为研究车车通信技术条件下车辆通过合流影响区时的运行情况,缓解快速路交通压力,提出车车通信环境下入口匝道车辆速度控制模型。首先,分析合流影响区车辆汇合存在的问题;然后,结合合流影响区车辆行驶速度需求,确定入口匝道车辆在加速车道上可汇合位置;接着,根据入口匝道车辆和主路最外侧车道车辆分别到达合流影响区汇合点的时间,建立入口匝道车辆汇入的车速控制模型;最后,对传统环境下和车车通信环境下车辆驶过合流影响区进行仿真。结果表明,在给定的仿真时间段,车车通信环境下,主路和匝道交通量分别为1 000veh/h和400veh/h时,合流影响区的交通量提高了19.5%,入口匝道车辆的平均行驶时间节约了26.9%、平均行驶速度提高了19.7%;主路交通量为1 800veh/h、匝道交通量为800veh/h时,传统环境下合流区车辆出现排队现象,车车通信环境下无排队现象。
Abstract
In order to study the operation situation of vehicles passing through the merging influence area under the technology condition of vehicle-vehicle communication and alleviate the traffic pressure on expressway, a speed control model for entrance ramp vehicle under vehicle-vehicle communication environment was proposed. Firstly, the problems of vehicle convergence in merging area were analyzed. Secondly, combined with the vehicle travel speed demand in merging area, the merging position of the entrance ramp vehicle on the acceleration lane was determined. Then, according to the arrival time of vehicle on the entrance ramp and vehicle on the outermost lane of the main road respectively reaching the convergence point of the merging influence area, the speed control model for entrance ramp vehicle was established. Finally, the simulation was carried out in which vehicle passed through the merging influence area under traditional environment and vehicle-vehicle communication environment. The results showed that in the given simulation period, under vehicle-vehicle communication environment, when the traffic volumes of the main road and the ramp were 1 000veh/h and 400veh/h respectively, the traffic volume in the merging influence area increased by 19.5%, the average driving time of entrance ramp vehicle saved by 26.9%, and the average driving speed increased by 19.7%. When the traffic volumes of the main road and the ramp were 1 800veh/h and 800veh/h, the vehicles in the merging area appeared queuing under the traditional environment, while there was no queuing phenomenon under the vehicle-vehicle communication.
关键词
车车通信 /
车速控制 /
合流区 /
仿真 /
交通流
Key words
vehicle-vehicle communication /
vehicle speed control /
merge area /
simulation /
traffic flow
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参考文献
[1]王振华. 车路协同环境下城市道路交织区驾驶行为特性及控制策略研究[D]. 北京:北京工业大学,2016.
[2]师建华. 中国汽车零部件产业发展报告[J]. 汽车与驾驶维修(维修版),2018(10):12-14.
[3]林奕钦,王伟智. 快速路行车安全的可变限速方法[J]. 福州大学学报(自然科学版),2019,47(4):509-514.
[4]Cheng Z Y, Lu J, Li Y X. Freeway Crash Risks Evaluation by Variable Speed Limit Strategy Using Real-World Traffic Flow Data[J]. Accident Analysis and Prevention, 2018(119): 176-187.
[5]郭代银,鲁兴举,金尚泰. 快速路入口匝道无模型自适应控制及ARM实现[J]. 森林工程,2015,31(1):97-102.
[6]李科志. 车联网环境下基于反馈的智能车辆编队控制研究[D]. 重庆:重庆邮电大学,2017.
[7]杨帆,云美萍,杨晓光. 车路协同系统下多智能体微观交通流模型[J]. 同济大学学报(自然科学版),2012,40(8):1189-1196.
[8]Abuamer I M, Sadat M, Silgu M A, et al. Analyzing the Effects of Driver Behavior within an Adaptive Ramp Control Scheme: a Case-Study with ALINEA[C]// 2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES). New York: IEEE, 2017: 109-114.
[9]Shladover S E, Nowakowski C, Lu X Y. Using Cooperative Adaptive Cruise Control (CACC) to Form High-Performance Vehicle Streams[R]. Berkeley: Institute of Transportation Studies, University of California, Berkeley, 2014.
[10]杨刚. 基于车车通信的多车协同自动换道控制策略研究[D]. 北京:清华大学,2016.
[11]陈友荣,万锦昊,陈俊洁,等. 基于车车通信的车辆防碰撞算法[J]. 电信科学,2016,32(11):32-41.
[12]Bento L C, Parafita R, Rakha H A. A Study of the Environmental Impacts of Intelligent Automated Vehicle Control at Intersections via V2V and V2I Communications[J]. Journal of Intelligent Transportation Systems, 2019, 23(1): 41-59.
[13]Wang R M, Xu Z G, Zhao X M, et al. a V2V-Based Method for the Detection of Road Traffic Congestion[J]. IET Intelligent Transport Systems, 2019, 13(5): 880-885.
[14]杨晓芳,郭倩,付强. 基于车车通信合流影响区外侧车辆决策模型[J]. 系统仿真学报,2015,27(5):1112-1119,1126.
[15]王东柱,陈艳艳,马建明,等. 车联网环境下的高速公路合流区协调控制方法及效果评价[J]. 公路交通科技,2016,33(9):99-105.
[16]陈宇峰,向郑涛,闫蓬,等. 浓雾环境下车车通信对交通事故的影响分析[J]. 交通运输系统工程与信息,2016,16(4):109-116,123.
[17]Hall R W, Li C. Evaluation of Priority Rules for Entrance to Automated Highways[J]. Intelligent Transportation Systems Journal, 2001, 6(2): 175-193.
[18]Park H, Bhamidipati C S, Smith B L. Development and Evaluation of Enhanced IntelliDrive-Enabled Lane Changing Advisory Algorithm to Address Freeway Merge Conflict[J]. Transportation Research Record: Journal of the Transportation Research Board, 2011, 2243(1): 146-157.
[19]姚佼,杨晓光. 车路协同环境下城市交通控制研究[J]. 上海理工大学学报,2013,35(4):397-403.
[20]林培群,卓福庆,姚凯斌,等. 车联网环境下交叉口交通流微观控制模型及其求解与仿真[J]. 中国公路学报,2015,28(8):82-90.
[21]鹿应荣,许晓彤,丁川,等. 车联网环境下信号交叉口车速控制策略[J]. 交通运输系统工程与信息,2018,18(1):50-58,95.
基金
国家自然科学基金项目(51308409);上海市浦江人才计划项目(15PJC075)