IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i13p2056-d1426603.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/13/2056/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/13/2056/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. Muralidharan, Ajith & Pedarsani, Ramtin & Varaiya, Pravin, 2015. "Analysis of fixed-time control," Transportation Research Part B: Methodological, Elsevier, vol. 73(C), pages 81-90.
    4. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Roman Ekhlakov & Nikita Andriyanov, 2024. "Multicriteria Assessment Method for Network Structure Congestion Based on Traffic Data Using Advanced Computer Vision," Mathematics, MDPI, vol. 12(4), pages 1-27, February.
    2. Yu, Chunhui & Ma, Wanjing & Yang, Xiaoguang, 2020. "A time-slot based signal scheme model for fixed-time control at isolated intersections," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 176-192.
    3. Kouvelas, Anastasios & Saeedmanesh, Mohammadreza & Geroliminis, Nikolas, 2017. "Enhancing model-based feedback perimeter control with data-driven online adaptive optimization," Transportation Research Part B: Methodological, Elsevier, vol. 96(C), pages 26-45.
    4. Smith, M.J. & Liu, R. & Mounce, R., 2015. "Traffic control and route choice: Capacity maximisation and stability," Transportation Research Part B: Methodological, Elsevier, vol. 81(P3), pages 863-885.
    5. Como, Giacomo & Nilsson, Gustav, 2021. "On the well-posedness of deterministic queuing networks with feedback control," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 323-335.
    6. Li Zhang & Lei Zhang, 2024. "Distributed Traffic Signal Optimization at V2X Intersections," Mathematics, MDPI, vol. 12(5), pages 1-16, March.
    7. Coogan, Samuel & Kim, Eric & Gomes, Gabriel & Arcak, Murat & Varaiya, Pravin, 2017. "Offset optimization in signalized traffic networks via semidefinite relaxation," Transportation Research Part B: Methodological, Elsevier, vol. 100(C), pages 82-92.
    8. Xiaoning Wang & Yi Tang & Anna Grazia Quaranta, 2024. "Machine Learning-Driven Lending Decisions in Bank Consumer Finance," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 17(1), pages 1-19, January.
    9. Mingwen Zheng & Lixiang Li & Haipeng Peng & Jinghua Xiao & Yixian Yang & Yanping Zhang & Hui Zhao, 2018. "Globally fixed-time synchronization of coupled neutral-type neural network with mixed time-varying delays," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-22, January.
    10. Hao, Zhenzhen & Boel, René, 2022. "Convergence analysis on control for traffic signals in urban road network," Transportation Research Part B: Methodological, Elsevier, vol. 165(C), pages 35-62.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:13:p:2056-:d:1426603. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.