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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