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车联网环境下连续信号交叉口协同控制模型

王庞伟 冯月 邓辉 汪云峰 王力

王庞伟, 冯月, 邓辉, 汪云峰, 王力. 车联网环境下连续信号交叉口协同控制模型[J]. 交通信息与安全, 2021, 39(1): 145-154. doi: 10.3963/j.jssn.1674-4861.2021.01.017
引用本文: 王庞伟, 冯月, 邓辉, 汪云峰, 王力. 车联网环境下连续信号交叉口协同控制模型[J]. 交通信息与安全, 2021, 39(1): 145-154. doi: 10.3963/j.jssn.1674-4861.2021.01.017
WANG Pangwei, FENG Yue, DENG Hui, WANG Yunfeng, WANG Li. A Cooperative Control Model of Continuous Signal Intersections for Connected Vehicles[J]. Journal of Transport Information and Safety, 2021, 39(1): 145-154. doi: 10.3963/j.jssn.1674-4861.2021.01.017
Citation: WANG Pangwei, FENG Yue, DENG Hui, WANG Yunfeng, WANG Li. A Cooperative Control Model of Continuous Signal Intersections for Connected Vehicles[J]. Journal of Transport Information and Safety, 2021, 39(1): 145-154. doi: 10.3963/j.jssn.1674-4861.2021.01.017

车联网环境下连续信号交叉口协同控制模型

doi: 10.3963/j.jssn.1674-4861.2021.01.017
基金项目: 

国家重点研发计划项目 2018YFB1600500

北京市自然科学基金项目 4212034

详细信息
    通讯作者:

    王庞伟(1982—),博士,副教授.研究方向:车路协同与智能驾驶. E-mail: wpw@ncut.edu.cn

  • 中图分类号: U491.2+5

A Cooperative Control Model of Continuous Signal Intersections for Connected Vehicles

  • 摘要: 智能交通信号控制技术是缓解交通拥堵的重要手段。为解决传统强化学习算法应用到连续多交叉口的局限性问题,提出了1种基于上下层神经网络的连续交叉口交通信号控制模型。控制模型由下层神经网络选择当前状态下可能的最优控制策略,再由上层神经网络根据各路口车均延误进行二次调整,将最终控制策略应用到多交叉口的相位配时中。以典型连续3个交叉口为例,通过SUMO仿真平台对模型进行仿真验证,在低与高饱和度下,该控制模型分别对车均延误降低了23.6%和26%,排队长度降低了8.4%和9.4%。实验数据表明,该模型可有效提高连续交叉口道路通行能力,为缓解城市交通拥堵提供了1种有效技术手段。

     

  • 图  1  连续交叉口上下层信号控制模型框架

    Figure  1.  Frame of upper-and-lower signal control model for continuous intersections

    图  2  路口矩阵化离散建模

    Figure  2.  Intersection matrix discrete modeling

    图  3  MDP循环流程图

    Figure  3.  Flow of the MDP cycle

    图  4  处理车辆信息的图卷积神经网络

    Figure  4.  Graph convolutional neural network for processing vehicle information

    图  5  DQN的模型框架图

    Figure  5.  Framework of the DQN model

    图  6  上层状态空间定义图

    Figure  6.  Definition of the upper state space

    图  7  上层状态空间示意图

    Figure  7.  Upper state space

    图  8  上下层网络的全局模型框架

    Figure  8.  Global model framework of upper and lower networks

    图  9  SUMO仿真平台示意图

    Figure  9.  SUMO Simulation platform

    图  10  连续交叉口仿真场景

    Figure  10.  Simulation scenario for continuous intersections

    图  11  各流量下的车均延误

    Figure  11.  Vehicle average delay at different circumstances

    图  12  各流量下的平均排队长度

    Figure  12.  Average queue length at different circumstances

    图  13  流量为2 400~3 600 veh/h的车均延误

    Figure  13.  Vehicle average delay at 2 400~3 600 veh / h

    表  1  神经网络参数表

    Table  1.   Parameters of the neural network

    参数
    重放内存大小M 20 000
    训练批次B 64
    初始贪心率ϵ 1
    最终贪心率ϵ 0.01
    目标网络更新率α衰减系数γ 0.001
    0.99
    Relu函数泄露值 0.01
    学习率 0.000 1
    下载: 导出CSV

    表  2  车辆参数表

    Table  2.   Parameters of vehicles

    车辆参数 数值
    最大速度/(km/h) 50
    最大加速度/(m/s2) 4.0
    减速加速度/(m/s2) 4.5
    车身长度/m 4.8
    最小车间距/m 1
    下载: 导出CSV

    表  3  车流到达率

    Table  3.   Traffic arrival rates

    交叉口 车流量/(Veh/h) 直行比例/% 右转比例/% 左转比例/%
    1 2 400 57.14 28.57 14.29
    2 2 400 54.55 27.27 18.18
    3 2 400 57.14 28.57 14.29
    1 3 600 44.44 33.33 22.22
    2 3 600 57.14 28.57 14.29
    3 3 600 50.00 25.00 25.00
    1 4 800 57.14 28.57 14.29
    2 4 800 60.00 20.00 20.00
    3 4 800 57.14 28.57 14.29
    下载: 导出CSV

    表  4  各模型在不同流量下的车均延误统计

    Table  4.   Vehicle delay under different flow rates

    车流量/(veh/h) 上下层Agent/m 单层DQN/m 数解法绿波带/m
    2 400 34.9 36.1 53.4
    3 600 46.3 50.6 57.4
    4 800 48.9 56.4 64.2
    下载: 导出CSV

    表  5  各模型在不同流量下的排队长度统计

    Table  5.   Average queue length under different flow rates

    车流量/(veh/h) 上下层Agent/m 单层DQN/m 数解法绿波带/m
    2 400 12.4 13.5 17.6
    3 600 15.3 16.4 18.4
    4 800 20.7 22.8 23.2
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-09-28
  • 刊出日期:  2021-02-28

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