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考虑交通事件影响的动态交通信号控制策略

俞灏 刘攀 柏璐 路小波

俞灏, 刘攀, 柏璐, 路小波. 考虑交通事件影响的动态交通信号控制策略[J]. 交通运输工程学报, 2019, 19(6): 182-190. doi: 10.19818/j.cnki.1671-1637.2019.06.017
引用本文: 俞灏, 刘攀, 柏璐, 路小波. 考虑交通事件影响的动态交通信号控制策略[J]. 交通运输工程学报, 2019, 19(6): 182-190. doi: 10.19818/j.cnki.1671-1637.2019.06.017
YU Hao, LIU Pan, BAI Lu, LU Xiao-bo. Dynamic traffic signal control strategies considering traffic incidents[J]. Journal of Traffic and Transportation Engineering, 2019, 19(6): 182-190. doi: 10.19818/j.cnki.1671-1637.2019.06.017
Citation: YU Hao, LIU Pan, BAI Lu, LU Xiao-bo. Dynamic traffic signal control strategies considering traffic incidents[J]. Journal of Traffic and Transportation Engineering, 2019, 19(6): 182-190. doi: 10.19818/j.cnki.1671-1637.2019.06.017

考虑交通事件影响的动态交通信号控制策略

doi: 10.19818/j.cnki.1671-1637.2019.06.017
基金项目: 

国家自然科学基金项目 61803083

详细信息
    作者简介:

    俞灏(1988-), 男, 江苏扬州人, 东南大学博士后, 从事智能交通系统研究

    通讯作者:

    刘攀(1979-), 男, 江苏扬州人, 东南大学教授, 工学博士

  • 中图分类号: U491.51

Dynamic traffic signal control strategies considering traffic incidents

More Information
  • 摘要: 采用数值仿真方法评价了固定式信号控制、延误最小自适应信号控制与通行能力最大自适应控制3种典型信号控制策略下的路网动态运行效率; 采用双排队模型构建了动态交通流仿真平台, 提出了交叉口流量传输优化模型, 分析了双排队模型中交叉口内交通流运行的状态; 假定用户依据瞬时用户最优原则选择路径, 提出了考虑信号控制惩罚时间的瞬时用户最优约束; 以系统总行程时间、有无交通事件影响的行程时间为评价指标, 研究了低、中、高3级不同交通需求下的信号控制效果。试验结果表明: 在低、中级交通需求下延误最小自适应控制策略的系统总行程时间最小, 比通行能力最大自适应控制在无交通事件影响下总行程时间分别降低0.45%和0.18%, 在有交通事件影响下总行程时间分别降低5.95%和2.52%;在高级交通需求下, 通行能力最大自适应控制总行程时间最小, 对比延误最小自适应控制, 在有、无交通事件影响下系统总行程时间分别降低5.46%、5.31%;对比有无交通事件影响下系统总行程时间变化幅度, 固定式信号控制在不同交通需求下均表现出最高的稳定性; 在低、中级交通需求下, 延误最小自适应控制策略较通行能力最大自适应信号控制策略更稳定, 在高级交通需求下, 两者的稳定性无显著差异。可见, 当交通需求较大时, 应提升交叉口通行能力, 当交通需求较小时, 应降低车辆延误。

     

  • 图  1  基于双排队仿真平台的信号控制流程

    Figure  1.  Signal control process based on DQ simulation platform

    图  2  测试路网

    Figure  2.  Testing road network

    图  3  系统总行程时间差值的分布曲线

    Figure  3.  Distribution curves of difference of system total travel time

    表  1  信号控制惩罚时间

    Table  1.   Penalty times of signal control

    路段 时间步
    1 2 3 4 5 6 7 8 9 10
    a 0 0 3 2 1 0 0 3 2 1
    b 2 1 0 0 0 2 1 0 0 0
    下载: 导出CSV

    表  2  低级交通需求下OD矩阵

    Table  2.   OD matrix under low traffic demand condition veh·h-1

    终点 起点
    1 2 3 4 5 6 7 8 9 10
    1 0 0 0 0 480 0 240 480 0 240
    2 240 0 0 240 0 480 0 0 240 0
    3 0 0 0 0 0 240 0 480 240 0
    4 240 0 0 0 240 240 0 240 240 240
    5 480 0 0 0 0 240 0 240 480 0
    6 0 0 240 0 240 0 240 0 0 240
    7 240 480 0 240 0 0 0 0 0 240
    8 240 240 0 0 240 0 240 0 0 240
    9 0 240 240 480 0 0 240 0 0 240
    10 0 0 240 240 0 240 0 0 0 0
    下载: 导出CSV

    表  3  系统总行程时间

    Table  3.   System total travel times

    控制策略 交通需求 有无交通事件影响 最小时间步 最大时间步 期望时间步 时间步标准差 t p
    FSC 1 658 437 1 672 055 1 665 567 2 710 2.649 0.008
    1 658 870 1 673 400 1 666 180 2 944
    2 353 151 2 367 275 2 360 012 3 117 2.131 0.034
    2 353 818 2 367 323 2 360 545 3 016
    8 091 426 8 170 419 8 132 731 17 929 2.378 0.018
    8 097 779 8 173 129 8 135 958 15 199
    ASC-VD 1 402 641 1 424 157 1 410 377 5 080 6.581 < 0.001
    1 400 369 1 428 398 1 414 790 10 444
    1 789 061 1 855 236 1 821 236 18 266 2.058 0.040
    1 775 724 1 862 327 1 824 170 16 607
    6 704 896 7 492 216 7 194 811 186 930 1.280 0.201
    6 790 442 7 454 694 7 215 798 213 710
    ASC-IC 1 403 790 1 436 459 1 416 767 11 460 7.683 < 0.001
    1 412 230 1 432 350 1 423 212 8 933
    1 776 424 1 894 483 1 824 600 47 911 8.281 < 0.001
    1 791 109 2 001 795 1 870 259 82 607
    6 552 413 7 150 553 6 813 046 227 472 0.570 0.569
    6 580 594 7 093 754 6 822 835 191 534
    下载: 导出CSV

    表  4  有无交通事件条件下系统总行程时间t检验结果

    Table  4.   t-test results of system total travel times with and without traffic incidents

    交通需求 无交通事件 有交通事件
    t p t p
    8.830 < 0.001 10.615 < 0.001
    1.136 0.256 9.474 < 0.001
    22.459 < 0.001 23.717 < 0.001
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-06-18
  • 刊出日期:  2019-12-25

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