A comprehensive comparison study of four classical car-following models based on the large-scale naturalistic driving experiment

Abstract 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 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.