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智能网联环境下信号交叉口车辆轨迹重构模型

杨涛 马玉琴 刘梦 姚志洪 蒋阳升

杨涛, 马玉琴, 刘梦, 姚志洪, 蒋阳升. 智能网联环境下信号交叉口车辆轨迹重构模型[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20220321
引用本文: 杨涛, 马玉琴, 刘梦, 姚志洪, 蒋阳升. 智能网联环境下信号交叉口车辆轨迹重构模型[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20220321
YANG Tao, MA Yuqin, LIU Meng, YAO Zhihong, JIANG Yangsheng. Vehicle Trajectory Reconstruction Model of Signalized Intersection in Connected Automated Environments[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20220321
Citation: YANG Tao, MA Yuqin, LIU Meng, YAO Zhihong, JIANG Yangsheng. Vehicle Trajectory Reconstruction Model of Signalized Intersection in Connected Automated Environments[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20220321

智能网联环境下信号交叉口车辆轨迹重构模型

doi: 10.3969/j.issn.0258-2724.20220321
基金项目: 四川省科技计划(2021YJ0535, 2022YFG0152).
详细信息
    作者简介:

    杨涛(1974—),男,正高级建筑师,研究方向为交通工程,E-mail:23432425@qq.com

    通讯作者:

    姚志洪(1991—),男,副教授,博士,研究方向为智能网联交通系统建模与优化,E-mail:zhyao@swjtu.edu.cn

  • 中图分类号: U491

Vehicle Trajectory Reconstruction Model of Signalized Intersection in Connected Automated Environments

  • 摘要:

    车辆轨迹数据提供了大量的时空交通流信息,可用于各类交通研究. 传统车辆轨迹模型多以人工驾驶环境为研究对象,普遍未考虑由常规车(RV)、网联人工驾驶车(CV)以及智能网联车(CAV)组成的混合交通流的影响. 为解决该问题,构建了智能网联环境下信号交叉口全样本车辆轨迹重构模型. 首先,分析并介绍智能网联环境下城市道路交叉口处车辆组成及排队通过情况;然后,构建城市道路混合交通流轨迹数量估计模型,并针对前后车的排队情况提出虚拟车的概念用于估计不同车辆的交通状态. 最后,设计数值仿真实验分析交通流密度和网联车渗透率对模型的影响,并基于NGSIM数据进行实例验证. 结果表明:轨迹重构模型的数量误差和位置误差均随着交通流密度和网联车渗透率的增大而减小,如交通流密度由20 veh/km增大至50 veh/km的过程中,模型数量误差和位置误差均呈现下降趋势,且最大误差不超过6.88%和8.02 m;与网联人工驾驶车渗透率相比,智能网联车的渗透率对模型结果影响更大.

     

  • 图 1  车辆跟驰示意

    Figure 1.  Car-following

    图 2  交叉口交通波理论图

    Figure 2.  Traffic wave theory of signalized intersection

    图 3  城市道路混合交通流车辆组成

    Figure 3.  Vehicle composition of mixed traffic flow in urban roads

    图 4  混合交通流下信号交叉口处时空轨迹

    Figure 4.  Spatial-temporal trajectories at signalized intersection under mixed traffic flow

    图 5  关键点示意

    Figure 5.  Key points

    图 6  虚拟车示意

    Figure 6.  Virtual vehicle

    图 7  交通流密度为40 veh/km时重构轨迹

    Figure 7.  Trajectory reconstruction with a traffic flow density of 40 veh/km

    图 8  智能网联车渗透率为14%时重构轨迹

    Figure 8.  Trajectory reconstruction with a CAV penetration rate of 14%

    图 9  均方根误差热力图

    Figure 9.  Heat map of RMSE

    图 10  Lankershim Boulevard街道重构轨迹

    Figure 10.  Trajectory reconstruction of Lankershim Boulevard Street

    表  1  车辆速度与视野和注视距离关系

    Table  1.   Relationship among vehicle speed, horizon, and gaze distance

    车辆速度/(km·h−1视野/(°)注视距离/m
    017080
    40100180
    6086355
    8060377
    10040564
    12022710
    下载: 导出CSV

    表  2  IDM跟驰模型参数

    Table  2.   Parameters of IDM car-following model

    模型$ {A_n} $/ (m·s−2$ {b_n} $/ (m·s−2${v_{\rm{f}}}$/ (m·s−1)Tn/ss0/m
    IDM1.002.0033.301.502.00
    虚拟IDM2.001.6828.921.452.00
    下载: 导出CSV

    表  3  不同交通流密度条件下轨迹重构结果

    Table  3.   Trajectory reconstruction results under different traffic density conditions

    交通流
    密度/
    (veh·km−1
    插入RV的数量 插入RV的位置
    实际车
    辆/veh
    MAEm/
    veh
    MAPEm /
    %
    MAEx/
    m
    RMSEx/
    m
    2029.052.006.88 4.158.02
    3029.091.856.363.187.45
    4029.001.826.272.937.06
    5023.501.335.672.886.61
    下载: 导出CSV

    表  4  不同CAV渗透率条件下轨迹重构结果

    Table  4.   Trajectory reconstruction results under different CAV penetration rates

    CAV渗
    透率/%
    CAV数
    量/veh
    插入RV的数量 插入RV的位置
    实际车辆/vehMAEm/
    veh
    MAPEm/
    %
    MAEx/
    m
    RMSEx/
    m
    2134.042.417.07 3.468.37
    4229.852.046.833.017.63
    6328.032.007.143.318.01
    8423.501.335.672.886.61
    10522.051.185.372.806.51
    12618.930.673.542.215.04
    14716.480.583.522.194.85
    下载: 导出CSV

    表  5  不同CV渗透率条件下轨迹重构结果

    Table  5.   Trajectory reconstruction results under different CV penetration rates

    CV渗透率/%CV辆数/veh插入RV的数量 插入RV的位置
    实际车辆/vehMAEm/
    veh
    MAPEm/
    %
    MAEx/
    m
    RMSEx/
    m
    8427.332.258.23 3.748.72
    12626.611.746.543.558.23
    16824.861.576.323.076.96
    201023.501.335.672.886.61
    241223.301.235.262.716.43
    281421.410.914.262.496.06
    下载: 导出CSV

    表  6  Lankershim Boulevard街道轨迹重构误差

    Table  6.   Trajectory reconstruction error of Lankershim Boulevard Street

    插入RV的数量插入RV的位置
    本文方法已有方法
    实际车
    辆/veh
    MAEm/
    veh
    MAPEm/
    %
    MAEx/
    m
    RMSEx/
    m
    MAEx/
    m
    RMSEx/
    m
    5.63 0.38 6.75 4.35 5.94 6.53 7.60
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-04-28
  • 修回日期:  2022-09-17
  • 网络出版日期:  2023-11-22

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