Car-following models, which are used to predict the acceleration-deceleration decisions of drivers in the presence of a closely spaced lead vehicle, are critical components of traffic microsimulation tools and useful for safety evaluation. Existing car-following models primarily account for the effects of surrounding traffic conditions on a driver’s decision to accelerate or decelerate. However, research in human factors and safety has demonstrated that driving decisions are also significantly affected by individuals’ characteristics and their emotional states like stress, fatigue, etc. This motivates us to develop a car-following model where we explicitly account for the stress level of the driver and quantify its impact on acceleration-deceleration decisions. An extension of the GM stimulus-response model framework is proposed in this regard, where stress is treated as a latent (unobserved) variable, while the specification also accounts for the effects of drivers’ sociodemographic characteristics. The proposed hybrid models are calibrated using data collected with the University of Leeds Driving Simulator where participants are deliberately subjected to stress in the form of aggressive surrounding vehicles, slow leaders and/or time pressure while driving in a motorway setting. Alongside commonly used variables, physiological measures of stress (i.e. heart rate, blood volume pulse, skin conductance) are collected with a non-intrusive wristband. These measurements are used as indicators of the latent stress level in a hybrid model framework and the model parameters are estimated using Maximum Likelihood Technique. Estimation results indicate that car-following behaviour is significantly influenced by stress alongside speed, headway and drivers’ characteristics. The findings can be used to improve the fidelity of simulation tools and designing interventions to improve safety.

跟车模型是交通微仿真工具的关键组成部分，可用于预测在间距很近的领先车辆的情况下驾驶员的加减速决定，并且对安全性评估很有用。现有的跟车模型主要考虑周围交通状况对驾驶员决定加速或减速的影响。但是，有关人为因素和安全性的研究表明，驾驶决策也会受到个人特征及其情绪状态（例如压力，疲劳等）的显着影响。这促使我们建立了汽车跟随模型，其中我们明确考虑了压力水平并确定其对加减速决策的影响。在这方面，提出了转基因刺激反应模型框架的扩展，其中压力被视为潜在（不可观察）变量，而规范也考虑了驾驶员的社会人口统计学特征的影响。拟议的混合模型是使用利兹大学驾驶模拟器收集的数据进行校准的，参与者在高速公路环境中驾驶时故意受到参与者以激进的周围车辆，缓慢的领导者和/或时间压力为形式的压力。除常用变量外，还使用非侵入式腕带收集压力的生理指标（即心率，血容量脉冲，皮肤电导）。这些测量值用作混合模型框架中潜在应力水平的指标，并且使用最大似然技术估算模型参数。估计结果表明，跟车行为受压力，速度，时速和驾驶员特征的影响很大。这些发现可用于提高仿真工具的保真度，并设计干预措施以提高安全性。