Volume 41 Issue 3
Jun.  2023
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Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(3): 1-11
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
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An Analysis of the Mechanism of Traffic Conflicts Considering Risky Lane-changing Behavior in Weaving Sections of Expressways

doi: 10.3963/j.jssn.1674-4861.2023.03.001
  • 1.

    School of Transportation, Southeast University, Nanjing 211189, China

  • 2.

    College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China

  • 3.

    Beijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing University of Technology, Beijing 100124, China

  • Received Date: 2023-01-20
    Available Online: 2023-09-16
    Abstract
  • Drivers' risky lane-changing (LC) behavior has remarkable impacts on road safety. To investigate the underlying mechanism of risky LC behavior that contributes to traffic conflicts in expressway weaving sections and further to enhance the safety of LC scenarios, a structural equation model (SEM) is developed in this study incorporating latent variables of LC benefits (LCB), subject vehicle performance features (SVP), evasive features of following vehicle on the target lane (TFVE) and conflict severity (CS). High-precision trajectory data is extracted from 200 samples of risky LC behavior, which are collected by unmanned aerial vehicle (UAV) in an expressway weaving section in Nanjing. The underlying mechanism of risky LC behavior that contributes to traffic conflicts and key indicators of such mechanism are analyzed. The severity of traffic conflicts is evaluated through the minimum time to collision. The causal relationship between LCB, SVP, TFVE and CS are analyzed using the structural model. Two types of risky LC behavior are proposed: the oppressive LC behavior and intrusive LC behavior. Several microscopic indicators characterizing LCB and SVP are adopted to develop the measurement model. Results of the SEM show that, LCB has a significant impact on SVP (p = 0.044), SVP significantly affects TFVE (p = 0.001) and CS (p = 0.021), and TFVE considerably influences CS (p < 0.001). At the beginning of LC behavior, three factors could effectively characterize LCB, which are the distance between the subject vehicle and the following vehicle on the target lane (p = 0.002), the speed difference between the leading vehicles at the adjacent lanes (p = 0.012) and the LC motivation (p < 0.001). During the LC processes, the dangerous driving behavior, the yaw angle and the lateral speed could characterize SVP (p < 0.001) well. This study provides effective indicators for assessing the collision risk of LC behavior from a microscopic perspective, which could be useful for the in-vehicle crash avoidance system and the design of short-distance expressway weaving sections.

     

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    • References(30)
  • [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.
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