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面向常发拥堵点的交通信号协调控制方法

张泰文 张存保 罗舒琳 曹雨

张泰文, 张存保, 罗舒琳, 曹雨. 面向常发拥堵点的交通信号协调控制方法[J]. 交通信息与安全, 2021, 39(6): 63-72. doi: 10.3963/j.jssn.1674-4861.2021.06.008
引用本文: 张泰文, 张存保, 罗舒琳, 曹雨. 面向常发拥堵点的交通信号协调控制方法[J]. 交通信息与安全, 2021, 39(6): 63-72. doi: 10.3963/j.jssn.1674-4861.2021.06.008
ZHANG Taiwen, ZHANG Cunbao, LUO Shulin, CAO Yu. A Coordinated Control Method of Traffic Signals for Recurrent Congested Network Locations[J]. Journal of Transport Information and Safety, 2021, 39(6): 63-72. doi: 10.3963/j.jssn.1674-4861.2021.06.008
Citation: ZHANG Taiwen, ZHANG Cunbao, LUO Shulin, CAO Yu. A Coordinated Control Method of Traffic Signals for Recurrent Congested Network Locations[J]. Journal of Transport Information and Safety, 2021, 39(6): 63-72. doi: 10.3963/j.jssn.1674-4861.2021.06.008

面向常发拥堵点的交通信号协调控制方法

doi: 10.3963/j.jssn.1674-4861.2021.06.008
基金项目: 

国家重点研发计划项目 2018YFB1601004

详细信息
    作者简介:

    张泰文(1994—), 硕士研究生.研究方向: 交通管理与控制.E-mail: zhang_taiwen@163.com

    通讯作者:

    张存保(1976—), 博士, 研究员.研究方向: 交通信息工程及控制、交通安全等.E-mail: zhangcunbao@163.com

  • 中图分类号: U491.5+4

A Coordinated Control Method of Traffic Signals for Recurrent Congested Network Locations

  • 摘要: 针对高峰期间常发拥堵点交通需求过大、周边关联交叉口交通负荷分布不均的问题, 研究了面向常发拥堵点的交通信号协调控制方法。通过对常发拥堵点的车流进行追踪与溯源, 根据交通量关联度确定信号协调控制范围, 然后基于路径的流量分担率与路段平均饱和度识别信号协调控制范围内的关键路径。基于宏观基本图理论, 考虑关键路径对路网运行状态的影响, 构建边界交叉口主动限流控制模型。同时, 利用元胞传输模型描述交叉口与路段的运行状态, 以关键路径通行能力最大化和进口道饱和度均衡化为信号控制优化目标, 建立均衡路网交通负荷的信号控制优化模型。以武汉市发展大道青年路交叉口以及关联交叉口为对象开展仿真实验, 结果表明: 虽然本文方法下的边界交叉口车均延误增加了6.8 s, 但常发拥堵点的车均延误降低了15.7 s; 关键路径的车均延误减少72.6 s, 平均排队长度减少26.1 m。并且, 路网整体的车均延误降低14.7%, 驶出车辆数增加26.6%, 验证了提出方法缓解常发拥堵点交通拥堵的有效性。

     

  • 图  1  交通信号协调控制策略动态决策流程

    Figure  1.  Dynamic decision process of traffic signal coordination control strategy

    图  2  信号协调控制范围以及关键路径示意图

    Figure  2.  Signal coordination control area and critical route

    图  3  区域路网宏观基本图

    Figure  3.  Macroscopic fundamental diagram of the road network

    图  4  NEMA双环结构示意图

    Figure  4.  Schematic of the NEMA dual-ring structure

    图  5  相位时间调整图示

    Figure  5.  Phase time adjustment

    图  6  可变元胞原理图

    Figure  6.  Schematic of variable cells

    图  7  测试区域示意图

    Figure  7.  Schematic of the test area

    图  8  测试区域Vissim仿真界面

    Figure  8.  Vissim simulation interface of the test area

    图  9  测试区域的宏观基本图

    Figure  9.  MFD of the test area

    图  10  高峰时段发展大道协调控制方案时距图

    Figure  10.  Optimized time-distance map of the coordinated control scheme for Fazhan Avenue during peak hours

    图  11  路网内车辆数变化对比

    Figure  11.  Comparison of vehicles in the network

    图  12  路网驶出交通量对比

    Figure  12.  Comparison of the outflow volumes of the network

    图  13  路网车均延误对比

    Figure  13.  Comparison of the average delays of network vehicles

    图  14  关键路径车均延误对比

    Figure  14.  Comparison of the average delays of critical route vehicles

    图  15  关键路径平均排队长度对比

    Figure  15.  Comparison of the average queue lengths of critical routes

    图  16  常发拥堵点的车均延误对比

    Figure  16.  Comparison of the vehicles'average delays of the recurrent congestion point

    图  17  边界交叉口车均延误对比

    Figure  17.  Comparison of the vehicles'average delays of perimeter intersections

    表  1  外部道路输入流量

    Table  1.   Input volume of the external road

    外部道路 流量/(veh/h) 外部道路 流量/(veh/h)
    R1 2 109 R10 1 998
    R2 1 702 R11 1 887
    R3 1 907 R12 2 204
    R4 1 675 R13 1 731
    R5 2 142 R14 2 372
    R6 2 263 R15 2 100
    R7 1 504 R16 1 794
    R8 2 134 R17 1 816
    R9 1 649
    下载: 导出CSV

    表  2  交通运行评价指标对比

    Table  2.   Comparison of traffic-operation evaluation indices

    交通运行评价指标 控制方法 优化效果/%
    干道协调 本文方法
    路网车辆平均延误/(s/veh) 276.5 235.9 -14.7
    驶离路网的车辆数/veh 17 262 21 854 26.6
    关键路径车辆平均延误/(s/veh) 357.7 285.1 -20.3
    关键路径平均排队长度/m 151.8 125.7 -17.2
    常发拥堵点的车辆平均延误/(s/veh) 83.2 67.5 -18.9
    边界交叉口车辆平均延误/(s/veh) 29.4 36.2 23.1
    下载: 导出CSV
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  • 收稿日期:  2021-08-26
  • 网络出版日期:  2022-01-12

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