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

2026-03-29

AI Summary

Traditional car-following models struggle to capture the stochastic nature of naturalistic driving behavior. This work proposes a Markov chain–based car-following model (MC-CF) grounded in empirical probabilities, eschewing parametric assumptions in favor of a fully data-driven approach. The model performs stochastic sampling from empirical acceleration distributions within discretized state intervals and represents state transitions via a Markov process. Requiring no calibration, MC-CF faithfully reproduces the probabilistic structure of real-world driving. Experiments on large-scale datasets such as Waymo demonstrate that MC-CF significantly outperforms classical models like IDM in trajectory prediction accuracy, exhibits strong zero-shot generalization, and achieves collision-free microscopic simulation while accurately replicating the propagation of stochastic traffic waves.

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 types. Zero-shot generalization on the Naturalistic Phoenix (PHX) dataset further confirms the model's robustness. Finally, microscopic ring road simulations validate the framework's scalability. By incrementally integrating unconstrained free-flow trajectories and high-speed freeway data (TGSIM) alongside a conservative inference strategy, the model drastically reduces collisions, achieving zero crashes in multiple equilibrium and shockwave scenarios, while successfully reproducing naturalistic and stochastic shockwave propagation. Overall, the proposed MC-CF model provides a robust, scalable, and calibration-free foundation for high-fidelity stochastic traffic modeling, uniquely suited for the data-rich future of intelligent transportation.

Problem

• car-following
• stochasticity
• naturalistic driving
• traffic flow
• probabilistic modeling

Innovation

• Markov Chain Car-Following
• empirical probabilistic paradigm
• stochastic traffic modeling
• data-driven trajectory prediction
• zero-shot generalization