为了更好地研究复杂情况下的车辆跟驰特性，将车辆跟驰行为类比为分子在一维管道中相互作用的结果。已有的基于分子动力学的车辆跟驰模型利用需求安全间距和车道限速作为因素建立分子跟驰模型，忽略了车辆相对速度对驾驶员跟驰行为的影响，不符合真实的跟驰情况。因此，将车辆相对速度纳入模型结构中，建立分子相互作用势函数和壁面势函数，并据此构建改进分子动力学的车辆跟驰模型。用高精度车载定位仪器对车辆跟驰过程中的参数进行采集，利用遗传算法对模型参数进行标定并对模型进行分析，分别验证模型在不同跟驰状态下的准确性，并与改进前的分子跟驰模型进行对比。结果表明，改进的分子跟驰模型可以更有效地预测车辆的跟驰行为。

In order to better study the car-following characteristics of vehicles in complex situations, the car-following behavior of vehicles is analogous to the result of the interaction of molecules in a one-dimensional pipeline. The existing car-following model based on molecular dynamics uses the demand safety interval and the expected vehicle speed as the main factors to establish the molecular car-following model, ignoring the influence of the relative speed on the driver’s car-following behavior, which does not conform to the real car-following situations. Therefore, by incorporating the relative velocity of the vehicle into the model structure, this paper establishes the molecular interaction potential function and the wall potential function, and constructs a car-following model with improved molecular dynamics accordingly. The parameters in the car-following process are collected by high-precision vehicle-mounted instruments and the model parameters are calibrated by genetic algorithm. Finally, the accuracy of the model under different car-following states is verified and compared with the previous molecular model. The results show that the improved molecular car-following model can more effectively predict the car-following behavior of the vehicles.

U491

国家自然科学基金资助项目（71471046）；吉林省交通运输厅交通运输科技资助项目（2017-1-18）。