Modeling accident risks in different lane-changing behavioral patterns
Modeling accident risks in different lane-changing behavioral patterns
复制标题
对不同变道行为模式下的事故风险进行建模
DOI：
10.1016/j.amar.2021.100159
复制
发表时间：
2021-03-10
影响因子：
12.9
通讯作者：
Zhai, Xiaoqi
中科院分区：
文献类型：
--
作者：
Chen, Qinghong;Huang, Helai;Zhai, Xiaoqi
换道是一项复杂的工作，发生事故的概率很大。虽然已有大量文献使用车辆轨迹来从微观上理解和模拟换道行为，但这些研究大多集中在换道决策以及换道对周围车辆的影响上，而不是对交通安全的影响。使用车辆轨迹数据，还没有从微观行为的角度充分探讨变道风险的影响因素。本研究在考虑未观察到的异质性的情况下，调查了不同换道模式下交通事故风险的影响因素。使用车辆轨迹数据集HighD，提取4842个变道车辆组进行分析。根据车辆类型将这些车辆组分为16种模式，并考察了三种主要模式。提出了换道风险指数(LCRI)来评价每组车辆的风险水平。提出并比较了两种研究换道风险的方法：(1)建立随机参数分数Logit模型；(2)用k-均值算法对LCRI进行分类，建立均值和方差具有异质性的随机参数有序Logit模型。建模结果表明，后一种方法效果较好，车辆群的风险水平与(1)车辆间距的均值和标准差，(2)车辆的纵向速度和加速度，(3)换道方向和持续时间有很强的相关性。然而，不同的模式被发现具有不同的贡献变量和影响。在不同的车辆组中，间隙距离的影响差别很大，车辆的纵向速度与间隙距离的随机参数的平均值有关。(C)2021年爱思唯尔有限公司。保留所有权利。
Lane-changing is a complicated task and has a high probability of accident occurrence. Although a large body of literature has used vehicle trajectories to microscopically understand and model lane-changing behavior, most of these studies focus on lane-changing decision making and lane changing's impacts on surrounding vehicles, not on traffic safety. The contributing factors to lane-changing risks have not been fully explored from the perspective of microscopic behavior using vehicle trajectory data. This study investigates the contributing factors to accident risks in different lane-changing patterns with taking unobserved heterogeneity into account. A vehicle trajectory dataset, HighD is used and 4842 lane-changing vehicle groups are extracted for analysis. These vehicle groups are divided into sixteen patterns according to the vehicle type, and three major patterns are examined. A lane-changing risk index (LCRI) is proposed to evaluate the risk level of each vehicle group. Two methods are developed and compared for exploring lane-changing risks of the three patterns including (1) establishing the random parameters fractional logit models; and (2) classifying LCRI by k-means algorithm and establishing random parameters ordered logit models with heterogeneity in means and variances. The modeling results show that the latter method performs better and the risk level of the vehicle group is strongly associated with (1) the mean and standard deviation of the gap distance between vehicles; (2) the longitudinal velocities and acceleration of vehicles; and (3) the lane-changing direction and duration. However, different patterns are found to have different contributing variables and effects. The effects of gap distances vary considerably across different vehicle groups and the longitudinal velocity of vehicles are associated with the means of random parameters for gap distance. (C) 2021 Elsevier Ltd. All rights reserved.