[2603.27909] Data is All You Need: Markov Chain Car-Following (MC-CF) Model

Electrical Engineering and Systems Science > Systems and Control arXiv:2603.27909 (eess) [Submitted on 29 Mar 2026] Title:Data is All You Need: Markov Chain Car-Following (MC-CF) Model Authors:Sungyong Chung, Yanlin Zhang, Nachuan Li, Dana Monzer, Alireza Talebpour View a PDF of the paper titled Data is All You Need: Markov Chain Car-Following (MC-CF) Model, by Sungyong Chung and 4 other authors View PDF HTML (experimental) Abstract:Car-following behavior is fundamental to traffic flow theory, yet traditional models often fail to capture the stochasticity of naturalistic driving. This paper introduces a new car-following modeling category called the empirical probabilistic paradigm, which bypasses conventional parametric assumptions. Within this paradigm, we propose the Markov Chain Car-Following (MC-CF) model, which represents state transitions as a Markov process and predicts behavior by randomly sampling accelerations from empirical distributions within discretized state bins. Evaluation of the MC-CF model trained on the Waymo Open Motion Dataset (WOMD) demonstrates that its variants significantly outperform physics-based models including IDM, Gipps, FVDM, and SIDM in both one-step and open-loop trajectory prediction accuracy. Statistical analysis of transition probabilities confirms that the model-generated trajectories are indistinguishable from real-world behavior, successfully reproducing the probabilistic structure of naturalistic driving across all interaction typ...