留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

考虑快速路交织区驾驶人强行变道行为的交通冲突机理分析

李佳硕 郑展骥 顾欣 项乔君 陈钢

李佳硕, 郑展骥, 顾欣, 项乔君, 陈钢. 考虑快速路交织区驾驶人强行变道行为的交通冲突机理分析[J]. 交通信息与安全, 2023, 41(3): 1-11. doi: 10.3963/j.jssn.1674-4861.2023.03.001
引用本文: 李佳硕, 郑展骥, 顾欣, 项乔君, 陈钢. 考虑快速路交织区驾驶人强行变道行为的交通冲突机理分析[J]. 交通信息与安全, 2023, 41(3): 1-11. doi: 10.3963/j.jssn.1674-4861.2023.03.001
LI Jiashuo, ZHENG Zhanji, GU Xin, XIANG Qiaojun, CHEN Gang. An Analysis of the Mechanism of Traffic Conflicts Considering Risky Lane-changing Behavior in Weaving Sections of Expressways[J]. Journal of Transport Information and Safety, 2023, 41(3): 1-11. doi: 10.3963/j.jssn.1674-4861.2023.03.001
Citation: LI Jiashuo, ZHENG Zhanji, GU Xin, XIANG Qiaojun, CHEN Gang. An Analysis of the Mechanism of Traffic Conflicts Considering Risky Lane-changing Behavior in Weaving Sections of Expressways[J]. Journal of Transport Information and Safety, 2023, 41(3): 1-11. doi: 10.3963/j.jssn.1674-4861.2023.03.001

考虑快速路交织区驾驶人强行变道行为的交通冲突机理分析

doi: 10.3963/j.jssn.1674-4861.2023.03.001
基金项目: 

国家自然科学基金项目 7187010568

江苏省研究生科研与实践创新计划项目 SJCX21_0065

重庆市自然科学基金项目 CSTB2022NSCQ-BHX0731

详细信息
    作者简介:

    李佳硕(1998—), 硕士研究生.研究方向:交通安全理论与设计.E-mail:bennyli_1998@163.com

    通讯作者:

    项乔君(1964—),博士,教授.研究方向:交通安全理论与设计、驾驶行为仿真建模与分析等. E-mail:xqj@seu.edu.cn

  • 中图分类号: U491.2+65

An Analysis of the Mechanism of Traffic Conflicts Considering Risky Lane-changing Behavior in Weaving Sections of Expressways

  • 摘要: 驾驶人的强行变道行为对交通安全具有较大影响。为研究快速路交织区驾驶人强行变道行为引发交通冲突的机理、提升变道场景的安全性,本研究选取变道收益、变道车辆特征、目标车道后方来车避险特征、交通冲突严重程度4个变量构建了结构方程模型(structural equation model, SEM)。选取南京市1处快速路交织区为研究区域,通过无人机采集200个强行变道行为样本,并从中提取高精度车辆轨迹数据,分析了强行变道行为引发交通冲突的微观机理与关键特征指标。基于最小碰撞时间评估交通冲突的严重程度, 以结构模型分析强行变道各环节因素引发事故风险的因果链路, 提出压迫式、侵入式2种强行变道形态, 综合考虑表征车辆变道收益与变道特征的多项微观指标,建立测量模型。SEM分析表明:变道收益显著影响变道车辆特征(p = 0.044);变道车辆特征显著影响后方来车避险特征(p = 0.001)与交通冲突严重程度(p = 0.021);后方来车避险特征显著影响交通冲突严重程度(p < 0.001)。在变道起始时刻,变道车辆与目标车道后车间距(p = 0.002)、相邻车道前车速度差(p = 0.012)与变道动机(p < 0.001)可以有效表征变道收益; 在变道过程中,驾驶人危险行为特征、车辆横摆角、横向速度均可有效表征变道车辆特征(p < 0.001)。研究结果为微观视角下刻画车辆强行变道风险提供了有效指标,可为车载碰撞预警系统与短距离交织区交通设计提供理论支撑。

     

  • 图  1  交织区构造

    Figure  1.  Structure of weaving section

    图  2  强行变道全过程

    Figure  2.  Full process of risky LC maneuver

    图  3  Davis等[20]提出的因果模型

    Figure  3.  Causal model adopted from Davis et al[20]

    图  4  强行变道演化的模型框架

    Figure  4.  Model framework for the evolution of risky LC maneuvers

    图  5  强行变道起始时刻

    Figure  5.  Beginning of risky LC maneuver

    图  6  结构方程模型路径图

    Figure  6.  Paths of structural equation model

    表  1  原始模型变量描述及统计

    Table  1.   Description and statistics of variables in initial model

    潜变量 显变量 变量解释 均值 标准差 最小值 最大值 比例
    变道收益 D1 变道起始时刻SV车尾与TFV车头纵向间距/m 5.67 8.393 -22.12 38
    D3-D2 变道起始时刻TLV车尾与LV车尾纵向间距/m -3.531 15.366 -36.25 123.1
    V4-V3 变道起始时刻TLV与LV速度差/(m/s) 1.521 3.179 -9.585 8.88
    LCBMOM 是否由主路外侧车道进入中间车道(0/1: 否/是) 0 1 101:99
    LCBD_MLC MLC车辆变道点与交织区尾端纵向间距(1~5:非常大~非常小) 1 5 138:14:15:14:19
    SV变道特征 SVPNSL 是否提前开启转向灯(0/1:是/否) 0 1 69:131
    SVPCLC 是否连续变更2条及以上车道(0/1:否/是) 0 1 126:74
    SVPSLLC 是否实线变道(0/1:否/是) 0 1 164:36:00
    SVPLC_Mode 变道形态(0/1:压迫式/侵入式) 0 1 82:118
    D_LC 变道时刻SV车尾与TFV车头纵向间距/m 2.95 4.707 -8.971 22.12
    Angle_LC 变道时刻SV的横摆角/rad 0.131 0.098 0 0.611
    Angle_Max 冲突过程中SV的最大横摆角/rad 0.172 0.096 0.028 0.649
    Acce_Long 冲突过程中SV的最大纵向加速度(m/s2) 0.902 0.751 0 3.205
    Dece_Long 冲突过程中SV的最大纵向减速度(m/s2) 1.244 0.998 0 4.6
    Acce_Lat 冲突过程中SV的最大横向加速度(m/s2) 0.845 0.615 0 5.31
    V_Lat 冲突过程中SV的最大横向速度/(m/s) 0.96 0.46 0.295 3.61
    TFV避险特征 Dece_TFV 冲突过程中TFV的最大减速度/(m/s2) 2.724 0.971 0.92 6.57
    交通冲突 TTC_Min 冲突过程中最小TTC值/s 1.353 0.518 0.05 2.754
    严重程度 D_Min 冲突结尾2车最小间距/m 1.082 0.932 0.08 5.68
    下载: 导出CSV

    表  2  处理后的模型变量描述及统计

    Table  2.   Description and statistics of variables in modified model

    潜变量 显变量 偏度 峰度 变量来源 正态化处理后偏度 正态化处理后峰度 K-S检验p
    变道收益 LCBD1 0.291 1.316 D1离散化 0.291 1.316 0.01
    LCBD3-D2 3.5 23.596 ln(D3-D2+45);离散化 0.034 4.73 < 0.001
    LCBV4-V3 -0.175 0.204 V4-V3离散化 -0.175 0.204 0.2
    LCBMD LCBMOM+ LCBD_MLC
    SV变道特征 SVPDB SVPNSL +SVPCLC+SVPSLLC+SVPLC_Mode+1
    SVPD_LC 1.016 2.144 ln(D_LC+18);离散化 -0.006 1.538 0.041
    SVPAngle_LC 1.545 3.53 ln(Angle_LC + 0.035);离散化 -0.028 -0.399 0.2
    SVPAngle_Max 1.402 3.173 ln(Angle_Max); 离散化 -0.137 -0.229 0.2
    SVPAcce_Long 0.407 -0.644 $\sqrt{Acce\_Long +0.5 }$;离散化 0.025 -1.175 < 0.001
    SVPDece_Long 0.871 0.241 $\sqrt{Dece\_Long +0.1 }$;离散化 0.071 -0.666 0.087
    SVPAcce_Lat 2.799 14.794 ln(Acce_Lat + 0.2);离散化 0.09 0.837 0.2
    SVPV_Lat 1.75 5.792 ln(V_Lat);离散化 0.12 -0.021 0.2
    TFV避险特征 TFVEDece 0.863 1.574 ln(Dece_TFV + 1);离散化 0.025 -0.028 0.2
    交通冲突 CSTTC_Min 0.128 -0.239 TTC_Min离散化 0.128 -0.239 0.2
    严重程度 CSD_Min 2.157 6.167 ln(D_Min);离散化 -0.172 -0.071 0.2
    下载: 导出CSV

    表  3  结构方程模型拟合指标

    Table  3.   Fitness indices of structural equation model

    拟合指标 指标解释 判断准则 拟合值 拟合结果
    c2/df(chi-square/degrees of freedom) 卡方自由度比 1 ~ 3 2.663 理想
    RMR(root mean square residual) 均方根误差 理想:<0.05
    可接受:<0.10
    0.093 可接受
      RMSEA(root mean square error of approximation) 近似误差均方根 理想:<0.05
    可接受:<0.10
    0.091 可接受
    GFI(goodness of fit index) 拟合优度指数 理想:>0.9
    可接受:>0.8
    0.915 理想
    AGFI(adjusted goodness of fit index) 调整拟合优度指数 理想:>0.9
    可接受:>0.8
    0.858 可接受
    CFI(comparative fit index) 比较拟合指数 理想:>0.9
    可接受:>0.8
    0.863 可接受
    下载: 导出CSV

    表  4  结构方程模型路径分析

    Table  4.   Path analysis of structural equation model

    路径编号 假设路径 标准化路径系数 标准差S.E. 临界比C.R. p value
    H1 SV变道收益潜变量LCB → SV变道特征潜变量SVP 0.222 0.185 2.010 0.044
    H2 SVP→冲突过程中TFV的最大减速度修正变量TFVEDece 0.278 0.22 3.268 0.001
    H3 TFVEDece →冲突过程中最小TTC修正变量CSTTC_Min -0.276 0.074 4.006 < 0.001
    H4 SVPCSTTC_Min -0.180 0.215 2.309 0.021
    H5 LCB →变道起始时刻SV车尾与TFV车头纵向间距修正变量LCBD1 0.586 0.738 3.050 0.002
    H6 LCB →变道起始时刻TLV车头与LV车头纵向间距修正变量LCBD3-D2 0.168 0.274 1.847 0.065
    H7 LCB→变道起始时刻TLV与LV速度差修正变量LCBV4-V3 0.934 1.418 2.512 0.012
    H8 LCB →变道动机收益组合变量LCBMD 0.237 < 0.001
    H9 SVP → SV驾驶人危险行为特征组合变量SVPDB 0.298 0.18 3.439 < 0.001
    H10 SVP→ SV变道时刻横摆角修正变量SVPAngle_LC 0.799 0.381 5.608 < 0.001
    H11 SVP → SV全过程最大横摆角修正变量SVPAngle_Max 0.950 0.469 5.420 < 0.001
    H12 SVP→ SV全过程最大横向速度修正变量SVPV_Lat 0.402 < 0.001
    下载: 导出CSV

    表  5  变道收益变量分布区间

    Table  5.   Distribution intervals of variables of LC benefits

    原始变量 原始变量区间分级
    1 2 3 4 5
    D1/m ≤-4.8 (-4.8, 2.5] (2.5, 8.5] (8.5, 17] > 17
    D3-D2/m ≤-17.61 (-17.61, -8.77] (-8.77, 1.62] (1.62, 13.56] > 13.56
    V4-V3/(m/s) ≤ -2.5 (-2.5, 0.2] (0.2, 3.2] (3.2, 6.5] > 6.5
    D_MLC/m > 80 (35, 80] (17, 35] (0, 17] =0
    下载: 导出CSV

    表  6  SV动力学指标分布区间

    Table  6.   Distribution intervals of SV kinetic variables

    原始变量 原始变量区间分级 相关性检验(双尾)p
    1 2 3 4 5 TTC_Min ln(D_Min) SVPLC_Mode
    D_LC/m > 9.66 (4.2, 9.66] (0.17, 4.2] (-1.88, 0.17] ≤-1.88 0.096 < 0.001 0.688
    Angle_LC/rad (0, 0.023] (0.023, 0.073] (0.073, 0.154] (0.154, 0.299] > 0.299 0.097 0.581 0.943
    Angle_Max/rad (0, 0.067] (0.067, 0.117] (0.117, 0.202] (0.202, 0.317] > 0.317 < 0.001 0.389 0.069
    Acce_Long /(m/s2) =0 (0, 0.69] (0.69, 1.32] (1.32, 1.9] > 1.9 0.445 0.13 0.43
    Dece_Long /(m/s2) =0 (0, 0.63] (0.63, 1.59] (1.59, 2.86] > 2.86 0.002 < 0.001 0.061
    Acce_Lat /(m/s2) (0, 0.34] (0.34, 0.62] (0.62, 1.05] (1.05, 1.62] > 1.62 0.003 0.541 0.003
    V_Lat /(m/s) (0, 0.5] (0.5, 0.7] (0.7, 1.07] (1.07, 1.57] > 1.57 < 0.001 0.548 0.101
    下载: 导出CSV
  • [1] CHEN T, WONG Y D, SHI X, et al. A data-driven feature learning approach based on Copula-Bayesian network and its application in comparative investigation on risky lane-changing and car-following maneuvers[J]. Accident Analysis & Prevention, 2021(154): 106061.
    [2] YURTSEVER E, LIU Y, LAMBERT J, et al. Risky action recognition in lane change video clips using deep spatiotemporal networks with segmentation mask transfer[C]. 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand: IEEE, 2019.
    [3] MA Y, GU X, YU Y, et al. Identification of contributing factors for driver's perceptual bias of aggressive driving in China[J]. Sustainability, 2021, 13(2): 766. doi: 10.3390/su13020766
    [4] WU Y, ABDEL-ATY M, WANG L, et al. Combined connected vehicles and variable speed limit strategies to reduce rear-end crash risk under fog conditions[J]. Journal of Intelligent Transportation Systems, 2020, 24(5): 494-513. doi: 10.1080/15472450.2019.1634560
    [5] BAO S, LEBLANC D J, SAYER J R, et al. Heavy-truck drivers' following behavior with intervention of an integrated, in-vehicle crash warning system: A field evaluation[J]. Human Factors, 2012, 54(5): 687-697. doi: 10.1177/0018720812439412
    [6] 李林恒, 甘婧, 曲栩, 等. 智能网联环境下基于安全势场理论的车辆换道模型[J]. 中国公路学报, 2021, 34(6): 184-195. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202106018.htm

    LI L, GAN J, QU X, et al. Lane-changing model based on safety potential field theory under the connected and automated vehicles environment[J]. China Journal of Highway and Transport, 2021, 34(6): 184-195. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202106018.htm
    [7] WU C, PENG L, HUANG Z, et al. A method of vehicle motion prediction and collision risk assessment with a simulated vehicular cyber physical system[J]. Transportation Research Part C: Emerging Technologies, 2014(47): 179-191.
    [8] WANG C, SUN Q, FU R, et al. Lane change warning threshold based on driver perception characteristics[J]. Accident Analysis & Prevention, 2018(117): 164-174.
    [9] BALALE, CHEURL, SARKODIE-GYANT. A binary decision model for discretionary lane changing move based on fuzzy inference system[J]. Transportation Research Part C: Emerging Technologies, 2016(67): 47-61.
    [10] CHEN T, SHI X, WONG Y D. A lane-changing risk profile analysis method based on time-series clustering[J]. Physica A: Statistical Mechanics and its Applications, 2021(565): 125567.
    [11] CHEN T, SHI X, WONG Y D. Key feature selection and risk prediction for lane-changing behaviors based on vehicles' trajectory data[J]. Accident Analysis & Prevention, 2019(129): 156-169.
    [12] CHEN Q, GU R, HUANG H, et al. Using vehicular trajectory data to explore risky factors and unobserved heterogeneity during lane-changing[J]. Accident Analysis & Prevention, 2021(151): 105871.
    [13] KATRAKAZAS C, QUDDUS M, CHEN W H. Anew integrated collision risk assessment methodology for autonomous vehicles[J]. Accident Analysis & Prevention, 2019 (127): 61-79.
    [14] 薛清文, 蒋愚明, 陆键. 基于轨迹数据的危险驾驶行为识别方法[J]. 中国公路学报, 2020, 3(6): 84-94. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202006009.htm

    XUE Q, JIANG Y, LU J. Risky driving behavior recognition based on trajectory data[J]. China Journal of Highway and Transport, 2020, 33(6): 84-94. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202006009.htm
    [15] TARKO A P. A unifying view on traffic conflicts and their connection with crashes[J]. Accident Analysis & Prevention, 2021(158): 106187.
    [16] ARUN A, HAQUE M M, BHASKAR A, et al. A systematic mapping review of surrogate safety assessment using traffic conflict techniques[J]. Accident Analysis & Prevention, 2021 (153): 106016.
    [17] WANG C, XU C, DAI Y. A crash prediction method based on bivariate extreme value theory and video-based vehicle trajectory data[J]. Accident Analysis & Prevention, 2019 (123): 365-373.
    [18] 何仁, 赵晓聪, 杨奕彬, 等. 基于驾驶人风险响应机制的人机共驾模型[J]. 吉林大学学报(工学版), 2021, 51 (3): 799-809. https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY202103003.htm

    HE R, ZHAO X, YANG Y, et al. Man-machine shared driving model using risk-response mechanism of human driver[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 799-809. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY202103003.htm
    [19] 《中国公路学报》编辑部. 中国交通工稈学术研究综述·2016[J]. 中国公路学报, 2016, 29(6): 1-161.

    Editorial Department of China Journal of Hightway and Transport. Review on China's traffic engineering research progress: 2016[J]. China Journal of Highway and Transport, 2016, 29(6): 1-161. (in Chinese)
    [20] DAVIS G A, HOURDOS J, XIONG H, et al. Outline for a causal model of traffic conflicts and crashes[J]. Accident Analysis & Prevention, 2011, 43(6): 1907-1919.
    [21] PEARL J. Causality[M]. London: Cambridge University Press, 2009.
    [22] 戢晓峰, 耿昭师, 普永明, 等. 接入道路对山区双车道公路穿村镇路段事故风险影响[J]. 中国安全科学学报, 2022, 32 (5) : 155-162. https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK202205023.htm

    JI X, GENG Z, PU Y, et al. Influence of access roads on accident risks of mountainous two-lane highway passing through villages and towns segments[J]. China Safety Science Journal, 2022, 32(5): 155-162. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK202205023.htm
    [23] 熊仁江, 赵航, 段梅花等. 山地城市建成环境对居民小汽车拥有影响研究——以贵阳市为例[J]. 交通信息与安全, 2022, 40(5): 169-180. doi: 10.3963/j.jssn.1674-4861.2022.05.018

    XIONG R, ZHAO H, DUAN M, et al. The effect of the terrain slope of mountainous city on car ownership: a case study of the city of Guiyang[J]. Journal of Transport Information and Safety, 2022, 40(5): 169-180. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2022.05.018
    [24] ZHENG Z. Recent developments and research needs in modeling lane changing[J]. Transportation Research Part B : Methodological, 2014(60): 16-32.
    [25] GUO F, FANG Y. Individual driver risk assessment using naturalistic driving data[J]. Accident Analysis & Prevention, 2013, (61): 3-9.
    [26] ZHENG Z, XIANG Q, GU X, et al. The influence of individual differences on diverging behavior at the weaving sections of an urban expressway[J]. International Journal of Environmental Research and Public Health, 2021, 18(1): 25.
    [27] KAPLAN S, PRATO C G. Associating crash avoidance maneuvers with driver attributes and accident characteristics : a mixed logit model approach[J]. Traffic Injury Prevention, 2012, 13(3): 315-326.
    [28] 吴艳, 温忠麟. 结构方稈建模中的题目打包策略[J]. 心理科学进展, 2011, 19(12): 1859-1867.

    WU Y, WEN Z. Item parceling strategies in structural equation modeling[J]. Advances in Psychological Science, 2011, 19(12): 1859-1867. (in Chinese)
    [29] 姚荣涵, 祁文彦, 郭伟伟. 自动驾驶环境下驾驶人接管行为结构方稈模型[J]. 交通运输工稈学报, 2021, 21(2): 209-221. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202102021.htm

    YAO R, QI W, GUO W. Structural equation model of drivers'takeover behaviors in autonomous driving environment[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 209-221. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202102021.htm
    [30] IACOBUCCI D. Structural equations modeling : Fit indices, sample size, and advanced topics[J]. Journal of Consumer Psychology, 2010, 20(1): 90-98.
  • 加载中
图(6) / 表(6)
计量
  • 文章访问数:  739
  • HTML全文浏览量:  396
  • PDF下载量:  91
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-01-20
  • 网络出版日期:  2023-09-16

目录

    /

    返回文章
    返回