城市交通流量预测与信号控制优化
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摘要
城市中车辆数量的快速增长会带来一系列交通问题,包括交通拥堵、交通事故率高等.高效的交通信号控制方法已被证明是缓解交通问题的重要途径之一.已有的信号灯控制工作主要集中在设计在线信号灯控制算法上,但该方法存在信号灯频繁切换的问题.这篇文章首先根据联合驾驶和变换车道模型预测出下一个时间段检测区域内的车辆数目,然后利用图论对该路口的交通动作进行建模,在此基础上设计了一种高效的基于图的车辆调度算法,并从理论上证明了该车辆调度算法的近似比为2.实验结果表明:首先,这篇文章所提出的车辆调度算法能够有效地减少车辆调度的时间,提高交通运输效率;其次,该算法能够大大减少信号灯切换的次数.
The rapid growth of the number of vehicles in cities will bring about a series of traffic problems,including traffic congestion,traffic accidents,etc. Efficient traffic signal control method has been proved to be one of the important ways to alleviate traffic problems. The existing signal control mainly focuses on the design of online signal control algorithm,but there is a problem of frequent switching of traffic lights. This article first predicts the number of vehicles in the detection area for the next time period based on the cooperative driving and lane changing model,and then uses the graph theory to model the traffic movement at the intersection. On the basis of this model,a highly efficient vehicle scheduling algorithm based on graph is designed. The approximation ratio of the algorithm is proved to be 2. The experimental results show that: first,the vehicle scheduling algorithm proposed in this paper can effectively reduce the time of scheduling the vehicles and improve the efficiency of traffic. Secondly,the algorithm can greatly reduce the number of switching of traffic lights.
The rapid growth of the number of vehicles in cities will bring about a series of traffic problems,including traffic congestion,traffic accidents,etc. Efficient traffic signal control method has been proved to be one of the important ways to alleviate traffic problems. The existing signal control mainly focuses on the design of online signal control algorithm,but there is a problem of frequent switching of traffic lights. This article first predicts the number of vehicles in the detection area for the next time period based on the cooperative driving and lane changing model,and then uses the graph theory to model the traffic movement at the intersection. On the basis of this model,a highly efficient vehicle scheduling algorithm based on graph is designed. The approximation ratio of the algorithm is proved to be 2. The experimental results show that: first,the vehicle scheduling algorithm proposed in this paper can effectively reduce the time of scheduling the vehicles and improve the efficiency of traffic. Secondly,the algorithm can greatly reduce the number of switching of traffic lights.
引文
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[7]Lee J,Park B.Development and evaluation of a cooperative vehicle intersection control algorithm under the connected vehicles environment[J].IEEE Transactions on Intelligent Transportation Systems,2012,13(1):81-90.
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[12]Treiber M,Helbing D.Memory effects in microscopic traffic models and wide scattering in flow-density data[J].Physical Review E,2003,68(4):46-119.
[13]Treiber M,Kesting A,Helbing D.Delays,inaccuracies and anticipation in microscopic traffic models[J].Physica A Statistical Mechanics&Its Applications,2004,360(1):71-88.
[14]Winsum W V,Waard D D,Brookhuis K A.Lane change manoeuvres and safety margins[J].Transportation Research Part F Traffic Psychology&Behaviour,2000,2(3):139-149.
[15]He Y,Sun D,Zhao M,et al.Cooperative driving and lane changing modeling for connected vehicles in the vicinity of traffic signals:a cyber-physical perspective[J].IEEE Access,2018,6:13891-13897.
[16]He Z,Zhang L,Zhang H,et al.Integrated modeling and control of urban road traffic networks[C]//Proceedings of IEEE Data Driven Control and Learning Systems(DDCLS),2017:733-738.
[1]吴黎兵,范静,聂雷,等.一种车联网环境下的城市车辆协同选路方法[J].计算机学报.2017,40(7):1600-1613.
[2]吴黎兵,聂雷,刘冰艺,等.一种VANET环境下的智能交通信号控制方法[J].计算机学报.2016,39(6):1105-1119.
[2]Wu Li-bing,Nie Lei,Liu Bing-yi,et al.An intelligent traffic signal control method in VANET[J].Chinese Journal of Computers,2016,39(6):1105-1119.
[3]WeiíC.V2X communication in europe-from research projects towards standardization and field testing of vehicle communication technology[J].Computer Networks,2011,55(14):3103-3119.
[4]Pandit K,Ghosal D,Zhang H M,et al.Adaptive traffic signal control with vehicular ad hoc networks[J].IEEE Transactions on Vehicular Technology,2013,62(4):1459-1471.
[5]Liu B,Kamel A E.V2X-Based decentralized cooperative adaptive cruise control in the vicinity of intersections[J].IEEE Transactions on Intelligent Transportation Systems,2016,17(3):644-658.
[6]Nunzio G D,Wit C C D,Moulin P,et al.Eco-driving in urban traffic networks using traffic signal information[J].International Journal of Robust and Nonlinear Control,2016,26(6):1307-1324.
[7]Lee J,Park B.Development and evaluation of a cooperative vehicle intersection control algorithm under the connected vehicles environment[J].IEEE Transactions on Intelligent Transportation Systems,2012,13(1):81-90.
[8]Li L,Wang F Y,Kim H.Cooperative driving and lane changing at blind crossings[C]//Proceedings of IEEE Intelligent Vehicles Symposium,2005:435-440.
[9]Li L,Wang F Y.Advanced motion control and sensing for intelligent vehicles[M].Springer Science&Business Media Press,2007.
[10]Wong S C,Sze N N,Li Y C.Contributory factors to traffic crashes at signalized intersections in Hong Kong[J].Accident Analysis&Prevention,2007,39(6):1107-1113.
[11]Treiber M,Hennecke A,Helbing D.Congested traffic states in empirical observations and microscopic simulations[J].Physical Review E,2000,62(2):1805-1824.
[12]Treiber M,Helbing D.Memory effects in microscopic traffic models and wide scattering in flow-density data[J].Physical Review E,2003,68(4):46-119.
[13]Treiber M,Kesting A,Helbing D.Delays,inaccuracies and anticipation in microscopic traffic models[J].Physica A Statistical Mechanics&Its Applications,2004,360(1):71-88.
[14]Winsum W V,Waard D D,Brookhuis K A.Lane change manoeuvres and safety margins[J].Transportation Research Part F Traffic Psychology&Behaviour,2000,2(3):139-149.
[15]He Y,Sun D,Zhao M,et al.Cooperative driving and lane changing modeling for connected vehicles in the vicinity of traffic signals:a cyber-physical perspective[J].IEEE Access,2018,6:13891-13897.
[16]He Z,Zhang L,Zhang H,et al.Integrated modeling and control of urban road traffic networks[C]//Proceedings of IEEE Data Driven Control and Learning Systems(DDCLS),2017:733-738.
[1]吴黎兵,范静,聂雷,等.一种车联网环境下的城市车辆协同选路方法[J].计算机学报.2017,40(7):1600-1613.
[2]吴黎兵,聂雷,刘冰艺,等.一种VANET环境下的智能交通信号控制方法[J].计算机学报.2016,39(6):1105-1119.