Lane changing is one of the basic driving behaviours, which may induce traffic oscillations and incidents. However, it is difficult to well model the lane-changing decision process due to the complex traffic status. To promote the prediction accuracy of lane-changing decisions, this paper presents a probability lane-changing model by taking into account the memory effect. That is, the lane-changing decision model considers a series of trajectory data rather than the data of a specific time utilized in most existing models. Furthermore, the drivers are classified in terms of lane-changing trajectories, which is expected to further promote the prediction accuracy of the lane-changing decision model. Calibrations and validations are carried out based on the NGSIM data, which indicate that the proposed model can significantly promote the prediction accuracy of lane-changing decisions.

中文翻译：

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考虑记忆效应和驾驶员异质性的概率换道模型

变道是基本的驾驶行为之一，可能会引发交通振荡和事故。然而，由于复杂的交通状况，很难很好地对换道决策过程进行建模。为了提高换道决策的预测精度，本文提出了一种考虑记忆效应的概率换道模型。也就是说，换道决策模型考虑的是一系列轨迹数据，而不是大多数现有模型中使用的特定时间的数据。此外，根据换道轨迹对驾驶员进行分类，有望进一步提升换道决策模型的预测精度。基于 NGSIM 数据进行校准和验证，