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Cooperative Control for Signalized Intersections in Intelligent Connected Vehicle Environments

Author

Listed:
  • Anton Agafonov

    (Department of Geoinformatics and Information Security, Samara National Research University, 443086 Samara, Russia)

  • Alexander Yumaganov

    (Department of Geoinformatics and Information Security, Samara National Research University, 443086 Samara, Russia)

  • Vladislav Myasnikov

    (Department of Geoinformatics and Information Security, Samara National Research University, 443086 Samara, Russia)

Abstract

Cooperative control of vehicle trajectories and traffic signal phases is a promising approach to improving the efficiency and safety of transportation systems. This type of traffic flow control refers to the coordination and optimization of vehicle trajectories and traffic signal phases to reduce congestion, travel time, and fuel consumption. In this paper, we propose a cooperative control method that combines a model predictive control algorithm for adaptive traffic signal control and a trajectory construction algorithm. For traffic signal phase selection, the proposed modification of the adaptive traffic signal control algorithm combines the travel time obtained using either the vehicle trajectory or a deep neural network model and stop delays. The vehicle trajectory construction algorithm takes into account the predicted traffic signal phase to achieve cooperative control. To evaluate the method performance, numerical experiments have been conducted for three real-world scenarios in the SUMO simulation package. The experimental results show that the proposed cooperative control method can reduce the average fuel consumption by 1% to 4.2%, the average travel time by 1% to 5.3%, and the average stop delays to 27% for different simulation scenarios compared to the baseline methods.

Suggested Citation

  • Anton Agafonov & Alexander Yumaganov & Vladislav Myasnikov, 2023. "Cooperative Control for Signalized Intersections in Intelligent Connected Vehicle Environments," Mathematics, MDPI, vol. 11(6), pages 1-19, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1540-:d:1104061
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    References listed on IDEAS

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    Cited by:

    1. Gongquan Zhang & Fangrong Chang & Helai Huang & Zilong Zhou, 2024. "Dual-Objective Reinforcement Learning-Based Adaptive Traffic Signal Control for Decarbonization and Efficiency Optimization," Mathematics, MDPI, vol. 12(13), pages 1-24, June.
    2. Li Zhang & Lei Zhang, 2024. "Distributed Traffic Signal Optimization at V2X Intersections," Mathematics, MDPI, vol. 12(5), pages 1-16, March.

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