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Dual-Objective Reinforcement Learning-Based Adaptive Traffic Signal Control for Decarbonization and Efficiency Optimization

Author

Listed:
  • Gongquan Zhang

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
    Harvard Medical School, Harvard University, Boston, MA 02138, USA)

  • Fangrong Chang

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

  • Helai Huang

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Zilong Zhou

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

Abstract

To improve traffic efficiency, adaptive traffic signal control (ATSC) systems have been widely developed. However, few studies have proactively optimized the air environmental issues in the development of ATSC. To fill this research gap, this study proposes an optimized ATSC algorithm to take into consideration both traffic efficiency and decarbonization. The proposed algorithm is developed based on the deep reinforcement learning (DRL) framework with dual goals (DRL-DG) for traffic control system optimization. A novel network structure combining Convolutional Neural Networks and Long Short-Term Memory Networks is designed to map the intersection traffic state to a Q-value, accelerating the learning process. The reward mechanism involves a multi-objective optimization function, employing the entropy weight method to balance the weights among dual goals. Based on a representative intersection in Changsha, Hunan Province, China, a simulated intersection scenario is constructed to train and test the proposed algorithm. The result shows that the ATSC system optimized by the proposed DRL-DG results in a reduction of more than 71% in vehicle waiting time and 46% in carbon emissions compared to traditional traffic signal control systems. It converges faster and achieves a balanced dual-objective optimization compared to the prevailing DRL-based ATSC.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:13:p:2056-:d:1426603
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    References listed on IDEAS

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    1. Vladimir Shepelev & Aleksandr Glushkov & Ivan Slobodin & Mohammed Balfaqih, 2023. "Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions," Mathematics, MDPI, vol. 11(16), pages 1-30, August.
    2. 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.
    3. Hua, Chengying & Fan, Wei (David), 2024. "Safety-oriented dynamic speed harmonization of mixed traffic flow in nonrecurrent congestion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 634(C).
    4. Muralidharan, Ajith & Pedarsani, Ramtin & Varaiya, Pravin, 2015. "Analysis of fixed-time control," Transportation Research Part B: Methodological, Elsevier, vol. 73(C), pages 81-90.
    5. Adel A. Ahmed & Sharaf J. Malebary & Waleed Ali & Omar M. Barukab, 2023. "Smart Traffic Shaping Based on Distributed Reinforcement Learning for Multimedia Streaming over 5G-VANET Communication Technology," Mathematics, MDPI, vol. 11(3), pages 1-20, January.
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