Modeling and Analysis of Car-Following Behavior Based on Macro–Micro Coupling

The car-following model, which describes how a vehicle follows its leading vehicle within the same lane, is a fundamental component of high-precision microscopic traffic simulation. However, most existing car-following models deal with vehicle interactions without accounting for the influence of macroscopic traffic state on microscopic driving behavior, which makes it difficult to accurately reflect drivers’ behavioral adaptations under varying traffic conditions. To address this limitation, we propose Multiscale Entanglement Theory of Traffic Flow (MET-TF), which explicates the two-way macro-micro causation of the traffic flow system. Based on MET-TF, we further develop the Macro-Micro Coupling Car-Following (MMC-CF) modeling framework, which incorporates both microscopic driving style and macroscopic traffic state as additional input features in a data-driven car-following model for trajectory prediction. To evaluate the effectiveness of the proposed model, extensive experiments with empirical car-following data collected from real-world highway driving scenarios were conducted. The results demonstrate that MMC-CF outperforms baseline models at both microscopic and macroscopic scales. At the microscopic scale, MMC-CF accurately captures the behavior of the following vehicle. At the macroscopic scale, it effectively reconstructs the traffic flow that emerges from the collective behaviors of multiple vehicles, achieving a reduction of at least 52.6% in traffic flow simulation error compared with baseline models. This cross-scale modeling method offers a novel perspective for understanding the microscopic interactions of vehicles and the co-evolutionary dynamics of traffic flow.

• Publication year

  2026

• Venue

  IEEE transactions on intelligent transportation systems (Print)

• Publication date

  2026-01-01

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1109/TITS.2025.3626565
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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