Traffic signal control(TSC)systems are one essential component in intelligent transport systems.However,relevant studies are usually independent of the urban traffic simulation environment,collaborative TSC algorithms...Traffic signal control(TSC)systems are one essential component in intelligent transport systems.However,relevant studies are usually independent of the urban traffic simulation environment,collaborative TSC algorithms and traffic signal communication.In this paper,we propose(1)an integrated and cooperative Internet-of-Things architecture,namely General City Traffic Computing System(GCTCS),which simultaneously leverages an urban traffic simulation environment,TSC algorithms,and traffic signal communication;and(2)a general multi-agent reinforcement learning algorithm,namely General-MARL,considering cooperation and communication between traffic lights for multi-intersection TSC.In experiments,we demonstrate that the integrated and cooperative architecture of GCTCS is much closer to the real-life traffic environment.The General-MARL increases the average movement speed of vehicles in traffic by 23.2%while decreases the network latency by 11.7%.展开更多
On-road Vehicular traffic congestion has detrimental effect on three lifelines: Economy, Productivity and Pollution (EPP). With ever increasing population of vehicles on road, traffic congestion is a major challenge t...On-road Vehicular traffic congestion has detrimental effect on three lifelines: Economy, Productivity and Pollution (EPP). With ever increasing population of vehicles on road, traffic congestion is a major challenge to the economy, productivity and pollution, notwithstanding continuous developments in alternative fuels, alternative sources of energy. The research develops accurate and precise model in real time which computes congestion detection, dynamic signaling algorithm to evenly distribute vehicle densities while ensuring avoidance of starvation and deadlock situation. The model incorporates road segment length and breadth, quality and achievable average speed to compute road capacity. Vehicles installed with GPS enabled devices provide their location, which enables computing road occupancy. Road occupancy is evaluated based on number of vehicles as well as area occupied by vehicles. Ratio of road occupancy and road capacity provides congestion index important to compute signal phases. The algorithm ensures every direction is serviced once during a signaling cycle ensuring no starvation. Secondly, the definition of minimum and maximum signal timings ensures against dead lock situation. A simulator is developed to validate the proposition and proves it can ease congestion by more than 50% which is better than any of the contemporary approaches offering 15% improvement. In case of higher congestion index, alternate routes are suggested based on evaluation of traffic density graphs for shortest route or knowledge database. The algorithm to compute shortest route is optimized drastically, reducing computation cost to 3*√2N vis-à-vis computation cost of N2 by classical algorithms. The proposal brings down the cost of implementation per traffic junction from USD 30,000 to USD 2000.展开更多
Many signalized intersections are characterized with frequent left-turn moves. Vehicles waiting for a protected left turn may form long queues, which will increase the intersection delay and negatively impact the netw...Many signalized intersections are characterized with frequent left-turn moves. Vehicles waiting for a protected left turn may form long queues, which will increase the intersection delay and negatively impact the network performance. Researchers and practitioners across various countries underline that access management leads to a smoother traffic flow. One way of access management at intersections is to eliminate the direct left-turn maneuver. This study aims to evaluate how the traffic conditions will be affected from replacing the direct left turn with the right-turn U-turn maneuver at intersections. In case of the right-turn U-turn maneuver, a vehicle turns right instead of making the left turn and travels either to the median opening or to the next intersection to make a U-turn. Two simulation models are built using the Synchro Studio and Aimsun simulation software packages based on the data, collected from one of the busiest intersections in Tehran (Iran), to quantify the effects of replacing the direct left turn with the right-turn U-turn maneuver on the intersection and network performance. Results of a comprehensive simulation analysis indicate that the proposed access management treatment not only significantly reduces the total vehicle queue length and the total delay at the considered intersection, but also decreases the total network delay and the total travel time. Furthermore, elimination of the direct left turn increases the number of vehicles entering the network.展开更多
Signal retiming is a prominent way that transportation agencies use to fight congestion and change of traffic pattern.Performance evaluations of traffic conditions at signalized intersections and arterials provide act...Signal retiming is a prominent way that transportation agencies use to fight congestion and change of traffic pattern.Performance evaluations of traffic conditions at signalized intersections and arterials provide actionable data for agencies to make well-informed and prioritized signal retiming decisions.However,the abundance of data sources,the lack of standardized evaluation methods and oftentimes the shortage of resources make it a difficult endeavor.The review detailed in this paper examines the advances made in traffic signal performance evaluation.We establish the necessity for the evaluations,study the process of continuous improvement of traffic signal performance using the evaluations,and then examine multiple methodologies in a plethora of research endeavors.Particularly,we focus on probe vehicles and sensors data,the two major sources of data.We discuss how sensors are connected to signal controllers to provide relevant in-depth traffic data including speed and occupancy measures.We also review the nature of probe vehicles and the level of penetration.We then define and summarize performance measures derived from both sources,to aid in performance evaluations.For performance evaluation methods,we discuss the research studies and provide summaries including advantages and disadvantages of the methods used,as well as a holistic outlook for future research.This paper is aimed to provide a comprehensive review on the state-of-the-art to benefit researcher,traffic agencies,and commercial entities that thrive to improve safety and efficiency of traffic signals through performance evaluations.展开更多
This paper presents development of a control system for ecological driving of a hybrid vehicle. Prediction using traffic signal and road slope information is considered to improve the fuel economy. It is assumed that ...This paper presents development of a control system for ecological driving of a hybrid vehicle. Prediction using traffic signal and road slope information is considered to improve the fuel economy. It is assumed that the automobile receives traffic signal information from intelligent transportation systems (ITS). Model predictive control is used to calculate optimal vehicle control inputs using traffic signal and road slope information. The performance of the proposed method was analyzed through computer simulation results. Both the fuel economy and the driving profile are optimized using the proposed approach. It was observed that fuel economy was improved compared with driving of a typical human driving model.展开更多
The importance of using adaptive traffic signal control for figuring out the unpredictable traffic congestion in today's metropolitan life cannot be overemphasized. The vehicular ad hoc network(VANET), as an integ...The importance of using adaptive traffic signal control for figuring out the unpredictable traffic congestion in today's metropolitan life cannot be overemphasized. The vehicular ad hoc network(VANET), as an integral component of intelligent transportation systems(ITSs), is a new potent technology that has recently gained the attention of academics to replace traditional instruments for providing information for adaptive traffic signal controlling systems(TSCSs). Meanwhile, the suggestions of VANET-based TSCS approaches have some weaknesses:(1) imperfect compatibility of signal timing algorithms with the obtained VANET-based data types, and(2) inefficient process of gathering and transmitting vehicle density information from the perspective of network quality of service(Qo S). This paper proposes an approach that reduces the aforementioned problems and improves the performance of TSCS by decreasing the vehicle waiting time, and subsequently their pollutant emissions at intersections. To achieve these goals, a combination of vehicle-to-vehicle(V2V) and vehicle-to-infrastructure(V2I) communications is used. The V2 V communication scheme incorporates the procedure of density calculation of vehicles in clusters, and V2 I communication is employed to transfer the computed density information and prioritized movements information to the road side traffic controller. The main traffic input for applying traffic assessment in this approach is the queue length of vehicle clusters at the intersections. The proposed approach is compared with one of the popular VANET-based related approaches called MC-DRIVE in addition to the traditional simple adaptive TSCS that uses the Webster method. The evaluation results show the superiority of the proposed approach based on both traffic and network Qo S criteria.展开更多
Simulation is a powerful tool for improving,evaluating and analyzing the performance of new and existing systems.Traffic simulators provide tools for studying transportation systems in smart cities as they describe th...Simulation is a powerful tool for improving,evaluating and analyzing the performance of new and existing systems.Traffic simulators provide tools for studying transportation systems in smart cities as they describe the evolution of traffic to the highest level of detail.There are many types of traffic simulators that allow simulating traffic in modern cities.The most popular traffic simulation approach is the microscopic traffic simulation because of its ability to model traffic in a realistic manner.In many cities of Saudi Arabia,traffic management represents a major challenge as a result of expansion in traffic demands and increasing number of incidents.Unfortunately,employing simulation to provide effective traffic management for local scenarios in Saudi Arabia is limited to a number of commercial products in both public and private sectors.Commercial simulators are usually expensive,closed source and inflexible as they allow limited functionalities.In this project,we developed a local traffic simulator“KSUtraffic”for traffic modeling,planning and analysis with respect to different traffic control strategies and considerations.We modeled information specified by GIS and real traffic data.Furthermore,we designed experiments that manipulate simulation parameters and the underlying area.KSUTraffic visualizes traffic and provides statistical results on the simulated traffic which would help to improve traffic management and efficiency.展开更多
现有交通信号灯控制策略大多针对单一交叉口展开分析,该策略仅考虑车流量的单一因素,难以适应动态的路网状态。对此,提出了一种结合模糊控制的深度强化学习交通灯控制策略,利用SAC(soft actor critic)深度强化学习对两交叉口的交通信号...现有交通信号灯控制策略大多针对单一交叉口展开分析,该策略仅考虑车流量的单一因素,难以适应动态的路网状态。对此,提出了一种结合模糊控制的深度强化学习交通灯控制策略,利用SAC(soft actor critic)深度强化学习对两交叉口的交通信号灯相位选择及配时进行联合优化,同时考虑车辆速度、路段车辆排队长度等因素,利用模糊控制对SAC的惩罚函数进行处理。实验结果表明,与固定循环周期策略、SAC控制策略和DDPG(deep deterministic policy gradient)控制策略相比,提出的交通信号灯控制策略能获得更快的车辆通行速度,车辆的油耗和尾气排放情况也得到了改善。展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.61673150,11622538).
文摘Traffic signal control(TSC)systems are one essential component in intelligent transport systems.However,relevant studies are usually independent of the urban traffic simulation environment,collaborative TSC algorithms and traffic signal communication.In this paper,we propose(1)an integrated and cooperative Internet-of-Things architecture,namely General City Traffic Computing System(GCTCS),which simultaneously leverages an urban traffic simulation environment,TSC algorithms,and traffic signal communication;and(2)a general multi-agent reinforcement learning algorithm,namely General-MARL,considering cooperation and communication between traffic lights for multi-intersection TSC.In experiments,we demonstrate that the integrated and cooperative architecture of GCTCS is much closer to the real-life traffic environment.The General-MARL increases the average movement speed of vehicles in traffic by 23.2%while decreases the network latency by 11.7%.
文摘On-road Vehicular traffic congestion has detrimental effect on three lifelines: Economy, Productivity and Pollution (EPP). With ever increasing population of vehicles on road, traffic congestion is a major challenge to the economy, productivity and pollution, notwithstanding continuous developments in alternative fuels, alternative sources of energy. The research develops accurate and precise model in real time which computes congestion detection, dynamic signaling algorithm to evenly distribute vehicle densities while ensuring avoidance of starvation and deadlock situation. The model incorporates road segment length and breadth, quality and achievable average speed to compute road capacity. Vehicles installed with GPS enabled devices provide their location, which enables computing road occupancy. Road occupancy is evaluated based on number of vehicles as well as area occupied by vehicles. Ratio of road occupancy and road capacity provides congestion index important to compute signal phases. The algorithm ensures every direction is serviced once during a signaling cycle ensuring no starvation. Secondly, the definition of minimum and maximum signal timings ensures against dead lock situation. A simulator is developed to validate the proposition and proves it can ease congestion by more than 50% which is better than any of the contemporary approaches offering 15% improvement. In case of higher congestion index, alternate routes are suggested based on evaluation of traffic density graphs for shortest route or knowledge database. The algorithm to compute shortest route is optimized drastically, reducing computation cost to 3*√2N vis-à-vis computation cost of N2 by classical algorithms. The proposal brings down the cost of implementation per traffic junction from USD 30,000 to USD 2000.
文摘Many signalized intersections are characterized with frequent left-turn moves. Vehicles waiting for a protected left turn may form long queues, which will increase the intersection delay and negatively impact the network performance. Researchers and practitioners across various countries underline that access management leads to a smoother traffic flow. One way of access management at intersections is to eliminate the direct left-turn maneuver. This study aims to evaluate how the traffic conditions will be affected from replacing the direct left turn with the right-turn U-turn maneuver at intersections. In case of the right-turn U-turn maneuver, a vehicle turns right instead of making the left turn and travels either to the median opening or to the next intersection to make a U-turn. Two simulation models are built using the Synchro Studio and Aimsun simulation software packages based on the data, collected from one of the busiest intersections in Tehran (Iran), to quantify the effects of replacing the direct left turn with the right-turn U-turn maneuver on the intersection and network performance. Results of a comprehensive simulation analysis indicate that the proposed access management treatment not only significantly reduces the total vehicle queue length and the total delay at the considered intersection, but also decreases the total network delay and the total travel time. Furthermore, elimination of the direct left turn increases the number of vehicles entering the network.
基金supported in part by Tennessee Department of Transportation(TDOT)and Federal Highway Administration(FHWA),under TDOT grant RES2021-09
文摘Signal retiming is a prominent way that transportation agencies use to fight congestion and change of traffic pattern.Performance evaluations of traffic conditions at signalized intersections and arterials provide actionable data for agencies to make well-informed and prioritized signal retiming decisions.However,the abundance of data sources,the lack of standardized evaluation methods and oftentimes the shortage of resources make it a difficult endeavor.The review detailed in this paper examines the advances made in traffic signal performance evaluation.We establish the necessity for the evaluations,study the process of continuous improvement of traffic signal performance using the evaluations,and then examine multiple methodologies in a plethora of research endeavors.Particularly,we focus on probe vehicles and sensors data,the two major sources of data.We discuss how sensors are connected to signal controllers to provide relevant in-depth traffic data including speed and occupancy measures.We also review the nature of probe vehicles and the level of penetration.We then define and summarize performance measures derived from both sources,to aid in performance evaluations.For performance evaluation methods,we discuss the research studies and provide summaries including advantages and disadvantages of the methods used,as well as a holistic outlook for future research.This paper is aimed to provide a comprehensive review on the state-of-the-art to benefit researcher,traffic agencies,and commercial entities that thrive to improve safety and efficiency of traffic signals through performance evaluations.
基金supported by National Natural Science Foundation of China(Nos.51405137,61403129)the Key Scientific Research Program of the Higher Education Institutions of Henan Province(No.15A470014)+1 种基金the Program for Innovative Research Team of Henan Polytechnic Universitythe Doctoral Program Foundation of Henan Polytechnic University
文摘This paper presents development of a control system for ecological driving of a hybrid vehicle. Prediction using traffic signal and road slope information is considered to improve the fuel economy. It is assumed that the automobile receives traffic signal information from intelligent transportation systems (ITS). Model predictive control is used to calculate optimal vehicle control inputs using traffic signal and road slope information. The performance of the proposed method was analyzed through computer simulation results. Both the fuel economy and the driving profile are optimized using the proposed approach. It was observed that fuel economy was improved compared with driving of a typical human driving model.
基金Project supported by the UM High Impact Research MoE Grant from the Ministry of Education,Malaysia(No.UM.C/625/1/HIR/MOHE/FCSIT/09)
文摘The importance of using adaptive traffic signal control for figuring out the unpredictable traffic congestion in today's metropolitan life cannot be overemphasized. The vehicular ad hoc network(VANET), as an integral component of intelligent transportation systems(ITSs), is a new potent technology that has recently gained the attention of academics to replace traditional instruments for providing information for adaptive traffic signal controlling systems(TSCSs). Meanwhile, the suggestions of VANET-based TSCS approaches have some weaknesses:(1) imperfect compatibility of signal timing algorithms with the obtained VANET-based data types, and(2) inefficient process of gathering and transmitting vehicle density information from the perspective of network quality of service(Qo S). This paper proposes an approach that reduces the aforementioned problems and improves the performance of TSCS by decreasing the vehicle waiting time, and subsequently their pollutant emissions at intersections. To achieve these goals, a combination of vehicle-to-vehicle(V2V) and vehicle-to-infrastructure(V2I) communications is used. The V2 V communication scheme incorporates the procedure of density calculation of vehicles in clusters, and V2 I communication is employed to transfer the computed density information and prioritized movements information to the road side traffic controller. The main traffic input for applying traffic assessment in this approach is the queue length of vehicle clusters at the intersections. The proposed approach is compared with one of the popular VANET-based related approaches called MC-DRIVE in addition to the traditional simple adaptive TSCS that uses the Webster method. The evaluation results show the superiority of the proposed approach based on both traffic and network Qo S criteria.
基金the Deanship of Scientific Research at King Saud University for funding this work through research Group No.RG-1441-331.
文摘Simulation is a powerful tool for improving,evaluating and analyzing the performance of new and existing systems.Traffic simulators provide tools for studying transportation systems in smart cities as they describe the evolution of traffic to the highest level of detail.There are many types of traffic simulators that allow simulating traffic in modern cities.The most popular traffic simulation approach is the microscopic traffic simulation because of its ability to model traffic in a realistic manner.In many cities of Saudi Arabia,traffic management represents a major challenge as a result of expansion in traffic demands and increasing number of incidents.Unfortunately,employing simulation to provide effective traffic management for local scenarios in Saudi Arabia is limited to a number of commercial products in both public and private sectors.Commercial simulators are usually expensive,closed source and inflexible as they allow limited functionalities.In this project,we developed a local traffic simulator“KSUtraffic”for traffic modeling,planning and analysis with respect to different traffic control strategies and considerations.We modeled information specified by GIS and real traffic data.Furthermore,we designed experiments that manipulate simulation parameters and the underlying area.KSUTraffic visualizes traffic and provides statistical results on the simulated traffic which would help to improve traffic management and efficiency.