Car-following (CF) is the most basic human driving behavior, which is the vital component of traffic flow theories, traffic simulation, and traffic operation. Over the past decades, numerous CF models have been developed based on different interaction logics between the following vehicle and the leading vehicle. Among these, four categories of CF models are the most widely studied and have been long adopted in different traffic simulation systems. The representative models are the Gazis-Herman-Rothery (GHR) model (stimuli-response category), Gipps model (safety distance category), intelligent driver model (IDM) (desired measures category), Wiedemann model (psycho-physical category). However, there is still a lack of comprehensive comparisons of the four classical CF models, especially their adaptabilities to key influencing factors (driving styles and traffic flow facilities). This study adopted the large-scale Shanghai naturalistic driving data to conduct a comprehensive comparison of four classical CF models through over 5,000 extracted CF events. The results prove that the IDM performs best in depicting the CF behavior overall, as well as in various driving styles and traffic flow facilities, since the error of the IDM is at least lower than the GHR model, Gipps model, and Wiedemann model 16.16%, 19.51%, and 56.75%, respectively. Then, the best-performed IDM model was further improved with an additional term described by an α-stable distribution, to better reproduce heterogeneity in simulation practice. It has a remarkable performance with only one parameter freedom, decreasing over 59% error than the fixed-parameter IDM. These findings could provide better guidance for the choice and the development of the basic CF model in traffic simulation systems.

跟车（CF）是人类最基本的驾驶行为，是交通流理论、交通仿真和交通运营的重要组成部分。在过去的几十年里，基于跟车和领车之间不同的交互逻辑，已经开发了许多 CF 模型。其中，四类CF模型是研究最广泛的，并且长期以来被不同的交通仿真系统采用。代表模型有Gazis-Herman-Rothery（GHR）模型（刺激-响应类）、Gipps模型（安全距离类）、智能驾驶员模型（IDM）（期望测量类）、Wiedemann模型（心理-物理类）。但是目前还缺乏对四种经典CF模型的综合比较，尤其是对关键影响因素（驾驶方式和交通流设施）的适应性。本研究采用上海大规模自然驾驶数据，通过提取的 5000 多个 CF 事件，对四种经典 CF 模型进行综合比较。结果证明，IDM 在描述 CF 的整体行为以及各种驾驶风格和交通流设施方面表现最好，因为 IDM 的误差至少低于 GHR 模型、Gipps 模型和 Wiedemann 模型 16.16% 、19.51% 和 56.75%。然后，性能最佳的 IDM 模型通过由 α 稳定分布描述的附加项进一步改进，以更好地再现模拟实践中的异质性。它具有卓越的性能，只有一个参数自由，比固定参数 IDM 减少了 59% 以上的误差。这些发现可以为交通仿真系统中基本CF模型的选择和开发提供更好的指导。