Saturated arterial coordinate control strategy optimization considering macroscopic fundamental diagram

Lin, Xuanhua; Lin, Xiaohui; Chen, Kelian · 2022 · DOAJ

DOI: 10.5604/01.3001.0015.9253

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Summary

This study addresses the optimization of signal coordination strategies for urban arterial road networks under saturated traffic conditions. Traditional control methods often rely on microscopic indicators like delay minimization or green wave maximization, which fail to capture the macroscopic operational state of the network. To bridge this gap, the authors integrate the Macroscopic Fundamental Diagram (MFD)—a model describing the relationship between network-wide traffic flow and density—into the optimization framework. The research aims to improve network efficiency by treating MFD characteristics as key performance indicators alongside traditional metrics. The methodology involves constructing a multi-objective optimization model that minimizes average vehicle delay and queue coefficient while maximizing the MFD’s ascending segment slope (representing free-flow efficiency) and the network’s maximum flow capacity (representing saturated flow efficiency). To handle the discrete and irregular nature of simulated MFD data, the authors propose a novel modeling technique. They use the “tic-tac-toe” method to extract boundary points from the raw data, followed by Gaussian Mixture Model (GMM) clustering to divide the MFD into its characteristic segments. This approach ensures accurate fitting of the MFD curve even when data is sparse or non-ideal. The optimization problem is solved using a hybrid algorithm combining Genetic Algorithm (GA) and Multi-Objective Particle Swarm Optimization (MOPSO), implemented via Python and the Vissim simulation software interface. The study validates the proposed model using a real-world three-intersection arterial network. The results demonstrate that the MFD-based optimization strategy significantly outperforms traditional models that ignore MFD characteristics, as well as those solved by standard algebraic methods or pure MOPSO. Specifically, the proposed model achieved superior performance in reducing total and average delays and lowering the queue coefficient. Furthermore, the resulting MFD curves exhibited higher stability, indicating more robust network performance under saturated conditions. The hybrid GA-MOPSO algorithm proved effective in finding optimal solutions for the complex, multi-objective problem. The significance of this work lies in providing a macroscopic perspective for arterial signal control, moving beyond localized delay metrics to evaluate overall network health. By incorporating MFD indicators, traffic engineers can design control strategies that maintain higher operational efficiency and stability during congestion. The proposed data processing technique for MFD modeling also offers a practical solution for handling noisy or incomplete traffic data, enhancing the applicability of MFD-based control in real-world scenarios.

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• traffic density