Scene-to-Scene Microscopic Traffic Dynamics Deduction: Integrating Simulation, Prediction, and Real-Time Monitoring

Microscopic traffic dynamics deduction, capable of reconstructing and predicting traffic states at 0.1–0.5-s temporal and 0.1–1-m spatial granularity with limited information, constitutes a critical foundation for traffic control and planning. Conventional deduction methods based on analytical car following models are oversimplified in parameters, and thus, they inadequately capture complex traffic dynamics, while higher-dimensional parametric deep learning-based methods suffer from poor iteration speed. As a compromise between speed and modeling capability, this article proposes a scene-to-scene autoregressive framework that fuses simulation, data-driven prediction, and real-time monitoring for microscopic traffic dynamics deduction. The methodology implements scene-level autoregressive deduction cycles rather than agent-based modeling while enhancing real-time accuracy through integration of connected vehicle (CV) future information. Evaluations using the International, Adversarial, and Cooperative Motion dataset demonstrate that the proposed simulation framework achieves comparable accuracy in a reasonable runtime. Real-time CV information incorporation is also proved to be effective for further accuracy improvements.