WorldWideScience

Sample records for network traffic prediction

  1. Predicting Information Flows in Network Traffic.

    Science.gov (United States)

    Hinich, Melvin J.; Molyneux, Robert E.

    2003-01-01

    Discusses information flow in networks and predicting network traffic and describes a study that uses time series analysis on a day's worth of Internet log data. Examines nonlinearity and traffic invariants, and suggests that prediction of network traffic may not be possible with current techniques. (Author/LRW)

  2. Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks.

    Science.gov (United States)

    Yu, Haiyang; Wu, Zhihai; Wang, Shuqin; Wang, Yunpeng; Ma, Xiaolei

    2017-06-26

    Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

  3. Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

    Directory of Open Access Journals (Sweden)

    Haiyang Yu

    2017-06-01

    Full Text Available Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs, for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs and long short-term memory (LSTM neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

  4. Network traffic anomaly prediction using Artificial Neural Network

    Science.gov (United States)

    Ciptaningtyas, Hening Titi; Fatichah, Chastine; Sabila, Altea

    2017-03-01

    As the excessive increase of internet usage, the malicious software (malware) has also increase significantly. Malware is software developed by hacker for illegal purpose(s), such as stealing data and identity, causing computer damage, or denying service to other user[1]. Malware which attack computer or server often triggers network traffic anomaly phenomena. Based on Sophos's report[2], Indonesia is the riskiest country of malware attack and it also has high network traffic anomaly. This research uses Artificial Neural Network (ANN) to predict network traffic anomaly based on malware attack in Indonesia which is recorded by Id-SIRTII/CC (Indonesia Security Incident Response Team on Internet Infrastructure/Coordination Center). The case study is the highest malware attack (SQL injection) which has happened in three consecutive years: 2012, 2013, and 2014[4]. The data series is preprocessed first, then the network traffic anomaly is predicted using Artificial Neural Network and using two weight update algorithms: Gradient Descent and Momentum. Error of prediction is calculated using Mean Squared Error (MSE) [7]. The experimental result shows that MSE for SQL Injection is 0.03856. So, this approach can be used to predict network traffic anomaly.

  5. A Wavelet Analysis-Based Dynamic Prediction Algorithm to Network Traffic

    Directory of Open Access Journals (Sweden)

    Meng Fan-Bo

    2016-01-01

    Full Text Available Network traffic is a significantly important parameter for network traffic engineering, while it holds highly dynamic nature in the network. Accordingly, it is difficult and impossible to directly predict traffic amount of end-to-end flows. This paper proposes a new prediction algorithm to network traffic using the wavelet analysis. Firstly, network traffic is converted into the time-frequency domain to capture time-frequency feature of network traffic. Secondly, in different frequency components, we model network traffic in the time-frequency domain. Finally, we build the prediction model about network traffic. At the same time, the corresponding prediction algorithm is presented to attain network traffic prediction. Simulation results indicates that our approach is promising.

  6. Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction

    OpenAIRE

    Cui, Zhiyong; Ke, Ruimin; Wang, Yinhai

    2018-01-01

    Short-term traffic forecasting based on deep learning methods, especially long short-term memory (LSTM) neural networks, has received much attention in recent years. However, the potential of deep learning methods in traffic forecasting has not yet fully been exploited in terms of the depth of the model architecture, the spatial scale of the prediction area, and the predictive power of spatial-temporal data. In this paper, a deep stacked bidirectional and unidirectional LSTM (SBU- LSTM) neura...

  7. Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks

    Directory of Open Access Journals (Sweden)

    Laisen Nie

    2018-01-01

    Full Text Available Wireless mesh network is prevalent for providing a decentralized access for users and other intelligent devices. Meanwhile, it can be employed as the infrastructure of the last few miles connectivity for various network applications, for example, Internet of Things (IoT and mobile networks. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep learning architecture and the Spatiotemporal Compressive Sensing method. The proposed method first adopts discrete wavelet transform to extract the low-pass component of network traffic that describes the long-range dependence of itself. Then, a prediction model is built by learning a deep architecture based on the deep belief network from the extracted low-pass component. Otherwise, for the remaining high-pass component that expresses the gusty and irregular fluctuations of network traffic, the Spatiotemporal Compressive Sensing method is adopted to predict it. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods.

  8. Comparison of classical statistical methods and artificial neural network in traffic noise prediction

    International Nuclear Information System (INIS)

    Nedic, Vladimir; Despotovic, Danijela; Cvetanovic, Slobodan; Despotovic, Milan; Babic, Sasa

    2014-01-01

    Traffic is the main source of noise in urban environments and significantly affects human mental and physical health and labor productivity. Therefore it is very important to model the noise produced by various vehicles. Techniques for traffic noise prediction are mainly based on regression analysis, which generally is not good enough to describe the trends of noise. In this paper the application of artificial neural networks (ANNs) for the prediction of traffic noise is presented. As input variables of the neural network, the proposed structure of the traffic flow and the average speed of the traffic flow are chosen. The output variable of the network is the equivalent noise level in the given time period L eq . Based on these parameters, the network is modeled, trained and tested through a comparative analysis of the calculated values and measured levels of traffic noise using the originally developed user friendly software package. It is shown that the artificial neural networks can be a useful tool for the prediction of noise with sufficient accuracy. In addition, the measured values were also used to calculate equivalent noise level by means of classical methods, and comparative analysis is given. The results clearly show that ANN approach is superior in traffic noise level prediction to any other statistical method. - Highlights: • We proposed an ANN model for prediction of traffic noise. • We developed originally designed user friendly software package. • The results are compared with classical statistical methods. • The results are much better predictive capabilities of ANN model

  9. Comparison of classical statistical methods and artificial neural network in traffic noise prediction

    Energy Technology Data Exchange (ETDEWEB)

    Nedic, Vladimir, E-mail: vnedic@kg.ac.rs [Faculty of Philology and Arts, University of Kragujevac, Jovana Cvijića bb, 34000 Kragujevac (Serbia); Despotovic, Danijela, E-mail: ddespotovic@kg.ac.rs [Faculty of Economics, University of Kragujevac, Djure Pucara Starog 3, 34000 Kragujevac (Serbia); Cvetanovic, Slobodan, E-mail: slobodan.cvetanovic@eknfak.ni.ac.rs [Faculty of Economics, University of Niš, Trg kralja Aleksandra Ujedinitelja, 18000 Niš (Serbia); Despotovic, Milan, E-mail: mdespotovic@kg.ac.rs [Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac (Serbia); Babic, Sasa, E-mail: babicsf@yahoo.com [College of Applied Mechanical Engineering, Trstenik (Serbia)

    2014-11-15

    Traffic is the main source of noise in urban environments and significantly affects human mental and physical health and labor productivity. Therefore it is very important to model the noise produced by various vehicles. Techniques for traffic noise prediction are mainly based on regression analysis, which generally is not good enough to describe the trends of noise. In this paper the application of artificial neural networks (ANNs) for the prediction of traffic noise is presented. As input variables of the neural network, the proposed structure of the traffic flow and the average speed of the traffic flow are chosen. The output variable of the network is the equivalent noise level in the given time period L{sub eq}. Based on these parameters, the network is modeled, trained and tested through a comparative analysis of the calculated values and measured levels of traffic noise using the originally developed user friendly software package. It is shown that the artificial neural networks can be a useful tool for the prediction of noise with sufficient accuracy. In addition, the measured values were also used to calculate equivalent noise level by means of classical methods, and comparative analysis is given. The results clearly show that ANN approach is superior in traffic noise level prediction to any other statistical method. - Highlights: • We proposed an ANN model for prediction of traffic noise. • We developed originally designed user friendly software package. • The results are compared with classical statistical methods. • The results are much better predictive capabilities of ANN model.

  10. A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

    Science.gov (United States)

    Huang, Darong; Bai, Xing-Rong

    Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

  11. Predicting Traffic Flow in Local Area Networks by the Largest Lyapunov Exponent

    Directory of Open Access Journals (Sweden)

    Yan Liu

    2016-01-01

    Full Text Available The dynamics of network traffic are complex and nonlinear, and chaotic behaviors and their prediction, which play an important role in local area networks (LANs, are studied in detail, using the largest Lyapunov exponent. With the introduction of phase space reconstruction based on the time sequence, the high-dimensional traffic is projected onto the low dimension reconstructed phase space, and a reduced dynamic system is obtained from the dynamic system viewpoint. Then, a numerical method for computing the largest Lyapunov exponent of the low-dimensional dynamic system is presented. Further, the longest predictable time, which is related to chaotic behaviors in the system, is studied using the largest Lyapunov exponent, and the Wolf method is used to predict the evolution of the traffic in a local area network by both Dot and Interval predictions, and a reliable result is obtained by the presented method. As the conclusion, the results show that the largest Lyapunov exponent can be used to describe the sensitivity of the trajectory in the reconstructed phase space to the initial values. Moreover, Dot Prediction can effectively predict the flow burst. The numerical simulation also shows that the presented method is feasible and efficient for predicting the complex dynamic behaviors in LAN traffic, especially for congestion and attack in networks, which are the main two complex phenomena behaving as chaos in networks.

  12. Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

    Science.gov (United States)

    Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

    2017-04-10

    This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

  13. Sensitivity Analysis of Wavelet Neural Network Model for Short-Term Traffic Volume Prediction

    Directory of Open Access Journals (Sweden)

    Jinxing Shen

    2013-01-01

    Full Text Available In order to achieve a more accurate and robust traffic volume prediction model, the sensitivity of wavelet neural network model (WNNM is analyzed in this study. Based on real loop detector data which is provided by traffic police detachment of Maanshan, WNNM is discussed with different numbers of input neurons, different number of hidden neurons, and traffic volume for different time intervals. The test results show that the performance of WNNM depends heavily on network parameters and time interval of traffic volume. In addition, the WNNM with 4 input neurons and 6 hidden neurons is the optimal predictor with more accuracy, stability, and adaptability. At the same time, a much better prediction record will be achieved with the time interval of traffic volume are 15 minutes. In addition, the optimized WNNM is compared with the widely used back-propagation neural network (BPNN. The comparison results indicated that WNNM produce much lower values of MAE, MAPE, and VAPE than BPNN, which proves that WNNM performs better on short-term traffic volume prediction.

  14. Integrated Control of Mixed Traffic Networks using Model Predictive Control

    NARCIS (Netherlands)

    Van den Berg, M.

    2010-01-01

    Motivation The growth of our road infrastructure cannot keep up with the growing mobility of people, and the corresponding increase in traffic demand. This results in daily congestion on the freeways. It is an illusion that the problem of congestion can be solved completely within a few years, but

  15. Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing

    Directory of Open Access Journals (Sweden)

    Zhaosheng Yang

    2014-01-01

    Full Text Available To increase the efficiency and precision of large-scale road network traffic flow prediction, a genetic algorithm-support vector machine (GA-SVM model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA-SVM model and parallel GA-SVM model based on MPI (message passing interface. The results demonstrate that the parallel GA-SVM model based on cloud computing has higher prediction accuracy, shorter running time, and higher speedup.

  16. Global forward-predicting dynamic routing for traffic concurrency space stereo multi-layer scale-free network

    International Nuclear Information System (INIS)

    Xie Wei-Hao; Zhou Bin; Liu En-Xiao; Lu Wei-Dang; Zhou Ting

    2015-01-01

    Many real communication networks, such as oceanic monitoring network and land environment observation network, can be described as space stereo multi-layer structure, and the traffic in these networks is concurrent. Understanding how traffic dynamics depend on these real communication networks and finding an effective routing strategy that can fit the circumstance of traffic concurrency and enhance the network performance are necessary. In this light, we propose a traffic model for space stereo multi-layer complex network and introduce two kinds of global forward-predicting dynamic routing strategies, global forward-predicting hybrid minimum queue (HMQ) routing strategy and global forward-predicting hybrid minimum degree and queue (HMDQ) routing strategy, for traffic concurrency space stereo multi-layer scale-free networks. By applying forward-predicting strategy, the proposed routing strategies achieve better performances in traffic concurrency space stereo multi-layer scale-free networks. Compared with the efficient routing strategy and global dynamic routing strategy, HMDQ and HMQ routing strategies can optimize the traffic distribution, alleviate the number of congested packets effectively and reach much higher network capacity. (paper)

  17. Virtual network topology reconfiguration based on big data analytics for traffic prediction

    OpenAIRE

    Morales Alcaide, Fernando; Ruiz Ramírez, Marc; Velasco Esteban, Luis Domingo

    2016-01-01

    Big data analytics is applied for IP traffic prediction. When the virtual topology needs to be reconfigured, predicted and current traffic matrices are used to find the optimal topology. Exhaustive simulation results reveal large benefits. Peer Reviewed

  18. Traffic Route Guidance using Feedback of Predicted Travel Times : Improving Travel Times in the Berlin Traffic Network

    OpenAIRE

    Bergsten, Arvid; Zetterberg, Daniel

    2008-01-01

    Traffic congestions constitute a problem in many large cities. Congestions can be handled by reducing the network demand, expanding the infrastructure, or by utilizing the road network more efficiently. This master thesis presents a methodology for route guidance, based on automatic feedback control from the current traffic situation. Through variable direction signs or individual in-car devices, all vehicles with a certain origin and destination (which are both normally intermediate) are gui...

  19. Predicting Free Flow Speed and Crash Risk of Bicycle Traffic Flow Using Artificial Neural Network Models

    Directory of Open Access Journals (Sweden)

    Cheng Xu

    2015-01-01

    Full Text Available Free flow speed is a fundamental measure of traffic performance and has been found to affect the severity of crash risk. However, the previous studies lack analysis and modelling of impact factors on bicycles’ free flow speed. The main focus of this study is to develop multilayer back propagation artificial neural network (BPANN models for the prediction of free flow speed and crash risk on the separated bicycle path. Four different models with considering different combinations of input variables (e.g., path width, traffic condition, bicycle type, and cyclists’ characteristics were developed. 459 field data samples were collected from eleven bicycle paths in Hangzhou, China, and 70% of total samples were used for training, 15% for validation, and 15% for testing. The results show that considering the input variables of bicycle types and characteristics of cyclists will effectively improve the accuracy of the prediction models. Meanwhile, the parameters of bicycle types have more significant effect on predicting free flow speed of bicycle compared to those of cyclists’ characteristics. The findings could contribute for evaluation, planning, and management of bicycle safety.

  20. Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier

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    Hesham El-Sayed

    2018-05-01

    Full Text Available Heterogeneous vehicular networks (HETVNETs evolve from vehicular ad hoc networks (VANETs, which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs. The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM kernels with a radial basis function (RBF. The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.

  1. Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene.

    Science.gov (United States)

    Li, Jun; Mei, Xue; Prokhorov, Danil; Tao, Dacheng

    2017-03-01

    Hierarchical neural networks have been shown to be effective in learning representative image features and recognizing object classes. However, most existing networks combine the low/middle level cues for classification without accounting for any spatial structures. For applications such as understanding a scene, how the visual cues are spatially distributed in an image becomes essential for successful analysis. This paper extends the framework of deep neural networks by accounting for the structural cues in the visual signals. In particular, two kinds of neural networks have been proposed. First, we develop a multitask deep convolutional network, which simultaneously detects the presence of the target and the geometric attributes (location and orientation) of the target with respect to the region of interest. Second, a recurrent neuron layer is adopted for structured visual detection. The recurrent neurons can deal with the spatial distribution of visible cues belonging to an object whose shape or structure is difficult to explicitly define. Both the networks are demonstrated by the practical task of detecting lane boundaries in traffic scenes. The multitask convolutional neural network provides auxiliary geometric information to help the subsequent modeling of the given lane structures. The recurrent neural network automatically detects lane boundaries, including those areas containing no marks, without any explicit prior knowledge or secondary modeling.

  2. Framework for Traffic Congestion Prediction

    NARCIS (Netherlands)

    Zaki, J.F.W.; Ali-Eldin, A.M.T.; Hussein, S.E.; Saraya, S.F.; Areed, F.F.

    2016-01-01

    Traffic Congestion is a complex dilemma facing most major cities. It has undergone a lot of research since the early 80s in an attempt to predict traffic in the short-term. Recently, Intelligent Transportation Systems (ITS) became an integral part of traffic research which helped in modeling and

  3. Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

    Directory of Open Access Journals (Sweden)

    Seyed Hadi Hosseini

    2014-10-01

    Full Text Available Traffic flow forecasting is useful for controlling traffic flow, traffic lights, and travel times. This study uses a multi-layer perceptron neural network and the mutual information (MI technique to forecast traffic flow and compares the prediction results with conventional traffic flow forecasting methods. The MI method is used to calculate the interdependency of historical traffic data and future traffic flow. In numerical case studies, the proposed traffic flow forecasting method was tested against data loss, changes in weather conditions, traffic congestion, and accidents. The outcomes were highly acceptable for all cases and showed the robustness of the proposed flow forecasting method.

  4. A State-of-the-Art Review of the Sensor Location, Flow Observability, Estimation, and Prediction Problems in Traffic Networks

    Directory of Open Access Journals (Sweden)

    Enrique Castillo

    2015-01-01

    Full Text Available A state-of-the-art review of flow observability, estimation, and prediction problems in traffic networks is performed. Since mathematical optimization provides a general framework for all of them, an integrated approach is used to perform the analysis of these problems and consider them as different optimization problems whose data, variables, constraints, and objective functions are the main elements that characterize the problems proposed by different authors. For example, counted, scanned or “a priori” data are the most common data sources; conservation laws, flow nonnegativity, link capacity, flow definition, observation, flow propagation, and specific model requirements form the most common constraints; and least squares, likelihood, possible relative error, mean absolute relative error, and so forth constitute the bases for the objective functions or metrics. The high number of possible combinations of these elements justifies the existence of a wide collection of methods for analyzing static and dynamic situations.

  5. Scalable data-driven short-term traffic prediction

    NARCIS (Netherlands)

    Friso, K.; Wismans, L. J.J.; Tijink, M. B.

    2017-01-01

    Short-term traffic prediction has a lot of potential for traffic management. However, most research has traditionally focused on either traffic models-which do not scale very well to large networks, computationally-or on data-driven methods for freeways, leaving out urban arterials completely. Urban

  6. Active Traffic Capture for Network Forensics

    Science.gov (United States)

    Slaviero, Marco; Granova, Anna; Olivier, Martin

    Network traffic capture is an integral part of network forensics, but current traffic capture techniques are typically passive in nature. Under heavy loads, it is possible for a sniffer to miss packets, which affects the quality of forensic evidence.

  7. Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Yang Dan

    2008-12-01

    Full Text Available Distributed network traffic anomaly refers to a traffic abnormal behavior involving many links of a network and caused by the same source (e.g., DDoS attack, worm propagation. The anomaly transiting in a single link might be unnoticeable and hard to detect, while the anomalous aggregation from many links can be prevailing, and does more harm to the networks. Aiming at the similar features of distributed traffic anomaly on many links, this paper proposes a network-wide detection method by performing anomalous correlation analysis of traffic signals' instantaneous parameters. In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction. Finally, an anomaly is detected by a global correlation coefficient of anomalous space. Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.

  8. Wireless traffic steering for green cellular networks

    CERN Document Server

    Zhang, Shan; Zhou, Sheng; Niu, Zhisheng; Shen, Xuemin (Sherman)

    2016-01-01

    This book introduces wireless traffic steering as a paradigm to realize green communication in multi-tier heterogeneous cellular networks. By matching network resources and dynamic mobile traffic demand, traffic steering helps to reduce on-grid power consumption with on-demand services provided. This book reviews existing solutions from the perspectives of energy consumption reduction and renewable energy harvesting. Specifically, it explains how traffic steering can improve energy efficiency through intelligent traffic-resource matching. Several promising traffic steering approaches for dynamic network planning and renewable energy demand-supply balancing are discussed. This book presents an energy-aware traffic steering method for networks with energy harvesting, which optimizes the traffic allocated to each cell based on the renewable energy status. Renewable energy demand-supply balancing is a key factor in energy dynamics, aimed at enhancing renewable energy sustainability to reduce on-grid energy consum...

  9. Proactive Traffic Information Control in Emergency Evacuation Network

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    Zhengfeng Huang

    2015-01-01

    Full Text Available Traffic demand in emergency evacuation is usually too large to be effectively managed with reactive traffic information control methods. These methods adapt to the road traffic passively by publishing real-time information without consideration of the routing behavior feedback produced by evacuees. Other remedy measures have to be prepared in case of nonrecurring congestion under these methods. To use the network capacity fully to mitigate near-future evacuation traffic congestion, we propose proactive traffic information control (PTIC model. Based on the mechanism between information and routing behavior feedback, this model can change the route choice of evacuees in advance by dissipating strategic traffic information. Generally, the near-future traffic condition is difficult to accurately predict because it is uncertain in evacuation. Assume that the value of traffic information obeys certain distribution within a range, and then real-time traffic information may reflect the most-likely near-future traffic condition. Unlike the real-time information, the proactive traffic information is a selection within the range to achieve a desired level of the road network performance index (total system travel time. In the aspect of the solution algorithm, differential equilibrium decomposed optimization (D-EDO is proposed to compare with other heuristic methods. A field study on a road network around a large stadium is used to validate the PTIC.

  10. Congestion transition in air traffic networks.

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    Bernardo Monechi

    Full Text Available Air Transportation represents a very interesting example of a complex techno-social system whose importance has considerably grown in time and whose management requires a careful understanding of the subtle interplay between technological infrastructure and human behavior. Despite the competition with other transportation systems, a growth of air traffic is still foreseen in Europe for the next years. The increase of traffic load could bring the current Air Traffic Network above its capacity limits so that safety standards and performances might not be guaranteed anymore. Lacking the possibility of a direct investigation of this scenario, we resort to computer simulations in order to quantify the disruptive potential of an increase in traffic load. To this end we model the Air Transportation system as a complex dynamical network of flights controlled by humans who have to solve potentially dangerous conflicts by redirecting aircraft trajectories. The model is driven and validated through historical data of flight schedules in a European national airspace. While correctly reproducing actual statistics of the Air Transportation system, e.g., the distribution of delays, the model allows for theoretical predictions. Upon an increase of the traffic load injected in the system, the model predicts a transition from a phase in which all conflicts can be successfully resolved, to a phase in which many conflicts cannot be resolved anymore. We highlight how the current flight density of the Air Transportation system is well below the transition, provided that controllers make use of a special re-routing procedure. While the congestion transition displays a universal scaling behavior, its threshold depends on the conflict solving strategy adopted. Finally, the generality of the modeling scheme introduced makes it a flexible general tool to simulate and control Air Transportation systems in realistic and synthetic scenarios.

  11. Congestion transition in air traffic networks.

    Science.gov (United States)

    Monechi, Bernardo; Servedio, Vito D P; Loreto, Vittorio

    2015-01-01

    Air Transportation represents a very interesting example of a complex techno-social system whose importance has considerably grown in time and whose management requires a careful understanding of the subtle interplay between technological infrastructure and human behavior. Despite the competition with other transportation systems, a growth of air traffic is still foreseen in Europe for the next years. The increase of traffic load could bring the current Air Traffic Network above its capacity limits so that safety standards and performances might not be guaranteed anymore. Lacking the possibility of a direct investigation of this scenario, we resort to computer simulations in order to quantify the disruptive potential of an increase in traffic load. To this end we model the Air Transportation system as a complex dynamical network of flights controlled by humans who have to solve potentially dangerous conflicts by redirecting aircraft trajectories. The model is driven and validated through historical data of flight schedules in a European national airspace. While correctly reproducing actual statistics of the Air Transportation system, e.g., the distribution of delays, the model allows for theoretical predictions. Upon an increase of the traffic load injected in the system, the model predicts a transition from a phase in which all conflicts can be successfully resolved, to a phase in which many conflicts cannot be resolved anymore. We highlight how the current flight density of the Air Transportation system is well below the transition, provided that controllers make use of a special re-routing procedure. While the congestion transition displays a universal scaling behavior, its threshold depends on the conflict solving strategy adopted. Finally, the generality of the modeling scheme introduced makes it a flexible general tool to simulate and control Air Transportation systems in realistic and synthetic scenarios.

  12. Classification and Analysis of Computer Network Traffic

    OpenAIRE

    Bujlow, Tomasz

    2014-01-01

    Traffic monitoring and analysis can be done for multiple different reasons: to investigate the usage of network resources, assess the performance of network applications, adjust Quality of Service (QoS) policies in the network, log the traffic to comply with the law, or create realistic models of traffic for academic purposes. We define the objective of this thesis as finding a way to evaluate the performance of various applications in a high-speed Internet infrastructure. To satisfy the obje...

  13. Traffic Dynamics of Computer Networks

    Science.gov (United States)

    Fekete, Attila

    2008-10-01

    Two important aspects of the Internet, namely the properties of its topology and the characteristics of its data traffic, have attracted growing attention of the physics community. My thesis has considered problems of both aspects. First I studied the stochastic behavior of TCP, the primary algorithm governing traffic in the current Internet, in an elementary network scenario consisting of a standalone infinite-sized buffer and an access link. The effect of the fast recovery and fast retransmission (FR/FR) algorithms is also considered. I showed that my model can be extended further to involve the effect of link propagation delay, characteristic of WAN. I continued my thesis with the investigation of finite-sized semi-bottleneck buffers, where packets can be dropped not only at the link, but also at the buffer. I demonstrated that the behavior of the system depends only on a certain combination of the parameters. Moreover, an analytic formula was derived that gives the ratio of packet loss rate at the buffer to the total packet loss rate. This formula makes it possible to treat buffer-losses as if they were link-losses. Finally, I studied computer networks from a structural perspective. I demonstrated through fluid simulations that the distribution of resources, specifically the link bandwidth, has a serious impact on the global performance of the network. Then I analyzed the distribution of edge betweenness in a growing scale-free tree under the condition that a local property, the in-degree of the "younger" node of an arbitrary edge, is known in order to find an optimum distribution of link capacity. The derived formula is exact even for finite-sized networks. I also calculated the conditional expectation of edge betweenness, rescaled for infinite networks.

  14. A Comparative Analysis and Prediction of Traffic Accident Causalities in the Sultanate of Oman using Artificial Neural Networks and Statistical methods

    Directory of Open Access Journals (Sweden)

    Galal A. Ali

    1998-12-01

    Full Text Available Traffic accidents are among the major causes of death in the Sultanate of Oman This is particularly the case in the age group of I6 to 25. Studies indicate that, in spite of Oman's high population-per-vehicle ratio, its fatality rate per l0,000 vehicles is one of the highest in the world. This alarming Situation underlines the importance of analyzing traffic accident data and predicting accident casualties. Such steps will lead to understanding the underlying causes of traffic accidents, and thereby to devise appropriate measures to reduce the number of car accidents and enhance safety standards. In this paper, a comparative study of car accident casualties in Oman was undertaken. Artificial Neural Networks (ANNs were used to analyze the data and make predictions of the number of accident casualties. The results were compared with those obtained from the analysis and predictions by regression techniques. Both approaches attempted to model accident casualties using historical  data on related factors, such as population, number of cars on the road and so on, covering the period from I976 to 1994. Forecasts for the years 1995 to 2000 were made using ANNs and regression equations. The results from ANNs provided the best fit for the data. However, it was found that ANNs gave lower forecasts relative to those obtained by the regression methods used, indicating that ANNs are suitable for interpolation but their use for extrapolation may be limited. Nevertheless, the study showed that ANNs provide a potentially powerful tool in analyzing and forecasting traffic accidents and casualties.

  15. Comparative Application of Radial Basis Function and Multilayer Perceptron Neural Networks to Predict Traffic Noise Pollution in Tehran Roads

    Directory of Open Access Journals (Sweden)

    Ali Mansourkhaki

    2018-01-01

    Full Text Available Noise pollution is a level of environmental noise which is considered as a disturbing and annoying phenomenon for human and wildlife. It is one of the environmental problems which has not been considered as harmful as the air and water pollution. Compared with other pollutants, the attempts to control noise pollution have largely been unsuccessful due to the inadequate knowledge of its effectson humans, as well as the lack of clear standards in previous years. However, with an increase of traveling vehicles, the adverse impact of increasing noise pollution on human health is progressively emerging. Hence, investigators all around the world are seeking to findnew approaches for predicting, estimating and controlling this problem and various models have been proposed. Recently, developing learning algorithms such as neural network has led to novel solutions for this challenge. These algorithms provide intelligent performance based on the situations and input data, enabling to obtain the best result for predicting noise level. In this study, two types of neural networks – multilayer perceptron and radial basis function – were developed for predicting equivalent continuous sound level (LA eq by measuring the traffivolume, average speed and percentage of heavy vehicles in some roads in west and northwest of Tehran. Then, their prediction results were compared based on the coefficienof determination (R 2 and the Mean Squared Error (MSE. Although both networks are of high accuracy in prediction of noise level, multilayer perceptron neural network based on selected criteria had a better performance.

  16. A Network Traffic Control Enhancement Approach over Bluetooth Networks

    DEFF Research Database (Denmark)

    Son, L.T.; Schiøler, Henrik; Madsen, Ole Brun

    2003-01-01

    This paper analyzes network traffic control issues in Bluetooth data networks as convex optimization problem. We formulate the problem of maximizing of total network flows and minimizing the costs of flows. An adaptive distributed network traffic control scheme is proposed as an approximated solu...... as capacity limitations and flow requirements in the network. Simulation shows that the performance of Bluetooth networks could be improved by applying the adaptive distributed network traffic control scheme...... solution of the stated optimization problem that satisfies quality of service requirements and topologically induced constraints in Bluetooth networks, such as link capacity and node resource limitations. The proposed scheme is decentralized and complies with frequent changes of topology as well......This paper analyzes network traffic control issues in Bluetooth data networks as convex optimization problem. We formulate the problem of maximizing of total network flows and minimizing the costs of flows. An adaptive distributed network traffic control scheme is proposed as an approximated...

  17. Self-Similar Traffic In Wireless Networks

    OpenAIRE

    Jerjomins, R.; Petersons, E.

    2005-01-01

    Many studies have shown that traffic in Ethernet and other wired networks is self-similar. This paper reveals that wireless network traffic is also self-similar and long-range dependant by analyzing big amount of data captured from the wireless router.

  18. Consistency analysis of network traffic repositories

    NARCIS (Netherlands)

    Lastdrager, Elmer; Lastdrager, E.E.H.; Pras, Aiko

    Traffic repositories with TCP/IP header information are very important for network analysis. Researchers often assume that such repositories reliably represent all traffic that has been flowing over the network; little thoughts are made regarding the consistency of these repositories. Still, for

  19. Traffic Dynamics on Complex Networks: A Survey

    Directory of Open Access Journals (Sweden)

    Shengyong Chen

    2012-01-01

    Full Text Available Traffic dynamics on complex networks are intriguing in recent years due to their practical implications in real communication networks. In this survey, we give a brief review of studies on traffic routing dynamics on complex networks. Strategies for improving transport efficiency, including designing efficient routing strategies and making appropriate adjustments to the underlying network structure, are introduced in this survey. Finally, a few open problems are discussed in this survey.

  20. Modeling, robust and distributed model predictive control for freeway networks

    NARCIS (Netherlands)

    Liu, S.

    2016-01-01

    In Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of

  1. On traffic modelling in GPRS networks

    DEFF Research Database (Denmark)

    Madsen, Tatiana Kozlova; Schwefel, Hans-Peter; Prasad, Ramjee

    2005-01-01

    Optimal design and dimensioning of wireless data networks, such as GPRS, requires the knowledge of traffic characteristics of different data services. This paper presents an in-detail analysis of an IP-level traffic measurements taken in an operational GPRS network. The data measurements reported...... here are done at the Gi interface. The aim of this paper is to reveal some key statistics of GPRS data applications and to validate if the existing traffic models can adequately describe traffic volume and inter-arrival time distribution for different services. Additionally, we present a method of user...

  2. Modeling Network Traffic in Wavelet Domain

    Directory of Open Access Journals (Sweden)

    Sheng Ma

    2004-12-01

    Full Text Available This work discovers that although network traffic has the complicated short- and long-range temporal dependence, the corresponding wavelet coefficients are no longer long-range dependent. Therefore, a "short-range" dependent process can be used to model network traffic in the wavelet domain. Both independent and Markov models are investigated. Theoretical analysis shows that the independent wavelet model is sufficiently accurate in terms of the buffer overflow probability for Fractional Gaussian Noise traffic. Any model, which captures additional correlations in the wavelet domain, only improves the performance marginally. The independent wavelet model is then used as a unified approach to model network traffic including VBR MPEG video and Ethernet data. The computational complexity is O(N for developing such wavelet models and generating synthesized traffic of length N, which is among the lowest attained.

  3. Evaluation and Simulation of Common Video Conference Traffics in Communication Networks

    Directory of Open Access Journals (Sweden)

    Farhad faghani

    2014-01-01

    Full Text Available Multimedia traffics are the basic traffics in data communication networks. Especially Video conferences are the most desirable traffics in huge networks(wired, wireless, …. Traffic modeling can help us to evaluate the real networks. So, in order to have good services in data communication networks which provide multimedia services, QoS will be very important .In this research we tried to have an exact traffic model design and simulation to overcome QoS challenges. Also, we predict bandwidth by Kalman filter in Ethernet networks.

  4. Characterization and Modeling of Network Traffic

    DEFF Research Database (Denmark)

    Shawky, Ahmed; Bergheim, Hans; Ragnarsson, Olafur

    2011-01-01

    -arrival time, IP addresses, port numbers and transport protocol are the only necessary parameters to model network traffic behaviour. In order to recreate this behaviour, a complex model is needed which is able to recreate traffic behaviour based on a set of statistics calculated from the parameters values...

  5. Traffic Predictive Control: Case Study and Evaluation

    Science.gov (United States)

    2017-06-26

    This project developed a quantile regression method for predicting future traffic flow at a signalized intersection by combining both historical and real-time data. The algorithm exploits nonlinear correlations in historical measurements and efficien...

  6. 4K Video Traffic Prediction using Seasonal Autoregressive Modeling

    Directory of Open Access Journals (Sweden)

    D. R. Marković

    2017-06-01

    Full Text Available From the perspective of average viewer, high definition video streams such as HD (High Definition and UHD (Ultra HD are increasing their internet presence year over year. This is not surprising, having in mind expansion of HD streaming services, such as YouTube, Netflix etc. Therefore, high definition video streams are starting to challenge network resource allocation with their bandwidth requirements and statistical characteristics. Need for analysis and modeling of this demanding video traffic has essential importance for better quality of service and experience support. In this paper we use an easy-to-apply statistical model for prediction of 4K video traffic. Namely, seasonal autoregressive modeling is applied in prediction of 4K video traffic, encoded with HEVC (High Efficiency Video Coding. Analysis and modeling were performed within R programming environment using over 17.000 high definition video frames. It is shown that the proposed methodology provides good accuracy in high definition video traffic modeling.

  7. Understanding the context of network traffic alerts

    NARCIS (Netherlands)

    Cappers, B.C.M.; van Wijk, J.J.; Best, D.M.; Staheli, D.; Prigent, N.; Engle, S.; Harrison, L.

    2016-01-01

    For the protection of critical infrastructures against complex virus attacks, automated network traffic analysis and deep packet inspection are unavoidable. However, even with the use of network intrusion detection systems, the number of alerts is still too large to analyze manually. In addition,

  8. Modelling traffic congestion using queuing networks

    Indian Academy of Sciences (India)

    Flow-density curves; uninterrupted traffic; Jackson networks. ... ness - also suffer from a big handicap vis-a-vis the Indian scenario: most of these models do .... more well-known queuing network models and onsite data, a more exact Road Cell ...

  9. Traffic Policing in Dynamic Military Networks Using Software Defined Networking

    OpenAIRE

    Skappel, Hans Fredrik

    2016-01-01

    This thesis looks at how Software Defined Networking (SDN) can be used to provide traffic engineering and to police traffic in an Operational Military Network (OMN). SDN is a concept where the control plane is separated from the forwarding plane, and the control plane is capable of controlling forwarding plane elements located on multiple network nodes using the OpenFlow protocol. Specifically, we have discussed the problems in OMNs, and possible SDN approaches to mitigate the challenges. Bas...

  10. Traffic of indistinguishable particles in complex networks

    International Nuclear Information System (INIS)

    Qing-Kuan, Meng; Jian-Yang, Zhu

    2009-01-01

    In this paper, we apply a simple walk mechanism to the study of the traffic of many indistinguishable particles in complex networks. The network with particles stands for a particle system, and every vertex in the network stands for a quantum state with the corresponding energy determined by the vertex degree. Although the particles are indistinguishable, the quantum states can be distinguished. When the many indistinguishable particles walk randomly in the system for a long enough time and the system reaches dynamic equilibrium, we find that under different restrictive conditions the particle distributions satisfy different forms, including the Bose–Einstein distribution, the Fermi–Dirac distribution and the non-Fermi distribution (as we temporarily call it). As for the Bose–Einstein distribution, we find that only if the particle density is larger than zero, with increasing particle density, do more and more particles condense in the lowest energy level. While the particle density is very low, the particle distribution transforms from the quantum statistical form to the classically statistical form, i.e., transforms from the Bose distribution or the Fermi distribution to the Boltzmann distribution. The numerical results fit well with the analytical predictions

  11. Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles.

    Science.gov (United States)

    Wan, Jiafu; Liu, Jianqi; Shao, Zehui; Vasilakos, Athanasios V; Imran, Muhammad; Zhou, Keliang

    2016-01-11

    The advances in wireless communication techniques, mobile cloud computing, automotive and intelligent terminal technology are driving the evolution of vehicle ad hoc networks into the Internet of Vehicles (IoV) paradigm. This leads to a change in the vehicle routing problem from a calculation based on static data towards real-time traffic prediction. In this paper, we first address the taxonomy of cloud-assisted IoV from the viewpoint of the service relationship between cloud computing and IoV. Then, we review the traditional traffic prediction approached used by both Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) communications. On this basis, we propose a mobile crowd sensing technology to support the creation of dynamic route choices for drivers wishing to avoid congestion. Experiments were carried out to verify the proposed approaches. Finally, we discuss the outlook of reliable traffic prediction.

  12. Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles

    Directory of Open Access Journals (Sweden)

    Jiafu Wan

    2016-01-01

    Full Text Available The advances in wireless communication techniques, mobile cloud computing, automotive and intelligent terminal technology are driving the evolution of vehicle ad hoc networks into the Internet of Vehicles (IoV paradigm. This leads to a change in the vehicle routing problem from a calculation based on static data towards real-time traffic prediction. In this paper, we first address the taxonomy of cloud-assisted IoV from the viewpoint of the service relationship between cloud computing and IoV. Then, we review the traditional traffic prediction approached used by both Vehicle to Infrastructure (V2I and Vehicle to Vehicle (V2V communications. On this basis, we propose a mobile crowd sensing technology to support the creation of dynamic route choices for drivers wishing to avoid congestion. Experiments were carried out to verify the proposed approaches. Finally, we discuss the outlook of reliable traffic prediction.

  13. Traffic Management for Next Generation Transport Networks

    DEFF Research Database (Denmark)

    Yu, Hao

    required by the next generation transport network to provide Quality-of-Service (QoS) guaranteed video services. Augmenting network capacity and upgrading network nodes indicate long deployment period, replacement of equipment and thus significant cost to the network service providers. This challenge may...... slacken the steps of some network operators towards providing IPTV services. In this dissertation, the topology-based hierarchical scheduling scheme is proposed to tackle the problem addressed. The scheme simplifies the deployment process by placing an intelligent switch with centralized traffic...... management functions at the edge of the network, scheduling traffic on behalf of the other nodes. The topology-based hierarchical scheduling scheme is able to provide outstanding flow isolation due to its centralized scheduling ability, which is essential for providing IPTV services. In order to reduce...

  14. Network Analysis of Urban Traffic with Big Bus Data

    OpenAIRE

    Zhao, Kai

    2016-01-01

    Urban traffic analysis is crucial for traffic forecasting systems, urban planning and, more recently, various mobile and network applications. In this paper, we analyse urban traffic with network and statistical methods. Our analysis is based on one big bus dataset containing 45 million bus arrival samples in Helsinki. We mainly address following questions: 1. How can we identify the areas that cause most of the traffic in the city? 2. Why there is a urban traffic? Is bus traffic a key cause ...

  15. Competitive Traffic Assignment in Road Networks

    Directory of Open Access Journals (Sweden)

    Krylatov Alexander Y.

    2016-09-01

    Full Text Available Recently in-vehicle route guidance and information systems are rapidly developing. Such systems are expected to reduce congestion in an urban traffic area. This social benefit is believed to be reached by imposing the route choices on the network users that lead to the system optimum traffic assignment. However, guidance service could be offered by different competitive business companies. Then route choices of different mutually independent groups of users may reject traffic assignment from the system optimum state. In this paper, a game theoretic approach is shown to be very efficient to formalize competitive traffic assignment problem with various groups of users in the form of non-cooperative network game with the Nash equilibrium search. The relationships between the Wardrop’s system optimum associated with the traffic assignment problem and the Nash equilibrium associated with the competitive traffic assignment problem are investigated. Moreover, some related aspects of the Nash equilibrium and the Wardrop’s user equilibrium assignments are also discussed.

  16. Linking network usage patterns to traffic Gaussianity fit

    NARCIS (Netherlands)

    de Oliveira Schmidt, R.; Sadre, R.; Melnikov, Nikolay; Schönwälder, Jürgen; Pras, Aiko

    Gaussian traffic models are widely used in the domain of network traffic modeling. The central assumption is that traffic aggregates are Gaussian distributed. Due to its importance, the Gaussian character of network traffic has been extensively assessed by researchers in the past years. In 2001,

  17. Traffic dynamics on coupled spatial networks

    International Nuclear Information System (INIS)

    Du, Wen-Bo; Zhou, Xing-Lian; Chen, Zhen; Cai, Kai-Quan; Cao, Xian-Bin

    2014-01-01

    With the rapid development of modern traffic, various means of transportation systems make it more convenient and diversified for passengers to travel out. In this paper, we establish a two-layered spatial network model where the low-speed lower layer is a regular lattice and the high-speed upper layer is a scale-free network embedded in the lattice. Passengers will travel along the path with the minimal travel time, and they can transfer from one layer to the other, which will induce extra transfer cost. We extensively investigate the traffic process on these coupled spatial networks and focus on the effect of the parameter α, the speed ratio between two networks. It is found that, as α grows, the network capacity of the coupled networks increases in the early stage and then decreases, indicating that cooperation between the coupled networks will induce the highest network capacity at an optimal α. We then provide an explanation for this non-monotonous dependence from a micro-scope point of view. The travel time reliability is also examined. Both in free-flow state and congestion state, the travel time is linearly related to the Euclidean distance. However, the variance of travel time in the congestion state is remarkably larger than that in the free-flow state, namely, people have to set aside more redundant time in an unreliable traffic system

  18. Highway traffic noise prediction based on GIS

    Science.gov (United States)

    Zhao, Jianghua; Qin, Qiming

    2014-05-01

    Before building a new road, we need to predict the traffic noise generated by vehicles. Traditional traffic noise prediction methods are based on certain locations and they are not only time-consuming, high cost, but also cannot be visualized. Geographical Information System (GIS) can not only solve the problem of manual data processing, but also can get noise values at any point. The paper selected a road segment from Wenxi to Heyang. According to the geographical overview of the study area and the comparison between several models, we combine the JTG B03-2006 model and the HJ2.4-2009 model to predict the traffic noise depending on the circumstances. Finally, we interpolate the noise values at each prediction point and then generate contours of noise. By overlaying the village data on the noise contour layer, we can get the thematic maps. The use of GIS for road traffic noise prediction greatly facilitates the decision-makers because of GIS spatial analysis function and visualization capabilities. We can clearly see the districts where noise are excessive, and thus it becomes convenient to optimize the road line and take noise reduction measures such as installing sound barriers and relocating villages and so on.

  19. The Stability of Multi-modal Traffic Network

    International Nuclear Information System (INIS)

    Han Linghui; Sun Huijun; Zhu Chengjuan; Jia Bin; Wu Jianjun

    2013-01-01

    There is an explicit and implicit assumption in multimodal traffic equilibrium models, that is, if the equilibrium exists, then it will also occur. The assumption is very idealized; in fact, it may be shown that the quite contrary could happen, because in multimodal traffic network, especially in mixed traffic conditions the interaction among traffic modes is asymmetric and the asymmetric interaction may result in the instability of traffic system. In this paper, to study the stability of multimodal traffic system, we respectively present the travel cost function in mixed traffic conditions and in traffic network with dedicated bus lanes. Based on a day-to-day dynamical model, we study the evolution of daily route choice of travelers in multimodal traffic network using 10000 random initial values for different cases. From the results of simulation, it can be concluded that the asymmetric interaction between the cars and buses in mixed traffic conditions can lead the traffic system to instability when traffic demand is larger. We also study the effect of travelers' perception error on the stability of multimodal traffic network. Although the larger perception error can alleviate the effect of interaction between cars and buses and improve the stability of traffic system in mixed traffic conditions, the traffic system also become instable when the traffic demand is larger than a number. For all cases simulated in this study, with the same parameters, traffic system with dedicated bus lane has better stability for traffic demand than that in mixed traffic conditions. We also find that the network with dedicated bus lane has higher portion of travelers by bus than it of mixed traffic network. So it can be concluded that building dedicated bus lane can improve the stability of traffic system and attract more travelers to choose bus reducing the traffic congestion. (general)

  20. Classification and Analysis of Computer Network Traffic

    DEFF Research Database (Denmark)

    Bujlow, Tomasz

    2014-01-01

    various classification modes (decision trees, rulesets, boosting, softening thresholds) regarding the classification accuracy and the time required to create the classifier. We showed how to use our VBS tool to obtain per-flow, per-application, and per-content statistics of traffic in computer networks...

  1. Multimodale trafiknet i GIS (Multimodal Traffic Network in GIS)

    DEFF Research Database (Denmark)

    Kronbak, Jacob; Brems, Camilla Riff

    1996-01-01

    The report introduces the use of multi-modal traffic networks within a geographical Information System (GIS). The necessary theory of modelling multi-modal traffic network is reviewed and applied to the ARC/INFO GIS by an explorative example.......The report introduces the use of multi-modal traffic networks within a geographical Information System (GIS). The necessary theory of modelling multi-modal traffic network is reviewed and applied to the ARC/INFO GIS by an explorative example....

  2. Traffic Congestion Evaluation and Signal Control Optimization Based on Wireless Sensor Networks: Model and Algorithms

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2012-01-01

    Full Text Available This paper presents the model and algorithms for traffic flow data monitoring and optimal traffic light control based on wireless sensor networks. Given the scenario that sensor nodes are sparsely deployed along the segments between signalized intersections, an analytical model is built using continuum traffic equation and develops the method to estimate traffic parameter with the scattered sensor data. Based on the traffic data and principle of traffic congestion formation, we introduce the congestion factor which can be used to evaluate the real-time traffic congestion status along the segment and to predict the subcritical state of traffic jams. The result is expected to support the timing phase optimization of traffic light control for the purpose of avoiding traffic congestion before its formation. We simulate the traffic monitoring based on the Mobile Century dataset and analyze the performance of traffic light control on VISSIM platform when congestion factor is introduced into the signal timing optimization model. The simulation result shows that this method can improve the spatial-temporal resolution of traffic data monitoring and evaluate traffic congestion status with high precision. It is helpful to remarkably alleviate urban traffic congestion and decrease the average traffic delays and maximum queue length.

  3. Networks and their traffic in multiplayer games

    Directory of Open Access Journals (Sweden)

    Cristian Andrés Melo López

    2016-06-01

    Full Text Available Computer games called multiplayer real-time, or (MCG are at the forefront of the use of the possibilities of the network. Research on this subject have been made for military simulations, virtual reality systems, computer support teamwork, the solutions diverge on the problems posed by MCG. With this in mind, this document provides an overview of the four issues affecting networking at the MCG. First, network resources (bandwidth, latency and computing capacity, together with the technical limits within which the MCG must operate. Second, the distribution concepts include communication architectures (peer-to-peer, client / server, server / network, and data and control architectures (centralized, distributed and reproduced .Thirdly, scalability allows the MCG to adapt to changes in parameterization resources. Finally, security is intended to fend off the traps and vandalism, which are common in online games; to check traffic, particularly these games we decided to take the massively multiplayer game League of Legends, a scene corresponding to a situation of real life in a network of ADSL access network is deployed has been simulated by using NS2 Three variants of TCP, it means SACK TCP, New Reno TCP, and TCP Vegas, have been considered for the cross traffic. The results show that TCP Vegas is able to maintain a constant speed while racing against the game traffic, since it avoids the packet loss and the delays in the tail caused by high peaks, without increasing the size of the sender window. SACK TCP and TCP New Reno, on the other hand, tend to increase continuously the sender window size, which could allow a greater loss of packages and also to cause unwanted delays for the game traffic.

  4. Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles

    OpenAIRE

    Wan, Jiafu; Liu, Jianqi; Shao, Zehui; Vasilakos, Athanasios V.; Imran, Muhammad; Zhou, Keliang

    2016-01-01

    The advances in wireless communication techniques, mobile cloud computing, automotive and intelligent terminal technology are driving the evolution of vehicle ad hoc networks into the Internet of Vehicles (IoV) paradigm. This leads to a change in the vehicle routing problem from a calculation based on static data towards real-time traffic prediction. In this paper, we first address the taxonomy of cloud-assisted IoV from the viewpoint of the service relationship between cloud computing and Io...

  5. Machine learning for identifying botnet network traffic

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2013-01-01

    . Due to promise of non-invasive and resilient detection, botnet detection based on network traffic analysis has drawn a special attention of the research community. Furthermore, many authors have turned their attention to the use of machine learning algorithms as the mean of inferring botnet......-related knowledge from the monitored traffic. This paper presents a review of contemporary botnet detection methods that use machine learning as a tool of identifying botnet-related traffic. The main goal of the paper is to provide a comprehensive overview on the field by summarizing current scientific efforts....... The contribution of the paper is three-fold. First, the paper provides a detailed insight on the existing detection methods by investigating which bot-related heuristic were assumed by the detection systems and how different machine learning techniques were adapted in order to capture botnet-related knowledge...

  6. Routing strategies in traffic network and phase transition in network ...

    Indian Academy of Sciences (India)

    3Department of Electronic Engineering, City University of Hong Kong, Hong Kong ... Routing strategy; network traffic flow; hysteretic loop; phase transition from ... ered from two aspects: modifying the underlying network structure or developing ... capacity corresponds to α = −1 in the case of identical nodes' delivering ability.

  7. A Traffic Prediction Algorithm for Street Lighting Control Efficiency

    Directory of Open Access Journals (Sweden)

    POPA Valentin

    2013-01-01

    Full Text Available This paper presents the development of a traffic prediction algorithm that can be integrated in a street lighting monitoring and control system. The prediction algorithm must enable the reduction of energy costs and improve energy efficiency by decreasing the light intensity depending on the traffic level. The algorithm analyses and processes the information received at the command center based on the traffic level at different moments. The data is collected by means of the Doppler vehicle detection sensors integrated within the system. Thus, two methods are used for the implementation of the algorithm: a neural network and a k-NN (k-Nearest Neighbor prediction algorithm. For 500 training cycles, the mean square error of the neural network is 9.766 and for 500.000 training cycles the error amounts to 0.877. In case of the k-NN algorithm the error increases from 8.24 for k=5 to 12.27 for a number of 50 neighbors. In terms of a root means square error parameter, the use of a neural network ensures the highest performance level and can be integrated in a street lighting control system.

  8. Traffic measurement for big network data

    CERN Document Server

    Chen, Shigang; Xiao, Qingjun

    2017-01-01

    This book presents several compact and fast methods for online traffic measurement of big network data. It describes challenges of online traffic measurement, discusses the state of the field, and provides an overview of the potential solutions to major problems. The authors introduce the problem of per-flow size measurement for big network data and present a fast and scalable counter architecture, called Counter Tree, which leverages a two-dimensional counter sharing scheme to achieve far better memory efficiency and significantly extend estimation range. Unlike traditional approaches to cardinality estimation problems that allocate a separated data structure (called estimator) for each flow, this book takes a different design path by viewing all the flows together as a whole: each flow is allocated with a virtual estimator, and these virtual estimators share a common memory space. A framework of virtual estimators is designed to apply the idea of sharing to an array of cardinality estimation solutions, achi...

  9. Nonlinearity and chaos in wireless network traffic

    International Nuclear Information System (INIS)

    Mukherjee, Somenath; Ray, Rajdeep; Samanta, Rajkumar; Khondekar, Mofazzal H.; Sanyal, Goutam

    2017-01-01

    The natural complexity of wireless mobile network traffic dynamics has been assessed in this article by tracing the presence of nonlinearity and chaos in the profile of daily peak hour call arrival and daily call drop of a sub-urban local mobile switching centre. The tools like Recurrence Plot and Recurrence Quantification Analysis (RQA) has been used to reveal the probable presence of non-stationarity, nonlinearity and chaosity in the network traffic. Information Entropy (IE) and 0–1 test have been employed to provide the quantitative support to the findings. Both the daily peak hour call arrival profile and the daily call drop profile exhibit non-stationarity, determinism and nonlinearity with the former one being more regular while the later one is chaotic.

  10. Discovering urban mobility patterns with PageRank based traffic modeling and prediction

    Science.gov (United States)

    Wang, Minjie; Yang, Su; Sun, Yi; Gao, Jun

    2017-11-01

    Urban transportation system can be viewed as complex network with time-varying traffic flows as links to connect adjacent regions as networked nodes. By computing urban traffic evolution on such temporal complex network with PageRank, it is found that for most regions, there exists a linear relation between the traffic congestion measure at present time and the PageRank value of the last time. Since the PageRank measure of a region does result from the mutual interactions of the whole network, it implies that the traffic state of a local region does not evolve independently but is affected by the evolution of the whole network. As a result, the PageRank values can act as signatures in predicting upcoming traffic congestions. We observe the aforementioned laws experimentally based on the trajectory data of 12000 taxies in Beijing city for one month.

  11. Scheduling Network Traffic for Grid Purposes

    DEFF Research Database (Denmark)

    Gamst, Mette

    This thesis concerns scheduling of network traffic in grid context. Grid computing consists of a number of geographically distributed computers, which work together for solving large problems. The computers are connected through a network. When scheduling job execution in grid computing, data...... transmission has so far not been taken into account. This causes stability problems, because data transmission takes time and thus causes delays to the execution plan. This thesis proposes the integration of job scheduling and network routing. The scientific contribution is based on methods from operations...... research and consists of six papers. The first four considers data transmission in grid context. The last two solves the data transmission problem, where the number of paths per data connection is bounded from above. The thesis shows that it is possible to solve the integrated job scheduling and network...

  12. The effects of redundancy and information manipulation on traffic networks

    OpenAIRE

    Özel, Berk; Ozel, Berk

    2014-01-01

    Traffic congestion is one of the most frequently encountered problems in real life. It is not only a scientific concern of scholars, but also an inevitable issue for most of the individuals living in urban areas. Since every driver in traffic networks tries to minimize own journey length, and volume of the traffic prevents coordination between individuals, a cooperative behavior will not be provided spontaneously in order to decrease the total cost of the network and the time spent on traffic...

  13. Vehicular traffic noise prediction using soft computing approach.

    Science.gov (United States)

    Singh, Daljeet; Nigam, S P; Agrawal, V P; Kumar, Maneek

    2016-12-01

    A new approach for the development of vehicular traffic noise prediction models is presented. Four different soft computing methods, namely, Generalized Linear Model, Decision Trees, Random Forests and Neural Networks, have been used to develop models to predict the hourly equivalent continuous sound pressure level, Leq, at different locations in the Patiala city in India. The input variables include the traffic volume per hour, percentage of heavy vehicles and average speed of vehicles. The performance of the four models is compared on the basis of performance criteria of coefficient of determination, mean square error and accuracy. 10-fold cross validation is done to check the stability of the Random Forest model, which gave the best results. A t-test is performed to check the fit of the model with the field data. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Minimal-delay traffic grooming for WDM star networks

    Science.gov (United States)

    Choi, Hongsik; Garg, Nikhil; Choi, Hyeong-Ah

    2003-10-01

    All-optical networks face the challenge of reducing slower opto-electronic conversions by managing assignment of traffic streams to wavelengths in an intelligent manner, while at the same time utilizing bandwidth resources to the maximum. This challenge becomes harder in networks closer to the end users that have insufficient data to saturate single wavelengths as well as traffic streams outnumbering the usable wavelengths, resulting in traffic grooming which requires costly traffic analysis at access nodes. We study the problem of traffic grooming that reduces the need to analyze traffic, for a class of network architecture most used by Metropolitan Area Networks; the star network. The problem being NP-complete, we provide an efficient twice-optimal-bound greedy heuristic for the same, that can be used to intelligently groom traffic at the LANs to reduce latency at the access nodes. Simulation results show that our greedy heuristic achieves a near-optimal solution.

  15. Network Traffic Monitoring Using Poisson Dynamic Linear Models

    Energy Technology Data Exchange (ETDEWEB)

    Merl, D. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2011-05-09

    In this article, we discuss an approach for network forensics using a class of nonstationary Poisson processes with embedded dynamic linear models. As a modeling strategy, the Poisson DLM (PoDLM) provides a very flexible framework for specifying structured effects that may influence the evolution of the underlying Poisson rate parameter, including diurnal and weekly usage patterns. We develop a novel particle learning algorithm for online smoothing and prediction for the PoDLM, and demonstrate the suitability of the approach to real-time deployment settings via a new application to computer network traffic monitoring.

  16. Routing strategies in traffic network and phase transition in network ...

    Indian Academy of Sciences (India)

    The dynamics of information traffic over scale-free networks has been investigated systematically. A series of routing strategies of data packets have been proposed, including the local routing strategy, the next-nearest-neighbour routing strategy, and the mixed routing strategy based on local static and dynamic information.

  17. Breakdown in traffic networks fundamentals of transportation science

    CERN Document Server

    Kerner, Boris S

    2017-01-01

    This book offers a detailed investigation of breakdowns in traffic and transportation networks. It shows empirically that transitions from free flow to so-called synchronized flow, initiated by local disturbances at network bottlenecks, display a nucleation-type behavior: while small disturbances in free flow decay, larger ones grow further and lead to breakdowns at the bottlenecks. Further, it discusses in detail the significance of this nucleation effect for traffic and transportation theories, and the consequences this has for future automatic driving, traffic control, dynamic traffic assignment, and optimization in traffic and transportation networks. Starting from a large volume of field traffic data collected from various sources obtained solely through measurements in real world traffic, the author develops his insights, with an emphasis less on reviewing existing methodologies, models and theories, and more on providing a detailed analysis of empirical traffic data and drawing consequences regarding t...

  18. Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method

    Directory of Open Access Journals (Sweden)

    Yuhan Jia

    2017-01-01

    Full Text Available Accurate traffic flow prediction is increasingly essential for successful traffic modeling, operation, and management. Traditional data driven traffic flow prediction approaches have largely assumed restrictive (shallow model architectures and do not leverage the large amount of environmental data available. Inspired by deep learning methods with more complex model architectures and effective data mining capabilities, this paper introduces the deep belief network (DBN and long short-term memory (LSTM to predict urban traffic flow considering the impact of rainfall. The rainfall-integrated DBN and LSTM can learn the features of traffic flow under various rainfall scenarios. Experimental results indicate that, with the consideration of additional rainfall factor, the deep learning predictors have better accuracy than existing predictors and also yield improvements over the original deep learning models without rainfall input. Furthermore, the LSTM can outperform the DBN to capture the time series characteristics of traffic flow data.

  19. Real-time network traffic classification technique for wireless local area networks based on compressed sensing

    Science.gov (United States)

    Balouchestani, Mohammadreza

    2017-05-01

    Network traffic or data traffic in a Wireless Local Area Network (WLAN) is the amount of network packets moving across a wireless network from each wireless node to another wireless node, which provide the load of sampling in a wireless network. WLAN's Network traffic is the main component for network traffic measurement, network traffic control and simulation. Traffic classification technique is an essential tool for improving the Quality of Service (QoS) in different wireless networks in the complex applications such as local area networks, wireless local area networks, wireless personal area networks, wireless metropolitan area networks, and wide area networks. Network traffic classification is also an essential component in the products for QoS control in different wireless network systems and applications. Classifying network traffic in a WLAN allows to see what kinds of traffic we have in each part of the network, organize the various kinds of network traffic in each path into different classes in each path, and generate network traffic matrix in order to Identify and organize network traffic which is an important key for improving the QoS feature. To achieve effective network traffic classification, Real-time Network Traffic Classification (RNTC) algorithm for WLANs based on Compressed Sensing (CS) is presented in this paper. The fundamental goal of this algorithm is to solve difficult wireless network management problems. The proposed architecture allows reducing False Detection Rate (FDR) to 25% and Packet Delay (PD) to 15 %. The proposed architecture is also increased 10 % accuracy of wireless transmission, which provides a good background for establishing high quality wireless local area networks.

  20. An analysis of network traffic classification for botnet detection

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2015-01-01

    of detecting botnet network traffic using three methods that target protocols widely considered as the main carriers of botnet Command and Control (C&C) and attack traffic, i.e. TCP, UDP and DNS. We propose three traffic classification methods based on capable Random Forests classifier. The proposed methods...

  1. Road traffic noise prediction model for heterogeneous traffic based on ASJ-RTN Model 2008 with consideration of horn

    Science.gov (United States)

    Hustim, M.; Arifin, Z.; Aly, S. H.; Ramli, M. I.; Zakaria, R.; Liputo, A.

    2018-04-01

    This research aimed to predict the noise produced by the traffic in the road network in Makassar City using ASJ-RTN Model 2008 by calculating the horn sound. Observations were taken at 37 survey points on road side. The observations were conducted at 06.00 - 18.00 and 06.00 - 21.00 which research objects were motorcycle (MC), light vehicle (LV) and heavy vehicle (HV). The observed data were traffic volume, vehicle speed, number of horn and traffic noise using Sound Level Meter Tenmars TM-103. The research result indicates that prediction noise model by calculating the horn sound produces the average noise level value of 78.5 dB having the Pearson’s correlation and RMSE of 0.95 and 0.87. Therefore, ASJ-RTN Model 2008 prediction model by calculating the horn sound is said to be sufficiently good for predicting noise level.

  2. Robust, Optimal, Predictive, and Integrated Road Traffic Control : Research proposal

    NARCIS (Netherlands)

    Van de Weg, G.S.; Hegyi, A.; Hoogendoorn, S.P.

    2014-01-01

    The development of control strategies for traffic lights, ramp metering installations, and variable speed limits to improve the throughput of road traffic networks can contribute to a more efficient use of road networks. In this project, a hierarchical controller will be developed for the

  3. Traffic sharing algorithms for hybrid mobile networks

    Science.gov (United States)

    Arcand, S.; Murthy, K. M. S.; Hafez, R.

    1995-01-01

    In a hybrid (terrestrial + satellite) mobile personal communications networks environment, a large size satellite footprint (supercell) overlays on a large number of smaller size, contiguous terrestrial cells. We assume that the users have either a terrestrial only single mode terminal (SMT) or a terrestrial/satellite dual mode terminal (DMT) and the ratio of DMT to the total terminals is defined gamma. It is assumed that the call assignments to and handovers between terrestrial cells and satellite supercells take place in a dynamic fashion when necessary. The objectives of this paper are twofold, (1) to propose and define a class of traffic sharing algorithms to manage terrestrial and satellite network resources efficiently by handling call handovers dynamically, and (2) to analyze and evaluate the algorithms by maximizing the traffic load handling capability (defined in erl/cell) over a wide range of terminal ratios (gamma) given an acceptable range of blocking probabilities. Two of the algorithms (G & S) in the proposed class perform extremely well for a wide range of gamma.

  4. Consensus-Based Cooperative Control Based on Pollution Sensing and Traffic Information for Urban Traffic Networks.

    Science.gov (United States)

    Artuñedo, Antonio; Del Toro, Raúl M; Haber, Rodolfo E

    2017-04-26

    Nowadays many studies are being conducted to develop solutions for improving the performance of urban traffic networks. One of the main challenges is the necessary cooperation among different entities such as vehicles or infrastructure systems and how to exploit the information available through networks of sensors deployed as infrastructures for smart cities. In this work an algorithm for cooperative control of urban subsystems is proposed to provide a solution for mobility problems in cities. The interconnected traffic lights controller ( TLC ) network adapts traffic lights cycles, based on traffic and air pollution sensory information, in order to improve the performance of urban traffic networks. The presence of air pollution in cities is not only caused by road traffic but there are other pollution sources that contribute to increase or decrease the pollution level. Due to the distributed and heterogeneous nature of the different components involved, a system of systems engineering approach is applied to design a consensus-based control algorithm. The designed control strategy contains a consensus-based component that uses the information shared in the network for reaching a consensus in the state of TLC network components. Discrete event systems specification is applied for modelling and simulation. The proposed solution is assessed by simulation studies with very promising results to deal with simultaneous responses to both pollution levels and traffic flows in urban traffic networks.

  5. Consensus-Based Cooperative Control Based on Pollution Sensing and Traffic Information for Urban Traffic Networks

    Directory of Open Access Journals (Sweden)

    Antonio Artuñedo

    2017-04-01

    Full Text Available Nowadays many studies are being conducted to develop solutions for improving the performance of urban traffic networks. One of the main challenges is the necessary cooperation among different entities such as vehicles or infrastructure systems and how to exploit the information available through networks of sensors deployed as infrastructures for smart cities. In this work an algorithm for cooperative control of urban subsystems is proposed to provide a solution for mobility problems in cities. The interconnected traffic lights controller (TLC network adapts traffic lights cycles, based on traffic and air pollution sensory information, in order to improve the performance of urban traffic networks. The presence of air pollution in cities is not only caused by road traffic but there are other pollution sources that contribute to increase or decrease the pollution level. Due to the distributed and heterogeneous nature of the different components involved, a system of systems engineering approach is applied to design a consensus-based control algorithm. The designed control strategy contains a consensus-based component that uses the information shared in the network for reaching a consensus in the state of TLC network components. Discrete event systems specification is applied for modelling and simulation. The proposed solution is assessed by simulation studies with very promising results to deal with simultaneous responses to both pollution levels and traffic flows in urban traffic networks.

  6. Learning Behavior Models for Interpreting and Predicting Traffic Situations

    OpenAIRE

    Gindele, Tobias

    2014-01-01

    In this thesis, we present Bayesian state estimation and machine learning methods for predicting traffic situations. The cognitive ability to assess situations and behaviors of traffic participants, and to anticipate possible developments is an essential requirement for several applications in the traffic domain, especially for self-driving cars. We present a method for learning behavior models from unlabeled traffic observations and develop improved learning methods for decision trees.

  7. Research of convolutional neural networks for traffic sign recognition

    OpenAIRE

    Stadalnikas, Kasparas

    2017-01-01

    In this thesis the convolutional neural networks application for traffic sign recognition is analyzed. Thesis describes the basic operations, techniques that are commonly used to apply in the image classification using convolutional neural networks. Also, this paper describes the data sets used for traffic sign recognition, their problems affecting the final training results. The paper reviews most popular existing technologies – frameworks for developing the solution for traffic sign recogni...

  8. Analysis and Classification of Traffic in Wireless Sensor Network

    National Research Council Canada - National Science Library

    Beng, Wang W

    2007-01-01

    .... Specifically, this thesis studied the traffic generated by wireless sensor networks by setting up two different commonly used network topologies, namely a direct connection to the base and a daisy...

  9. Efficient Algorithms for Network-Wide Road Traffic Control

    NARCIS (Netherlands)

    van de Weg, G.S.

    2017-01-01

    Controlling road traffic networks is a complex problem. One of the difficulties is the coordination of actuators, such as traffic lights, variables speed limits, ramp metering and route guidance, with the aim to improve the network performance over a near-future time horizon. This dissertation

  10. Self-Adapting Routing Overlay Network for Frequently Changing Application Traffic in Content-Based Publish/Subscribe System

    Directory of Open Access Journals (Sweden)

    Meng Chi

    2014-01-01

    Full Text Available In the large-scale distributed simulation area, the topology of the overlay network cannot always rapidly adapt to frequently changing application traffic to reduce the overall traffic cost. In this paper, we propose a self-adapting routing strategy for frequently changing application traffic in content-based publish/subscribe system. The strategy firstly trains the traffic information and then uses this training information to predict the application traffic in the future. Finally, the strategy reconfigures the topology of the overlay network based on this predicting information to reduce the overall traffic cost. A predicting path is also introduced in this paper to reduce the reconfiguration numbers in the process of the reconfigurations. Compared to other strategies, the experimental results show that the strategy proposed in this paper could reduce the overall traffic cost of the publish/subscribe system in less reconfigurations.

  11. Big Data-Driven Based Real-Time Traffic Flow State Identification and Prediction

    Directory of Open Access Journals (Sweden)

    Hua-pu Lu

    2015-01-01

    Full Text Available With the rapid development of urban informatization, the era of big data is coming. To satisfy the demand of traffic congestion early warning, this paper studies the method of real-time traffic flow state identification and prediction based on big data-driven theory. Traffic big data holds several characteristics, such as temporal correlation, spatial correlation, historical correlation, and multistate. Traffic flow state quantification, the basis of traffic flow state identification, is achieved by a SAGA-FCM (simulated annealing genetic algorithm based fuzzy c-means based traffic clustering model. Considering simple calculation and predictive accuracy, a bilevel optimization model for regional traffic flow correlation analysis is established to predict traffic flow parameters based on temporal-spatial-historical correlation. A two-stage model for correction coefficients optimization is put forward to simplify the bilevel optimization model. The first stage model is built to calculate the number of temporal-spatial-historical correlation variables. The second stage model is present to calculate basic model formulation of regional traffic flow correlation. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods.

  12. Multicast traffic grooming in flexible optical WDM networks

    Science.gov (United States)

    Patel, Ankitkumar N.; Ji, Philip N.; Jue, Jason P.; Wang, Ting

    2012-12-01

    In Metropolitan Area Networks (MANs), point-to-multipoint applications, such as IPTV, video-on-demand, distance learning, and content distribution, can be efficiently supported through light-tree-based multicastcommunications instead of lightpath-based unicast-communications. The application of multicasting for such traffic is justified by its inherent benefits of reduced control and management overhead and elimination of redundant resource provisioning. Supporting such multicast traffic in Flexible optical WDM (FWDM) networks that can provision light-trees using optimum amount of spectrum within flexible channel spacing leads to higher wavelength and spectral efficiencies compared to the conventional ITU-T fixed grid networks. However, in spite of such flexibility, the residual channel capacity of stranded channels may not be utilized if the network does not offer channels with arbitrary line rates. Additionally, the spectrum allocated to guard bands used to isolate finer granularity channels remains unutilized. These limitations can be addressed by using traffic grooming in which low-rate multicast connections are aggregated and switched over high capacity light-trees. In this paper, we address the multicast traffic grooming problem in FWDM networks, and propose a novel auxiliary graph-based algorithm for the first time. The performance of multicast traffic grooming is evaluated in terms of spectral, cost, and energy efficiencies compared to lightpath-based transparent FWDM networks, lightpathbased traffic grooming-capable FWDM networks, multicast-enabled transparent FWDM networks, and multicast traffic grooming-capable fixed grid networks. Simulation results demonstrate that multicast traffic grooming in FWDM networks not only improves spectral efficiency, but also cost, and energy efficiencies compared to other multicast traffic provisioning approaches of FWDM and fixed grid networks.

  13. Accurate Multisteps Traffic Flow Prediction Based on SVM

    Directory of Open Access Journals (Sweden)

    Zhang Mingheng

    2013-01-01

    Full Text Available Accurate traffic flow prediction is prerequisite and important for realizing intelligent traffic control and guidance, and it is also the objective requirement for intelligent traffic management. Due to the strong nonlinear, stochastic, time-varying characteristics of urban transport system, artificial intelligence methods such as support vector machine (SVM are now receiving more and more attentions in this research field. Compared with the traditional single-step prediction method, the multisteps prediction has the ability that can predict the traffic state trends over a certain period in the future. From the perspective of dynamic decision, it is far important than the current traffic condition obtained. Thus, in this paper, an accurate multi-steps traffic flow prediction model based on SVM was proposed. In which, the input vectors were comprised of actual traffic volume and four different types of input vectors were compared to verify their prediction performance with each other. Finally, the model was verified with actual data in the empirical analysis phase and the test results showed that the proposed SVM model had a good ability for traffic flow prediction and the SVM-HPT model outperformed the other three models for prediction.

  14. Sonification of network traffic flow for monitoring and situational awareness

    Science.gov (United States)

    2018-01-01

    Maintaining situational awareness of what is happening within a computer network is challenging, not only because the behaviour happens within machines, but also because data traffic speeds and volumes are beyond human ability to process. Visualisation techniques are widely used to present information about network traffic dynamics. Although they provide operators with an overall view and specific information about particular traffic or attacks on the network, they often still fail to represent the events in an understandable way. Also, because they require visual attention they are not well suited to continuous monitoring scenarios in which network administrators must carry out other tasks. Here we present SoNSTAR (Sonification of Networks for SiTuational AwaReness), a real-time sonification system for monitoring computer networks to support network administrators’ situational awareness. SoNSTAR provides an auditory representation of all the TCP/IP traffic within a network based on the different traffic flows between between network hosts. A user study showed that SoNSTAR raises situational awareness levels by enabling operators to understand network behaviour and with the benefit of lower workload demands (as measured by the NASA TLX method) than visual techniques. SoNSTAR identifies network traffic features by inspecting the status flags of TCP/IP packet headers. Combinations of these features define particular traffic events which are mapped to recorded sounds to generate a soundscape that represents the real-time status of the network traffic environment. The sequence, timing, and loudness of the different sounds allow the network to be monitored and anomalous behaviour to be detected without the need to continuously watch a monitor screen. PMID:29672543

  15. Sonification of network traffic flow for monitoring and situational awareness.

    Science.gov (United States)

    Debashi, Mohamed; Vickers, Paul

    2018-01-01

    Maintaining situational awareness of what is happening within a computer network is challenging, not only because the behaviour happens within machines, but also because data traffic speeds and volumes are beyond human ability to process. Visualisation techniques are widely used to present information about network traffic dynamics. Although they provide operators with an overall view and specific information about particular traffic or attacks on the network, they often still fail to represent the events in an understandable way. Also, because they require visual attention they are not well suited to continuous monitoring scenarios in which network administrators must carry out other tasks. Here we present SoNSTAR (Sonification of Networks for SiTuational AwaReness), a real-time sonification system for monitoring computer networks to support network administrators' situational awareness. SoNSTAR provides an auditory representation of all the TCP/IP traffic within a network based on the different traffic flows between between network hosts. A user study showed that SoNSTAR raises situational awareness levels by enabling operators to understand network behaviour and with the benefit of lower workload demands (as measured by the NASA TLX method) than visual techniques. SoNSTAR identifies network traffic features by inspecting the status flags of TCP/IP packet headers. Combinations of these features define particular traffic events which are mapped to recorded sounds to generate a soundscape that represents the real-time status of the network traffic environment. The sequence, timing, and loudness of the different sounds allow the network to be monitored and anomalous behaviour to be detected without the need to continuously watch a monitor screen.

  16. Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection.

    Directory of Open Access Journals (Sweden)

    Su Yang

    Full Text Available Spatial-temporal correlations among the data play an important role in traffic flow prediction. Correspondingly, traffic modeling and prediction based on big data analytics emerges due to the city-scale interactions among traffic flows. A new methodology based on sparse representation is proposed to reveal the spatial-temporal dependencies among traffic flows so as to simplify the correlations among traffic data for the prediction task at a given sensor. Three important findings are observed in the experiments: (1 Only traffic flows immediately prior to the present time affect the formation of current traffic flows, which implies the possibility to reduce the traditional high-order predictors into an 1-order model. (2 The spatial context relevant to a given prediction task is more complex than what is assumed to exist locally and can spread out to the whole city. (3 The spatial context varies with the target sensor undergoing prediction and enlarges with the increment of time lag for prediction. Because the scope of human mobility is subject to travel time, identifying the varying spatial context against time lag is crucial for prediction. Since sparse representation can capture the varying spatial context to adapt to the prediction task, it outperforms the traditional methods the inputs of which are confined as the data from a fixed number of nearby sensors. As the spatial-temporal context for any prediction task is fully detected from the traffic data in an automated manner, where no additional information regarding network topology is needed, it has good scalability to be applicable to large-scale networks.

  17. Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection.

    Science.gov (United States)

    Yang, Su; Shi, Shixiong; Hu, Xiaobing; Wang, Minjie

    2015-01-01

    Spatial-temporal correlations among the data play an important role in traffic flow prediction. Correspondingly, traffic modeling and prediction based on big data analytics emerges due to the city-scale interactions among traffic flows. A new methodology based on sparse representation is proposed to reveal the spatial-temporal dependencies among traffic flows so as to simplify the correlations among traffic data for the prediction task at a given sensor. Three important findings are observed in the experiments: (1) Only traffic flows immediately prior to the present time affect the formation of current traffic flows, which implies the possibility to reduce the traditional high-order predictors into an 1-order model. (2) The spatial context relevant to a given prediction task is more complex than what is assumed to exist locally and can spread out to the whole city. (3) The spatial context varies with the target sensor undergoing prediction and enlarges with the increment of time lag for prediction. Because the scope of human mobility is subject to travel time, identifying the varying spatial context against time lag is crucial for prediction. Since sparse representation can capture the varying spatial context to adapt to the prediction task, it outperforms the traditional methods the inputs of which are confined as the data from a fixed number of nearby sensors. As the spatial-temporal context for any prediction task is fully detected from the traffic data in an automated manner, where no additional information regarding network topology is needed, it has good scalability to be applicable to large-scale networks.

  18. A measure theoretic approach to traffic flow optimization on networks

    OpenAIRE

    Cacace, Simone; Camilli, Fabio; De Maio, Raul; Tosin, Andrea

    2018-01-01

    We consider a class of optimal control problems for measure-valued nonlinear transport equations describing traffic flow problems on networks. The objective isto minimise/maximise macroscopic quantities, such as traffic volume or average speed,controlling few agents, for example smart traffic lights and automated cars. The measuretheoretic approach allows to study in a same setting local and nonlocal drivers interactionsand to consider the control variables as additional measures interacting ...

  19. Outer Synchronization between Two Coupled Complex Networks and Its Application in Public Traffic Supernetwork

    Directory of Open Access Journals (Sweden)

    Wen-ju Du

    2016-01-01

    Full Text Available The paper presents a new urban public traffic supernetwork model by using the existing bus network modeling method, consisting of the conventional bus traffic network and the urban rail traffic network. We investigate the synchronization problem of urban public traffic supernetwork model by using the coupled complex network’s outer synchronization theory. Analytical and numerical simulations are given to illustrate the impact of traffic dispatching frequency and traffic lines optimization to the urban public traffic supernetwork balance.

  20. Effects of node buffer and capacity on network traffic

    International Nuclear Information System (INIS)

    Ling Xiang; Ding Jian-Xun; Hu Mao-Bin

    2012-01-01

    In this paper, we study the optimization of network traffic by considering the effects of node buffer ability and capacity. Two node buffer settings are considered. The node capacity is considered to be proportional to its buffer ability. The node effects on network traffic systems are studied with the shortest path protocol and an extension of the optimal routing [Phys. Rev. E 74 046106 (2006)]. In the diagrams of flux—density relationships, it is shown that a nodes buffer ability and capacity have profound effects on the network traffic

  1. Cooperative driving in mixed traffic networks - Optimizing for performance

    NARCIS (Netherlands)

    Calvert, S.C.; Broek, T.H.A. van den; Noort, M. van

    2012-01-01

    This paper discusses a cooperative adaptive cruise control application and its effects on the traffic system. In previous work this application has been tested on the road, and traffic simulation has been used to scale up the results of the field test to larger networks and more vehicles. The

  2. A Bayes Theory-Based Modeling Algorithm to End-to-end Network Traffic

    OpenAIRE

    Zhao Hong-hao; Meng Fan-bo; Zhao Si-wen; Zhao Si-hang; Lu Yi

    2016-01-01

    Recently, network traffic has exponentially increasing due to all kind of applications, such as mobile Internet, smart cities, smart transportations, Internet of things, and so on. the end-to-end network traffic becomes more important for traffic engineering. Usually end-to-end traffic estimation is highly difficult. This paper proposes a Bayes theory-based method to model the end-to-end network traffic. Firstly, the end-to-end network traffic is described as a independent identically distrib...

  3. Joint QoS and Congestion Control Based on Traffic Prediction in SDN

    Directory of Open Access Journals (Sweden)

    Mohammad Mahdi Tajiki

    2017-12-01

    Full Text Available Due to the various network requirements of applications, quality of service (QoS-aware routing plays an important role in the networks. Recently proposed resource allocation algorithms focus on the current traffic matrix, which is not applicable for dynamic networks. In this paper, we exploit an estimation of flow matrix that gives our scheme the ability to sufficiently reduce the total packet loss and simultaneously raise the network throughput. In this way, we mathematically formulate the QoS-aware resource reallocation in software-defined networking (SDN networks based on the traffic prediction. To solve this optimization problem, two schemes are proposed: (i exact solution; and (ii fast suboptimal one. The proposed schemes are compared with the accuracy perspective. Moreover, the impact of prediction on resource reallocation is discussed. In this regard, it is shown that, compared with the conventional scheme, the proposed scheme decreases the packet loss and increases the throughput significantly.

  4. Wavelength converter placement in optical networks with dynamic traffic

    DEFF Research Database (Denmark)

    Buron, Jakob Due; Ruepp, Sarah Renée; Wessing, Henrik

    2008-01-01

    We evaluate the connection provisioning performance of GMPLS-controlled wavelength routed networks under dynamic traffic load and using three different wavelength converter placement heuristics. Results show that a simple uniform placement heuristic matches the performance of complex heuristics...

  5. Using OpenSSH to secure mobile LAN network traffic

    Science.gov (United States)

    Luu, Brian B.; Gopaul, Richard D.

    2002-08-01

    Mobile Internet Protocol (IP) Local Area Network (LAN) is a technique, developed by the U.S. Army Research Laboratory, which allows a LAN to be IP mobile when attaching to a foreign IP-based network and using this network as a means to retain connectivity to its home network. In this paper, we describe a technique that uses Open Secure Shell (OpenSSH) software to ensure secure, encrypted transmission of a mobile LAN's network traffic. Whenever a mobile LAN, implemented with Mobile IP LAN, moves to a foreign network, its gateway (router) obtains an IP address from the new network. IP tunnels, using IP encapsulation, are then established from the gateway through the foreign network to a home agent on its home network. These tunnels provide a virtual two-way connection to the home network for the mobile LAN as if the LAN were connected directly to its home network. Hence, when IP mobile, a mobile LAN's tunneled network traffic must traverse one or more foreign networks that may not be trusted. This traffic could be subject to eavesdropping, interception, modification, or redirection by malicious nodes in these foreign networks. To protect network traffic passing through the tunnels, OpenSSH is used as a means of encryption because it prevents surveillance, modification, and redirection of mobile LAN traffic passing across foreign networks. Since the software is found in the public domain, is available for most current operating systems, and is commonly used to provide secure network communications, OpenSSH is the software of choice.

  6. Validation of a multi-objective, predictive urban traffic model

    NARCIS (Netherlands)

    Wilmink, I.R.; Haak, P. van den; Woldeab, Z.; Vreeswijk, J.

    2013-01-01

    This paper describes the results of the verification and validation of the ecoStrategic Model, which was developed, implemented and tested in the eCoMove project. The model uses real-time and historical traffic information to determine the current, predicted and desired state of traffic in a

  7. Traffic sign recognition with deep convolutional neural networks

    OpenAIRE

    Karamatić, Boris

    2016-01-01

    The problem of detection and recognition of traffic signs is becoming an important problem when it comes to the development of self driving cars and advanced driver assistance systems. In this thesis we will develop a system for detection and recognition of traffic signs. For the problem of detection we will use aggregate channel features and for the problem of recognition we will use a deep convolutional neural network. We will describe how convolutional neural networks work, how they are co...

  8. Behavioral Profiling of Scada Network Traffic Using Machine Learning Algorithms

    Science.gov (United States)

    2014-03-27

    Acquisition ( SCADA ) System Overview SCADA systems control and monitor processes for water distribution, oil and natural gas pipelines , electrical...the desire for remote control and monitoring of industrial processes. The ability to identify SCADA devices on a mixed traffic network with zero...optimal attribute subset, while maintaining the desired TPR of .99 for SCADA network traffic. The attributes and ML algorithms chosen for

  9. SAE for the prediction of road traffic status from taxicab operating data and bus smart card data

    Science.gov (United States)

    Zhengfeng, Huang; Pengjun, Zheng; Wenjun, Xu; Gang, Ren

    Road traffic status is significant for trip decision and traffic management, and thus should be predicted accurately. A contribution is that we consider multi-modal data for traffic status prediction than only using single source data. With the substantial data from Ningbo Passenger Transport Management Sector (NPTMS), we wished to determine whether it was possible to develop Stacked Autoencoders (SAEs) for accurately predicting road traffic status from taxicab operating data and bus smart card data. We show that SAE performed better than linear regression model and Back Propagation (BP) neural network for determining the relationship between road traffic status and those factors. In a 26-month data experiment using SAE, we show that it is possible to develop highly accurate predictions (91% test accuracy) of road traffic status from daily taxicab operating data and bus smart card data.

  10. INTELLIGENT TRAFFIC-SAFETY MIRROR BY USING WIRELESS SENSOR NETWORK

    Directory of Open Access Journals (Sweden)

    Peter Danišovič

    2014-03-01

    Full Text Available This article is focused on the problematic of traffic safety, dealing with the problem of car intersections with blocked view crossing by a special wireless sensor network (WSN proposed for the traffic monitoring, concretely for vehicle’s detection at places, where it is necessary. Some ultra-low-power TI products were developed due to this reason: microcontroller MSP430F2232, 868MHz RF transceiver CC1101 and LDO voltage regulator TPS7033. The WSN consist of four network nodes supplied with the special safety lightings which serve the function of intelligent traffic safety mirror.

  11. Switching performance of OBS network model under prefetched real traffic

    Science.gov (United States)

    Huang, Zhenhua; Xu, Du; Lei, Wen

    2005-11-01

    Optical Burst Switching (OBS) [1] is now widely considered as an efficient switching technique in building the next generation optical Internet .So it's very important to precisely evaluate the performance of the OBS network model. The performance of the OBS network model is variable in different condition, but the most important thing is that how it works under real traffic load. In the traditional simulation models, uniform traffics are usually generated by simulation software to imitate the data source of the edge node in the OBS network model, and through which the performance of the OBS network is evaluated. Unfortunately, without being simulated by real traffic, the traditional simulation models have several problems and their results are doubtable. To deal with this problem, we present a new simulation model for analysis and performance evaluation of the OBS network, which uses prefetched IP traffic to be data source of the OBS network model. The prefetched IP traffic can be considered as real IP source of the OBS edge node and the OBS network model has the same clock rate with a real OBS system. So it's easy to conclude that this model is closer to the real OBS system than the traditional ones. The simulation results also indicate that this model is more accurate to evaluate the performance of the OBS network system and the results of this model are closer to the actual situation.

  12. The Effect of Queueing Strategy on Network Traffic

    International Nuclear Information System (INIS)

    Zhang Xue-Jun; Guan Xiang-Min; Sun Deng-Feng; Tang Shao-Ting

    2013-01-01

    In recent years, the transportation system has been faced by increasing challenge in congestion and inefficiency, and research in traffic network has become a significant area of interest. In this paper, we introduce a dynamic-information-based (DIB) queueing strategy into network traffic model under the efficient routing strategy. DIB makes a packet with higher priority to be delivered if there are less packets travelling along its path from the current node to the destination. It is found that, compared with the traditional first-in-first-out (FIFO) queueing strategy, DIB can effectively balance the traffic load of the system via delaying packets to be delivered to congested nodes. Although the network capacity has no obvious changes, some other indexes which reflect transportation efficiency are efficiently improved in the congestion state. Besides, extensive simulation results and discussions are provided to explain the phenomena. The results may provide novel insights for research on traffic systems. (condensed matter: structural, mechanical, and thermal properties)

  13. Betweenness centrality and its applications from modeling traffic flows to network community detection

    Science.gov (United States)

    Ren, Yihui

    As real-world complex networks are heterogeneous structures, not all their components such as nodes, edges and subgraphs carry the same role or importance in the functions performed by the networks: some elements are more critical than others. Understanding the roles of the components of a network is crucial for understanding the behavior of the network as a whole. One the most basic function of networks is transport; transport of vehicles/people, information, materials, forces, etc., and these quantities are transported along edges between source and destination nodes. For this reason, network path-based importance measures, also called centralities, play a crucial role in the understanding of the transport functions of the network and the network's structural and dynamical behavior in general. In this thesis we study the notion of betweenness centrality, which measures the fraction of lowest-cost (or shortest) paths running through a network component, in particular through a node or an edge. High betweenness centrality nodes/edges are those that will be frequently used by the entities transported through the network and thus they play a key role in the overall transport properties of the network. In the first part of the thesis we present a first-principles based method for traffic prediction using a cost-based generalization of the radiation model (emission/absorbtion model) for human mobility, coupled with a cost-minimizing algorithm for efficient distribution of the mobility fluxes through the network. Using US census and highway traffic data, we show that traffic can efficiently and accurately be computed from a range-limited, network betweenness type calculation. The model based on travel time costs captures the log-normal distribution of the traffic and attains a high Pearson correlation coefficient (0.75) when compared with real traffic. We then focus on studying the extent of changes in traffic flows in the wake of a localized damage or alteration to the

  14. A paradox for traffic dynamics in complex networks with ATIS

    International Nuclear Information System (INIS)

    Zheng Jianfeng; Gao Ziyou

    2008-01-01

    In this work, we study the statistical properties of traffic (e.g., vehicles) dynamics in complex networks, by introducing advanced transportation information systems (ATIS). The ATIS can provide the information of traffic flow pattern throughout the network and have an obvious effect on path routing strategy for such vehicles equipped with ATIS. The ATIS can be described by the understanding of link cost functions. Different indices such as efficiency and system total cost are discussed in depth. It is found that, for random networks (scale-free networks), the efficiency is effectively improved (decreased) if ATIS is properly equipped; however the system total cost is largely increased (decreased). It indicates that there exists a paradox between the efficiency and system total cost in complex networks. Furthermore, we report the simulation results by considering different kinds of link cost functions, and the paradox is recovered. Finally, we extend our traffic model, and also find the existence of the paradox

  15. Cooperative Learning for Distributed In-Network Traffic Classification

    Science.gov (United States)

    Joseph, S. B.; Loo, H. R.; Ismail, I.; Andromeda, T.; Marsono, M. N.

    2017-04-01

    Inspired by the concept of autonomic distributed/decentralized network management schemes, we consider the issue of information exchange among distributed network nodes to network performance and promote scalability for in-network monitoring. In this paper, we propose a cooperative learning algorithm for propagation and synchronization of network information among autonomic distributed network nodes for online traffic classification. The results show that network nodes with sharing capability perform better with a higher average accuracy of 89.21% (sharing data) and 88.37% (sharing clusters) compared to 88.06% for nodes without cooperative learning capability. The overall performance indicates that cooperative learning is promising for distributed in-network traffic classification.

  16. Adaptive traffic control systems for urban networks

    Directory of Open Access Journals (Sweden)

    Radivojević Danilo

    2017-01-01

    Full Text Available Adaptive traffic control systems represent complex, but powerful tool for improvement of traffic flow conditions in locations or zones where applied. Many traffic agencies, especially those that have a large number of signalized intersections with high variability of the traffic demand, choose to apply some of the adaptive traffic control systems. However, those systems are manufactured and offered by multiple vendors (companies that are competing for the market share. Due to that fact, besides the information available from the vendors themselves, or the information from different studies conducted on different continents, very limited amount of information is available about the details how those systems are operating. The reason for that is the protecting of the intellectual property from plagiarism. The primary goal of this paper is to make a brief analysis of the functionalities, characteristics, abilities and results of the most recognized, but also less known adaptive traffic control systems to the professional public and other persons with interest in this subject.

  17. Traffic engineering and regenerator placement in GMPLS networks with restoration

    Science.gov (United States)

    Yetginer, Emre; Karasan, Ezhan

    2002-07-01

    In this paper we study regenerator placement and traffic engineering of restorable paths in Generalized Multipro-tocol Label Switching (GMPLS) networks. Regenerators are necessary in optical networks due to transmission impairments. We study a network architecture where there are regenerators at selected nodes and we propose two heuristic algorithms for the regenerator placement problem. Performances of these algorithms in terms of required number of regenerators and computational complexity are evaluated. In this network architecture with sparse regeneration, offline computation of working and restoration paths is studied with bandwidth reservation and path rerouting as the restoration scheme. We study two approaches for selecting working and restoration paths from a set of candidate paths and formulate each method as an Integer Linear Programming (ILP) prob-lem. Traffic uncertainty model is developed in order to compare these methods based on their robustness with respect to changing traffic patterns. Traffic engineering methods are compared based on number of additional demands due to traffic uncertainty that can be carried. Regenerator placement algorithms are also evaluated from a traffic engineering point of view.

  18. Traffic networks as information systems a viability approach

    CERN Document Server

    Aubin, Jean-Pierre

    2017-01-01

    This authored monograph covers a viability to approach to traffic management by advising to vehicles circulated on the network the velocity they should follow for satisfying global traffic conditions;. It presents an investigation of three structural innovations: The objective is to broadcast at each instant and at each position the advised celerity to vehicles, which could be read by auxiliary speedometers or used by cruise control devices. Namely, 1. Construct regulation feedback providing at each time and position advised velocities (celerities) for minimizing congestion or other requirements. 2. Taking into account traffic constraints of different type, the first one being to remain on the roads, to stop at junctions, etc. 3. Use information provided by the probe vehicles equipped with GPS to the traffic regulator; 4. Use other global traffic measures of vehicles provided by different types of sensors; These results are based on convex analysis, intertemporal optimization and viability theory as mathemati...

  19. Model for Predicting Traffic Signs Functional Service Life – The Republic of Croatia Case Study

    Directory of Open Access Journals (Sweden)

    Dario Babić

    2017-06-01

    Full Text Available Traffic signs are the basic elements of communication between the relevant road authorities and road users. They manage, regulate, inform and warn road users to ensure their safe movement throughout transport networks. Traffic signs must be timely visible to all traffic participants in all weather and traffic conditions in order to fulfil their function, which means they must have satisfactory retroreflection properties. This paper presents a research of the deterioration of traffic signs retroreflection. The aim of this article is to develop models that will effectively enable predicting the retroreflection of traffic signs and thus optimize the maintenance activities and replacement of road signs to increase road safety. The research included measurements of retroreflection of retroreflective material Classes I and II (white, red and blue colour and Class III (red and yellow colour. Based on the collected data from the City of Zagreb (Republic of Croatia, the authors developed the models to estimate the functional service life of certain colours and materials used to make traffic signs. Considering that the average coefficient of determination for all the models is between 0.55-0.60, they present an effective tool in the traffic sign maintenance system.

  20. SNAPS : semantic network traffic analysis through projection and selection

    NARCIS (Netherlands)

    Cappers, B.C.M.; van Wijk, J.J.; Harrison, L.; Prigent, N.; Engle, S.; Best, D.; Goodall, J.

    2015-01-01

    Most network traffic analysis applications are designed to discover malicious activity by only relying on high-level flow-based message properties. However, to detect security breaches that are specifically designed to target one network (e.g., Advanced Persistent Threats), deep packet inspection

  1. Optimization of TTEthernet Networks to Support Best-Effort Traffic

    DEFF Research Database (Denmark)

    Tamas-Selicean, Domitian; Pop, Paul

    2014-01-01

    This paper focuses on the optimization of the TTEthernet communication protocol, which offers three traffic classes: time-triggered (TT), sent according to static schedules, rate-constrained (RC) that has bounded end-to-end latency, and best-effort (BE), the classic Ethernet traffic, with no timing...... guarantees. In our earlier work we have proposed an optimization approach named DOTTS that performs the routing, scheduling and packing / fragmenting of TT and RC messages, such that the TT and RC traffic is schedulable. Although backwards compatibility with classic Ethernet networks is one of TTEthernet...

  2. Traffic sign classification with dataset augmentation and convolutional neural network

    Science.gov (United States)

    Tang, Qing; Kurnianggoro, Laksono; Jo, Kang-Hyun

    2018-04-01

    This paper presents a method for traffic sign classification using a convolutional neural network (CNN). In this method, firstly we transfer a color image into grayscale, and then normalize it in the range (-1,1) as the preprocessing step. To increase robustness of classification model, we apply a dataset augmentation algorithm and create new images to train the model. To avoid overfitting, we utilize a dropout module before the last fully connection layer. To assess the performance of the proposed method, the German traffic sign recognition benchmark (GTSRB) dataset is utilized. Experimental results show that the method is effective in classifying traffic signs.

  3. Delay Bound: Fractal Traffic Passes through Network Servers

    Directory of Open Access Journals (Sweden)

    Ming Li

    2013-01-01

    Full Text Available Delay analysis plays a role in real-time systems in computer communication networks. This paper gives our results in the aspect of delay analysis of fractal traffic passing through servers. There are three contributions presented in this paper. First, we will explain the reasons why conventional theory of queuing systems ceases in the general sense when arrival traffic is fractal. Then, we will propose a concise method of delay computation for hard real-time systems as shown in this paper. Finally, the delay computation of fractal traffic passing through severs is presented.

  4. Traffic Adaptive MAC Protocols in Wireless Body Area Networks

    Directory of Open Access Journals (Sweden)

    Farhan Masud

    2017-01-01

    Full Text Available In Wireless Body Area Networks (WBANs, every healthcare application that is based on physical sensors is responsible for monitoring the vital signs data of patient. WBANs applications consist of heterogeneous and dynamic traffic loads. Routine patient’s observation is described as low-load traffic while an alarming situation that is unpredictable by nature is referred to as high-load traffic. This paper offers a thematic review of traffic adaptive Medium Access Control (MAC protocols in WBANs. First, we have categorized them based on their goals, methods, and metrics of evaluation. The Zigbee standard IEEE 802.15.4 and the baseline MAC IEEE 802.15.6 are also reviewed in terms of traffic adaptive approaches. Furthermore, a comparative analysis of the protocols is made and their performances are analyzed in terms of delay, packet delivery ratio (PDR, and energy consumption. The literature shows that no review work has been done on traffic adaptive MAC protocols in WBANs. This review work, therefore, could add enhancement to traffic adaptive MAC protocols and will stimulate a better way of solving the traffic adaptivity problem.

  5. Fragmented network subsystem with traffic filtering for microkernel environment

    Directory of Open Access Journals (Sweden)

    Anna Urievna Budkina

    2016-06-01

    Full Text Available The TCP/IP stack in a microkernel operating system executed in a user space, which requires the development of a distributed network infrastructure within a single software environment. Its functions are the organization of interaction between the components of the stack with different processes, as well as the organization of filtering mechanisms and routing of internal network traffic. Use of architectural approaches applicable in monolithic-modular systems is impossible, because the network stack is not a shareable component of the system. As a consequence, the microkernel environment requires development of special network subsystem. In this work we provide overview of major conceptions of network architectures in microkernel environments. Also, we provide own architecture which supports filtering of internal network traffic. We evaluate the architecture by development of high-performance "key-value" store.

  6. Self-organized natural roads for predicting traffic flow: a sensitivity study

    International Nuclear Information System (INIS)

    Jiang, Bin; Zhao, Sijian; Yin, Junjun

    2008-01-01

    In this paper, we extended road-based topological analysis to both nationwide and urban road networks, and concentrated on a sensitivity study with respect to the formation of self-organized natural roads based on the Gestalt principle of good continuity. Both annual average daily traffic (AADT) and global positioning system (GPS) data were used to correlate with a series of ranking metrics including five centrality-based metrics and two PageRank metrics. It was found that there exists a tipping point from segment-based to road-based network topology in terms of correlation between ranking metrics and their traffic. To our great surprise, (1) this correlation is significantly improved if a selfish rather than utopian strategy is adopted in forming the self-organized natural roads, and (2) point-based metrics assigned by summation into individual roads tend to have a much better correlation with traffic flow than line-based metrics. These counter-intuitive surprising findings constitute emergent properties of self-organized natural roads, which are intelligent enough for predicting traffic flow, thus shedding substantial light on the understanding of road networks and their traffic from the perspective of complex networks

  7. A cyberciege traffic analysis extension for teaching network security

    OpenAIRE

    Chang, Xuquan Stanley.; Chua, Kim Yong.

    2011-01-01

    CyberCIEGE is an interactive game simulating realistic scenarios that teaches the players Information Assurance (IA) concepts. The existing game scenarios only provide a high-level abstraction of the networked environment, e.g., nodes do not have Internet protocol (IP) addresses or belong to proper subnets, and there is no packet-level network simulation. This research explored endowing the game with network level traffic analysis, and implementing a game scenario to take advantage of this ne...

  8. Synchronization between Different Networks with Time-Varying Delay and Its Application in Bilayer Coupled Public Traffic Network

    Directory of Open Access Journals (Sweden)

    Wenju Du

    2016-01-01

    Full Text Available In order to study the dynamic characteristics of urban public traffic network, this paper establishes the conventional bus traffic network and the urban rail traffic network based on the space R modeling method. Then regarding these two networks as the subnetwork, the paper presents a new bilayer coupled public traffic network through the transfer relationship between subway and bus, and this model well reflects the connection between the passengers and bus operating vehicles. Based on the synchronization theory of coupling network with time-varying delay and taking “Lorenz system” as the network node, the paper studies the synchronization of bilayer coupled public traffic network. Finally, numerical results are given to show the impact of public traffic dispatching, delayed departure, the number of public bus stops between bus lines, and the number of transfer stations between two traffic modes on the bilayer coupled public traffic network balance through Matlab simulation.

  9. Estimating Urban Traffic Patterns through Probabilistic Interconnectivity of Road Network Junctions.

    Directory of Open Access Journals (Sweden)

    Ed Manley

    Full Text Available The emergence of large, fine-grained mobility datasets offers significant opportunities for the development and application of new methodologies for transportation analysis. In this paper, the link between routing behaviour and traffic patterns in urban areas is examined, introducing a method to derive estimates of traffic patterns from a large collection of fine-grained routing data. Using this dataset, the interconnectivity between road network junctions is extracted in the form of a Markov chain. This representation encodes the probability of the successive usage of adjacent road junctions, encoding routes as flows between decision points rather than flows along road segments. This network of functional interactions is then integrated within a modified Markov chain Monte Carlo (MCMC framework, adapted for the estimation of urban traffic patterns. As part of this approach, the data-derived links between major junctions influence the movement of directed random walks executed across the network to model origin-destination journeys. The simulation process yields estimates of traffic distribution across the road network. The paper presents an implementation of the modified MCMC approach for London, United Kingdom, building an MCMC model based on a dataset of nearly 700000 minicab routes. Validation of the approach clarifies how each element of the MCMC framework contributes to junction prediction performance, and finds promising results in relation to the estimation of junction choice and minicab traffic distribution. The paper concludes by summarising the potential for the development and extension of this approach to the wider urban modelling domain.

  10. Early Model of Traffic Sign Reminder Based on Neural Network

    Directory of Open Access Journals (Sweden)

    Budi Rahmani

    2012-12-01

    Full Text Available Recognizing the traffic signs installed on the streets is one of the requirements of driving on the road. Laxity in driving may result in traffic accident. This paper describes a real-time reminder model, by utilizing a camera that can be installed in a car to capture image of traffic signs, and is processed and later to inform the driver. The extracting feature harnessing the morphological elements (strel is used in this paper. Artificial Neural Networks is used to train the system and to produce a final decision. The result shows that the accuracy in detecting and recognizing the ten types of traffic signs in real-time is 80%.

  11. Traffic Profiling in Wireless Sensor Networks

    National Research Council Canada - National Science Library

    Kirykos, Georgios

    2006-01-01

    .... Wireless sensor networks pose unique challenges and limitations to the traditional schemes, which are used in the other wireless networks for security protection, and are due mainly to the increased...

  12. A First Look into SCADA Network Traffic

    NARCIS (Netherlands)

    Barbosa, R.R.R.; Sadre, R.; Pras, Aiko

    Supervisory Control and Data Acquisition (SCADA) networks are commonly deployed to aid the operation of critical infrastructures, such as water distribution facilities. These networks provide automated processes that ensure the correct functioning of these infrastructures, in a operation much

  13. Failure cascade in interdependent network with traffic loads

    International Nuclear Information System (INIS)

    Hong, Sheng; Wang, Baoqing; Wang, Jianghui; Zhao, Tingdi; Ma, Xiaomin

    2015-01-01

    Complex networks have been widely studied recent years, but most researches focus on the single, non-interacting networks. With the development of modern systems, many infrastructure networks are coupled together and therefore should be modeled as interdependent networks. For interdependent networks, failure of nodes in one network may lead to failure of dependent nodes in the other networks. This may happen recursively and lead to a failure cascade. In the real world, different networks carry different traffic loads. Overload and load redistribution may lead to more nodes’ failure. Considering the dependency between the interdependent networks and the traffic load, a small fraction of fault nodes may lead to complete fragmentation of a system. Based on the robust analysis of interdependent networks, we propose a costless defense strategy to suppress the failure cascade. Our findings highlight the need to consider the load and coupling preference when designing robust interdependent networks. And it is necessary to take actions in the early stage of the failure cascade to decrease the losses caused by the large-scale breakdown of infrastructure networks. (paper)

  14. Probabilistic Predictions of Traffic Demand for En Route Sectors Based on Individual Flight Data

    Science.gov (United States)

    2010-01-01

    The Traffic Flow Management System (TFMS) predicts the demand for each sector, and traffic managers use these predictions to spot possible congestion and to take measures to prevent it. These predictions of sector demand, however, are currently made ...

  15. Communication efficiency and congestion of signal traffic in large-scale brain networks.

    Science.gov (United States)

    Mišić, Bratislav; Sporns, Olaf; McIntosh, Anthony R

    2014-01-01

    The complex connectivity of the cerebral cortex suggests that inter-regional communication is a primary function. Using computational modeling, we show that anatomical connectivity may be a major determinant for global information flow in brain networks. A macaque brain network was implemented as a communication network in which signal units flowed between grey matter nodes along white matter paths. Compared to degree-matched surrogate networks, information flow on the macaque brain network was characterized by higher loss rates, faster transit times and lower throughput, suggesting that neural connectivity may be optimized for speed rather than fidelity. Much of global communication was mediated by a "rich club" of hub regions: a sub-graph comprised of high-degree nodes that are more densely interconnected with each other than predicted by chance. First, macaque communication patterns most closely resembled those observed for a synthetic rich club network, but were less similar to those seen in a synthetic small world network, suggesting that the former is a more fundamental feature of brain network topology. Second, rich club regions attracted the most signal traffic and likewise, connections between rich club regions carried more traffic than connections between non-rich club regions. Third, a number of rich club regions were significantly under-congested, suggesting that macaque connectivity actively shapes information flow, funneling traffic towards some nodes and away from others. Together, our results indicate a critical role of the rich club of hub nodes in dynamic aspects of global brain communication.

  16. Towards Mining Latent Client Identifiers from Network Traffic

    Directory of Open Access Journals (Sweden)

    Jain Sakshi

    2016-04-01

    Full Text Available Websites extensively track users via identifiers that uniquely map to client machines or user accounts. Although such tracking has desirable properties like enabling personalization and website analytics, it also raises serious concerns about online user privacy, and can potentially enable illicit surveillance by adversaries who broadly monitor network traffic.

  17. Traffic Analysis for Real-Time Communication Networks onboard Ships

    DEFF Research Database (Denmark)

    Schiøler, Henrik; Nielsen, Jens Frederik Dalsgaard; Jørgensen, N.

    1998-01-01

    The paper presents a novel method for establishing worst case estimates of queue lenghts and transmission delays in networks of interconnected segments each of ring topology as defined by the ATOMOS project for marine automation. A non probalistic model for describing traffic is introduced as well...

  18. Traffic Analysis for Real-Time Communication Networks onboard Ships

    DEFF Research Database (Denmark)

    Schiøler, Henrik; Nielsen, Jens Frederik Dalsgaard; Jørgensen, N.

    The paper presents a novel method for establishing worst case estimates of queue lenghts and transmission delays in networks of interconnected segments each of ring topology as defined by the ATOMOS project for marine automation. A non probalistic model for describing traffic is introduced as well...

  19. Energy Saving: Scaling Network Energy Efficiency Faster than Traffic Growth

    NARCIS (Netherlands)

    Chen, Y.; Blume, O.; Gati, A.; Capone, A.; Wu, C.-E.; Barth, U.; Marzetta, T.; Zhang, H.; Xu, S.

    2013-01-01

    As the mobile traffic is expected to continue its exponential growth in the near future, energy efficiency has gradually become a must criterion for wireless network design. Three fundamental questions need to be answered before the detailed design could be carried out, namely what energy efficiency

  20. Simulation of traffic capacity of inland waterway network

    NARCIS (Netherlands)

    Chen, L.; Mou, J.; Ligteringen, H.

    2013-01-01

    The inland waterborne transportation is viewed as an economic, safe and environmentally friendly alternative to the congested road network. The traffic capacity are the critical indicator of the inland shipping performance. Actually, interacted under the complicated factors, it is challenging to

  1. Power Consumption Evaluation of Distributed Computing Network Considering Traffic Locality

    Science.gov (United States)

    Ogawa, Yukio; Hasegawa, Go; Murata, Masayuki

    When computing resources are consolidated in a few huge data centers, a massive amount of data is transferred to each data center over a wide area network (WAN). This results in increased power consumption in the WAN. A distributed computing network (DCN), such as a content delivery network, can reduce the traffic from/to the data center, thereby decreasing the power consumed in the WAN. In this paper, we focus on the energy-saving aspect of the DCN and evaluate its effectiveness, especially considering traffic locality, i.e., the amount of traffic related to the geographical vicinity. We first formulate the problem of optimizing the DCN power consumption and describe the DCN in detail. Then, numerical evaluations show that, when there is strong traffic locality and the router has ideal energy proportionality, the system's power consumption is reduced to about 50% of the power consumed in the case where a DCN is not used; moreover, this advantage becomes even larger (up to about 30%) when the data center is located farthest from the center of the network topology.

  2. Traffic-aware Elastic Optical Networks to leverage Energy Savings

    DEFF Research Database (Denmark)

    Turus, Ioan; Fagertun, Anna Manolova; Dittmann, Lars

    2014-01-01

    Because of the static nature of the deployed optical networks, large energy wastage is experienced today in production networks such as Telecom networks . With power-adaptive optical interfaces and suitable grooming procedures, we propose the design of more energy efficient transport networks....... Optical network reconfigurations are performed by GMPLS node controllers according to monitored traffic information. The investigated energy reduction strategies are simulated on two large scale transport networks (DT17 and COST37). The results show that the energy savings obtained by these strategies......-Europea n COST37 network, for both symbol-rate and modulation format adaptations significant savings are obtained . Mixed adaptation (jointly performing symbol-rate and modulation format adaptations) used together with optical grooming allows up to 4 4 % and 4 7 % power savings in DT17 and COST37 networks...

  3. Uncovering the footprints of malicious traffic in cellular data networks

    OpenAIRE

    Raghuramu, A; Zang, H; Chuah, CN

    2015-01-01

    © Springer International Publishing Switzerland 2015. In this paper, we present a comprehensive characterization of malicious traffic generated by mobile devices using Deep Packet Inspection (DPI) records and security event logs from a large US based cellular provider network. Our analysis reveals that 0.17% of mobile devices in the cellular network are affected by security threats. This proportion, while small, is orders of magnitude higher than the last reported (in 2013) infection rate of ...

  4. Consistency of Network Traffic Repositories: An Overview

    NARCIS (Netherlands)

    Lastdrager, E.; Lastdrager, E.E.H.; Pras, Aiko

    2009-01-01

    Traffc repositories with TCP/IP header information are very important for network analysis. Researchers often assume that such repositories reliably represent all traffc that has been flowing over the network; little thoughts are made regarding the consistency of these repositories. Still, for

  5. Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm

    Directory of Open Access Journals (Sweden)

    Li Wang

    2017-01-01

    Full Text Available Traffic congestion is a common problem in many countries, especially in big cities. At present, China’s urban road traffic accidents occur frequently, the occurrence frequency is high, the accident causes traffic congestion, and accidents cause traffic congestion and vice versa. The occurrence of traffic accidents usually leads to the reduction of road traffic capacity and the formation of traffic bottlenecks, causing the traffic congestion. In this paper, the formation and propagation of traffic congestion are simulated by using the improved medium traffic model, and the control strategy of congestion dissipation is studied. From the point of view of quantitative traffic congestion, the paper provides the fact that the simulation platform of urban traffic integration is constructed, and a feasible data analysis, learning, and parameter calibration method based on RBF neural network is proposed, which is used to determine the corresponding decision support system. The simulation results prove that the control strategy proposed in this paper is effective and feasible. According to the temporal and spatial evolution of the paper, we can see that the network has been improved on the whole.

  6. Generic Traffic Descriptors in Managing Service Quality in BISDN/ATM Network

    Directory of Open Access Journals (Sweden)

    Ivan Bošnjak

    2002-03-01

    Full Text Available Traffic models for multiservice broadband networks differsignificantly regarding simple analytic models applicable intelephone traffic and circuit-switch network. The paper presentsa clear analysis of standardised traffic descriptors andquality parameters of the main services in BISDNIATM. Trafficdescriptors have been associated with the basic values andconcepts developed within generic traffic theory. Part systematisationof traffic parameters has been performed as basis for formalisedgeneralised description of parameters and effectivequality management of A TM services.

  7. Urban Traffic Signal System Control Structural Optimization Based on Network Analysis

    Directory of Open Access Journals (Sweden)

    Li Wang

    2013-01-01

    Full Text Available Advanced urban traffic signal control systems such as SCOOT and SCATS normally coordinate traffic network using multilevel hierarchical control mechanism. In this mechanism, several key intersections will be selected from traffic signal network and the network will be divided into different control subareas. Traditionally, key intersection selection and control subareas division are executed according to dynamic traffic counts and link length between intersections, which largely rely on traffic engineers’ experience. However, it omits important inherent characteristics of traffic network topology. In this paper, we will apply network analysis approach into these two aspects for traffic system control structure optimization. Firstly, the modified C-means clustering algorithm will be proposed to assess the importance of intersections in traffic network and furthermore determine the key intersections based on three indexes instead of merely on traffic counts in traditional methods. Secondly, the improved network community discovery method will be used to give more reasonable evidence in traffic control subarea division. Finally, to test the effectiveness of network analysis approach, a hardware-in-loop simulation environment composed of regional traffic control system, microsimulation software and signal controller hardware, will be built. Both traditional method and proposed approach will be implemented on simulation test bed to evaluate traffic operation performance indexes, for example, travel time, stop times, delay and average vehicle speed. Simulation results show that the proposed network analysis approach can improve the traffic control system operation performance effectively.

  8. Algorithm for queueing networks with multi-rate traffic

    DEFF Research Database (Denmark)

    Iversen, Villy Bæk; Ko, King-Tim

    2011-01-01

    the nodes behave as independent nodes. For closed queueing networks with multiple servers in every node and multi-rate services we may apply multidimensional convolution algorithm to aggregate the nodes so that we end up with two nodes, the aggregated node and a single node, for which we can calculate......In this paper we present a new algorithm for evaluating queueing networks with multi-rate traffic. The detailed state space of a node is evaluated by explicit formulæ. We consider reversible nodes with multi-rate traffic and find the state probabilities by taking advantage of local balance. Theory...... of queueing networks in general, presumes that we have product form between the nodes. Otherwise, we have the state space explosion. Even so, the detailed state space of each node may become very large because there is no product form between chains inside a node. A prerequisite for product form...

  9. Algorithm for queueing networks with multi-rate traffic

    DEFF Research Database (Denmark)

    Iversen, Villy Bæk; King-Tim, Ko

    2011-01-01

    the nodes behave as independent nodes. For closed queueing networks with multiple servers in every node and multi-rate services we may apply multidimensional convolutions to aggregate the nodes so that we end up with two nodes, the aggregated node and a single node, for which we can calculate the detailed......In this paper we present a new algorithm for evaluating queueing networks with multi-rate traffic. The detailed state space of a node is evaluated by explicit formulæ. We consider reversible nodes with multi-rate traffic and find the state probabilities by taking advantage of local balance. Theory...... of queueing networks in general presumes that we have product form between the nodes. Other ways we have the state space explosion. Even so the detailed state space of each node may easily become very large because there is no product form between chains inside a node. A prerequisite for product form...

  10. Traffic Rules in Electronic Financial Transactions (EFT Networks

    Directory of Open Access Journals (Sweden)

    Vedran Batoš

    2002-01-01

    Full Text Available This paper presents the traffic rules in the EFT (ElectronicFinancial Transactions networks, based on the implementationof the solution called Gold-Net developed and implementedby Euronet Worldwide Inc. Following the traffic rulesin EFT networks, out of its worldwide experience, Gold-Netevolved a comprehensive and expandable EFT network solutiondesigned to meet an institution's needs today and in the future.It is an ITM (Integrated Transaction Management solution,modular and expandable, and consists of a comprehensiveEFT software modules with ATM and POS driving capabilities.The combination of ATM management and the onlineconnection form the intercept processing control module. Asthe marketplace grows, this solution ensures that an ente1prisemay position itself for future growth and expanded service offerings.

  11. Traffic analysis and signal processing in optical packet switched networks

    DEFF Research Database (Denmark)

    Fjelde, Tina

    2002-01-01

    /s optical packet switched network exploiting the best of optics and electronics, is used as a thread throughout the thesis. An overview of the DAVID network architecture is given, focussing on the MAN and WAN architecture as well as the MPLS-based network hierarchy. Subsequently, the traffic performance...... of the DAVID core optical packet router, which exploits wavelength conversion and fibre delay-line buffers for contention resolution, is analysed using a numerical model developed for that purpose. The robustness of the shared recirculating loop buffer with respect to´bursty traffic is demonstrated...... the injection of an additional clock signal into the IWC is presented. Results show very good transmission capabilities combined with a high-speed response. It is argued that signal regeneration is an inherent attribute of the IWC employed as a wavelength converter due to the sinusoidal transfer function...

  12. Fine-granularity inference and estimations to network traffic for SDN.

    Directory of Open Access Journals (Sweden)

    Dingde Jiang

    Full Text Available An end-to-end network traffic matrix is significantly helpful for network management and for Software Defined Networks (SDN. However, the end-to-end network traffic matrix's inferences and estimations are a challenging problem. Moreover, attaining the traffic matrix in high-speed networks for SDN is a prohibitive challenge. This paper investigates how to estimate and recover the end-to-end network traffic matrix in fine time granularity from the sampled traffic traces, which is a hard inverse problem. Different from previous methods, the fractal interpolation is used to reconstruct the finer-granularity network traffic. Then, the cubic spline interpolation method is used to obtain the smooth reconstruction values. To attain an accurate the end-to-end network traffic in fine time granularity, we perform a weighted-geometric-average process for two interpolation results that are obtained. The simulation results show that our approaches are feasible and effective.

  13. Fine-granularity inference and estimations to network traffic for SDN.

    Science.gov (United States)

    Jiang, Dingde; Huo, Liuwei; Li, Ya

    2018-01-01

    An end-to-end network traffic matrix is significantly helpful for network management and for Software Defined Networks (SDN). However, the end-to-end network traffic matrix's inferences and estimations are a challenging problem. Moreover, attaining the traffic matrix in high-speed networks for SDN is a prohibitive challenge. This paper investigates how to estimate and recover the end-to-end network traffic matrix in fine time granularity from the sampled traffic traces, which is a hard inverse problem. Different from previous methods, the fractal interpolation is used to reconstruct the finer-granularity network traffic. Then, the cubic spline interpolation method is used to obtain the smooth reconstruction values. To attain an accurate the end-to-end network traffic in fine time granularity, we perform a weighted-geometric-average process for two interpolation results that are obtained. The simulation results show that our approaches are feasible and effective.

  14. Controlling P2P File-Sharing Networks Traffic

    OpenAIRE

    García Pineda, Miguel; HAMMOUMI, MOHAMMED; Canovas Solbes, Alejandro; Lloret, Jaime

    2011-01-01

    Since the appearance of Peer-To-Peer (P2P) file-sharing networks some time ago, many Internet users have chosen this technology to share and search programs, videos, music, documents, etc. The total number of P2P file-sharing users has been increasing and decreasing in the last decade depending on the creation or end of some well known P2P file-sharing systems. P2P file-sharing networks traffic is currently overloading some data networks and it is a major headache for netw...

  15. The wireshark field guide analyzing and troubleshooting network traffic

    CERN Document Server

    Shimonski, Robert

    2013-01-01

    The Wireshark Field Guide provides hackers, pen testers, and network administrators with practical guidance on capturing and interactively browsing computer network traffic. Wireshark is the world's foremost network protocol analyzer, with a rich feature set that includes deep inspection of hundreds of protocols, live capture, offline analysis and many other features. The Wireshark Field Guide covers the installation, configuration and use of this powerful multi-platform tool. The book give readers the hands-on skills to be more productive with Wireshark as they drill

  16. Traffic

    International Nuclear Information System (INIS)

    Lichtblau, G.

    2001-01-01

    This chapter deals with passenger and freight traffic, public and private transportation, traffic related environmental impacts, future developments, traffic indicators, regional traffic planning, health costs due to road traffic related air pollution, noise pollution, measures and regulations for traffic control and fuels for traffic. In particular energy consumption, energy efficiency, pollutant emissions ( CO 2 , SO 2 , NO x , HC, CO, N 2 O, NH 3 and particulates) and environmental effects of the different types of traffic and different types of fuels are compared and studied. Legal regulations and measures for an effective traffic control are discussed. (a.n.)

  17. Performability indicators for the traffic analysis of wide area networks

    International Nuclear Information System (INIS)

    Tsopelas, Panagiotis; Platis, Agapios

    2003-01-01

    In connecting computing networks, reliability term is strongly related to the availability of connections of Wide Area networks (WANs) or Local Area networks (LANs). In this paper we will examine the network connections activity of a Greek University in order to provide two sources of information: The Quantity of Information Not Delivered (QIND) and the Information Flow Interruption (IFI). These indicators will provide us with the inference of information from observable characteristics of data flow(s), even when the data is encrypted or otherwise not directly available (traffic), which is lost due to failures or upgrades inside this network. The reliability analysis is obtained by collecting the network failures data (duration and frequency) and traffic (total and average) for a specified period of 1 year. It is assumed that the numerical analysis is based on the fact that the lifetime follows and exponential distribution (here as we are working on discrete time the distribution must be the geometric distribution). Hence a Markov chain model seems suitable for modelling the functioning of this system. An algorithm concentrates the results in a transition probability matrix and calculates the reward functions for the QIND/IFI indicators with the use of the power method. Finally, the application part provides an example of how final results can be used to evaluate the observed network

  18. Traffic sign recognition based on deep convolutional neural network

    Science.gov (United States)

    Yin, Shi-hao; Deng, Ji-cai; Zhang, Da-wei; Du, Jing-yuan

    2017-11-01

    Traffic sign recognition (TSR) is an important component of automated driving systems. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we propose a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the network, a novel structure (dubbed block-layer below) which combines network-in-network and residual connection is designed. Our network has 10 layers with parameters (block-layer seen as a single layer): the first seven are alternate convolutional layers and block-layers, and the remaining three are fully-connected layers. We train our TSR network on the German traffic sign recognition benchmark (GTSRB) dataset. To reduce overfitting, we perform data augmentation on the training images and employ a regularization method named "dropout". The activation function we employ in our network adopts scaled exponential linear units (SELUs), which can induce self-normalizing properties. To speed up the training, we use an efficient GPU to accelerate the convolutional operation. On the test dataset of GTSRB, we achieve the accuracy rate of 99.67%, exceeding the state-of-the-art results.

  19. Detecting Target Data in Network Traffic

    Science.gov (United States)

    2017-03-01

    perimeter around a network and create a single point of entry where security policies can be enforced and auditing can be performed [16]. Security...that we used in this thesis. 2.4.1 bulk_extractor Bulk_extractor is a forensic analysis tool designed for directly extracting artifacts of forensic ...educated guess whether or not the file is there. Shields et al. explains that they need other forensics tools to ensure that all forensic evidence is used

  20. Probabilistic prediction of aggregate traffic demand using uncertainty in individual flight predictions.

    Science.gov (United States)

    2009-08-01

    Federal Aviation Administration (FAA) air traffic flow management (TFM) : decision-making is based primarily on a comparison of deterministic predictions of demand : and capacity at National Airspace System (NAS) elements such as airports, fixes and ...

  1. From trees to forest: relational complexity network and workload of air traffic controllers.

    Science.gov (United States)

    Zhang, Jingyu; Yang, Jiazhong; Wu, Changxu

    2015-01-01

    In this paper, we propose a relational complexity (RC) network framework based on RC metric and network theory to model controllers' workload in conflict detection and resolution. We suggest that, at the sector level, air traffic showing a centralised network pattern can provide cognitive benefits in visual search and resolution decision which will in turn result in lower workload. We found that the network centralisation index can account for more variance in predicting perceived workload and task completion time in both a static conflict detection task (Study 1) and a dynamic one (Study 2) in addition to other aircraft-level and pair-level factors. This finding suggests that linear combination of aircraft-level or dyad-level information may not be adequate and the global-pattern-based index is necessary. Theoretical and practical implications of using this framework to improve future workload modelling and management are discussed. We propose a RC network framework to model the workload of air traffic controllers. The effect of network centralisation was examined in both a static conflict detection task and a dynamic one. Network centralisation was predictive of perceived workload and task completion time over and above other control variables.

  2. Forecasting short-term data center network traffic load with convolutional neural networks

    Science.gov (United States)

    Ordozgoiti, Bruno; Gómez-Canaval, Sandra

    2018-01-01

    Efficient resource management in data centers is of central importance to content service providers as 90 percent of the network traffic is expected to go through them in the coming years. In this context we propose the use of convolutional neural networks (CNNs) to forecast short-term changes in the amount of traffic crossing a data center network. This value is an indicator of virtual machine activity and can be utilized to shape the data center infrastructure accordingly. The behaviour of network traffic at the seconds scale is highly chaotic and therefore traditional time-series-analysis approaches such as ARIMA fail to obtain accurate forecasts. We show that our convolutional neural network approach can exploit the non-linear regularities of network traffic, providing significant improvements with respect to the mean absolute and standard deviation of the data, and outperforming ARIMA by an increasingly significant margin as the forecasting granularity is above the 16-second resolution. In order to increase the accuracy of the forecasting model, we exploit the architecture of the CNNs using multiresolution input distributed among separate channels of the first convolutional layer. We validate our approach with an extensive set of experiments using a data set collected at the core network of an Internet Service Provider over a period of 5 months, totalling 70 days of traffic at the one-second resolution. PMID:29408936

  3. Forecasting short-term data center network traffic load with convolutional neural networks.

    Science.gov (United States)

    Mozo, Alberto; Ordozgoiti, Bruno; Gómez-Canaval, Sandra

    2018-01-01

    Efficient resource management in data centers is of central importance to content service providers as 90 percent of the network traffic is expected to go through them in the coming years. In this context we propose the use of convolutional neural networks (CNNs) to forecast short-term changes in the amount of traffic crossing a data center network. This value is an indicator of virtual machine activity and can be utilized to shape the data center infrastructure accordingly. The behaviour of network traffic at the seconds scale is highly chaotic and therefore traditional time-series-analysis approaches such as ARIMA fail to obtain accurate forecasts. We show that our convolutional neural network approach can exploit the non-linear regularities of network traffic, providing significant improvements with respect to the mean absolute and standard deviation of the data, and outperforming ARIMA by an increasingly significant margin as the forecasting granularity is above the 16-second resolution. In order to increase the accuracy of the forecasting model, we exploit the architecture of the CNNs using multiresolution input distributed among separate channels of the first convolutional layer. We validate our approach with an extensive set of experiments using a data set collected at the core network of an Internet Service Provider over a period of 5 months, totalling 70 days of traffic at the one-second resolution.

  4. Forecasting of passenger traffic in Moscow metro applying artificial neural networks

    International Nuclear Information System (INIS)

    Ivanov, V.V.; Natsional'nyj Issledovatel'skij Yadernyj Univ. MIFI, Moscow; FKU Rostransmodernizatsiya, Moscow

    2016-01-01

    Methods for the forecasting of passenger traffic in Moscow metro have been developed using artificial neural networks. To this end, the factors primarily determining passenger traffic in the subway have been analyzed and selected [ru

  5. Ant colony optimization algorithm for signal coordination of oversaturated traffic networks.

    Science.gov (United States)

    2010-05-01

    Traffic congestion is a daily and growing problem of the modern era in mostly all major cities in the world. : Increasing traffic demand strains the existing transportation system, leading to oversaturated network : conditions, especially at peak hou...

  6. Using mobile probes to inform and measure the effectiveness of traffic control strategies on urban networks.

    Science.gov (United States)

    2015-07-01

    Urban traffic congestion is a problem that plagues many cities in the United States. Testing strategies to alleviate this : congestion is especially challenging due to the difficulty of modeling complex urban traffic networks. However, recent work ha...

  7. Traffic Steering Framework for Mobile-Assisted Resource Management in Heterogeneous Networks

    DEFF Research Database (Denmark)

    Dogadaev, Anton Konstantinovich; Checko, Aleksandra; Popovska Avramova, Andrijana

    2013-01-01

    With the expected growth of mobile data traffic it is essential to manage the network resources efficiently. In order to undertake this challenge, we propose a framework for network-centric, mobile-assisted resource management, which facilitates traffic offloading from mobile network to Wi-Fi...... to the network backbone. What is more, we give an overview of existing standardization activities on offloading the mobile traffic through Wi-Fi....

  8. Predicting commuter flows in spatial networks using a radiation model based on temporal ranges

    Science.gov (United States)

    Ren, Yihui; Ercsey-Ravasz, Mária; Wang, Pu; González, Marta C.; Toroczkai, Zoltán

    2014-11-01

    Understanding network flows such as commuter traffic in large transportation networks is an ongoing challenge due to the complex nature of the transportation infrastructure and human mobility. Here we show a first-principles based method for traffic prediction using a cost-based generalization of the radiation model for human mobility, coupled with a cost-minimizing algorithm for efficient distribution of the mobility fluxes through the network. Using US census and highway traffic data, we show that traffic can efficiently and accurately be computed from a range-limited, network betweenness type calculation. The model based on travel time costs captures the log-normal distribution of the traffic and attains a high Pearson correlation coefficient (0.75) when compared with real traffic. Because of its principled nature, this method can inform many applications related to human mobility driven flows in spatial networks, ranging from transportation, through urban planning to mitigation of the effects of catastrophic events.

  9. Entropy-based heavy tailed distribution transformation and visual analytics for monitoring massive network traffic

    Science.gov (United States)

    Han, Keesook J.; Hodge, Matthew; Ross, Virginia W.

    2011-06-01

    For monitoring network traffic, there is an enormous cost in collecting, storing, and analyzing network traffic datasets. Data mining based network traffic analysis has a growing interest in the cyber security community, but is computationally expensive for finding correlations between attributes in massive network traffic datasets. To lower the cost and reduce computational complexity, it is desirable to perform feasible statistical processing on effective reduced datasets instead of on the original full datasets. Because of the dynamic behavior of network traffic, traffic traces exhibit mixtures of heavy tailed statistical distributions or overdispersion. Heavy tailed network traffic characterization and visualization are important and essential tasks to measure network performance for the Quality of Services. However, heavy tailed distributions are limited in their ability to characterize real-time network traffic due to the difficulty of parameter estimation. The Entropy-Based Heavy Tailed Distribution Transformation (EHTDT) was developed to convert the heavy tailed distribution into a transformed distribution to find the linear approximation. The EHTDT linearization has the advantage of being amenable to characterize and aggregate overdispersion of network traffic in realtime. Results of applying the EHTDT for innovative visual analytics to real network traffic data are presented.

  10. A Bayes Theory-Based Modeling Algorithm to End-to-end Network Traffic

    Directory of Open Access Journals (Sweden)

    Zhao Hong-hao

    2016-01-01

    Full Text Available Recently, network traffic has exponentially increasing due to all kind of applications, such as mobile Internet, smart cities, smart transportations, Internet of things, and so on. the end-to-end network traffic becomes more important for traffic engineering. Usually end-to-end traffic estimation is highly difficult. This paper proposes a Bayes theory-based method to model the end-to-end network traffic. Firstly, the end-to-end network traffic is described as a independent identically distributed normal process. Then the Bases theory is used to characterize the end-to-end network traffic. By calculating the parameters, the model is determined correctly. Simulation results show that our approach is feasible and effective.

  11. Multiobjective Reinforcement Learning for Traffic Signal Control Using Vehicular Ad Hoc Network

    Directory of Open Access Journals (Sweden)

    Houli Duan

    2010-01-01

    Full Text Available We propose a new multiobjective control algorithm based on reinforcement learning for urban traffic signal control, named multi-RL. A multiagent structure is used to describe the traffic system. A vehicular ad hoc network is used for the data exchange among agents. A reinforcement learning algorithm is applied to predict the overall value of the optimization objective given vehicles' states. The policy which minimizes the cumulative value of the optimization objective is regarded as the optimal one. In order to make the method adaptive to various traffic conditions, we also introduce a multiobjective control scheme in which the optimization objective is selected adaptively to real-time traffic states. The optimization objectives include the vehicle stops, the average waiting time, and the maximum queue length of the next intersection. In addition, we also accommodate a priority control to the buses and the emergency vehicles through our model. The simulation results indicated that our algorithm could perform more efficiently than traditional traffic light control methods.

  12. Methodology for neural networks prototyping. Application to traffic control

    Energy Technology Data Exchange (ETDEWEB)

    Belegan, I.C.

    1998-07-01

    The work described in this report was carried out in the context of the European project ASTORIA (Advanced Simulation Toolbox for Real-World Industrial Application in Passenger Management and Adaptive Control), and concerns the development of an advanced toolbox for complex transportation systems. Our work was focused on the methodology for prototyping a set of neural networks corresponding to specific strategies for traffic control and congestion management. The tool used for prototyping is SNNS (Stuggart Neural Network Simulator), developed at the University of Stuggart, Institute for Parallel and Distributed High Performance Systems, and the real data from the field were provided by ZELT. This report is structured into six parts. The introduction gives some insights about traffic control and its approaches. The second chapter discusses the various control strategies existing. The third chapter is an introduction to the field of neural networks. The data analysis and pre-processing is described in the fourth chapter. In the fifth chapter, the methodology for prototyping the neural networks is presented. Finally, conclusions and further work are presented. (author) 14 refs.

  13. A Unified Monitoring Framework for Energy Consumption and Network Traffic

    Directory of Open Access Journals (Sweden)

    Florentin Clouet

    2015-08-01

    Full Text Available Providing experimenters with deep insight about the effects of their experiments is a central feature of testbeds. In this paper, we describe Kwapi, a framework designed in the context of the Grid'5000 testbed, that unifies measurements for both energy consumption and network traffic. Because all measurements are taken at the infrastructure level (using sensors in power and network equipment, using this framework has no dependencies on the experiments themselves. Initially designed for OpenStack infrastructures, the Kwapi framework allows monitoring and reporting of energy consumption of distributed platforms. In this article, we present the extension of Kwapi to network monitoring, and outline how we overcame several challenges: scaling to a testbed the size of Grid'5000 while still providing high-frequency measurements; providing long-term loss-less storage of measurements; handling operational issues when deploying such a tool on a real infrastructure.

  14. Fiber fault location utilizing traffic signal in optical network.

    Science.gov (United States)

    Zhao, Tong; Wang, Anbang; Wang, Yuncai; Zhang, Mingjiang; Chang, Xiaoming; Xiong, Lijuan; Hao, Yi

    2013-10-07

    We propose and experimentally demonstrate a method for fault location in optical communication network. This method utilizes the traffic signal transmitted across the network as probe signal, and then locates the fault by correlation technique. Compared with conventional techniques, our method has a simple structure and low operation expenditure, because no additional device is used, such as light source, modulator and signal generator. The correlation detection in this method overcomes the tradeoff between spatial resolution and measurement range in pulse ranging technique. Moreover, signal extraction process can improve the location result considerably. Experimental results show that we achieve a spatial resolution of 8 cm and detection range of over 23 km with -8-dBm mean launched power in optical network based on synchronous digital hierarchy protocols.

  15. A NEURAL NETWORK BASED TRAFFIC-AWARE FORWARDING STRATEGY IN NAMED DATA NETWORKING

    OpenAIRE

    Parisa Bazmi; Manijeh Keshtgary

    2016-01-01

    Named Data Networking (NDN) is a new Internet architecture which has been proposed to eliminate TCP/IP Internet architecture restrictions. This architecture is abstracting away the notion of host and working based on naming datagrams. However, one of the major challenges of NDN is supporting QoS-aware forwarding strategy so as to forward Interest packets intelligently over multiple paths based on the current network condition. In this paper, Neural Network (NN) Based Traffic-aware Forwarding ...

  16. Effects of traffic generation patterns on the robustness of complex networks

    Science.gov (United States)

    Wu, Jiajing; Zeng, Junwen; Chen, Zhenhao; Tse, Chi K.; Chen, Bokui

    2018-02-01

    Cascading failures in communication networks with heterogeneous node functions are studied in this paper. In such networks, the traffic dynamics are highly dependent on the traffic generation patterns which are in turn determined by the locations of the hosts. The data-packet traffic model is applied to Barabási-Albert scale-free networks to study the cascading failures in such networks and to explore the effects of traffic generation patterns on network robustness. It is found that placing the hosts at high-degree nodes in a network can make the network more robust against both intentional attacks and random failures. It is also shown that the traffic generation pattern plays an important role in network design.

  17. Monitoring individual traffic flows within the ATLAS TDAQ network

    CERN Document Server

    Sjoen, R; Ciobotaru, M; Batraneanu, S M; Leahu, L; Martin, B; Al-Shabibi, A

    2010-01-01

    The ATLAS data acquisition system consists of four different networks interconnecting up to 2000 processors using up to 200 edge switches and five multi-blade chassis devices. The architecture of the system has been described in [1] and its operational model in [2]. Classical, SNMP-based, network monitoring provides statistics on aggregate traffic, but for performance monitoring and troubleshooting purposes there was an imperative need to identify and quantify single traffic flows. sFlow [3] is an industry standard based on statistical sampling which attempts to provide a solution to this. Due to the size of the ATLAS network, the collection and analysis of the sFlow data from all devices generates a data handling problem of its own. This paper describes how this problem is addressed by making it possible to collect and store data either centrally or distributed according to need. The methods used to present the results in a relevant fashion for system analysts are discussed and we explore the possibilities a...

  18. Monitoring individual traffic flows within the ATLAS TDAQ network

    International Nuclear Information System (INIS)

    Sjoen, R; Batraneanu, S M; Leahu, L; Martin, B; Al-Shabibi, A; Stancu, S; Ciobotaru, M

    2010-01-01

    The ATLAS data acquisition system consists of four different networks interconnecting up to 2000 processors using up to 200 edge switches and five multi-blade chassis devices. The architecture of the system has been described in [1] and its operational model in [2]. Classical, SNMP-based, network monitoring provides statistics on aggregate traffic, but for performance monitoring and troubleshooting purposes there was an imperative need to identify and quantify single traffic flows. sFlow [3] is an industry standard based on statistical sampling which attempts to provide a solution to this. Due to the size of the ATLAS network, the collection and analysis of the sFlow data from all devices generates a data handling problem of its own. This paper describes how this problem is addressed by making it possible to collect and store data either centrally or distributed according to need. The methods used to present the results in a relevant fashion for system analysts are discussed and we explore the possibilities and limitations of this diagnostic tool, giving an example of its use in solving system problems that arise during the ATLAS data taking.

  19. Control of Networked Traffic Flow Distribution - A Stochastic Distribution System Perspective

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Hong [Pacific Northwest National Laboratory (PNNL); Aziz, H M Abdul [ORNL; Young, Stan [National Renewable Energy Laboratory (NREL); Patil, Sagar [Pacific Northwest National Laboratory (PNNL)

    2017-10-01

    Networked traffic flow is a common scenario for urban transportation, where the distribution of vehicle queues either at controlled intersections or highway segments reflect the smoothness of the traffic flow in the network. At signalized intersections, the traffic queues are controlled by traffic signal control settings and effective traffic lights control would realize both smooth traffic flow and minimize fuel consumption. Funded by the Energy Efficient Mobility Systems (EEMS) program of the Vehicle Technologies Office of the US Department of Energy, we performed a preliminary investigation on the modelling and control framework in context of urban network of signalized intersections. In specific, we developed a recursive input-output traffic queueing models. The queue formation can be modeled as a stochastic process where the number of vehicles entering each intersection is a random number. Further, we proposed a preliminary B-Spline stochastic model for a one-way single-lane corridor traffic system based on theory of stochastic distribution control.. It has been shown that the developed stochastic model would provide the optimal probability density function (PDF) of the traffic queueing length as a dynamic function of the traffic signal setting parameters. Based upon such a stochastic distribution model, we have proposed a preliminary closed loop framework on stochastic distribution control for the traffic queueing system to make the traffic queueing length PDF follow a target PDF that potentially realizes the smooth traffic flow distribution in a concerned corridor.

  20. Radio resource management for mobile traffic offloading in heterogeneous cellular networks

    CERN Document Server

    Wu, Yuan; Huang, Jianwei; Shen, Xuemin (Sherman)

    2017-01-01

    This SpringerBrief offers two concrete design examples for traffic offloading. The first is an optimal resource allocation for small-cell based traffic offloading that aims at minimizing mobile users’ data cost. The second is an optimal resource allocation for device-to-device assisted traffic offloading that also minimizes the total energy consumption and cellular link usage (while providing an overview of the challenging issues). Both examples illustrate the importance of proper resource allocation to the success of traffic offloading, show the consequent performance advantages of executing optimal resource allocation, and present the methodologies to achieve the corresponding optimal offloading solution for traffic offloading in heterogeneous cellular networks. The authors also include an overview of heterogeneous cellular networks and explain different traffic offloading paradigms ranging from uplink traffic offloading through small cells to downlink traffic offloading via mobile device-to-device cooper...

  1. A new traffic control design method for large networks with signalized intersections

    Science.gov (United States)

    Leininger, G. G.; Colony, D. C.; Seldner, K.

    1979-01-01

    The paper presents a traffic control design technique for application to large traffic networks with signalized intersections. It is shown that the design method adopts a macroscopic viewpoint to establish a new traffic modelling procedure in which vehicle platoons are subdivided into main stream queues and turning queues. Optimization of the signal splits minimizes queue lengths in the steady state condition and improves traffic flow conditions, from the viewpoint of the traveling public. Finally, an application of the design method to a traffic network with thirty-three signalized intersections is used to demonstrate the effectiveness of the proposed technique.

  2. Mathematical programs with complementarity constraints in traffic and telecommunications networks.

    Science.gov (United States)

    Ralph, Daniel

    2008-06-13

    Given a suitably parametrized family of equilibrium models and a higher level criterion by which to measure an equilibrium state, mathematical programs with equilibrium constraints (MPECs) provide a framework for improving or optimizing the equilibrium state. An example is toll design in traffic networks, which attempts to reduce total travel time by choosing which arcs to toll and what toll levels to impose. Here, a Wardrop equilibrium describes the traffic response to each toll design. Communication networks also have a deep literature on equilibrium flows that suggest some MPECs. We focus on mathematical programs with complementarity constraints (MPCCs), a subclass of MPECs for which the lower level equilibrium system can be formulated as a complementarity problem and therefore, importantly, as a nonlinear program (NLP). Although MPECs and MPCCs are typically non-convex, which is a consequence of the upper level objective clashing with the users' objectives in the lower level equilibrium program, the last decade of research has paved the way for finding local solutions of MPCCs via standard NLP techniques.

  3. Fermi-Dirac statistics and traffic in complex networks.

    Science.gov (United States)

    de Moura, Alessandro P S

    2005-06-01

    We propose an idealized model for traffic in a network, in which many particles move randomly from node to node, following the network's links, and it is assumed that at most one particle can occupy any given node. This is intended to mimic the finite forwarding capacity of nodes in communication networks, thereby allowing the possibility of congestion and jamming phenomena. We show that the particles behave like free fermions, with appropriately defined energy-level structure and temperature. The statistical properties of this system are thus given by the corresponding Fermi-Dirac distribution. We use this to obtain analytical expressions for dynamical quantities of interest, such as the mean occupation of each node and the transport efficiency, for different network topologies and particle densities. We show that the subnetwork of free nodes always fragments into small isolated clusters for a sufficiently large number of particles, implying a communication breakdown at some density for all network topologies. These results are compared to direct simulations.

  4. Understanding characteristics in multivariate traffic flow time series from complex network structure

    Science.gov (United States)

    Yan, Ying; Zhang, Shen; Tang, Jinjun; Wang, Xiaofei

    2017-07-01

    Discovering dynamic characteristics in traffic flow is the significant step to design effective traffic managing and controlling strategy for relieving traffic congestion in urban cities. A new method based on complex network theory is proposed to study multivariate traffic flow time series. The data were collected from loop detectors on freeway during a year. In order to construct complex network from original traffic flow, a weighted Froenius norm is adopt to estimate similarity between multivariate time series, and Principal Component Analysis is implemented to determine the weights. We discuss how to select optimal critical threshold for networks at different hour in term of cumulative probability distribution of degree. Furthermore, two statistical properties of networks: normalized network structure entropy and cumulative probability of degree, are utilized to explore hourly variation in traffic flow. The results demonstrate these two statistical quantities express similar pattern to traffic flow parameters with morning and evening peak hours. Accordingly, we detect three traffic states: trough, peak and transitional hours, according to the correlation between two aforementioned properties. The classifying results of states can actually represent hourly fluctuation in traffic flow by analyzing annual average hourly values of traffic volume, occupancy and speed in corresponding hours.

  5. The impact of real-time and predictive traffic information on travelers' behavior on the I-4 corridor. Final report.

    Science.gov (United States)

    2003-07-01

    Real time and predicted traffic information plays a key role in the successful implementation of advanced traveler information systems (ATIS) and advance traffic management systems (ATMS). Traffic information is essentially valuable to both transport...

  6. Model for Detection and Classification of DDoS Traffic Based on Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    D. Peraković

    2017-06-01

    Full Text Available Detection of DDoS (Distributed Denial of Service traffic is of great importance for the availability protection of services and other information and communication resources. The research presented in this paper shows the application of artificial neural networks in the development of detection and classification model for three types of DDoS attacks and legitimate network traffic. Simulation results of developed model showed accuracy of 95.6% in classification of pre-defined classes of traffic.

  7. Feasibility of Optical Packet Switched WDM Networks without Packet Synchronisation Under Bursty Traffic Conditions

    DEFF Research Database (Denmark)

    Fjelde, Tina; Hansen, Peter Bukhave; Kloch, Allan

    1999-01-01

    We show that complex packet synchronisation may be avoided in optical packetswitched networks. Detailed traffic analysis demonstrates that packet lossratios of 1e-10 are feasible under bursty traffic conditions for a highcapacity network consisting of asynchronously operated add-drop switch...

  8. Networked traffic state estimation involving mixed fixed-mobile sensor data using Hamilton-Jacobi equations

    KAUST Repository

    Canepa, Edward S.; Claudel, Christian G.

    2017-01-01

    Nowadays, traffic management has become a challenge for urban areas, which are covering larger geographic spaces and facing the generation of different kinds of traffic data. This article presents a robust traffic estimation framework for highways modeled by a system of Lighthill Whitham Richards equations that is able to assimilate different sensor data available. We first present an equivalent formulation of the problem using a Hamilton–Jacobi equation. Then, using a semi-analytic formula, we show that the model constraints resulting from the Hamilton–Jacobi equation are linear ones. We then pose the problem of estimating the traffic density given incomplete and inaccurate traffic data as a Mixed Integer Program. We then extend the density estimation framework to highway networks with any available data constraint and modeling junctions. Finally, we present a travel estimation application for a small network using real traffic measurements obtained obtained during Mobile Century traffic experiment, and comparing the results with ground truth data.

  9. Networked traffic state estimation involving mixed fixed-mobile sensor data using Hamilton-Jacobi equations

    KAUST Repository

    Canepa, Edward S.

    2017-06-19

    Nowadays, traffic management has become a challenge for urban areas, which are covering larger geographic spaces and facing the generation of different kinds of traffic data. This article presents a robust traffic estimation framework for highways modeled by a system of Lighthill Whitham Richards equations that is able to assimilate different sensor data available. We first present an equivalent formulation of the problem using a Hamilton–Jacobi equation. Then, using a semi-analytic formula, we show that the model constraints resulting from the Hamilton–Jacobi equation are linear ones. We then pose the problem of estimating the traffic density given incomplete and inaccurate traffic data as a Mixed Integer Program. We then extend the density estimation framework to highway networks with any available data constraint and modeling junctions. Finally, we present a travel estimation application for a small network using real traffic measurements obtained obtained during Mobile Century traffic experiment, and comparing the results with ground truth data.

  10. Correlation dimension based nonlinear analysis of network traffics with different application protocols

    International Nuclear Information System (INIS)

    Wang Jun-Song; Yuan Jing; Li Qiang; Yuan Rui-Xi

    2011-01-01

    This paper uses a correlation dimension based nonlinear analysis approach to analyse the dynamics of network traffics with three different application protocols—HTTP, FTP and SMTP. First, the phase space is reconstructed and the embedding parameters are obtained by the mutual information method. Secondly, the correlation dimensions of three different traffics are calculated and the results of analysis have demonstrated that the dynamics of the three different application protocol traffics is different from each other in nature, i.e. HTTP and FTP traffics are chaotic, furthermore, the former is more complex than the later; on the other hand, SMTP traffic is stochastic. It is shown that correlation dimension approach is an efficient method to understand and to characterize the nonlinear dynamics of HTTP, FTP and SMTP protocol network traffics. This analysis provided insight into and a more accurate understanding of nonlinear dynamics of internet traffics which have a complex mixture of chaotic and stochastic components. (general)

  11. Trading network predicts stock price.

    Science.gov (United States)

    Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi

    2014-01-16

    Stock price prediction is an important and challenging problem for studying financial markets. Existing studies are mainly based on the time series of stock price or the operation performance of listed company. In this paper, we propose to predict stock price based on investors' trading behavior. For each stock, we characterize the daily trading relationship among its investors using a trading network. We then classify the nodes of trading network into three roles according to their connectivity pattern. Strong Granger causality is found between stock price and trading relationship indices, i.e., the fraction of trading relationship among nodes with different roles. We further predict stock price by incorporating these trading relationship indices into a neural network based on time series of stock price. Experimental results on 51 stocks in two Chinese Stock Exchanges demonstrate the accuracy of stock price prediction is significantly improved by the inclusion of trading relationship indices.

  12. Performance Evaluation of Hadoop-based Large-scale Network Traffic Analysis Cluster

    Directory of Open Access Journals (Sweden)

    Tao Ran

    2016-01-01

    Full Text Available As Hadoop has gained popularity in big data era, it is widely used in various fields. The self-design and self-developed large-scale network traffic analysis cluster works well based on Hadoop, with off-line applications running on it to analyze the massive network traffic data. On purpose of scientifically and reasonably evaluating the performance of analysis cluster, we propose a performance evaluation system. Firstly, we set the execution times of three benchmark applications as the benchmark of the performance, and pick 40 metrics of customized statistical resource data. Then we identify the relationship between the resource data and the execution times by a statistic modeling analysis approach, which is composed of principal component analysis and multiple linear regression. After training models by historical data, we can predict the execution times by current resource data. Finally, we evaluate the performance of analysis cluster by the validated predicting of execution times. Experimental results show that the predicted execution times by trained models are within acceptable error range, and the evaluation results of performance are accurate and reliable.

  13. Variable speed limit strategies analysis with mesoscopic traffic flow model based on complex networks

    Science.gov (United States)

    Li, Shu-Bin; Cao, Dan-Ni; Dang, Wen-Xiu; Zhang, Lin

    As a new cross-discipline, the complexity science has penetrated into every field of economy and society. With the arrival of big data, the research of the complexity science has reached its summit again. In recent years, it offers a new perspective for traffic control by using complex networks theory. The interaction course of various kinds of information in traffic system forms a huge complex system. A new mesoscopic traffic flow model is improved with variable speed limit (VSL), and the simulation process is designed, which is based on the complex networks theory combined with the proposed model. This paper studies effect of VSL on the dynamic traffic flow, and then analyzes the optimal control strategy of VSL in different network topologies. The conclusion of this research is meaningful to put forward some reasonable transportation plan and develop effective traffic management and control measures to help the department of traffic management.

  14. Model Predictive Control for Integrating Traffic Control Measures

    NARCIS (Netherlands)

    Hegyi, A.

    2004-01-01

    Dynamic traffic control measures, such as ramp metering and dynamic speed limits, can be used to better utilize the available road capacity. Due to the increasing traffic volumes and the increasing number of traffic jams the interaction between the control measures has increased such that local

  15. Optimum principle for a vehicular traffic network: minimum probability of congestion

    Energy Technology Data Exchange (ETDEWEB)

    Kerner, Boris S, E-mail: boris.kerner@daimler.com [Daimler AG, GR/PTF, HPC: G021, 71059 Sindelfingen (Germany)

    2011-03-04

    We introduce an optimum principle for a vehicular traffic network with road bottlenecks. This network breakdown minimization (BM) principle states that the network optimum is reached when link flow rates are assigned in the network in such a way that the probability for spontaneous occurrence of traffic breakdown in at least one of the network bottlenecks during a given observation time reaches the minimum possible value. Based on numerical simulations with a stochastic three-phase traffic flow model, we show that in comparison to the well-known Wardrop's principles, the application of the BM principle permits considerably greater network inflow rates at which no traffic breakdown occurs and, therefore, free flow remains in the whole network. (fast track communication)

  16. Optimum principle for a vehicular traffic network: minimum probability of congestion

    International Nuclear Information System (INIS)

    Kerner, Boris S

    2011-01-01

    We introduce an optimum principle for a vehicular traffic network with road bottlenecks. This network breakdown minimization (BM) principle states that the network optimum is reached when link flow rates are assigned in the network in such a way that the probability for spontaneous occurrence of traffic breakdown in at least one of the network bottlenecks during a given observation time reaches the minimum possible value. Based on numerical simulations with a stochastic three-phase traffic flow model, we show that in comparison to the well-known Wardrop's principles, the application of the BM principle permits considerably greater network inflow rates at which no traffic breakdown occurs and, therefore, free flow remains in the whole network. (fast track communication)

  17. Nuclear traffic and peloton formation in fungal networks

    Science.gov (United States)

    Roper, Marcus; Hickey, Patrick; Lewkiewicz, Stephanie; Dressaire, Emilie; Read, Nick

    2013-11-01

    Hyphae, the network of microfluidic pipes that make up a growing fungal cell, must balance their function as conduits for the transport of nuclei with other cellular functions including secretion and growth. Constant flow of nuclei may interfere with the protein traffic that enables other functions to be performed. Live-cell imaging reveals that nuclear flows are anti-congestive; that groups of nuclei flow faster than single nuclei, and that nuclei sweep through the colony in dense clumps. We call these clumps pelotons, after the term used to describe groups of cycle racers slip-streaming off each other. Because of the pelotons, individual hyphae transport nuclei only intermittently, producing long intervals in which hyphae can perform their other functions. Modeling reveals how pelotons are created by interactions between nuclei and the hyphal cytoskeleton, and reveal the control that the fungus enjoys over peloton assembly and timing.

  18. File Detection On Network Traffic Using Approximate Matching

    Directory of Open Access Journals (Sweden)

    Frank Breitinger

    2014-09-01

    Full Text Available In recent years, Internet technologies changed enormously and allow faster Internet connections, higher data rates and mobile usage. Hence, it is possible to send huge amounts of data / files easily which is often used by insiders or attackers to steal intellectual property. As a consequence, data leakage prevention systems (DLPS have been developed which analyze network traffic and alert in case of a data leak. Although the overall concepts of the detection techniques are known, the systems are mostly closed and commercial.Within this paper we present a new technique for network trac analysis based on approximate matching (a.k.a fuzzy hashing which is very common in digital forensics to correlate similar files. This paper demonstrates how to optimize and apply them on single network packets. Our contribution is a straightforward concept which does not need a comprehensive conguration: hash the file and store the digest in the database. Within our experiments we obtained false positive rates between 10-4 and 10-5 and an algorithm throughput of over 650 Mbit/s.

  19. Predicting future traffic offenders by pre-drivers’ attitudes towards risky driving

    OpenAIRE

    Slavinskienė, Justina; Žardeckaitė-Matulaitienė, Kristina; Endriulaitienė, Auksė; Šeibokaitė, Laura; Markšaitytė, Rasa

    2017-01-01

    Worldwide statistics indicate that novice drivers are still one of the riskiest drivers’ groups as they highly contribute to road accidents and traffic rules violations. Thus, the psychological variables that allow predicting whether novice drivers will violate traffic rules are important in risky driving research. The aim of this study is to find out if pre-drivers’ attitudes towards risky driving measured before obtaining driving license could predict future traffic offences during the firs...

  20. Network Traffic Features for Anomaly Detection in Specific Industrial Control System Network

    Directory of Open Access Journals (Sweden)

    Matti Mantere

    2013-09-01

    Full Text Available The deterministic and restricted nature of industrial control system networks sets them apart from more open networks, such as local area networks in office environments. This improves the usability of network security, monitoring approaches that would be less feasible in more open environments. One of such approaches is machine learning based anomaly detection. Without proper customization for the special requirements of the industrial control system network environment, many existing anomaly or misuse detection systems will perform sub-optimally. A machine learning based approach could reduce the amount of manual customization required for different industrial control system networks. In this paper we analyze a possible set of features to be used in a machine learning based anomaly detection system in the real world industrial control system network environment under investigation. The network under investigation is represented by architectural drawing and results derived from network trace analysis. The network trace is captured from a live running industrial process control network and includes both control data and the data flowing between the control network and the office network. We limit the investigation to the IP traffic in the traces.

  1. Impact of Bimodal Traffic on Latency in Optical Burst Switching Networks

    Directory of Open Access Journals (Sweden)

    Yuhua Chen

    2008-01-01

    Full Text Available This paper analyzes the impact of bimodal traffic composition on latency in optical burst switching networks. In particular, it studies the performance degradation to short-length packets caused by longer packets, both of which are part of a heterogeneous traffic model. The paper defines a customer satisfaction index for each of the classes of traffic, and a composite satisfaction index. The impact of higher overall utilization of the network as well as that of the ratio of the traffic mix on each of the customer satisfaction indices is specifically addressed.

  2. The Impact of Traffic Prioritization on Deep Space Network Mission Traffic

    Science.gov (United States)

    Jennings, Esther; Segui, John; Gao, Jay; Clare, Loren; Abraham, Douglas

    2011-01-01

    A select number of missions supported by NASA's Deep Space Network (DSN) are demanding very high data rates. For example, the Kepler Mission was launched March 7, 2009 and at that time required the highest data rate of any NASA mission, with maximum rates of 4.33 Mb/s being provided via Ka band downlinks. The James Webb Space Telescope will require a maximum 28 Mb/s science downlink data rate also using Ka band links; as of this writing the launch is scheduled for a June 2014 launch. The Lunar Reconnaissance Orbiter, launched June 18, 2009, has demonstrated data rates at 100 Mb/s at lunar-Earth distances using NASA's Near Earth Network (NEN) and K-band. As further advances are made in high data rate space telecommunications, particularly with emerging optical systems, it is expected that large surges in demand on the supporting ground systems will ensue. A performance analysis of the impact of high variance in demand has been conducted using our Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) simulation tool. A comparison is made regarding the incorporation of Quality of Service (QoS) mechanisms and the resulting ground-to-ground Wide Area Network (WAN) bandwidth necessary to meet latency requirements across different user missions. It is shown that substantial reduction in WAN bandwidth may be realized through QoS techniques when low data rate users with low-latency needs are mixed with high data rate users having delay-tolerant traffic.

  3. Analysis of Roadway Traffic Accidents Based on Rough Sets and Bayesian Networks

    Directory of Open Access Journals (Sweden)

    Xiaoxia Xiong

    2018-02-01

    Full Text Available The paper integrates Rough Sets (RS and Bayesian Networks (BN for roadway traffic accident analysis. RS reduction of attributes is first employed to generate the key set of attributes affecting accident outcomes, which are then fed into a BN structure as nodes for BN construction and accident outcome classification. Such RS-based BN framework combines the advantages of RS in knowledge reduction capability and BN in describing interrelationships among different attributes. The framework is demonstrated using the 100-car naturalistic driving data from Virginia Tech Transportation Institute to predict accident type. Comparative evaluation with the baseline BNs shows the RS-based BNs generally have a higher prediction accuracy and lower network complexity while with comparable prediction coverage and receiver operating characteristic curve area, proving that the proposed RS-based BN overall outperforms the BNs with/without traditional feature selection approaches. The proposed RS-based BN indicates the most significant attributes that affect accident types include pre-crash manoeuvre, driver’s attention from forward roadway to centre mirror, number of secondary tasks undertaken, traffic density, and relation to junction, most of which feature pre-crash driver states and driver behaviours that have not been extensively researched in literature, and could give further insight into the nature of traffic accidents.

  4. Traffic signal synchronization in the saturated high-density grid road network.

    Science.gov (United States)

    Hu, Xiaojian; Lu, Jian; Wang, Wei; Zhirui, Ye

    2015-01-01

    Most existing traffic signal synchronization strategies do not perform well in the saturated high-density grid road network (HGRN). Traffic congestion often occurs in the saturated HGRN, and the mobility of the network is difficult to restore. In order to alleviate traffic congestion and to improve traffic efficiency in the network, the study proposes a regional traffic signal synchronization strategy, named the long green and long red (LGLR) traffic signal synchronization strategy. The essence of the strategy is to control the formation and dissipation of queues and to maximize the efficiency of traffic flows at signalized intersections in the saturated HGRN. With this strategy, the same signal control timing plan is used at all signalized intersections in the HGRN, and the straight phase of the control timing plan has a long green time and a long red time. Therefore, continuous traffic flows can be maintained when vehicles travel, and traffic congestion can be alleviated when vehicles stop. Using the strategy, the LGLR traffic signal synchronization model is developed, with the objective of minimizing the number of stops. Finally, the simulation is executed to analyze the performance of the model by comparing it to other models, and the superiority of the LGLR model is evident in terms of delay, number of stops, queue length, and overall performance in the saturated HGRN.

  5. Next generation network based carrier ethernet test bed for IPTV traffic

    DEFF Research Database (Denmark)

    Fu, Rong; Berger, Michael Stübert; Zheng, Yu

    2009-01-01

    This paper presents a Carrier Ethernet (CE) test bed based on the Next Generation Network (NGN) framework. After the concept of CE carried out by Metro Ethernet Forum (MEF), the carrier-grade Ethernet are obtaining more and more interests and being investigated as the low cost and high performanc...... services of transport network to carry the IPTV traffic. This test bed is approaching to support the research on providing a high performance carrier-grade Ethernet transport network for IPTV traffic....

  6. Network Traffic Forensics on Firefox Mobile OS: Facebook, Twitter and Telegram as Case Studies

    OpenAIRE

    Yusoff, Mohd Najwadi; Dehghantanha, Ali; Mahmod, Ramlan

    2017-01-01

    Development of mobile web-centric OS such as Firefox OS has created new challenges, and opportunities for digital investigators. Network traffic forensic plays an important role in cybercrime investigation to detect subject(s) and object(s) of the crime. In this chapter, we detect and analyze residual network traffic artefacts of Firefox OS in relation to two popular social networking applications (Facebook and Twitter) and one instant messaging application (Telegram). We utilized a Firefox O...

  7. Understanding structure of urban traffic network based on spatial-temporal correlation analysis

    Science.gov (United States)

    Yang, Yanfang; Jia, Limin; Qin, Yong; Han, Shixiu; Dong, Honghui

    2017-08-01

    Understanding the structural characteristics of urban traffic network comprehensively can provide references for improving road utilization rate and alleviating traffic congestion. This paper focuses on the spatial-temporal correlations between different pairs of traffic series and proposes a complex network-based method of constructing the urban traffic network. In the network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding spatial-temporal correlation. Further, a modified PageRank algorithm, named the geographical weight-based PageRank algorithm (GWPA), is proposed to analyze the spatial distribution of important segments in the road network. Finally, experiments are conducted by using three kinds of traffic series collected from the urban road network in Beijing. Experimental results show that the urban traffic networks constructed by three traffic variables all indicate both small-world and scale-free characteristics. Compared with the results of PageRank algorithm, GWPA is proved to be valid in evaluating the importance of segments and identifying the important segments with small degree.

  8. Robust and Agile System against Fault and Anomaly Traffic in Software Defined Networks

    Directory of Open Access Journals (Sweden)

    Mihui Kim

    2017-03-01

    Full Text Available The main advantage of software defined networking (SDN is that it allows intelligent control and management of networking though programmability in real time. It enables efficient utilization of network resources through traffic engineering, and offers potential attack defense methods when abnormalities arise. However, previous studies have only identified individual solutions for respective problems, instead of finding a more global solution in real time that is capable of addressing multiple situations in network status. To cover diverse network conditions, this paper presents a comprehensive reactive system for simultaneously monitoring failures, anomalies, and attacks for high availability and reliability. We design three main modules in the SDN controller for a robust and agile defense (RAD system against network anomalies: a traffic analyzer, a traffic engineer, and a rule manager. RAD provides reactive flow rule generation to control traffic while detecting network failures, anomalies, high traffic volume (elephant flows, and attacks. The traffic analyzer identifies elephant flows, traffic anomalies, and attacks based on attack signatures and network monitoring. The traffic engineer module measures network utilization and delay in order to determine the best path for multi-dimensional routing and load balancing under any circumstances. Finally, the rule manager generates and installs a flow rule for the selected best path to control traffic. We implement the proposed RAD system based on Floodlight, an open source project for the SDN controller. We evaluate our system using simulation with and without the aforementioned RAD modules. Experimental results show that our approach is both practical and feasible, and can successfully augment an existing SDN controller in terms of agility, robustness, and efficiency, even in the face of link failures, attacks, and elephant flows.

  9. On the Use of Machine Learning for Identifying Botnet Network Traffic

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2016-01-01

    contemporary approaches use machine learning techniques for identifying malicious traffic. This paper presents a survey of contemporary botnet detection methods that rely on machine learning for identifying botnet network traffic. The paper provides a comprehensive overview on the existing scientific work thus...... contributing to the better understanding of capabilities, limitations and opportunities of using machine learning for identifying botnet traffic. Furthermore, the paper outlines possibilities for the future development of machine learning-based botnet detection systems....

  10. Modeling Air Traffic Situation Complexity with a Dynamic Weighted Network Approach

    Directory of Open Access Journals (Sweden)

    Hongyong Wang

    2018-01-01

    Full Text Available In order to address the flight delays and risks associated with the forecasted increase in air traffic, there is a need to increase the capacity of air traffic management systems. This should be based on objective measurements of traffic situation complexity. In current air traffic complexity research, no simple means is available to integrate airspace and traffic flow characteristics. In this paper, we propose a new approach for the measurement of air traffic situation complexity. This approach considers the effects of both airspace and traffic flow and objectively quantifies air traffic situation complexity. Considering the aircraft, waypoints, and airways as nodes, and the complexity relationships among these nodes as edges, a dynamic weighted network is constructed. Air traffic situation complexity is defined as the sum of the weights of all edges in the network, and the relationships of complexity with some commonly used indices are statistically analyzed. The results indicate that the new complexity index is more accurate than traffic count and reflects the number of trajectory changes as well as the high-risk situations. Additionally, analysis of potential applications reveals that this new index contributes to achieving complexity-based management, which represents an efficient method for increasing airspace system capacity.

  11. Exploiting Spatial Abstraction in Predictive Analytics of Vehicle Traffic

    Directory of Open Access Journals (Sweden)

    Natalia Andrienko

    2015-04-01

    Full Text Available By applying visual analytics techniques to vehicle traffic data, we found a way to visualize and study the relationships between the traffic intensity and movement speed on links of a spatially abstracted transportation network. We observed that the traffic intensities and speeds in an abstracted network are interrelated in the same way as they are in a detailed street network at the level of street segments. We developed interactive visual interfaces that support representing these interdependencies by mathematical models. To test the possibility of utilizing them for performing traffic simulations on the basis of abstracted transportation networks, we devised a prototypical simulation algorithm employing these dependency models. The algorithm is embedded in an interactive visual environment for defining traffic scenarios, running simulations, and exploring their results. Our research demonstrates a principal possibility of performing traffic simulations on the basis of spatially abstracted transportation networks using dependency models derived from real traffic data. This possibility needs to be comprehensively investigated and tested in collaboration with transportation domain specialists.

  12. Enhancing the Quality of Service for Real Time Traffic over Optical Burst Switching (OBS Networks with Ensuring the Fairness for Other Traffics.

    Directory of Open Access Journals (Sweden)

    Mohammed A Al-Shargabi

    Full Text Available Optical burst switching (OBS networks have been attracting much consideration as a promising approach to build the next generation optical Internet. A solution for enhancing the Quality of Service (QoS for high priority real time traffic over OBS with the fairness among the traffic types is absent in current OBS' QoS schemes. In this paper we present a novel Real Time Quality of Service with Fairness Ratio (RT-QoSFR scheme that can adapt the burst assembly parameters according to the traffic QoS needs in order to enhance the real time traffic QoS requirements and to ensure the fairness for other traffic. The results show that RT-QoSFR scheme is able to fulfill the real time traffic requirements (end to end delay, and loss rate ensuring the fairness for other traffics under various conditions such as the type of real time traffic and traffic load. RT-QoSFR can guarantee that the delay of the real time traffic packets does not exceed the maximum packets transfer delay value. Furthermore, it can reduce the real time traffic packets loss, at the same time guarantee the fairness for non real time traffic packets by determining the ratio of real time traffic inside the burst to be 50-60%, 30-40%, and 10-20% for high, normal, and low traffic loads respectively.

  13. A NEURAL NETWORK BASED TRAFFIC-AWARE FORWARDING STRATEGY IN NAMED DATA NETWORKING

    Directory of Open Access Journals (Sweden)

    Parisa Bazmi

    2016-11-01

    Full Text Available Named Data Networking (NDN is a new Internet architecture which has been proposed to eliminate TCP/IP Internet architecture restrictions. This architecture is abstracting away the notion of host and working based on naming datagrams. However, one of the major challenges of NDN is supporting QoS-aware forwarding strategy so as to forward Interest packets intelligently over multiple paths based on the current network condition. In this paper, Neural Network (NN Based Traffic-aware Forwarding strategy (NNTF is introduced in order to determine an optimal path for Interest forwarding. NN is embedded in NDN routers to select next hop dynamically based on the path overload probability achieved from the NN. This solution is characterized by load balancing and QoS-awareness via monitoring the available path and forwarding data on the traffic-aware shortest path. The performance of NNTF is evaluated using ndnSIM which shows the efficiency of this scheme in terms of network QoS improvementof17.5% and 72% reduction in network delay and packet drop respectively.

  14. Injury risk prediction for traffic accidents in Porto Alegre/RS, Brazil

    OpenAIRE

    Perone, Christian S.

    2015-01-01

    This study describes the experimental application of Machine Learning techniques to build prediction models that can assess the injury risk associated with traffic accidents. This work uses an freely available data set of traffic accident records that took place in the city of Porto Alegre/RS (Brazil) during the year of 2013. This study also provides an analysis of the most important attributes of a traffic accident that could produce an outcome of injury to the people involved in the accident.

  15. High-speed and high-fidelity system and method for collecting network traffic

    Science.gov (United States)

    Weigle, Eric H [Los Alamos, NM

    2010-08-24

    A system is provided for the high-speed and high-fidelity collection of network traffic. The system can collect traffic at gigabit-per-second (Gbps) speeds, scale to terabit-per-second (Tbps) speeds, and support additional functions such as real-time network intrusion detection. The present system uses a dedicated operating system for traffic collection to maximize efficiency, scalability, and performance. A scalable infrastructure and apparatus for the present system is provided by splitting the work performed on one host onto multiple hosts. The present system simultaneously addresses the issues of scalability, performance, cost, and adaptability with respect to network monitoring, collection, and other network tasks. In addition to high-speed and high-fidelity network collection, the present system provides a flexible infrastructure to perform virtually any function at high speeds such as real-time network intrusion detection and wide-area network emulation for research purposes.

  16. A hierarchical clustering scheme approach to assessment of IP-network traffic using detrended fluctuation analysis

    Science.gov (United States)

    Takuma, Takehisa; Masugi, Masao

    2009-03-01

    This paper presents an approach to the assessment of IP-network traffic in terms of the time variation of self-similarity. To get a comprehensive view in analyzing the degree of long-range dependence (LRD) of IP-network traffic, we use a hierarchical clustering scheme, which provides a way to classify high-dimensional data with a tree-like structure. Also, in the LRD-based analysis, we employ detrended fluctuation analysis (DFA), which is applicable to the analysis of long-range power-law correlations or LRD in non-stationary time-series signals. Based on sequential measurements of IP-network traffic at two locations, this paper derives corresponding values for the LRD-related parameter α that reflects the degree of LRD of measured data. In performing the hierarchical clustering scheme, we use three parameters: the α value, average throughput, and the proportion of network traffic that exceeds 80% of network bandwidth for each measured data set. We visually confirm that the traffic data can be classified in accordance with the network traffic properties, resulting in that the combined depiction of the LRD and other factors can give us an effective assessment of network conditions at different times.

  17. An optimal general type-2 fuzzy controller for Urban Traffic Network

    DEFF Research Database (Denmark)

    Khooban, Mohammad Hassan; Vafamand, Navid; Liaghat, Alireza

    2017-01-01

    Urban traffic network model is illustrated by state-charts and object-diagram. However, they have limitations to show the behavioral perspective of the Traffic Information flow. Consequently, a state space model is used to calculate the half-value waiting time of vehicles. In this study......, a combination of the general type-2 fuzzy logic sets and the Modified Backtracking Search Algorithm (MBSA) techniques are used in order to control the traffic signal scheduling and phase succession so as to guarantee a smooth flow of traffic with the least wait times and average queue length. The parameters...

  18. A method for classification of network traffic based on C5.0 Machine Learning Algorithm

    DEFF Research Database (Denmark)

    Bujlow, Tomasz; Riaz, M. Tahir; Pedersen, Jens Myrup

    2012-01-01

    current network traffic. To overcome the drawbacks of existing methods for traffic classification, usage of C5.0 Machine Learning Algorithm (MLA) was proposed. On the basis of statistical traffic information received from volunteers and C5.0 algorithm we constructed a boosted classifier, which was shown...... and classification, an algorithm for recognizing flow direction and the C5.0 itself. Classified applications include Skype, FTP, torrent, web browser traffic, web radio, interactive gaming and SSH. We performed subsequent tries using different sets of parameters and both training and classification options...

  19. Minimal-Intrusion Traffic Monitoring And Analysis In Mission-Critical Communication Networks

    Directory of Open Access Journals (Sweden)

    Alberto Domingo Ajenjo

    2003-10-01

    Full Text Available A good knowledge of expected and actual traffic patterns is an essential tool for network planning, design and operation in deployed, mission-critical applications. This paper describes those needs, and explains the Traffic Monitoring and Analysis Platform (TMAP concept, as developed in support of NATO deployed military headquarters Communications and Information Systems. It shows how a TMAP was deployed to a real NATO exercise, to prove the concept and baseline the traffic needs per application, per user community and per time of day. Then, it analyses the obtained results and derives conclusions on how to integrate traffic monitoring and analysis platforms in future deployments.

  20. COMPARISON OF TREND PROJECTION METHODS AND BACKPROPAGATION PROJECTIONS METHODS TREND IN PREDICTING THE NUMBER OF VICTIMS DIED IN TRAFFIC ACCIDENT IN TIMOR TENGAH REGENCY, NUSA TENGGARA

    Directory of Open Access Journals (Sweden)

    Aleksius Madu

    2016-10-01

    Full Text Available The purpose of this study is to predict the number of traffic accident victims who died in Timor Tengah Regency with Trend Projection method and Backpropagation method, and compare the two methods based on the degree of guilt and predict the number traffic accident victims in the Timor Tengah Regency for the coming year. This research was conducted in Timor Tengah Regency where data used in this study was obtained from Police Unit in Timor Tengah Regency. The data is on the number of traffic accidents in Timor Tengah Regency from 2000 – 2013, which is obtained by a quantitative analysis with Trend Projection and Backpropagation method. The results of the data analysis predicting the number of traffic accidents victims using Trend Projection method obtained the best model which is the quadratic trend model with equation Yk = 39.786 + (3.297 X + (0.13 X2. Whereas by using back propagation method, it is obtained the optimum network that consists of 2 inputs, 3 hidden screens, and 1 output. Based on the error rates obtained, Back propagation method is better than the Trend Projection method which means that the predicting accuracy with Back propagation method is the best method to predict the number of traffic accidents victims in Timor Tengah Regency. Thus obtained predicting the numbers of traffic accident victims for the next 5 years (Years 2014-2018 respectively - are 106 person, 115 person, 115 person, 119 person and 120 person.   Keywords: Trend Projection, Back propagation, Predicting.

  1. Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management.

    Science.gov (United States)

    Cruz-Piris, Luis; Rivera, Diego; Fernandez, Susel; Marsa-Maestre, Ivan

    2018-02-02

    One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these systems is in the deployment of sensors to provide accurate information about the traffic. Our proposal is using the centrality measurement of a graph as a base to locate the best locations for sensor installation in a traffic network. After integrating these sensors in a simulation scenario, we define a Multi-Agent Systems composed of three types of agents: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. The ultimate goal of these Multi-Agent Systems is to improve the trip duration for vehicles in the network. To validate our solution, we have developed the needed elements for modelling the sensors and agents in the simulation environment. We have carried out experiments using the Simulation of Urban MObility (SUMO) traffic simulator and the Travel and Activity PAtterns Simulation (TAPAS) Cologne traffic scenario. The obtained results show that our proposal allows to reduce the sensor network while still obtaining relevant information to have a global view of the environment. Finally, regarding the Multi-Agent Systems, we have carried out experiments that show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic) in terms of reduction of vehicle trip duration and reduction of the message exchange overhead in the sensor network.

  2. Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management

    Directory of Open Access Journals (Sweden)

    Luis Cruz-Piris

    2018-02-01

    Full Text Available One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these systems is in the deployment of sensors to provide accurate information about the traffic. Our proposal is using the centrality measurement of a graph as a base to locate the best locations for sensor installation in a traffic network. After integrating these sensors in a simulation scenario, we define a Multi-Agent Systems composed of three types of agents: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. The ultimate goal of these Multi-Agent Systems is to improve the trip duration for vehicles in the network. To validate our solution, we have developed the needed elements for modelling the sensors and agents in the simulation environment. We have carried out experiments using the Simulation of Urban MObility (SUMO traffic simulator and the Travel and Activity PAtterns Simulation (TAPAS Cologne traffic scenario. The obtained results show that our proposal allows to reduce the sensor network while still obtaining relevant information to have a global view of the environment. Finally, regarding the Multi-Agent Systems, we have carried out experiments that show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic in terms of reduction of vehicle trip duration and reduction of the message exchange overhead in the sensor network.

  3. Entropy Based Analysis of DNS Query Traffic in the Campus Network

    Directory of Open Access Journals (Sweden)

    Dennis Arturo Ludeña Romaña

    2008-10-01

    Full Text Available We carried out the entropy based study on the DNS query traffic from the campus network in a university through January 1st, 2006 to March 31st, 2007. The results are summarized, as follows: (1 The source IP addresses- and query keyword-based entropies change symmetrically in the DNS query traffic from the outside of the campus network when detecting the spam bot activity on the campus network. On the other hand (2, the source IP addresses- and query keywordbased entropies change similarly each other when detecting big DNS query traffic caused by prescanning or distributed denial of service (DDoS attack from the campus network. Therefore, we can detect the spam bot and/or DDoS attack bot by only watching DNS query access traffic.

  4. Distributed Traffic Control for Reduced Fuel Consumption and Travel Time in Transportation Networks

    Science.gov (United States)

    2018-04-01

    Current technology in traffic control is limited to a centralized approach that has not paid appropriate attention to efficiency of fuel consumption and is subject to the scale of transportation networks. This project proposes a transformative approa...

  5. Developing a New HSR Switching Node (SwitchBox for Improving Traffic Performance in HSR Networks

    Directory of Open Access Journals (Sweden)

    Nguyen Xuan Tien

    2016-01-01

    Full Text Available High availability is crucial for industrial Ethernet networks as well as Ethernet-based control systems such as automation networks and substation automation systems (SAS. Since standard Ethernet does not support fault tolerance capability, the high availability of Ethernet networks can be increased by using redundancy protocols. Various redundancy protocols for Ethernet networks have been developed and standardized, such as rapid spanning tree protocol (RSTP, media redundancy protocol (MRP, parallel redundancy protocol (PRP, high-availability seamless redundancy (HSR and others. RSTP and MRP have switchover delay drawbacks. PRP provides zero recovery time, but requires a duplicate network infrastructure. HSR operation is similar to PRP, but HSR uses a single network. However, the standard HSR protocol is mainly applied to ring-based topologies and generates excessively unnecessary redundant traffic in the network. In this paper, we develop a new switching node for the HSR protocol, called SwitchBox, which is used in HSR networks in order to support any network topology and significantly reduce redundant network traffic, including unicast, multicast and broadcast traffic, compared with standard HSR. By using the SwitchBox, HSR not only provides seamless communications with zero switchover time in case of failure, but it is also easily applied to any network topology and significantly reduces unnecessary redundant traffic in HSR networks.

  6. Energy Saving Scheme Based On Traffic Forwarding For Optical Fiber Access Networks

    DEFF Research Database (Denmark)

    Lopez, G. Arturo Rodes; Estaran Tolosa, Jose Manuel; Vegas Olmos, Juan José

    2013-01-01

    We report on an energy saving block that regroups and powers off OLTs during low traffic periods, resulting in energy savings up to 87,5% in the central office of optical access networks.......We report on an energy saving block that regroups and powers off OLTs during low traffic periods, resulting in energy savings up to 87,5% in the central office of optical access networks....

  7. End-to-End Traffic Flow Modeling of the Integrated SCaN Network

    Science.gov (United States)

    Cheung, K.-M.; Abraham, D. S.

    2012-05-01

    In this article, we describe the analysis and simulation effort of the end-to-end traffic flow for the Integrated Space Communications and Navigation (SCaN) Network. Using the network traffic derived for the 30-day period of July 2018 from the Space Communications Mission Model (SCMM), we generate the wide-area network (WAN) bandwidths of the ground links for different architecture options of the Integrated SCaN Network. We also develop a new analytical scheme to model the traffic flow and buffering mechanism of a store-and-forward network. It is found that the WAN bandwidth of the Integrated SCaN Network is an important differentiator of different architecture options, as the recurring circuit costs of certain architecture options can be prohibitively high.

  8. Dynamic traffic grooming with Spectrum Engineering (TG-SE) in flexible grid optical networks

    Science.gov (United States)

    Yu, Xiaosong; Zhao, Yongli; Zhang, Jiawei; Wang, Jianping; Zhang, Guoying; Chen, Xue; Zhang, Jie

    2015-12-01

    Flexible grid has emerged as an evolutionary technology to satisfy the ever increasing demand for higher spectrum efficiency and operational flexibility. To optimize the spectrum resource utilization, this paper introduces the concept of Spectrum Engineering in flex-grid optical networks. The sliceable optical transponder has been proposed to offload IP traffic to the optical layer and reduce the number of IP router ports and transponders. We discuss the impact of sliceable transponder in traffic grooming and propose several traffic-grooming schemes with Spectrum Engineering (TG-SE). Our results show that there is a tradeoff among different traffic grooming policies, which should be adopted based on the network operator's objectives. The proposed traffic grooming with Spectrum Engineering schemes can reduce OPEX as well as increase spectrum efficiency by efficiently utilizing the bandwidth variability and capability of sliceable optical transponders.

  9. An Architecture to Manage Incoming Traffic of Inter-Domain Routing Using OpenFlow Networks

    Directory of Open Access Journals (Sweden)

    Walber José Adriano Silva

    2018-04-01

    Full Text Available The Border Gateway Protocol (BGP is the current state-of-the-art inter-domain routing between Autonomous Systems (ASes. Although BGP has different mechanisms to manage outbound traffic in an AS domain, it lacks an efficient tool for inbound traffic control from transit ASes such as Internet Service Providers (ISPs. For inter-domain routing, the BGP’s destination-based forwarding paradigm limits the granularity of distributing the network traffic among the multiple paths of the current Internet topology. Thus, this work offered a new architecture to manage incoming traffic in the inter-domain using OpenFlow networks. The architecture explored direct inter-domain communication to exchange control information and the functionalities of the OpenFlow protocol. Based on the achieved results of the size of exchanging messages, the proposed architecture is not only scalable, but also capable of performing load balancing for inbound traffic using different strategies.

  10. Incorporation of Duffing Oscillator and Wigner-Ville Distribution in Traffic Flow Prediction

    Directory of Open Access Journals (Sweden)

    Anamarija L. Mrgole

    2017-02-01

    Full Text Available The main purpose of this study was to investigate the use of various chaotic pattern recognition methods for traffic flow prediction. Traffic flow is a variable, dynamic and complex system, which is non-linear and unpredictable. The emergence of traffic flow congestion in road traffic is estimated when the traffic load on a specific section of the road in a specific time period is close to exceeding the capacity of the road infrastructure. Under certain conditions, it can be seen in concentrating chaotic traffic flow patterns. The literature review of traffic flow theory and its connection with chaotic features implies that this kind of method has great theoretical and practical value. Researched methods of identifying chaos in traffic flow have shown certain restrictions in their techniques but have suggested guidelines for improving the identification of chaotic parameters in traffic flow. The proposed new method of forecasting congestion in traffic flow uses Wigner-Ville frequency distribution. This method enables the display of a chaotic attractor without the use of reconstruction phase space.

  11. Using mobile probes to inform and measure the effectiveness of macroscopic traffic control strategies on urban networks.

    Science.gov (United States)

    2015-06-01

    Urban traffic congestion is a problem that plagues many cities in the United States. Testing strategies to alleviate this : congestion is especially challenging due to the difficulty of modeling complex urban traffic networks. However, recent work ha...

  12. Traffic Management by Using Admission Control Methods in Multiple Node IMS Network

    Directory of Open Access Journals (Sweden)

    Filip Chamraz

    2016-01-01

    Full Text Available The paper deals with Admission Control methods (AC as a possible solution for traffic management in IMS networks (IP Multimedia Subsystem - from the point of view of an efficient redistribution of the available network resources and keeping the parameters of Quality of Service (QoS. The paper specifically aims at the selection of the most appropriate method for the specific type of traffic and traffic management concept using AC methods on multiple nodes. The potential benefit and disadvantage of the used solution is evaluated.

  13. Day-to-day evolution of the traffic network with Advanced Traveler Information System

    International Nuclear Information System (INIS)

    Han Linghui; Sun Huijun; Wu Jianjun; Zhu Chengjuan

    2011-01-01

    Highlights: → We develop a dynamical system with Advanced Travelers Information System (ATIS). → We use the dynamical system to study stability of the traffic network with ATIS. → It is found that some periodic attractors appear in some cases. → A road pricing is implemented to alleviate the instability of the traffic network with ATIS. - Abstract: Since the notion of user equilibrium (UE) was proposed by Wardrop , it has become a cornerstone for traffic assignment analysis. But, it is not sufficient to only ask whether equilibrium exists or not; it is equally important to ask whether and how the system can achieve equilibrium. Meanwhile, stability is an important performance in the sense that if equilibrium is unsustainable, both the equilibrium and the trajectory are sensitive to disturbances, even a small perturbation will result in the system evolution away from the equilibrium point. These incentive a growing interest in day-to-day dynamics. In this paper, we develop a dynamical system with Advanced Traveler Information System (ATIS) and study the stability of the network with ATIS. A simple network is used to simulate the model, and the results show that there exist periodic attractors in the traffic network in some cases (for example, the market penetration level of ATIS is 0.25 and traffic demand is 2 unit). It is found that the logit parameter of the dynamical model and the traffic demand can also affect the stability of the traffic network. More periodic attractors appear in the system when the traffic demand is large and the low logit parameter can delay the appearance of periodic attractors. By simulation, it can be concluded that if the range of the periodic attractors' domain of the simple network is known, the road pricing based on the range of the attraction domain is effective to alleviate the instability of the system.

  14. Real time network traffic monitoring for wireless local area networks based on compressed sensing

    Science.gov (United States)

    Balouchestani, Mohammadreza

    2017-05-01

    A wireless local area network (WLAN) is an important type of wireless networks which connotes different wireless nodes in a local area network. WLANs suffer from important problems such as network load balancing, large amount of energy, and load of sampling. This paper presents a new networking traffic approach based on Compressed Sensing (CS) for improving the quality of WLANs. The proposed architecture allows reducing Data Delay Probability (DDP) to 15%, which is a good record for WLANs. The proposed architecture is increased Data Throughput (DT) to 22 % and Signal to Noise (S/N) ratio to 17 %, which provide a good background for establishing high qualified local area networks. This architecture enables continuous data acquisition and compression of WLAN's signals that are suitable for a variety of other wireless networking applications. At the transmitter side of each wireless node, an analog-CS framework is applied at the sensing step before analog to digital converter in order to generate the compressed version of the input signal. At the receiver side of wireless node, a reconstruction algorithm is applied in order to reconstruct the original signals from the compressed signals with high probability and enough accuracy. The proposed algorithm out-performs existing algorithms by achieving a good level of Quality of Service (QoS). This ability allows reducing 15 % of Bit Error Rate (BER) at each wireless node.

  15. Main concept of local area network protection on the basis of the SAAM 'TRAFFIC'

    International Nuclear Information System (INIS)

    Vasil'ev, P.M.; Kryukov, Yu.A.; Kuptsov, S.I.; Ivanov, V.V.; Koren'kov, V.V.

    2002-01-01

    In our previous paper we developed a system for acquisition, analysis and management of the network traffic (SAAM 'Traffic') for a segment of the JINR local area computer network (JINR LAN). In our present work we consider well-known scenarios of attacks on local area networks and propose protection methods based on the SAAM 'Traffic'. Although the system for LAN protection is installed on a router computer, it is not analogous to the firewall scheme and, thus, it does not hinder the performance of distributed network applications. This provides a possibility to apply such an approach to GRID-technologies, where network protection on the firewall basis can not be basically used. (author)

  16. A Survey on Urban Traffic Management System Using Wireless Sensor Networks

    Science.gov (United States)

    Nellore, Kapileswar; Hancke, Gerhard P.

    2016-01-01

    Nowadays, the number of vehicles has increased exponentially, but the bedrock capacities of roads and transportation systems have not developed in an equivalent way to efficiently cope with the number of vehicles traveling on them. Due to this, road jamming and traffic correlated pollution have increased with the associated adverse societal and financial effect on different markets worldwide. A static control system may block emergency vehicles due to traffic jams. Wireless Sensor networks (WSNs) have gained increasing attention in traffic detection and avoiding road congestion. WSNs are very trendy due to their faster transfer of information, easy installation, less maintenance, compactness and for being less expensive compared to other network options. There has been significant research on Traffic Management Systems using WSNs to avoid congestion, ensure priority for emergency vehicles and cut the Average Waiting Time (AWT) of vehicles at intersections. In recent decades, researchers have started to monitor real-time traffic using WSNs, RFIDs, ZigBee, VANETs, Bluetooth devices, cameras and infrared signals. This paper presents a survey of current urban traffic management schemes for priority-based signalling, and reducing congestion and the AWT of vehicles. The main objective of this survey is to provide a taxonomy of different traffic management schemes used for avoiding congestion. Existing urban traffic management schemes for the avoidance of congestion and providing priority to emergency vehicles are considered and set the foundation for further research. PMID:26828489

  17. Developing a stochastic traffic volume prediction model for public-private partnership projects

    Science.gov (United States)

    Phong, Nguyen Thanh; Likhitruangsilp, Veerasak; Onishi, Masamitsu

    2017-11-01

    Transportation projects require an enormous amount of capital investment resulting from their tremendous size, complexity, and risk. Due to the limitation of public finances, the private sector is invited to participate in transportation project development. The private sector can entirely or partially invest in transportation projects in the form of Public-Private Partnership (PPP) scheme, which has been an attractive option for several developing countries, including Vietnam. There are many factors affecting the success of PPP projects. The accurate prediction of traffic volume is considered one of the key success factors of PPP transportation projects. However, only few research works investigated how to predict traffic volume over a long period of time. Moreover, conventional traffic volume forecasting methods are usually based on deterministic models which predict a single value of traffic volume but do not consider risk and uncertainty. This knowledge gap makes it difficult for concessionaires to estimate PPP transportation project revenues accurately. The objective of this paper is to develop a probabilistic traffic volume prediction model. First, traffic volumes were estimated following the Geometric Brownian Motion (GBM) process. Monte Carlo technique is then applied to simulate different scenarios. The results show that this stochastic approach can systematically analyze variations in the traffic volume and yield more reliable estimates for PPP projects.

  18. Low Cost Wireless Network Camera Sensors for Traffic Monitoring

    Science.gov (United States)

    2012-07-01

    Many freeways and arterials in major cities in Texas are presently equipped with video detection cameras to : collect data and help in traffic/incident management. In this study, carefully controlled experiments determined : the throughput and output...

  19. Traffic Command Gesture Recognition for Virtual Urban Scenes Based on a Spatiotemporal Convolution Neural Network

    Directory of Open Access Journals (Sweden)

    Chunyong Ma

    2018-01-01

    Full Text Available Intelligent recognition of traffic police command gestures increases authenticity and interactivity in virtual urban scenes. To actualize real-time traffic gesture recognition, a novel spatiotemporal convolution neural network (ST-CNN model is presented. We utilized Kinect 2.0 to construct a traffic police command gesture skeleton (TPCGS dataset collected from 10 volunteers. Subsequently, convolution operations on the locational change of each skeletal point were performed to extract temporal features, analyze the relative positions of skeletal points, and extract spatial features. After temporal and spatial features based on the three-dimensional positional information of traffic police skeleton points were extracted, the ST-CNN model classified positional information into eight types of Chinese traffic police gestures. The test accuracy of the ST-CNN model was 96.67%. In addition, a virtual urban traffic scene in which real-time command tests were carried out was set up, and a real-time test accuracy rate of 93.0% was achieved. The proposed ST-CNN model ensured a high level of accuracy and robustness. The ST-CNN model recognized traffic command gestures, and such recognition was found to control vehicles in virtual traffic environments, which enriches the interactive mode of the virtual city scene. Traffic command gesture recognition contributes to smart city construction.

  20. Development of traffic light control algorithm in smart municipal network

    OpenAIRE

    Kuzminykh, Ievgeniia

    2016-01-01

    This paper presents smart system that bypasses the normal functioning algorithm of traffic lights, triggers a green light when the lights are red or reset the timer of the traffic lights when they are about to turn red. Different pieces of hardware like microcontroller units, transceivers, resistors, diodes, LEDs, a digital compass and accelerometer will be coupled together and programed to create unified complex intelligent system.

  1. Cybersecurity and Network Forensics: Analysis of Malicious Traffic towards a Honeynet with Deep Packet Inspection

    OpenAIRE

    Gabriel Arquelau Pimenta Rodrigues; Robson de Oliveira Albuquerque; Flávio Elias Gomes de Deus; Rafael Timóteo de Sousa Jr.; Gildásio Antônio de Oliveira Júnior; Luis Javier García Villalba; Tai-Hoon Kim

    2017-01-01

    Any network connected to the Internet is subject to cyber attacks. Strong security measures, forensic tools, and investigators contribute together to detect and mitigate those attacks, reducing the damages and enabling reestablishing the network to its normal operation, thus increasing the cybersecurity of the networked environment. This paper addresses the use of a forensic approach with Deep Packet Inspection to detect anomalies in the network traffic. As cyber attacks may occur on any laye...

  2. Efficient traffic grooming with dynamic ONU grouping for multiple-OLT-based access network

    Science.gov (United States)

    Zhang, Shizong; Gu, Rentao; Ji, Yuefeng; Wang, Hongxiang

    2015-12-01

    Fast bandwidth growth urges large-scale high-density access scenarios, where the multiple Passive Optical Networking (PON) system clustered deployment can be adopted as an appropriate solution to fulfill the huge bandwidth demands, especially for a future 5G mobile network. However, the lack of interaction between different optical line terminals (OLTs) results in part of the bandwidth resources waste. To increase the bandwidth efficiency, as well as reduce bandwidth pressure at the edge of a network, we propose a centralized flexible PON architecture based on Time- and Wavelength-Division Multiplexing PON (TWDM PON). It can provide flexible affiliation for optical network units (ONUs) and different OLTs to support access network traffic localization. Specifically, a dynamic ONU grouping algorithm (DGA) is provided to obtain the minimal OLT outbound traffic. Simulation results show that DGA obtains an average 25.23% traffic gain increment under different OLT numbers within a small ONU number situation, and the traffic gain will increase dramatically with the increment of the ONU number. As the DGA can be deployed easily as an application running above the centralized control plane, the proposed architecture can be helpful to improve the network efficiency for future traffic-intensive access scenarios.

  3. A Big Network Traffic Data Fusion Approach Based on Fisher and Deep Auto-Encoder

    Directory of Open Access Journals (Sweden)

    Xiaoling Tao

    2016-03-01

    Full Text Available Data fusion is usually performed prior to classification in order to reduce the input space. These dimensionality reduction techniques help to decline the complexity of the classification model and thus improve the classification performance. The traditional supervised methods demand labeled samples, and the current network traffic data mostly is not labeled. Thereby, better learners will be built by using both labeled and unlabeled data, than using each one alone. In this paper, a novel network traffic data fusion approach based on Fisher and deep auto-encoder (DFA-F-DAE is proposed to reduce the data dimensions and the complexity of computation. The experimental results show that the DFA-F-DAE improves the generalization ability of the three classification algorithms (J48, back propagation neural network (BPNN, and support vector machine (SVM by data dimensionality reduction. We found that the DFA-F-DAE remarkably improves the efficiency of big network traffic classification.

  4. Predicting Posttraumatic Stress Symptoms in Children after Road Traffic Accidents

    Science.gov (United States)

    Landolt, Markus A.; Vollrath, Margarete; Timm, Karin; Gnehm, Hanspeter E.; Sennhauser, Felix H.

    2005-01-01

    Objective: To prospectively assess the prevalence, course, and predictors of posttraumatic stress symptoms (PTSSs) in children after road traffic accidents (RTAs). Method: Sixty-eight children (6.5-14.5 years old) were interviewed 4-6 weeks and 12 months after an RTA with the Child PTSD Reaction Index (response rate 58.6%). Their mothers (n = 60)…

  5. Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

    Science.gov (United States)

    Wen, Shameng; Meng, Qingkun; Feng, Chao; Tang, Chaojing

    2017-01-01

    Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

  6. Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

    Directory of Open Access Journals (Sweden)

    Shameng Wen

    Full Text Available Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

  7. An implementation of traffic light system using multi-hop Ad hoc networks

    KAUST Repository

    Ansari, Imran Shafique

    2009-08-01

    In ad hoc networks nodes cooperate with each other to form a temporary network without the aid of any centralized administration. No wired base station or infrastructure is supported, and each host communicates via radio packets. Each host must act as a router, since routes are mostly multi-hop, due to the limited power transmission set by government agencies, (e.g. the Federal Communication Commission (FCC), which is 1 Watt in Industrial Scientific and Medical (ISM) band. The natures of wireless mobile ad hoc networks depend on batteries or other fatiguing means for their energy. A limited energy capacity may be the most significant performance constraint. Therefore, radio resource and power management is an important issue of any wireless network. In this paper, a design for traffic light system employing ad hoc networks is proposed. The traffic light system runs automatically based on signals sent through a multi-hop ad hoc network of \\'n\\' number of nodes utilizing the Token Ring protocol, which is efficient for this application from the energy prospective. The experiment consists of a graphical user interface that simulates the traffic lights and laptops (which have wireless network adapters) are used to run the graphical user interface and are responsible for setting up the ad hoc network between them. The traffic light system has been implemented utilizing A Mesh Driver (which allows for more than one wireless device to be connected simultaneously) and Java-based client-server programs. © 2009 IEEE.

  8. Assembly and offset assignment scheme for self-similar traffic in optical burst switched networks

    CSIR Research Space (South Africa)

    Muwonge, KB

    2007-10-01

    Full Text Available at the Label Edge Router (LER) to buffer traffic in the electronic domain. Burst assembly and offset assignment schemes are implemented in a complementary manner to improve QoS of an OBS network. The authors show that OBS network performance is directly related...

  9. Research on the Application of Wireless Network in Collecting Road Traffic Information

    Institute of Scientific and Technical Information of China (English)

    DU Hui-jiang

    2015-01-01

    Due to the characteristics of variability and dispersion in traffic information, to get the reliable real-time traffic information has been a bottleneck in the development of intelligent transportation systems. However, with the development of wireless network technology and mobile Internet, the mobile phones are rapidly developed and more popular, so it is possible to get road traffic information by locating the mobile phones in vehicles. The system structure for the road traffic information collection is designed based on wireless network and mobile phones in vehicles, and the vehicle recognition and its information computation methods are given and discussed. Also the simulation is done for vehicle recognition and computation based on fuzzy cluster analysis method and the results are obtained and analyzed.

  10. Resilience of traffic networks: From perturbation to recovery via a dynamic restricted equilibrium model

    International Nuclear Information System (INIS)

    Nogal, Maria; O'Connor, Alan; Caulfield, Brian; Martinez-Pastor, Beatriz

    2016-01-01

    When a disruptive event takes place in a traffic network some important questions arise, such as how stressed the traffic network is, whether the system is able to respond to this stressful situation, or how long the system needs to recover a new equilibrium position after suffering this perturbation. Quantifying these aspects allows the comparison of different systems, to scale the degree of damage, to identify traffic network weaknesses, and to analyse the effect of user knowledge about the traffic network state. The indicator that accounts for performance and recovery pattern under disruptive events is known as resilience. This paper presents a methodology to assess the resilience of a traffic network when a given perturbation occurs, from the beginning of the perturbation to the total system recovery. To consider the dynamic nature of the problem, a new dynamic equilibrium-restricted assignment model is presented to simulate the network performance evolution, which takes into consideration important aspects, such as the cost increment due to the perturbation, the system impedance to alter its previous state and the user stress level. Finally, this methodology is used to evaluate the resilience indices of a real network. - Highlights: • Method to assess the resilience of a traffic network suffering progressive impacts. • It simulates the dynamic response during the perturbation and system recovery. • The resilience index is based on the travel costs and the stress level of users. • It considers the capacity of adaptation of the system to the new situations. • The model evaluates redundancy, adaptability, ability to recover, etc.

  11. Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks.

    Science.gov (United States)

    Taamneh, Madhar; Taamneh, Salah; Alkheder, Sharaf

    2017-09-01

    Artificial neural networks (ANNs) have been widely used in predicting the severity of road traffic crashes. All available information about previously occurred accidents is typically used for building a single prediction model (i.e., classifier). Too little attention has been paid to the differences between these accidents, leading, in most cases, to build less accurate predictors. Hierarchical clustering is a well-known clustering method that seeks to group data by creating a hierarchy of clusters. Using hierarchical clustering and ANNs, a clustering-based classification approach for predicting the injury severity of road traffic accidents was proposed. About 6000 road accidents occurred over a six-year period from 2008 to 2013 in Abu Dhabi were used throughout this study. In order to reduce the amount of variation in data, hierarchical clustering was applied on the data set to organize it into six different forms, each with different number of clusters (i.e., clusters from 1 to 6). Two ANN models were subsequently built for each cluster of accidents in each generated form. The first model was built and validated using all accidents (training set), whereas only 66% of the accidents were used to build the second model, and the remaining 34% were used to test it (percentage split). Finally, the weighted average accuracy was computed for each type of models in each from of data. The results show that when testing the models using the training set, clustering prior to classification achieves (11%-16%) more accuracy than without using clustering, while the percentage split achieves (2%-5%) more accuracy. The results also suggest that partitioning the accidents into six clusters achieves the best accuracy if both types of models are taken into account.

  12. Competition between Local Collisions and Collective Hydrodynamic Feedback Controls Traffic Flows in Microfluidic Networks

    Science.gov (United States)

    Belloul, M.; Engl, W.; Colin, A.; Panizza, P.; Ajdari, A.

    2009-05-01

    By studying the repartition of monodisperse droplets at a simple T junction, we show that the traffic of discrete fluid systems in microfluidic networks results from two competing mechanisms, whose significance is driven by confinement. Traffic is dominated by collisions occurring at the junction for small droplets and by collective hydrodynamic feedback for large ones. For each mechanism, we present simple models in terms of the pertinent dimensionless parameters of the problem.

  13. Traffic-Adaptive and Energy-Efficient Small Cell Networks-Energy, Delay and Throughput

    OpenAIRE

    Nazrul Alam, Mirza

    2016-01-01

    The low power small cell network has emerged as a promising and feasible solution to address the massive wireless traffic resulting from the aggressive growth of wireless applications. It is also estimated that Internet of things (IoT) will consist of around 50 billion physical objects by 2020. As a result, besides capacity enhancement, other challenges, e.g., energy efficiency, dynamic addressing of UL/DL traffic asymmetry, low latency, multi-hop communications, reliability and coverage have...

  14. Logistic centres in the Hungarian traffic network -A Current Smvey

    Directory of Open Access Journals (Sweden)

    Geza Schubert

    2003-07-01

    Full Text Available As Europe's economic integration proceeds, Hungary- situatedin the continent's geometric centre - is called upon to accommodatea huge volume of transit traffic. Congestion on thecountry's highways, already a serious problem, makes it desirableto shift transit freight traffic onto railways. For this purpose,and also to make transportation generally more efficient,the so-called logistic service centres are being established. Theseare expected to play a decisive role in the European freight trafficnetwork. An expeditious extension of their services is urgentlyneeded.

  15. Services and traffic policing mechanisms in B-ISDN networks

    International Nuclear Information System (INIS)

    Nleya, B.M.

    1995-10-01

    The paper looks at some of the services that will be offered by ATM in future and their general characteristics. The paper then reviews ATM technology and the various traffic and control functions. Finally the performance comparisons of both the static and dynamic rate Leaky Bucket schemes is presented. The conclusions are that the dynamic rate scheme can control several traffic parameters as compared to the static one, but the complexity in its realization might mean higher costs. (author). 3 refs, 13 figs, 2 tabs

  16. Development of a Software Based Firewall System for Computer Network Traffic Control

    Directory of Open Access Journals (Sweden)

    Ikhajamgbe OYAKHILOME

    2009-12-01

    Full Text Available The connection of an internal network to an external network such as Internet has made it vulnerable to attacks. One class of network attack is unauthorized penetration into network due to the openness of networks. It is possible for hackers to sum access to an internal network, this pose great danger to the network and network resources. Our objective and major concern of network design was to build a secured network, based on software firewall that ensured the integrity and confidentiality of information on the network. We studied several mechanisms to achieve this; one of such mechanism is the implementation of firewall system as a network defence. Our developed firewall has the ability to determine which network traffic should be allowed in or out of the network. Part of our studied work was also channelled towards a comprehensive study of hardware firewall security system with the aim of developing this software based firewall system. Our software firewall goes a long way in protecting an internal network from external unauthorized traffic penetration. We included an anti virus software which is lacking in most firewalls.

  17. A network centrality measure framework for analyzing urban traffic flow: A case study of Wuhan, China

    Science.gov (United States)

    Zhao, Shuangming; Zhao, Pengxiang; Cui, Yunfan

    2017-07-01

    In this paper, we propose an improved network centrality measure framework that takes into account both the topological characteristics and the geometric properties of a road network in order to analyze urban traffic flow in relation to different modes: intersection, road, and community, which correspond to point mode, line mode, and area mode respectively. Degree, betweenness, and PageRank centralities are selected as the analysis measures, and GPS-enabled taxi trajectory data is used to evaluate urban traffic flow. The results show that the mean value of the correlation coefficients between the modified degree, the betweenness, and the PageRank centralities and the traffic flow in all periods are higher than the mean value of the correlation coefficients between the conventional degree, the betweenness, the PageRank centralities and the traffic flow at different modes; this indicates that the modified measurements, for analyzing traffic flow, are superior to conventional centrality measurements. This study helps to shed light into the understanding of urban traffic flow in relation to different modes from the perspective of complex networks.

  18. Assessment of traffic noise levels in urban areas using different soft computing techniques.

    Science.gov (United States)

    Tomić, J; Bogojević, N; Pljakić, M; Šumarac-Pavlović, D

    2016-10-01

    Available traffic noise prediction models are usually based on regression analysis of experimental data, and this paper presents the application of soft computing techniques in traffic noise prediction. Two mathematical models are proposed and their predictions are compared to data collected by traffic noise monitoring in urban areas, as well as to predictions of commonly used traffic noise models. The results show that application of evolutionary algorithms and neural networks may improve process of development, as well as accuracy of traffic noise prediction.

  19. The improved degree of urban road traffic network: A case study of Xiamen, China

    Science.gov (United States)

    Wang, Shiguang; Zheng, Lili; Yu, Dexin

    2017-03-01

    The complex network theory is applied to the study of urban road traffic network topology, and we constructed a new measure to characterize an urban road network. It is inspiring to quantify the interaction more appropriately between nodes in complex networks, especially in the field of traffic. The measure takes into account properties of lanes (e.g. number of lanes, width, traffic direction). As much, it is a more comprehensive measure in comparison to previous network measures. It can be used to grasp the features of urban street network more clearly. We applied this measure to the road network in Xiamen, China. Based on a standard method from statistical physics, we examined in more detail the distribution of this new measure and found that (1) due to the limitation of space geographic attributes, traditional research conclusions acquired by using the original definition of degree to study the primal approach modeled urban street network are not very persuasive; (2) both of the direction of the network connection and the degree's odd or even classifications need to be analyzed specifically; (3) the improved degree distribution presents obvious hierarchy, and hierarchical values conform to the power-law distribution, and correlation of our new measure shows some significant segmentation of the urban road network.

  20. Crash Prediction and Risk Evaluation Based on Traffic Analysis Zones

    Directory of Open Access Journals (Sweden)

    Cuiping Zhang

    2014-01-01

    Full Text Available Traffic safety evaluation for traffic analysis zones (TAZs plays an important role in transportation safety planning and long-range transportation plan development. This paper aims to present a comprehensive analysis of zonal safety evaluation. First, several criteria are proposed to measure the crash risk at zonal level. Then these criteria are integrated into one measure-average hazard index (AHI, which is used to identify unsafe zones. In addition, the study develops a negative binomial regression model to statistically estimate significant factors for the unsafe zones. The model results indicate that the zonal crash frequency can be associated with several social-economic, demographic, and transportation system factors. The impact of these significant factors on zonal crash is also discussed. The finding of this study suggests that safety evaluation and estimation might benefit engineers and decision makers in identifying high crash locations for potential safety improvements.

  1. Predicting and analyzing the trend of traffic accidents deaths in Iran in 2014 and 2015.

    Science.gov (United States)

    Mehmandar, Mohammadreza; Soori, Hamid; Mehrabi, Yadolah

    2016-01-01

    Predicting the trend in traffic accidents deaths and its analysis can be a useful tool for planning and policy-making, conducting interventions appropriate with death trend, and taking the necessary actions required for controlling and preventing future occurrences. Predicting and analyzing the trend of traffic accidents deaths in Iran in 2014 and 2015. It was a cross-sectional study. All the information related to fatal traffic accidents available in the database of Iran Legal Medicine Organization from 2004 to the end of 2013 were used to determine the change points (multi-variable time series analysis). Using autoregressive integrated moving average (ARIMA) model, traffic accidents death rates were predicted for 2014 and 2015, and a comparison was made between this rate and the predicted value in order to determine the efficiency of the model. From the results, the actual death rate in 2014 was almost similar to that recorded for this year, while in 2015 there was a decrease compared with the previous year (2014) for all the months. A maximum value of 41% was also predicted for the months of January and February, 2015. From the prediction and analysis of the death trends, proper application and continuous use of the intervention conducted in the previous years for road safety improvement, motor vehicle safety improvement, particularly training and culture-fostering interventions, as well as approval and execution of deterrent regulations for changing the organizational behaviors, can significantly decrease the loss caused by traffic accidents.

  2. Monitoring and Predicting Traffic Induced Vertebrate Mortality Near Wetlands

    OpenAIRE

    DeWoody, J. Andrew; Nogle, Jamie M.; Hoover, Melissa; Dunning, Barny

    2010-01-01

    Animal-vehicle collisions are undesirable to the general public, to drivers, to insurance providers, to biologists, and presumably to the animals themselves. However, traffic-induced mortality (―roadkill‖) is difficult to mitigate in large part because scientists lack the empirical data required to understand the patterns and processes associated with roadkill. Roadkill is not randomly distributed in space or in time, but what are the primary determinants of roadkill? And do they differ acros...

  3. RESEARCH OF ENGINEERING TRAFFIC IN COMPUTER UZ NETWORK USING MPLS TE TECHNOLOGY

    Directory of Open Access Journals (Sweden)

    V. M. Pakhomovа

    2014-12-01

    Full Text Available Purpose. In railway transport of Ukraine one requires the use of computer networks of different technologies: Ethernet, Token Bus, Token Ring, FDDI and others. In combined computer networks on the railway transport it is necessary to use packet switching technology in multiprotocol networks MPLS (MultiProtocol Label Switching more effectively. They are based on the use of tags. Packet network must transmit different types of traffic with a given quality of service. The purpose of the research is development a methodology for determining the sequence of destination flows for the considered fragment of computer network of UZ. Methodology. When optimizing traffic management in MPLS networks has the important role of technology traffic engineering (Traffic Engineering, TE. The main mechanism of TE in MPLS is the use of unidirectional tunnels (MPLS TE tunnel to specify the path of the specified traffic. The mathematical model of the problem of traffic engineering in computer network of UZ technology MPLS TE was made. Computer UZ network is represented with the directed graph, their vertices are routers of computer network, and each arc simulates communication between nodes. As an optimization criterion serves the minimum value of the maximum utilization of the TE-tunnel. Findings. The six options destination flows were determined; rational sequence of flows was found, at which the maximum utilization of TE-tunnels considered a simplified fragment of a computer UZ network does not exceed 0.5. Originality. The method of solving the problem of traffic engineering in Multiprotocol network UZ technology MPLS TE was proposed; for different classes its own way is laid, depending on the bandwidth and channel loading. Practical value. Ability to determine the values of the maximum coefficient of use of TE-tunnels in computer UZ networks based on developed software model «TraffEng». The input parameters of the model: number of routers, channel capacity, the

  4. Communications network traffic data : technical and legal aspects

    NARCIS (Netherlands)

    Fischer, J.C.

    2010-01-01

    Communications traffic data is a legal data concept that can be explained by the example of traditional, non-digital mail: For a letter to be sent by post it must be enclosed in an envelope. On the envelope the name of the addressee, his address and some information about the sender are noted.

  5. Trunk Reservation in Multi-service Networks with BPP Traffic

    DEFF Research Database (Denmark)

    Zheng, H.; Zhang, Qi; Iversen, Villy Bæk

    2006-01-01

    In this paper we develop approximate models for trunk reservation in multi-service systems with BPP (Binomial-Poisson-Pascal) multi-rate traffic streams. The approximation is a generalization of previous work by Tran-Gia & Hubner who assumed Poisson arrival processes. It is based on a generalized...

  6. Design Issues for Traffic Management for the ATM UBR + Service for TCP Over Satellite Networks

    Science.gov (United States)

    Jain, Raj

    1999-01-01

    This project was a comprehensive research program for developing techniques for improving the performance of Internet protocols over Asynchronous Transfer Mode (ATM) based satellite networks. Among the service categories provided by ATM networks, the most commonly used category for data traffic is the unspecified bit rate (UBR) service. UBR allows sources to send data into the network without any feedback control. The project resulted in the numerous ATM Forum contributions and papers.

  7. Bandwidth Impacts of Localizing Peer-to-Peer IP Video Traffic in Access and Aggregation Networks

    Directory of Open Access Journals (Sweden)

    Kerpez Kenneth

    2008-01-01

    Full Text Available Abstract This paper examines the burgeoning impact of peer-to-peer (P2P traffic IP video traffic. High-quality IPTV or Internet TV has high-bandwidth requirements, and P2P IP video could severely strain broadband networks. A model for the popularity of video titles is given, showing that some titles are very popular and will often be available locally; making localized P2P attractive for video titles. The bandwidth impacts of localizing P2P video to try and keep traffic within a broadband access network area or within a broadband access aggregation network area are examined. Results indicate that such highly localized P2P video can greatly lower core bandwidth usage.

  8. Bandwidth Impacts of Localizing Peer-to-Peer IP Video Traffic in Access and Aggregation Networks

    Directory of Open Access Journals (Sweden)

    Kenneth Kerpez

    2008-10-01

    Full Text Available This paper examines the burgeoning impact of peer-to-peer (P2P traffic IP video traffic. High-quality IPTV or Internet TV has high-bandwidth requirements, and P2P IP video could severely strain broadband networks. A model for the popularity of video titles is given, showing that some titles are very popular and will often be available locally; making localized P2P attractive for video titles. The bandwidth impacts of localizing P2P video to try and keep traffic within a broadband access network area or within a broadband access aggregation network area are examined. Results indicate that such highly localized P2P video can greatly lower core bandwidth usage.

  9. From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks.

    Science.gov (United States)

    Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming

    2016-03-14

    The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains.

  10. Congestion Control and Traffic Scheduling for Collaborative Crowdsourcing in SDN Enabled Mobile Wireless Networks

    Directory of Open Access Journals (Sweden)

    Dawei Shen

    2018-01-01

    Full Text Available Currently, a number of crowdsourcing-based mobile applications have been implemented in mobile networks and Internet of Things (IoT, targeted at real-time services and recommendation. The frequent information exchanges and data transmissions in collaborative crowdsourcing are heavily injected into the current communication networks, which poses great challenges for Mobile Wireless Networks (MWN. This paper focuses on the traffic scheduling and load balancing problem in software-defined MWN and designs a hybrid routing forwarding scheme as well as a congestion control algorithm to achieve the feasible solution. The traffic scheduling algorithm first sorts the tasks in an ascending order depending on the amount of tasks and then solves it using a greedy scheme. In the proposed congestion control scheme, the traffic assignment is first transformed into a multiknapsack problem, and then the Artificial Fish Swarm Algorithm (AFSA is utilized to solve this problem. Numerical results on practical network topology reveal that, compared with the traditional schemes, the proposed congestion control and traffic scheduling schemes can achieve load balancing, reduce the probability of network congestion, and improve the network throughput.

  11. Inside the Mechanics of Network Development: How Competition and Strategy Reorganize European Air Traffic

    Science.gov (United States)

    Huber, Hans

    2006-01-01

    Air transport forms complex networks that can be measured in order to understand its structural characteristics and functional properties. Recent models for network growth (i.e., preferential attachment, etc.) remain stochastic and do not seek to understand other network-specific mechanisms that may account for their development in a more microscopic way. Air traffic is made up of many constituent airlines that are either privately or publicly owned and that operate their own networks. They follow more or less similar business policies each. The way these airline networks organize among themselves into distinct traffic distributions reveals complex interaction among them, which in turn can be aggregated into larger (macro-) traffic distributions. Our approach allows for a more deterministic methodology that will assess the impact of airline strategies on the distinct distributions for air traffic, particularly inside Europe. One key question this paper is seeking to answer is whether there are distinct patterns of preferential attachment for given classes of airline networks to distinct types of European airports. Conclusions about the advancing degree of concentration in this industry and the airline operators that accelerate this process can be drawn.

  12. Phase dynamics of complex-valued neural networks and its application to traffic signal control.

    Science.gov (United States)

    Nishikawa, Ikuko; Iritani, Takeshi; Sakakibara, Kazutoshi; Kuroe, Yasuaki

    2005-01-01

    Complex-valued Hopfield networks which possess the energy function are analyzed. The dynamics of the network with certain forms of an activation function is de-composable into the dynamics of the amplitude and phase of each neuron. Then the phase dynamics is described as a coupled system of phase oscillators with a pair-wise sinusoidal interaction. Therefore its phase synchronization mechanism is useful for the area-wide offset control of the traffic signals. The computer simulations show the effectiveness under the various traffic conditions.

  13. Advanced Models and Algorithms for Self-Similar IP Network Traffic Simulation and Performance Analysis

    Science.gov (United States)

    Radev, Dimitar; Lokshina, Izabella

    2010-11-01

    The paper examines self-similar (or fractal) properties of real communication network traffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. Advanced fractal models of sequentional generators and fixed-length sequence generators, and efficient algorithms that are used to simulate self-similar behavior of IP network traffic data are developed and applied. Numerical examples are provided; and simulation results are obtained and analyzed.

  14. A Fair Contention Access Scheme for Low-Priority Traffic in Wireless Body Area Networks

    Directory of Open Access Journals (Sweden)

    Shagufta Henna

    2017-08-01

    Full Text Available Recently, wireless body area networks (WBANs have attracted significant consideration in ubiquitous healthcare. A number of medium access control (MAC protocols, primarily derived from the superframe structure of the IEEE 802.15.4, have been proposed in literature. These MAC protocols aim to provide quality of service (QoS by prioritizing different traffic types in WBANs. A contention access period (CAPwith high contention in priority-based MAC protocols can result in higher number of collisions and retransmissions. During CAP, traffic classes with higher priority are dominant over low-priority traffic; this has led to starvation of low-priority traffic, thus adversely affecting WBAN throughput, delay, and energy consumption. Hence, this paper proposes a traffic-adaptive priority-based superframe structure that is able to reduce contention in the CAP period, and provides a fair chance for low-priority traffic. Simulation results in ns-3 demonstrate that the proposed MAC protocol, called traffic- adaptive priority-based MAC (TAP-MAC, achieves low energy consumption, high throughput, and low latency compared to the IEEE 802.15.4 standard, and the most recent priority-based MAC protocol, called priority-based MAC protocol (PA-MAC.

  15. Modelling Altitude Information in Two-Dimensional Traffic Networks for Electric Mobility Simulation

    Directory of Open Access Journals (Sweden)

    Diogo Santos

    2016-06-01

    Full Text Available Elevation data is important for electric vehicle simulation. However, traffic simulators are often two-dimensional and do not offer the capability of modelling urban networks taking elevation into account. Specifically, SUMO - Simulation of Urban Mobility, a popular microscopic traffic simulator, relies on networks previously modelled with elevation data as to provide this information during simulations. This work tackles the problem of adding elevation data to urban network models - particularly for the case of the Porto urban network, in Portugal. With this goal in mind, a comparison between different altitude information retrieval approaches is made and a simple tool to annotate network models with altitude data is proposed. The work starts by describing the methodological approach followed during research and development, then describing and analysing its main findings. This description includes an in-depth explanation of the proposed tool. Lastly, this work reviews some related work to the subject.

  16. Effect and Analysis of Sustainable Cell Rate using MPEG video Traffic in ATM Networks

    Directory of Open Access Journals (Sweden)

    Sakshi Kaushal

    2006-04-01

    Full Text Available The broadband networks inhibit the capability to carry multiple types of traffic – voice, video and data, but these services need to be controlled according to the traffic contract negotiated at the time of the connection to maintain desired Quality of service. Such control techniques use traffic descriptors to evaluate its performance and effectiveness. In case of Variable Bit Rate (VBR services, Peak Cell Rate (PCR and its Cell Delay Variation Tolerance (CDVTPCR are mandatory descriptors. In addition to these, ATM Forum proposed Sustainable Cell Rate (SCR and its Cell delay variation tolerance (CDVTSCR. In this paper, we evaluated the impact of specific SCR and CDVTSCR values on the Usage Parameter Control (UPC performance in case of measured MPEG traffic for improving the efficiency

  17. Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode

    Directory of Open Access Journals (Sweden)

    Tao Ye

    2018-06-01

    Full Text Available Many accidents happen under shunting mode when the speed of a train is below 45 km/h. In this mode, train attendants observe the railway condition ahead using the traditional manual method and tell the observation results to the driver in order to avoid danger. To address this problem, an automatic object detection system based on convolutional neural network (CNN is proposed to detect objects ahead in shunting mode, which is called Feature Fusion Refine neural network (FR-Net. It consists of three connected modules, i.e., the depthwise-pointwise convolution, the coarse detection module, and the object detection module. Depth-wise-pointwise convolutions are used to improve the detection in real time. The coarse detection module coarsely refine the locations and sizes of prior anchors to provide better initialization for the subsequent module and also reduces search space for the classification, whereas the object detection module aims to regress accurate object locations and predict the class labels for the prior anchors. The experimental results on the railway traffic dataset show that FR-Net achieves 0.8953 mAP with 72.3 FPS performance on a machine with a GeForce GTX1080Ti with the input size of 320 × 320 pixels. The results imply that FR-Net takes a good tradeoff both on effectiveness and real time performance. The proposed method can meet the needs of practical application in shunting mode.

  18. Study on network traffic forecast model of SVR optimized by GAFSA

    International Nuclear Information System (INIS)

    Liu, Yuan; Wang, RuiXue

    2016-01-01

    There are some problems, such as low precision, on existing network traffic forecast model. In accordance with these problems, this paper proposed the network traffic forecast model of support vector regression (SVR) algorithm optimized by global artificial fish swarm algorithm (GAFSA). GAFSA constitutes an improvement of artificial fish swarm algorithm, which is a swarm intelligence optimization algorithm with a significant effect of optimization. The optimum training parameters used for SVR could be calculated by optimizing chosen parameters, which would make the forecast more accurate. With the optimum training parameters searched by GAFSA algorithm, a model of network traffic forecast, which greatly solved problems of great errors in SVR improved by others intelligent algorithms, could be built with the forecast result approaching stability and the increased forecast precision. The simulation shows that, compared with other models (e.g. GA-SVR, CPSO-SVR), the forecast results of GAFSA-SVR network traffic forecast model is more stable with the precision improved to more than 89%, which plays an important role on instructing network control behavior and analyzing security situation.

  19. USAF Enlisted Air Traffic Controller Selection: Examination of the Predictive Validity of the FAA Air Traffic Selection and Training Battery versus Training Performance

    National Research Council Canada - National Science Library

    Carretta, Thomas R; King, Raymond E

    2008-01-01

    .... The current study examined the utility of the FAA Air Traffic Selection and Training (AT-SAT) battery for incrementing the predictiveness of the ASVAB versus several enlisted ATC training criteria...

  20. 3D Markov Process for Traffic Flow Prediction in Real-Time

    Directory of Open Access Journals (Sweden)

    Eunjeong Ko

    2016-01-01

    Full Text Available Recently, the correct estimation of traffic flow has begun to be considered an essential component in intelligent transportation systems. In this paper, a new statistical method to predict traffic flows using time series analyses and geometric correlations is proposed. The novelty of the proposed method is two-fold: (1 a 3D heat map is designed to describe the traffic conditions between roads, which can effectively represent the correlations between spatially- and temporally-adjacent traffic states; and (2 the relationship between the adjacent roads on the spatiotemporal domain is represented by cliques in MRF and the clique parameters are obtained by example-based learning. In order to assess the validity of the proposed method, it is tested using data from expressway traffic that are provided by the Korean Expressway Corporation, and the performance of the proposed method is compared with existing approaches. The results demonstrate that the proposed method can predict traffic conditions with an accuracy of 85%, and this accuracy can be improved further.

  1. Predictive control strategies for energy saving of hybrid electric vehicles based on traffic light information

    Directory of Open Access Journals (Sweden)

    Kaijiang YU

    2015-10-01

    Full Text Available As the conventional control method for hybrid electric vehicle doesn’t consider the effect of known traffic light information on the vehicle energy management, this paper proposes a model predictive control intelligent optimization strategies based on traffic light information for hybrid electric vehicles. By building the simplified model of the hybrid electric vehicle and adopting the continuation/generalized minimum residual method, the model prediction problem is solved. The simulation is conducted by using MATLAB/Simulink platform. The simulation results show the effectiveness of the proposed model of the traffic light information, and that the proposed model predictive control method can improve fuel economy and the real-time control performance significantly. The research conclusions show that the proposed control strategy can achieve optimal control of the vehicle trajectory, significantly improving fuel economy of the vehicle, and meet the system requirements for the real-time optimal control.

  2. Effects of packet retransmission with finite packet lifetime on traffic capacity in scale-free networks

    Science.gov (United States)

    Jiang, Zhong-Yuan; Ma, Jian-Feng

    Existing routing strategies such as the global dynamic routing [X. Ling, M. B. Hu, R. Jiang and Q. S. Wu, Phys. Rev. E 81, 016113 (2010)] can achieve very high traffic capacity at the cost of extremely long packet traveling delay. In many real complex networks, especially for real-time applications such as the instant communication software, extremely long packet traveling time is unacceptable. In this work, we propose to assign a finite Time-to-Live (TTL) parameter for each packet. To guarantee every packet to arrive at its destination within its TTL, we assume that a packet is retransmitted by its source once its TTL expires. We employ source routing mechanisms in the traffic model to avoid the routing-flaps induced by the global dynamic routing. We compose extensive simulations to verify our proposed mechanisms. With small TTL, the effects of packet retransmission on network traffic capacity are obvious, and the phase transition from flow free state to congested state occurs. For the purpose of reducing the computation frequency of the routing table, we employ a computing cycle Tc within which the routing table is recomputed once. The simulation results show that the traffic capacity decreases with increasing Tc. Our work provides a good insight into the understanding of effects of packet retransmission with finite packet lifetime on traffic capacity in scale-free networks.

  3. Best response game of traffic on road network of non-signalized intersections

    Science.gov (United States)

    Yao, Wang; Jia, Ning; Zhong, Shiquan; Li, Liying

    2018-01-01

    This paper studies the traffic flow in a grid road network with non-signalized intersections. The nature of the drivers in the network is simulated such that they play an iterative snowdrift game with other drivers. A cellular automata model is applied to study the characteristics of the traffic flow and the evolution of the behaviour of the drivers during the game. The drivers use best-response as their strategy to update rules. Three major findings are revealed. First, the cooperation rate in simulation experiences staircase-shaped drop as cost to benefit ratio r increases, and cooperation rate can be derived analytically as a function of cost to benefit ratio r. Second, we find that higher cooperation rate corresponds to higher average speed, lower density and higher flow. This reveals that defectors deteriorate the efficiency of traffic on non-signalized intersections. Third, the system experiences more randomness when the density is low because the drivers will not have much opportunity to update strategy when the density is low. These findings help to show how the strategy of drivers in a traffic network evolves and how their interactions influence the overall performance of the traffic system.

  4. Estimation and Control of Networked Distributed Parameter Systems: Application to Traffic Flow

    KAUST Repository

    Canepa, Edward

    2016-11-01

    The management of large-scale transportation infrastructure is becoming a very complex task for the urban areas of this century which are covering bigger geographic spaces and facing the inclusion of connected and self-controlled vehicles. This new system paradigm can leverage many forms of sensing and interaction, including a high-scale mobile sensing approach. To obtain a high penetration sensing system on urban areas more practical and scalable platforms are needed, combined with estimation algorithms suitable to the computational capabilities of these platforms. The purpose of this work was to develop a transportation framework that is able to handle different kinds of sensing data (e.g., connected vehicles, loop detectors) and optimize the traffic state on a defined traffic network. The framework estimates the traffic on road networks modeled by a family of Lighthill-Whitham-Richards equations. Based on an equivalent formulation of the problem using a Hamilton-Jacobi equation and using a semi-analytic formula, I will show that the model constraints resulting from the Hamilton-Jacobi equation are linear, albeit with unknown integer variables. This general framework solve exactly a variety of problems arising in transportation networks: traffic estimation, traffic control (including robust control), cybersecurity and sensor fault detection, or privacy analysis of users in probe-based traffic monitoring systems. This framework is very flexible, fast, and yields exact results. The recent advances in sensors (GPS, inertial measurement units) and microprocessors enable the development low-cost dedicated devices for traffic sensing in cities, 5 which are highly scalable, providing a feasible solution to cover large urban areas. However, one of the main problems to address is the privacy of the users of the transportation system, the framework presented here is a viable option to guarantee the privacy of the users by design.

  5. Neural Networks for protein Structure Prediction

    DEFF Research Database (Denmark)

    Bohr, Henrik

    1998-01-01

    This is a review about neural network applications in bioinformatics. Especially the applications to protein structure prediction, e.g. prediction of secondary structures, prediction of surface structure, fold class recognition and prediction of the 3-dimensional structure of protein backbones...

  6. Capacity planning of link restorable optical networks under dynamic change of traffic

    Science.gov (United States)

    Ho, Kwok Shing; Cheung, Kwok Wai

    2005-11-01

    Future backbone networks shall require full-survivability and support dynamic changes of traffic demands. The Generalized Survivable Networks (GSN) was proposed to meet these challenges. GSN is fully-survivable under dynamic traffic demand changes, so it offers a practical and guaranteed characterization framework for ASTN / ASON survivable network planning and bandwidth-on-demand resource allocation 4. The basic idea of GSN is to incorporate the non-blocking network concept into the survivable network models. In GSN, each network node must specify its I/O capacity bound which is taken as constraints for any allowable traffic demand matrix. In this paper, we consider the following generic GSN network design problem: Given the I/O bounds of each network node, find a routing scheme (and the corresponding rerouting scheme under failure) and the link capacity assignment (both working and spare) which minimize the cost, such that any traffic matrix consistent with the given I/O bounds can be feasibly routed and it is single-fault tolerant under the link restoration scheme. We first show how the initial, infeasible formal mixed integer programming formulation can be transformed into a more feasible problem using the duality transformation of the linear program. Then we show how the problem can be simplified using the Lagrangian Relaxation approach. Previous work has outlined a two-phase approach for solving this problem where the first phase optimizes the working capacity assignment and the second phase optimizes the spare capacity assignment. In this paper, we present a jointly optimized framework for dimensioning the survivable optical network with the GSN model. Experiment results show that the jointly optimized GSN can bring about on average of 3.8% cost savings when compared with the separate, two-phase approach. Finally, we perform a cost comparison and show that GSN can be deployed with a reasonable cost.

  7. Efficient IP Traffic over Optical Network Based on Wavelength Translation Switching

    DEFF Research Database (Denmark)

    Jha, Vikas; Kalia, Kartik; Chowdhary, Bhawani Shankar

    2016-01-01

    With the advent of TCP/IP protocol suite the overall era of communication technologies had been redefined. Now, we can’t ignore the presence of huge amount of IP traffic; data, voice or video increasing day by day creating more pressure on existing communicating media and supporting back bone....... With the humongous popularity of Internet the overall traffic on Internet has the same story. Focusing on transmission of IP traffic in an optical network with signals remaining in their optical nature generated at particular wavelength, proposed is the switching of optically generated IP packets through optical...... cross connects based on translation of wavelength when an IP packet is crossing the optical cross connect. Adding the concepts of layer 3 routing protocols along with the wavelength translation scheme, will help in spanning the overall optical network for a larger area....

  8. A Feature Selection Method for Large-Scale Network Traffic Classification Based on Spark

    Directory of Open Access Journals (Sweden)

    Yong Wang

    2016-02-01

    Full Text Available Currently, with the rapid increasing of data scales in network traffic classifications, how to select traffic features efficiently is becoming a big challenge. Although a number of traditional feature selection methods using the Hadoop-MapReduce framework have been proposed, the execution time was still unsatisfactory with numeral iterative computations during the processing. To address this issue, an efficient feature selection method for network traffic based on a new parallel computing framework called Spark is proposed in this paper. In our approach, the complete feature set is firstly preprocessed based on Fisher score, and a sequential forward search strategy is employed for subsets. The optimal feature subset is then selected using the continuous iterations of the Spark computing framework. The implementation demonstrates that, on the precondition of keeping the classification accuracy, our method reduces the time cost of modeling and classification, and improves the execution efficiency of feature selection significantly.

  9. Wireless Magnetic Sensor Network for Road Traffic Monitoring and Vehicle Classification

    Directory of Open Access Journals (Sweden)

    Velisavljevic Vladan

    2016-12-01

    Full Text Available Efficiency of transportation of people and goods is playing a vital role in economic growth. A key component for enabling effective planning of transportation networks is the deployment and operation of autonomous monitoring and traffic analysis tools. For that reason, such systems have been developed to register and classify road traffic usage. In this paper, we propose a novel system for road traffic monitoring and classification based on highly energy efficient wireless magnetic sensor networks. We develop novel algorithms for vehicle speed and length estimation and vehicle classification that use multiple magnetic sensors. We also demonstrate that, using such a low-cost system with simplified installation and maintenance compared to current solutions, it is possible to achieve highly accurate estimation and a high rate of positive vehicle classification.

  10. The Detection of Emerging Trends Using Wikipedia Traffic Data and Context Networks

    Science.gov (United States)

    Kämpf, Mirko; Tessenow, Eric; Kenett, Dror Y.; Kantelhardt, Jan W.

    2015-01-01

    Can online media predict new and emerging trends, since there is a relationship between trends in society and their representation in online systems? While several recent studies have used Google Trends as the leading online information source to answer corresponding research questions, we focus on the online encyclopedia Wikipedia often used for deeper topical reading. Wikipedia grants open access to all traffic data and provides lots of additional (semantic) information in a context network besides single keywords. Specifically, we suggest and study context-normalized and time-dependent measures for a topic’s importance based on page-view time series of Wikipedia articles in different languages and articles related to them by internal links. As an example, we present a study of the recently emerging Big Data market with a focus on the Hadoop ecosystem, and compare the capabilities of Wikipedia versus Google in predicting its popularity and life cycles. To support further applications, we have developed an open web platform to share results of Wikipedia analytics, providing context-rich and language-independent relevance measures for emerging trends. PMID:26720074

  11. The Detection of Emerging Trends Using Wikipedia Traffic Data and Context Networks.

    Directory of Open Access Journals (Sweden)

    Mirko Kämpf

    Full Text Available Can online media predict new and emerging trends, since there is a relationship between trends in society and their representation in online systems? While several recent studies have used Google Trends as the leading online information source to answer corresponding research questions, we focus on the online encyclopedia Wikipedia often used for deeper topical reading. Wikipedia grants open access to all traffic data and provides lots of additional (semantic information in a context network besides single keywords. Specifically, we suggest and study context-normalized and time-dependent measures for a topic's importance based on page-view time series of Wikipedia articles in different languages and articles related to them by internal links. As an example, we present a study of the recently emerging Big Data market with a focus on the Hadoop ecosystem, and compare the capabilities of Wikipedia versus Google in predicting its popularity and life cycles. To support further applications, we have developed an open web platform to share results of Wikipedia analytics, providing context-rich and language-independent relevance measures for emerging trends.

  12. The Detection of Emerging Trends Using Wikipedia Traffic Data and Context Networks.

    Science.gov (United States)

    Kämpf, Mirko; Tessenow, Eric; Kenett, Dror Y; Kantelhardt, Jan W

    2015-01-01

    Can online media predict new and emerging trends, since there is a relationship between trends in society and their representation in online systems? While several recent studies have used Google Trends as the leading online information source to answer corresponding research questions, we focus on the online encyclopedia Wikipedia often used for deeper topical reading. Wikipedia grants open access to all traffic data and provides lots of additional (semantic) information in a context network besides single keywords. Specifically, we suggest and study context-normalized and time-dependent measures for a topic's importance based on page-view time series of Wikipedia articles in different languages and articles related to them by internal links. As an example, we present a study of the recently emerging Big Data market with a focus on the Hadoop ecosystem, and compare the capabilities of Wikipedia versus Google in predicting its popularity and life cycles. To support further applications, we have developed an open web platform to share results of Wikipedia analytics, providing context-rich and language-independent relevance measures for emerging trends.

  13. Link prediction in weighted networks

    DEFF Research Database (Denmark)

    Wind, David Kofoed; Mørup, Morten

    2012-01-01

    Many complex networks feature relations with weight information. Some models utilize this information while other ignore the weight information when inferring the structure. In this paper we investigate if edge-weights when modeling real networks, carry important information about the network...... is to infer presence of edges, but that simpler models are better at inferring the actual weights....

  14. Software defined multi-OLT passive optical network for flexible traffic allocation

    Science.gov (United States)

    Zhang, Shizong; Gu, Rentao; Ji, Yuefeng; Zhang, Jiawei; Li, Hui

    2016-10-01

    With the rapid growth of 4G mobile network and vehicular network services mobile terminal users have increasing demand on data sharing among different radio remote units (RRUs) and roadside units (RSUs). Meanwhile, commercial video-streaming, video/voice conference applications delivered through peer-to-peer (P2P) technology are still keep on stimulating the sharp increment of bandwidth demand in both business and residential subscribers. However, a significant issue is that, although wavelength division multiplexing (WDM) and orthogonal frequency division multiplexing (OFDM) technology have been proposed to fulfil the ever-increasing bandwidth demand in access network, the bandwidth of optical fiber is not unlimited due to the restriction of optical component properties and modulation/demodulation technology, and blindly increase the wavelength cannot meet the cost-sensitive characteristic of the access network. In this paper, we propose a software defined multi-OLT PON architecture to support efficient scheduling of access network traffic. By introducing software defined networking technology and wavelength selective switch into TWDM PON system in central office, multiple OLTs can be considered as a bandwidth resource pool and support flexible traffic allocation for optical network units (ONUs). Moreover, under the configuration of the control plane, ONUs have the capability of changing affiliation between different OLTs under different traffic situations, thus the inter-OLT traffic can be localized and the data exchange pressure of the core network can be released. Considering this architecture is designed to be maximum following the TWDM PON specification, the existing optical distribution network (ODN) investment can be saved and conventional EPON/GPON equipment can be compatible with the proposed architecture. What's more, based on this architecture, we propose a dynamic wavelength scheduling algorithm, which can be deployed as an application on control plane

  15. Multi Service Proxy: Mobile Web Traffic Entitlement Point in 4G Core Network

    Directory of Open Access Journals (Sweden)

    Dalibor Uhlir

    2015-05-01

    Full Text Available Core part of state-of-the-art mobile networks is composed of several standard elements like GGSN (Gateway General Packet Radio Service Support Node, SGSN (Serving GPRS Support Node, F5 or MSP (Multi Service Proxy. Each node handles network traffic from a slightly different perspective, and with various goals. In this article we will focus only on the MSP, its key features and especially on related security issues. MSP handles all HTTP traffic in the mobile network and therefore it is a suitable point for the implementation of different optimization functions, e.g. to reduce the volume of data generated by YouTube or similar HTTP-based service. This article will introduce basic features and functions of MSP as well as ways of remote access and security mechanisms of this key element in state-of-the-art mobile networks.

  16. Improved routing strategies for data traffic in scale-free networks

    International Nuclear Information System (INIS)

    Wu, Zhi-Xi; Peng, Gang; Wong, Wing-Ming; Yeung, Kai-Hau

    2008-01-01

    We study the information packet routing process in scale-free networks by mimicking Internet traffic delivery. We incorporate both the global shortest paths information and local degree information of the network in the dynamic process, via two tunable parameters, α and β, to guide the packet routing. We measure the performance of the routing method by both the average transit times of packets and the critical packet generation rate (above which packet aggregation occurs in the network). We found that the routing strategies which integrate ingredients of both global and local topological information of the underlying networks perform much better than the traditional shortest path routing protocol taking into account the global topological information only. Moreover, by doing comparative studies with some related works, we found that the performance of our proposed method shows universal efficiency characteristic against the amount of traffic

  17. Integration of Body Sensor Networks and Vehicular Ad-hoc Networks for Traffic Safety

    Directory of Open Access Journals (Sweden)

    Angelica Reyes-Muñoz

    2016-01-01

    Full Text Available The emergence of Body Sensor Networks (BSNs constitutes a new and fast growing trend for the development of daily routine applications. However, in the case of heterogeneous BSNs integration with Vehicular ad hoc Networks (VANETs a large number of difficulties remain, that must be solved, especially when talking about the detection of human state factors that impair the driving of motor vehicles. The main contributions of this investigation are principally three: (1 an exhaustive review of the current mechanisms to detect four basic physiological behavior states (drowsy, drunk, driving under emotional state disorders and distracted driving that may cause traffic accidents is presented; (2 A middleware architecture is proposed. This architecture can communicate with the car dashboard, emergency services, vehicles belonging to the VANET and road or street facilities. This architecture seeks on the one hand to improve the car driving experience of the driver and on the other hand to extend security mechanisms for the surrounding individuals; and (3 as a proof of concept, an Android real-time attention low level detection application that runs in a next-generation smartphone is developed. The application features mechanisms that allow one to measure the degree of attention of a driver on the base of her/his EEG signals, establish wireless communication links via various standard wireless means, GPRS, Bluetooth and WiFi and issue alarms of critical low driver attention levels.

  18. Integration of Body Sensor Networks and Vehicular Ad-hoc Networks for Traffic Safety.

    Science.gov (United States)

    Reyes-Muñoz, Angelica; Domingo, Mari Carmen; López-Trinidad, Marco Antonio; Delgado, José Luis

    2016-01-15

    The emergence of Body Sensor Networks (BSNs) constitutes a new and fast growing trend for the development of daily routine applications. However, in the case of heterogeneous BSNs integration with Vehicular ad hoc Networks (VANETs) a large number of difficulties remain, that must be solved, especially when talking about the detection of human state factors that impair the driving of motor vehicles. The main contributions of this investigation are principally three: (1) an exhaustive review of the current mechanisms to detect four basic physiological behavior states (drowsy, drunk, driving under emotional state disorders and distracted driving) that may cause traffic accidents is presented; (2) A middleware architecture is proposed. This architecture can communicate with the car dashboard, emergency services, vehicles belonging to the VANET and road or street facilities. This architecture seeks on the one hand to improve the car driving experience of the driver and on the other hand to extend security mechanisms for the surrounding individuals; and (3) as a proof of concept, an Android real-time attention low level detection application that runs in a next-generation smartphone is developed. The application features mechanisms that allow one to measure the degree of attention of a driver on the base of her/his EEG signals, establish wireless communication links via various standard wireless means, GPRS, Bluetooth and WiFi and issue alarms of critical low driver attention levels.

  19. Integration of Body Sensor Networks and Vehicular Ad-hoc Networks for Traffic Safety

    Science.gov (United States)

    Reyes-Muñoz, Angelica; Domingo, Mari Carmen; López-Trinidad, Marco Antonio; Delgado, José Luis

    2016-01-01

    The emergence of Body Sensor Networks (BSNs) constitutes a new and fast growing trend for the development of daily routine applications. However, in the case of heterogeneous BSNs integration with Vehicular ad hoc Networks (VANETs) a large number of difficulties remain, that must be solved, especially when talking about the detection of human state factors that impair the driving of motor vehicles. The main contributions of this investigation are principally three: (1) an exhaustive review of the current mechanisms to detect four basic physiological behavior states (drowsy, drunk, driving under emotional state disorders and distracted driving) that may cause traffic accidents is presented; (2) A middleware architecture is proposed. This architecture can communicate with the car dashboard, emergency services, vehicles belonging to the VANET and road or street facilities. This architecture seeks on the one hand to improve the car driving experience of the driver and on the other hand to extend security mechanisms for the surrounding individuals; and (3) as a proof of concept, an Android real-time attention low level detection application that runs in a next-generation smartphone is developed. The application features mechanisms that allow one to measure the degree of attention of a driver on the base of her/his EEG signals, establish wireless communication links via various standard wireless means, GPRS, Bluetooth and WiFi and issue alarms of critical low driver attention levels. PMID:26784204

  20. Traffic Scheduling in WDM Passive Optical Network with Delay Guarantee

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    WDM passive optical network becomes more favorable as the required bandwidth increases, but currently few media access control algorithms adapted to WDM access network. This paper presented a new scheduling algorithm for bandwidth sharing in WDM passive optical networks, which provides per-flow delay guarantee and supports variable-length packets scheduling. Through theoretical analysis and simulation, the end-to-end delay bound and throughput fairness of the algorithm was demonstrated.

  1. Road safety and bicycle usage impacts of unbundling vehicular and cycle traffic in Dutch urban networks

    NARCIS (Netherlands)

    Schepers, Paul; Heinen, Eva; Methorst, Rob; Wegman, Fred

    2013-01-01

    Bicycle-motor vehicle crashes are concentrated along distributor roads where cyclists are exposed to greater volumes of high-speed motorists than they would experience on access roads. This study examined the road safety impact of network-level separation of vehicular and cycle traffic in Dutch

  2. Road safety and bicycle usage impacts of unbundling vehicular and cycle traffic in Dutch urban networks.

    NARCIS (Netherlands)

    Schepers, P. Heinen, E. Methorst, R. & Wegman, F.

    2015-01-01

    Bicycle-motor vehicle crashes are concentrated along distributor roads where cyclists are exposed to greater volumes of high-speed motorists than they would experience on access roads. This study examined the road safety impact of network-level separation of vehicular and cycle traffic in Dutch

  3. Multivariate correlation analysis technique based on euclidean distance map for network traffic characterization

    NARCIS (Netherlands)

    Tan, Zhiyuan; Jamdagni, Aruna; He, Xiangjian; Nanda, Priyadarsi; Liu, Ren Ping; Qing, Sihan; Susilo, Willy; Wang, Guilin; Liu, Dongmei

    2011-01-01

    The quality of feature has significant impact on the performance of detection techniques used for Denial-of-Service (DoS) attack. The features that fail to provide accurate characterization for network traffic records make the techniques suffer from low accuracy in detection. Although researches

  4. Dynamic Flow Migration for Delay Constrained Traffic in Software-Defined Networks

    NARCIS (Netherlands)

    Berger, Andre; Gross, James; Danielis, Peter; Dán, György

    2017-01-01

    Various industrial control applications have stringent end-to-end latency requirements in the order of a few milliseconds. Software-defined networking (SDN) is a promising solution in order to meet these stringent requirements under varying traffic patterns, as it enables the flexible management of

  5. Downlink Performance of a Multi-Carrier MIMO System in a Bursty Traffic Cellular Network

    DEFF Research Database (Denmark)

    Nguyen, Hung Tuan; Kovacs, Istvan; Wang, Yuanye

    2011-01-01

    In this paper we analyse the downlink performance of a rank adaptive multiple input multiple output (MIMO) system in a busty traffic cellular network. A LTE-Advanced system with multiple component carriers was selected as a study case. To highlight the advantage of using MIMO techniques, we used ...

  6. Energy-minimized design in all-optical networks using unicast/multicast traffic grooming

    Science.gov (United States)

    Puche, William S.; Amaya, Ferney O.; Sierra, Javier E.

    2013-09-01

    The increased bandwidth required by applications, tends to raise the amount of optical equipment, for this reason, it is essential to maintain a balance between the wavelength allocation, available capacity and number of optical devices to achieve the lowest power consumption. You could say that we propose a model that minimizes energy consumption, using unicast / multicast traffic grooming in optical networks.

  7. Network traffic intelligence using a low interaction honeypot

    Science.gov (United States)

    Nyamugudza, Tendai; Rajasekar, Venkatesh; Sen, Prasad; Nirmala, M.; Madhu Viswanatham, V.

    2017-11-01

    Advancements in networking technology have seen more and more devices becoming connected day by day. This has given organizations capacity to extend their networks beyond their boundaries to remote offices and remote employees. However as the network grows security becomes a major challenge since the attack surface also increases. There is need to guard the network against different types of attacks like intrusion and malware through using different tools at different networking levels. This paper describes how network intelligence can be acquired through implementing a low-interaction honeypot which detects and track network intrusion. Honeypot allows an organization to interact and gather information about an attack earlier before it compromises the network. This process is important because it allows the organization to learn about future attacks of the same nature and allows them to develop counter measures. The paper further shows how honeypot-honey net based model for interruption detection system (IDS) can be used to get the best valuable information about the attacker and prevent unexpected harm to the network.

  8. Stock market index prediction using neural networks

    Science.gov (United States)

    Komo, Darmadi; Chang, Chein-I.; Ko, Hanseok

    1994-03-01

    A neural network approach to stock market index prediction is presented. Actual data of the Wall Street Journal's Dow Jones Industrial Index has been used for a benchmark in our experiments where Radial Basis Function based neural networks have been designed to model these indices over the period from January 1988 to Dec 1992. A notable success has been achieved with the proposed model producing over 90% prediction accuracies observed based on monthly Dow Jones Industrial Index predictions. The model has also captured both moderate and heavy index fluctuations. The experiments conducted in this study demonstrated that the Radial Basis Function neural network represents an excellent candidate to predict stock market index.

  9. Link prediction in multiplex online social networks

    Science.gov (United States)

    Jalili, Mahdi; Orouskhani, Yasin; Asgari, Milad; Alipourfard, Nazanin; Perc, Matjaž

    2017-02-01

    Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

  10. Optimal traffic control in highway transportation networks using linear programming

    KAUST Repository

    Li, Yanning; Canepa, Edward S.; Claudel, Christian G.

    2014-01-01

    of the Hamilton-Jacobi PDE, the problem of controlling the state of the system on a network link in a finite horizon can be posed as a Linear Program. Assuming all intersections in the network are controllable, we show that the optimization approach can

  11. Hyper-Spectral Networking Concept of Operations and Future Air Traffic Management Simulations

    Science.gov (United States)

    Davis, Paul; Boisvert, Benjamin

    2017-01-01

    The NASA sponsored Hyper-Spectral Communications and Networking for Air Traffic Management (ATM) (HSCNA) project is conducting research to improve the operational efficiency of the future National Airspace System (NAS) through diverse and secure multi-band, multi-mode, and millimeter-wave (mmWave) wireless links. Worldwide growth of air transportation and the coming of unmanned aircraft systems (UAS) will increase air traffic density and complexity. Safe coordination of aircraft will require more capable technologies for communications, navigation, and surveillance (CNS). The HSCNA project will provide a foundation for technology and operational concepts to accommodate a significantly greater number of networked aircraft. This paper describes two of the HSCNA projects technical challenges. The first technical challenge is to develop a multi-band networking concept of operations (ConOps) for use in multiple phases of flight and all communication link types. This ConOps will integrate the advanced technologies explored by the HSCNA project and future operational concepts into a harmonized vision of future NAS communications and networking. The second technical challenge discussed is to conduct simulations of future ATM operations using multi-bandmulti-mode networking and technologies. Large-scale simulations will assess the impact, compared to todays system, of the new and integrated networks and technologies under future air traffic demand.

  12. Optimal traffic control in highway transportation networks using linear programming

    KAUST Repository

    Li, Yanning

    2014-06-01

    This article presents a framework for the optimal control of boundary flows on transportation networks. The state of the system is modeled by a first order scalar conservation law (Lighthill-Whitham-Richards PDE). Based on an equivalent formulation of the Hamilton-Jacobi PDE, the problem of controlling the state of the system on a network link in a finite horizon can be posed as a Linear Program. Assuming all intersections in the network are controllable, we show that the optimization approach can be extended to an arbitrary transportation network, preserving linear constraints. Unlike previously investigated transportation network control schemes, this framework leverages the intrinsic properties of the Halmilton-Jacobi equation, and does not require any discretization or boolean variables on the link. Hence this framework is very computational efficient and provides the globally optimal solution. The feasibility of this framework is illustrated by an on-ramp metering control example.

  13. Predicting traffic volumes and estimating the effects of shocks in massive transportation systems.

    Science.gov (United States)

    Silva, Ricardo; Kang, Soong Moon; Airoldi, Edoardo M

    2015-05-05

    Public transportation systems are an essential component of major cities. The widespread use of smart cards for automated fare collection in these systems offers a unique opportunity to understand passenger behavior at a massive scale. In this study, we use network-wide data obtained from smart cards in the London transport system to predict future traffic volumes, and to estimate the effects of disruptions due to unplanned closures of stations or lines. Disruptions, or shocks, force passengers to make different decisions concerning which stations to enter or exit. We describe how these changes in passenger behavior lead to possible overcrowding and model how stations will be affected by given disruptions. This information can then be used to mitigate the effects of these shocks because transport authorities may prepare in advance alternative solutions such as additional buses near the most affected stations. We describe statistical methods that leverage the large amount of smart-card data collected under the natural state of the system, where no shocks take place, as variables that are indicative of behavior under disruptions. We find that features extracted from the natural regime data can be successfully exploited to describe different disruption regimes, and that our framework can be used as a general tool for any similar complex transportation system.

  14. Method for predicting future developments of traffic noise in urban areas in Europe

    NARCIS (Netherlands)

    Salomons, E.; Hout, D. van den; Janssen, S.; Kugler, U.; MacA, V.

    2010-01-01

    Traffic noise in urban areas in Europe is a major environmental stressor. In this study we present a method for predicting how environmental noise can be expected to develop in the future. In the project HEIMTSA scenarios were developed for all relevant environmental stressors to health, for all

  15. Incidents Prediction in Road Junctions Using Artificial Neural Networks

    Science.gov (United States)

    Hajji, Tarik; Alami Hassani, Aicha; Ouazzani Jamil, Mohammed

    2018-05-01

    The implementation of an incident detection system (IDS) is an indispensable operation in the analysis of the road traffics. However the IDS may, in no case, represent an alternative to the classical monitoring system controlled by the human eye. The aim of this work is to increase detection and prediction probability of incidents in camera-monitored areas. Knowing that, these areas are monitored by multiple cameras and few supervisors. Our solution is to use Artificial Neural Networks (ANN) to analyze moving objects trajectories on captured images. We first propose a modelling of the trajectories and their characteristics, after we develop a learning database for valid and invalid trajectories, and then we carry out a comparative study to find the artificial neural network architecture that maximizes the rate of valid and invalid trajectories recognition.

  16. An efficient strategy for enhancing traffic capacity by removing links in scale-free networks

    International Nuclear Information System (INIS)

    Huang, Wei; Chow, Tommy W S

    2010-01-01

    An efficient link-removal strategy, called the variance-of-neighbor-degree-reduction (VNDR) strategy, for enhancing the traffic capacity of scale-free networks is proposed in this paper. The VNDR strategy, which considers the important role of hub nodes, balances the amounts of packets routed from each node to the node's neighbors. Compared against the outcomes of strategies that remove links among hub nodes, our results show that the traffic capacity can be greatly enhanced, especially under the shortest path routing strategy. It is also found that the average transport time is effectively reduced by using the VNDR strategy only under the shortest path routing strategy

  17. Characterization of Background Traffic in Hybrid Network Simulation

    National Research Council Canada - National Science Library

    Lauwens, Ben; Scheers, Bart; Van de Capelle, Antoine

    2006-01-01

    .... Two approaches are common: discrete event simulation and fluid approximation. A discrete event simulation generates a huge amount of events for a full-blown battlefield communication network resulting in a very long runtime...

  18. Prediction of traffic fatalities and prospects for mobility becoming ...

    Indian Academy of Sciences (India)

    edge transfer from developed countries. Four prediction ...... also depends on the average vehicle speed by the dependence of crash avoidance (reaction time, braking .... and political priority of road safety actions. ..... It is a man-made problem.

  19. HOW TRAVEL DEMAND AFFECTS DETECTION OF NON-RECURRENT TRAFFIC CONGESTION ON URBAN ROAD NETWORKS

    Directory of Open Access Journals (Sweden)

    B. Anbaroglu

    2016-06-01

    Full Text Available Occurrence of non-recurrent traffic congestion hinders the economic activity of a city, as travellers could miss appointments or be late for work or important meetings. Similarly, for shippers, unexpected delays may disrupt just-in-time delivery and manufacturing processes, which could lose them payment. Consequently, research on non-recurrent congestion detection on urban road networks has recently gained attention. By analysing large amounts of traffic data collected on a daily basis, traffic operation centres can improve their methods to detect non-recurrent congestion rapidly and then revise their existing plans to mitigate its effects. Space-time clusters of high link journey time estimates correspond to non-recurrent congestion events. Existing research, however, has not considered the effect of travel demand on the effectiveness of non-recurrent congestion detection methods. Therefore, this paper investigates how travel demand affects detection of non-recurrent traffic congestion detection on urban road networks. Travel demand has been classified into three categories as low, normal and high. The experiments are carried out on London’s urban road network, and the results demonstrate the necessity to adjust the relative importance of the component evaluation criteria depending on the travel demand level.

  20. Based on BP Neural Network Stock Prediction

    Science.gov (United States)

    Liu, Xiangwei; Ma, Xin

    2012-01-01

    The stock market has a high profit and high risk features, on the stock market analysis and prediction research has been paid attention to by people. Stock price trend is a complex nonlinear function, so the price has certain predictability. This article mainly with improved BP neural network (BPNN) to set up the stock market prediction model, and…

  1. Traffic routing for multicomputer networks with virtual cut-through capability

    Science.gov (United States)

    Kandlur, Dilip D.; Shin, Kang G.

    1992-01-01

    Consideration is given to the problem of selecting routes for interprocess communication in a network with virtual cut-through capability, while balancing the network load and minimizing the number of times that a message gets buffered. An approach is proposed that formulates the route selection problem as a minimization problem with a link cost function that depends upon the traffic through the link. The form of this cost function is derived using the probability of establishing a virtual cut-through route. The route selection problem is shown to be NP-hard, and an algorithm is developed to incrementally reduce the cost by rerouting the traffic. The performance of this algorithm is exemplified by two network topologies: the hypercube and the C-wrapped hexagonal mesh.

  2. A Bio-Inspired Approach to Traffic Network Equilibrium Assignment Problem.

    Science.gov (United States)

    Zhang, Xiaoge; Mahadevan, Sankaran

    2018-04-01

    Finding an equilibrium state of the traffic assignment plays a significant role in the design of transportation networks. We adapt the path finding mathematical model of slime mold Physarum polycephalum to solve the traffic equilibrium assignment problem. We make three contributions in this paper. First, we propose a generalized Physarum model to solve the shortest path problem in directed and asymmetric graphs. Second, we extend it further to resolve the network design problem with multiple source nodes and sink nodes. At last, we demonstrate that the Physarum solver converges to the user-optimized (Wardrop) equilibrium by dynamically updating the costs of links in the network. In addition, convergence of the developed algorithm is proved. Numerical examples are used to demonstrate the efficiency of the proposed algorithm. The superiority of the proposed algorithm is demonstrated in comparison with several other algorithms, including the Frank-Wolfe algorithm, conjugate Frank-Wolfe algorithm, biconjugate Frank-Wolfe algorithm, and gradient projection algorithm.

  3. Dynamic segment shared protection for multicast traffic in meshed wavelength-division-multiplexing optical networks

    Science.gov (United States)

    Liao, Luhua; Li, Lemin; Wang, Sheng

    2006-12-01

    We investigate the protection approach for dynamic multicast traffic under shared risk link group (SRLG) constraints in meshed wavelength-division-multiplexing optical networks. We present a shared protection algorithm called dynamic segment shared protection for multicast traffic (DSSPM), which can dynamically adjust the link cost according to the current network state and can establish a primary light-tree as well as corresponding SRLG-disjoint backup segments for a dependable multicast connection. A backup segment can efficiently share the wavelength capacity of its working tree and the common resources of other backup segments based on SRLG-disjoint constraints. The simulation results show that DSSPM not only can protect the multicast sessions against a single-SRLG breakdown, but can make better use of the wavelength resources and also lower the network blocking probability.

  4. Anticipation of Traffic Demands to Guarantee QoS in IP/Optical Networks

    Directory of Open Access Journals (Sweden)

    Carolina Pinart

    2010-09-01

    Full Text Available Traffic in the Internet backbone is expected to grow above a few Tbit/s in 2020. To cope with this, operators are moving to IP/optical network architectures, where IP is the convergence layer for all services. On the other hand, the quality of service (QoS requirements of future applications encompasses the individualization of services and the assurance of stricter quality parameters such as latency, jitter or capacity. In other words, future optical networks will not only transport more IP data, but they will also have to offer differentiated QoS requirements to services. Finally, some emerging applications, e.g., grid computing, need greater flexibility in the usage of network resources, which involves establishing and releasing connections as if they were virtualized resources controlled by other elements or layers. In this context, traffic-driven lightpath provisioning and service-plane approaches arise as very interesting candidate solutions to solve the main challenges described above. This work reviews the concepts of service-oriented and self-managed networks and relates them to propose an integrated approach to assure QoS by offering flow-aware networking in the sense that traffic demands will be anticipated in a suitable way, lightpaths will be established taking into account QoS information (i.e., impairments and complex services will be decomposed into optical connections so that the above techniques can be employed to assure QoS for any service.

  5. Differing Air Traffic Controller Responses to Similar Trajectory Prediction Errors

    Science.gov (United States)

    Mercer, Joey; Hunt-Espinosa, Sarah; Bienert, Nancy; Laraway, Sean

    2016-01-01

    A Human-In-The-Loop simulation was conducted in January of 2013 in the Airspace Operations Laboratory at NASA's Ames Research Center. The simulation airspace included two en route sectors feeding the northwest corner of Atlanta's Terminal Radar Approach Control. The focus of this paper is on how uncertainties in the study's trajectory predictions impacted the controllers ability to perform their duties. Of particular interest is how the controllers interacted with the delay information displayed in the meter list and data block while managing the arrival flows. Due to wind forecasts with 30-knot over-predictions and 30-knot under-predictions, delay value computations included errors of similar magnitude, albeit in opposite directions. However, when performing their duties in the presence of these errors, did the controllers issue clearances of similar magnitude, albeit in opposite directions?

  6. Heterogeneous Cellular Networks with Spatio-Temporal Traffic: Delay Analysis and Scheduling

    OpenAIRE

    Zhong, Yi; Quek, Tony Q. S.; Ge, Xiaohu

    2016-01-01

    Emergence of new types of services has led to various traffic and diverse delay requirements in fifth generation (5G) wireless networks. Meeting diverse delay requirements is one of the most critical goals for the design of 5G wireless networks. Though the delay of point-to-point communications has been well investigated, the delay of multi-point to multi-point communications has not been thoroughly studied since it is a complicated function of all links in the network. In this work, we propo...

  7. Optimal Traffic Allocation for Multi-Stream Aggregation in Heterogeneous Networks

    DEFF Research Database (Denmark)

    Popovska Avramova, Andrijana; Iversen, Villy Bæk

    2015-01-01

    nature of radio access networks are considered as important factors for performance improvement by multi-stream aggregation. Therefore, in our model, the networks are represented by different queueing systems in order to indicate networks with opposite quality of service provisioning, capacity and delay...... variations. Furthermore, services with different traffic characteristics in terms of quality of service requirements are considered. The simulation results show the advantages of our scheme with respect to efficient increase in data rate and delay performance compared to traditional schemes....

  8. Pattern Recognition and Classification of Fatal Traffic Accidents in Israel A Neural Network Approach

    DEFF Research Database (Denmark)

    Prato, Carlo Giacomo; Gitelman, Victoria; Bekhor, Shlomo

    2011-01-01

    on 1,793 fatal traffic accidents occurred during the period between 2003 and 2006 and applies Kohonen and feed-forward back-propagation neural networks with the objective of extracting from the data typical patterns and relevant factors. Kohonen neural networks reveal five compelling accident patterns....... Feed-forward back-propagation neural networks indicate that sociodemographic characteristics of drivers and victims, accident location, and period of the day are extremely relevant factors. Accident patterns suggest that countermeasures are necessary for identified problems concerning mainly vulnerable...

  9. Short-Range Prediction of the Zone of Moving Vehicles in Arterial Networks

    Directory of Open Access Journals (Sweden)

    Rouzbeh Forouzandeh Jonaghani

    2018-01-01

    Full Text Available In many moving object databases, future locations of vehicles in arterial networks are predicted. While most of studies apply the frequent behavior of historical trajectories or vehicles’ recent kinematics as the basis of predictions, consideration of the dynamics of the intersections is mostly neglected. Signalized intersections make vehicles experience different delays, which vary from zero to some minutes based on the traffic state at intersections. In the absence of traffic signal information (red and green times of traffic signal phases, the queue lengths, approaching traffic volume, turning volumes to each intersection leg, etc., the experienced delays in traffic signals are random variables. In this paper, we model the probability distribution function (PDF and cumulative distribution function (CDF of the delay for any point in the arterial networks based on a spatiotemporal model of the queue at the intersection. The probability of the presence of a vehicle in a zone is determined based on the modeled probability function of the delay. A comparison between the results of the proposed method and a well-known kinematic-based method indicates a significant improvement in the precisions of the predictions.

  10. Noise-Assisted Concurrent Multipath Traffic Distribution in Ad Hoc Networks

    Science.gov (United States)

    Murata, Masayuki

    2013-01-01

    The concept of biologically inspired networking has been introduced to tackle unpredictable and unstable situations in computer networks, especially in wireless ad hoc networks where network conditions are continuously changing, resulting in the need of robustness and adaptability of control methods. Unfortunately, existing methods often rely heavily on the detailed knowledge of each network component and the preconfigured, that is, fine-tuned, parameters. In this paper, we utilize a new concept, called attractor perturbation (AP), which enables controlling the network performance using only end-to-end information. Based on AP, we propose a concurrent multipath traffic distribution method, which aims at lowering the average end-to-end delay by only adjusting the transmission rate on each path. We demonstrate through simulations that, by utilizing the attractor perturbation relationship, the proposed method achieves a lower average end-to-end delay compared to other methods which do not take fluctuations into account. PMID:24319375

  11. A network traffic reduction method for cooperative positioning

    NARCIS (Netherlands)

    Das, Kallol; Wymeersch, Henk

    Cooperative positioning is suitable for applications where conventional positioning fails due to lack of connectivity with a sufficient number of reference nodes. In a dense network, as the number of cooperating devices increases, the number of packet exchanges also increases proportionally. This

  12. Best Practices Handbook: Traffic Engineering in Range Networks

    Science.gov (United States)

    2016-03-01

    Cisco ) .................................................................................................. 4-7 4.4.2 Vendor 2 (Brocade...by Cisco Systems for collecting OAM information. NetFlow functions similarly to sFlow, using packet sampling technology embedded in switches and...transport and entry to the network core. In the merchant market this function is typically designated by the following products:  Aggregation Switch

  13. Statistical Traffic Anomaly Detection in Time-Varying Communication Networks

    Science.gov (United States)

    2015-02-01

    PLs can be generated using tad and (7). Otherwise, the network is periodic according to feature a, and a family of candidate PLs can be generated...using tad , t a p, and (8). In addition, in case that some prior knowledge of td and tp is available, the family of candidate PLs can include the PLs

  14. Statistical Traffic Anomaly Detection in Time Varying Communication Networks

    Science.gov (United States)

    2015-02-01

    PLs can be generated using tad and (7). Otherwise, the network is periodic according to feature a, and a family of candidate PLs can be generated...using tad , t a p, and (8). In addition, in case that some prior knowledge of td and tp is available, the family of candidate PLs can include the PLs

  15. A latency analysis for M2M and OG-like traffic patterns in different HSPA core network configurations

    Directory of Open Access Journals (Sweden)

    M. V. Popović

    2014-11-01

    Full Text Available In this paper we present an analysis intended to reveal possible impacts of core network features on latency for modelled M2M and Online Gaming traffic. Simulations were performed in a live 3G/HSPA network. Test traffic simulating multiplayer real-time games and M2M applications was generated on 10 mobile phones in parallel, sending data to a remote server. APNs with different combinations of hardware and features (proxy server, different GGSNs and firewalls, usage of Service Awareness feature were chosen. The traffic was recorded on the Gn interface in the mobile core. The goal of experiments was to evaluate any eventually significant variation of average recorded RTTs in the core part of mobile network that would clearly indicate either the impact of used APN on delay for a specific traffic pattern, or selectivity of the APN towards different traffic patterns.

  16. Adaptive traffic signal control with actor-critic methods in a real-world traffic network with different traffic disruption events

    NARCIS (Netherlands)

    Aslani, Mohammad; Mesgari, Mohammad Saadi; Wiering, Marco

    2017-01-01

    The transportation demand is rapidly growing in metropolises, resulting in chronic traffic con-gestions in dense downtown areas. Adaptive traffic signal control as the principle part of in-telligent transportation systems has a primary role to effectively reduce traffic congestion by making a

  17. Contribution to the Management of Traffic in Networks

    Directory of Open Access Journals (Sweden)

    Filip Chamraz

    2014-01-01

    Full Text Available The paper deals with Admission control methods (AC in IMS networks (IP multimedia subsystem as one of the elements that help ensure QoS (Quality of service. In the paper we are trying to choose the best AC method for selected IMS network to allow access to the greatest number of users. Of the large number of methods that were tested and considered good we chose two. The paper compares diffusion method and one of the measurement based method, specifically „Simple Sum“. Both methods estimate effective bandwidth to allow access for the greatest number of users/devices and allow them access to prepaid services or multimedia content.

  18. Traffic Dimensioning and Performance Modeling of 4G LTE Networks

    Science.gov (United States)

    Ouyang, Ye

    2011-01-01

    Rapid changes in mobile techniques have always been evolutionary, and the deployment of 4G Long Term Evolution (LTE) networks will be the same. It will be another transition from Third Generation (3G) to Fourth Generation (4G) over a period of several years, as is the case still with the transition from Second Generation (2G) to 3G. As a result,…

  19. Integration of a network aware traffic generation device into a computer network emulation platform

    CSIR Research Space (South Africa)

    Von Solms, S

    2014-07-01

    Full Text Available Flexible, open source network emulation tools can provide network researchers with significant benefits regarding network behaviour and performance. The evaluation of these networks can benefit greatly from the integration of realistic, network...

  20. Comparison between genetic algorithm and self organizing map to detect botnet network traffic

    Science.gov (United States)

    Yugandhara Prabhakar, Shinde; Parganiha, Pratishtha; Madhu Viswanatham, V.; Nirmala, M.

    2017-11-01

    In Cyber Security world the botnet attacks are increasing. To detect botnet is a challenging task. Botnet is a group of computers connected in a coordinated fashion to do malicious activities. Many techniques have been developed and used to detect and prevent botnet traffic and the attacks. In this paper, a comparative study is done on Genetic Algorithm (GA) and Self Organizing Map (SOM) to detect the botnet network traffic. Both are soft computing techniques and used in this paper as data analytics system. GA is based on natural evolution process and SOM is an Artificial Neural Network type, uses unsupervised learning techniques. SOM uses neurons and classifies the data according to the neurons. Sample of KDD99 dataset is used as input to GA and SOM.

  1. Game theoretic analysis of congestion, safety and security networks, air traffic and emergency departments

    CERN Document Server

    Zhuang, Jun

    2015-01-01

    Maximizing reader insights into the roles of intelligent agents in networks, air traffic and emergency departments, this volume focuses on congestion in systems where safety and security are at stake, devoting special attention to applying game theoretic analysis of congestion to: protocols in wired and wireless networks; power generation, air transportation and emergency department overcrowding. Reviewing exhaustively the key recent research into the interactions between game theory, excessive crowding, and safety and security elements, this book establishes a new research angle by illustrating linkages between the different research approaches and serves to lay the foundations for subsequent analysis. Congestion (excessive crowding) is defined in this work as all kinds of flows; e.g., road/sea/air traffic, people, data, information, water, electricity, and organisms. Analyzing systems where congestion occurs – which may be in parallel, series, interlinked, or interdependent, with flows one way or both way...

  2. Smart Collection and Storage Method for Network Traffic Data

    Science.gov (United States)

    2014-09-01

    to the root of an incident or under- stand what goes on in a network may mean looking at data from weeks, months, or even years ago, as has been the...KB 1.01% 69.42 TB 694.20 TB 6,941.99 TB SuSE 6.3 .pcap 51,706 KB 1.01% 104.03 TB 1,040.27 TB 10,402.68 TB HP-UX nettl .trc0 53,391 KB 1.04% 451.13

  3. Property relationships of the physical infrastructure and the traffic flow networks

    Science.gov (United States)

    Zhou, Ta; Zou, Sheng-Rong; He, Da-Ren

    2010-03-01

    We studied both empirically and analytically the correlation between the degrees or the clustering coefficients, respectively, of the networks in the physical infrastructure and the traffic flow layers in three Chinese transportation systems. The systems are bus transportation systems in Beijing and Hangzhou, and the railway system in the mainland. It is found that the correlation between the degrees obey a linear function; while the correlation between the clustering coefficients obey a power law. A possible dynamic explanation on the rules is presented.

  4. Monitor Network Traffic with Packet Capture (pcap) on an Android Device

    Science.gov (United States)

    2015-09-01

    administrative privileges . Under the current design Android development requirement, an Android Graphical User Interface (GUI) application cannot directly...build an Android application to monitor network traffic using open source packet capture (pcap) libraries. 15. SUBJECT TERMS ELIDe, Android , pcap 16...Building Application with Native Codes 5 8.1 Calling Native Codes Using JNI 5 8.2 Calling Native Codes from an Android Application 8 9. Retrieve Live

  5. Testing Application (End-to-End Performance of Networks With EFT Traffic

    Directory of Open Access Journals (Sweden)

    Vlatko Lipovac

    2009-01-01

    Full Text Available This paper studies how end-to-end application peiformance(of Electronic Financial Transaction traffic, in particulardepends on the actual protocol stacks, operating systemsand network transmission rates. With this respect, the respectivesimulation tests of peiformance of TCP and UDP protocolsrunning on various operating systems, ranging from Windows,Sun Solmis, to Linux have been implemented, and thedifferences in peiformance addressed focusing on throughputand response time.

  6. Value of Information for Optimal Adaptive Routing in Stochastic Time-Dependent Traffic Networks: Algorithms and Computational Tools

    Science.gov (United States)

    2010-10-25

    Real-time information is important for travelers' routing decisions in uncertain networks by enabling online adaptation to revealed traffic conditions. Usually there are spatial and/or temporal limitations in traveler information. In this research, a...

  7. Decentralized State-Observer-Based Traffic Density Estimation of Large-Scale Urban Freeway Network by Dynamic Model

    Directory of Open Access Journals (Sweden)

    Yuqi Guo

    2017-08-01

    Full Text Available In order to estimate traffic densities in a large-scale urban freeway network in an accurate and timely fashion when traffic sensors do not cover the freeway network completely and thus only local measurement data can be utilized, this paper proposes a decentralized state observer approach based on a macroscopic traffic flow model. Firstly, by using the well-known cell transmission model (CTM, the urban freeway network is modeled in the way of distributed systems. Secondly, based on the model, a decentralized observer is designed. With the help of the Lyapunov function and S-procedure theory, the observer gains are computed by using linear matrix inequality (LMI technique. So, the traffic densities of the whole road network can be estimated by the designed observer. Finally, this method is applied to the outer ring of the Beijing’s second ring road and experimental results demonstrate the effectiveness and applicability of the proposed approach.

  8. Predicting and Controlling Complex Networks

    Science.gov (United States)

    2015-06-22

    ubiquitous in nature and fundamental to evolution in ecosystems. However, a significant chal- lenge remains in understanding biodiversity since, by the...networks and control . . . . . . . . . . . . . . . . . . . 7 3.4 Pattern formation, synchronization and outbreak of biodiversity in cyclically...Ni, Y.-C. Lai, and C. Grebogi, “Pattern formation, synchronization and outbreak of biodiversity in cyclically competing games,” Physical Review E 83

  9. An empirical model to predict road dust emissions based on pavement and traffic characteristics.

    Science.gov (United States)

    Padoan, Elio; Ajmone-Marsan, Franco; Querol, Xavier; Amato, Fulvio

    2018-06-01

    The relative impact of non-exhaust sources (i.e. road dust, tire wear, road wear and brake wear particles) on urban air quality is increasing. Among them, road dust resuspension has generally the highest impact on PM concentrations but its spatio-temporal variability has been rarely studied and modeled. Some recent studies attempted to observe and describe the time-variability but, as it is driven by traffic and meteorology, uncertainty remains on the seasonality of emissions. The knowledge gap on spatial variability is much wider, as several factors have been pointed out as responsible for road dust build-up: pavement characteristics, traffic intensity and speed, fleet composition, proximity to traffic lights, but also the presence of external sources. However, no parameterization is available as a function of these variables. We investigated mobile road dust smaller than 10 μm (MF10) in two cities with different climatic and traffic conditions (Barcelona and Turin), to explore MF10 seasonal variability and the relationship between MF10 and site characteristics (pavement macrotexture, traffic intensity and proximity to braking zone). Moreover, we provide the first estimates of emission factors in the Po Valley both in summer and winter conditions. Our results showed a good inverse relationship between MF10 and macro-texture, traffic intensity and distance from the nearest braking zone. We also found a clear seasonal effect of road dust emissions, with higher emission in summer, likely due to the lower pavement moisture. These results allowed building a simple empirical mode, predicting maximal dust loadings and, consequently, emission potential, based on the aforementioned data. This model will need to be scaled for meteorological effect, using methods accounting for weather and pavement moisture. This can significantly improve bottom-up emission inventory for spatial allocation of emissions and air quality management, to select those roads with higher emissions

  10. SmartCop: Enabling Smart Traffic Violations Ticketing in Vehicular Named Data Networks

    Directory of Open Access Journals (Sweden)

    Syed Hassan Ahmed

    2016-01-01

    Full Text Available Recently, various applications for Vehicular Ad hoc Networks (VANETs have been proposed and smart traffic violation ticketing is one of them. On the other hand, the new Information-Centric Networking (ICN architectures have emerged and been investigated into VANETs, such as Vehicular Named Data Networking (VNDN. However, the existing applications in VANETs are not suitable for VNDN paradigm due to the dependency on a “named content” instead of a current “host-centric” approach. Thus, we need to design the emerging and new architectures for VNDN applications. In this paper, we propose a smart traffic violation ticketing (TVT system for VNDN, named as SmartCop, that enables a cop vehicle (CV to issue tickets for traffic violation(s to the offender(s autonomously, once they are in the transmission range of that CV. The ticket issuing delay, messaging cost, and percentage of violations detected for varying number of vehicles, violators, CVs, and vehicles speeds are estimated through simulations. In addition, we provide a road map of future research directions for enabling safe driving experience in future cars aided with VNDN technology.

  11. Admission Control for Multiservices Traffic in Hierarchical Mobile IPv6 Networks by Using Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Jung-Shyr Wu

    2012-01-01

    Full Text Available CAC (Call Admission Control plays a significant role in providing QoS (Quality of Service in mobile wireless networks. In addition to much research that focuses on modified Mobile IP to get better efficient handover performance, CAC should be introduced to Mobile IP-based network to guarantee the QoS for users. In this paper, we propose a CAC scheme which incorporates multiple traffic types and adjusts the admission threshold dynamically using fuzzy control logic to achieve better usage of resources. The method can provide QoS in Mobile IPv6 networks with few modifications on MAP (Mobility Anchor Point functionality and slight change in BU (Binding Update message formats. According to the simulation results, the proposed scheme presents good performance of voice and video traffic at the expenses of poor performance on data traffic. It is evident that these CAC schemes can reduce the probability of the handoff dropping and the cell overload and limit the probability of the new call blocking.

  12. Green provisioning of the traffic partition grooming in robust, reconfigurable and heterogeneous optical networks

    Science.gov (United States)

    Hou, Weigang; Yu, Yao; Song, Qingyang; Gong, Xiaoxue

    2013-01-01

    In recent years, various high-speed network architectures have been widespread deployed. Dense Wavelength Division Multiplexing (DWDM) has gained favor as a terabit solution. The optical circuit switching has also been provided for "sub-rate" aggregation. Such that, the granular types of demands tend to be diverse and must be evaluated. However, current dedicated optical networks do not offer sufficient flexibility to satisfy the requirements of demands with such wide range of granularities. The traffic grooming becomes a power-efficient one only when it does not utilize the aggregation of Coarse-Granularity (CG) demands. The waveband switching merely provides port-cost-effective connections for CG demands regardless of fine-granularity ones. Consequently, in this paper, we devise a heterogeneous grooming method called traffic partition grooming. It combines the power efficiency advantage of the traffic grooming under fine-granularity environment and the port savings advantage of the waveband switching under coarse-granularity environment to provide green provisioning. In addition, the optical virtual topology self-reconfigures along with various optimization objectives variation and has the robustness to determine the pre-unknown information. This paper is also the first work on investigating the issue of Robust, Reconfigurable and Heterogeneous Optical Networking (R2HON). The effective green provisioning and OPEX savings of our R2HON have been demonstrated by numerical simulations.

  13. Prediction Based Energy Balancing Forwarding in Cellular Networks

    Directory of Open Access Journals (Sweden)

    Yang Jian-Jun

    2017-01-01

    Full Text Available In the recent cellular network technologies, relay stations extend cell coverage and enhance signal strength for mobile users. However, busy traffic makes the relay stations in hot area run out of energy quickly. Energy is a very important factor in the forwarding of cellular network since mobile users(cell phones in hot cells often suffer from low throughput due to energy lack problems. In many situations, the energy lack problems take place because the energy loading is not balanced. In this paper, we present a prediction based forwarding algorithm to let a mobile node dynamically select the next relay station with highest potential energy capacity to resume communication. Key to this strategy is that a relay station only maintains three past status, and then it is able to predict the potential energy capacity. Then, the node selects the next hop with potential maximal energy. Moreover, a location based algorithm is developed to let the mobile node figure out the target region in order to avoid flooding. Simulations demonstrate that our approach significantly increase the aggregate throughput and decrease the delay in cellular network environment.

  14. A multiclass vehicular dynamic traffic flow model for main roads and dedicated lanes/roads of multimodal transport network

    Energy Technology Data Exchange (ETDEWEB)

    Sossoe, K.S., E-mail: kwami.sossoe@irt-systemx.fr [TECHNOLOGICAL RESEARCH INSTITUTE SYSTEMX (France); Lebacque, J-P., E-mail: jean-patrick.lebacque@ifsttar.fr [UPE/IFSTTAR-COSYS-GRETTIA (France)

    2015-03-10

    We present in this paper a model of vehicular traffic flow for a multimodal transportation road network. We introduce the notion of class of vehicles to refer to vehicles of different transport modes. Our model describes the traffic on highways (which may contain several lanes) and network transit for pubic transportation. The model is drafted with Eulerian and Lagrangian coordinates and uses a Logit model to describe the traffic assignment of our multiclass vehicular flow description on shared roads. The paper also discusses traffic streams on dedicated lanes for specific class of vehicles with event-based traffic laws. An Euler-Lagrangian-remap scheme is introduced to numerically approximate the model’s flow equations.

  15. A QoS Framework with Traffic Request in Wireless Mesh Network

    Science.gov (United States)

    Fu, Bo; Huang, Hejiao

    In this paper, we consider major issues in ensuring greater Quality-of-Service (QoS) in Wireless Mesh Networks (WMNs), specifically with regard to reliability and delay. To this end, we use traffic request to record QoS requirements of data flows. In order to achieve required QoS for all data flows efficiently and with high portability, we develop Network State Update Algorithm. All assumptions, definitions, and algorithms are made exclusively with WMNs in mind, guaranteeing the portability of our framework to various environments in WMNs. The simulation results in proof that our framework is correct.

  16. International scale implementation of the CNOSSOS-EU road traffic noise prediction model for epidemiological studies

    International Nuclear Information System (INIS)

    Morley, D.W.; Hoogh, K. de; Fecht, D.; Fabbri, F.; Bell, M.; Goodman, P.S.; Elliott, P.; Hodgson, S.; Hansell, A.L.; Gulliver, J.

    2015-01-01

    The EU-FP7-funded BioSHaRE project is using individual-level data pooled from several national cohort studies in Europe to investigate the relationship of road traffic noise and health. The detailed input data (land cover and traffic characteristics) required for noise exposure modelling are not always available over whole countries while data that are comparable in spatial resolution between different countries is needed for harmonised exposure assessment. Here, we assess the feasibility using the CNOSSOS-EU road traffic noise prediction model with coarser input data in terms of model performance. Starting with a model using the highest resolution datasets, we progressively introduced lower resolution data over five further model runs and compared noise level estimates to measurements. We conclude that a low resolution noise model should provide adequate performance for exposure ranking (Spearman's rank = 0.75; p < 0.001), but with relatively large errors in predicted noise levels (RMSE = 4.46 dB(A)). - Highlights: • The first implementation of CNOSSOS-EU for national scale noise exposure assessment. • Road traffic noise model performance with varying resolution of inputs is assessed. • Model performance is good with low resolution inputs (r_s = 0.75). • This model will be applied in epidemiological studies of European cohorts. - The CNOSSOS-EU road traffic noise model estimates can be used for international scale exposure assessment when parameterised with freely available low resolution covering a large geographic area.

  17. Prediction, Expectation, and Surprise: Methods, Designs, and Study of a Deployed Traffic Forecasting Service

    OpenAIRE

    Horvitz, Eric J.; Apacible, Johnson; Sarin, Raman; Liao, Lin

    2012-01-01

    We present research on developing models that forecast traffic flow and congestion in the Greater Seattle area. The research has led to the deployment of a service named JamBayes, that is being actively used by over 2,500 users via smartphones and desktop versions of the system. We review the modeling effort and describe experiments probing the predictive accuracy of the models. Finally, we present research on building models that can identify current and future surprises, via efforts on mode...

  18. Predicting Hidden Links in Supply Networks

    Directory of Open Access Journals (Sweden)

    A. Brintrup

    2018-01-01

    Full Text Available Manufacturing companies often lack visibility of the procurement interdependencies between the suppliers within their supply network. However, knowledge of these interdependencies is useful to plan for potential operational disruptions. In this paper, we develop the Supply Network Link Predictor (SNLP method to infer supplier interdependencies using the manufacturer’s incomplete knowledge of the network. SNLP uses topological data to extract relational features from the known network to train a classifier for predicting potential links. Using a test case from the automotive industry, four features are extracted: (i number of existing supplier links, (ii overlaps between supplier product portfolios, (iii product outsourcing associations, and (iv likelihood of buyers purchasing from two suppliers together. Naïve Bayes and Logistic Regression are then employed to predict whether these features can help predict interdependencies between two suppliers. Our results show that these features can indeed be used to predict interdependencies in the network and that predictive accuracy is maximised by (i and (iii. The findings give rise to the exciting possibility of using data analytics for improving supply chain visibility. We then proceed to discuss to what extent such approaches can be adopted and their limitations, highlighting next steps for future work in this area.

  19. A wireless sensor network for urban traffic characterization and trend monitoring.

    Science.gov (United States)

    Fernández-Lozano, J J; Martín-Guzmán, Miguel; Martín-Ávila, Juan; García-Cerezo, A

    2015-10-15

    Sustainable mobility requires a better management of the available infrastructure resources. To achieve this goal, it is necessary to obtain accurate data about road usage, in particular in urban areas. Although a variety of sensor alternates for urban traffic exist, they usually require extensive investments in the form of construction works for installation, processing means, etc. Wireless Sensor Networks (WSN) are an alternative to acquire urban traffic data, allowing for flexible, easy deployment. Together with the use of the appropriate sensors, like Bluetooth identification, and associate processing, WSN can provide the means to obtain in real time data like the origin-destination matrix, a key tool for trend monitoring which previously required weeks or months to be completed. This paper presents a system based on WSN designed to characterize urban traffic, particularly traffic trend monitoring through the calculation of the origin-destination matrix in real time by using Bluetooth identification. Additional sensors are also available integrated in different types of nodes. Experiments in real conditions have been performed, both for separate sensors (Bluetooth, ultrasound and laser), and for the whole system, showing the feasibility of this approach.

  20. CONSTRUCTION COST PREDICTION USING NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Smita K Magdum

    2017-10-01

    Full Text Available Construction cost prediction is important for construction firms to compete and grow in the industry. Accurate construction cost prediction in the early stage of project is important for project feasibility studies and successful completion. There are many factors that affect the cost prediction. This paper presents construction cost prediction as multiple regression model with cost of six materials as independent variables. The objective of this paper is to develop neural networks and multilayer perceptron based model for construction cost prediction. Different models of NN and MLP are developed with varying hidden layer size and hidden nodes. Four artificial neural network models and twelve multilayer perceptron models are compared. MLP and NN give better results than statistical regression method. As compared to NN, MLP works better on training dataset but fails on testing dataset. Five activation functions are tested to identify suitable function for the problem. ‘elu' transfer function gives better results than other transfer function.

  1. Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network

    OpenAIRE

    Gao, Juntao; Shen, Yulong; Liu, Jia; Ito, Minoru; Shiratori, Norio

    2017-01-01

    Adaptive traffic signal control, which adjusts traffic signal timing according to real-time traffic, has been shown to be an effective method to reduce traffic congestion. Available works on adaptive traffic signal control make responsive traffic signal control decisions based on human-crafted features (e.g. vehicle queue length). However, human-crafted features are abstractions of raw traffic data (e.g., position and speed of vehicles), which ignore some useful traffic information and lead t...

  2. Neural networks to predict exosphere temperature corrections

    Science.gov (United States)

    Choury, Anna; Bruinsma, Sean; Schaeffer, Philippe

    2013-10-01

    Precise orbit prediction requires a forecast of the atmospheric drag force with a high degree of accuracy. Artificial neural networks are universal approximators derived from artificial intelligence and are widely used for prediction. This paper presents a method of artificial neural networking for prediction of the thermosphere density by forecasting exospheric temperature, which will be used by the semiempirical thermosphere Drag Temperature Model (DTM) currently developed. Artificial neural network has shown to be an effective and robust forecasting model for temperature prediction. The proposed model can be used for any mission from which temperature can be deduced accurately, i.e., it does not require specific training. Although the primary goal of the study was to create a model for 1 day ahead forecast, the proposed architecture has been generalized to 2 and 3 days prediction as well. The impact of artificial neural network predictions has been quantified for the low-orbiting satellite Gravity Field and Steady-State Ocean Circulation Explorer in 2011, and an order of magnitude smaller orbit errors were found when compared with orbits propagated using the thermosphere model DTM2009.

  3. Link Label Prediction in Signed Citation Network

    KAUST Repository

    Akujuobi, Uchenna

    2016-04-12

    Link label prediction is the problem of predicting the missing labels or signs of all the unlabeled edges in a network. For signed networks, these labels can either be positive or negative. In recent years, different algorithms have been proposed such as using regression, trust propagation and matrix factorization. These approaches have tried to solve the problem of link label prediction by using ideas from social theories, where most of them predict a single missing label given that labels of other edges are known. However, in most real-world social graphs, the number of labeled edges is usually less than that of unlabeled edges. Therefore, predicting a single edge label at a time would require multiple runs and is more computationally demanding. In this thesis, we look at link label prediction problem on a signed citation network with missing edge labels. Our citation network consists of papers from three major machine learning and data mining conferences together with their references, and edges showing the relationship between them. An edge in our network is labeled either positive (dataset relevant) if the reference is based on the dataset used in the paper or negative otherwise. We present three approaches to predict the missing labels. The first approach converts the label prediction problem into a standard classification problem. We then, generate a set of features for each edge and then adopt Support Vector Machines in solving the classification problem. For the second approach, we formalize the graph such that the edges are represented as nodes with links showing similarities between them. We then adopt a label propagation method to propagate the labels on known nodes to those with unknown labels. In the third approach, we adopt a PageRank approach where we rank the nodes according to the number of incoming positive and negative edges, after which we set a threshold. Based on the ranks, we can infer an edge would be positive if it goes a node above the

  4. Neural Network Classifiers for Local Wind Prediction.

    Science.gov (United States)

    Kretzschmar, Ralf; Eckert, Pierre; Cattani, Daniel; Eggimann, Fritz

    2004-05-01

    This paper evaluates the quality of neural network classifiers for wind speed and wind gust prediction with prediction lead times between +1 and +24 h. The predictions were realized based on local time series and model data. The selection of appropriate input features was initiated by time series analysis and completed by empirical comparison of neural network classifiers trained on several choices of input features. The selected input features involved day time, yearday, features from a single wind observation device at the site of interest, and features derived from model data. The quality of the resulting classifiers was benchmarked against persistence for two different sites in Switzerland. The neural network classifiers exhibited superior quality when compared with persistence judged on a specific performance measure, hit and false-alarm rates.

  5. Optimized Virtual Machine Placement with Traffic-Aware Balancing in Data Center Networks

    Directory of Open Access Journals (Sweden)

    Tao Chen

    2016-01-01

    Full Text Available Virtualization has been an efficient method to fully utilize computing resources such as servers. The way of placing virtual machines (VMs among a large pool of servers greatly affects the performance of data center networks (DCNs. As network resources have become a main bottleneck of the performance of DCNs, we concentrate on VM placement with Traffic-Aware Balancing to evenly utilize the links in DCNs. In this paper, we first proposed a Virtual Machine Placement Problem with Traffic-Aware Balancing (VMPPTB and then proved it to be NP-hard and designed a Longest Processing Time Based Placement algorithm (LPTBP algorithm to solve it. To take advantage of the communication locality, we proposed Locality-Aware Virtual Machine Placement Problem with Traffic-Aware Balancing (LVMPPTB, which is a multiobjective optimization problem of simultaneously minimizing the maximum number of VM partitions of requests and minimizing the maximum bandwidth occupancy on uplinks of Top of Rack (ToR switches. We also proved it to be NP-hard and designed a heuristic algorithm (Least-Load First Based Placement algorithm, LLBP algorithm to solve it. Through extensive simulations, the proposed heuristic algorithm is proven to significantly balance the bandwidth occupancy on uplinks of ToR switches, while keeping the number of VM partitions of each request small enough.

  6. Artificial neural network intelligent method for prediction

    Science.gov (United States)

    Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi

    2017-09-01

    Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.

  7. Incremental Validity of Biographical Data in the Prediction of En Route Air Traffic Control Specialist Technical Skills

    Science.gov (United States)

    2012-07-01

    Previous research demonstrated that an empirically-keyed, response-option scored biographical data (biodata) : scale predicted supervisory ratings of air traffic control specialist (ATCS) job performance (Dean & Broach, : 2011). This research f...

  8. Big data analytics : predicting traffic flow regimes from simulated connected vehicle messages using data analytics and machine learning.

    Science.gov (United States)

    2016-12-25

    The key objectives of this study were to: 1. Develop advanced analytical techniques that make use of a dynamically configurable connected vehicle message protocol to predict traffic flow regimes in near-real time in a virtual environment and examine ...

  9. Differential Prediction of FAA Academy Performance on the Basis of Race and Written Air Traffic Control Specialist Aptitude Test Scores

    National Research Council Canada - National Science Library

    Broach, Dana

    1999-01-01

    The written air traffic control specialist (ATCS) aptitude test battery was evaluated for evidence of predictive bias within the framework of the Uniform Guidelines on Employee Selection Procedures (29 CFR 1607...

  10. Early Obstacle Detection and Avoidance for All to All Traffic Pattern in Wireless Sensor Networks

    Science.gov (United States)

    Huc, Florian; Jarry, Aubin; Leone, Pierre; Moraru, Luminita; Nikoletseas, Sotiris; Rolim, Jose

    This paper deals with early obstacles recognition in wireless sensor networks under various traffic patterns. In the presence of obstacles, the efficiency of routing algorithms is increased by voluntarily avoiding some regions in the vicinity of obstacles, areas which we call dead-ends. In this paper, we first propose a fast convergent routing algorithm with proactive dead-end detection together with a formal definition and description of dead-ends. Secondly, we present a generalization of this algorithm which improves performances in all to many and all to all traffic patterns. In a third part we prove that this algorithm produces paths that are optimal up to a constant factor of 2π + 1. In a fourth part we consider the reactive version of the algorithm which is an extension of a previously known early obstacle detection algorithm. Finally we give experimental results to illustrate the efficiency of our algorithms in different scenarios.

  11. A Practical Method for Multilevel Classification and Accounting of Traffic in Computer Networks

    DEFF Research Database (Denmark)

    Bujlow, Tomasz; Pedersen, Jens Myrup

    Learning Algorithms (MLAs) require good quality training data, which are difficult to obtain. MLAs usually cannot properly deal with other types of traffic, than they are trained to work with -- such traffic is identified as the most probable class, instead of being left unclassified. Another drawback...... Learning Algorithm. Finally, content and service provider levels are identified based on IP addresses. The training data for the statistical classifier and the mappings between the different types of content and the IP addresses are created based on the data collected by Volunteer-Based System, while...... the service provider (as Facebook, YouTube, or Google). Furthermore, Deep Packet Inspection (DPI), which seems to be the most accurate technique, in addition to the extensive needs for resources, often cannot be used by ISPs in their networks due to privacy or legal reasons. Techniques based on Machine...

  12. DECISION WITH ARTIFICIAL NEURAL NETWORKS IN DISCRETE EVENT SIMULATION MODELS ON A TRAFFIC SYSTEM

    Directory of Open Access Journals (Sweden)

    Marília Gonçalves Dutra da Silva

    2016-04-01

    Full Text Available ABSTRACT This work aims to demonstrate the use of a mechanism to be applied in the development of the discrete-event simulation models that perform decision operations through the implementation of an artificial neural network. Actions that involve complex operations performed by a human agent in a process, for example, are often modeled in simplified form with the usual mechanisms of simulation software. Therefore, it was chosen a traffic system controlled by a traffic officer with a flow of vehicles and pedestrians to demonstrate the proposed solution. From a module built in simulation software itself, it was possible to connect the algorithm for intelligent decision to the simulation model. The results showed that the model elaborated responded as expected when it was submitted to actions, which required different decisions to maintain the operation of the system with changes in the flow of people and vehicles.

  13. Combined Prediction Model of Death Toll for Road Traffic Accidents Based on Independent and Dependent Variables

    Directory of Open Access Journals (Sweden)

    Feng Zhong-xiang

    2014-01-01

    Full Text Available In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability.

  14. Combined prediction model of death toll for road traffic accidents based on independent and dependent variables.

    Science.gov (United States)

    Feng, Zhong-xiang; Lu, Shi-sheng; Zhang, Wei-hua; Zhang, Nan-nan

    2014-01-01

    In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability.

  15. Energy savings in mobile broadband network based on load predictions

    DEFF Research Database (Denmark)

    Samulevicius, Saulius; Pedersen, Torben Bach; Sørensen, Troels Bundgaard

    2012-01-01

    Abstract—The deployment of new network equipment is resulting in increasing energy consumption in mobile broadband networks (MBNs). This contributes to higher CO2 emissions. Over the last 10 years MBNs have grown considerably, and are still growing to meet the evolution in traffic volume carried...

  16. Performance Evaluation of IEEE 802.11ah Networks With High-Throughput Bidirectional Traffic.

    Science.gov (United States)

    Šljivo, Amina; Kerkhove, Dwight; Tian, Le; Famaey, Jeroen; Munteanu, Adrian; Moerman, Ingrid; Hoebeke, Jeroen; De Poorter, Eli

    2018-01-23

    So far, existing sub-GHz wireless communication technologies focused on low-bandwidth, long-range communication with large numbers of constrained devices. Although these characteristics are fine for many Internet of Things (IoT) applications, more demanding application requirements could not be met and legacy Internet technologies such as Transmission Control Protocol/Internet Protocol (TCP/IP) could not be used. This has changed with the advent of the new IEEE 802.11ah Wi-Fi standard, which is much more suitable for reliable bidirectional communication and high-throughput applications over a wide area (up to 1 km). The standard offers great possibilities for network performance optimization through a number of physical- and link-layer configurable features. However, given that the optimal configuration parameters depend on traffic patterns, the standard does not dictate how to determine them. Such a large number of configuration options can lead to sub-optimal or even incorrect configurations. Therefore, we investigated how two key mechanisms, Restricted Access Window (RAW) grouping and Traffic Indication Map (TIM) segmentation, influence scalability, throughput, latency and energy efficiency in the presence of bidirectional TCP/IP traffic. We considered both high-throughput video streaming traffic and large-scale reliable sensing traffic and investigated TCP behavior in both scenarios when the link layer introduces long delays. This article presents the relations between attainable throughput per station and attainable number of stations, as well as the influence of RAW, TIM and TCP parameters on both. We found that up to 20 continuously streaming IP-cameras can be reliably connected via IEEE 802.11ah with a maximum average data rate of 160 kbps, whereas 10 IP-cameras can achieve average data rates of up to 255 kbps over 200 m. Up to 6960 stations transmitting every 60 s can be connected over 1 km with no lost packets. The presented results enable the fine tuning

  17. Problems in air traffic management. VII., Job training performance of air traffic control specialists - measurement, structure, and prediction.

    Science.gov (United States)

    1965-07-01

    A statistical study of training- and job-performance measures of several hundred Air Traffic Control Specialists (ATCS) representing Enroute, Terminal, and Flight Service Station specialties revealed that training-performance measures reflected: : 1....

  18. Comparing detection and disclosure of traffic incidents in social networks: an intelligent approach based on Twitter vs. Waze

    Directory of Open Access Journals (Sweden)

    Sebastián Vallejos

    2018-03-01

    Full Text Available Nowadays, social networks have become  in a  communication  medium widely  used to disseminate any type  of  information. In  particular,  the  shared  information  in  social  networks  usually  includes  a  considerable number of traffic incidents reports of specific cities. In light of this, specialized social networks have emerged for detecting and disseminating traffic incidents, differentiating from generic social networks in which a wide variety of  topics  are  communicated.  In this  context,  Twitter  is  a  case  in  point  of  a  generic  social  network  in  which  its users often share information about traffic incidents, while Waze is a social network specialized in traffic. In this paper we present a comparative study between Waze and an intelligent approach that detects traffic incidents by analyzing publications shared in Twitter. The comparative study was carried out considering Ciudad Autónoma de Buenos  Aires  (CABA,  Argentina,  as  the  region  of  interest.  The results of this work suggest that both social networks should be considered as complementary sources of information. This conclusion is based on the fact that the proportion of mutual detections, i.e. traffic incidents detected by both approaches, was considerably low since it did not exceed 6% of the cases. Moreover, the results do not show that any of the approaches tend to anticipate in time to the other one in the detection of traffic incidents.

  19. A Model for Traffic Accidents Prediction Based on Driver Personality Traits Assessment

    Directory of Open Access Journals (Sweden)

    Marjana Čubranić-Dobrodolac

    2017-12-01

    Full Text Available The model proposed in this paper uses four psychological instruments for assessing driver behaviour and personality traits aiming to find a relationship between the considered constructs and the occurrence of traffic accidents. A Barratt Impulsiveness Scale (BIS-11 was used for the assessment of impulsivity, Aggressive Driving Behaviour Questionnaire (ADBQ for assessing the aggressiveness while driving, Manchester Driver Attitude Questionnaire (DAQ and the Questionnaire for self-assessment of driving ability. Besides these instruments, the participants filled out an extensive demographic survey. Within the statistical analysis, in addition to the descriptive indicators, correlation coefficients were calculated and four hierarchical regression analyses were performed to determine the predictive power of personality traits on the occurrence of traffic accidents. Further, to confirm the results and to obtain additional information about the relationship between the considered variables, the structural equation modelling and binary logistic regression have been implemented. A sample of this research covered 305 drivers, of which there were 100 bus drivers and 102 truck drivers, as well as 103 drivers of privately owned vehicles. The results indicate that BIS-11 and ADBQ questionnaires show the best predictive power which means that impulsivity and aggressiveness as personality traits have the greatest influence on the occurrence of traffic accidents. This research could be useful in many fields, such as the design of selection procedures for professional drivers, development of programs for the prevention of traffic accidents and violations of law, rehabilitation of drivers who have been deprived of the driving license, etc.

  20. A Space-Time Network-Based Modeling Framework for Dynamic Unmanned Aerial Vehicle Routing in Traffic Incident Monitoring Applications

    Directory of Open Access Journals (Sweden)

    Jisheng Zhang

    2015-06-01

    Full Text Available It is essential for transportation management centers to equip and manage a network of fixed and mobile sensors in order to quickly detect traffic incidents and further monitor the related impact areas, especially for high-impact accidents with dramatic traffic congestion propagation. As emerging small Unmanned Aerial Vehicles (UAVs start to have a more flexible regulation environment, it is critically important to fully explore the potential for of using UAVs for monitoring recurring and non-recurring traffic conditions and special events on transportation networks. This paper presents a space-time network- based modeling framework for integrated fixed and mobile sensor networks, in order to provide a rapid and systematic road traffic monitoring mechanism. By constructing a discretized space-time network to characterize not only the speed for UAVs but also the time-sensitive impact areas of traffic congestion, we formulate the problem as a linear integer programming model to minimize the detection delay cost and operational cost, subject to feasible flying route constraints. A Lagrangian relaxation solution framework is developed to decompose the original complex problem into a series of computationally efficient time-dependent and least cost path finding sub-problems. Several examples are used to demonstrate the results of proposed models in UAVs’ route planning for small and medium-scale networks.

  1. The performance evaluation of a new neural network based traffic management scheme for a satellite communication network

    Science.gov (United States)

    Ansari, Nirwan; Liu, Dequan

    1991-01-01

    A neural-network-based traffic management scheme for a satellite communication network is described. The scheme consists of two levels of management. The front end of the scheme is a derivation of Kohonen's self-organization model to configure maps for the satellite communication network dynamically. The model consists of three stages. The first stage is the pattern recognition task, in which an exemplar map that best meets the current network requirements is selected. The second stage is the analysis of the discrepancy between the chosen exemplar map and the state of the network, and the adaptive modification of the chosen exemplar map to conform closely to the network requirement (input data pattern) by means of Kohonen's self-organization. On the basis of certain performance criteria, whether a new map is generated to replace the original chosen map is decided in the third stage. A state-dependent routing algorithm, which arranges the incoming call to some proper path, is used to make the network more efficient and to lower the call block rate. Simulation results demonstrate that the scheme, which combines self-organization and the state-dependent routing mechanism, provides better performance in terms of call block rate than schemes that only have either the self-organization mechanism or the routing mechanism.

  2. A Network Traffic Generator Model for Fast Network-on-Chip Simulation

    DEFF Research Database (Denmark)

    Mahadevan, Shankar; Angiolini, Frederico; Storgaard, Michael

    2005-01-01

    For Systems-on-Chip (SoCs) development, a predominant part of the design time is the simulation time. Performance evaluation and design space exploration of such systems in bit- and cycle-true fashion is becoming prohibitive. We propose a traffic generation (TG) model that provides a fast...

  3. Effective distance adaptation traffic dispatching in software defined IP over optical network

    Science.gov (United States)

    Duan, Zhiwei; Li, Hui; Liu, Yuze; Ji, Yuefeng; Li, Hongfa; Lin, Yi

    2017-10-01

    The rapid growth of IP traffic has contributed to the wide deployment of optical devices (ROADM/OXC, etc.). Meanwhile, with the emergence and application of high-performance network services such as ultra-high video transmission, people are increasingly becoming more and more particular about the quality of service (QoS) of network. However, the pass-band shape of WSSs which is utilized in the ROADM/OXC is not ideal, causing narrowing of spectrum. Spectral narrowing can lead to signal impairment. Therefore, guard-bands need to be inserted between adjacent paths. In order to minimize the bandwidth waste due to guard bands, we propose an effective distance-adaptation traffic dispatching algorithm in IP over optical network based on SDON architecture. We use virtualization technology to set up virtual resources direct links by extracting part of the resources on paths which meet certain specific constraints. We also assign different bandwidth to each IP request based on path length. There is no need for guard-bands between the adjacent paths on the virtual link, which can effectively reduce the number of guard-bands and save the spectrum.

  4. 77 FR 3544 - Meeting and Webinar on the Active Traffic and Demand Management and Intelligent Network Flow...

    Science.gov (United States)

    2012-01-24

    ... Intelligent Network Flow Optimization Operational Concepts; Notice of Public Meeting AGENCY: Research and... Demand Management (ADTM) and Intelligent Network Flow Optimization (INFLO) operational concepts. The ADTM... infrastructure. The vision for ATDM research is to allow transportation agencies to increase traffic flow...

  5. Large-scale transportation network congestion evolution prediction using deep learning theory.

    Science.gov (United States)

    Ma, Xiaolei; Yu, Haiyang; Wang, Yunpeng; Wang, Yinhai

    2015-01-01

    Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.

  6. Large-scale transportation network congestion evolution prediction using deep learning theory.

    Directory of Open Access Journals (Sweden)

    Xiaolei Ma

    Full Text Available Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS and Internet of Things (IoT, transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.

  7. Ontology-Based Architecture for Intelligent Transportation Systems Using a Traffic Sensor Network

    Directory of Open Access Journals (Sweden)

    Susel Fernandez

    2016-08-01

    Full Text Available Intelligent transportation systems are a set of technological solutions used to improve the performance and safety of road transportation. A crucial element for the success of these systems is the exchange of information, not only between vehicles, but also among other components in the road infrastructure through different applications. One of the most important information sources in this kind of systems is sensors. Sensors can be within vehicles or as part of the infrastructure, such as bridges, roads or traffic signs. Sensors can provide information related to weather conditions and traffic situation, which is useful to improve the driving process. To facilitate the exchange of information between the different applications that use sensor data, a common framework of knowledge is needed to allow interoperability. In this paper an ontology-driven architecture to improve the driving environment through a traffic sensor network is proposed. The system performs different tasks automatically to increase driver safety and comfort using the information provided by the sensors.

  8. Combination Adaptive Traffic Algorithm and Coordinated Sleeping in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    M. Udin Harun Al Rasyid

    2014-12-01

    Full Text Available Wireless sensor network (WSN uses a battery as its primary power source, so that WSN will be limited to battery power for long operations. The WSN should be able to save the energy consumption in order to operate in a long time.WSN has the potential to be the future of wireless communications solutions. WSN are small but has a variety of functions that can help human life. WSN has the wide variety of sensors and can communicate quickly making it easier for people to obtain information accurately and quickly. In this study, we combine adaptive traffic algorithms and coordinated sleeping as power‐efficient WSN solution. We compared the performance of our proposed ideas combination adaptive traffic and coordinated sleeping algorithm with non‐adaptive scheme. From the simulation results, our proposed idea has good‐quality data transmission and more efficient in energy consumption, but it has higher delay than that of non‐adaptive scheme. Keywords:WSN,adaptive traffic,coordinated sleeping,beacon order,superframe order.

  9. Adaptive EWMA Method Based on Abnormal Network Traffic for LDoS Attacks

    Directory of Open Access Journals (Sweden)

    Dan Tang

    2014-01-01

    Full Text Available The low-rate denial of service (LDoS attacks reduce network services capabilities by periodically sending high intensity pulse data flows. For their concealed performance, it is more difficult for traditional DoS detection methods to detect LDoS attacks; at the same time the accuracy of the current detection methods for LDoS attacks is relatively low. As the fact that LDoS attacks led to abnormal distribution of the ACK traffic, LDoS attacks can be detected by analyzing the distribution characteristics of ACK traffic. Then traditional EWMA algorithm which can smooth the accidental error while being the same as the exceptional mutation may cause some misjudgment; therefore a new LDoS detection method based on adaptive EWMA (AEWMA algorithm is proposed. The AEWMA algorithm which uses an adaptive weighting function instead of the constant weighting of EWMA algorithm can smooth the accidental error and retain the exceptional mutation. So AEWMA method is more beneficial than EWMA method for analyzing and measuring the abnormal distribution of ACK traffic. The NS2 simulations show that AEWMA method can detect LDoS attacks effectively and has a low false negative rate and a false positive rate. Based on DARPA99 datasets, experiment results show that AEWMA method is more efficient than EWMA method.

  10. Alternative method of highway traffic safety analysis for developing countries using delphi technique and Bayesian network.

    Science.gov (United States)

    Mbakwe, Anthony C; Saka, Anthony A; Choi, Keechoo; Lee, Young-Jae

    2016-08-01

    Highway traffic accidents all over the world result in more than 1.3 million fatalities annually. An alarming number of these fatalities occurs in developing countries. There are many risk factors that are associated with frequent accidents, heavy loss of lives, and property damage in developing countries. Unfortunately, poor record keeping practices are very difficult obstacle to overcome in striving to obtain a near accurate casualty and safety data. In light of the fact that there are numerous accident causes, any attempts to curb the escalating death and injury rates in developing countries must include the identification of the primary accident causes. This paper, therefore, seeks to show that the Delphi Technique is a suitable alternative method that can be exploited in generating highway traffic accident data through which the major accident causes can be identified. In order to authenticate the technique used, Korea, a country that underwent similar problems when it was in its early stages of development in addition to the availability of excellent highway safety records in its database, is chosen and utilized for this purpose. Validation of the methodology confirms the technique is suitable for application in developing countries. Furthermore, the Delphi Technique, in combination with the Bayesian Network Model, is utilized in modeling highway traffic accidents and forecasting accident rates in the countries of research. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Implementation of a FPGA-Based Feature Detection and Networking System for Real-time Traffic Monitoring

    OpenAIRE

    Chen, Jieshi; Schafer, Benjamin Carrion; Ho, Ivan Wang-Hei

    2016-01-01

    With the growing demand of real-time traffic monitoring nowadays, software-based image processing can hardly meet the real-time data processing requirement due to the serial data processing nature. In this paper, the implementation of a hardware-based feature detection and networking system prototype for real-time traffic monitoring as well as data transmission is presented. The hardware architecture of the proposed system is mainly composed of three parts: data collection, feature detection,...

  12. Visualizing Network Traffic to Understand the Performance of Massively Parallel Simulations

    KAUST Repository

    Landge, A. G.

    2012-12-01

    The performance of massively parallel applications is often heavily impacted by the cost of communication among compute nodes. However, determining how to best use the network is a formidable task, made challenging by the ever increasing size and complexity of modern supercomputers. This paper applies visualization techniques to aid parallel application developers in understanding the network activity by enabling a detailed exploration of the flow of packets through the hardware interconnect. In order to visualize this large and complex data, we employ two linked views of the hardware network. The first is a 2D view, that represents the network structure as one of several simplified planar projections. This view is designed to allow a user to easily identify trends and patterns in the network traffic. The second is a 3D view that augments the 2D view by preserving the physical network topology and providing a context that is familiar to the application developers. Using the massively parallel multi-physics code pF3D as a case study, we demonstrate that our tool provides valuable insight that we use to explain and optimize pF3D-s performance on an IBM Blue Gene/P system. © 1995-2012 IEEE.

  13. Traffic modelling for Big Data backed telecom cloud

    OpenAIRE

    Via Baraldés, Anna

    2016-01-01

    The objective of this project is to provide traffic models based on new services characteristics. Specifically, we focus on modelling the traffic between origin-destination node pairs (also known as OD pairs) in a telecom network. Two use cases are distinguished: i) traffic generation in the context of simulation, and ii) traffic modelling for prediction in the context of big-data backed telecom cloud systems. To this aim, several machine learning and statistical models and technics are studi...

  14. Network information improves cancer outcome prediction.

    Science.gov (United States)

    Roy, Janine; Winter, Christof; Isik, Zerrin; Schroeder, Michael

    2014-07-01

    Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. One approach to deal with these two problems employs protein-protein interaction networks and ranks genes using the random surfer model of Google's PageRank algorithm. In this work, we created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and systematically evaluated the use of networks and a PageRank derivative, NetRank, for signature identification. We show that the NetRank performs significantly better than classical methods such as fold change or t-test. Despite an order of magnitude difference in network size, a regulatory and protein-protein interaction network perform equally well. Experimental evaluation on cancer outcome prediction in all of the 25 underlying datasets suggests that the network-based methodology identifies highly overlapping signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. Integration of network information into gene expression analysis allows the identification of more reliable and accurate biomarkers and provides a deeper understanding of processes occurring in cancer development and progression. © The Author 2012. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  15. Social Trust Prediction Using Heterogeneous Networks

    Science.gov (United States)

    HUANG, JIN; NIE, FEIPING; HUANG, HENG; TU, YI-CHENG; LEI, YU

    2014-01-01

    Along with increasing popularity of social websites, online users rely more on the trustworthiness information to make decisions, extract and filter information, and tag and build connections with other users. However, such social network data often suffer from severe data sparsity and are not able to provide users with enough information. Therefore, trust prediction has emerged as an important topic in social network research. Traditional approaches are primarily based on exploring trust graph topology itself. However, research in sociology and our life experience suggest that people who are in the same social circle often exhibit similar behaviors and tastes. To take advantage of the ancillary information for trust prediction, the challenge then becomes what to transfer and how to transfer. In this article, we address this problem by aggregating heterogeneous social networks and propose a novel joint social networks mining (JSNM) method. Our new joint learning model explores the user-group-level similarity between correlated graphs and simultaneously learns the individual graph structure; therefore, the shared structures and patterns from multiple social networks can be utilized to enhance the prediction tasks. As a result, we not only improve the trust prediction in the target graph but also facilitate other information retrieval tasks in the auxiliary graphs. To optimize the proposed objective function, we use the alternative technique to break down the objective function into several manageable subproblems. We further introduce the auxiliary function to solve the optimization problems with rigorously proved convergence. The extensive experiments have been conducted on both synthetic and real- world data. All empirical results demonstrate the effectiveness of our method. PMID:24729776

  16. A comprehensive model for the prediction of vibrations due to underground railway traffic: formulation and validation

    International Nuclear Information System (INIS)

    Costa, Pedro Alvares; Cardoso Silva, Antonio; Calçada, Rui; Lopes, Patricia; Fernandez, Jesus

    2016-01-01

    n this communication, a numerical approach for the prediction of vibrations induced in buildings due to railway traffic in tunnels is presented. The numerical model is based on the concept of dynamic sub structuring, being composed by three autonomous models to simulate the following main parts of the problem: i) generation of vibrations (train-track interaction); ii) propagation of vibrations (track - tunnel-ground system); iii) reception of vibrations (building coupled to the ground). The methodology proposed allows dealing with the three-dimensional characteristics of the problem with a reasonable computational effort [ 1 , 2 ] . After the brief description of the model, its experimental validation is performed. For that, a case study about vibrations inside of a building close to a shallow railway tunnel in Madrid are simulated and the experimental data [ 3 ] is compared with the predicted results [ 4 ]. Finally, the communication finishes with some insights about the potentialities and challenges of this numerical modelling approach on the prediction of the behavior of ancient structures subjected to vibrations induced by human sources (railway and road traffic, pile driving, etc)

  17. Traffic Incident Clearance Time and Arrival Time Prediction Based on Hazard Models

    Directory of Open Access Journals (Sweden)

    Yang beibei Ji

    2014-01-01

    Full Text Available Accurate prediction of incident duration is not only important information of Traffic Incident Management System, but also an effective input for travel time prediction. In this paper, the hazard based prediction models are developed for both incident clearance time and arrival time. The data are obtained from the Queensland Department of Transport and Main Roads’ STREAMS Incident Management System (SIMS for one year ending in November 2010. The best fitting distributions are drawn for both clearance and arrival time for 3 types of incident: crash, stationary vehicle, and hazard. The results show that Gamma, Log-logistic, and Weibull are the best fit for crash, stationary vehicle, and hazard incident, respectively. The obvious impact factors are given for crash clearance time and arrival time. The quantitative influences for crash and hazard incident are presented for both clearance and arrival. The model accuracy is analyzed at the end.

  18. File access prediction using neural networks.

    Science.gov (United States)

    Patra, Prashanta Kumar; Sahu, Muktikanta; Mohapatra, Subasish; Samantray, Ronak Kumar

    2010-06-01

    One of the most vexing issues in design of a high-speed computer is the wide gap of access times between the memory and the disk. To solve this problem, static file access predictors have been used. In this paper, we propose dynamic file access predictors using neural networks to significantly improve upon the accuracy, success-per-reference, and effective-success-rate-per-reference by using neural-network-based file access predictor with proper tuning. In particular, we verified that the incorrect prediction has been reduced from 53.11% to 43.63% for the proposed neural network prediction method with a standard configuration than the recent popularity (RP) method. With manual tuning for each trace, we are able to improve upon the misprediction rate and effective-success-rate-per-reference using a standard configuration. Simulations on distributed file system (DFS) traces reveal that exact fit radial basis function (RBF) gives better prediction in high end system whereas multilayer perceptron (MLP) trained with Levenberg-Marquardt (LM) backpropagation outperforms in system having good computational capability. Probabilistic and competitive predictors are the most suitable for work stations having limited resources to deal with and the former predictor is more efficient than the latter for servers having maximum system calls. Finally, we conclude that MLP with LM backpropagation algorithm has better success rate of file prediction than those of simple perceptron, last successor, stable successor, and best k out of m predictors.

  19. Cybersecurity and Network Forensics: Analysis of Malicious Traffic towards a Honeynet with Deep Packet Inspection

    Directory of Open Access Journals (Sweden)

    Gabriel Arquelau Pimenta Rodrigues

    2017-10-01

    Full Text Available Any network connected to the Internet is subject to cyber attacks. Strong security measures, forensic tools, and investigators contribute together to detect and mitigate those attacks, reducing the damages and enabling reestablishing the network to its normal operation, thus increasing the cybersecurity of the networked environment. This paper addresses the use of a forensic approach with Deep Packet Inspection to detect anomalies in the network traffic. As cyber attacks may occur on any layer of the TCP/IP networking model, Deep Packet Inspection is an effective way to reveal suspicious content in the headers or the payloads in any packet processing layer, excepting of course situations where the payload is encrypted. Although being efficient, this technique still faces big challenges. The contributions of this paper rely on the association of Deep Packet Inspection with forensics analysis to evaluate different attacks towards a Honeynet operating in a network laboratory at the University of Brasilia. In this perspective, this work could identify and map the content and behavior of attacks such as the Mirai botnet and brute-force attacks targeting various different network services. Obtained results demonstrate the behavior of automated attacks (such as worms and bots and non-automated attacks (brute-force conducted with different tools. The data collected and analyzed is then used to generate statistics of used usernames and passwords, IP and services distribution, among other elements. This paper also discusses the importance of network forensics and Chain of Custody procedures to conduct investigations and shows the effectiveness of the mentioned techniques in evaluating different attacks in networks.

  20. Impact of the traffic load on performance of an alternative LTE railway communication network

    DEFF Research Database (Denmark)

    Sniady, Aleksander; Soler, José

    2013-01-01

    communication infrastructure supporting railway signaling. This work is based on OPNET realistic network simulations, which show the relation between the traffic load (the number of trains transmitting and receiving data in an LTE cell) and the delay performance of the European Train Control System (ETCS......Although many countries only now begin to invest in deployment of GSM-Railways (GSM-R) networks, this technology is already obsolete and reveals its significant shortcomings. The most troublesome one is the insufficient number of communication channels offered by GSM-R. This is a major problem...... obstructing railway operations at big train stations and junctions. Hence, other technologies, such as Long Term Evolution (LTE), need to be considered as an alternative to GSM-R. The goal of this paper is to demonstrate the capacity increase that railways can expect, from the introduction of LTE as internal...

  1. Reliable Path Selection Problem in Uncertain Traffic Network after Natural Disaster

    Directory of Open Access Journals (Sweden)

    Jing Wang

    2013-01-01

    Full Text Available After natural disaster, especially for large-scale disasters and affected areas, vast relief materials are often needed. In the meantime, the traffic networks are always of uncertainty because of the disaster. In this paper, we assume that the edges in the network are either connected or blocked, and the connection probability of each edge is known. In order to ensure the arrival of these supplies at the affected areas, it is important to select a reliable path. A reliable path selection model is formulated, and two algorithms for solving this model are presented. Then, adjustable reliable path selection model is proposed when the edge of the selected reliable path is broken. And the corresponding algorithms are shown to be efficient both theoretically and numerically.

  2. Defending Tor from Network Adversaries: A Case Study of Network Path Prediction

    Directory of Open Access Journals (Sweden)

    Juen Joshua

    2015-06-01

    Full Text Available The Tor anonymity network has been shown vulnerable to traffic analysis attacks by autonomous systems (ASes and Internet exchanges (IXes, which can observe different overlay hops belonging to the same circuit. We evaluate whether network path prediction techniques provide an accurate picture of the threat from such adversaries, and whether they can be used to avoid this threat. We perform a measurement study by collecting 17.2 million traceroutes from Tor relays to destinations around the Internet. We compare the collected traceroute paths to predicted paths using state-of-the-art path inference techniques. We find that traceroutes present a very different picture, with the set of ASes seen in the traceroute path differing from the predicted path 80% of the time. We also consider the impact that prediction errors have on Tor security. Using a simulator to choose paths over a week, our traceroutes indicate a user has nearly a 100% chance of at least one compromise in a week with 11% of total paths containing an AS compromise and less than 1% containing an IX compromise when using default Tor selection. We find modifying the path selection to choose paths predicted to be safe lowers total paths with an AS compromise to 0.14% but still presents a 5–11% chance of at least one compromise in a week while making 5% of paths fail, with 96% of failures due to false positives in path inferences. Our results demonstrate more measurement and better path prediction is necessary to mitigate the risk of AS and IX adversaries to Tor.

  3. Cardiac abnormality prediction using HMLP network

    Science.gov (United States)

    Adnan, Ja'afar; Ahmad, K. A.; Mat, Muhamad Hadzren; Rizman, Zairi Ismael; Ahmad, Shahril

    2018-02-01

    Cardiac abnormality often occurs regardless of gender, age and races but depends on the lifestyle. This problem sometimes does not show any symptoms and usually detected once it already critical which lead to a sudden death to the patient. Basically, cardiac abnormality is the irregular electrical signal that generate by the pacemaker of the heart. This paper attempts to develop a program that can detect cardiac abnormality activity through implementation of Hybrid Multilayer Perceptron (HMLP) network. A certain amount of data of the heartbeat signals from the electrocardiogram (ECG) will be used in this project to train the MLP and HMLP network by using Modified Recursive Prediction Error (MRPE) algorithm and to test the network performance.

  4. Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Rechargeable Sensor Networks.

    Science.gov (United States)

    Wei, Yunkai; Ma, Xiaohui; Yang, Ning; Chen, Yijin

    2017-09-15

    Software Defined Wireless Rechargeable Sensor Networks (SDWRSNs) are an inexorable trend for Wireless Sensor Networks (WSNs), including Wireless Rechargeable Sensor Network (WRSNs). However, the traditional network devices cannot be completely substituted in the short term. Hybrid SDWRSNs, where software defined devices and traditional devices coexist, will last for a long time. Hybrid SDWRSNs bring new challenges as well as opportunities for energy saving issues, which is still a key problem considering that the wireless chargers are also exhaustible, especially in some rigid environment out of the main supply. Numerous energy saving schemes for WSNs, or even some works for WRSNs, are no longer suitable for the new features of hybrid SDWRSNs. To solve this problem, this paper puts forward an Energy-saving Traffic Scheduling (ETS) algorithm. The ETS algorithm adequately considers the new characters in hybrid SDWRSNs, and takes advantage of the Software Defined Networking (SDN) controller's direct control ability on SDN nodes and indirect control ability on normal nodes. The simulation results show that, comparing with traditional Minimum Transmission Energy (MTE) protocol, ETS can substantially improve the energy efficiency in hybrid SDWRSNs for up to 20-40% while ensuring feasible data delay.

  5. Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Rechargeable Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yunkai Wei

    2017-09-01

    Full Text Available Software Defined Wireless Rechargeable Sensor Networks (SDWRSNs are an inexorable trend for Wireless Sensor Networks (WSNs, including Wireless Rechargeable Sensor Network (WRSNs. However, the traditional network devices cannot be completely substituted in the short term. Hybrid SDWRSNs, where software defined devices and traditional devices coexist, will last for a long time. Hybrid SDWRSNs bring new challenges as well as opportunities for energy saving issues, which is still a key problem considering that the wireless chargers are also exhaustible, especially in some rigid environment out of the main supply. Numerous energy saving schemes for WSNs, or even some works for WRSNs, are no longer suitable for the new features of hybrid SDWRSNs. To solve this problem, this paper puts forward an Energy-saving Traffic Scheduling (ETS algorithm. The ETS algorithm adequately considers the new characters in hybrid SDWRSNs, and takes advantage of the Software Defined Networking (SDN controller’s direct control ability on SDN nodes and indirect control ability on normal nodes. The simulation results show that, comparing with traditional Minimum Transmission Energy (MTE protocol, ETS can substantially improve the energy efficiency in hybrid SDWRSNs for up to 20–40% while ensuring feasible data delay.

  6. Model Predictive Control of Sewer Networks

    DEFF Research Database (Denmark)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik

    2016-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....

  7. Predicting economic growth with stock networks

    Science.gov (United States)

    Heiberger, Raphael H.

    2018-01-01

    Networks derived from stock prices are often used to model developments on financial markets and are tightly intertwined with crises. Yet, the influence of changing market topologies on the broader economy (i.e. GDP) is unclear. In this paper, we propose a Bayesian approach that utilizes individual-level network measures of companies as lagged probabilistic features to predict national economic growth. We use a comprehensive data set consisting of Standard and Poor's 500 corporations from January 1988 until October 2016. The final model forecasts correctly all major recession and prosperity phases of the U.S. economy up to one year ahead. By employing different network measures on the level of corporations, we can also identify which companies' stocks possess a key role in a changing economic environment and may be used as indication of critical (and prosperous) developments. More generally, the proposed approach allows to predict probabilities for different overall states of social entities by using local network positions and could be applied on various phenomena.

  8. Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods.

    Science.gov (United States)

    Arcos-García, Álvaro; Álvarez-García, Juan A; Soria-Morillo, Luis M

    2018-03-01

    This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural Network which comprises Convolutional layers and Spatial Transformer Networks. Such trials are built to measure the impact of diverse factors with the end goal of designing a Convolutional Neural Network that can improve the state-of-the-art of traffic sign classification task. First, different adaptive and non-adaptive stochastic gradient descent optimisation algorithms such as SGD, SGD-Nesterov, RMSprop and Adam are evaluated. Subsequently, multiple combinations of Spatial Transformer Networks placed at distinct positions within the main neural network are analysed. The recognition rate of the proposed Convolutional Neural Network reports an accuracy of 99.71% in the German Traffic Sign Recognition Benchmark, outperforming previous state-of-the-art methods and also being more efficient in terms of memory requirements. Copyright © 2018 Elsevier Ltd. All rights reserved.

  9. Cyber-Threat Assessment for the Air Traffic Management System: A Network Controls Approach

    Science.gov (United States)

    Roy, Sandip; Sridhar, Banavar

    2016-01-01

    Air transportation networks are being disrupted with increasing frequency by failures in their cyber- (computing, communication, control) systems. Whether these cyber- failures arise due to deliberate attacks or incidental errors, they can have far-reaching impact on the performance of the air traffic control and management systems. For instance, a computer failure in the Washington DC Air Route Traffic Control Center (ZDC) on August 15, 2015, caused nearly complete closure of the Centers airspace for several hours. This closure had a propagative impact across the United States National Airspace System, causing changed congestion patterns and requiring placement of a suite of traffic management initiatives to address the capacity reduction and congestion. A snapshot of traffic on that day clearly shows the closure of the ZDC airspace and the resulting congestion at its boundary, which required augmented traffic management at multiple locations. Cyber- events also have important ramifications for private stakeholders, particularly the airlines. During the last few months, computer-system issues have caused several airlines fleets to be grounded for significant periods of time: these include United Airlines (twice), LOT Polish Airlines, and American Airlines. Delays and regional stoppages due to cyber- events are even more common, and may have myriad causes (e.g., failure of the Department of Homeland Security systems needed for security check of passengers, see [3]). The growing frequency of cyber- disruptions in the air transportation system reflects a much broader trend in the modern society: cyber- failures and threats are becoming increasingly pervasive, varied, and impactful. In consequence, an intense effort is underway to develop secure and resilient cyber- systems that can protect against, detect, and remove threats, see e.g. and its many citations. The outcomes of this wide effort on cyber- security are applicable to the air transportation infrastructure

  10. Prediction of traffic-related nitrogen oxides concentrations using Structural Time-Series models

    Science.gov (United States)

    Lawson, Anneka Ruth; Ghosh, Bidisha; Broderick, Brian

    2011-09-01

    Ambient air quality monitoring, modeling and compliance to the standards set by European Union (EU) directives and World Health Organization (WHO) guidelines are required to ensure the protection of human and environmental health. Congested urban areas are most susceptible to traffic-related air pollution which is the most problematic source of air pollution in Ireland. Long-term continuous real-time monitoring of ambient air quality at such urban centers is essential but often not realistic due to financial and operational constraints. Hence, the development of a resource-conservative ambient air quality monitoring technique is essential to ensure compliance with the threshold values set by the standards. As an intelligent and advanced statistical methodology, a Structural Time Series (STS) based approach has been introduced in this paper to develop a parsimonious and computationally simple air quality model. In STS methodology, the different components of a time-series dataset such as the trend, seasonal, cyclical and calendar variations can be modeled separately. To test the effectiveness of the proposed modeling strategy, average hourly concentrations of nitrogen dioxide and nitrogen oxides from a congested urban arterial in Dublin city center were modeled using STS methodology. The prediction error estimates from the developed air quality model indicate that the STS model can be a useful tool in predicting nitrogen dioxide and nitrogen oxides concentrations in urban areas and will be particularly useful in situations where the information on external variables such as meteorology or traffic volume is not available.

  11. Temperature prediction model of asphalt pavement in cold regions based on an improved BP neural network

    International Nuclear Information System (INIS)

    Xu, Bo; Dan, Han-Cheng; Li, Liang

    2017-01-01

    Highlights: • Pavement temperature prediction model is presented with improved BP neural network. • Dynamic and static methods are presented to predict pavement temperature. • Pavement temperature can be excellently predicted in next 3 h. - Abstract: Ice cover on pavement threatens traffic safety, and pavement temperature is the main factor used to determine whether the wet pavement is icy or not. In this paper, a temperature prediction model of the pavement in winter is established by introducing an improved Back Propagation (BP) neural network model. Before the application of the BP neural network model, many efforts were made to eliminate chaos and determine the regularity of temperature on the pavement surface (e.g., analyze the regularity of diurnal and monthly variations of pavement temperature). New dynamic and static prediction methods are presented by improving the algorithms to intelligently overcome the prediction inaccuracy at the change point of daily temperature. Furthermore, some scenarios have been compared for different dates and road sections to verify the reliability of the prediction model. According to the analysis results, the daily pavement temperatures can be accurately predicted for the next 3 h from the time of prediction by combining the dynamic and static prediction methods. The presented method in this paper can provide technical references for temperature prediction of the pavement and the development of an early-warning system for icy pavements in cold regions.

  12. Energy-Saving Mechanism in WDM/TDM-PON Based on Upstream Network Traffic

    Directory of Open Access Journals (Sweden)

    Paola Garfias

    2014-08-01

    Full Text Available One of the main challenges of Passive Optical Networks (PONs is the resource (bandwidth and wavelength management. Since it has been shown that access networks consume a significant part of the overall energy of the telecom networks, the resource management schemes should also consider energy minimization strategies. To sustain the increased bandwidth demand of emerging applications in the access section of the network, it is expected that next generation optical access networks will adopt the wavelength division/time division multiplexing (WDM/TDM technique to increase PONs capacity. Compared with traditional PONs, the architecture of a WDM/TDM-PON requires more transceivers/receivers, hence they are expected to consume more energy. In this paper, we focus on the energy minimization in WDM/TDM-PONs and we propose an energy-efficient Dynamic Bandwidth and Wavelength Allocation mechanism whose objective is to turn off, whenever possible, the unnecessary upstream traffic receivers at the Optical Line Terminal (OLT. We evaluate our mechanism in different scenarios and show that the proper use of upstream channels leads to relevant energy savings. Our proposed energy-saving mechanism is able to save energy at the OLT while maintaining the introduced penalties in terms of packet delay and cycle time within an acceptable range. We might highlight the benefits of our proposal as a mechanism that maximizes the channel utilization. Detailed implementation of the proposed algorithm is presented, and simulation results are reported to quantify energy savings and effects on network performance on different network scenarios.

  13. Energy prediction using spatiotemporal pattern networks

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Zhanhong; Liu, Chao; Akintayo, Adedotun; Henze, Gregor P.; Sarkar, Soumik

    2017-11-01

    This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamical filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependencies among different sub-systems. To quantify causal dependencies, a mutual information based metric is presented and an energy prediction approach is subsequently proposed based on the STPN framework. To validate the proposed scheme, two case studies are presented, one involving wind turbine power prediction (supply side energy) using the Western Wind Integration data set generated by the National Renewable Energy Laboratory (NREL) for identifying spatiotemporal characteristics, and the other, residential electric energy disaggregation (demand side energy) using the Building America 2010 data set from NREL for exploring temporal features. In the energy disaggregation context, convex programming techniques beyond the STPN framework are developed and applied to achieve improved disaggregation performance.

  14. Influence of Traffic Vehicles Against Ground Fundamental Frequency Prediction using Ambient Vibration Technique

    Science.gov (United States)

    Kamarudin, A. F.; Noh, M. S. Md; Mokhatar, S. N.; Anuar, M. A. Mohd; Ibrahim, A.; Ibrahim, Z.; Daud, M. E.

    2018-04-01

    Ambient vibration (AV) technique is widely used nowadays for ground fundamental frequency prediction. This technique is easy, quick, non-destructive, less operator required and reliable result. The input motions of ambient vibration are originally collected from surrounding natural and artificial excitations. But, careful data acquisition controlled must be implemented to reduce the intrusion of short period noise that could imply the quality of frequency prediction of an investigated site. In this study, investigation on the primary noise intrusion under peak (morning, afternoon and evening) and off peak (early morning) traffic flows (only 8 meter from sensor to road shoulder) against the stability and quality of ground fundamental frequency prediction were carried out. None of specific standard is available for AV data acquisition and processing. Thus, some field and processing parameters recommended by previous studies and guideline were considered. Two units of 1 Hz tri-axial seismometer sensor were closely positioned in front of the main entrance Universiti Tun Hussein Onn Malaysia. 15 minutes of recording length were taken during peak and off peak periods of traffic flows. All passing vehicles were counted and grouped into four classes. Three components of ambient vibration time series recorded in the North-South: NS, East-West: EW and vertical: UD directions were automatically computed into Horizontal to Vertical Spectral Ratio (HVSR), by using open source software of GEOPSY for fundamental ground frequency, Fo determination. Single sharp peak pattern of HVSR curves have been obtained at peak frequencies between 1.33 to 1.38 Hz which classified under soft to dense soil classification. Even identical HVSR curves pattern with close frequencies prediction were obtained under both periods of AV measurement, however the total numbers of stable and quality windows selected for HVSR computation were significantly different but both have satisfied the requirement

  15. An Interference-Aware Traffic-Priority-Based Link Scheduling Algorithm for Interference Mitigation in Multiple Wireless Body Area Networks

    Directory of Open Access Journals (Sweden)

    Thien T. T. Le

    2016-12-01

    Full Text Available Currently, wireless body area networks (WBANs are effectively used for health monitoring services. However, in cases where WBANs are densely deployed, interference among WBANs can cause serious degradation of network performance and reliability. Inter-WBAN interference can be reduced by scheduling the communication links of interfering WBANs. In this paper, we propose an interference-aware traffic-priority-based link scheduling (ITLS algorithm to overcome inter-WBAN interference in densely deployed WBANs. First, we model a network with multiple WBANs as an interference graph where node-level interference and traffic priority are taken into account. Second, we formulate link scheduling for multiple WBANs as an optimization model where the objective is to maximize the throughput of the entire network while ensuring the traffic priority of sensor nodes. Finally, we propose the ITLS algorithm for multiple WBANs on the basis of the optimization model. High spatial reuse is also achieved in the proposed ITLS algorithm. The proposed ITLS achieves high spatial reuse while considering traffic priority, packet length, and the number of interfered sensor nodes. Our simulation results show that the proposed ITLS significantly increases spatial reuse and network throughput with lower delay by mitigating inter-WBAN interference.

  16. Spatio-Temporal Ensemble Prediction on Mobile Broadband Network Data

    DEFF Research Database (Denmark)

    Samulevicius, Saulius; Pitarch, Yoann; Pedersen, Torben Bach

    2013-01-01

    Facing the huge success of mobile devices, network providers ceaselessly deploy new nodes (cells) to always guarantee a high quality of service. Nevertheless, keeping turned on all the nodes when traffic is low is energy inefficient. This has led to investigations on the possibility to turn off...

  17. A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine.

    Directory of Open Access Journals (Sweden)

    Qiang Shang

    Full Text Available Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS. Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM is proposed based on singular spectrum analysis (SSA and kernel extreme learning machine (KELM. SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA. Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.

  18. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  19. Local and nonlocal information in a traffic network: how important is the horizon?

    International Nuclear Information System (INIS)

    Petri, G.

    2010-01-01

    Recent advances in distributed sensor network technology have changed the landscape of traffic optimization in which small, mobile devices are able to sense local information and communicate in real time with one another. Naive optimization algorithms that operate solely on the local or global level are inherently flawed, as global optimization requires every local sensor to communicate with a centralized base-station, creating prohibitive bandwidth, robustness, and security concerns, while local optimization methods are limited by a near information horizon as they are unable to propagate or react to information beyond their immediate vicinity. This paper investigates an intermediate approach where individual sensors are able to propagate congestion information over a variable distance that is determined in real-time. This strategy consistently out-performs a naive strategy where every car simply takes the shortest path to its destination, but does worse than a simpler optimization algorithm that only incorporates local information. This is most likely because the intermediate solution directs cars along the same alternate path when attempting to free a congested area, thus creating new congestion along the detour. The results suggest that local information might set an upper bound on performance in models of cascading in- formation. Further work is required to confirm this observation and develop an algorithm able to join both local and global information to effectively diffuse traffic around congestion. (author)

  20. An efficient mechanism for dynamic multicast traffic grooming in overlay IP/MPLS over WDM networks

    Science.gov (United States)

    Yu, Xiaojun; Xiao, Gaoxi; Cheng, Tee Hiang

    2014-08-01

    This paper proposes an efficient overlay multicast provisioning (OMP) mechanism for dynamic multicast traffic grooming in overlay IP/MPLS over WDM networks. To facilitate request provisioning, OMP jointly utilizes a data learning (DL) scheme on the IP/MPLS layer for logical link cost estimation, and a lightpath fragmentation (LPF) based method on the WDM layer for improving resource sharing in grooming process. Extensive simulations are carried out to evaluate the performance of OMP mechanism under different traffic loads, with either limited or unlimited port resources. Simulation results demonstrate that OMP significantly outperforms the existing methods. To evaluate the respective influences of the DL scheme and the LPF method on OMP performance, provisioning mechanisms only utilizing either the IP/MPLS layer DL scheme or the WDM layer LPF method are also devised. Comparison results show that both DL and LPF methods help improve OMP blocking performance, and contribution from the DL scheme is more significant when the fixed routing and first-fit wavelength assignment (RWA) strategy is adopted on the WDM layer. Effects of a few other factors, including definition of connection cost to be reported by the WDM layer to the IP/MPLS layer and WDM-layer routing method, on OMP performance are also evaluated.

  1. Human Factors of Automated Driving : Predicting the Effects of Authority Transitions on Traffic Flow Efficiency

    NARCIS (Netherlands)

    Varotto, S.F.; Hoogendoorn, R.G.; Van Arem, B.; Hoogendoorn, S.P.

    2014-01-01

    Automated driving potentially has a significant impact on traffic flow efficiency. Automated vehicles, which possess cooperative capabilities, are expected to reduce congestion levels for instance by increasing road capacity, by anticipating traffic conditions further downstream and also by

  2. On-Chip SDM Switching for Unicast, Multicast and Traffic Grooming in Data Center Networks

    DEFF Research Database (Denmark)

    Kamchevska, Valerija; Ding, Yunhong; Dalgaard, Kjeld

    2017-01-01

    This paper reports on the use of a novel photonic integrated circuit that facilitates multicast and grooming in an optical data center architecture. The circuit allows for on-chip spatial multiplexing and demultiplexing as well as fiber core switching. Using this device, we experimentally verify...... that multicast and/or grooming can be successfully performed along the full range of output ports, for different group size and different power ratio. Moreover, we experimentally demonstrate SDM transmission and 5 Tbit/s switching using the on-chip fiber switch with integrated fan-in/fan-out devices and achieve...... errorfree performance (BER≤10-9) for a network scenario including simultaneous unicast/multicast switching and traffic grooming....

  3. Hybrid Electromagnetism-Like Algorithm for Dynamic Supply Chain Network Design under Traffic Congestion and Uncertainty

    Directory of Open Access Journals (Sweden)

    Javid Jouzdani

    2016-01-01

    Full Text Available With the constantly increasing pressure of the competitive environment, supply chain (SC decision makers are forced to consider several aspects of business climate. More specifically, they should take into account the endogenous features (e.g., available means of transportation, and the variety of products and exogenous criteria (e.g., the environmental uncertainty, and transportation system conditions. In this paper, a mixed integer nonlinear programming (MINLP model for dynamic design of a supply chain network is proposed. In this model, multiple products and multiple transportation modes, the time value of money, traffic congestion, and both supply-side and demand-side uncertainties are considered. Due to the complexity of such models, conventional solution methods are not applicable; therefore, two hybrid Electromagnetism-Like Algorithms (EMA are designed and discussed for tackling the problem. The numerical results show the applicability of the proposed model and the capabilities of the solution approaches to the MINLP problem.

  4. Epidemic metapopulation model with traffic routing in scale-free networks

    International Nuclear Information System (INIS)

    Huang, Wei; Chen, Shengyong

    2011-01-01

    In this paper, we propose a model incorporating both the traffic routing dynamics and the virus prevalence dynamics. In this model, each packet may be isolated from the network on its transporting path, which means that the packet cannot be successfully delivered to its destination. In contrast, a successful transport means that a packet can be delivered from source to destination without being isolated. The effects of model parameters on the delivery success rate and the delivery failure rate are intensively studied and analyzed. Several routing strategies are performed for our model. Results show that the shortest path routing strategy is the most effective for enhancing the delivery success rate, especially when each packet is only allowed to be delivered to the neighbor with the lowest degree along the shortest path. We also find that, by minimizing the sum of the nodes' degree along the transporting path, we can also obtain a satisfactory delivery success rate

  5. Traffic-Adaptive Proactive Sp ectrum Handoff Strategy for Graded Secondary Users in Cognitive Radio Networks

    Institute of Scientific and Technical Information of China (English)

    ZHANG Lei; SONG Tiecheng; WU Ming; BAO Xu; GUO Jie; HU Jing

    2015-01-01

    In order to meet diff erent delay require-ments of various communication services in Cognitive ra-dio (CR) networks, Secondary users (SUs) are divided into two classes according to the priority of accessing to spec-trum in this paper. Based on the proactive spectrum hand-off scheme, the Preemptive resume priority (PRP) M/G/1 queueing is used to characterize multiple spectrum hand-off s under two diff erent spectrum handoff strategies. The traffic-adaptive spectrum handoff strategy is proposed for graded SUs so as to minimize the average cumulative hand-off delay. Simulation results not only verify that our theo-retical analysis is valid, but also show that the strategy we proposed can reduce the average cumulative handoff delay evidently. The eff ect of service rate on the proposed spec-trum switching point and the admissible access region are provided.

  6. On the Impact of Zero-padding in Network Coding Efficiency with Internet Traffic and Video Traces

    DEFF Research Database (Denmark)

    Taghouti, Maroua; Roetter, Daniel Enrique Lucani; Pedersen, Morten Videbæk

    2016-01-01

    Random Linear Network Coding (RLNC) theoretical results typically assume that packets have equal sizes while in reality, data traffic presents a random packet size distribution. Conventional wisdom considers zero-padding of original packets as a viable alternative, but its effect can reduce the e...

  7. A cost-effective traffic data collection system based on the iDEN mobile telecommunication network.

    Science.gov (United States)

    2008-10-01

    This report describes a cost-effective data collection system for Caltrans 170 traffic signal : controller. The data collection system is based on TCP/IP communication over existing : low-cost mobile communication networks and Motorola iDEN1 mobile...

  8. Predicting Community Evolution in Social Networks

    Directory of Open Access Journals (Sweden)

    Stanisław Saganowski

    2015-05-01

    Full Text Available Nowadays, sustained development of different social media can be observed worldwide. One of the relevant research domains intensively explored recently is analysis of social communities existing in social media as well as prediction of their future evolution taking into account collected historical evolution chains. These evolution chains proposed in the paper contain group states in the previous time frames and its historical transitions that were identified using one out of two methods: Stable Group Changes Identification (SGCI and Group Evolution Discovery (GED. Based on the observed evolution chains of various length, structural network features are extracted, validated and selected as well as used to learn classification models. The experimental studies were performed on three real datasets with different profile: DBLP, Facebook and Polish blogosphere. The process of group prediction was analysed with respect to different classifiers as well as various descriptive feature sets extracted from evolution chains of different length. The results revealed that, in general, the longer evolution chains the better predictive abilities of the classification models. However, chains of length 3 to 7 enabled the GED-based method to almost reach its maximum possible prediction quality. For SGCI, this value was at the level of 3–5 last periods.

  9. Performance of FHWA model for predicting traffic noise: a case study of metropolitan city, Lucknow (India

    Directory of Open Access Journals (Sweden)

    J. B. Srivastava

    2009-09-01

    Full Text Available Industrial and transport activities are the two major sources of noise pollution in any metropolitan city. Lucknow city, the capital of the largest populated state Uttar Pradesh in India has an area of 310 sq. km and is rapidly growing as a commercial, industrial and trading centre of northern India. The population of Lucknow city as per census 2001 is 22.45 Lacs. It is expected that by the year 2021 it will make 45 Lacs. The total vehicle population in Lucknow city on 31 March 2008, was nearly 1 million with almost 80% two wheelers, 12% cars, 1.36% three wheelers, 0.45% buses etc. A study was carried out to assess the existing status of noise levels and its impacts on the environment with a possibility of further expansion of the city. Ambient noise levels were measured at different locations selected on the basis of land use such as silence, heavy traffic and residential and commercial zones. It was found that noise levels at all selected locations were much higher (75–90 dB than the prescribed limits. The observed traffic volume and data on road geometry were used to predict noise levels using Federal Highway Administration Agency (FHWA model and the calculated noise levels were compared with the observed levels for checking the suitability of this model for predicting the future levels. It was established that the results obtained by FHWA model were very close to the observed noise levels and that the model was suitable to be used for other similar metropolitan cities in India.

  10. Indicators for traffic safety assessment and prediction and their application in micro-simulation modelling : a study of urban and suburban intersections

    OpenAIRE

    Archer, Jeffery

    2005-01-01

    In order to achieve sustainable long-term transport infrastructure development, there is a growing need for fast, reliable and effective methods to evaluate and predict the impact of traffic safety measures. Recognising this need, and the need for an active traffic safety approach, this thesis focuses on traffic safety assessment and prediction based on the use of safety indicators that measure the spatial and/or temporal proximity of safety critical events. The main advantage of such measure...

  11. A Spike Neural Controller for Traffic Load Parameter with Priority-Based Rate in Wireless Multimedia Sensor Networks

    Directory of Open Access Journals (Sweden)

    Nadia Adnan Shiltagh

    2015-11-01

    Full Text Available Wireless Multimedia Sensor Networks (WMSNs are a type of sensor network that contains sensor nodes equipped with cameras, microphones; therefore the WMSNS are able to produce multimedia data such as video and audio streams, still images, and scalar data from the surrounding environment. Most multimedia applications typically produce huge volumes of data, this leads to congestion. To address this challenge, This paper proposes Modify Spike Neural Network control for Traffic Load Parameter with Exponential Weight of Priority Based Rate Control algorithm (MSNTLP with EWBPRC. The Modify Spike Neural Network controller (MSNC can calculate the appropriate traffic load parameter μ for each parent node and then use in the EWPBRC algorithm to estimate the transmission rate of parent nodes and then assign a suitable transmission rate for each child node. A comparative study between (MSNTLP with EWBPRC and fuzzy logic controller for traffic load parameter with Exponential Weight of Priority Based Rate Control algorithm (FTLP with EWBPRC algorithm shows that the (MSNTLP with EWBPRC is more efficient than (FTLP with EWBPRC algorithm in terms of packet loss, queue delay and throughput. Another comparative study between (MSNTLP with EWBPRC and EWBPRC with fixed traffic load parameter (µ shows that the MSNTLP with EWBPRC is more efficient than EWBPRC with fixed traffic load parameter (µ in terms of packet loss ratio and queue delay. A simulation process is developed and tested using the network simulator _2 (NS2 in a computer having the following properties: windows 7 (64-bit, core i7, RAM 8GB, hard 1TB.

  12. Time-Predictable Communication on a Time-Division Multiplexing Network-on-Chip Multicore

    DEFF Research Database (Denmark)

    Sørensen, Rasmus Bo

    This thesis presents time-predictable inter-core communication on a multicore platform with a time-division multiplexing (TDM) network-on-chip (NoC) for hard real-time systems. The thesis is structured as a collection of papers that contribute within the areas of: reconfigurable TDM NoCs, static...... TDM scheduling, and time-predictable inter-core communication. More specifically, the work presented in this thesis investigates the interaction between hardware and software involved in time-predictable inter-core communication on the multicore platform. The thesis presents: a new generation...... of the Argo NoC network interface (NI) that supports instantaneous reconfiguration, a TDM traffic scheduler that generates virtual circuit (VC) configurations for the Argo NoC, and software functions for two types of intercore communication. The new generation of the Argo NoC adds the capability...

  13. Predicting local field potentials with recurrent neural networks.

    Science.gov (United States)

    Kim, Louis; Harer, Jacob; Rangamani, Akshay; Moran, James; Parks, Philip D; Widge, Alik; Eskandar, Emad; Dougherty, Darin; Chin, Sang Peter

    2016-08-01

    We present a Recurrent Neural Network using LSTM (Long Short Term Memory) that is capable of modeling and predicting Local Field Potentials. We train and test the network on real data recorded from epilepsy patients. We construct networks that predict multi-channel LFPs for 1, 10, and 100 milliseconds forward in time. Our results show that prediction using LSTM outperforms regression when predicting 10 and 100 millisecond forward in time.

  14. Traffic data collection and anonymous vehicle detection using wireless sensor networks : research summary.

    Science.gov (United States)

    2012-05-01

    Problem: : Most Intelligent Transportation System (ITS) applications require distributed : acquisition of various traffic metrics such as traffic speed, volume, and density. : The existing measurement technologies, such as inductive loops, infrared, ...

  15. Timing analysis of rate-constrained traffic in TTEthernet using network calculus

    DEFF Research Database (Denmark)

    Zhao, Luxi; Pop, Paul; Li, Qiao

    2017-01-01

    calculus (NC) to determine the worst-case end-to-end delays of RC traffic in TTEthernet. The main contribution of this paper is capturing the effects of all the integration policies on the latency bounds of RC traffic using NC, and the consideration of relative frame offsets of TT traffic to reduce...

  16. Prediction based chaos control via a new neural network

    International Nuclear Information System (INIS)

    Shen Liqun; Wang Mao; Liu Wanyu; Sun Guanghui

    2008-01-01

    In this Letter, a new chaos control scheme based on chaos prediction is proposed. To perform chaos prediction, a new neural network architecture for complex nonlinear approximation is proposed. And the difficulty in building and training the neural network is also reduced. Simulation results of Logistic map and Lorenz system show the effectiveness of the proposed chaos control scheme and the proposed neural network

  17. Predicting tax avoidance by means of social network analytics

    NARCIS (Netherlands)

    Jasmien, Lismont; Cardinaels, Eddy; Bruynseels, L.M.L.; De Groote, Sander; Baesens, B.; Lemahieu, W.; Vanthienen, J.

    This study predicts tax avoidance by means of social network analytics. We extend previous literature by being the first to build a predictive model including a larger variation of network features. We construct a network of firms connected through shared board membership. Then, we apply three

  18. Composite Social Network for Predicting Mobile Apps Installation

    Science.gov (United States)

    2011-06-02

    Social network tools (such as the Facebook app and the Twitter app) can observe users’ online friendship network . In this work, our key idea is...the friendship network from phones by collecting data from social networking apps such as the Facebook and Twitter apps. We summarize all the networks ...ar X iv :1 10 6. 03 59 v1 [ cs .S I] 2 J un 2 01 1 Composite Social Network for Predicting Mobile Apps Installation Wei Pan

  19. Supervised learning from human performance at the computationally hard problem of optimal traffic signal control on a network of junctions.

    Science.gov (United States)

    Box, Simon

    2014-12-01

    Optimal switching of traffic lights on a network of junctions is a computationally intractable problem. In this research, road traffic networks containing signallized junctions are simulated. A computer game interface is used to enable a human 'player' to control the traffic light settings on the junctions within the simulation. A supervised learning approach, based on simple neural network classifiers can be used to capture human player's strategies in the game and thus develop a human-trained machine control (HuTMaC) system that approaches human levels of performance. Experiments conducted within the simulation compare the performance of HuTMaC to two well-established traffic-responsive control systems that are widely deployed in the developed world and also to a temporal difference learning-based control method. In all experiments, HuTMaC outperforms the other control methods in terms of average delay and variance over delay. The conclusion is that these results add weight to the suggestion that HuTMaC may be a viable alternative, or supplemental method, to approximate optimization for some practical engineering control problems where the optimal strategy is computationally intractable.

  20. Predicting long-term average concentrations of traffic-related air pollutants using GIS-based information

    Science.gov (United States)

    Hochadel, Matthias; Heinrich, Joachim; Gehring, Ulrike; Morgenstern, Verena; Kuhlbusch, Thomas; Link, Elke; Wichmann, H.-Erich; Krämer, Ursula

    Global regression models were developed to estimate individual levels of long-term exposure to traffic-related air pollutants. The models are based on data of a one-year measurement programme including geographic data on traffic and population densities. This investigation is part of a cohort study on the impact of traffic-related air pollution on respiratory health, conducted at the westerly end of the Ruhr-area in North-Rhine Westphalia, Germany. Concentrations of NO 2, fine particle mass (PM 2.5) and filter absorbance of PM 2.5 as a marker for soot were measured at 40 sites spread throughout the study region. Fourteen-day samples were taken between March 2002 and March 2003 for each season and site. Annual average concentrations for the sites were determined after adjustment for temporal variation. Information on traffic counts in major roads, building densities and community population figures were collected in a geographical information system (GIS). This information was used to calculate different potential traffic-based predictors: (a) daily traffic flow and maximum traffic intensity of buffers with radii from 50 to 10 000 m and (b) distances to main roads and highways. NO 2 concentration and PM 2.5 absorbance were strongly correlated with the traffic-based variables. Linear regression prediction models, which involved predictors with radii of 50 to 1000 m, were developed for the Wesel region where most of the cohort members lived. They reached a model fit ( R2) of 0.81 and 0.65 for NO 2 and PM 2.5 absorbance, respectively. Regression models for the whole area required larger spatial scales and reached R2=0.90 and 0.82. Comparison of predicted values with NO 2 measurements at independent public monitoring stations showed a satisfactory association ( r=0.66). PM 2.5 concentration, however, was only slightly correlated and thus poorly predictable by traffic-based variables ( rGIS-based regression models offer a promising approach to assess individual levels of

  1. STUDY ON SUPPORTING FOR DRAWING UP THE BCP FOR URBAN EXPRESSWAY NETWORK USING BY TRAFFIC SIMULATION SYSTEM

    Science.gov (United States)

    Yamawaki, Masashi; Shiraki, Wataru; Inomo, Hitoshi; Yasuda, Keiichi

    The urban expressway network is an important infrastructure to execute a disaster restoration. Therefore, it is necessary to draw up the BCP (Business Continuity Plan) to enable securing of road user's safety and restoration of facilities, etc. It is important that each urban expressway manager execute decision and improvement of effective BCP countermeasures when disaster occurs by assuming various disaster situations. Then, in this study, we develop the traffic simulation system that can reproduce various disaster situations and traffic actions, and examine some methods supporting for drawing up the BCP for an urban expressway network. For disaster outside assumption such as tsunami generated by a huge earthquake, we examine some approaches securing safety of users and cars on the Hanshin Expressway Network as well as on general roads. And, we aim to propose a tsunami countermeasure not considered in the current urban expressway BCP.

  2. Forex Market Prediction Using NARX Neural Network with Bagging

    Directory of Open Access Journals (Sweden)

    Shahbazi Nima

    2016-01-01

    Full Text Available We propose a new methodfor predicting movements in Forex market based on NARX neural network withtime shifting bagging techniqueand financial indicators, such as relative strength index and stochastic indicators. Neural networks have prominent learning ability but they often exhibit bad and unpredictable performance for noisy data. When compared with the static neural networks, our method significantly reducesthe error rate of the responseandimproves the performance of the prediction. We tested three different types ofarchitecture for predicting the response and determined the best network approach. We applied our method to prediction the hourly foreign exchange rates and found remarkable predictability in comprehensive experiments with 2 different foreign exchange rates (GBPUSD and EURUSD.

  3. Joint Content Placement and Traffic Management for Cloud Mobile Video Distribution over Software-Defined Networks

    Institute of Scientific and Technical Information of China (English)

    Zhenghuan Zhang; Xiaofeng Jiang; Hongsheng Xi

    2016-01-01

    To cope with the rapid growth of mobile video,video providers have leveraged cloud technologies to deploy their mobile video service system for more cost-effective and scalable performance.The emergence of Software-Defined Networking (SDN) provides a promising solution to manage the underlying network.In this paper,we introduce an SDN-enabled cloud mobile video distribution architecture and propose a joint video placement,request dispatching and traffic management mechanism to improve user experience and reduce the system operational cost.We use a utility function to capture the two aspects of user experience:the level of satisfaction and average latency,and formulate the joint optimization problem as a mixed integer programming problem.We develop an optimal algorithm based on dual decomposition and prove its optimality.We conduct simulations to evaluate the performance of our algorithm and the results show that our strategy can effectively cut down the total cost and guarantee user experience.

  4. An auxiliary graph based dynamic traffic grooming algorithm in spatial division multiplexing enabled elastic optical networks with multi-core fibers

    Science.gov (United States)

    Zhao, Yongli; Tian, Rui; Yu, Xiaosong; Zhang, Jiawei; Zhang, Jie

    2017-03-01

    A proper traffic grooming strategy in dynamic optical networks can improve the utilization of bandwidth resources. An auxiliary graph (AG) is designed to solve the traffic grooming problem under a dynamic traffic scenario in spatial division multiplexing enabled elastic optical networks (SDM-EON) with multi-core fibers. Five traffic grooming policies achieved by adjusting the edge weights of an AG are proposed and evaluated through simulation: maximal electrical grooming (MEG), maximal optical grooming (MOG), maximal SDM grooming (MSG), minimize virtual hops (MVH), and minimize physical hops (MPH). Numeric results show that each traffic grooming policy has its own features. Among different traffic grooming policies, an MPH policy can achieve the lowest bandwidth blocking ratio, MEG can save the most transponders, and MSG can obtain the fewest cores for each request.

  5. Feasibility of a Networked Air Traffic Infrastructure Validation Environment for Advanced NextGen Concepts

    Science.gov (United States)

    McCormack, Michael J.; Gibson, Alec K.; Dennis, Noah E.; Underwood, Matthew C.; Miller,Lana B.; Ballin, Mark G.

    2013-01-01

    Abstract-Next Generation Air Transportation System (NextGen) applications reliant upon aircraft data links such as Automatic Dependent Surveillance-Broadcast (ADS-B) offer a sweeping modernization of the National Airspace System (NAS), but the aviation stakeholder community has not yet established a positive business case for equipage and message content standards remain in flux. It is necessary to transition promising Air Traffic Management (ATM) Concepts of Operations (ConOps) from simulation environments to full-scale flight tests in order to validate user benefits and solidify message standards. However, flight tests are prohibitively expensive and message standards for Commercial-off-the-Shelf (COTS) systems cannot support many advanced ConOps. It is therefore proposed to simulate future aircraft surveillance and communications equipage and employ an existing commercial data link to exchange data during dedicated flight tests. This capability, referred to as the Networked Air Traffic Infrastructure Validation Environment (NATIVE), would emulate aircraft data links such as ADS-B using in-flight Internet and easily-installed test equipment. By utilizing low-cost equipment that is easy to install and certify for testing, advanced ATM ConOps can be validated, message content standards can be solidified, and new standards can be established through full-scale flight trials without necessary or expensive equipage or extensive flight test preparation. This paper presents results of a feasibility study of the NATIVE concept. To determine requirements, six NATIVE design configurations were developed for two NASA ConOps that rely on ADS-B. The performance characteristics of three existing in-flight Internet services were investigated to determine whether performance is adequate to support the concept. Next, a study of requisite hardware and software was conducted to examine whether and how the NATIVE concept might be realized. Finally, to determine a business case

  6. Wave transmission prediction of multilayer floating breakwater using neural network

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Patil, S.G.; Hegde, A.V.

    In the present study, an artificial neural network method has been applied for wave transmission prediction of multilayer floating breakwater. Two neural network models are constructed based on the parameters which influence the wave transmission...

  7. Stability prediction of berm breakwater using neural network

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Rao, S.; Manjunath, Y.R.

    In the present study, an artificial neural network method has been applied to predict the stability of berm breakwaters. Four neural network models are constructed based on the parameters which influence the stability of breakwater. Training...

  8. A study of modelling simplifications in ground vibration predictions for railway traffic at grade

    Science.gov (United States)

    Germonpré, M.; Degrande, G.; Lombaert, G.

    2017-10-01

    Accurate computational models are required to predict ground-borne vibration due to railway traffic. Such models generally require a substantial computational effort. Therefore, much research has focused on developing computationally efficient methods, by either exploiting the regularity of the problem geometry in the direction along the track or assuming a simplified track structure. This paper investigates the modelling errors caused by commonly made simplifications of the track geometry. A case study is presented investigating a ballasted track in an excavation. The soil underneath the ballast is stiffened by a lime treatment. First, periodic track models with different cross sections are analyzed, revealing that a prediction of the rail receptance only requires an accurate representation of the soil layering directly underneath the ballast. A much more detailed representation of the cross sectional geometry is required, however, to calculate vibration transfer from track to free field. Second, simplifications in the longitudinal track direction are investigated by comparing 2.5D and periodic track models. This comparison shows that the 2.5D model slightly overestimates the track stiffness, while the transfer functions between track and free field are well predicted. Using a 2.5D model to predict the response during a train passage leads to an overestimation of both train-track interaction forces and free field vibrations. A combined periodic/2.5D approach is therefore proposed in this paper. First, the dynamic axle loads are computed by solving the train-track interaction problem with a periodic model. Next, the vibration transfer to the free field is computed with a 2.5D model. This combined periodic/2.5D approach only introduces small modelling errors compared to an approach in which a periodic model is used in both steps, while significantly reducing the computational cost.

  9. Optimum Neural Network Architecture for Precipitation Prediction of Myanmar

    OpenAIRE

    Khaing Win Mar; Thinn Thu Naing

    2008-01-01

    Nowadays, precipitation prediction is required for proper planning and management of water resources. Prediction with neural network models has received increasing interest in various research and application domains. However, it is difficult to determine the best neural network architecture for prediction since it is not immediately obvious how many input or hidden nodes are used in the model. In this paper, neural network model is used as a forecasting tool. The major aim is to evaluate a s...

  10. Predicting cryptic links in host-parasite networks.

    Directory of Open Access Journals (Sweden)

    Tad Dallas

    2017-05-01

    Full Text Available Networks are a way to represent interactions among one (e.g., social networks or more (e.g., plant-pollinator networks classes of nodes. The ability to predict likely, but unobserved, interactions has generated a great deal of interest, and is sometimes referred to as the link prediction problem. However, most studies of link prediction have focused on social networks, and have assumed a completely censused network. In biological networks, it is unlikely that all interactions are censused, and ignoring incomplete detection of interactions may lead to biased or incorrect conclusions. Previous attempts to predict network interactions have relied on known properties of network structure, making the approach sensitive to observation errors. This is an obvious shortcoming, as networks are dynamic, and sometimes not well sampled, leading to incomplete detection of links. Here, we develop an algorithm to predict missing links based on conditional probability estimation and associated, node-level features. We validate this algorithm on simulated data, and then apply it to a desert small mammal host-parasite network. Our approach achieves high accuracy on simulated and observed data, providing a simple method to accurately predict missing links in networks without relying on prior knowledge about network structure.

  11. Signal Control for Reducing Vehicle NOx and CO2 Emissions Based on Prediction of Arrival Traffic Flows at Intersections

    Science.gov (United States)

    Oda, Toshihiko

    Nitrogen oxide (NOx) and carbon dioxide (CO2) emissions from vehicles have been increasing every year because of the growing number of vehicles, and they cause serious environmental problems such as air pollution and global warming. To alleviate these problems, this paper proposes a new traffic signal control method for reducing vehicle NOx and CO2 emissions on arterial roads. To this end, we first model the amount of vehicle emissions as a function of the traffic delay and the number of stops at intersections. This step is necessary because it is difficult to obtain the amount of emissions directly using traffic control systems. Second, we introduce a signal control model in which the control parameters are continuously updated on the basis of predictions of arrival traffic flows at intersections. The signal timings are calculated in such a manner so as to minimize the weighted sum of the two emissions, which depend on the traffic flow. To evaluate the validity of this method, simulation experiments are carried out on an arterial road. The experiments show that the proposed method significantly outperforms existing methods in reducing both the emissions and travel time.

  12. Multi-agent model predictive control for transportation networks : Serial versus parallel schemes

    NARCIS (Netherlands)

    Negenborn, R.R.; De Schutter, B.; Hellendoorn, J.

    2006-01-01

    We consider the control of large-scale transportation networks, like road traffic networks, power distribution networks, water distribution networks, etc. Control of these networks is often not possible from a single point by a single intelligent control agent; instead control has to be performed

  13. Predictive Control of Networked Multiagent Systems via Cloud Computing.

    Science.gov (United States)

    Liu, Guo-Ping

    2017-01-18

    This paper studies the design and analysis of networked multiagent predictive control systems via cloud computing. A cloud predictive control scheme for networked multiagent systems (NMASs) is proposed to achieve consensus and stability simultaneously and to compensate for network delays actively. The design of the cloud predictive controller for NMASs is detailed. The analysis of the cloud predictive control scheme gives the necessary and sufficient conditions of stability and consensus of closed-loop networked multiagent control systems. The proposed scheme is verified to characterize the dynamical behavior and control performance of NMASs through simulations. The outcome provides a foundation for the development of cooperative and coordinative control of NMASs and its applications.

  14. Using Generalized Entropies and OC-SVM with Mahalanobis Kernel for Detection and Classification of Anomalies in Network Traffic

    Directory of Open Access Journals (Sweden)

    Jayro Santiago-Paz

    2015-09-01

    Full Text Available Network anomaly detection and classification is an important open issue in network security. Several approaches and systems based on different mathematical tools have been studied and developed, among them, the Anomaly-Network Intrusion Detection System (A-NIDS, which monitors network traffic and compares it against an established baseline of a “normal” traffic profile. Then, it is necessary to characterize the “normal” Internet traffic. This paper presents an approach for anomaly detection and classification based on Shannon, Rényi and Tsallis entropies of selected features, and the construction of regions from entropy data employing the Mahalanobis distance (MD, and One Class Support Vector Machine (OC-SVM with different kernels (Radial Basis Function (RBF and Mahalanobis Kernel (MK for “normal” and abnormal traffic. Regular and non-regular regions built from “normal” traffic profiles allow anomaly detection, while the classification is performed under the assumption that regions corresponding to the attack classes have been previously characterized. Although this approach allows the use of as many features as required, only four well-known significant features were selected in our case. In order to evaluate our approach, two different data sets were used: one set of real traffic obtained from an Academic Local Area Network (LAN, and the other a subset of the 1998 MIT-DARPA set. For these data sets, a True positive rate up to 99.35%, a True negative rate up to 99.83% and a False negative rate at about 0.16% were yielded. Experimental results show that certain q-values of the generalized entropies and the use of OC-SVM with RBF kernel improve the detection rate in the detection stage, while the novel inclusion of MK kernel in OC-SVM and k-temporal nearest neighbors improve accuracy in classification. In addition, the results show that using the Box-Cox transformation, the Mahalanobis distance yielded high detection rates with

  15. Traffic Responsive Control of Intersections with Predicted Arrival Times: A Markovian Approach

    NARCIS (Netherlands)

    Haijema, R.; Hendrix, E.M.T.

    2014-01-01

    The dynamic adaptive control of traffic lights can be formulated as a Markov decision problem (MDP). This framework is hardly used, as solving an MDP can be very time-consuming and is only possible for simple infrastructures with a small number of traffic flows. Nevertheless, we show that the MDP

  16. Prediction of tides using back-propagation neural networks

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.

    Prediction of tides is very much essential for human activities and to reduce the construction cost in marine environment. This paper presents an application of the artificial neural network with back-propagation procedures for accurate prediction...

  17. Predicting Expressive Dynamics in Piano Performances using Neural Networks

    NARCIS (Netherlands)

    van Herwaarden, Sam; Grachten, Maarten; de Haas, W. Bas

    2014-01-01

    This paper presents a model for predicting expressive accentuation in piano performances with neural networks. Using Restricted Boltzmann Machines (RBMs), features are learned from performance data, after which these features are used to predict performed loudness. During feature learning, data

  18. Millifluidics as a simple tool to optimize droplet networks: Case study on drop traffic in a bifurcated loop

    Science.gov (United States)

    Wang, William S.; Vanapalli, Siva A.

    2014-01-01

    We report that modular millifluidic networks are simpler, more cost-effective alternatives to traditional microfluidic networks, and they can be rapidly generated and altered to optimize designs. Droplet traffic can also be studied more conveniently and inexpensively at the millimeter scale, as droplets are readily visible to the naked eye. Bifurcated loops, ladder networks, and parking networks were made using only Tygon® tubing and plastic T-junction fittings and visualized using an iPod® camera. As a case study, droplet traffic experiments through a millifluidic bifurcated loop were conducted, and the periodicity of drop spacing at the outlet was mapped over a wide range of inlet drop spacing. We observed periodic, intermittent, and aperiodic behaviors depending on the inlet drop spacing. The experimentally observed periodic behaviors were in good agreement with numerical simulations based on the simple network model. Our experiments further identified three main sources of intermittency between different periodic and/or aperiodic behaviors: (1) simultaneous entering and exiting events, (2) channel defects, and (3) equal or nearly equal hydrodynamic resistances in both sides of the bifurcated loop. In cases of simultaneous events and/or channel defects, the range of input spacings where intermittent behaviors are observed depends on the degree of inherent variation in input spacing. Finally, using a time scale analysis of syringe pump fluctuations and experiment observation times, we find that in most cases, more consistent results can be generated in experiments conducted at the millimeter scale than those conducted at the micrometer scale. Thus, millifluidic networks offer a simple means to probe collective interactions due to drop traffic and optimize network geometry to engineer passive devices for biological and material analysis. PMID:25553188

  19. Social network models predict movement and connectivity in ecological landscapes

    Science.gov (United States)

    Fletcher, Robert J.; Acevedo, M.A.; Reichert, Brian E.; Pias, Kyle E.; Kitchens, Wiley M.

    2011-01-01

    Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.

  20. Social network models predict movement and connectivity in ecological landscapes.

    Science.gov (United States)

    Fletcher, Robert J; Acevedo, Miguel A; Reichert, Brian E; Pias, Kyle E; Kitchens, Wiley M

    2011-11-29

    Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.

  1. BER and FER Prediction of Control and Traffic Channels for a GSM type of interface

    DEFF Research Database (Denmark)

    Wigard, Jeroen; Nielsen, Thomas Toftegaard; Michaelsen, Per Henrik

    1998-01-01

    in a network simulator, but without having to simulate every single link, since this would be to time consuming. In this paper a method is presented to find the BER and FER from the signal to interference (C/I) values for a GSM type of air-interface, which can be used for integration of link aspects...... in a network simulator. The accuracy is within 0.2 dB in case of the BER and 0.5 for the FER. Both traffic and control channels are studied and the method is independent of hopping sequences and speed...

  2. POLLING AND DUAL-LEVEL TRAFFIC ANALYSIS FOR IMPROVED DOS DETECTION IN IEEE 802.21 NETWORKS

    Directory of Open Access Journals (Sweden)

    Nygil Alex Vadakkan

    2014-06-01

    Full Text Available The IEEE 802.21 standard was developed for communication of devices in a heterogeneous environment which included greater support for handoffs. This paper focuses on the denial of service (DoS vulnerabilities faced by such Media Independent Handover (MIH networks & various effective countermeasures that can be deployed to prevent their impact on such heterogeneous networks. The use of polling mechanism coupled with real time as well as offline traffic analysis can keep a good number of attacks at bay. The use of offline traffic analysis is to use the model and compare it with a lighter model and see if any of the excluded features in the lighter model have had suspicious variations which could be a varied form of DoS attack or an attack that is completely new. The countermeasures that have been developed also allows for the increase in efficiency of data transfer as well as higher rates of success in handoffs.

  3. Enhancing traffic performance in hierarchical DHT system by exploiting network proximity

    Science.gov (United States)

    Zhong, Haifeng; Wu, Wei; Pei, Canhao; Zhang, Chengfeng

    2009-08-01

    Nowadays P2P systems have become increasingly popular for object distribution and file sharing, and the majority of Internet traffic is generated by P2P file sharing applications. However, those applications usually ignored the underlying proximity of physical nodes and regionalization of file accessing. As a result, they generate a large amount of unnecessary interdomain transit traffic and increase response latency. In this paper, we proposed a new traffic control approach to enhance p2p traffic locality and reduce the cross-group transfer. Using analysis, we show that the method substantially improves node transfer efficiency and significantly reduces file access latency compared with native P2P applications.

  4. Metabolic network prediction through pairwise rational kernels.

    Science.gov (United States)

    Roche-Lima, Abiel; Domaratzki, Michael; Fristensky, Brian

    2014-09-26

    Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations. We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy

  5. Models for discrete-time self-similar vector processes with application to network traffic

    Science.gov (United States)

    Lee, Seungsin; Rao, Raghuveer M.; Narasimha, Rajesh

    2003-07-01

    The paper defines self-similarity for vector processes by employing the discrete-time continuous-dilation operation which has successfully been used previously by the authors to define 1-D discrete-time stochastic self-similar processes. To define self-similarity of vector processes, it is required to consider the cross-correlation functions between different 1-D processes as well as the autocorrelation function of each constituent 1-D process in it. System models to synthesize self-similar vector processes are constructed based on the definition. With these systems, it is possible to generate self-similar vector processes from white noise inputs. An important aspect of the proposed models is that they can be used to synthesize various types of self-similar vector processes by choosing proper parameters. Additionally, the paper presents evidence of vector self-similarity in two-channel wireless LAN data and applies the aforementioned systems to simulate the corresponding network traffic traces.

  6. Evaluating the Influence of Road Lighting on Traffic Safety at Accesses Using An Artificial Neural Network.

    Science.gov (United States)

    Xu, Yueru; Ye, Zhirui; Wang, Yuan; Wang, Chao; Sun, Cuicui

    2018-05-18

    This paper focuses on the effect of road lighting on road safety at accesses and tries to quantitatively analyze the relationship between road lighting and road safety. An Artificial Neural Network (ANN) was applied in this study. This method is one of the most popular machine-learning methods in recent years and does not require any pre-defined assumptions. This method was applied using field data collected from ten road segments in Nanjing, Jiangsu Province, China. The results show that the impact of road lighting on road safety at accesses is significant. In addition, road lighting has greater influence when vehicle speeds are higher or the number of lanes is larger. A threshold illuminance was also found in this paper, and the results show that the safety level at accesses will become stable when reaching this value. The improvement of illuminance can decrease the speed variation among vehicles and improve the safety level. In addition, high-grade roads need better illuminance at accesses. A threshold value can also be obtained based on related variables and used to develop scientific guidelines for traffic management organizations.

  7. A Visual Analytics Approach for Extracting Spatio-Temporal Urban Mobility Information from Mobile Network Traffic

    Directory of Open Access Journals (Sweden)

    Euro Beinat

    2012-11-01

    Full Text Available In this paper we present a visual analytics approach for deriving spatio-temporal patterns of collective human mobility from a vast mobile network traffic data set. More than 88 million movements between pairs of radio cells—so-called handovers—served as a proxy for more than two months of mobility within four urban test areas in Northern Italy. In contrast to previous work, our approach relies entirely on visualization and mapping techniques, implemented in several software applications. We purposefully avoid statistical or probabilistic modeling and, nonetheless, reveal characteristic and exceptional mobility patterns. The results show, for example, surprising similarities and symmetries amongst the total mobility and people flows between the test areas. Moreover, the exceptional patterns detected can be associated to real-world events such as soccer matches. We conclude that the visual analytics approach presented can shed new light on large-scale collective urban mobility behavior and thus helps to better understand the “pulse” of dynamic urban systems.

  8. Predicting the evolution of social networks with life cycle events

    NARCIS (Netherlands)

    Sharmeen, F.; Arentze, T.A.; Timmermans, H.J.P.

    2015-01-01

    This paper presents a model of social network evolution, to predict and simulate changes in social networks induced by lifecycle events. We argue that social networks change with lifecycle events, and we extend a model of friendship selection to incorporate these dynamics of personal social

  9. Time series prediction with simple recurrent neural networks ...

    African Journals Online (AJOL)

    A hybrid of the two called Elman-Jordan (or Multi-recurrent) neural network is also being used. In this study, we evaluated the performance of these neural networks on three established bench mark time series prediction problems. Results from the experiments showed that Jordan neural network performed significantly ...

  10. Efficient disk-to-disk copy through long-distance high-speed networks with background traffic

    International Nuclear Information System (INIS)

    Tanida, Naoki; Hiraki, Kei; Inaba, Mary

    2010-01-01

    We propose 'ICDC - InterContinental Disk Copy', a data sharing facility between distant places. ICDC aims to transfer huge amount of data files easily on Long Fat-pipe Networks with some background traffic. ICDC consists of commodity PCs and Solid State Drives and we apply Inter Packet Gap tuning technique to it. Using 'ICDC-1 Gbps model', we transferred data between Tokyo and Cadarache via New York. We attained about 860 Mbps, i.e., 86% usage of the network bottleneck bandwidth.

  11. Centrality Robustness and Link Prediction in Complex Social Networks

    DEFF Research Database (Denmark)

    Davidsen, Søren Atmakuri; Ortiz-Arroyo, Daniel

    2012-01-01

    . Secondly, we present a method to predict edges in dynamic social networks. Our experimental results indicate that the robustness of the centrality measures applied to more realistic social networks follows a predictable pattern and that the use of temporal statistics could improve the accuracy achieved......This chapter addresses two important issues in social network analysis that involve uncertainty. Firstly, we present am analysis on the robustness of centrality measures that extend the work presented in Borgati et al. using three types of complex network structures and one real social network...

  12. Bridging the gap between traffic generated health stressors in urban areas: Predicting xylene levels in EU cities

    International Nuclear Information System (INIS)

    Vlachokostas, Ch.; Michailidou, A.V.; Spyridi, D.; Moussiopoulos, N.

    2013-01-01

    Many citizens live, work, commute, or visit traffic intensive spaces and are exposed to high levels of chemical health stressors. However, urban conurbations worldwide present monitoring “shortage” – due to economical and/or practical constraints – for toxic stressors such as xylene isomers, which can pose human health risks. This “shortage” may be covered by the establishment of associations between rarely monitored substances such as xylenes and more frequently monitored (i.e. benzene) or usually monitored (i.e. CO). Regression analysis is used and strong statistical relationships are detected. The adopted models are applied to EU cities and comparison between measurements and predictions depicts their representativeness. The analysis provides transferability insights in an effort to bridge the gap between traffic-related stressors. Strong associations between substances of the air pollution mixture may be influential to interpret the complexity of the causal chain, especially if a synergetic exposure assessment in traffic intensive spaces is considered. -- Highlights: •EU cities present monitoring shortage for health stressors such as xylenes. •The multi-stressor multi-city stepwise regression modelling approach is presented. •Strong linear relationships between xylenes and toluene, benzene, CO are detected. •Modelling results are in good agreement with the respective available measurements. •Toluene seems the optimal marker to predict xylene trends in traffic environments. -- The multi-stressor, multi-city stepwise regression modelling approach develops reliable statistical associations which capture m,p-xylene and o-xylene trends in EU traffic intensive environments

  13. Can the traffic locus of control (T-LOC) scale be successfully used to predict Swedish drivers' speeding behaviour?

    Science.gov (United States)

    Warner, Henriette Wallén; Ozkan, Türker; Lajunen, Timo

    2010-07-01

    The first aim of the present study was to examine the factor structure of the traffic locus of control (T-LOC) scale in a Swedish sample of drivers. The second aim was to examine if this scale can be used to predict drivers' speeding behaviour. A sample of Swedish car owners (N=223) completed a questionnaire including questions based on the traffic locus of control (T-LOC) scale as well as questions about their speeding behaviour. The results showed a five factor solution including own skills, own behaviour, other drivers, vehicle/environment and fate. Own behaviour and vehicle/environment could be used to predict drivers' speeding behaviour on roads with a 90 km/h speed limit while none of the variables included in the traffic locus of control (T-LOC) scale could be used to predict drivers' speeding behaviour on roads with a 50 km/h speed limit. On 90 km/h roads own behaviour was positively related to drivers' speeding behaviour while vehicle/environment was negatively related to their speeding behaviour. Copyright 2010 Elsevier Ltd. All rights reserved.

  14. Using the Weighted Rich-Club Coefficient to Explore Traffic Organization in Mobility Networks

    Science.gov (United States)

    Ramasco, José J.; Colizza, Vittoria; Panzarasa, Pietro

    The aim of a transportation system is to enable the movement of goods or persons between any two locations with the highest possible efficiency. This simple principle inspires highly complex structures in a number of real-world mobility networks of different kind that often exhibit a hierarchical organization. In this paper, we rely on a framework that has been recently introduced for the study of the management and distribution of resources in different real-world systems. This framework offers a new method for exploring the tendency of the top elements to form clubs with exclusive control over the system’s resources. Such tendency is known as the weighted rich-club effect. We apply the method to three cases of mobility networks at different scales of resolution: the US air transportation network, the US counties daily commuting, and the Italian municipalities commuting datasets. In all cases, a strong weighted rich-club effect is found. We also show that a very simple model can account for part of the intrinsic features of mobility networks, while deviations found between the theoretical predictions and the empirical observations point to the presence of higher levels of organization.

  15. Link Prediction Methods and Their Accuracy for Different Social Networks and Network Metrics

    Directory of Open Access Journals (Sweden)

    Fei Gao

    2015-01-01

    Full Text Available Currently, we are experiencing a rapid growth of the number of social-based online systems. The availability of the vast amounts of data gathered in those systems brings new challenges that we face when trying to analyse it. One of the intensively researched topics is the prediction of social connections between users. Although a lot of effort has been made to develop new prediction approaches, the existing methods are not comprehensively analysed. In this paper we investigate the correlation between network metrics and accuracy of different prediction methods. We selected six time-stamped real-world social networks and ten most widely used link prediction methods. The results of the experiments show that the performance of some methods has a strong correlation with certain network metrics. We managed to distinguish “prediction friendly” networks, for which most of the prediction methods give good performance, as well as “prediction unfriendly” networks, for which most of the methods result in high prediction error. Correlation analysis between network metrics and prediction accuracy of prediction methods may form the basis of a metalearning system where based on network characteristics it will be able to recommend the right prediction method for a given network.

  16. Call Arrival Rate Prediction and Blocking Probability Estimation for Infrastructure based Mobile Cognitive Radio Personal Area Network

    Directory of Open Access Journals (Sweden)

    Neeta Nathani

    2017-08-01

    Full Text Available The Cognitive Radio usage has been estimated as non-emergency service with low volume traffic. Present work proposes an infrastructure based Cognitive Radio network and probability of success of CR traffic in licensed band. The Cognitive Radio nodes will form cluster. The cluster nodes will communicate on Industrial, Scientific and Medical band using IPv6 over Low-Power Wireless Personal Area Network based protocol from sensor to Gateway Cluster Head. For Cognitive Radio-Media Access Control protocol for Gateway to Cognitive Radio-Base Station communication, it will use vacant channels of licensed band. Standalone secondary users of Cognitive Radio Network shall be considered as a Gateway with one user. The Gateway will handle multi-channel multi radio for communication with Base Station. Cognitive Radio Network operators shall define various traffic data accumulation counters at Base Station for storing signal strength, Carrier-to-Interference and Noise Ratio, etc. parameters and record channel occupied/vacant status. The researches has been done so far using hour as interval is too long for parameters like holding time expressed in minutes and hence channel vacant/occupied status time is only probabilistically calculated. In the present work, an infrastructure based architecture has been proposed which polls channel status each minute in contrary to hourly polling of data. The Gateways of the Cognitive Radio Network shall monitor status of each Primary User periodically inside its working range and shall inform to Cognitive Radio- Base Station for preparation of minutewise database. For simulation, the occupancy data for all primary user channels were pulled in one minute interval from a live mobile network. Hourly traffic data and minutewise holding times has been analyzed to optimize the parameters of Seasonal Auto Regressive Integrated Moving Average prediction model. The blocking probability of an incoming Cognitive Radio call has been

  17. Financial time series prediction using spiking neural networks.

    Science.gov (United States)

    Reid, David; Hussain, Abir Jaafar; Tawfik, Hissam

    2014-01-01

    In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.

  18. Predicting Employee Turnover from Communication Networks.

    Science.gov (United States)

    Feeley, Thomas H.; Barnett, George A.

    1997-01-01

    Investigates three social network models of employee turnover: a structural equivalence model, a social influence model, and an erosion model. Administers a communication network questionnaire to all 170 employees of an organization. Finds support for all three models of turnover, with the erosion model explaining more of the variance than do the…

  19. Data mining tools for the support of traffic signal timing plan development in arterial networks

    Science.gov (United States)

    2001-05-01

    Intelligent transportation systems (ITS) include large numbers of traffic sensors that collect enormous quantities of data. The data provided by ITS is necessary for advanced forms of control; however, basic forms of control, primarily time-of-day (T...

  20. Characterizing and predicting the robustness of power-law networks

    International Nuclear Information System (INIS)

    LaRocca, Sarah; Guikema, Seth D.

    2015-01-01

    Power-law networks such as the Internet, terrorist cells, species relationships, and cellular metabolic interactions are susceptible to node failures, yet maintaining network connectivity is essential for network functionality. Disconnection of the network leads to fragmentation and, in some cases, collapse of the underlying system. However, the influences of the topology of networks on their ability to withstand node failures are poorly understood. Based on a study of the response of 2000 randomly-generated power-law networks to node failures, we find that networks with higher nodal degree and clustering coefficient, lower betweenness centrality, and lower variability in path length and clustering coefficient maintain their cohesion better during such events. We also find that network robustness, i.e., the ability to withstand node failures, can be accurately predicted a priori for power-law networks across many fields. These results provide a basis for designing new, more robust networks, improving the robustness of existing networks such as the Internet and cellular metabolic pathways, and efficiently degrading networks such as terrorist cells. - Highlights: • Examine relationship between network topology and robustness to failures. • Relationship is statistically significant for scale-free networks. • Use statistical models to estimate robustness to failures for real-world networks

  1. The Algorithm of Link Prediction on Social Network

    Directory of Open Access Journals (Sweden)

    Liyan Dong

    2013-01-01

    Full Text Available At present, most link prediction algorithms are based on the similarity between two entities. Social network topology information is one of the main sources to design the similarity function between entities. But the existing link prediction algorithms do not apply the network topology information sufficiently. For lack of traditional link prediction algorithms, we propose two improved algorithms: CNGF algorithm based on local information and KatzGF algorithm based on global information network. For the defect of the stationary of social network, we also provide the link prediction algorithm based on nodes multiple attributes information. Finally, we verified these algorithms on DBLP data set, and the experimental results show that the performance of the improved algorithm is superior to that of the traditional link prediction algorithm.

  2. Predictive power control in wireless sensor networks

    NARCIS (Netherlands)

    Chincoli, M.; Syed, Aly; Mocanu, D.C.; Liotta, A.

    2016-01-01

    Communications in Wireless Sensor Networks (WSNs) are affected by dynamic environments, variable signal fluctuations and interference. Thus, prompt actions are necessary to achieve dependable communications and meet quality of service requirements. To this end, the reactive algorithms used in

  3. water demand prediction using artificial neural network

    African Journals Online (AJOL)

    user

    2017-01-01

    Jan 1, 2017 ... Interface for activation and deactivation of valves. •. Interface demand ... process could be done and monitored at the computer terminal as expected of a .... [15] Arbib, M. A.The Handbook of Brain Theory and Neural. Networks.

  4. CNNcon: improved protein contact maps prediction using cascaded neural networks.

    Directory of Open Access Journals (Sweden)

    Wang Ding

    Full Text Available BACKGROUNDS: Despite continuing progress in X-ray crystallography and high-field NMR spectroscopy for determination of three-dimensional protein structures, the number of unsolved and newly discovered sequences grows much faster than that of determined structures. Protein modeling methods can possibly bridge this huge sequence-structure gap with the development of computational science. A grand challenging problem is to predict three-dimensional protein structure from its primary structure (residues sequence alone. However, predicting residue contact maps is a crucial and promising intermediate step towards final three-dimensional structure prediction. Better predictions of local and non-local contacts between residues can transform protein sequence alignment to structure alignment, which can finally improve template based three-dimensional protein structure predictors greatly. METHODS: CNNcon, an improved multiple neural networks based contact map predictor using six sub-networks and one final cascade-network, was developed in this paper. Both the sub-networks and the final cascade-network were trained and tested with their corresponding data sets. While for testing, the target protein was first coded and then input to its corresponding sub-networks for prediction. After that, the intermediate results were input to the cascade-network to finish the final prediction. RESULTS: The CNNcon can accurately predict 58.86% in average of contacts at a distance cutoff of 8 Å for proteins with lengths ranging from 51 to 450. The comparison results show that the present method performs better than the compared state-of-the-art predictors. Particularly, the prediction accuracy keeps steady with the increase of protein sequence length. It indicates that the CNNcon overcomes the thin density problem, with which other current predictors have trouble. This advantage makes the method valuable to the prediction of long length proteins. As a result, the effective

  5. Using machine learning, neural networks and statistics to predict bankruptcy

    NARCIS (Netherlands)

    Pompe, P.P.M.; Feelders, A.J.; Feelders, A.J.

    1997-01-01

    Recent literature strongly suggests that machine learning approaches to classification outperform "classical" statistical methods. We make a comparison between the performance of linear discriminant analysis, classification trees, and neural networks in predicting corporate bankruptcy. Linear

  6. Neural Network Predictions of the 4-Quadrant Wageningen Propeller Series

    National Research Council Canada - National Science Library

    Roddy, Robert F; Hess, David E; Faller, Will

    2006-01-01

    .... This report describes the development of feedforward neural network (FFNN) predictions of four-quadrant thrust and torque behavior for the Wageningen B-Screw Series of propellers and for two Wageningen ducted propeller series...

  7. Ocean wave prediction using numerical and neural network models

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Prabaharan, N.

    This paper presents an overview of the development of the numerical wave prediction models and recently used neural networks for ocean wave hindcasting and forecasting. The numerical wave models express the physical concepts of the phenomena...

  8. Predicting drunk driving: contribution of alcohol use and related problems, traffic behaviour, personality and platelet monoamine oxidase (MAO) activity.

    Science.gov (United States)

    Eensoo, Diva; Paaver, Marika; Harro, Maarike; Harro, Jaanus

    2005-01-01

    The aim of the study was to characterize the predictive value of socio-economic data, alcohol consumption measures, smoking, platelet monoamine oxidase (MAO) activity, traffic behaviour habits and impulsivity measures for actual drunk driving. Data were collected from 203 male drunk driving offenders and 211 control subjects using self-reported questionnaires, and blood samples were obtained from the two groups. We identified the combination of variables, which predicted correctly, approximately 80% of the subjects' belonging to the drunk driving and control groups. Significant independent discriminators in the final model were, among the health-behaviour measures, alcohol-related problems, frequency of using alcohol, the amount of alcohol consumed and smoking. Predictive traffic behaviour measures were seat belt use and paying for parking. Among the impulsivity measures, dysfunctional impulsivity was the best predictor; platelet MAO activity and age also had an independent predictive value. Our results support the notion that drunk driving is the result of a combination of various behavioural, biological and personality-related risk factors.

  9. Congestion Prediction Modeling for Quality of Service Improvement in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Ga-Won Lee

    2014-04-01

    Full Text Available Information technology (IT is pushing ahead with drastic reforms of modern life for improvement of human welfare. Objects constitute “Information Networks” through smart, self-regulated information gathering that also recognizes and controls current information states in Wireless Sensor Networks (WSNs. Information observed from sensor networks in real-time is used to increase quality of life (QoL in various industries and daily life. One of the key challenges of the WSNs is how to achieve lossless data transmission. Although nowadays sensor nodes have enhanced capacities, it is hard to assure lossless and reliable end-to-end data transmission in WSNs due to the unstable wireless links and low hard ware resources to satisfy high quality of service (QoS requirements. We propose a node and path traffic prediction model to predict and minimize the congestion. This solution includes prediction of packet generation due to network congestion from both periodic and event data generation. Simulation using NS-2 and Matlab is used to demonstrate the effectiveness of the proposed solution.

  10. Financial Time Series Prediction Using Elman Recurrent Random Neural Networks

    Directory of Open Access Journals (Sweden)

    Jie Wang

    2016-01-01

    (ERNN, the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.

  11. Prediction of Electricity Usage Using Convolutional Neural Networks

    OpenAIRE

    Hansen, Martin

    2017-01-01

    Master's thesis Information- and communication technology IKT590 - University of Agder 2017 Convolutional Neural Networks are overwhelmingly accurate when attempting to predict numbers using the famous MNIST-dataset. In this paper, we are attempting to transcend these results for time- series forecasting, and compare them with several regression mod- els. The Convolutional Neural Network model predicted the same value through the entire time lapse in contrast with the other ...

  12. Cloudified Mobility and Bandwidth Prediction in Virtualized LTE Networks

    NARCIS (Netherlands)

    Zhao, Zongliang; Karimzadeh Motallebi Azar, Morteza; Braun, Torsten; Pras, Aiko; van den Berg, Hans Leo

    Network Function Virtualization involves implementing network functions (e.g., virtualized LTE component) in software that can run on a range of industry standard server hardware, and can be migrated or instantiated on demand. A prediction service hosted on cloud infrastructures enables consumers to

  13. Prediction of littoral drift with artificial neural networks

    Digital Repository Service at National Institute of Oceanography (India)

    Singh, A.K.; Deo, M.C.; SanilKumar, V.

    of the rate of sand drift has still remained as a problem. The current study addresses this issue through the use of artificial neural networks (ANN). Feed forward networks were developed to predict the sand drift from a variety of causative variables...

  14. The predictive validity of personality tests in air traffic controller selection

    NARCIS (Netherlands)

    Roe, R.A.; Oprins, E.A.P.B.; Geven, E.

    2012-01-01

    A brief historical review of test methods used for selecting air traffic controllers (ATCOs) shows that in contrast to e.g. ability tests and job samples, personality tests have been used rather infrequently. The lesser popularity of personality tests may be explained from the belief that

  15. Vehicle-class Specific Route-guidance of Freeway Traffic by Model-predictive Control

    NARCIS (Netherlands)

    Schreiter, T.; Landman, R.L.; Van Lint, J.W.C.; Hegyi, A.; Hoogendoorn, S.P.

    2012-01-01

    Few Active Traffic Management measures proposed in the past consider the distinction of different vehicle classes. Examples of vehicle-class specific measures are truck lanes and high-occupancy/toll (HOT) lanes. We propose that the distinction of different vehicle classes, with different flow

  16. Predicting Positive and Negative Relationships in Large Social Networks.

    Directory of Open Access Journals (Sweden)

    Guan-Nan Wang

    Full Text Available In a social network, users hold and express positive and negative attitudes (e.g. support/opposition towards other users. Those attitudes exhibit some kind of binary relationships among the users, which play an important role in social network analysis. However, some of those binary relationships are likely to be latent as the scale of social network increases. The essence of predicting latent binary relationships have recently began to draw researchers' attention. In this paper, we propose a machine learning algorithm for predicting positive and negative relationships in social networks inspired by structural balance theory and social status theory. More specifically, we show that when two users in the network have fewer common neighbors, the prediction accuracy of the relationship between them deteriorates. Accordingly, in the training phase, we propose a segment-based training framework to divide the training data into two subsets according to the number of common neighbors between users, and build a prediction model for each subset based on support vector machine (SVM. Moreover, to deal with large-scale social network data, we employ a sampling strategy that selects small amount of training data while maintaining high accuracy of prediction. We compare our algorithm with traditional algorithms and adaptive boosting of them. Experimental results of typical data sets show that our algorithm can deal with large social networks and consistently outperforms other methods.

  17. Predicting Positive and Negative Relationships in Large Social Networks.

    Science.gov (United States)

    Wang, Guan-Nan; Gao, Hui; Chen, Lian; Mensah, Dennis N A; Fu, Yan

    2015-01-01

    In a social network, users hold and express positive and negative attitudes (e.g. support/opposition) towards other users. Those attitudes exhibit some kind of binary relationships among the users, which play an important role in social network analysis. However, some of those binary relationships are likely to be latent as the scale of social network increases. The essence of predicting latent binary relationships have recently began to draw researchers' attention. In this paper, we propose a machine learning algorithm for predicting positive and negative relationships in social networks inspired by structural balance theory and social status theory. More specifically, we show that when two users in the network have fewer common neighbors, the prediction accuracy of the relationship between them deteriorates. Accordingly, in the training phase, we propose a segment-based training framework to divide the training data into two subsets according to the number of common neighbors between users, and build a prediction model for each subset based on support vector machine (SVM). Moreover, to deal with large-scale social network data, we employ a sampling strategy that selects small amount of training data while maintaining high accuracy of prediction. We compare our algorithm with traditional algorithms and adaptive boosting of them. Experimental results of typical data sets show that our algorithm can deal with large social networks and consistently outperforms other methods.

  18. Link Label Prediction in Signed Citation Network

    KAUST Repository

    Akujuobi, Uchenna Thankgod

    2016-01-01

    such as using regression, trust propagation and matrix factorization. These approaches have tried to solve the problem of link label prediction by using ideas from social theories, where most of them predict a single missing label given that labels of other

  19. The Road Traffic Injuries Research Network: a decade of research capacity strengthening in low- and middle-income countries.

    Science.gov (United States)

    Hyder, Adnan A; Norton, Robyn; Pérez-Núñez, Ricardo; Mojarro-Iñiguez, Francisco R; Peden, Margie; Kobusingye, Olive

    2016-02-27

    Road traffic crashes have been an increasing threat to the wellbeing of road users worldwide; an unacceptably high number of people die or become disabled from them. While high-income countries have successfully implemented effective interventions to help reduce the burden of road traffic injuries (RTIs) in their countries, low- and middle-income countries (LMICs) have not yet achieved similar results. Both scientific research and capacity development have proven to be useful for preventing RTIs in high-income countries. In 1999, a group of leading researchers from different countries decided to join efforts to help promote research on RTIs and develop the capacity of professionals from LMICs. This translated into the creation of the Road Traffic Injuries Research Network (RTIRN) - a partnership of over 1,100 road safety professionals from 114 countries collaborating to facilitate reductions in the burden of RTIs in LMICs by identifying and promoting effective, evidenced-based interventions and supporting research capacity building in road safety research in LMICs. This article presents the work that RTIRN has done over more than a decade, including production of a dozen scientific papers, support of nearly 100 researchers, training of nearly 1,000 people and 35 scholarships granted to researchers from LMICs to attend world conferences, as well as lessons learnt and future challenges to maximize its work.

  20. Energy-efficient multicast traffic grooming strategy based on light-tree splitting for elastic optical networks

    Science.gov (United States)

    Liu, Huanlin; Yin, Yarui; Chen, Yong

    2017-07-01

    In order to address the problem of optimizing the spectrum resources and power consumption in elastic optical networks (EONs), we investigate the potential gains by jointly employing the light-tree splitting and traffic grooming for multicast requests. An energy-efficient multicast traffic grooming strategy based on light-tree splitting (EED-MTGS-LS) is proposed in this paper. Firstly, we design a traffic pre-processing mechanism to decide the multicast requests' routing order, which considers the request's bandwidth requirement and physical hops synthetically. Then, by dividing a light-tree to some sub-light-trees and grooming the request to these sub-light-trees, the light-tree sharing ratios of multicast requests can be improved. What's more, a priority scheduling vector is constructed, which aims to improve the success rate of spectrum assignment for grooming requests. Finally, a grooming strategy is designed to optimize the total power consumption by reducing the use of transponders and IP routers during routing. Simulation results show that the proposed strategy can significantly improve the spectrum utilization and save the power consumption.