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考虑交通流非线性特性的交通信号迭代学习控制策略

闫飞 李浦 阎高伟 任密蜂

闫飞, 李浦, 阎高伟, 任密蜂. 考虑交通流非线性特性的交通信号迭代学习控制策略. 自动化学报, 2021, 47(9): 2238−2249 doi: 10.16383/j.aas.c190183
引用本文: 闫飞, 李浦, 阎高伟, 任密蜂. 考虑交通流非线性特性的交通信号迭代学习控制策略. 自动化学报, 2021, 47(9): 2238−2249 doi: 10.16383/j.aas.c190183
Yan Fei, Li Pu, Yan Gao-Wei, Ren Mi-Feng. An iterative learning control strategy for traffic signals considering nonlinear characteristics of traffic flow. Acta Automatica Sinica, 2021, 47(9): 2238−2249 doi: 10.16383/j.aas.c190183
Citation: Yan Fei, Li Pu, Yan Gao-Wei, Ren Mi-Feng. An iterative learning control strategy for traffic signals considering nonlinear characteristics of traffic flow. Acta Automatica Sinica, 2021, 47(9): 2238−2249 doi: 10.16383/j.aas.c190183

考虑交通流非线性特性的交通信号迭代学习控制策略

doi: 10.16383/j.aas.c190183
基金项目: 国家自然科学基金(61703300), 中国博士后科学基金(2019M651082), 山西省应用基础研究项目(201801D221191)资助
详细信息
    作者简介:

    闫飞:太原理工大学电气与动力工程学院副教授. 主要研究方向为迭代学习控制, 城市交通信号控制, 智能交通系统. 本文通信作者. E-mail: yanfei@tyut.edu.cn

    李浦:太原理工大学电气与动力工程学院硕士研究生. 主要研究方向为迭代学习控制, 城市交通信号控制. E-mail: lipu12281@163.com

    阎高伟:太原理工大学电气与动力工程学院教授. 主要研究方向为复杂工业控制系统, 智能控制理论及其应用, 智能信息处理. E-mail: yangaowei@tyut.edu.cn

    任密蜂:太原理工大学电气与动力工程学院副教授. 主要研究方向为随机控制, 性能评价, 故障诊断. E-mail: renmifeng@126.com

An Iterative Learning Control Strategy for Traffic Signals Considering NonlinearCharacteristics of Traffic Flow

Funds: Supported by National Natural Science Foundation of China (61703300), China Postdoctoral Science Foundation (2019M651082), and Applied Basic Research Program of Shanxi Province (201801D221191)
More Information
    Author Bio:

    YAN Fei Associate professor at the College of Electrical and Power Engineering, Taiyuan University of Technology. His research interest covers iterative learning control, urban traffic signal control, and intelligent traffic system. Corresponding author of this paper

    LI Pu Master student at the College of Electrical and Power Engineering, Taiyuan University of Technology. His research interest covers iterative learning control and urban traffic signal control

    YAN Gao-Wei Professor at the College of Electrical and Power Engineering, Taiyuan University of Technology. His research interest covers complex industrial control system, intelligent control theory and application, and intelligent information processing

    REN Mi-Feng Associate professor at the College of Electrical and Power Engineering, Taiyuan University of Technology. Her research interest covers stochastic control, control performance assessment, and fault diagnosis

  • 摘要: 现实中城市交通流的运行具有很强的非线性特性, 采用简单的线性模型难以全面描述交通流的实际运行过程. 本文在考虑城市交通流非线性动态特性的基础上, 提出了一种非线性交通流排队模型, 并基于宏观交通流固有的周期性特征, 设计了交叉口信号的迭代学习控制策略. 通过对交叉口信号的迭代学习控制, 使交叉口各进口道的车辆排队长度尽可能趋于均衡, 提高交叉口信号有效绿灯时间的利用率, 从而提高路网的通行效率. 最后通过严格的数学推导证明了该方法的收敛性, 仿真研究及实验结果验证了所提方法的有效性.
  • 图  1  交叉口相位示意图与路段交通流模型

    Fig.  1  The intersection phase and road traffic flow model

    图  2  太原市某区域路网结构简图

    Fig.  2  The road network structure of one region in Taiyuan

    图  3  三种控制方案下各交叉口相位车辆排队长度差值

    Fig.  3  The differences of queue lengths at different phases of each intersection for the three control schemes

    图  4  三种控制方案下交叉口4各相位的车辆排队情况

    Fig.  4  The queue lengths at Intersection 4 for the three control schemes

    图  5  三种控制方案下交叉口9各相位的车辆排队情况

    Fig.  5  The queue lengths at Intersection 9 for the three control schemes

    图  6  平均车辆排队长度差值随迭代次数的变化情况

    Fig.  6  The average difference of queue lengths of allintersections after each iteration

    图  7  三种控制方案下路网的平均延迟时间

    Fig.  7  The average delay time of road network for thethree control schemes

    图  8  三种控制方案下路网的平均停车次数

    Fig.  8  The average number of stops for the threecontrol schemes

    图  9  三种控制方案下路网内车辆的平均速度

    Fig.  9  The average speed of vehicles for the threecontrol schemes

    表  1  各路段车道数

    Table  1  The number of lanes in each link

    道路情况道路编号
    双向 3 车道道路$x_{1},x_{2},x_{3},x_{4},x_{9},x_{10},x_{11},x_{14},x_{17},x_{20},x_{23}$
    双向 4 车道道路$x_{12},x_{13},x_{15},x_{16},x_{18},x_{19},x_{21},x_{22}$
    双向 7 车道道路$x_{5},x_{6},x_{7},x_{8}$
    下载: 导出CSV

    表  2  路网的输入流量(veh/h)

    Table  2  The inflows of the road network (veh/h)

    时段/min$x_{1},x_{4},x_{9},x_{12},x_{14},x_{21},x_{23}$$x_{13},x_{22}$$x_{5},x_{8}$
    $0\sim20$1 6003 0002 000
    $20\sim40$2 0003 4002 400
    $40\sim60$1 8003 2002 200
    下载: 导出CSV

    表  3  交叉口的迭代学习增益

    Table  3  The iterative learning gains at different intersections

    交叉口编号学习增益值
    1, 2, 3, 4, 5, 6, 7, 8, 90.1
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-03-19
  • 录用日期:  2019-08-15
  • 刊出日期:  2021-10-13

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