Improper lane-changing behaviors may result in breakdown of traffic flow and the occurrence of various types of collisions. This study investigates lane-changing behaviors of multiple vehicles and the stimulative effect on following drivers in a consecutive lane-changing scenario. The microscopic trajectory data from the dataset are used for driving behavior analysis.Two discretionary lane-changing vehicle groups constitute a consecutive lane-changing scenario, and not only distance- and speed-related factors but also driving behaviors are taken into account to examine the impacts on the utility of following lane-changing vehicles.A random parameters logit model is developed to capture the driver psychological heterogeneity in the consecutive lane-changing situation.Furthermore, a lane-changing utility prediction model is established based on three supervised learning algorithms to detect the improper lane-changing decision. Results indicate that (1) the consecutive lane-changing behaviors have a significant negative effect on the following lane-changing vehicles after lane-change; (2) the stimulative effect exists in a consecutive lane-change situation and its influence is heterogeneous due to different psychological activities of drivers; and (3) the utility prediction model can be used to detect an improper lane-changing decision.
翻译:该研究调查了多辆汽车的车道变化行为和在连续更换车道的情况下对司机的刺激作用。数据集的微细轨迹数据用于驾驶行为分析。有两个自行决定的车道变化组构成连续更换车道的情况,不仅与距离和速度有关,而且与驾驶行为有关,以审查对更换车道后的效用的影响。 开发了一个随机参数日志模型,以便在连续更换车道的情况下捕捉驾驶员的心理异质性。此外,还建立了一个改变车道的效用预测模型,以三个受监督的学习算法为基础,发现不适当的更换车道的决定。结果显示:(1) 更换车道后,连续更换车道的行为对随后更换车道的车辆具有重大的负面影响;(2) 连续更换车道的情况具有刺激效应,其影响因司机的心理活动不同而各异;(3) 使用效用预测模型可用来探测不适当的改变车道决定。