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The Macroscopic Fundamental Diagram (MFD) is a promising paradigm for network-level traffic management, providing a robust framework for monitoring congestion and maximizing network production. This paper surveys recent MFD advances across three key domains: theoretical modeling, state estimation, and coordinated control. It highlights key methods, from classical dynamic models and EKF/MHE estimators to data-driven (ML/DL) modeling, and control frameworks from PID to Model Predictive Control (MPC) and multi-region coordination. This review analyzes major challenges, including network heterogeneity, data sparsity, boundary queuing, and multi-modal interactions. Looking ahead, the paper discusses emerging trends like hybrid model-driven/data-driven frameworks, multi-modal 3D-MFDs, and control strategies for mixed-autonomy (CAV) environments. Ultimately, this paper's goal is to provide theoretical insights and practical guidance for developing robust MFD-based control systems for complex urban networks.

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Macroscopic Fundamental Diagram of Urban Traffic: From Theoretical Modeling and State Estimation to Coordinated Control

  • 会议论文

  • 发表日期：

    2026年

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  • Honghai Ji,
  • Siran Liu,
  • Li Wang,
  • Ye Ren,
  • Zhonghe He

摘要

The Macroscopic Fundamental Diagram (MFD) is a promising paradigm for network-level traffic management, providing a robust framework for monitoring congestion and maximizing network production. This paper surveys recent MFD advances across three key domains: theoretical modeling, state estimation, and coordinated control. It highlights key methods, from classical dynamic models and EKF/MHE estimators to data-driven (ML/DL) modeling, and control frameworks from PID to Model Predictive Control (MPC) and multi-region coordination. This review analyzes major challenges, including network heterogeneity, data sparsity, boundary queuing, and multi-modal interactions. Looking ahead, the paper discusses emerging trends like hybrid model-driven/data-driven frameworks, multi-modal 3D-MFDs, and control strategies for mixed-autonomy (CAV) environments. Ultimately, this paper's goal is to provide theoretical insights and practical guidance for developing robust MFD-based control systems for complex urban networks.