Road traffic congestion can inevitably de-grade road infrastructure and decrease travel efficiency in urban traffic networks,which can be relieved by employing appropriate congestion control.Accord-ing to different de...Road traffic congestion can inevitably de-grade road infrastructure and decrease travel efficiency in urban traffic networks,which can be relieved by employing appropriate congestion control.Accord-ing to different developmental driving forces,in this paper,the evolution of road traffic congestion control is divided into two stages.The ever-growing num-ber of advanced sensing techniques can be seen as the key driving force of the first stage,called the sens-ing stage,in which congestion control strategies ex-perienced rapid growth owing to the accessibility of traffic data.At the second stage,i.e.,the communica-tion stage,communication and computation capabil-ity can be regarded as the identifying symbols for this stage,where the ability of collecting finer-grained in-sight into transportation and mobility reality improves dramatically with advances in vehicular networks,Big Data,and artificial intelligence.Specifically,as the pre-requisite for congestion control,in this paper,ex-isting congestion detection techniques are first elab-orated and classified.Then,a comprehensive survey of the recent advances for current congestion control strategies with a focus on traffic signal control,vehi-cle route guidance,and their combined techniques is provided.In this regard,the evolution of these strate-gies with continuous development of sensing,com-munication,and computation capability are also intro-duced.Finally,the paper concludes with several re-search challenges and trends to fully promote the in-tegration of advanced techniques for traffic congestion mitigation in transportation systems.展开更多
The growing number of vehicles makes traffic jams and accidents significant problems. Making people get to know the real-time road condition can mitigate the effect of congestions greatly, but this is not supported by...The growing number of vehicles makes traffic jams and accidents significant problems. Making people get to know the real-time road condition can mitigate the effect of congestions greatly, but this is not supported by traditional traffic assistant systems. The intelligent traffic system is born to settle these problems. By making full use of the ArcGIS （Arc Geographic Information System） Engine characteristics, this paper designs and imple- ments an urban traffic monitoring system. The main functions of the system include the real-time road condition information display, layer-control, supervisory control management and the basic operations of a map. With the data collected by monitors deployed in intersections, different road conditions are calculated and shown with dif- ferent colors on the map and users can choose suitable roads to get away from the traffic congestion; meanwhile it can offer a reference for a traffic management department to make decisions on traffic control. The system has been deployed and shows high practicability and reliability in practical use.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
基金the National Key R&D Program of China(2019YFB1600100)National Nat-ural Science Foundation of China(U1801266)the Youth Innovation Team of Shaanxi Universities.
文摘Road traffic congestion can inevitably de-grade road infrastructure and decrease travel efficiency in urban traffic networks,which can be relieved by employing appropriate congestion control.Accord-ing to different developmental driving forces,in this paper,the evolution of road traffic congestion control is divided into two stages.The ever-growing num-ber of advanced sensing techniques can be seen as the key driving force of the first stage,called the sens-ing stage,in which congestion control strategies ex-perienced rapid growth owing to the accessibility of traffic data.At the second stage,i.e.,the communica-tion stage,communication and computation capabil-ity can be regarded as the identifying symbols for this stage,where the ability of collecting finer-grained in-sight into transportation and mobility reality improves dramatically with advances in vehicular networks,Big Data,and artificial intelligence.Specifically,as the pre-requisite for congestion control,in this paper,ex-isting congestion detection techniques are first elab-orated and classified.Then,a comprehensive survey of the recent advances for current congestion control strategies with a focus on traffic signal control,vehi-cle route guidance,and their combined techniques is provided.In this regard,the evolution of these strate-gies with continuous development of sensing,com-munication,and computation capability are also intro-duced.Finally,the paper concludes with several re-search challenges and trends to fully promote the in-tegration of advanced techniques for traffic congestion mitigation in transportation systems.
文摘The growing number of vehicles makes traffic jams and accidents significant problems. Making people get to know the real-time road condition can mitigate the effect of congestions greatly, but this is not supported by traditional traffic assistant systems. The intelligent traffic system is born to settle these problems. By making full use of the ArcGIS （Arc Geographic Information System） Engine characteristics, this paper designs and imple- ments an urban traffic monitoring system. The main functions of the system include the real-time road condition information display, layer-control, supervisory control management and the basic operations of a map. With the data collected by monitors deployed in intersections, different road conditions are calculated and shown with dif- ferent colors on the map and users can choose suitable roads to get away from the traffic congestion; meanwhile it can offer a reference for a traffic management department to make decisions on traffic control. The system has been deployed and shows high practicability and reliability in practical use.
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.
