A fundamental challenge in car-following (CF) modeling lies in accurately representing the multiscale complexity of driving behaviors, particularly the intradriver heterogeneity where a single driver's actions fluctuate dynamically under varying conditions. While existing models, both conventional and data-driven, address behavioral heterogeneity to some extent, they often emphasize interdriver heterogeneity or rely on simplified assumptions, limiting their ability to capture the dynamic heterogeneity of a single driver under different driving conditions. To address this gap, we propose a novel data-driven CF framework that systematically embeds discrete driving regimes (e.g., steady-state following, acceleration, cruising) into vehicular motion predictions. Leveraging high-resolution traffic trajectory datasets, the proposed hybrid deep learning architecture combines gated recurrent units (GRUs) for discrete driving regime classification with long short-term memory networks (LSTMs) for continuous kinematic prediction, unifying discrete decision-making processes and continuous vehicular dynamics to comprehensively represent interdriver and intradriver heterogeneity. Driving regimes are identified using a bottom-up segmentation algorithm and dynamic time warping (DTW), ensuring robust characterization of behavioral states across diverse traffic scenarios. Comparative analyses demonstrate that the framework significantly reduces prediction errors for multiple metrics while reproducing critical traffic phenomena, such as stop-and-go wave propagation and oscillatory dynamics.
Zhou et al. (Thu,) studied this question.