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Dynamic traffic graph based risk assessment of multivehicle lane change interaction scenarios

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  • Guo, Yinjia
  • Chen, Yanyan
  • Gu, Xin
  • Guo, Jifu
  • Zheng, Shuyan
  • Zhou, Yuntong

Abstract

Vehicles' lane-changing behavior can potentially result in traffic conflicts and crash risks, particularly in scenarios with interactions among multiple vehicles. To assess the crash risk of multi-vehicle interaction lane-changing (MILC) scenarios, this study presents a dynamic traffic graph-based risk assessment method. First, a method for constructing dynamic scene graphs is proposed, along with graph-based indicators for assessing scene and scenario risks. Second, the Gaussian mixture model-latent Dirichlet allocation (GMM-LDA) algorithm is utilized to cluster risk sequences of MILC scenarios, enabling the classification of scenario risks into different levels. The method considers both dynamic and static elements in these scenarios, as well as the spatial relationships among these elements. A case study was conducted to illustrate this approach at a weaving area in China. The findings reveal that diverging segments have the highest probability of high-risk MILC scenarios. Furthermore, the likelihood of high-risk scenarios involving consecutive lane-changing maneuvers is higher compared to single lane changes. Left lane-changing behavior exhibits a higher probability of high-risk scenarios compared to right lane-changing. The proposed risk assessment method facilitates the identification and construction of high-risk MILC scenarios. It also enables the exploration of the risk evolution process.

Suggested Citation

  • Guo, Yinjia & Chen, Yanyan & Gu, Xin & Guo, Jifu & Zheng, Shuyan & Zhou, Yuntong, 2024. "Dynamic traffic graph based risk assessment of multivehicle lane change interaction scenarios," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 643(C).
    • Handle: RePEc:eee:phsmap:v:643:y:2024:i:c:s0378437124003005
    DOI: 10.1016/j.physa.2024.129791
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    References listed on IDEAS

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    1. Chen, Tianyi & Shi, Xiupeng & Wong, Yiik Diew, 2021. "A lane-changing risk profile analysis method based on time-series clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    2. Ma, Changxi & Li, Dong, 2023. "A review of vehicle lane change research," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    3. Xiaoxiao Wang & Liangjie Xu, 2021. "Factors Influencing Young Drivers’ Willingness to Engage in Risky Driving Behavior: Continuous Lane-Changing," Sustainability, MDPI, vol. 13(11), pages 1-18, June.
    4. Xiao-Yun Lu & Jianqiang Wang & Shengbo Eben Li & Yang Zheng, 2014. "Multiple-Vehicle Longitudinal Collision Mitigation by Coordinated Brake Control," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-13, September.
    5. Li, Linheng & Gan, Jing & Zhou, Kun & Qu, Xu & Ran, Bin, 2020. "A novel lane-changing model of connected and automated vehicles: Using the safety potential field theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
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