Shinkarev, A. A.
This paper describes continuation of the authors’ work in the field of traffic flow mathematical models based on the cellular automata theory. The refactored representation of the multifactorial traffic flow model based on the cellular automata theory is used for a representation of an adaptive deceleration step implementation. The adaptive deceleration step in the case of a leader deceleration allows slowing down smoothly but not instantly. Concepts of the number of time steps without confli...
Despite the bursty and highly volatile traffic, routing in the Internet today is optimised only on coarse time scales, as load-adaptive routing is known to induce performance deterioration by causing massive oscillations. We describe ReplEx, an universally applicable distributed algorithm for dynamic routing/traffic engineering, which is based on game theory. We show through extensive realistic simulations that ReplEx does not oscillate, and that it achieves performance gains comparable to tr...
Meng Chi; Shufen Liu; Changhong Hu
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 strat...
Singh, Manoj Kumar
Because of the stochastic nature of traffic requirement matrix, it is very difficult to get the optimal traffic distribution to minimize the delay even with adaptive routing protocol in a fixed connection network where capacity already defined for each link. Hence there is a requirement to define such a method, which could generate the optimal solution very quickly and efficiently. This paper presenting a new concept to provide the adaptive optimal traffic distribution for dynamic condition of traffic matrix using nature based intelligence methods. With the defined load and fixed capacity of links, average delay for packet has minimized with various variations of evolutionary programming and particle swarm optimization. Comparative study has given over their performance in terms of converging speed. Universal approximation capability, the key feature of feed forward neural network has applied to predict the flow distribution on each link to minimize the average delay for a total load available at present on t...
de Gier, Jan; Rojas, Omar
We present a model of traffic flow on generic urban road networks based on cellular automata. We apply this model to an existing road network in the Australian city of Melbourne, using empirical data as input. For comparison, we also apply this model to a square-grid network using hypothetical input data. On both networks we compare the effects of non-adative vs adaptive traffic lights, in which instantaneous traffic state information feeds back into the traffic signal schedule. We observe that not only do adaptive traffic lights result in better averages of network observables, they also lead to significantly smaller fluctuations in these observables. We furthermore compare two different systems of adaptive traffic signals, one which is informed by the traffic state on both upstream and downstream links, and one which is informed by upstream links only. We find that, in general, the total travel time is smallest when using the joint upstream-downstream control strategy.
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.
Aly, Salah A.; Ansari, Nirwan; Walid, Anwar I.; Poor, H. Vincent
There have been several approaches to provisioning traffic between core network nodes in Internet Service Provider (ISP) networks. Such approaches aim to minimize network delay, increase network capacity, and enhance network security services. MATE (Multipath Adaptive Traffic Engineering) protocol has been proposed for multipath adaptive traffic engineering between an ingress node (source) and an egress node (destination). Its novel idea is to avoid network congestion and attacks that might e...
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.
Wang, N.; Ho, KH; Pavlou, G
Handling traffic dynamics in order to avoid network congestion and subsequent service disruptions is one of the key tasks performed by contemporary network management systems. Given the simple but rigid routing and forwarding functionalities in IP base environments, efficient resource management and control solutions against dynamic traffic conditions is still yet to be obtained. In this article, we introduce AMPLE - an efficient traffic engineering and management system that performs adaptiv...
Czura, Guillaume; Taillandier, Patrick; Tranouez, Pierrick; Daudé, Éric
International audience In this paper, we present MOSAIIC, an agent-based model to simulate the road traffic of a city in the context of a catastrophic event. Whether natural (cyclone, earthquake, flood) or human (industrial accident) in origin, catastrophic situations modify both infrastructures (buildings, road networks) and human behaviors, which can have a huge impact on human safety. Because the heterogeneities of human behaviors, of land- uses and of network topology have a great impa...
Full Text Available Current traffic lights are pre-programmed and use daily signal timing schedules, which contribute to traffic congestion and delay. Thus, with the increase in the number of vehicles on road, need for adaptive signal technology arises which has the potential to adjust the timing of red, yellow and green lights in order to accommodate changing traffic patterns and ease traffic congestion. In this paper, we present a model for adaptive traffic signalization, which uses fuzzy neural network for designing traffic signal controller. The controllers use vehicle detectors in order to detect the number of incoming vehicles. Based on the number of approaching vehicles, the current signal phase is either extended or terminated. The traffic volume at one particular region in an intersection is compared with that in the competing regions of the same intersection. The decision made is thus robust and results in less congestion and delays.
Krogh, Benjamin Bjerre; Andersen, Ove; Lewis-Kelham, Edwin;
We present the INTRA system for interactive path-based traffic analysis. The analyses are developed in collaboration with traffic researchers and provide novel insights into conditions such as congestion, travel-time, choice of route, and traffic-flow. INTRA supports interactive point-and-click a......We present the INTRA system for interactive path-based traffic analysis. The analyses are developed in collaboration with traffic researchers and provide novel insights into conditions such as congestion, travel-time, choice of route, and traffic-flow. INTRA supports interactive point......-and-click analysis, due to a novel and efficient indexing structure. With the web-site daisy.aau.dk/its/spqdemo/we will demonstrate several analyses, using a very large real-world data set consisting of 1.9 billion GPS records (1.5 million trajectories) recorded from more than 13000 vehicles, and touching most of...
Cordier, A.; Domingues, Rémi; Labaere, Anthony; Noel, Nicolas; Thiery, Adrien; Cerqueus, Thomas; Perry, Philip; Ventresque, Anthony; et al.
This paper demonstrates how we applied a constraint-based dynamic adaptation approach on CarDemo, a traffic management system. The approach allows domain experts to describe the adaptation goals as declarative constraints, and automatically plan the adaptation decisions to satisfy these constraints. We demonstrate how to utilise this approach to realise the dynamic switch of routing services of the traffic management system, according to the change of global system states and user requests.
Full Text Available Back pressure-based adaptive routing algorithms where each packet is routed along a possibly different pathhave been extensively studied in the literature. However, suchalgorithms typically result in poor delay performance and involvehigh implementation complexity. In this paper, we develop anew adaptive routing algorithm built upon the widely-studiedback-pressure algorithm. We decouple the routing and schedulingcomponents of the algorithm by designing a probabilistic routingtable which is used to route packets to per-destination queues.The scheduling decisions in the case of wireless networks aremade using counters called shadow queues. The results arealso extended to the case of networks which employ simpleforms of network coding. In that case, our algorithm provides alow-complexity solution to optimally exploit the routing-codingtrade-off.
<正>Bus dispatching has been studied,and also the bus dispatching model is set up.Then,Genetic Algorithm is adaptively improved in order to avoid premature problem and the slow convergence,and then the keeping optimal strategy is used to the Genetic Algorithm,so formed the Improved Adaptive Genetic Algorithm,namely IAGA. Finally,the IAGA is used to optimizing the bus dispatching model,and the results of the simulation indicate IAGA has the higher efficiency than simple GA and is one effective way to optimizing the bus dispatching.
Chabchoub, Yousra; Guillemin, Fabrice; Robert, Philippe
We develop in this paper an adaptive algorithm based on Bloom filters in order to identify large flows. While most algorithms proposed so far in the technical literature rely on a periodic erasure of the Bloom filter, we propose in this paper to progressively decrement the various counters of the filter according to some overload criteria. When tested against real traffic traces, the proposed algorithm performs well in the sense that a high percentage of large flows in traffic are detected by the algorithm. In order to improve the accuracy of the algorithm, we introduce a shadow Bloom filter, which is less frequently decremented so that elephants have more chance of being identified. Since elephant detection issue is very close to flood attack detection, we adapt the proposed algorithm in order to detect SYN and volume flood attack in Internet traffic. The attack detection algorithm is tested against traffic traces from France Telecom collect and transit networks. Some performance issues are finally discussed...
Devesh Batra; Pragya Verma
Current traffic lights are pre-programmed and use daily signal timing schedules, which contribute to traffic congestion and delay. Thus, with the increase in the number of vehicles on road, need for adaptive signal technology arises which has the potential to adjust the timing of red, yellow and green lights in order to accommodate changing traffic patterns and ease traffic congestion. In this paper, we present a model for adaptive traffic signalization, which uses fuzzy neura...
为了减少车辆通过路口的延误,提出了一种基于云模型的单路口交通信号自适应控制方法；使用云模型作为信号控制的基础模型,利用云模型中的正态云发生器和前件云发生器算法分别对道路交通信息进行处理并产生自适应的控制规则,以实现单路口交通信号的自适应控制；通过仿真实验,结果表明,使用云模型作为控制方法,比较传统控制方式更具智能化,更接近于人脑思维过程的控制方法,这也是将来交通信号控制的发展方向.%In order to reduce traffic delays through the intersection, this paper presents, a single intersection based on cloud model adaptive traffic signal control. Signal control using the cloud model as the basis model, the cloud model in the normal cloud generator and the former pieces of cloud generator algorithms are processed on road traffic information and generates adaptive control rules in order to achieve a single self-intersection traffic signal adaptive control. The simulation experiment results show that the control method using the cloud model as the more traditional control method is more intelligent, more close to the brain control the process of thinking, this is the future direction of development of traffic signal control.
Sameera Pallavi; Ch.Sandeep; P.Pramod Kumar
Network management systems are to handle traffic dynamics in order to ensure congestion free network with highest throughput. IP environments are able to provide simple facilities for forwarding and routing packets. However, in presence of dynamic traffic conditions efficient management of resources is yet to be achieved. Recently Ning Wang et al. proposed a traffic engineering system which can ynamically adapt to traffic conditions with the help of virtual routing topologies. It has two majo...
Full Text Available Preceding vehicle detection and tracking at nighttime are challenging problems due to the disturbance of other extraneous illuminant sources coexisting with the vehicle lights. To improve the detection accuracy and robustness of vehicle detection, a novel method for vehicle detection and tracking at nighttime is proposed in this paper. The characteristics of taillights in the gray level are applied to determine the lower boundary of the threshold for taillights segmentation, and the optimal threshold for taillight segmentation is calculated using the OTSU algorithm between the lower boundary and the highest grayscale of the region of interest. The candidate taillight pairs are extracted based on the similarity between left and right taillights, and the non-vehicle taillight pairs are removed based on the relevance analysis of vehicle location between frames. To reduce the false negative rate of vehicle detection, a vehicle tracking method based on taillights estimation is applied. The taillight spot candidate is sought in the region predicted by Kalman filtering, and the disturbed taillight is estimated based on the symmetry and location of the other taillight of the same vehicle. Vehicle tracking is completed after estimating its location according to the two taillight spots. The results of experiments on a vehicle platform indicate that the proposed method could detect vehicles quickly, correctly and robustly in the actual traffic environments with illumination variation.
Montero Mercadé, Lídia; Barceló Bugeda, Jaime; Codina Sancho, Esteve
The primary data input used in principal traffic models comes from Origin-Destination (OD) trip matrices, which describe the patterns of commuters across the network. In this way, OD matrices become a critical requirement in Advanced Transport Control and Management and/or Information Systems that are supported by Dynamic Traffic Assignment models (DTA models). Dynamic Transit Assignment models are a research topic, but once a dynamic transit assignment be available to practitioners, the prob...
Oliver, Iain Angus
Multi User Virtual Environments (MUVE) are a new class of Internet application with a significant user base. This thesis adds to our understanding of how MUVE network traffic fits into the mix of Internet traffic, and how this relates to the application's needs. MUVEs differ from established Internet traffic types in their requirements from the network. They differ from traditional data traffic in that they have soft real-time constraints, from game traffic in that their bandwidth requi...
Marakhimov, A. R.; Igamberdiev, H. Z.; Umarov, Sh. X.
This paper considers the problem of building adaptive fuzzy-logic traffic control systems (AFLTCS) to deal with information fuzziness and uncertainty in case of heavy traffic streams. Methods of formal description of traffic control on the crossroads based on fuzzy sets and fuzzy logic are proposed. This paper also provides efficient algorithms for implementing AFLTCS and develops the appropriate simulation models to test the efficiency of suggested approach. PMID:27517081
Anesa Maolod Omar Al-Najeh
Full Text Available The packet loss has become an important issue to the research community, which needs to be addressed. In FMIPv6, Packet losses are significantly related to the handover latency and buffer size used for packet buffering. In the case of increased handover latency or decreased buffer size, packet losses will be increased. To solve the problem, we propose an adaptive packet buffering (APT algorithm based on priority of packets and traffic throughput in layer 3 (L3 were the packets are buffered by the predefined rule in the new access point during handover. This algorithm is designed to reduce packet loss in FMIPv6 and high level of throughput and low delay can be achieved through the proposed technique. To achieve a fair comparison with Adaptive Buffer Limit Tuning (ALT algorithm, we have implemented the APT algorithm in Omnet++ along with the FMIPv6 to develop the model and the algorithm. The results of the simulation study show that the proposed algorithm can reduce the packet loss as well as the delay.
Wanscher, Jørgen Bundgaard
vehicle can be obtained through cellular phone tracking or GPS systems. This information can then be used to provide individual traffic guidance as opposed to the mass information systems of today -- dynamic roadsigns and trafficradio. The goal is to achieve better usage of road and time. The main topic......When working with traffic planning or guidance it is common practice to view the vehicles as a combined mass. >From this models are employed to specify the vehicle supply and demand for each region. As the models are complex and the calculations are equally demanding the regions and the detail of...... the road network is aggregated. As a result the calculations reveal only what the mass of vehicles are doing and not what a single vehicle is doing. This is the crucial difference to ABIT (Agent Based Individual Trafficguidance). ABIT is based on the fact that information on the destination of each...
This thesis investigates the possibilities in applying Operations Research (OR) to autonomous vehicular traffic. The explicit difference to most other research today is that we presume that an agent is present in every vehicle - hence Agent Based Individual Traffic guidance (ABIT). The next...... that the system can be divided into two separate constituents. The immediate dispersion, which is used for small areas and quick response, and the individual alleviation, which considers the longer distance decision support. Both of these require intrinsicate models and cost functions which at the...... beginning of the project were not previously considered. We define a special inseparable cost function and develop a solution complex capable of using this cost function. In relation to calibration and estimation of statistical models used for dynamic route guidance we worked with generating random number...
Callantine, Todd J.
This report describes intelligent agents that function as air traffic controllers. Each agent controls traffic in a single sector in real time; agents controlling traffic in adjoining sectors can coordinate to manage an arrival flow across a given meter fix. The purpose of this research is threefold. First, it seeks to study the design of agents for controlling complex systems. In particular, it investigates agent planning and reactive control functionality in a dynamic environment in which a variety perceptual and decision making skills play a central role. It examines how heuristic rules can be applied to model planning and decision making skills, rather than attempting to apply optimization methods. Thus, the research attempts to develop intelligent agents that provide an approximation of human air traffic controller behavior that, while not based on an explicit cognitive model, does produce task performance consistent with the way human air traffic controllers operate. Second, this research sought to extend previous research on using the Crew Activity Tracking System (CATS) as the basis for intelligent agents. The agents use a high-level model of air traffic controller activities to structure the control task. To execute an activity in the CATS model, according to the current task context, the agents reference a 'skill library' and 'control rules' that in turn execute the pattern recognition, planning, and decision-making required to perform the activity. Applying the skills enables the agents to modify their representation of the current control situation (i.e., the 'flick' or 'picture'). The updated representation supports the next activity in a cycle of action that, taken as a whole, simulates air traffic controller behavior. A third, practical motivation for this research is to use intelligent agents to support evaluation of new air traffic control (ATC) methods to support new Air Traffic Management (ATM) concepts. Current approaches that use large, human
Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming
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.
Al-Naima, Fawzi M.; Hamd, Hassan A.
Due to the proliferation in the number of vehicles on the road, traffic problems are bound to exist. Therefore, the use of Intelligent Transportation Systems (ITS) has become mandatory for obtaining traffic information from roads. Radio Frequency Identification (RFID) technology has been used to obtain vehicles’ IDs (tag ID) from RFID readers and to collect traffic information in real‐time. This paper proposes a simulation system for the Vehicle Traffic Congestion Estimation (VTCE) based on R...
City intersection traffic signal control is an important method to improve the efficiency of road network and alleviate traffic congestion. This paper researches traffic signal fuzzy control method on a single intersection. A two-stage traffic signal control method based on traffic urgency degree is proposed according to two-stage fuzzy inference on single intersection. At the first stage, calculate traffic urgency degree for all red phases using traffic urgency evaluation module and select t...
To achieve the optimal allocation of urban road resources,a simulation research of self-adaptive traffic control system based on independent intersections in the LabVIEW software platform has been carried out.This system controls run and stop of the vehicle by its traffic lights ,makes self-adaptive changes according to current traffic flow and saturation rate of various roads,adjusts the running cycle of traffic lights,and corrects green light ratios of all directions,improves the efficiency of the intersection in unit time.Test results show that,this simulation system runs stability,achieves the design requirement of the simulation research,can provide theoretical references to urban traffic monitoring and controlling and decision bases to urban transport organization and management.%为实现城市道路资源优化配置,在LabVIEW软件平台上对基于独立交叉口的自适应交通控制系统进行了仿真研究。该系统以交通信号灯对车辆的行与停进行管制,同时根据各路段实时车流量与道路饱和率进行自适应变化,调整交通信号灯的运行周期,并修正各交通方向上绿信比,以提高交叉口单位时间通车率。测试结果表明,该仿真系统运行稳定,满足仿真研究的设计需求,可以为城市交通监测控制提供理论参考和城市交通组织管理提供决策依据。
Neha M. Betgeri
Full Text Available Road traffic signs provide important information for guiding, warning, or regulating the behaviors of driver in order to make driving safer and easier. Automatic recognition of traffic signs is important for an automated intelligent driving vehicle or for driver assistance systems. We have designed such a robust and a fault tolerant system so that it can be a part of the so called “Driver Support Systems”. This paper presents a study to recognize traffic sign patterns using Hough transform and slope detection method. Images are pre-processed with several image processing techniques, such as, boundary trace, edge detection, erosion etc. And then using slope detection technique, which is different and new approach than color based and shape based technique, respective traffic sign is detected. Which in turns give commands to wireless robot to move according to the detected traffic sign. (Here we are specifically considering traffic sign boards of arrow.
Full Text Available Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.
Zhou, Xiaoke; Zhu, Fei; Liu, Quan; Fu, Yuchen; Huang, Wei
Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control. PMID:24592183
Full Text Available Urban traffic self-adaptive control problem is dynamic and uncertain, so the states of traffic environment are hard to be observed. Efficient agent which controls a single intersection can be discovered automatically via multiagent reinforcement learning. However, in the majority of the previous works on this approach, each agent needed perfect observed information when interacting with the environment and learned individually with less efficient coordination. This study casts traffic self-adaptive control as a multiagent Markov game problem. The design employs traffic signal control agent (TSCA for each signalized intersection that coordinates with neighboring TSCAs. A mathematical model for TSCAs’ interaction is built based on nonzero-sum markov game which has been applied to let TSCAs learn how to cooperate. A multiagent Markov game reinforcement learning approach is constructed on the basis of single-agent Q-learning. This method lets each TSCA learn to update its Q-values under the joint actions and imperfect information. The convergence of the proposed algorithm is analyzed theoretically. The simulation results show that the proposed method is convergent and effective in realistic traffic self-adaptive control setting.
MONIKA JOSHI, CHIRAG GOHEL
Full Text Available Raspberry Pi the credit-card-sized single board computer device which adopts a high performance embedded microprocessor and an embedded real-time Linux operating system, which hangs the characteristics of miniaturization, digitalization and network together well. It has virtues of low cost, small volume and flexible networking, etc, because of embedded technology adoption. Network administrators need to see what’s going on with their network. They need to know what the traffic on their network is comprised of, who's using the bandwidth, and how their infrastructure is handling the load. Fortunately, Linux runs a wide variety of free, open source network monitoring and traffic analysis applications that can give net administrator this type of insight. So this paper focuses on how to deploy network monitoring tool within this device which monitors the network traffic within LAN using the tool ntop. Ntop is a simple, free, portable traffic measurement and monitoring tool, which supports various management activities, including network optimization and to plan, and detection of network security violations.
Bisaga, J. J.; Blank, H. A.; Klein, S. A.
The analysis of the performance of the various implementations of the simultaneous system in the Atlantic and Pacific Oceans has demonstrated that the use of adaptive system concepts in satellite traffic management systems can greatly improve the performance capabilities of these systems as compared to the corresponding performance capabilities of conventional nonadaptive satellite communications systems. It is considered that the techniques developed and analyzed represent a significant technological advance, and that the performance improvement achieved will generally outweigh the increased cost and implementation factors.
Zhao Yongli; Zhang Jie; Han Dahai; Wang Lei; Chen Xiuzhong; Gu Wanyi
This paper researched the traffic of optical networks in time-space complexity, proposed a novel traffic model for complex optical networks based on traffic grooming, designed a traffic generator GTS (generator based on time and space) with "centralized + distributed" idea, and then made a simulation in C language. Experiments results show that GTS can produce the virtual network topology which can change dynamically with the characteristic of scaling-free network. GTS can also groom the different traffic and trigger them under real-time or scheduling mechanisms, generating different optical connections. This traffic model is convenient for the simulation of optical networks considering the traffic complexity.
K.Ram Mohan Rao; P. L. N. Raju; Hari Shankar
In this study, the road traffic congestion of Dehradun city is evaluated from traffic flow information using fuzzy techniques. Three different approaches namely Sugeno, Mamdani models which are manually tuned techniques, and an Adaptive Neuo-Fuzzy Inference System (ANFIS) which an automated model decides the ranges and parameters of the membership functions using grid partition technique, based on fuzzy logic. The systems are designed to human’s feelings on inputs and output levels. There are...
Walker, T. Owens; Tummala, Murali; McEachen, John
In this report, we formally introduce the novel concept of traffic-adaptive, flow-specific medium access control and show that it outperforms contention, non-contention and hybrid medium access schemes. A traffic-adaptive, flow-specific mechanism is proposed that utilizes flow-specific queue size statistics to select between medium access modes. A general model for traffic adaptive, flow-specific medium access control is developed and it is shown that hybrid medium access as well as traditio...
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 ( CO2, SO2, NOx, HC, CO, N2O, NH3 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.)
Aly, Salah A.; Ansari, Nirwan; Poor, H. Vincent; Walid, Anwar I.
Several approaches have been proposed to the problem of provisioning traffic engineering between core network nodes in Internet Service Provider (ISP) networks. Such approaches aim to minimize network delay, increase capacity, and enhance security services between two core (relay) network nodes, an ingress node and an egress node. MATE (Multipath Adaptive Traffic Engineering) has been proposed for multipath adaptive traffic engineering between an ingress node (source) and an egress node (dest...
赵运; 冯永新; 刘恒驰; 刘猛
In order to solve performance degradation for advanced orbiting system (AOS)space data system scheduling caused by high burst and heavy tailed nature of self-similar traffic,the existing problems of the AOS virtual channel access (VCA)layer scheduling strategy and the short correlation model of the scheduling algo-rithm are analyzed.A novel scheduling algorithm based on AOS delay accumulated adaptive polling (SDAAP)is proposed.Based on Hurst parameters,urgency,flow rate deviation,and framing time factor,the novel schedu-ling algorithm adaptively change the delay threshold factor to realize multi-service by different operation meth-ods.The SDAAP algorithm optimizes the AOS virtual channel service quality and scheduling performance. With heavy tailed distribution of the ON/OFF traffic model,the experimental results show that,for the AOS self-similar traffic,the SDAAP algorithm executes more well in terms of the overflow rate and average delay than the AOS fixed threshold and the equal time scheduling algorithm.%针对自相似业务流量下的高突发性及重尾性所引起的空间数据系统调度性能下降问题，分析了高级在轨系统（advanced orbiting system，AOS）虚拟信道存取（virtual channel access，VCA）子层调度策略以及现有基于短相关模型调度算法的不足，引入 Hurst 参数、紧迫度、流量离差、成帧时间因子等权值参量，提出一种基于延时累积的自适应轮询调度（scheduling of delay accumulated adaptive polling，SDAAP）算法，通过自适应改变延时阀值因子实现多业务的差异化调度，从而优化 AOS 虚拟信道服务质量及调度性能。采用多信源重尾分布的 ON／OFF 流量分布模型进行仿真验证，实验结果表明，针对自相似业务流，SDAAP 算法在溢出率、平均延迟等方面优于 AOS 固定阀值和等时调度算法。
Full Text Available Purpose: In this paper, the impact of Cooperative Adaptive Cruise Control (CACC systems on traffic performance is examined using microscopic agent-based simulation. Using a developed traffic simulation model of a freeway with an on-ramp - created to induce perturbations and to trigger stop-and-go traffic, the CACC system’s effect on the traffic performance is studied. The previously proposed traffic simulation model is extended and validated. By embedding CACC vehicles in different penetration levels, the results show significance and indicate the potential of CACC systems to improve traffic characteristics and therefore can be used to reduce traffic congestion. The study shows that the impact of CACC is positive but is highly dependent on the CACC market penetration. The flow rate of the traffic using CACC is proportional to the market penetration rate of CACC equipped vehicles and the density of the traffic.Design/methodology/approach: This paper uses microscopic simulation experiments followed by a quantitative statistical analysis. Simulation enables researchers manipulating the system variables to straightforwardly predict the outcome on the overall system, giving researchers the unique opportunity to interfere and make improvements to performance. Thus with simulation, changes to variables that might require excessive time, or be unfeasible to carry on real systems, are often completed within seconds.Findings: The findings of this paper are summarized as follow:•\tProvide and validate a platform (agent-based microscopic traffic simulator in which any CACC algorithm (current or future may be evaluated.•\tProvide detailed analysis associated with implementation of CACC vehicles on freeways.•\tInvestigate whether embedding CACC vehicles on freeways has a significant positive impact or not.Research limitations/implications: The main limitation of this research is that it has been conducted solely in a computer laboratory. Laboratory
S. Lokesh; , T.Prahlad Reddy
By increasing of population the usage of vehicles have been increasing and controlling of traffic is one of the challenging works. The frequent traffic jams at major junctions call for an efficient traffic management system in place. The resulting wastage of time and increase in pollution levels can be eliminated on a city-wide scale by these systems. Previously the traffic control techniques used like magnetic loop detectors, induction loop detectors are buried on the road si...
Juan C. Burguillo-Rial; Rodríguez-Hernández, Pedro S.; Enrique Costa Montenegro; Felipe Gil Castiñeira
Managing traffic in cities is nowadays a complex problem involving considerable physical and economical resources. Multi-agent Systems (MAS) consist of a set of distributed, usually co-operating, agents that act autonomously. The traffic in a city can be simulated by a MAS with different agents, cars and traffic lights, that interact to obtain an overall goal: to reduce average waiting times for the traffic users. In this paper, we describe an agent-based simulator to model traffic in cities....
Yang Wang; Yanyan Chen; Ning Chen
In urban traffic, of particular interest the traffic breakdown which is primarily resulted from the driving behaviors is emerged to respond to the traffic signal. To investigate the influences of driving behaviors on the traffic breakdown, a cellular automaton model has been developed by incorporating a number of driving behaviors typically manifesting during the different stages when the vehicle approaching a traffic light. Numerical simulations have been performed based on a road segment co...
Full Text Available By increasing of population the usage of vehicles have been increasing and controlling of traffic is one of the challenging works. The frequent traffic jams at major junctions call for an efficient traffic management system in place. The resulting wastage of time and increase in pollution levels can be eliminated on a city-wide scale by these systems. Previously the traffic control techniques used like magnetic loop detectors, induction loop detectors are buried on the road side provide the limited traffic information and require separate systems for traffic counting and for traffic surveillance. Here the project proposes to implement an artificial density traffic control system using image processing and Raspberrypi. The hardware here we are using is webcam, pc, Raspberry pi and the software used is OCCIDENTALIS and MATLAB. In this project the camera is get interfaced with a Raspberry pi. The image sequences from a camera are analyzed using thresholding method to find the density of vehicles. Subsequently, the number of vehicles at the intersection is evaluated and traffic is efficiently managed. In this project we implemented a real-time emergency vehicle detection system. In case an emergency vehicle is detected, the lane is given priority over all the others.
Bilal Ahmed Khan; Nai Shyan Lai
Traffic light plays an important role in the urban traffic management. Therefore, it is necessary to improve the traffic controller for effective traffic management and better traffic flow leading to greener environment. In this paper, an advanced and intelligent traffic light controller is proposed, utilising the fuzzy logic technology and image processing technique. A fuzzy logic control has been implemented to provide the attribute of intelligence to the system. For real-time image acquisi...
Lun-Hui Xu; Xin-Hai Xia; Qiang Luo
Urban traffic self-adaptive control problem is dynamic and uncertain, so the states of traffic environment are hard to be observed. Efficient agent which controls a single intersection can be discovered automatically via multiagent reinforcement learning. However, in the majority of the previous works on this approach, each agent needed perfect observed information when interacting with the environment and learned individually with less efficient coordination. This study casts traffic self-ad...
Klunder, G.; Li, M.; Minderhoud, M.
In 2006 in the Netherlands, a field operational test was carried out to study the effect of adaptive cruise control (ACC) and lane departure warning on driver behavior and traffic flow in real traffic. To estimate the effect for larger penetration rates, simulations were needed. For a reliable impac
Salden, Ron; Paas, Fred; Van Merriënboer, Jeroen
Salden, R.J.C.M., Paas, F., & Van Merriënboer, J.J.G. (2006). Personalised adaptive task selection in air traffic control: Effects on training efficiency and transfer. Learning and Instruction, 16, 350-362
Agerholm, Niels; Olesen, Anne Vingaard
Adaptive Traffic Control Systems (ATCS) are aimed at reducing congestion. ATCS adapt to approaching traffic to continuously optimise the traffic flows in question. ATCS have been implemented in many locations, including the Scandinavian countries, with various effects. Due to congestion problems......, ATCS were installed in the eight signalised intersections of a 1.7 km stretch of the ring road in the medium-sized Danish city of Aalborg. To measure the effect of ATCS a with/without study was carried out. GPS data from a car following the traffic, recorded transportation times for buses in service...... morning peak and midday off-peak. The effect on crossing and turning traffic was slight, and while reduced transportation time was found in one part of the ring road in another part transportation time was seen to increase. The benefit to the ring road was partly gained at the cost of slightly increased...
The paper studies the modern methods and tools to simulate the behavior of complex adaptive systems (CAS), the existing systems of traffic modeling in simulators and their characteristics; proposes requirements for assessing the suitability of the system to simulate the CAS behavior in simulators. The author has developed a model of adaptive agent representation and its functioning environment to meet certain requirements set above, and has presented methods of agents' interactions and methods of conflict resolution in simulated traffic situations. A simulation system realizing computer modeling for simulating the behavior of CAS in traffic situations has been created
In this paper, we consider a method to enhance the throughput of HSDPA systems in the mixed traffic scenario. A channel-dependent adaptive delay barrier (DB) function is proposed to maximize throughput of best-effort (BE) traffic while satisfying the delay latency of voice over internet protocol (VoIP) service. Simulations show that the proposed channel-adaptive DB function raises the throughput of BE traffic service by 30% compared to the conventional scheme, without degrading the capacity of VoIP service over HSDPA system.
XU DongWei; DONG HongHui; JIA LiMin; QIN Yong
The accurate estimation of expressway traffic state can provide decision-making for both travelers and traffic managers.The speed is one of the most representative parameter of the traffic state.So the expressway speed spatial distribution can be taken as the expressway traffic state equivalent.In this paper,an algorithm based on virtual speed sensors (VSS) is presented to estimate the expressway traffic state (the speed spatial distribution).To gain the spatial distribution of expressway traffic state,virtual speed sensors are defined between adjacent traffic flow sensors.Then,the speed data extracted from traffic flow sensors in time series are mapped to space series to design virtual speed sensors.Then the speed of virtual speed sensors can be calculated with the weight matrix which is related with the speed of virtual speed sensors and the speed data extracted from traffic flow sensors and the speed data extracted from traffic flow sensors in time series.Finally,the expressway traffic state (the speed spatial distribution) can be gained.The acquisition of average travel speed of the expressway is taken for application of this traffic state estimation algorithm.One typical expressway in Beijing is adopted for the experiment analysis.The results prove that this traffic state estimation approach based on VSS is feasible and can achieve a high accuracy.
Heng Hong-jun; Yang Jue
According to the characteristics of airport pavement traffic, we discuss a method of building an airport pavement traffic model which is based on CPN theory and simulate a practical situation as well. The method overcomes the shortage of modelling with normal Petri Net theory, solves the difficult problems of airport pavement traffic such as complex traffic nets, frequent road changing, etc., refines the process of the model, and will be good for the model’s analysis and simulation.
Traffic information acquisition system is an essential part of the intelligent transportation system (ITS). This paper presents a novel traffic information acquisition system, i.e., a dynamic traffic information acquisition system based on wireless mesh networks (WMN). The system comprises of probe vehicles and wireless mesh networks. Compared to the conventional systems, it is independent of other network infrastructures and easy to implement. It can acquire the real-time traffic information correctly with lower cost.
Sapkota, Brahmananda; Sinderen, van Marten
The increasing number of road vehicles has given rise to increasingly adverse consequences in the society. Some of the major concerns that arise due to such an increase in road vehicles are: safety of the people using the road, cost and efficiency of the traffic management and the environmental foot
Arem, van, Bart; Driel, van, J.; Visser, Ruben
Cooperative adaptive cruise control (CACC) is an extension of ACC. In addition to measuring the distance to a predecessor, a vehicle can also exchange information with a predecessor by wireless communication. This enables a vehicle to follow its predecessor at a closer distance under tighter control. This paper focuses on the impact of CACC on traffic-flow characteristics. It uses the traffic-flow simulation model MIXIC that was specially designed to study the impact of intelligent vehicles o...
Huberman, Bernardo A.; Helbing, Dirk
We present an economics-based method for deciding the optimal rates at which vehicles are allowed to enter a highway. The method exploits the naturally occuring fluctuations of traffic flow and is flexible enough to adapt in real time to the transient flow characteristics of road traffic. Simulations based on realistic parameter values show that this strategy is feasible for naturally occuring traffic, and that even far from optimality, injection policies can improve traffic flow. Our results...
Full Text Available A modified cell transmission model (CTM is proposed to depict the temporal-spatial evolution of traffic congestion on urban freeways. Specifically, drivers’ adaptive behaviors and the corresponding influence on traffic flows are emphasized. Two piecewise linear regression models are proposed to describe the relationship of flow and density (occupancy. Several types of cellular connections are designed to depict urban rapid roads with on/off-ramps and junctions. Based on the data collected on freeway of Queen Elizabeth, Ontario, Canada, we show that the new model provides a relatively higher accuracy of temporal-spatial evolution of traffic congestions.
Wang, Zuchao; Lu, Min; Yuan, Xiaoru; Zhang, Junping; van de Wetering, Huub
In this work, we present an interactive system for visual analysis of urban traffic congestion based on GPS trajectories. For these trajectories we develop strategies to extract and derive traffic jam information. After cleaning the trajectories, they are matched to a road network. Subsequently, traffic speed on each road segment is computed and traffic jam events are automatically detected. Spatially and temporally related events are concatenated in, so-called, traffic jam propagation graphs. These graphs form a high-level description of a traffic jam and its propagation in time and space. Our system provides multiple views for visually exploring and analyzing the traffic condition of a large city as a whole, on the level of propagation graphs, and on road segment level. Case studies with 24 days of taxi GPS trajectories collected in Beijing demonstrate the effectiveness of our system. PMID:24051782
This article focuses on identifying file-sharing peer-to-peer (P2P) (such as BitTorrent (BT)) traffic at the borders of a stub network. By analyzing protocols and traffic of applications, it is found that file-sharing P2P traffic of a single user differs greatly from traditional and other P2P (such as QQ) applications' traffic in the distribution of involved remote hosts and remote ports. Therefore, a method based on discreteness of remote hosts (RHD) and discreteness of remote ports (RPD) is proposed to identify BT-like traffic. This method only relies on flow information of each user host in a stub network, and no packet payload needs to be monitored. At intervals, instant RHD for concurrent transmission control protocol and user datagram protocol flows for each host are calculated respectively through grouping flows by the stub network that the remote host of each flow belongs to. On given conditions, instant RPD are calculated through grouping flows by the remote port to amend instant RHD. Whether a host has been using a BT-like application or not can be deduced from instant RHD or average RHD for a period of time. The proposed method based on traffic characteristics is more suitable for identifying protean file-sharing P2P traffic than content-based methods. Experimental results show that this method is effective with high accuracy.
Full Text Available This study proposes an architecture for an interactive motion-based traffic simulation environment. In order to enhance modeling realism involving actual human beings, the proposed architecture integrates multiple types of simulation, including: (i motion-based driving simulation, (ii pedestrian simulation, (iii motorcycling and bicycling simulation, and (iv traffic flow simulation. The architecture has been designed to enable the simulation of the entire network; as a result, the actual driver, pedestrian, and bike rider can navigate anywhere in the system. In addition, the background traffic interacts with the actual human beings. This is accomplished by using a hybrid mesomicroscopic traffic flow simulation modeling approach. The mesoscopic traffic flow simulation model loads the results of a user equilibrium traffic assignment solution and propagates the corresponding traffic through the entire system. The microscopic traffic flow simulation model provides background traffic around the vicinities where actual human beings are navigating the system. The two traffic flow simulation models interact continuously to update system conditions based on the interactions between actual humans and the fully simulated entities. Implementation efforts are currently in progress and some preliminary tests of individual components have been conducted. The implementation of the proposed architecture faces significant challenges ranging from multiplatform and multilanguage integration to multievent communication and coordination.
Paz, Alexander; Veeramisti, Naveen; Khaddar, Romesh; de la Fuente-Mella, Hanns; Modorcea, Luiza
This study proposes an architecture for an interactive motion-based traffic simulation environment. In order to enhance modeling realism involving actual human beings, the proposed architecture integrates multiple types of simulation, including: (i) motion-based driving simulation, (ii) pedestrian simulation, (iii) motorcycling and bicycling simulation, and (iv) traffic flow simulation. The architecture has been designed to enable the simulation of the entire network; as a result, the actual driver, pedestrian, and bike rider can navigate anywhere in the system. In addition, the background traffic interacts with the actual human beings. This is accomplished by using a hybrid mesomicroscopic traffic flow simulation modeling approach. The mesoscopic traffic flow simulation model loads the results of a user equilibrium traffic assignment solution and propagates the corresponding traffic through the entire system. The microscopic traffic flow simulation model provides background traffic around the vicinities where actual human beings are navigating the system. The two traffic flow simulation models interact continuously to update system conditions based on the interactions between actual humans and the fully simulated entities. Implementation efforts are currently in progress and some preliminary tests of individual components have been conducted. The implementation of the proposed architecture faces significant challenges ranging from multiplatform and multilanguage integration to multievent communication and coordination. PMID:26491711
Full Text Available A problem of network traffic anomalies detection in the computer networks is analyzed. Overview of anomalies detection methods is given then advantages and disadvantages of the different methods are analyzed. Model for the traffic anomalies detection was developed based on IBM SPSS Modeler and is used to analyze SNMP data of the router. Investigation of the traffic anomalies was done using three classification methods and different sets of the learning data. Based on the results of investigation it was determined that C5.1 decision tree method has the largest accuracy and performance and can be successfully used for identification of the network traffic anomalies.
ZHANG Lei; SONG Tiecheng; WU Ming; BAO Xu; GUO Jie; HU Jing
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.
Full Text Available Detecting traffic flow by in-road inductive loop is the most common methods, but inductive loop is physically large, it is hard to install and maintain, also the classification rate is low. The inductive loops cannot communicate with each other, so they cannot share traffic data with each other. The wireless sensor network has these features: real-time, fault tolerance, scalability and coordination. Applying wireless sensor network into traffic area for traffic flow detection is easier to install, and provide real-time traffic flow for coordinate traffic control, also it can improve the classification rate. A lot of researchers applied the wireless sensor network for traffic flow detection, but no one referred to the coordinate traffic flow detection method. In this paper, we provided a coordinate traffic flow detection framework. Based on this framework, we set up a simple traffic flow detection platform by wireless sensor nodes produced by cross box company to verify this method. We selected four periods, for each period, we got a more than 90% classification rate.
An expert system for traffic safety uses a "knowledge-base" for the interpretation of the "databases" in which accident data and the characteristics of roads and traffic are stored. Computerized procedures are developed for detection, diagnosis, and remedy. The procedures will be based on what is kn
Davis, L C
Wirelessly connected vehicles that exchange information about traffic conditions can reduce delays caused by congestion. At a 2-to-1 lane reduction, the improvement in flow past a bottleneck due to traffic with a random mixture of 40% connected vehicles is found to be 52%. Control is based on connected-vehicle-reported velocities near the bottleneck. In response to indications of congestion the connected vehicles, which are also adaptive cruise control vehicles, reduce their speed in slowdown regions. Early lane changes of manually driven vehicles from the terminated lane to the continuous lane are induced by the slowing connected vehicles. Self-organized congestion at the bottleneck is thus delayed or eliminated, depending upon the incoming flow magnitude. For the large majority of vehicles, travel times past the bottleneck are substantially reduced. Control is responsible for delaying the onset of congestion as the incoming flow increases. Adaptive cruise control increases the flow out of the congested stat...
LI Yan; FAN Xiao-ping
An adaptive fuzzy logic controller (AFC) is presented for the signal control of the urban traffic network.The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuzzy rules regulation level.The control level decides the signal tunings in an intersection with a fuzzy logic controller.The regulation level optimizes the fuzzy rules by the Adaptive Rule Module in AFC according to both the system performance index in current control period and the traffic flows in the last one.Consequently the system performances are improved.A weight coefficient controller (WCC) is also developed to describe the interactions of traffic flow among the adjacent intersections.So the AFC combined with the WCC can be applied in a road network for signal timings.Simulations of the AFC on a real traffic scenario have been conducted.Simulation results indicate that the adaptive controller for traffic control shows better performance than the actuated one.
Full Text Available In view of the traffic flow data of non-stationary, the abnormal data detection is difficult.proposed basing on the wavelet analysis and least squares method of abnormal traffic flow data detection in this paper.First using wavelet analysis to make the traffic flow data of high frequency and low frequency component and separation, and then, combined with least square method to find abnormal points in the reconstructed signal data.Wavelet analysis and least square method, the simulation results show that using wavelet analysis of abnormal traffic flow data detection, effectively reduce the detection results of misjudgment rate and false negative rate.
Full Text Available Based on the local support vector algorithm of chaotic time series analysis, the Hannan-Quinn information criterion and SAX symbolization are introduced. Then a novel prediction algorithm is proposed, which is successfully applied to the prediction of wireless network traffic. For the correct prediction problems of short-term flow with smaller data set size, the weakness of the algorithms during model construction is analyzed by study and comparison to LDK prediction algorithm. It is verified the Hannan-Quinn information principle can be used to calculate the number of neighbor points to replace pervious empirical method, which uses the number of neighbor points to acquire more accurate prediction model. Finally, actual flow data is applied to confirm the accuracy rate of the proposed algorithm LSDHQ. It is testified by our experiments that it also has higher performance in adaptability than that of LSDHQ algorithm.
Yadong ZHOU; Xiaohong GUAN; Qindong SUN; Wei LI; Jing TAO
This article presents the formal definition and description of popular topics on the Internet,analyzes the relationship between popular words and topics,and finally introduces a method that uses statistics and correlation of the popular words in traffic content and network flow characteristics as input for extracting popular topics on the Internet.Based on this,this article adapts a clustering algorithm to extract popular topics and gives formalized results.The test results show that this method has an accuracy of 16.7% in extracting popular topics on the Internet.Compared with web mining and topic detection and tracking (TDT),it can provide a more suitable data source for effective recovery of Internet public opinions.
Bonald, Thomas; Elayoubi, Salah-Eddine; Lin, Yu-Ting
International audience This paper proposes a performance model for mobile networks carrying adaptive streaming traffic. The proposed model takes into account the flow dynamics in addition to the main parameters influencing the performance of adaptive streaming, such as the playout buffer and the video bit rates. We show how to compute several performance metrics like the average video bit rate, the deficit rate, defined as the probability of having an instantaneous throughput lower than th...
Carrillo, Snaider; Harkin, Jim; McDaid, Liam; Pande, Sandeep; Cawley, Seamus; McGinley, Brian; Morgan, Fearghal
The brain is highly efficient in how it processes information and tolerates faults. Arguably, the basic processing units are neurons and synapses that are interconnected in a complex pattern. Computer scientists and engineers aim to harness this efficiency and build artificial neural systems that can emulate the key information processing principles of the brain. However, existing approaches cannot provide the dense interconnect for the billions of neurons and synapses that are required. Recently a reconfigurable and biologically inspired paradigm based on network-on-chip (NoC) and spiking neural networks (SNNs) has been proposed as a new method of realising an efficient, robust computing platform. However, the use of the NoC as an interconnection fabric for large-scale SNNs demands a good trade-off between scalability, throughput, neuron/synapse ratio and power consumption. This paper presents a novel traffic-aware, adaptive NoC router, which forms part of a proposed embedded mixed-signal SNN architecture called EMBRACE (EMulating Biologically-inspiRed ArChitectures in hardwarE). The proposed adaptive NoC router provides the inter-neuron connectivity for EMBRACE, maintaining router communication and avoiding dropped router packets by adapting to router traffic congestion. Results are presented on throughput, power and area performance analysis of the adaptive router using a 90 nm CMOS technology which outperforms existing NoCs in this domain. The adaptive behaviour of the router is also verified on a Stratix II FPGA implementation of a 4 × 2 router array with real-time traffic congestion. The presented results demonstrate the feasibility of using the proposed adaptive NoC router within the EMBRACE architecture to realise large-scale SNNs on embedded hardware. PMID:22561008
Full Text Available Traffic noise is a major environmental problem in many urban areas and frequently causes complaints from urban residents. An accurate traffic noise map of urban areas can facilitate noise monitoring, traffic strategic planning, street planning, residential area planning and noise prevention or reduction. An SOA based platform for urban traffic strategic noise mapping is proposed in this paper. Service Oriented Computing Environment (SORCER is adopted to build the highly flexible distributed platform for noise monitoring and noise mapping. The platform architecture and the hierarchical services structure based on SOA are presented. The major services in the platform, including the task scheduler service, prediction service and noise propagation calculation service are analyzed in details. To demonstrate the function and mechanism of the platform, a real traffic noise mapping project for a Beijing area is presented.
In a cognitive-relay system, the secondary user is permitted to transmit data via a relay when the spectrum bands are detected to be free. The miss detection of spectrum sensing and the primary user traffic will affect the data transmission performance of the secondary user. In this paper, we investigate the impact of the status change of the primary user on the bit error rate (BER) of the adaptive transmission of the secondary user in a cognitive-relay system. Numerical results show that the primary user traffic can significantly degrade the BER of the secondary user transmission.
Jens Milbrandt; Michael Menth; Jan Junker
Experience-based admission control (EBAC) is a hybrid approach combining the classical parameter-based and measurement-based admission control. EBAC calculates an appropriate overbooking factor used to overbook link capacities with resource reservations in packet-switched networks. This overbooking factor correlates with the average peak-to-mean rate ratio of all admitted traffic flows on the link. So far, a single overbooking factor is calculated for the entire traffic aggregate. In this pap...
Sun, Li; Ling, Ximan; He, Kun; Tan, Qian
Large structure in complex networks can be studied by dividing it into communities or modules. Urban traffic system is one of the most critical infrastructures. It can be abstracted into a complex network composed of tightly connected groups. Here, we analyze community structure in urban traffic zones based on the community detection method in network science. Spectral algorithm using the eigenvectors of matrices is employed. Our empirical results indicate that the traffic communities are variant with the travel demand distribution, since in the morning the majority of the passengers are traveling from home to work and in the evening they are traveling a contrary direction. Meanwhile, the origin-destination pairs with large number of trips play a significant role in urban traffic network's community division. The layout of traffic community in a city also depends on the residents' trajectories.
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.
National Aeronautics and Space Administration — We propose agent-based game-theoretic approaches for simulation of strategies involved in multi-objective collaborative traffic flow management (CTFM). Intelligent...
Tian, Bin; Hou, Kun Mean; Zhou, Haiying
The worldwide economic cost of road crashes and injuries is estimated to be US$518 billion per year and the annual congestion cost in France is estimated to be €5.9 billion. Vehicular Ad hoc Networks (VANETs) are one solution to improve transport features such as traffic safety, traffic jam and infotainment on wheels, where a great number of event-driven messages need to be disseminated in a timely way in a region of interest. In comparison with traditional wireless networks, VANETs have to consider the highly dynamic network topology and lossy links due to node mobility. Inter-Vehicle Communication (IVC) protocols are the keystone of VANETs. According to our survey, most of the proposed IVC protocols focus on either highway or urban scenarios, but not on both. Furthermore, too few protocols, considering both scenarios, can achieve high performance. In this paper, an infrastructure-less Traffic Adaptive data Dissemination (TrAD) protocol which takes into account road traffic and network traffic status for both highway and urban scenarios will be presented. TrAD has double broadcast suppression techniques and is designed to adapt efficiently to the irregular road topology. The performance of the TrAD protocol was evaluated quantitatively by means of realistic simulations taking into account different real road maps, traffic routes and vehicular densities. The obtained simulation results show that TrAD is more efficient in terms of packet delivery ratio, number of transmissions and delay in comparison with the performance of three well-known reference protocols. Moreover, TrAD can also tolerate a reasonable degree of GPS drift and still achieve efficient data dissemination. PMID:27338393
Han, Ke; Yao, Tao; Terry L. Friesz
This paper is concerned with highway traffic estimation using traffic sensing data, in a Lagrangian-based modeling framework. We consider the Lighthill-Whitham-Richards (LWR) model (Lighthill and Whitham, 1955; Richards, 1956) in Lagrangian-coordinates, and provide rigorous mathematical results regarding the equivalence of viscosity solutions to the Hamilton-Jacobi equations in Eulerian and Lagrangian coordinates. We derive closed-form solutions to the Lagrangian-based Hamilton-Jacobi equatio...
Full Text Available Abstract In this article, an adaptive scheduling packets algorithm for the uplink traffic in WiMAX networks is proposed. The proposed algorithm is designed to be completely dynamic, mainly in networks that use various modulation and coding schemes (MCSs. Using a cross-layer approach and the states of the uplink virtual queues in the base station, it was defined a new deadlines-based scheme, aiming at limiting the maximum delay to the real-time applications. Moreover, a method which interacts with the polling mechanisms of the base station was developed. This method controls the periodicity of sending unicast polling to the real-time and non-real-time service classes, in accordance with the quality of service requirements of the applications. The proposed algorithm was evaluated by means of modeling and simulation in environments where various MCSs were used and also in an environment where only one type of MCS was used. The simulations showed satisfactory results in both environments.
Full Text Available Traffic and spam are the main problems in the data transmission through the network. Many traffic filtering systems have been proposed to find and filter the traffic over the network. The system Optimal Source Filtering (OSF has implemented a new and optimal filtering mechanism. The new mechanism named as DROP, which monitors and filters the spam and malicious traffic over a network effectively. Traffic filtering systems have been proposed to detect the spammer and malicious traffic, using the optimal rules and policies. Further these systems are highly ineffective when they encounter malicious traffic. The proposed system introduced OSF protocol, which helps to improve the efficiency of the firewall and filters based on the user rule. The proposed filtering scheme provides TFS false filtering when the flash crowd occurred. The protocol verifies users and firewall rules and policies with the data priority model, which makes the filtering process more robust and fastest manner. The Proposed spam detection project identifies and eliminates unwanted messages by monitoring outgoing messages. The spam detection is the main challenging task in the network. In the existing system spam detection has implemented after the data received. According to the user rule and request the current system identifies the spam and zombies by monitoring every outgoing message from the sender.
Ahmed M. Manasrah
Full Text Available The botnet is considered as a critical issue of the Internet due to its fast growing mechanism and affect. Recently, Botnets have utilized the DNS and query DNS server just like any legitimate hosts. In this case, it is difficult to distinguish between the legitimate DNS traffic and illegitimate DNS traffic. It is important to build a suitable solution for botnet detection in the DNS traffic and consequently protect the network from the malicious Botnets activities. In this paper, a simple mechanism is proposed to monitors the DNS traffic and detects the abnormal DNS traffic issued by the botnet based on the fact that botnets appear as a group of hosts periodically. The proposed mechanism is also able to classify the DNS traffic requested by group of hosts (group behavior and single hosts (individual behavior, consequently detect the abnormal domain name issued by the malicious Botnets. Finally, the experimental results proved that the proposed mechanism is robust and able to classify DNS traffic, and efficiently detects the botnet activity with average detection rate of 89%.
Liu-Jian; Li-Qingsong; Li-Hui; Guo-Hanying; Pan-Heng
In order to improve the labor efficiency and economic benefit of road traffic facilities system and reduce resource waste, a scheme of road traffic facilities system based on GIS is provided in this paper. In the new scheme, firstly, we proposed Visual C++ embedding MapX component to program for the visualization of data and function analysis of space, and constructed core table in database and established property database and space database to improve efficiency; then we put forward the sys...
Wei Zhang; Guozhen Tan; Nan Ding; Guangyuan Wang
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 ...
Full Text Available Road section data packet is very necessary for the estimation and prediction in short-time traffic condition. However, previous researches on this problem are lack of quantitative analysis. A section correlation analyzing method with traffic flow microwave data is proposed for this problem. It is based on the metric multidimensional scaling theory. With a dissimilarity matrix, scalar product matrix can be calculated. Subsequently, a reconstructing matrix of section traffic flow could be got with principal components factor analysis, which could display section groups in low dimension. It is verified that the new method is reliable and effective. After that, Auto Regressive Moving Average (A RMA model is used for forecasting traffic flow and lane occupancy. Finally, a simulated example has shown that the technique is effective and exact. The theoretical analysis indicates that the forecasting model and algorithms have a broad prospect for practical application.
Kenedy Aliila Greyson
Full Text Available Currently, traffic congestions are common events in road networks of main cities in developing countries. It has been observed that, the size of congestion increases year after year. For traffic congestion management to work efficiently, sufficiently and accurately information are needed. In this research we present an alternative method using agent technology to collect and manipulate data so as to be used in optimizing the vehicle flow within the road networks. The objective is to design an agent based system to provide sufficient and accurate information used in traffic flow management, and vehicle traffic congestion mitigation. The implementation approach is presented. The case study is a portion of the road network from the city of Dar es Salaam.
Vu, Duc; Guo, Bin; Xu, Luzhou; Li, Jian
We consider ground moving target indication (GMTI) and target velocity estimation based on multi-channel synthetic aperture radar (SAR) images. Via forming velocity versus cross-range images, we show that small moving targets can be detected even in the presence of strong stationary ground clutter. Moreover, the velocities of the moving targets can be estimated, and the misplaced moving targets can be placed back to their original locations based on the estimated velocities. Adaptive beamforming techniques, including Capon and generalizedlikelihood ratio test (GLRT), are used to form velocity versus cross-range images for each range bin of interest. The velocity estimation ambiguities caused by the multi-channel array geometry are analyzed. We also demonstrate the effectiveness of our approaches using the Air Force Research Laboratory (AFRL) publicly-released Gotcha SAR based GMTI data set.
In the last decades, the air traffic system has been changing to adapt itself to new social demands, mainly the safe growth of worldwide traffic capacity. Those changes are ruled by the Communication, Navigation, Surveillance/Air Traffic Management (CNS/ATM) paradigm , based on digital communication technologies (mainly satellites) as a way of improving communication, surveillance, navigation and air traffic management services. However, CNS/ATM poses new challenges and needs, mainly related to the safety assessment process. In face of these new challenges, and considering the main characteristics of the CNS/ATM, a methodology is proposed at this work by combining 'absolute' and 'relative' safety assessment methods adopted by the International Civil Aviation Organization (ICAO) in ICAO Doc.9689 , using Fluid Stochastic Petri Nets (FSPN) as the modeling formalism, and compares the safety metrics estimated from the simulation of both the proposed (in analysis) and the legacy system models. To demonstrate its usefulness, the proposed methodology was applied to the 'Automatic Dependent Surveillance-Broadcasting' (ADS-B) based air traffic control system. As conclusions, the proposed methodology assured to assess CNS/ATM system safety properties, in which FSPN formalism provides important modeling capabilities, and discrete event simulation allowing the estimation of the desired safety metric.
Full Text Available Short-term traffic flow prediction is one of the most important issues in the field of adaptive traffic control system and dynamic traffic guidance system. In order to improve the accuracy of short-term traffic flow prediction, a short-term traffic flow local prediction method based on combined kernel function relevance vector machine (CKF-RVM model is put forward. The C-C method is used to calculate delay time and embedding dimension. The number of neighboring points is determined by use of Hannan-Quinn criteria, and the CKF-RVM model is built based on genetic algorithm. Finally, case validation is carried out using inductive loop data measured from the north–south viaduct in Shanghai. The experimental results demonstrate that the CKF-RVM model is 31.1% and 52.7% higher than GKF-RVM model and GKF-SVM model in the aspect of MAPE. Moreover, it is also superior to the other two models in the aspect of EC.
Besides the traditional data collection by stationary detectors, recent advances in wireless and sensor technologies have promoted new potentials for a vehicle-based data collection and local dissemination of information. By means of microscopic traffic simulations we study the problem of online estimation of the current traffic situation based on floating car data. Our focus is on the estimation on the up- and downstream jam fronts determining the extension of traffic congestion. We study the impact of delayed information transmission by short-range communication via wireless LAN in contrast to instantaneous information transmission to the roadside units by means of mobile radio. The delayed information transmission leads to systematic estimation errors which cannot be compensated for by a higher percentage of probe vehicles. Additional flow measurements from stationary detectors allow for a model-based prediction which is effective for much lower floating car percentages than 1%.
Full Text Available Experience-based admission control (EBAC is a hybrid approach combining the classical parameter-based and measurement-based admission control. EBAC calculates an appropriate overbooking factor used to overbook link capacities with resource reservations in packet-switched networks. This overbooking factor correlates with the average peak-to-mean rate ratio of all admitted traffic flows on the link. So far, a single overbooking factor is calculated for the entire traffic aggregate. In this paper, we propose typespecific EBAC which provides a compound overbooking factor considering different types of traffic that subsume flows with similar peak-to-mean rate ratios. The concept can be well implemented since it does not require measurements of type-specific traffic aggregates. We give a proof of concept for this extension and compare it with the conventional EBAC approach. We show that EBAC with type-specific overbooking leads to better resource utilization under normal conditions and to faster response times for changing traffic mixes.
Zhang, Junyou; Jian, Meng; Tang, Rui
Based on the analysis of floating car traffic information acquisition and processing system structure and construction frame, combining the Zibo floating car features and road conditions, using historical data provided by Zibo city bus companies, adopting the ArcGIS Engine of ESRI company as a map components, putting forward the nearest point estimate map matching algorithms, combining data fusion technology based on Kalman filter and road running speed calibration algorithm, predicting road traffic running status in certain period and express it in the GIS map, this paper completed the design, the practice has proved the suggested method is feasible.
E. R. Naganathan
Full Text Available Problem statement: Multi-Protocol Label Switching (MPLS is a mechanism which is used in high-performance telecommunications networks that directs and carries data from one network node to the next with the help of labels. Traffic management is still an issue in MPLS network as it involves high speed internet. Approach: This study proposed a traffic flow analysis of the real time MPLS traffic and segregates the MPLS traffic as three major class based on the outcome of traffic flow analysis. Using the traffic class. This study proposed a reliable transmission methodology which provides traffic free routing in the MPLS networks. Results: The proposed traffic flow analysis based reliable routing model overcomes the network traffic and provides effective routing by offering traffic free path. Conclusion: The proposed traffic flow analysis model outperforms existing routing protocol and offers comparatively negligible packet loss.
Aizen, Jonathan; Huttenlocher, Daniel; Kleinberg, Jon; Novak, Antal
Usage data at a high-traffic web site can expose information about external events and surges in popularity that may not be accessible solely from analyses of content and link structure. We consider sites that are organized around a set of items available for purchase or download, consider, for example, an e-commerce site or collection of online research papers, and we study a simple indicator of collective user interest in an item, the batting average, defined as the fraction of visits to an item's description that result in an acquisition of that item. We develop a stochastic model for identifying points in time at which an item's batting average experiences significant change. In experiments with usage data from the Internet Archive, we find that such changes often occur in an abrupt, discrete fashion, and that these changes can be closely aligned with events such as the highlighting of an item on the site or the appearance of a link from an active external referrer. In this way, analyzing the dynamics of item popularity at an active web site can help characterize the impact of a range of events taking place both on and off the site. PMID:14709676
Full Text Available Because in the traffic video image processing, the background image gotten from background modeling by traditional k-means clustering algorithm shows a lot of noises, thus the improvement of k-means clustering algorithm is proposed, and has been applied to the vehicle flow detection of traffic video image. By analyzing the vehicle detection method and comparing the flow detection algorithm, the improved k-means clustering algorithm is experimentally tested at last, and carries out software implementation. The experiment shows that the improved algorithm after background modeling is superior to the traditional one in time complexity, it has better adaptivity and robustness, which has increased the effect of vehicle flow detection.
Full Text Available Based on the information present in cumulative acoustic signal acquired from a roadside-installed single microphone, this paper considers the problem of vehicular traffic density state estimation. The occurrence and mixture weightings of traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc are determined by the prevalent traffic density conditions on the road segment. In this work, we extract the short-term spectral envelope features of the cumulative acoustic signals using MFCC (Mel-Frequency Cepstral Coefficients. Support Vector Machines (SVM is used as classifier is used to model the traffic density state as Low (40 Km/h and above, Medium (20-40 Km/h, and Heavy (0-20 Km/h. For the developing geographies where the traffic is non-lane driven and chaotic, other techniques (magnetic loop detectors are inapplicable. SVM classifier with different kernels are used to classify the acoustic signal segments spanning duration of 20–40 s, which results in average classification accuracy of 96.67% for Quadratic kernel function and 98.33% for polynomial kernel function, when entire frames are considered for classification.
Full Text Available Since compared with the Support Vector Machine (SVM, the Relevance Vector Machine (RVM not only has the advantage of avoiding the over- learn which is the characteristic of the SVM, but also greatly reduces the amount of computation of the kernel function and avoids the defects of the SVM that the scarcity is not strong, the large amount of calculation as well as the kernel function must satisfy the Mercer's condition and that human empirically determined parameters, so we proposed a new online traffic classification algorithm base on the RVM for this purpose. Through the analysis of the basic principles of RVM and the steps of the modeling, we made use of the training traffic classification model of the RVM to identify the network traffic in the real time through this model and the “port number+ DPI”. When the RVM predicts that the probability is in the query interval, we jointly used the "port number" and "DPI". Finally, we made a detailed experimental validation which shows that: compared with the Support Vector Machine (SVM network traffic classification algorithm, this algorithm can achieve the online network traffic classification, and the classification predication probability is greatly improved.
In a cognitive relay system, the secondary user is permitted to transmit data via a relay when licensed frequency bands are detected to be free. Previous studies mainly focus on reducing or limiting the interference of the secondary transmission on the primary users. On the other hand, however, the primary user traffic will also affect the data transmission performance of the secondary users. In this paper, we investigate the impact of the primary user traffic on the bit error rate (BER) of the secondary transmission, when the secondary user adopts adaptive transmission with a relay partially selected. From the numerical results, we can see that the primary user traffic seriously degrades average BER. The worse-link partial selection can perform almost as well as the global selection when the channel conditions of the source-relay links and the relay-destination links differ a lot. In addition, although the relay selection improves the spectral efficiency of the secondary transmission, numerical results show that it only has slight impact on the overall average BER, so that the robustness of the system will not be affected by the relay selection.
Joutsensalo, Jyrki; Hamalainen, Timo; Zhang, Jian
In the future Internet, different applications such as Voice over IP (VoIP) and Video-on-Demand (VoD) arise with different Quality of Service (QoS) parameters including e.g. guaranteed bandwidth, delay jitter, and latency. Different kinds of service classes (e.g. gold, silver, bronze) arise. The customers of different classes pay different prices to the service provider, who must share resources in a plausible way. In a router, packets are queued using a multi-queue system, where each queue corresponds to one service class. In this paper, an adaptive Weighted Fair Queue based algorithm for traffic allocation is presented and studied. The weights in gradient type WFQ algorithm are adapted using revenue as a target function.
Davis, L. C.
Wirelessly connected vehicles that exchange information about traffic conditions can reduce delays caused by congestion. At a 2-to-1 lane reduction, the improvement in flow past a bottleneck due to traffic with a random mixture of 40% connected vehicles is found to be 52%. Control is based on connected-vehicle-reported velocities near the bottleneck. In response to indications of congestion the connected vehicles, which are also adaptive cruise control vehicles, reduce their speed in slowdown regions. Early lane changes of manually driven vehicles from the terminated lane to the continuous lane are induced by the slowing connected vehicles. Self-organized congestion at the bottleneck is thus delayed or eliminated, depending upon the incoming flow magnitude. For the large majority of vehicles, travel times past the bottleneck are substantially reduced. Control is responsible for delaying the onset of congestion as the incoming flow increases. Adaptive cruise control increases the flow out of the congested state at the bottleneck. The nature of the congested state, when it occurs, appears to be similar under a variety of conditions. Typically 80-100 vehicles are approximately equally distributed between the lanes in the 500 m region prior to the end of the terminated lane. Without the adaptive cruise control capability, connected vehicles can delay the onset of congestion but do not increase the asymptotic flow past the bottleneck. Calculations are done using the Kerner-Klenov three-phase theory, stochastic discrete-time model for manual vehicles. The dynamics of the connected vehicles is given by a conventional adaptive cruise control algorithm plus commanded deceleration. Because time in the model for manual vehicles is discrete (one-second intervals), it is assumed that the acceleration of any vehicle immediately in front of a connected vehicle is constant during the time interval, thereby preserving the computational simplicity and speed of a discrete-time model.
Scellato, S.; Fortuna, L.; Frasca, M.; Gómez-Gardeñes, J.; Latora, V.
Congestion in transport networks is a topic of theoretical interest and practical importance. In this paper we study the flow of vehicles in urban street networks. In particular, we use a cellular automata model on a complex network to simulate the motion of vehicles along streets, coupled with a congestion-aware routing at street crossings. Such routing makes use of the knowledge of agents about traffic in nearby roads and allows the vehicles to dynamically update the routes towards their destinations. By implementing the model in real urban street patterns of various cities, we show that it is possible to achieve a global traffic optimization based on local agent decisions.
Full Text Available We propose a more practical spectrum sensing optimization problem in cognitive radio networks (CRN, by considering the data traffic of second user (SU. Compared with most existing work, we do not assume that SU always has packets to transmit; instead, we use the actual data transmitted per second rather than the channel capacity as the achievable throughput, to reformulate the Sensing-Throughput Tradeoff problem. We mathematically analyze the problem of optimal sensing time to maximize the achievable throughput, based on the data traffic of SU. Our model is more general because the traditional Sensing-Throughput Tradeoff model can be seen as a special case of our model. We also prove that the throughput is a concave function of sensing time and there is only one optimal sensing time value which is determined by the data traffic. Simulation results show that the proposed approach outperforms existing methods.
The ever-increasing traffic demand makes the efficient use of airspace an imperative mission, and this paper presents an effort in response to this call. Firstly, a new aggregate model, called Link Transmission Model (LTM), is proposed, which models the nationwide traffic as a network of flight routes identified by origin-destination pairs. The traversal time of a flight route is assumed to be the mode of distribution of historical flight records, and the mode is estimated by using Kernel Density Estimation. As this simplification abstracts away physical trajectory details, the complexity of modeling is drastically decreased, resulting in efficient traffic forecasting. The predicative capability of LTM is validated against recorded traffic data. Secondly, a nationwide traffic flow optimization problem with airport and en route capacity constraints is formulated based on LTM. The optimization problem aims at alleviating traffic congestions with minimal global delays. This problem is intractable due to millions of variables. A dual decomposition method is applied to decompose the large-scale problem such that the subproblems are solvable. However, the whole problem is still computational expensive to solve since each subproblem is an smaller integer programming problem that pursues integer solutions. Solving an integer programing problem is known to be far more time-consuming than solving its linear relaxation. In addition, sequential execution on a standalone computer leads to linear runtime increase when the problem size increases. To address the computational efficiency problem, a parallel computing framework is designed which accommodates concurrent executions via multithreading programming. The multithreaded version is compared with its monolithic version to show decreased runtime. Finally, an open-source cloud computing framework, Hadoop MapReduce, is employed for better scalability and reliability. This framework is an "off-the-shelf" parallel computing model
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.
Bernhardsson, Viktor; Ringdahl, Rasmus
Traffic problems caused by congestion are increasing in cities all over the world. As a traffic management tool traffic predictions can be used in order to make prevention actions against traffic congestion. There is one software for traffic state estimations called Mobile Millennium Stockholm (MMS) that are a part of a project for estimate real-time traffic information.In this thesis a framework for running traffic predictions in the MMS software have been implemented and tested on a stretch...
Palubinskas, Gintautas; Kurz, Franz; Reinartz, Peter
Purpose A new model based approach for the traffic congestion detection in time series of airborne optical digital camera images is proposed. Methods It is based on the estimation of the average vehicle speed on road segments. The method puts various techniques together: the vehicle detection on road segments by change detection between two images with a short time lag, the usage of a priori information such as road data base, vehicle sizes and road parameters and a si...
Bottino, Andrea; Garbo, Alessandro; Loiacono, Carmelo; Quer, Stefano
The development of intelligent transportation systems requires the availability of both accurate traffic information in real time and a cost-effective solution. In this paper, we describe Street Viewer, a system capable of analyzing the traffic behavior in different scenarios from images taken with an off-the-shelf optical camera. Street Viewer operates in real time on embedded hardware architectures with limited computational resources. The system features a pipelined architecture that, on one side, allows one to exploit multi-threading intensively and, on the other side, allows one to improve the overall accuracy and robustness of the system, since each layer is aimed at refining for the following layers the information it receives as input. Another relevant feature of our approach is that it is self-adaptive. During an initial setup, the application runs in learning mode to build a model of the flow patterns in the observed area. Once the model is stable, the system switches to the on-line mode where the flow model is used to count vehicles traveling on each lane and to produce a traffic information summary. If changes in the flow model are detected, the system switches back autonomously to the learning mode. The accuracy and the robustness of the system are analyzed in the paper through experimental results obtained on several different scenarios and running the system for long periods of time. PMID:27271627
The purpose of this research is to design and implement a road traffic congestion and traffic patterns simulation (TPS) model and integrate it with extension-information model (EIM). The problems of road traffic simulation and control are studied according to the method of extension information model, and from the spatio-temporal analysis point of view. The rules of the traffic simulation from existence to evolution are analyzed using theories. Based on this study, the concept of traffic syst...
Tavassoli Hojati, Ahmad; Ferreira, Luis; Washington, Simon; Charles, Phil
Assessing and prioritising cost-effective strategies to mitigate the impacts of traffic incidents and accidents on non-recurrent congestion on major roads represents a significant challenge for road network managers. This research examines the influence of numerous factors associated with incidents of various types on their duration. It presents a comprehensive traffic incident data mining and analysis by developing an incident duration model based on twelve months of incident data obtained from the Australian freeway network. Parametric accelerated failure time (AFT) survival models of incident duration were developed, including log-logistic, lognormal, and Weibul-considering both fixed and random parameters, as well as a Weibull model with gamma heterogeneity. The Weibull AFT models with random parameters were appropriate for modelling incident duration arising from crashes and hazards. A Weibull model with gamma heterogeneity was most suitable for modelling incident duration of stationary vehicles. Significant variables affecting incident duration include characteristics of the incidents (severity, type, towing requirements, etc.), and location, time of day, and traffic characteristics of the incident. Moreover, the findings reveal no significant effects of infrastructure and weather on incident duration. A significant and unique contribution of this paper is that the durations of each type of incident are uniquely different and respond to different factors. The results of this study are useful for traffic incident management agencies to implement strategies to reduce incident duration, leading to reduced congestion, secondary incidents, and the associated human and economic losses. PMID:23333698
Full Text Available Periodic lightpath reconfiguration of virtual topologies in transparent optical networks has been recently investigated as a mechanism to more efficiently adapt the network to predictable periodic traffic variations along a day or week. Scheduling periodic reconfigurations involves tuning a trade-off between a lower network cost obtained through better resource allocation, and undesired traffic disruptions that these reconfigurations may cause. This paper presents and compares two algorithms for planning a reconfigurable virtual topology suitable for exploring this trade-off. The first is based on a Lagrangean Relaxation of the planning problem, and the second is based on a Tabu Search meta-heuristic. The merits of both algorithms are assessed for moderate network sizes through comparison with analytical lower bounds and exact solutions obtained by a MILP formulation.
Full Text Available The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Particle swarm optimization is an evolutionary computation technology based on swarm intelligence which can not guarantee global convergence. Artificial Bee Colony algorithm is a global optimum algorithm with many advantages such as simple, convenient and strong robust. In this paper, a new BP neural network based on Artificial Bee Colony algorithm and particle swarm optimization algorithm is proposed to optimize the weight and threshold value of BP neural network. After network traffic prediction experiment, we can conclude that optimized BP network traffic prediction based on PSO-ABC has high prediction accuracy and has stable prediction performance.
Wedde, Horst F.; Lehnhoff, Sebastian; van Bonn, Bernhard; Bay, Z.; Becker, S.; Böttcher, S.; Brunner, C.; Büscher, A.; Fürst, T.; Lazarescu, A. M.; Rotaru, E.; Senge, S.; Steinbach, B.; Yilmaz, F.; Zimmermann, T.
Traffic congestions have become a major problem in metropolitan areas world-wide, within and between cities, to an extent where they make driving and transportation times largely unpredictable. Due to the highly dynamic character of congestion building and dissolving this phenomenon appears even to resist a formal treatment. Static approaches, and even more their global management, have proven counterproductive in practice. Given the latest progress in VANET technology and the remarkable commercially driven efforts like in the European C2C consortium, or the VSC Project in the US, allow meanwhile to tackle various aspects of traffic regulation through VANET communication. In this paper we introduce a novel, completely decentralized multi-agent routing algorithm (termed BeeJamA) which we have derived from the foraging behavior of honey bees. It is highly dynamic, adaptive, robust, and scalable, and it allows for both avoiding congestions, and minimizing traveling times to individual destinations. Vehicle guidance is provided well ahead of every intersection, depending on the individual speeds. Thus strict deadlines are imposed on, and respected by, the BeeJamA algorithm. We report on extensive simulation experiments which show the superior performance of BeeJamA over conventional approaches.
Ramaekers, Katrien; Kochan, Bruno; BELLEMANS, Tom; JANSSENS, Davy; Wets, Geert
A custom agent-based simulation framework is developed that combines the fields of traffic demand modeling and traffic assignment, applied to the region of Flanders (Belgium). The framework uses an activity-based approach to model traffic demand and an assignment module that is linked to the traffic demand module. Activity data for the framework is provided by a large scale survey, conducted on 2500 households in the study area. The agent-based simulation model consists of over six million ag...
Bujlow, Tomasz; Pedersen, Jens Myrup
Understanding Internet traffic is crucial in order to facilitate academic research and practical network engineering, e.g. when doing traffic classification, prioritization of traffic, creating realistic scenarios and models for Internet traffic development etc. In this paper we demonstrate how the...... Volunteer-Based System for Research on the Internet, developed at Aalborg University, is capable of providing detailed statistics of Internet usage. Since an increasing amount of HTTP traffic has been observed during the last few years, the system also supports creating statistics of different kinds of HTTP...... be useful for studying characteristics of computer network traffic in application-oriented or content-type- oriented way, and is now ready for a larger-scale implementation. The paper is concluded with a discussion about various applications of the system and possibilities of further enhancement....
This thesis describes the detection and reconstruction of traffic accidents with event data recorders. The underlying idea is to describe the vehicle motion and dynamics up to the stability limit by means of linear and non-linear vehicle models. These models are used to categorize the driving behavior and to freeze the recorded data in a memory if an accident occurs. Based on these data, among others the vehicle trajectory is reconstructed with fuzzy data fusion. The side slip angle whi...
Most mobile traffic simulators of today depend on the user to supply the mobility behavior of the simulated UEs. This becomes a problem when certain wanted mobility characteristics are to be tested, since the user have to go trough a trial-and-error procedure to come up with the proper mobility behavior. This thesis presents two approaches to mobility control, where the aim is to control UE mobility based on certain mobility characteristics supplied by the end user. The first approach introdu...
Bujlow, Tomasz; Balachandran, Kartheepan; Hald, Sara Ligaard;
To overcome the drawbacks of existing methods for traffic classification (by ports, Deep Packet Inspection, statistical classification) a new system was developed, in which the data are collected and classified directly by clients installed on machines belonging to volunteers. Our approach combines....... We released the system under The GNU General Public License v3.0 and published as a SourceForge project called Volunteer-Based System for Research on the Internet....
Bell, Alan E.
Since the emergence of commercial aviation in the early part of last century, economic forces have driven a steadily increasing demand for air transportation. Increasing density of aircraft operating in a finite volume of airspace is accompanied by a corresponding increase in the risk of collision, and in response to a growing number of incidents and accidents involving collisions between aircraft, governments worldwide have developed air traffic control systems and procedures to mitigate this risk. The objective of any collision risk management system is to project conflicts and provide operators with sufficient opportunity to recognize potential collisions and take necessary actions to avoid them. It is therefore the assertion of this research that the currency of collision risk management is time. Future Air Traffic Management Systems are being designed around the foundational principle of four dimensional trajectory based operations, a method that replaces legacy first-come, first-served sequencing priorities with time-based reservations throughout the airspace system. This research will demonstrate that if aircraft are to be sequenced in four dimensions, they must also be separated in four dimensions. In order to separate aircraft in four dimensions, time must emerge as the primary tool by which air traffic is managed. A functional relationship exists between the time-based performance of aircraft, the interval between aircraft scheduled to cross some three dimensional point in space, and the risk of collision. This research models that relationship and presents two key findings. First, a method is developed by which the ability of an aircraft to meet a required time of arrival may be expressed as a robust standard for both industry and operations. Second, a method by which airspace system capacity may be increased while maintaining an acceptable level of collision risk is presented and demonstrated for the purpose of formulating recommendations for procedures
Soldo, Fabio; Markopoulou, Athina
In this paper, we consider the problem of blocking malicious traffic on the Internet, via source-based filtering. In particular, we consider filtering via access control lists (ACLs): these are already available at the routers today but are a scarce resource because they are stored in the expensive ternary content addressable memory (TCAM). Aggregation (by filtering source prefixes instead of individual IP addresses) helps reduce the number of filters, but comes also at the cost of blocking legitimate traffic originating from the filtered prefixes. We show how to optimally choose which source prefixes to filter, for a variety of realistic attack scenarios and operators' policies. In each scenario, we design optimal, yet computationally efficient, algorithms. Using logs from Dshield.org, we evaluate the algorithms and demonstrate that they bring significant benefit in practice.
Zhao Qigang; Li Qunzhan; He Zhengyou
By using netflow traffic collecting technology, some traffic data for analysis are collected from a next generation network (NGN) operator. To build a wavelet basis neural network (NN), the Sigmoid function is replaced with the wavelet in NN. Then the wavelet multiresolution analysis method is used to decompose the traffic signal, and the decomposed component sequences are employed to train the NN. By using the methods, an NGN traffic prediction model is built to predict one day's traffic. The experimental results show that the traffic prediction method of wavelet NN is more accurate than that without using wavelet in the NGN traffic forecasting.
Fortz, Bernard; Rexford, Jennifer; Thorup, Mikkel
Traffic engineering involves adapting the routing of traffic to the network conditions, with the joint goals of good user performance and efficient use of network resources. In this paper, we describe an approach to intradomain traffic engineering that works within the existing deployed base of Interior Gateway Protocols (IGPs), such as Open Shortest Path First (OSPF) and Intermediate System-Intermediate System (IS-IS). We explain how to adapt the configuration of link weights, based on a ...
Full Text Available Nowadays, number of vehicles has been increased and traditional systems of traffic controlling couldn’t be able to meet the needs that cause to emergence of Intelligent Traffic Controlling Systems. They improve controlling and urban management and increase confidence index in roads and highways. The goal of thisarticle is vehicles classification base on neural networks. In this research, it has been used a immovable camera which is located in nearly close height of the road surface to detect and classify the vehicles. The algorithm that used is included two general phases; at first, we are obtaining mobile vehicles in the traffic situations by using some techniques included image processing and remove background of the images and performing edge detection and morphology operations. In the second phase, vehicles near the camera areselected and the specific features are processed and extracted. These features apply to the neural networks as a vector so the outputs determine type of vehicle. This presented model is able to classify the vehicles in three classes; heavy vehicles, light vehicles and motorcycles. Results demonstrate accuracy of the algorithm and its highly functional level.
Wang, Li-li; Ngan, Henry Y. T.; Yung, Nelson H. C.
Modern cities experience heavy traffic flows and congestions regularly across space and time. Monitoring traffic situations becomes an important challenge for the Traffic Control and Surveillance Systems (TCSS). In advanced TCSS, it is helpful to automatically detect and classify different traffic incidents such as severity of congestion, abnormal driving pattern, abrupt or illegal stop on road, etc. Although most TCSS are equipped with basic incident detection algorithms, they are however cr...
Hasan Omar Al-Sakran
In recent years popularity of private cars is getting urban traffic more and more crowded. As result traffic is becoming one of important problems in big cities in all over the world. Some of the traffic concerns are congestions and accidents which have caused a huge waste of time, property damage and environmental pollution. This research paper presents a novel intelligent traffic administration system, based on Internet of Things, which is featured by low cost, high scalability, high compat...
Zhenshan Yang; Yunli Zhang
Elevator traffic demand forecasting is the essential prerequisite for effectively implementing elevator group control system (EGCS). Considering that there exists lots of abnomal information in elevator traffic caused by subjectivity and occasionality in human behaviour and that observing traffic information continuously is costly and difficult, an improved grey forecasting based method using multi-model to forecast future elevator traffic demand of EGCS is proposed, the abnomal information w...
Lack of traffic safety has become a serious issue in residential areas. In this paper, a web-based advisory expert system for the purpose of applying traffic calming strategies on residential streets is described because there currently lacks a structured framework for the implementation of such strategies. Developing an expert system can assist and advise engineers for dealing with traffic safety problems. This expert system is developed to fill the gap between the traffic safety experts and...
Marinas, Javier; Salgado, Luis; Arróspide, Jon; Camplani, Massimo
In this paper we propose an innovative method for the automatic detection and tracking of road traffic signs using an onboard stereo camera. It involves a combination of monocular and stereo analysis strategies to increase the reliability of the detections such that it can boost the performance of any traffic sign recognition scheme. Firstly, an adaptive color and appearance based detection is applied at single camera level to generate a set of traffic sign hypotheses. In turn, stereo information allows for sparse 3D reconstruction of potential traffic signs through a SURF-based matching strategy. Namely, the plane that best fits the cloud of 3D points traced back from feature matches is estimated using a RANSAC based approach to improve robustness to outliers. Temporal consistency of the 3D information is ensured through a Kalman-based tracking stage. This also allows for the generation of a predicted 3D traffic sign model, which is in turn used to enhance the previously mentioned color-based detector through a feedback loop, thus improving detection accuracy. The proposed solution has been tested with real sequences under several illumination conditions and in both urban areas and highways, achieving very high detection rates in challenging environments, including rapid motion and significant perspective distortion.
Full Text Available The existing dynamic traffic assignment researches mostly based on ideal hypothesis conditions which can analyze the affection of all kinds of traffic parameters on traffic flow and find out characteristics of various types of traffic distribution, but there is rarely have accurate calculation of flow distribution model. The study will first apply the network equilibrium theory into dynamic traffic flow assignment. Using Leaky Bucket Controller and Network Calculus, complicated traffic elements will incorporate into unified mathematical model called T-S Constrained Model, we can deduce flow assignment rate which is in a delay-limited constraints. The simulation results manifest that the model can not only solves congestion, but also reduce average delay of every path, it can extremely improve the traffic capacity of road network. The accurate assignment solutions will have significant impact on traffic engineering implementation.
Full Text Available Classification of network traffic is basic and essential for manynetwork researches and managements. With the rapid development ofpeer-to-peer (P2P application using dynamic port disguisingtechniques and encryption to avoid detection, port-based and simplepayload-based network traffic classification methods were diminished.An alternative method based on statistics and machine learning hadattracted researchers' attention in recent years. However, most ofthe proposed algorithms were off-line and usually used a single classifier.In this paper a new hierarchical real-time model was proposed which comprised of a three tuple (source ip, destination ip and destination portlook up table(TT-LUT part and layered milestone part. TT-LUT was used to quickly classify short flows whichneed not to pass the layered milestone part, and milestones in layered milestone partcould classify the other flows in real-time with the real-time feature selection and statistics.Every milestone was a ECOC(Error-Correcting Output Codes based model which was usedto improve classification performance. Experiments showed that the proposedmodel can improve the efficiency of real-time to 80%, and themulti-class classification accuracy encouragingly to 91.4% on the datasets which had been captured from the backbone router in our campus through a week.
Detecting traffic flow by in-road inductive loop is the most common methods, but inductive loop is physically large, it is hard to install and maintain, also the classification rate is low. The inductive loops cannot communicate with each other, so they cannot share traffic data with each other. The wireless sensor network has these features: real-time, fault tolerance, scalability and coordination. Applying wireless sensor network into traffic area for traffic flow detection is easier to ins...
Dr.V. Palanisamy#1 , K. Gowri
Traffic engineering is an important mechanism for Internet network providers seeking to optimize network performance and traffic delivery. Routing optimization plays a key role in traffic engineering, finding efficient routes so as to achieve the desired network performance. BGP is the de facto protocol used for inter-autonomous system routing in the Internet. BGP has been proven to be secure, efficient, scalable, and robust. In proposed introduced AMPLE – an efficient traffic engineering and...
The accurate and timely traffic state prediction has become increasingly important for the traffic participants,especially for the traffic managements. In this paper,the traffic state is described by Micro-LOS,and a direct prediction method is introduced. The development of the proposed method is based on Maximum Entropy (ME) models trained for multiple modes. In the Multimode Maximum Entropy (MME) framework,the different features like temporal and spatial features of traffic systems,regional traffic state are integrated simultaneously,and the different state behaviors based on 14 traffic modes defined by average speed according to the date-time division are also dealt with. The experiments based on the real data in Beijing expressway prove that the MME models outperforms the already existing model in both effectiveness and robustness.
Liu, Chunfeng; Shu, Yantai; Yang, Oliver W. W.; Liu, Jiakun; Dong, Linfang
In this paper, Extreme Value Theory (EVT) is presented to analyze wireless network traffic. The role of EVT is to allow the development of procedures that are scientifically and statistically rational to estimate the extreme behavior of random processes. There are two primary methods for studying extremes: the Block Maximum (BM) method and the Points Over Threshold (POT) method. By taking limited traffic data that is greater than the threshold value, our experiment and analysis show the wireless network traffic model obtained with the EVT fits well with that of empirical distribution of traffic, thus illustrating that EVT has a good application foreground in the analysis of wireless network traffic.
Full Text Available In recent years, the volume of traffic is rapidly increasing. When vehicles running through the tunnel are more intensive or move slowly, the tunnel environment occurs deteriorated sharply, which affects the normal operation of the vehicle in the tunnel. This paper uses the result of previous mining association rules to select feature items and to establish four training samples divided by time. Then the training samples are utilized to create the SVM classification model. Finally the trained SVM model is used to prediction the tunnel traffic situation. Through traffic situation prediction, effective decisions can be made before traffic jams, and ensure that the tunnel traffic is normal.
Davis, L. C.
Mixed traffic flow consisting of vehicles equipped with adaptive cruise control (ACC) and manually driven vehicles is analyzed using car-following simulations. Simulations of merging from an on-ramp onto a freeway reported in the literature have not thus far demonstrated a substantial positive impact of ACC. In this paper cooperative merging for ACC vehicles is proposed to improve throughput and increase distance traveled in a fixed time. In such a system an ACC vehicle senses not only the preceding vehicle in the same lane but also the vehicle immediately in front in the other lane. Prior to reaching the merge region, the ACC vehicle adjusts its velocity to ensure that a safe gap for merging is obtained. If on-ramp demand is moderate, cooperative merging produces significant improvement in throughput (20%) and increases up to 3.6 km in distance traveled in 600 s for 50% ACC mixed flow relative to the flow of all-manual vehicles. For large demand, it is shown that autonomous merging with cooperation in the flow of all ACC vehicles leads to throughput limited only by the downstream capacity, which is determined by speed limit and headway time.
A method is proposed to find key components of traffic networks with homogenous and heterogeneous topologies, in which heavier traffic flow is transported. One component, called the skeleton, is the minimum spanning tree (MST) based on the zero flow cost (ZCMST). The other component is the infinite incipient percolation cluster (IIC) which represents the spine of the traffic network. Then, a new method to analysis the property of the bottleneck in a large scale traffic network is given from a macroscopic and statistical viewpoint. Moreover, three effective strategies are proposed to alleviate traffic congestion. The significance of the findings is that one can significantly improve the global transport by enhancing the capacity in the ZCMST with a few links, while for improving the local traffic property, improving a tiny fraction of the traffic network in the IIC is effective. The result can be used to help traffic managers prevent and alleviate traffic congestion in time, guard against the formation of congestion bottleneck, and make appropriate policies for traffic demand management. Meanwhile, the method has very important theoretical significance and practical worthiness in optimizing traffic organization, traffic control, and disposal of emergency.
Khan, F; Gokhale, M; Chuah, C N
The problem of Network Traffic Classification (NTC) has attracted significant amount of interest in the research community, offering a wide range of solutions at various levels. The core challenge is in addressing high amounts of traffic diversity found in today's networks. The problem becomes more challenging if a quick detection is required as in the case of identifying malicious network behavior or new applications like peer-to-peer traffic that have potential to quickly throttle the network bandwidth or cause significant damage. Recently, Traffic Dispersion Graphs (TDGs) have been introduced as a viable candidate for NTC. The TDGs work by forming a network wide communication graphs that embed characteristic patterns of underlying network applications. However, these patterns need to be quickly evaluated for mounting real-time response against them. This paper addresses these concerns and presents a novel solution for real-time analysis of Traffic Dispersion Metrics (TDMs) in the TDGs. We evaluate the dispersion metrics of interest and present a dedicated solution on an FPGA for their analysis. We also present analytical measures and empirically evaluate operating effectiveness of our design. The mapped design on Virtex-5 device can process 7.4 million packets/second for a TDG comprising of 10k flows at very high accuracies of over 96%.
Maravas, Alexander; Stengel, Robert F.
Transportation network capacity variations arising from accidents, roadway maintenance activity, and special events as well as fluctuations in commuters' travel demands complicate traffic management. Artificial intelligence concepts and expert systems can be useful in framing policies for incident detection, congestion anticipation, and optimal traffic management. This paper examines the applicability of intelligent route guidance and control as decision aids for traffic management. Basic requirements for managing traffic are reviewed, concepts for studying traffic flow are introduced, and mathematical models for modeling traffic flow are examined. Measures for quantifying transportation network performance levels are chosen, and surveillance and control strategies are evaluated. It can be concluded that automated decision support holds great promise for aiding the efficient flow of automobile traffic over limited-access roadways, bridges, and tunnels.
JIN Xuexiang; ZHANG Yi; LI Li; HU Jianming
One key function of intelligent transportation systems is to automatically detect abnormal traffic phenomena and to help further investigations of the cause of the abnormality. This paper describes a robust principal components analysis (RPCA)-based abnormal traffic flow pattern isolation and loop detector fault detection method. The results show that RPCA is a useful tool to distinguish regular traffic flow from abnor-mal traffic flow patterns caused by accidents and loop detector faults. This approach gives an effective traffic flow data pre-processing method to reduce the human effort in finding potential loop detector faults. The method can also be used to further investigate the causes of the abnormality.
Traffic network is an importance aspect of researching controllable parameters of an urban spatial morpholo-gy. Based on GIS, traffic network structure complexity can be understood by using fractal geometry in which thelength-radius dimension describes change of network density, and ramification-radius dimension describes complexity andaccessibility of urban network. It is propitious to analyze urban traffic network and to understand dynamic change processof traffic network using expanding fractal-dimension quantification. Meanwhile the length-radius dimension and ramifica-tion-radius dimension could be regard as reference factor of quantitative describing urban traffic network.
E.R. Naganathan; Rajagopalan, S.; P. H. Raj
Problem statement: Multi-Protocol Label Switching (MPLS) is a mechanism which is used in high-performance telecommunications networks that directs and carries data from one network node to the next with the help of labels. Traffic management is still an issue in MPLS network as it involves high speed internet. Approach: This study proposed a traffic flow analysis of the real time MPLS traffic and segregates the MPLS traffic as three major class based on the outcome of traffic flow analysis. U...
Shigehiro, Yuji; Miyakawa, Takuya; Masuda, Tatsuya
In this paper, we propose a road traffic control method for reducing traffic congestion with genetic algorithm. In the not too distant future, the system which controls the routes of all vehicles in a certain area must be realized. The system should optimize the routes of all vehicles, however the solution space of this problem is enormous. Therefore we apply the genetic algorithm to this problem, by encoding the route of all vehicles to a fixed length chromosome. To improve the search performance, a new genetic operator called “path shortening” is also designed. The effectiveness of the proposed method is shown by the experiment.
HE HaiTao; LUO XiaoNan; MA FeiTeng; CHE ChunHui; WANG JianMin
Classification of network traffic Is the essential step for many network researches. However, with the rapid evolution of Internet applications the effectiveness of the port-based or payload-based identifi-cation approaches has been greatly diminished In recent years. And many researchers begin to turn their attentions to an alternative machine learning based method. This paper presents a novel machine learning-based classification model, which combines ensemble learning paradigm with co-training tech-niques. Compared to previous approaches, most of which only employed single classifier, multiple clas-sifiers and semi-supervised learning are applied in our method and it mainly helps to overcome three shortcomings: limited flow accuracy rate, weak adaptability and huge demand of labeled training set. In this paper, statistical characteristics of IP flows are extracted from the packet level traces to establish the feature set, then the classification model is created and tested and the empirical results prove its feasibility and effectiveness.
Full Text Available Based on microscopic traffic characteristics of two-lane highway and different driving characteristics for drivers, the characteristics of drivers and vehicle structure are introduced into Cellular Automation model for establishing new Cellular Automation model of two-lane highway. Through computer simulation, the paper analyzes the effect of the promotion of different vehicles, drivers and arrival rates on traffic conflicts of two-lane highway, which gets the relationship between the parameters such as road traffic and velocity variance and collision. The results indicate that the frequency of traffic conflicts has close relationship with the product of traffic flow and velocity variation. When the traffic flow and velocity variation are great, the frequency of the conflict is the greatest, and when the traffic flow and velocity variation are little, the frequency of the conflict is the least.
Wang, Zhe; Xu, Ke; Yin, Baolin
The network traffic matrix is a kind of flow-level Internet traffic data and is widely applied to network operation and management. It is a crucial problem to analyze the composition and structure of traffic matrix; some mathematical approaches such as Principal Component Analysis (PCA) were used to handle that problem. In this paper, we first argue that PCA performs poorly for analyzing traffic matrixes polluted by large volume anomalies, then propose a new composition model of the network traffic matrix. According to our model, structure analysis can be formally defined as decomposing a traffic matrix into low-rank, sparse, and noise sub-matrixes, which is equal to the Robust Principal Component Analysis (RPCA) problem defined in . Based on the Relaxed Principal Component Pursuit (Relaxed PCP) method and the Accelerated Proximal Gradient (APG) algorithm, an iterative algorithm for decomposing a traffic matrix is presented, and our experiment results demonstrate its efficiency and flexibility. At last, f...
Wang Jun-Song; Yuan Jing; Li Qiang; Yuan Rui-Xi
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.
Hasan Omar Al-Sakran
Full Text Available In recent years popularity of private cars is getting urban traffic more and more crowded. As result traffic is becoming one of important problems in big cities in all over the world. Some of the traffic concerns are congestions and accidents which have caused a huge waste of time, property damage and environmental pollution. This research paper presents a novel intelligent traffic administration system, based on Internet of Things, which is featured by low cost, high scalability, high compatibility, easy to upgrade, to replace traditional traffic management system and the proposed system can improve road traffic tremendously. The Internet of Things is based on the Internet, network wireless sensing and detection technologies to realize the intelligent recognition on the tagged traffic object, tracking, monitoring, managing and processed automatically. The paper proposes an architecture that integrates internet of things with agent technology into a single platform where the agent technology handles effective communication and interfaces among a large number of heterogeneous highly distributed, and decentralized devices within the IoT. The architecture introduces the use of an active radio-frequency identification (RFID, wireless sensor technologies, object ad-hoc networking, and Internet-based information systems in which tagged traffic objects can be automatically represented, tracked, and queried over a network. This research presents an overview of a framework distributed traffic simulation model within NetLogo, an agent-based environment, for IoT traffic monitoring system using mobile agent technology.
Full Text Available The main object of this study was to design and implement a suitable algorithm and its simulation for an intelligent traffic signal simulator. The system developed is able to sense the presence or absence of vehicles within certain range by setting the appropriate duration for the traffic signals to react accordingly. By employing mathematical functions to calculate the appropriate timing for the green signal to illuminate, the system can help to solve the problem of traffic congestion. The simulation of the algorithm of the traffic signal system was done using MATLAB software. Hardware simulation tests were successfully performed on the algorithm implemented into a controller. The new timing scheme that was implemented promises an improvement in the current traffic light system and this system is feasible, affordable and ready to be implemented especially during peak hours. A countdown timer interfacing according to the traffic system using Lab VIEW software was also created.
Zhang Mingheng; Zhen Yaobao; Hui Ganglong; Chen Gang
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 mul...
Touko Tcheumadjeu, Louis Calvin; Ruppe, Sten; Brockfeld, Elmar; Yahyaoui, Younes
The DLR Traffic Data Platform (TDP) that is currently being developed by the German Aerospace Center (DLR) is an autonomous decentralised ITS system for distributed intelligent traffic data management and dissemination. Of course, there are many possibilities to design the architecture of such a traffic data platform where service oriented architecture (SOA) has been chosen for the current design of the TDP. In this paper the SOA design aspect of the TDP will be analysed and presented. The...
Tcheumadjeu, Touko; Calvin, Louis; Ruppe, Sten; Brockfeld, Elmar; Yahyaoui, Younes
The DLR Traffic Data Platform (TDP) that is currently being developed by the German Aerospace Center (DLR) is an autonomous decentralised ITS system for distributed intelligent traffic data management and dissemination. Of course, there are many possibilities to design the architecture of such a traffic data platform where service oriented architecture (SOA) has been chosen for the current design of the TDP. In this paper the SOA design aspect of the TDP will be analysed and presented. The TD...
Nowadays one of the hottest theme in wireless environments research is the application of the newest technologies to road safety problems and traffic management exploiting the (VANET) architecture. In this work, a novel protocol that aims to achieve a better traffic management is proposed. The overal system is able to reduce traffic level inside the city exploiting inter-communication among vehicles and support infrastructures also known as (V2V) and (V2I) communications. We design a network ...
Xuetao Du; Jian Yuan; Peng Gao; Jia Liu
Mechanisms to extract the characteristics of network traffic play a significant role in traffic monitoring, offering helpful information for network management and control. In this paper, a method based on Random Matrix Theory (RMT) and Principal Components Analysis (PCA) is proposed for monitoring and analyzing large-scale traffic patterns in the Internet. Besides the analysis of the largest eigenvalue in RMT, useful information is also extracted from small eigenvalues by a method based on P...
S H Melouk; B B Keskin; C Armbrester; M. Anderson
Traffic congestion has grown considerably in the United States over the past 20 years. In this paper, we develop a robust decision support tool based on simulation optimization to evaluate and recommend congestion mitigation strategies to transportation system decision-makers. A tabu search-based optimizer determines different network design strategies on the road network while a traffic simulator evaluates the goodness of fit. The tool is tested with real-world traffic data.
Yiliang Zeng; Jinhui Lan; Bin Ran; Yaoliang Jiang
A novel multisensor system with incomplete data is presented for traffic state assessment. The system comprises probe vehicle detection sensors, fixed detection sensors, and traffic state assessment algorithm. First of all, the validity checking of the traffic flow data is taken as preprocessing of this method. And then a new method based on the history data information is proposed to fuse and recover the incomplete data. According to the characteristics of space complementary of data based o...
M, Venkatesh; V, Srinivas
The way of analyzing, designing and building of real-time projects has been changed due to the rapid growth of internet, mobile technologies and intelligent applications. Most of these applications are intelligent, tiny and distributed components called as agent. Agent works like it takes the input from numerous real-time sources and gives back the real-time response. In this paper how these agents can be implemented in vehicle traffic management especially in large cities and identifying various challenges when there is a rapid growth of population and vehicles. In this paper our proposal gives a solution for using autonomous or agent based technology. These autonomous or intelligent agents have the capability to observe, act and learn from their past experience. This system uses the knowledge flow of precedent signal or data to identify the incoming flow of forthcoming signal. Our architecture involves the video analysis and exploration using some Intelligence learning algorithm to estimate and identify the...
Landry, Steven J.; Farley, Todd; Hoang, Ty
Time-based metering is an efficient air traffic management alternative to the more common practice of distance-based metering (or "miles-in-trail spacing"). Despite having demonstrated significant operational benefit to airspace users and service providers, time-based metering is used in the United States for arrivals to just nine airports and is not used at all for non-arrival traffic flows. The Multi-Center Traffic Management Advisor promises to bring time-based metering into the mainstream of air traffic management techniques. Not constrained to operate solely on arrival traffic, Multi-Center Traffic Management Advisor is flexible enough to work in highly congested or heavily partitioned airspace for any and all traffic flows in a region. This broader and more general application of time-based metering is expected to bring the operational benefits of time-based metering to a much wider pool of beneficiaries than is possible with existing technology. It also promises to facilitate more collaborative traffic management on a regional basis. This paper focuses on the operational concept of the Multi-Center Traffic Management Advisor, touching also on its system architecture, field test results, and prospects for near-term deployment to the United States National Airspace System.
Han, Keesook J.; Hodge, Matthew; Ross, Virginia W.
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.
Full Text Available These days, mass-produced vehicles benefit from research on Intelligent Transportation System (ITS. One prime example of ITS is vehicle Cruise Control (CC, which allows it to maintain a pre-defined reference speed, to economize on fuel or energy consumption, to avoid speeding fines, or to focus all of the driver’s attention on the steering of the vehicle. However, achieving efficient Cruise Control is not easy in roads or urban streets where sudden changes of the speed limit can happen, due to the presence of unexpected obstacles or maintenance work, causing, in inattentive drivers, traffic accidents. In this communication we present a new Infrastructure to Vehicles (I2V communication and control system for intelligent speed control, which is based upon Radio Frequency Identification (RFID technology for identification of traffic signals on the road, and high accuracy vehicle speed measurement with a Hall effect-based sensor. A fuzzy logic controller, based on sensor fusion of the information provided by the I2V infrastructure, allows the efficient adaptation of the speed of the vehicle to the circumstances of the road. The performance of the system is checked empirically, with promising results.
Pérez, Joshué; Seco, Fernando; Milanés, Vicente; Jiménez, Antonio; Díaz, Julio C.; de Pedro, Teresa
These days, mass-produced vehicles benefit from research on Intelligent Transportation System (ITS). One prime example of ITS is vehicle Cruise Control (CC), which allows it to maintain a pre-defined reference speed, to economize on fuel or energy consumption, to avoid speeding fines, or to focus all of the driver’s attention on the steering of the vehicle. However, achieving efficient Cruise Control is not easy in roads or urban streets where sudden changes of the speed limit can happen, due to the presence of unexpected obstacles or maintenance work, causing, in inattentive drivers, traffic accidents. In this communication we present a new Infrastructure to Vehicles (I2V) communication and control system for intelligent speed control, which is based upon Radio Frequency Identification (RFID) technology for identification of traffic signals on the road, and high accuracy vehicle speed measurement with a Hall effect-based sensor. A fuzzy logic controller, based on sensor fusion of the information provided by the I2V infrastructure, allows the efficient adaptation of the speed of the vehicle to the circumstances of the road. The performance of the system is checked empirically, with promising results. PMID:22219692
Bin He; Qiang Lu
In city traffic, it is important to improve transportation efficiency and the spacing of platoon should be shortened when crossing the street. The best method to deal with this problem is automatic control of vehicles. In this paper, a mathematical model is established for the platoon’s longitudinal movement. A systematic analysis of longitudinal control law is presented for the platoon of vehicles. However, the parameter calibration for the platoon model is relatively difficult because the p...
Coensel, B.de; Vanhove, F.; Logghe, S.; Wilmink, I.; Botteldooren, D.
One of the goals of the European IMAGINE project, is to formulate strategies to improve traffic modelling for application in noise mapping. It is well known that the specific deceleration and acceleration dynamics of traffic at junctions can influence local noise emission. However, macroscopic traff
Zhijie, Han; Ruchuang, Wang
In this paper, the authors first propose an efficient traffic prediction algorithm for sensor nodes which exploits the Markov model. Based on this algorithm, a distributed anomaly detection scheme, TPID(Traffic Prediction based Intrusion Detection), is designed to detect the attacks which make more influence on packet traffic, such as selective forwarding attacks, DOS attacks. In TPID, each node acts independently when predicting the traffic and detecting an anomaly. Neither special hardware nor nodes cooperation is needed. The scheme is evaluated and compared with other method in experiments. Results show that the proposed scheme obtain high detection ratio with less computation and communication cost.
Full Text Available The purpose of this research is to design and implement a road traffic congestion and traffic patterns simulation (TPS model and integrate it with extension-information model (EIM. The problems of road traffic simulation and control are studied according to the method of extension information model, and from the spatio-temporal analysis point of view. The rules of the traffic simulation from existence to evolution are analyzed using theories. Based on this study, the concept of traffic system entropy is introduced, and resulted in the establishment of a fundamental frame work for the road traffic simulation system based on extension spatio-temporal information system. Moreover, a practicable methodology is presented.
Full Text Available Paper presents basic information about application of Optical Correlator (OC, specifically Cambridge Correlator, in system to recognize of traffic sign. Traffic Sign Recognition System consists of three main blocks, Preprocessing, Optical Correlator and Traffic Sign Identification. The Region of Interest (ROI is defined and chosen in preprocessing block and then goes to Optical Correlator, where is compared with database of Traffic Sign. Output of Optical Correlation is correlation plane, which consist of highly localized intensities, know as correlation peaks. The intensity of spots provides a measure of similarity and position of spots, how images (traffic signs are relatively aligned in the input scene. Several experiments have been done with proposed system and results and conclusion are discussed.
Andersson, Henrik; Ögren, Mikael
One way of mitigating the negative effects of noise from road traffic is to include the external cost of noise in a road charging system. This study shows how standardized calculation methods for road traffic noise can be used together with monetary estimates of the social cost of noise exposure to calculate charges based on the social marginal cost. Using Swedish data on traffic volume and individuals exposed to road noise, together with official Swedish monetary values for noise exposure, w...
WANG Wei-gong; LI Zheng; CHENG Mei-ling
The local multiple regression fuzzy(LMRF)model based on Takagi-Sugeno fuzzy logical system and its application in traffic forecasting is proposed. Besides its prediction accuracy is testified and the model is proved much better than conventional forecasting methods. According to the regional traffic system, the model perfectly states the complex non-linear relation of the traffic and the local social economy. The model also efficiently deals with the system lack of enough data.
Angela-Aida Karugila Runyoro
Full Text Available Vehicles saturation in transportation infrastructure causes traffic congestion, accidents, transportation delays and environment pollution. This problem can be resolved with proper management of traffic flow. Existing traffic management systems are challenged on capturing and processing real-time road data from wide area road networks. The main purpose of this study is to address the gap by implementing a mobile phone based Road Information Management System. The proposed system integrates three modules for data collection, storage and information dissemination. The modules works together to enable real-time traffic control. Disseminated information from the system, enables road users to adjust their travelling habit, also it allows the traffic lights to control the traffic in relation to the real-time situation occurring on the road. In this paper the system implementation and testing was performed. The results indicated that there is a possibility to track traffic data using Global Positioning System enabled mobile phones, and after processing the collected data, real-time traffic status was displayed on web interface. This enabled road users to know in advance the situation occurring on the roads and hence make proper travelling decision. Further research should consider adjusting the traffic lights control system to understand the disseminated real-time traffic information.
Yu, Hao; Yan, Ying; Berger, Michael Stubert
Service (QoS) provisioning abilities, which guarantee end-to-end performances of voice, video and data traffic delivered over networks. This paper introduces a topology-based hierarchical scheduler scheme, which controls the incoming traffic at the edge of the network based on the network topology. This...
Full Text Available Scheduling in piconets has emerged as a challenging research area. Interpiconet scheduling focuses on when a bridge is switched among various piconets and how a bridge node communicates with the masters in different piconets. This paper proposes an interpiconet scheduling algorithm named, hold mode based dynamic traffic priority load adaptive scheduling. The bridges are adaptively switched between the piconets according to various traffic loads. The main goal is to maximize the utilization of the bridge by reducing the bridge switch wastes, utilize intelligent decision making algorithm, resolve conflict between the masters, and allow negotiation for bridge utilization in HDPLIS using bridge failure-bridge repair procedure . The Hold mode - dynamic traffic - priority based - load adaptive scheduling reduces the number of bridge switch wastes and hence increases the efficiency of the bridge which results in increased performance of the system.
This dissertation describes a new approach of the online traffic flow modelling based on the hydrodynamic traffic flow model and an online process to adapt the flow-density relation dynamically. The new modelling approach was tested based on the real traffic situations in various homogeneous motorway sections and a motorway section with ramps and gave encouraging simulation results. This work is composed of two parts: first the analysis of traffic flow characteristics and second the development of a new online traffic flow model applying these characteristics. For homogeneous motorway sections traffic flow is classified into six different traffic states with different characteristics. Delimitation criteria were developed to separate these states. The hysteresis phenomena were analysed during the transitions between these traffic states. The traffic states and the transitions are represented on a states diagram with the flow axis and the density axis. For motorway sections with ramps the complicated traffic fl...
Helbing, D; Lebacque, J P; Helbing, Dirk; L\\"ammer, Stefan; Lebacque, Jean-Patrick
We present a fluid-dynamic model for the simulation of urban traffic networks with road sections of different lengths and capacities. The model allows one to efficiently simulate the transitions between free and congested traffic, taking into account congestion-responsive traffic assignment and adaptive traffic control. We observe dynamic traffic patterns which significantly depend on the respective network topology. Synchronization is only one interesting example and implies the emergence of green waves. In this connection, we will discuss adaptive strategies of traffic light control which can considerably improve throughputs and travel times, using self-organization principles based on local interactions between vehicles and traffic lights. Similar adaptive control principles can be applied to other queueing networks such as production systems. In fact, we suggest to turn push operation of traffic systems into pull operation: By removing vehicles as fast as possible from the network, queuing effects can be ...
Ditze, Michael; Becker, Markus
This paper presents an adaptive near-optimal scheduler for multimedia traffic for the 802.11e Enhanced Distributed Channel Access (EDCA) medium access control scheme. The scheduler exploits the ant colony optimization (ACO) meta heuristic to tackle the challenge of packet scheduling. ACO is a biologically inspired algorithm that is known to find near-optimal solutions for combinatorial optimization problems. Thus, we expect that ACO scheduling produces more efficient schedules than comparable deterministic scheduling approaches at the expenses of a computational overhead it introduces. We compare ACO scheduling relevant deterministic scheduling approaches, and in particular the MLLF scheduler that is specifically designed for the needs of compressed multimedia applications. The purpose of the evaluation is twofold. It allows to draw conclusions on the feasibility of ACO scheduling for multimedia traffic while it serves as a benchmark to determine to what extent deterministic schedulers fall short of a near-optimal solution.
Full Text Available A number of emerging dynamic traffic analysis applications, such as regional or statewide traffic assignment, require a theoretically rigorous and computationally efficient model to describe the propagation and dissipation of system congestion with bottleneck capacity constraints. An open-source light-weight dynamic traffic assignment (DTA package, namely DTALite, has been developed to allow a rapid utilization of advanced dynamic traffic analysis capabilities. This paper describes its three major modeling components: (1 a light-weight dynamic network loading simulator that embeds Newell’s simplified kinematic wave model; (2 a mesoscopic agent-based DTA procedure to incorporate driver’s heterogeneity; and (3 an integrated traffic assignment and origin–destination demand calibration system that can iteratively adjust path flow volume and distribution to match the observed traffic counts. A number of real-world test cases are described to demonstrate the effectiveness and performance of the proposed models under different network and data availability conditions.
Xian-Qing Zheng; Zheng Wu; Shi-Xiong Xu; Ming-Min Guo; Zhan-Xi Lin; Ying-Ying Zhang
A new video-based measurement is proposed to collect and investigate traffic flow parameters.The output of the measurement is velocity-headway distance data pairs.Because density can be directly acquired by the reciprocal of headway distance, the data pairs have the advantage of better simultaneity than those from common detectors.By now,over 33 000 pairs of data have been collected from two road sections in the cities of Shanghai and Zhengzhou.Through analyzing the video files recording traffic movements on urban expressways, the following issues are studied: laws of vehicle velocity changing with headway distance, proportions of different driving behaviors in the traffic system, and characteristics of traffic flow in snowy days.The results show that the real road traffic is very complex, and factors such as location and climate need to be taken into consideration in the formation of traffic flow models.
Hai-Tao Zhang; Fang Yu; Wen Li
Aiming at the deficiency of conventional traffic control method, this paper proposes a new method based on multi-agent technology for traffic control. Different from many existing methods, this paper distinguishes traffic control on the basis of the agent technology from conventional traffic control method. The composition and structure of a multi-agent system (MAS) is first discussed. Then, the step-coordination strategies of intersection-agent, segment-agent, and area-agent are put forward. The advantages of the algorithm are demonstrated by a simulation study.
Eriksen, Torkild; Høye, Gudrun; Narheim, Bjørn; Meland, Bente Jensløkken
The Automatic Identification System (AIS) is a maritime safety and vessel traffic system imposed by the International Maritime Organization (IMO). The system broadcasts position reports and short messages with information about the ship and the voyage. Using frequencies in the maritime VHF band, the coverage is similar to other VHF applications, and is essentially dependent on the altitude of the antenna. For ship-to-ship communications the range is typically 20 nautical miles and for ship-to-shore up to 40 nm. A space-based AIS receiver in low earth orbit will have a range to the horizon of more than 1000 nm, giving an excellent opportunity for large-area ocean surveillance. The Norwegian Defence Research Establishment (FFI) has performed a feasibility study on reception of AIS messages from space. The results show that a ship detection probability of near 100% can be obtained for up to 1000 ships within the coverage area, and that for a standard AIS receiver a signal power margin of 10-20 dB can be achieved. On this background, swath-width analyses for European scenarios are done. It is argued that space-based reception of AIS messages is a promising way of achieving long-range identification and tracking services at marginal cost.
Kenedy Aliila Greyson
Currently, traffic congestions are common events in road networks of main cities in developing countries. It has been observed that, the size of congestion increases year after year. For traffic congestion management to work efficiently, sufficiently and accurately information are needed. In this research we present an alternative method using agent technology to collect and manipulate data so as to be used in optimizing the vehicle flow within the road networks. The objective is to design an...
Full Text Available Vehicles traffic congestion on the road is reflected as delays while traveling. This congestion has a number of negative effects such as energy consumption, wastage of time and increased tailpipes emission of idling vehicles probably bad for our health. Vehicular congestion has become the serious problem and it is getting worse day by day as the growth of the vehicles significantly increased. In this paper, we proposed a novel counter approach to avoid such vehicular congestion on the road. We have also proposed a path selection algorithm that ensures best path suggestion to vehicles in terms of reduction in trip time and less fuel consumption during whole trip. The whole traffic management solution is combination of "stochastic turn" (i.e. vehicles choose a new direction at each intersection or any other way point and path planning (i.e. origin and destination of the vehicle required in advance that ensured by suggested path selection algorithm. In the later part of this paper simulation results prove the effectiveness of our traffic management scheme in terms of reducing traffic congestion on the road. In addition, this scheme utilizes best of the resources and characteristics of vehicular networks to provide less congested path prediction and also smoothed flow of traffic for vehicles in high density vehicular traffic conditions.
Amilcare Francesco Santamaria
Full Text Available Nowadays one of the hottest theme in wireless environments research is the application of the newest technologies to road safety problems and traffic management exploiting the (VANET architecture. In this work, a novel protocol that aims to achieve a better traffic management is proposed. The overal system is able to reduce traffic level inside the city exploiting inter-communication among vehicles and support infrastructures also known as (V2V and (V2I communications. We design a network protocol called (STMP that takes advantages of IEEE 802.11p standard. On each road several sensors system are placed and they are responsible of monitoring. Gathered data are spread in the network exploiting ad-hoc protocol messages. The increasing knowledge about environment conditions make possible to take preventive actions. Moreover, having a realtime monitoring of the lanes it is possible to reveal roads and city blocks congestions in a shorter time. An external entity to the (VANET is responsible to manage traffic and rearrange traffic along the lanes of the city avoiding huge traffic levels.
Gimenez, Lucas Chavarria; Kovacs, Istvan Z.; Wigard, Jeroen;
The objective of this paper is to propose traffic steering solutions that aim at optimizing the end-user throughput. Two different implementations of an active mode throughput-based traffic steering algorithm for Heterogeneous Networks (HetNet) are introduced. One that always forces handover of the...
Bujlow, Tomasz; Riaz, Tahir; Pedersen, Jens Myrup
Our previous work demonstrated the possibility of distinguishing several groups of traffic with accuracy of over 99%. Today, most of the traffic is generated by web browsers, which provide different kinds of services based on the HTTP protocol: web browsing, file downloads, audio and voice...
Full Text Available Mechanisms to extract the characteristics of network traffic play a significant role in traffic monitoring, offering helpful information for network management and control. In this paper, a method based on Random Matrix Theory (RMT and Principal Components Analysis (PCA is proposed for monitoring and analyzing large-scale traffic patterns in the Internet. Besides the analysis of the largest eigenvalue in RMT, useful information is also extracted from small eigenvalues by a method based on PCA. And then an appropriate approach is put forward to select some observation points on the base of the eigen analysis. Finally, some experiments about peer-to-peer traffic pattern recognition and backbone aggregate flow estimation are constructed. The simulation results show that using about 10% of nodes as observation points, our method can monitor and extract key information about Internet traffic patterns.
Meng Qing-Fang; Chen Yue-Hui; Peng Yu-Hua
In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.
Full Text Available This paperresearches on the prediction of traffic flow chaotic time series based on VNNTF neural network. First, the traffic flow time series chaotic feature is extracted by chaos theory. Pretreatment for traffic flow time series and the VNNTP neural networks model was build by this. Second, principles of neural network learning algorithm VNNTF is described. Based on chaotic learning algorithm, the neural network traffic Volterra learning algorithm isdesigned for fast learning algorithm. Last, a single-step prediction of traffic flow chaotic time series is researched by VNNTF network model based on chaotic algorithm. The results showed that the VNNTF network model predictive performance is better than the Volterra prediction filter and the BP neural network by the simulation results and root-mean-square value.
Full Text Available Lack of traffic safety has become a serious issue in residential areas. In this paper, a web-based advisory expert system for the purpose of applying traffic calming strategies on residential streets is described because there currently lacks a structured framework for the implementation of such strategies. Developing an expert system can assist and advise engineers for dealing with traffic safety problems. This expert system is developed to fill the gap between the traffic safety experts and people who seek to employ traffic calming strategies including decision makers, engineers, and students. In order to build the expert system, examining sources related to traffic calming studies as well as interviewing with domain experts have been carried out. The system includes above 150 rules and 200 images for different types of measures. The system has three main functions including classifying traffic calming measures, prioritizing traffic calming strategies, and presenting solutions for different traffic safety problems. Verifying, validating processes, and comparing the system with similar works have shown that the system is consistent and acceptable for practical uses. Finally, some recommendations for improving the system are presented.
Full Text Available To overcome the drawbacks of existing methods for traffic classification (by ports, Deep Packet Inspection, statistical classification a new system has been developed, in which data are collected and classified directly by clients installed on machines belonging to volunteers. Our approach combines the information obtained from the system sockets, the HTTP content types, and the data transmitted through network interfaces. It allows grouping packets into flows and associating them with particular applications or types of service. This paper presents the design of our system, implementation, the testing phase and the obtained results. The performed threat assessment highlights potential security issues and proposes solutions in order to mitigate the risks. Furthermore, it proves that the system is feasible in terms of uptime and resource usage, assesses its performance and proposes future enhancements. We released the system under The GNU General Public License v3.0 and published as a SourceForge project called Volunteer-Based System for Research on the Internet.
Rivera, Iris Daliz
The field of training has been changing rapidly due to advances in technology such as videogame-based adaptive training. Videogame-based adaptive training has provided flexibility and adaptability for training in cost-effective ways. Although this method of training may have many benefits for the trainee, current research has not kept up to pace…
Ullah, Sana; Kwak, Kyung Sup
Wireless Body Area Network (WBAN) consists of low-power, miniaturized, and autonomous wireless sensor nodes that enable physicians to remotely monitor vital signs of patients and provide real-time feedback with medical diagnosis and consultations. It is the most reliable and cheaper way to take care of patients suffering from chronic diseases such as asthma, diabetes and cardiovascular diseases. Some of the most important attributes of WBAN is low-power consumption and delay. This can be achieved by introducing flexible duty cycling techniques on the energy constraint sensor nodes. Stated otherwise, low duty cycle nodes should not receive frequent synchronization and control packets if they have no data to send/receive. In this paper, we introduce a Traffic-adaptive MAC protocol (TaMAC) by taking into account the traffic information of the sensor nodes. The protocol dynamically adjusts the duty cycle of the sensor nodes according to their traffic-patterns, thus solving the idle listening and overhearing problems. The traffic-patterns of all sensor nodes are organized and maintained by the coordinator. The TaMAC protocol is supported by a wakeup radio that is used to accommodate emergency and on-demand events in a reliable manner. The wakeup radio uses a separate control channel along with the data channel and therefore it has considerably low power consumption requirements. Analytical expressions are derived to analyze and compare the performance of the TaMAC protocol with the well-known beacon-enabled IEEE 802.15.4 MAC, WiseMAC, and SMAC protocols. The analytical derivations are further validated by simulation results. It is shown that the TaMAC protocol outperforms all other protocols in terms of power consumption and delay. PMID:20703634
Full Text Available Base stations represent the main contributor to the energy consumption of a mobile cellular network. Since traffic load in mobile networks significantly varies during a working or weekend day, it is important to quantify the influence of these variations on the base station power consumption. Therefore, this paper investigates changes in the instantaneous power consumption of GSM (Global System for Mobile Communications and UMTS (Universal Mobile Telecommunications System base stations according to their respective traffic load. The real data in terms of the power consumption and traffic load have been obtained from continuous measurements performed on a fully operated base station site. Measurements show the existence of a direct relationship between base station traffic load and power consumption. According to this relationship, we develop a linear power consumption model for base stations of both technologies. This paper also gives an overview of the most important concepts which are being proposed to make cellular networks more energy-efficient.
Drayer, Elisabeth; Hegemann, Jan; Lazarus, Marc; Caire, Raphael; Braun, Martin
Compared to a centralised grid operation management for the distribution grid, a distributed and decentralised agent-based operation has a lot of advantages, like scalability, modularity and robustness. We propose the concept for an agent-based distribution grid operation management based on a traffic light concept. Depending on the situation in the grid, the operation management can be in different modes, which define the way how the grid is operated.
Yamamoto, Toshiaki; Ueda, Tetsuro; Obana, Sadao
As one of the dynamic spectrum access technologies, “cognitive radio technology,” which aims to improve the spectrum efficiency, has been studied. In cognitive radio networks, each node recognizes radio conditions, and according to them, optimizes its wireless communication routes. Cognitive radio systems integrate the heterogeneous wireless systems not only by switching over them but also aggregating and utilizing them simultaneously. The adaptive control of switchover use and concurrent use of various wireless systems will offer a stable and flexible wireless communication. In this paper, we propose the adaptive traffic route control scheme that provides high quality of service (QoS) for cognitive radio technology, and examine the performance of the proposed scheme through the field trials and computer simulations. The results of field trials show that the adaptive route control according to the radio conditions improves the user IP throughput by more than 20% and reduce the one-way delay to less than 1/6 with the concurrent use of IEEE802.16 and IEEE802.11 wireless media. Moreover, the simulation results assuming hundreds of mobile terminals reveal that the number of users receiving the required QoS of voice over IP (VoIP) service and the total network throughput of FTP users increase by more than twice at the same time with the proposed algorithm. The proposed adaptive traffic route control scheme can enhance the performances of the cognitive radio technologies by providing the appropriate communication routes for various applications to satisfy their required QoS.
Johansson, M S; Boyle, E; Hartvigsen, Jan;
BACKGROUND: Traffic collisions often result in a wide range of symptoms included in the umbrella term whiplash-associated disorders. Mid-back pain (MBP) is one of these symptoms. The incidence and prognosis of different traffic injuries and their related conditions (e.g. neck pain, low back pain......, depression or others) has been investigated previously; however, knowledge about traffic collision-related MBP is lacking. The study objectives were to describe the incidence, course of recovery and prognosis of MBP after traffic collisions, in terms of global self-reported recovery. METHODS: Longitudinal...... data from a population-based inception cohort of all traffic injuries occurring in Saskatchewan, Canada, during a 2-year period were used. Annual overall and age-sex-specific incidence rates were calculated, the course of recovery was described using the Kaplan-Meier technique, and associations between...
LI Ke-Ping; GAO Zi-You
@@ We propose a new cellular automation (CA) traffic model that is based on the car-following model. A class of driving strategies is used in the car-following model instead of the acceleration in the NaSch traffic model. In our model, some realistic driver behaviour and detailed vehicle characteristics have been taken into account, such as distance-headway and safe distance, etc. The simulation results show that our model can exhibit some traffic flow states that have been observed in the real traffic, and both of the maximum flux and the critical density are very close to the real measurement. Moreover, it is easy to extend our method to multi-lane traffic.
Canepa, Edward S.
Traffic sensing systems rely more and more on user generated (insecure) data, which can pose a security risk whenever the data is used for traffic flow control. In this article, we propose a new formulation for detecting malicious data injection in traffic flow monitoring systems by using the underlying traffic flow model. The state of traffic is modeled by the Lighthill-Whitham- Richards traffic flow model, which is a first order scalar conservation law with concave flux function. Given a set of traffic flow data, we show that the constraints resulting from this partial differential equation are mixed integer linear inequalities for some decision variable. We use this fact to pose the problem of detecting spoofing cyber-attacks in probe-based traffic flow information systems as mixed integer linear feasibility problem. The resulting framework can be used to detect spoofing attacks in real time, or to evaluate the worst-case effects of an attack offline. A numerical implementation is performed on a cyber-attack scenario involving experimental data from the Mobile Century experiment and the Mobile Millennium system currently operational in Northern California. © 2013 IEEE.
Canepa, Edward S.
Traffic sensing systems rely more and more on user generated (insecure) data, which can pose a security risk whenever the data is used for traffic flow control. In this article, we propose a new formulation for detecting malicious data injection in traffic flow monitoring systems by using the underlying traffic flow model. The state of traffic is modeled by the Lighthill- Whitham-Richards traffic flow model, which is a first order scalar conservation law with concave flux function. Given a set of traffic flow data generated by multiple sensors of different types, we show that the constraints resulting from this partial differential equation are mixed integer linear inequalities for a specific decision variable. We use this fact to pose the problem of detecting spoofing cyber attacks in probe-based traffic flow information systems as mixed integer linear feasibility problem. The resulting framework can be used to detect spoofing attacks in real time, or to evaluate the worst-case effects of an attack offliine. A numerical implementation is performed on a cyber attack scenario involving experimental data from the Mobile Century experiment and the Mobile Millennium system currently operational in Northern California. © American Institute of Mathematical Sciences.
D'Aquin, Mathieu; Badra, Fadi; Lafrogne, Sandrine; Lieber, Jean; Napoli, Amedeo; Szathmary, Laszlo
In case-based reasoning, the adaptation step depends in general on domain-dependent knowledge, which motivates studies on adaptation knowledge acquisition (AKA). CABAMAKA is an AKA system based on principles of knowledge discovery from databases. This system explores the variations within the case base to elicit adaptation knowledge. It has been successfully tested in an application of case-based decision support to breast cancer treatment.
赵晓华; 陈阳舟; 崔平远
A single intersection of two phases is selected as a model to put forward a new optimal time-planning scheme for traffic light based on the model of hybrid automata for single intersection. A method of optimization is proposed for hybrid systems, and the average queue length over all queues is used as an objective function to find an optimal switching scheme for traffic light. It is illustrated that traffic light control for single intersection is a typical hybrid system, and the optimal planning-time scheme can be obtained using the optimal hybrid systems control based on the two stages method.
Kurt Derr; Milos Manic
Wireless technology is a central component of many factory automation infrastructures in both the commercial and government sectors, providing connectivity among various components in industrial realms (distributed sensors, machines, mobile process controllers). However wireless technologies provide more threats to computer security than wired environments. The advantageous features of Bluetooth technology resulted in Bluetooth units shipments climbing to five million per week at the end of 2005 [1, 2]. This is why the real-time interpretation and understanding of Bluetooth traffic behavior is critical in both maintaining the integrity of computer systems and increasing the efficient use of this technology in control type applications. Although neuro-fuzzy approaches have been applied to wireless 802.11 behavior analysis in the past, a significantly different Bluetooth protocol framework has not been extensively explored using this technology. This paper presents a new neurofuzzy traffic analysis algorithm of this still new territory of Bluetooth traffic. Further enhancements of this algorithm are presented along with the comparison against the traditional, numerical approach. Through test examples, interesting Bluetooth traffic behavior characteristics were captured, and the comparative elegance of this computationally inexpensive approach was demonstrated. This analysis can be used to provide directions for future development and use of this prevailing technology in various control type applications, as well as making the use of it more secure.
Kurt Derr; Milos Manic
Wireless technology is a central component of many factory automation infrastructures in both the commercial and government sectors, providing connectivity among various components in industrial realms (distributed sensors, machines, mobile process controllers). However wireless technologies provide more threats to computer security than wired environments. The advantageous features of Bluetooth technology resulted in Bluetooth units shipments climbing to five million per week at the end of 2005 [1, 2]. This is why the real-time interpretation and understanding of Bluetooth traffic behavior is critical in both maintaining the integrity of computer systems and increasing the efficient use of this technology in control type applications. Although neuro-fuzzy approaches have been applied to wireless 802.11 behavior analysis in the past, a significantly different Bluetooth protocol framework has not been extensively explored using this technology. This paper presents a new neurofuzzy traffic analysis algorithm of this still new territory of Bluetooth traffic. Further enhancements of this algorithm are presented along with the comparison against the traditional, numerical approach. Through test examples, interesting Bluetooth traffic behavior characteristics were captured, and the comparative elegance of this computationally inexpensive approach was demonstrated. This analysis can be used to provide directions for future development and use of this prevailing technology in various control type applications, as well as making the use of it more secure.
Full Text Available We present an embedded DSL to support adaptation-based programming (ABP in Haskell. ABP is an abstract model for defining adaptive values, called adaptives, which adapt in response to some associated feedback. We show how our design choices in Haskell motivate higher-level combinators and constructs and help us derive more complicated compositional adaptives. We also show an important specialization of ABP is in support of reinforcement learning constructs, which optimize adaptive values based on a programmer-specified objective function. This permits ABP users to easily define adaptive values that express uncertainty anywhere in their programs. Over repeated executions, these adaptive values adjust to more efficient ones and enable the user's programs to self optimize. The design of our DSL depends significantly on the use of type classes. We will illustrate, along with presenting our DSL, how the use of type classes can support the gradual evolution of DSLs.
Bauer, Tim; Fern, Alan; Pinto, Jervis; 10.4204/EPTCS.66.1
We present an embedded DSL to support adaptation-based programming (ABP) in Haskell. ABP is an abstract model for defining adaptive values, called adaptives, which adapt in response to some associated feedback. We show how our design choices in Haskell motivate higher-level combinators and constructs and help us derive more complicated compositional adaptives. We also show an important specialization of ABP is in support of reinforcement learning constructs, which optimize adaptive values based on a programmer-specified objective function. This permits ABP users to easily define adaptive values that express uncertainty anywhere in their programs. Over repeated executions, these adaptive values adjust to more efficient ones and enable the user's programs to self optimize. The design of our DSL depends significantly on the use of type classes. We will illustrate, along with presenting our DSL, how the use of type classes can support the gradual evolution of DSLs.
Zheng, Nan; Waraich, Rashid A.; Axhausen, Kay W.; Geroliminis, Nikolaos
Pricing is considered an effective management policy to reduce traffic congestion in transportation networks. In this paper we combine a macroscopic model of traffic congestion in urban networks with an agent-based simulator to study congestion pricing schemes. The macroscopic model, which has been tested with real data in previous studies, represents an accurate and robust approach to model the dynamics of congestion. The agent-based simulator can reproduce the complexity of travel behavior ...
We propose here an autonomous traffic signal control model based on analogy with neural networks. In this model, the length of cycle time period of traffic lights at each signal is autonomously adapted. We find a self-organizing collective behavior of such a model through simulation on a one-dimensional lattice model road: traffic congestion is greatly diffused when traffic signals have such autonomous adaptability with suitably tuned parameters. We also find that effectiveness of the system emerges through interactions between units and shows a threshold transition as a function of proportion of adaptive signals in the model.
G. R. LAI; A. CHE SOH; H. MD. SARKAN; R. Z. ABDUL RAHMAN; Hassan, M. K.
Many controllers have applied the Adaptive Neural-Fuzzy Inference System (ANFIS) concept for optimizing the controller performance. However, there are less traffic signal controllers developed using the ANFIS concept. ANFIS traffic signal controller with its fuzzy rule base and its ability to learn from a set of sample data could improve the performance of Existing traffic signal controlling system to reduce traffic congestions at most of the busy traffic intersections in city such as Kuala L...
Full Text Available Traffic subarea division is vital for traffic system management and traffic network analysis in intelligent transportation systems (ITSs. Since existing methods may not be suitable for big traffic data processing, this paper presents a MapReduce-based Parallel Three-Phase K-Means (Par3PKM algorithm for solving traffic subarea division problem on a widely adopted Hadoop distributed computing platform. Specifically, we first modify the distance metric and initialization strategy of K-Means and then employ a MapReduce paradigm to redesign the optimized K-Means algorithm for parallel clustering of large-scale taxi trajectories. Moreover, we propose a boundary identifying method to connect the borders of clustering results for each cluster. Finally, we divide traffic subarea of Beijing based on real-world trajectory data sets generated by 12,000 taxis in a period of one month using the proposed approach. Experimental evaluation results indicate that when compared with K-Means, Par2PK-Means, and ParCLARA, Par3PKM achieves higher efficiency, more accuracy, and better scalability and can effectively divide traffic subarea with big taxi trajectory data.
Zeng, Yiliang; Lan, Jinhui; Ran, Bin; Jiang, Yaoliang
A novel multisensor system with incomplete data is presented for traffic state assessment. The system comprises probe vehicle detection sensors, fixed detection sensors, and traffic state assessment algorithm. First of all, the validity checking of the traffic flow data is taken as preprocessing of this method. And then a new method based on the history data information is proposed to fuse and recover the incomplete data. According to the characteristics of space complementary of data based on the probe vehicle detector and fixed detector, a fusion model of space matching is presented to estimate the mean travel speed of the road. Finally, the traffic flow data include flow, speed and, occupancy rate, which are detected between Beijing Deshengmen bridge and Drum Tower bridge, are fused to assess the traffic state of the road by using the fusion decision model of rough sets and cloud. The accuracy of experiment result can reach more than 98%, and the result is in accordance with the actual road traffic state. This system is effective to assess traffic state, and it is suitable for the urban intelligent transportation system. PMID:25162055
Full Text Available A novel multisensor system with incomplete data is presented for traffic state assessment. The system comprises probe vehicle detection sensors, fixed detection sensors, and traffic state assessment algorithm. First of all, the validity checking of the traffic flow data is taken as preprocessing of this method. And then a new method based on the history data information is proposed to fuse and recover the incomplete data. According to the characteristics of space complementary of data based on the probe vehicle detector and fixed detector, a fusion model of space matching is presented to estimate the mean travel speed of the road. Finally, the traffic flow data include flow, speed and, occupancy rate, which are detected between Beijing Deshengmen bridge and Drum Tower bridge, are fused to assess the traffic state of the road by using the fusion decision model of rough sets and cloud. The accuracy of experiment result can reach more than 98%, and the result is in accordance with the actual road traffic state. This system is effective to assess traffic state, and it is suitable for the urban intelligent transportation system.
Cement-based piezoelectric composites are employed as the sensing elements of a new smart traffic monitoring system. The piezoelectricity of the cement-based piezoelectric sensors enables powerful and accurate real-time detection of the pressure induced by the traffic flow. To describe the mechanical-electrical conversion mechanism between traffic flow and the electrical output of the embedded piezoelectric sensors, a mathematical model is established based on Duhamel’s integral, the constitutive law and the charge-leakage characteristics of the piezoelectric composite. Laboratory tests show that the voltage magnitude of the sensor is linearly proportional to the applied pressure, which ensures the reliability of the cement-based piezoelectric sensors for traffic monitoring. A series of on-site road tests by a 10 tonne truck and a 6.8 tonne van show that vehicle weight-in-motion can be predicted based on the mechanical-electrical model by taking into account the vehicle speed and the charge-leakage property of the piezoelectric sensor. In the speed range from 20 km h−1 to 70 km h−1, the error of the repeated weigh-in-motion measurements of the 6.8 tonne van is less than 1 tonne. The results indicate that the embedded cement-based piezoelectric sensors and associated measurement setup have good capability of smart traffic monitoring, such as traffic flow detection, vehicle speed detection and weigh-in-motion measurement. (paper)
Warberg, Andreas; Larsen, Jesper; Jørgensen, Rene Munk
important conflicts, which support the notion that traffic signal optimization is a multi-objective problem, and relates this to the most common measures of effectiveness. A distinction can be made between classical systems, which operate with a common cycle time, and the more flexible, phase......-based, approach, which is shown to be more suitable for adaptive traffic control. To support this claim three adaptive systems, which use alternatives to the classical optimization procedures, are described in detail....
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.
In the study of weighted complex networks, the interplay between traffic and topology have been paid much attention. However, the variation of topology and weight brought by new added vertices or edges should also be considered. In this paper, an evolution model of weighted networks driven by traffic dynamics with local perturbation is proposed. The model gives power-law distribution of degree, weight and strength, as confirmed by empirical measurements. By choosing appropriate parameters W and δ, the exponents of various power law distributions can be adjusted to meet real world networks. Nontrivial clustering coefficient C, degree assortativity coefficient r, and strength-degree correlation are also considered. What should be emphasized is that, with the consideration of local perturbation, one can adjust the exponent of strength-degree correlation more effectively. It makes our model more general than previous ones and may help reproducing real world networks more appropriately.
Takahashi, G.; Takeda, H.; Nakamura, K.
Recently, the development of autonomous cars is accelerating on the integration of highly advanced artificial intelligence, which increases demand for a digital map with high accuracy. In particular, traffic markings are required to be precisely digitized since automatic driving utilizes them for position detection. To draw traffic markings, we benefit from Mobile Mapping Systems (MMS) equipped with high-density Laser imaging Detection and Ranging (LiDAR) scanners, which produces large amount of data efficiently with XYZ coordination along with reflectance intensity. Digitizing this data, on the other hand, conventionally has been dependent on human operation, which thus suffers from human errors, subjectivity errors, and low reproductivity. We have tackled this problem by means of automatic extraction of traffic marking, which partially accomplished to draw several traffic markings (G. Takahashi et al., 2014). The key idea of the method was extracting lines using the Hough transform strategically focused on changes in local reflection intensity along scan lines. However, it failed to extract traffic markings properly in a densely marked area, especially when local changing points are close each other. In this paper, we propose a bidirectional gradient-based detection method where local changing points are labelled with plus or minus group. Given that each label corresponds to the boundary between traffic markings and background, we can identify traffic markings explicitly, meaning traffic lines are differentiated correctly by the proposed method. As such, our automated method, a highly accurate and non-human-operator-dependent method using bidirectional gradient-based algorithm, can successfully extract traffic lines composed of complex shapes such as a cross walk, resulting in minimizing cost and obtaining highly accurate results.
Tarun Prakash; Ritu Tiwari
Vehicles traffic congestion on the road is reflected as delays while traveling. This congestion has a number of negative effects such as energy consumption, wastage of time and increased tailpipes emission of idling vehicles probably bad for our health. Vehicular congestion has become the serious problem and it is getting worse day by day as the growth of the vehicles significantly increased. In this paper, we proposed a novel counter approach to avoid such vehicular congestion on the road. W...
Nowadays, accounting, charging and billing users' network resource consumption are commonly used for the purpose of facilitating reasonable network usage, controlling congestion, allocating cost, gaining revenue, etc. In traditional IP traffic accounting systems, IP addresses are used to identify the corresponding consumers of the network resources. However, there are some situations in which IP addresses cannot be used to identify users uniquely, for example, in multi-user systems. In these ...
Changhua Yao; Qihui Wu; Linfang Zhou
We propose a more practical spectrum sensing optimization problem in cognitive radio networks (CRN), by considering the data traffic of second user (SU). Compared with most existing work, we do not assume that SU always has packets to transmit; instead, we use the actual data transmitted per second rather than the channel capacity as the achievable throughput, to reformulate the Sensing-Throughput Tradeoff problem. We mathematically analyze the problem of optimal sensing time to maximize the ...
This paper aims at proposing a methodology and the required tools for evaluating current IDS (commercial ones, as well as prototypes resulting from advanced research projects) capabilities of detecting attacks targeting the networks and their services. This methodology tries to be as realistic as possible and reproducible, i.e. it works with real attacks and real traffic in controlled environments. It especially relies on a database containing attack traces specifically created for that evalu...
Yao Xiao; Jing Shi
This paper aimed to analyze the influence of drivers’ behavior of phone use while driving on traffic flow, including both traffic efficiency and traffic safety. An improved cellular automaton model was proposed to simulate traffic flow with distracted drivers based on the Nagel-Schreckenberg model. The driving characters of drivers using a phone were first discussed and a value representing the probability to use a phone while driving was put into the CA model. Simulation results showed that ...
Due to the maturing of Internet technology, the adaptive testing can be utilized in the web-based environment and the examinee can take the test anywhere and any time. The purpose of the research is to apply item response theory (IRT), adaptive testing theory and web-service technique to construct an XML format itembank and a system of web-based adaptive testing (WAT) by the framework of three-tiered client server distance testing.
D'Aquin, Mathieu; Badra, Fadi; Lafrogne, Sandrine; Lieber, Jean; Napoli, Amedeo; Szathmary, Laszlo
International audience In case-based reasoning, the adaptation of a source case in order to solve the target problem is at the same time crucial and difficult to implement. The reason for this difficulty is that, in general, adaptation strongly depends on domain-dependent knowledge. This fact motivates research on adaptation knowledge acquisition (AKA). This paper presents an approach to AKA based on the principles and techniques of knowledge discovery from databases and data-mining. It is...
Fu, Rong; Berger, Michael Stübert; Zheng, Yu; Brewka, Lukasz Jerzy; Wessing, Henrik
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....
Neumann, Thorsten; Ebendt, Rüdiger; Kuhns, Günter
Traffic data fusion has much to do with combining available or considered data sources in the best possible way. In this, it is very similar to optimizing a portfolio of financial assets in regard of return and risk. This article draws the analogy between these two mostly different scientific worlds, i.e. finance and engineering. Similarities and differences in context of weighted-mean data fusion based on numerical traffic flow measurements such as travel times or speeds are discussed. This,...
Fu, Rong; Berger, Michael Stübert; Zheng, Yu; Brewka, Lukasz Jerzy; Wessing, Henrik
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 performance 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.
Amico, P.; Santos, P.; Summers, D.; Duhoux, Ph.; Arsenault, R.; Bierwirth, Th.; Kuntschner, H.; Madec, P.-Y.; Prümm, M.; Rejkuba, M.
The Laser Traffic Control System (LTCS) entered routine operations on 1 October 2015 at the Paranal Observatory as the first component of the Adaptive Optics Facility (AOF). LTCS allows the night operators to plan and execute the observations without having to worry about possible collisions between the AOF's powerful laser beams and other telescopes with laser-sensitive instruments. LTCS provides observers with real-time information about ongoing collisions, predictive information for possible collisions and priority resolution between telescope pairs, where at least one telescope is operating a laser. LTCS is now deployed and embedded in the observatory's operational environment, supporting high configurability of telescopes and instruments, right-of-way priority rules and interfacing with ESO's observing tools for Service and Visitor Mode observations.
Wang, Honghuan; Xing, Fangyuan; Yin, Hongxi; Zhao, Nan; Lian, Bizhan
With the explosive growth of network services, the reasonable traffic scheduling and efficient configuration of network resources have an important significance to increase the efficiency of the network. In this paper, an adaptive traffic scheduling policy based on the priority and time window is proposed and the performance of this algorithm is evaluated in terms of scheduling ratio. The routing and spectrum allocation are achieved by using the Floyd shortest path algorithm and establishing a node spectrum resource allocation model based on greedy algorithm, which is proposed by us. The fairness index is introduced to improve the capability of spectrum configuration. The results show that the designed traffic scheduling strategy can be applied to networks with multicast and broadcast functionalities, and makes them get real-time and efficient response. The scheme of node spectrum configuration improves the frequency resource utilization and gives play to the efficiency of the network.
Full Text Available Due to the rapid development of motor vehicle Driver Assistance Systems (DAS, the safety problems associated with automatic driving have become a hot issue in Intelligent Transportation. The traffic sign is one of the most important tools used to reinforce traffic rules. However, traffic sign image degradation based on computer vision is unavoidable during the vehicle movement process. In order to quickly and accurately recognize traffic signs in motion-blurred images in DAS, a new image restoration algorithm based on border deformation detection in the spatial domain is proposed in this paper. The border of a traffic sign is extracted using color information, and then the width of the border is measured in all directions. According to the width measured and the corresponding direction, both the motion direction and scale of the image can be confirmed, and this information can be used to restore the motion-blurred image. Finally, a gray mean grads (GMG ratio is presented to evaluate the image restoration quality. Compared to the traditional restoration approach which is based on the blind deconvolution method and Lucy-Richardson method, our method can greatly restore motion blurred images and improve the correct recognition rate. Our experiments show that the proposed method is able to restore traffic sign information accurately and efficiently.
Richard J. Harris
Full Text Available In this paper, we consider statistical characteristics of real User Datagram Protocol (UDP traffic. Fourmain issues in the study include(i the presence of long rangedependence (LRD in the UDP traffic,(ii themarginal distribution of the UDP traces,(iii dependence structure of wavelet coefficients,(iv andperformance evaluation of the Hurst parameter estimation based on different numbers of vanishingmoments of the mother wavelet. By analyzing a large set of real traffic data, it is evident that theUDP Internet traffic reveals the LRD properties with considerably high non-stationaryprocesses.Furthermore, it exhibits non-Gaussian marginal distributions. However, byincreasing the number of vanishing moments,it is impossible to achieve reduction fromLRD tobecome a short range dependence. Thus, it can be shown that there is no significant differencein performance estimation of the Hurst parameter for different numbers of vanishing momentsof the mother wavelet.
Extraction of traffic information from image or video sequence is a hot research topic in intelligenttransportation system and computer vision. A real-time traffic information extraction method based on com-pressed video with interframe motion vectors for speed, density and flow detection, has been proposed for ex-traction of traffic information under fixed camera setting and well-defined environment. The motion vectors arefirst separated from the compressed video streams, and then filtered to eliminate incorrect and noisy vectors u-sing the well-defined environmental knowledge. By applying the projective transform and using the filtered mo-tion vectors, speed can be calculated from motion vector statistics, density can be estimated using the motionvector occupancy, and flow can be detected using the combination of speed and density. The embodiment of aprototype system for sky camera traffic monitoring using the MPEG video has been implemented, and experi-mental results proved the effectiveness of the method proposed.
Tobias, L.; Scoggins, J. L.
A prototype expert system was developed for the time scheduling of aircraft into the terminal area. The three functions of the air traffic control schedule advisor are as follows: first, for each new arrival, it develops an admissible flight plan for that aircraft. Second, as the aircraft progresses through the terminal area, it monitors deviations from the flight plan and provides advisories to return the aircraft to its assigned schedule. Third, if major disruptions such as missed approaches occur, it develops a revised plan. The advisor is operational on a Symbolics 3600, and is programed in MRS (a logic programming language), Lisp, and FORTRAN.
Full Text Available Smart cities are proposed as a medium-term option for all cities. This article aims to propose an architecture that allows cities to provide solutions to interconnect all their elements. The study case focuses in locating and optimized regulation of traffic in cities. However, thanks to the proposed structure and the applied algorithms, the architecture is scalable in size of the sensor network, in functionality or even in the use of resources. A simulation environment that is able to show the operation of the architecture in the same way that a real city would, is presented.
Shengyong Chen; Wei Zhao; Ming Li
Scaling phenomena of the Internet traffic gain people's interests, ranging from computer scientists to statisticians. There are two types of scales. One is small-time scaling and the other large-time one. Tools to separately describe them are desired in computer communications, such as performance analysis of network systems. Conventional tools, such as the standard fractional Brownian motion (fBm), or its increment process, or the standard multifractional fBm (mBm) indexed by the local Hölde...
Salvador, Paulo S.; Nogueira, Antonio; Valadas, Rui
In a previous work we have introduced a multifractal traffic model based on so-called stochastic L-Systems, which were introduced by biologist A. Lindenmayer as a method to model plant growth. L-Systems are string rewriting techniques, characterized by an alphabet, an axiom (initial string) and a set of production rules. In this paper, we propose a novel traffic model, and an associated parameter fitting procedure, which describes jointly the packet arrival and the packet size processes. The packet arrival process is modeled through a L-System, where the alphabet elements are packet arrival rates. The packet size process is modeled through a set of discrete distributions (of packet sizes), one for each arrival rate. In this way the model is able to capture correlations between arrivals and sizes. We applied the model to measured traffic data: the well-known pOct Bellcore, a trace of aggregate WAN traffic and two traces of specific applications (Kazaa and Operation Flashing Point). We assess the multifractality of these traces using Linear Multiscale Diagrams. The suitability of the traffic model is evaluated by comparing the empirical and fitted probability mass and autocovariance functions; we also compare the packet loss ratio and average packet delay obtained with the measured traces and with traces generated from the fitted model. Our results show that our L-System based traffic model can achieve very good fitting performance in terms of first and second order statistics and queuing behavior.
Lüßmann, J.; Vreeswijk, J.; Katwijk, R. van; Blokpoel, R.; Fullerton, M.
The EU FP7 project eCoMove created an integrated solution for more energy efficient road transport based on cooperative technology. The project's core concept assumed that there is theoretical minimum energy consumption achievable with the "perfect eco-driver" travelling through the "perfectly eco-m
An adaptive endpoint detection algorithm based on band energy and adaptive smoothing algorithm is described.This algorithm utilizes the capability of adaptive smoothing algorithm that intensifies the discontinuity between local areas. The band energy features are selected because of their usefulness in detecting high energy regions (in the incoming signal) and making the distinction between speech and noise. Heuristic "edge-focusing" is used to endpoint detection to save the time in iteration.
Zhang, Binbin; Chen, Jun; Jin, Long; Deng, Weili; Zhang, Lei; Zhang, Haitao; Zhu, Minhao; Yang, Weiqing; Wang, Zhong Lin
Wireless traffic volume detectors play a critical role for measuring the traffic-flow in a real-time for current Intelligent Traffic System. However, as a battery-operated electronic device, regularly replacing battery remains a great challenge, especially in the remote area and wide distribution. Here, we report a self-powered active wireless traffic volume sensor by using a rotating-disk-based hybridized nanogenerator of triboelectric nanogenerator and electromagnetic generator as the sustainable power source. Operated at a rotating rate of 1000 rpm, the device delivered an output power of 17.5 mW, corresponding to a volume power density of 55.7 W/m(3) (Pd = P/V, see Supporting Information for detailed calculation) at a loading resistance of 700 Ω. The hybridized nanogenerator was demonstrated to effectively harvest energy from wind generated by a moving vehicle through the tunnel. And the delivered power is capable of triggering a counter via a wireless transmitter for real-time monitoring the traffic volume in the tunnel. This study further expands the applications of triboelectric nanogenerators for high-performance ambient mechanical energy harvesting and as sustainable power sources for driving wireless traffic volume sensors. PMID:27232668
Di Castro, Dotan
We consider the problem of reinforcement learning using function approximation, where the approximating basis can change dynamically while interacting with the environment. A motivation for such an approach is maximizing the value function fitness to the problem faced. Three errors are considered: approximation square error, Bellman residual, and projected Bellman residual. Algorithms under the actor-critic framework are presented, and shown to converge. The advantage of such an adaptive basis is demonstrated in simulations.
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.
Fu Haiyang; Yang Longxiang; Peng Jianglong
This paper presents a new blind XPIC and a new adaptive blind deconvolutional algorithm based on HOS processing, which separates and equalizes the signals in real time. The simulation results demonstrate that the performance of the proposed adaptive blind algorithm,compared with the conventional algorithms, is outstanding with the feature of feasibility, stability and fast convergence rate.
Cesar Augusto Hernández Suarez
Full Text Available El objetivo de esta investigación es demostrar que las series de tiempo son una excelente herramienta para el modelamiento de tráfico de datos en redes Wimax. Para lograr este objetivo se utilizó la metodología de Box-Jenkins, la cual se describe en este artículo. El modelamiento de tráfico Wimax a través de modelos correlacionados como las series de tiempo permiten ajustar gran parte de la dinámica del comportamiento de los datos en una ecuación y con base en esto estimar valores futuros de tráfico. Lo anterior es una ventaja para la planeación de cobertura, reservación de recursos y la realización de un control más oportuno y eficiente en forma integrada a diferentes niveles de la jerarquía funcional de la red de datos Wimax. Como resultado de la investigación se obtuvo un modelo de tráfico ARIMA de orden 18, el cual realizó pronósticos de tráfico con valores del error cuadrático medio relativamente pequeños, para un periodo de 10 días.
Zhao, Guofeng; Tang, Hong; Zhang, Yi
Applying multi-protocol label switching techniques to IP-based backbone for traffic engineering goals has shown advantageous. Obtaining a volume of load on each internal link of the network is crucial for traffic engineering applying. Though collecting can be available for each link, such as applying traditional SNMP scheme, the approach may cause heavy processing load and sharply degrade the throughput of the core routers. Then monitoring merely at the edge of the network and mapping the measurements onto the core provides a good alternative way. In this paper, we explore a scheme for traffic mapping with edge-based measurements in MPLS network. It is supposed that the volume of traffic on each internal link over the domain would be mapped onto by measurements available only at ingress nodes. We apply path-based measurements at ingress nodes without enabling measurements in the core of the network. We propose a method that can infer a path from the ingress to the egress node using label distribution protocol without collecting routing data from core routers. Based on flow theory and queuing theory, we prove that our approach is effective and present the algorithm for traffic mapping. We also show performance simulation results that indicate potential of our approach.
胡婷; 王勇; 陶晓玲
In order to solve the problems in current work that relies on well known TCP or UDP port numbers or interpreting the contents of packet payloads, such as low accuracy and limited application region. This paper proposes a method based on a Supervised Self-Organizing Maps (SSOM) network classification method for traffic classification. The method uses training dataset of network traffic which has been labeled the traffic classes, and changes the adaptation rule of weighs in SOM training process, which easier to choose the winning neuron of output layer and divides each class more clearly, and improves the performance of classification. Experimental results show the SSOM network algorithm has a better resolution and a more continuous mapping than SOM. Its applying to network traffic classification has a higher accuracy rate.%针对目前基于端口号匹配和特征码识别的流量分类方法准确率低、应用范围受限等问题,提出一种基于有监督的自组织映射(SSOM)的网络流量分类方法.该方法使用已标注类别的网络流量训练集,通过改变自组织映射(SOM)训练过程中的权值调整规则,使输出层中获胜神经元的选择更容易,各类别之间划分更清晰,从而提高分类性能.实验结果表明,SSOM的分辨率及拓扑连续性均优于SOM,对网络流量分类具有更高的准确率.
This paper is a summary of the main contribu- tions of the PhD thesis published in . The main research contributions of the thesis are driven by the research question how to design simple, yet efficient and robust run-time adaptive resource allocation schemes within the commu- nication stack of Wireless Sensor Network (WSN) nodes. The thesis addresses several problem domains with con- tributions on different layers of the WSN communication stack. The main contributions can be summarized...
Adaptive fuzzy controllers by means of variable universe are proposed based on interpolation forms of fuzzy control. First, monotonicity of control rules is defined, and it is proved that the monotonicity of interpolation functions of fuzzy control is equivalent to the monotonicity of control rules. This means that there is not any contradiction among the control rules under the condition for the control rules being monotonic. Then structure of the contraction-expansion factor is discussed. At last, three models of adaptive fuzzy control based on variable universe are given which are adaptive fuzzy control model with potential heredity, adaptive fuzzy control model with obvious heredity and adaptive fuzzy control model with successively obvious heredity.
This thesis discusses the implications of the traffic characteristics on interdomain traffic engineering with BGP. We first provide an overview of the interdomain traffic control problem. Then, we present results concerning the characteristics of the interdomain traffic, based on the analysis of real traffic traces gathered from non-transit ASes. We discuss the implications of the topological properties of the traffic on interdomain traffic engineering. Based on this knowledge of the...