考虑快速路交织区驾驶人强行变道行为的交通冲突机理分析

An Analysis of the Mechanism of Traffic Conflicts Considering Risky Lane-changing Behavior in Weaving Sections of Expressways

驾驶人的强行变道行为对交通安全具有较大影响。为研究快速路交织区驾驶人强行变道行为引发交通冲突的机理、提升变道场景的安全性,本研究选取变道收益、变道车辆特征、目标车道后方来车避险特征、交通冲突严重程度4个变量构建了结构方程模型(structural equation model,SEM)。选取南京市1处快速路交织区为研究区域,通过无人机采集200个强行变道行为样本,并从中提取高精度车辆轨迹数据,分析了强行变道行为引发交通冲突的微观机理与关键特征指标。基于最小碰撞时间评估交通冲突的严重程度,以结构模型分析强行变道各环节因素引发事故风险的因果链路,提出压迫式、侵入式2种强行变道形态,综合考虑表征车辆变道收益与变道特征的多项微观指标,建立测量模型。SEM分析表明:变道收益显著影响变道车辆特征(p=0.044);变道车辆特征显著影响后方来车避险特征(p=0.001)与交通冲突严重程度(p=0.021);后方来车避险特征显著影响交通冲突严重程度(p<0.001)。在变道起始时刻,变道车辆与目标车道后车间距(p=0.002)、相邻车道前车速度差(p=0.012)与变道动机(p<0.001)可以有效表征变道收益;在变道过程中,驾驶人危险行为特征、车辆横摆角、横向速度均可有效表征变道车辆特征(p<0.001)。研究结果为微观视角下刻画车辆强行变道风险提供了有效指标,可为车载碰撞预警系统与短距离交织区交通设计提供理论支撑。

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.