This study provides a new method for better incorporating human factors in modeling car-following behavior. As the primary decision maker and vehicle operator, human driver is the vital component of the driving process. During the driving process, an external stimulus may trigger short-term psychological changes, and these changes are considered as the endogenous cause of many abnormal driving behaviors, which often lead to unsafe traffic disturbances and even crashes. In this paper, we investigate the intrinsic long-term driving characteristics and its short-term changes after driver experiences an external stimulus. A long- and short-term driving (LSTD) model is proposed to incorporate such changes into car-following driving behavior modelling. The long-term driving characteristics are extracted through a cluster analysis, and the changes after an external stimulus are identified and measured as the indicator of the short-term driving characteristics. NGSIM data are used to demonstrate the existence of LSTD characteristics, and the soundness of the LSTD model. Two classical car-following models (i.e. the intelligent driver model, Gipps’ model) are integrated with the LSTD model, and the integrated models show a promising performance as the errors decrease by 36.7% and 35.7%, respectively.

这项研究提供了一种新方法，可以更好地将人为因素纳入对跟车行为的建模中。作为主要的决策者和车辆操作员，驾驶员是驾驶过程中至关重要的组成部分。在驾驶过程中，外部刺激可能会触发短期的心理变化，这些变化被认为是许多异常驾驶行为的内在原因，通常会导致不安全的交通干扰，甚至导致交通事故。在本文中，我们研究了内在的长期驾驶特性及其在驾驶员受到外部刺激后的短期变化。提出了长期和短期驾驶（LSTD）模型，以将此类更改纳入跟车驾驶行为建模。通过聚类分析提取长期驾驶特征，识别并测量外部刺激后的变化作为短期驾驶特性的指标。NGSIM数据用于证明LSTD特性的存在以及LSTD模型的可靠性。LSTD模型集成了两个经典的跟车模型（即智能驾驶员模型，Gipps模型），并且集成模型显示出了有希望的性能，因为误差分别减少了36.7％和35.7％。