This study aims to model traffic flow under weak lane based heterogonous (mixed) traffic conditions. Unlike homogeneous traffic, when a follower (subject) vehicle in mixed traffic moves closer to its leader vehicle, it tends to adjust its longitudinal movement or change its lane and acts discretely. Due to this phenomenon, traffic flow modeling under such conditions is always challenging. A new driver behavioral logic is conceptualized for the vehicles’ movement within a combination of surrounding vehicles. In which the following behavior was dissected with the lateral shift distance between vehicles. Two car-following models for homogeneous traffic conditions, the IDM and Gipps models were adapted with relevant lateral behavior parameters to different vehicle classes under mixed-traffic conditions. The new driving behavior logic was incorporated externally in place of default logic. The results showed that the performance of the adapted models was better accurate than the classical models.

本研究旨在模拟基于弱车道的异质（混合）交通条件下的交通流量。与同质交通不同，当混合交通中的跟随（主体）车辆靠近其领导车辆时，它倾向于调整其纵向运动或改变其车道并离散地行动。由于这种现象，这种情况下的交通流建模总是具有挑战性。为车辆在周围车辆组合中的运动概念化了一种新的驾驶员行为逻辑。其中以下行为与车辆之间的横向移动距离进行了剖析。两种用于同质交通条件的跟驰模型，IDM 和 Gipps 模型在混合交通条件下使用相关的横向行为参数适应不同的车辆类别。新的驾驶行为逻辑被外部合并以代替默认逻辑。结果表明，适应模型的性能优于经典模型。