交通运输系统工程与信息

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (1): 298-310.DOI: 10.16097/j.cnki.1009-6744.2025.01.028

• 工程应用与案例分析 • 上一篇    下一篇

高速公路隧道不同车辆实时跟驰风险影响因素分析

林译峰，温惠英*   

  1. 华南理工大学，土木与交通学院，广州510641
  • 收稿日期:2024-10-16 修回日期:2024-12-13 接受日期:2024-12-25 出版日期:2025-02-25 发布日期:2025-02-24
  • 作者简介:林译峰(1997—)，男，广东湛江人，博士生。
  • 基金资助:
    国家自然科学基金(52172345, 52372329)。

Freeway Tunnel Real-time Car Following Risk Impact Factors Analysis

LIN Yifeng, WEN huiying*   

  1. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
  • Received:2024-10-16 Revised:2024-12-13 Accepted:2024-12-25 Online:2025-02-25 Published:2025-02-24
  • Supported by:
    Nationa lNatural Science Foundation of China (52172345, 52372329)。

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摘要： 为研究小型车和大型车两类车辆在高速公路隧道路段内的跟驰行为，本文使用“卡口相机结合激光雷达”的数据采集模式，采集广东省祈福隧道的车辆行驶轨迹，并分别提取两类车辆的跟驰轨迹数据；提出实时安全裕度偏差(Real-timeDeviation of Safety Margin, RDSM)评估车辆实时跟驰风险水平，采用模糊C-均值聚类方法将风险水平划分为无风险或低风险、中风险及高风险；从跟驰前车类型、车辆在隧道内位置、驾驶环境、当前时刻的车辆驾驶和交互状态，以及历史的车辆驾驶和交互状态这5个方面，在数据中选取26项潜在影响因素，构建两类车辆的多项Logit模型和相关随机参数Logit模型，分析和比较各项因素对两类车辆的高速公路隧道实时跟驰风险的影响，揭示影响因素的异质性。结果表明：大型车在隧道内的实时跟驰风险受到更多因素的影响；跟驰车辆与其前车的车辆类型不同时，隧道实时跟驰风险会相对降低；前车驾驶状态的波动更容易导致跟驰高风险；平均边际效应显示，相比于在隧道进口段，小型车在隧道出口段实时跟驰风险为高风险的概率增加了0.0413，大型车在隧道内部路段实时跟驰风险为高风险的概率增加了0.0155；高风险状态下的跟驰间距标准差在两类车辆中均表现出异质性。

关键词: 交通工程, 车辆实时跟驰风险, 相关随机参数Logit模型, 高速公路隧道路段, 影响因素, 异质性

Abstract: To investigate car-following behaviors of light vehicles and heavy vehicles in freeway tunnels, this paper first used cameras and laser radars to collect vehicle driving trajectories at the Qifu Tunnel in Guangdong Province, and further extracted car following trajectory data for both vehicle types. Then, the Real-time Deviation of Safety Margin (RDSM) was proposed to assess the real-time following risk level, and the Fuzzy C-means algorithm was used to classify the risk level into no risk or low risk, moderate risk and high risk. Subsequently, 26 potential impact factors were selected from five dimensions, including preceding vehicle types, vehicle position in the tunnel, driving environment, current vehicle driving and interaction states, and historical vehicle driving and interaction states. Multinomial Logit model and correlated random parameter Logit model were applied to analyze the effects of each factor on real-time following risk in the freeway tunnel for light and heavy vehicles, and reveal the heterogeneity of the impact factors. The results show that heavy vehicles are affected by more factors regarding their real-time following risk in the tunnel. When the following vehicle differs in vehicle type from the preceding vehicle, the real-time following risk is relatively reduced. Fluctuations in the driving states of the preceding vehicle are more likely to cause high-risk following. Average marginal effects indicate that, compared with the car-following real-time risk at the tunnel entrance, the occurrence probability of high-risk car-following behavior at the tunnel exit for light vehicles increases by 0.0413, while that at the tunnel internal segment increases by 0.0155 for heavy vehicles. Moreover, the standard deviation of the following distance in high-risk states exhibits heterogeneity in both vehicle types.

Key words: traffic engineering, real-time car-following risk, correlated random parameter Logit model, freeway tunnel, impact factors, heterogeneity

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