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基于交通流稳定性系数的高速公路交通事故实时风险预测

刘星良 单珏 刘唐志 饶畅 刘通

刘星良, 单珏, 刘唐志, 饶畅, 刘通. 基于交通流稳定性系数的高速公路交通事故实时风险预测[J]. 交通信息与安全, 2022, 40(4): 71-81. doi: 10.3963/j.jssn.1674-4861.2022.04.008
引用本文: 刘星良, 单珏, 刘唐志, 饶畅, 刘通. 基于交通流稳定性系数的高速公路交通事故实时风险预测[J]. 交通信息与安全, 2022, 40(4): 71-81. doi: 10.3963/j.jssn.1674-4861.2022.04.008
LIU Xingliang, SHAN Jue, LIU Tangzhi, RAO Chang, LIU Tong. Real-time Forecast Models for Traffic Accidents on Expressways Using Stability Coefficients of Traffic Flow[J]. Journal of Transport Information and Safety, 2022, 40(4): 71-81. doi: 10.3963/j.jssn.1674-4861.2022.04.008
Citation: LIU Xingliang, SHAN Jue, LIU Tangzhi, RAO Chang, LIU Tong. Real-time Forecast Models for Traffic Accidents on Expressways Using Stability Coefficients of Traffic Flow[J]. Journal of Transport Information and Safety, 2022, 40(4): 71-81. doi: 10.3963/j.jssn.1674-4861.2022.04.008

基于交通流稳定性系数的高速公路交通事故实时风险预测

doi: 10.3963/j.jssn.1674-4861.2022.04.008
基金项目: 

国家自然科学基金项目 52172341

重庆市教育委员会项目 KJQN202100718

重庆市高校创新研究群体项目 CXQT21022

详细信息
    作者简介:

    刘星良(1989—),博士,讲师. 研究方向:交通流理论、交通安全等. E-mail:xingliang1125@outlook.com

    通讯作者:

    刘唐志(1976—),博士,教授. 研究方向:山区道路交通安全、智慧交通、应急救援等. E-mail:391873717@qq.com

  • 中图分类号: U491.3

Real-time Forecast Models for Traffic Accidents on Expressways Using Stability Coefficients of Traffic Flow

  • 摘要: 预测交通事故实时风险时,存在大量指标变量,导致数据难以采集,不仅不利于构建预测模型,且带来的过拟合问题会降低模型预测可靠性。为了减少预测指标数量,提升预测模型可用性,降低预测模型过拟合影响,构建具有可解释性的2种交通流稳定性系数以简化指标集,分别为纵向交通流稳定系数和横向交通流稳定系数。采集西安市G3001高速公路交通事故与交通流历史数据,选用支持向量机、随机森林、Logistic回归模型,分别构建高速公路交通事故实时风险预测模型。通过改进的GI指数评估交通流稳定性系数的显著性,以检验其有效性;通过指标集在训练与测试数据中的预测精度、AUC值差异评估交通流稳定性系数对降低预测模型过拟合的作用,并通过训练耗时评估模型的计算效率,以检验新方法的可靠性。研究结果表明:2种交通流稳定性系数对应的改进GI指数分别为0.952和0.922,显著大于其他受试指标,与交通事故实时风险显著相关。在3种预测模型中,包含2种交通流稳定性系数的简化指标集在训练和测试数据中的预测精度分别为91.1%和90.5%,与完整指标集相近。2种指标集在训练与测试数据中的平均预测精度差异分别为0.69%和4.87%;平均AUC值差异分别为1.61%和5.87%;平均训练时间下降了15.2%。交通流稳定性系数大幅提高了预测模型的可靠性,同时显著提升了模型的计算效率。

     

  • 图  1  G3001道路基本线形与交通流监控系统布置

    Figure  1.  Layout of G3001 basic alignment and traffic flow monitoring system

    图  2  2015—2019年G3001交通事故数

    Figure  2.  Number of G3001 traffic incidents in 2015—2019

    图  3  各测试组预测精度

    Figure  3.  Forecast Accuracy of Each Tested Group

    图  4  各测试组AUC

    Figure  4.  AUC in Each Tested Group

    表  1  高速公路交通事故实时风险预测指标集

    Table  1.   Predictor set of expressway traffic accidents real-time risk forecast

    主要类别 次级类别 基础指标 基础指标代码
    交通状态 交通量(VC 上游平均交通量/(veh/h) VCup
    上游交通量标准差/(veh/h) Std. VCup
    上游相邻车道间平均交通量差值/(veh/h) Dif. VCup
    下游平均交通量/(veh/h) VCdo
    下游交通量标准差/(veh/h) Std. VCdo
    下游相邻车道间平均交通量差值/(veh/h) Dif. VCdo
    上下游平均交通量差值/(veh/h) Dif. VCup - do
    占有率(OCC 上游平均占有率/% OCCup
    上游占有率标准差/% Std. OCCup
    上游相邻车道间平均占用率差值/% Dif. OCCup
    下游平均占有率/% OCCdo
    下游占有率标准差/% Std. OCCdo
    下游相邻车道间平均占用率差值/% Dif. OCCdo
    上下游平均占有率差值/% Dif. OCCup - do
    速度(S 上游平均速度/(km/h) Sup
    上游速度标准差/(km/h) Std. Sup
    上游相邻车道间平均速度差值/(km/h) Dif. Sup
    下游平均速度/(km/h) Sdo
    下游速度标准差/(km/h) Std. Sdo
    下游相邻车道间平均速度差值/(km/h) Dif. Sdo
    上下游平均速度差值/(km/h) Dif. Sup - do
    道路几何线形 主线 路段长度/m SL
    车道数/条 NL
    路面宽度/m RSW
    车道宽度/m LW
    内侧路肩宽度/m ISW
    外侧路肩宽度/m OSW
    分隔带宽度/ m MW
    匝道 合流区占路段总长的比例/% MA
    分流区占路段总长的比例/% DA
    分合流区间距/m DMR
    环境 天气情况 WC
    时间 TD
    咼峰时间段 PP
    限速(km/h) VL
    下载: 导出CSV

    表  2  基于交通流稳定性系数的高速公路交通事故实时风险预测简化指标集

    Table  2.   Traffic flow stability coefficients based simplified predictor set of expressway traffic accidents real-time prediction

    基础指标 指标代码
    交通流纵向稳定性系数 Dif.DEup - do
    交通流横向稳定性系数 Dif. DEdo
    重车混人率/% PT
    合流区占路段总长的比例/% MA
    分流区占路段总长的比例/% DA
    天气情况 WC
    下载: 导出CSV

    表  3  各路段中受试指标的改进GI指数

    Table  3.   Improved Gini index of tested predictors in each road section

    改进GI指数指标代码 路段编码
    1-2# 2-3# 3-4# 4-5# 5-6# 6-7# 7-8# 8-9# 9-10# 10-11# 11-12# 12-13# 13-14# 14-1# 均值 标准差
    Dif.DEup-do 0.968 0.934 0.967 0.948 0.976 0.978 0.947 0.941 0.956 0.934 0.945 0.970 0.938 0.932 0.952 0.016
    Dif. DEdo 0.932 0.887 0.956 0.939 0.919 0.931 0.883 0.935 0.938 0.921 0.885 0.957 0.895 0.930 0.922 0.025
    Sdo 0.886 0.838 0.907 0.890 0.894 0.845 0.877 0.867 0.905 0.899 0.872 0.846 0.834 0.887 0.875 0.025
    OCCdo 0.791 0.756 0.805 0.820 0.825 0.770 0.761 0.781 0.775 0.821 0.791 0.798 0.823 0.786 0.793 0.023
    Sup 0.724 0.678 0.749 0.708 0.752 0.688 0.683 0.672 0.703 0.685 0.680 0.770 0.761 0.690 0.710 0.034
    DA 0.685 0.638 0.741 0.640 0.642 0.737 0.683 0.668 0.725 0.688 0.679 0.640 0.723 0.730 0.687 0.039
    MA 0.667 0.625 0.621 0.642 0.691 0.638 0.652 0.668 0.651 0.653 0.683 0.633 0.680 0.674 0.655 0.022
    WC 0.653 0.614 0.640 0.655 0.652 0.667 0.643 0.629 0.655 0.635 0.639 0.626 0.661 0.625 0.643 0.016
    PT 0.619 0.585 0.602 0.588 0.631 0.589 0.622 0.584 0.635 0.598 0.643 0.611 0.635 0.599 0.610 0.021
    VCdo 0.598 0.565 0.563 0.620 0.615 0.571 0.580 0.579 0.591 0.612 0.588 0.597 0.613 0.593 0.592 0.019
    OCCup 0.553 0.523 0.558 0.549 0.578 0.543 0.536 0.549 0.571 0.547 0.564 0.561 0.555 0.557 0.553 0.014
    SL 0.514 0.455 0.502 0.518 0.509 0.530 0.497 0.519 0.492 0.480 0.474 0.537 0.504 0.510 0.503 0.022
    VCup 0.419 0.383 0.381 0.429 0.418 0.435 0.391 0.417 0.456 0.390 0.402 0.413 0.443 0.428 0.415 0.023
    下载: 导出CSV

    表  4  各测试组训练耗时情况

    Table  4.   Train time in each tested group

    路段编号 SVM RF LR
    简化 完整 简化 完整 简化 完整
    1-2# 2.45 3.05 2.35 2.83 2.27 2.58
    2-3# 2.51 3.03 2.40 2.85 2.25 2.62
    3-4# 2.62 3.05 2.37 2.91 2.25 2.60
    4-5# 2.70 3.15 2.44 2.97 2.35 2.71
    5-6# 2.68 3.16 2.46 2.97 2.40 2.69
    6-7# 2.73 3.15 2.46 2.99 2.39 2.70
    7-8# 2.73 3.15 2.44 2.97 2.39 2.70
    8-9# 2.70 3.19 2.48 3.05 2.37 2.68
    9-10# 2.67 3.19 2.44 3.01 2.41 2.71
    10-11# 2.73 3.21 2.43 2.97 2.35 2.75
    11-12# 2.65 3.07 2.38 2.92 2.17 2.58
    12-13# 2.63 3.05 2.38 2.93 2.20 2.60
    13-14# 2.63 3.05 2.40 2.94 2.25 2.55
    14-1# 2.64 3.08 2.41 2.94 2.29 2.59
    平均值训练耗时/s 2.65 3.11 2.42 2.95 2.31 2.65
    耗时差异/% 14.79 17.97 12.83
    下载: 导出CSV
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  • 收稿日期:  2022-04-22
  • 网络出版日期:  2022-09-17

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