IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i10p4492-d1656128.html
   My bibliography  Save this article

Max-Pressure Controller for Traffic Networks Considering the Phase Switching Loss

Author

Listed:
  • Jiayu Sun

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China
    Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China
    National Traffic Management Engineering & Technology Research Center, Ningbo University Sub-Center, Ningbo 315832, China)

  • Yibing Wang

    (Institute of Intelligent Transportation Systems, Zhejiang University, Hangzhou 310058, China)

  • Hang Yang

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China
    Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China
    National Traffic Management Engineering & Technology Research Center, Ningbo University Sub-Center, Ningbo 315832, China)

  • Zhao Zhang

    (School of Transportation Science and Engineering, Beihang University, Beijing 100191, China)

  • Markos Papageorgiou

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China
    Dynamic Systems and Simulation Laboratory, Technical University of Crete, 73100 Chania, Greece)

  • Guiyun Liu

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China
    Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China
    National Traffic Management Engineering & Technology Research Center, Ningbo University Sub-Center, Ningbo 315832, China)

  • Pengjun Zheng

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China
    Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China
    National Traffic Management Engineering & Technology Research Center, Ningbo University Sub-Center, Ningbo 315832, China)

Abstract

Efficient traffic signal control plays a critical role in promoting sustainable mobility by reducing congestion and minimizing vehicle emissions. This paper proposes an enhanced max-pressure (MP) signal control strategy that explicitly accounts for phase switching time losses in grid road networks. While the traditional MP control strategy is recognized for its decentralized architecture and simplicity, it often neglects the delays introduced by frequent phase changes, limiting its real-world effectiveness. To address this issue, three key improvements are introduced in this study. First, a redefined phase pressure formulation is presented, which incorporates imbalances in traffic demand across multiple inlet roads within a single phase. Second, a dynamic green phase extension mechanism is developed, which adjusts phase durations in real time based on queue lengths to improve traffic flow responsiveness. Third, a current-phase protection mechanism is implemented by applying an amplification factor to the current-phase pressure calculations, thereby mitigating unnecessary phase switching. Simulation results using SUMO on a grid network demonstrate that the proposed strategy significantly reduces average vehicle delays and queue lengths compared with traditional MP, travel-time based MP, and fixed-time control strategies, leading to improved overall traffic efficiency. Specifically, the proposed method reduces total delay by 24.83%, 26.67%, and 47.11%, and average delay by approximately 16.18%, 18.91%, and 36.22%, respectively, while improving traffic throughput by 2.25%, 2.76%, and 5.84%. These improvements directly contribute to reducing traffic congestion, fuel consumption, and greenhouse gas emissions, thereby reinforcing the role of adaptive signal control in achieving smart and sustainable cities. The proposed approach can serve as a practical reference for improving real-world traffic signal control systems, particularly in regions seeking to improve sustainability and operational efficiency.

Suggested Citation

  • Jiayu Sun & Yibing Wang & Hang Yang & Zhao Zhang & Markos Papageorgiou & Guiyun Liu & Pengjun Zheng, 2025. "Max-Pressure Controller for Traffic Networks Considering the Phase Switching Loss," Sustainability, MDPI, vol. 17(10), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:10:p:4492-:d:1656128
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/10/4492/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/10/4492/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Elżbieta Macioszek & Anna Granà & Paulo Fernandes & Margarida C. Coelho, 2022. "New Perspectives and Challenges in Traffic and Transportation Engineering Supporting Energy Saving in Smart Cities—A Multidisciplinary Approach to a Global Problem," Energies, MDPI, vol. 15(12), pages 1-8, June.
    2. Li, Li & Jabari, Saif Eddin, 2019. "Position weighted backpressure intersection control for urban networks," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 435-461.
    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. Yuting Wang & Zhaocheng He & Wangyong Xing & Chengchuang Lin, 2025. "Understanding Congestion Risk and Emissions of Various Travel Behavior Patterns Based on License Plate Recognition Data," Sustainability, MDPI, vol. 17(2), pages 1-22, January.
    2. Ioana Popescu & Camelia Teau & Cristian Moisescu-Ciocan & Constantin Florescu & Relu Adam & Albert Titus Constantin, 2024. "Evaluating the Sustainability of Longtime Operating Infrastructure for Romanian Flood Risk Protection," Sustainability, MDPI, vol. 16(23), pages 1-19, December.
    3. Lin, Dianchao & Li, Li, 2025. "Unveiling network capacity potential with imminent supply information part II: Backpressure-based validation," Transportation Research Part B: Methodological, Elsevier, vol. 192(C).
    4. Cui, Shaohua & Xue, Yongjie & Gao, Kun & Wang, Kai & Yu, Bin & Qu, Xiaobo, 2024. "Delay-throughput tradeoffs for signalized networks with finite queue capacity," Transportation Research Part B: Methodological, Elsevier, vol. 180(C).
    5. Milan Dedík & Martin Kendra & Matúš Senci & Daniel Pribula & Martin Vojtek, 2024. "Progressive Methods of the Harmfulness Evaluation of Transport in Terms of Emission Production," Sustainability, MDPI, vol. 16(23), pages 1-24, November.
    6. Muhammad Adeel & Biao Wang & Ji Ke & Israel Muaka Mvitu, 2024. "The Nonlinear Dynamics of CO 2 Emissions in Pakistan: A Comprehensive Analysis of Transportation, Electricity Consumption, and Foreign Direct Investment," Sustainability, MDPI, vol. 17(1), pages 1-26, December.
    7. Hang Nguyen Thi Bich & Thuc Le Dinh, 2024. "Overcoming Barriers to Sustainable Green Transportation in Ho Chi Minh City: A Pathway Toward Achieving SDGs 11 and 13," Sustainability, MDPI, vol. 16(23), pages 1-20, December.
    8. Fei, Xinyu & Wang, Xingmin & Yu, Xian & Feng, Yiheng & Liu, Henry & Shen, Siqian & Yin, Yafeng, 2023. "Traffic signal control under stochastic traffic demand and vehicle turning via decentralized decomposition approaches," European Journal of Operational Research, Elsevier, vol. 310(2), pages 712-736.
    9. Bander Fahad Alkrides, 2025. "Promoting Sustainable Urban Walkability: A Modified Delphi Study on Key Indicators for Urban Walkability in Gulf Cooperation Council Urban Streets," Sustainability, MDPI, vol. 17(3), pages 1-34, February.
    10. Volodymyr N. Skoropad & Stevica Deđanski & Vladan Pantović & Zoran Injac & Slađana Vujičić & Marina Jovanović-Milenković & Boris Jevtić & Violeta Lukić-Vujadinović & Dejan Vidojević & Ištvan Bodolo, 2025. "Dynamic Traffic Flow Optimization Using Reinforcement Learning and Predictive Analytics: A Sustainable Approach to Improving Urban Mobility in the City of Belgrade," Sustainability, MDPI, vol. 17(8), pages 1-31, April.
    11. Lin, Dianchao & Li, Li, 2025. "Unveiling network capacity potential with imminent supply information part I: Theoretical derivation," Transportation Research Part B: Methodological, Elsevier, vol. 192(C).
    12. Du, Yu & Kouvelas, Anastasios & ShangGuan, Wei & Makridis, Michail A., 2022. "Dynamic capacity estimation of mixed traffic flows with application in adaptive traffic signal control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    13. Yeran Huang & Jian Gao & Lin Wang & Jierui Zhu & Wanjun Li, 2025. "A Framework of Life-Cycle Infrastructure Digitalization for Highway Asset Management," Sustainability, MDPI, vol. 17(3), pages 1-30, January.
    14. Li, Li & Pantelidis, Theodoros & Chow, Joseph Y.J. & Jabari, Saif Eddin, 2021. "A real-time dispatching strategy for shared automated electric vehicles with performance guarantees," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    15. Bing Zhang & Yiling Zong & Fang Liu, 2025. "Carbon Emission Prediction of the Transportation Industry in Jiangsu Province Based on the WOA-SVM Model," Sustainability, MDPI, vol. 17(10), pages 1-19, May.
    16. Ragosebo Kgaugelo Modise & Khumbulani Mpofu & Tshifhiwa Nenzhelele & Olukorede Tijani Adenuga, 2025. "Enhancing Energy Consumption in Automotive Component Manufacturing: A Hybrid Autoregressive Integrated Moving Average–Long Short-Term Memory Prediction Model," Sustainability, MDPI, vol. 17(4), pages 1-19, February.
    17. Yanni Liang & Jianxin You & Ran Wang & Bo Qin & Shuo Han, 2024. "Urban Transportation Data Research Overview: A Bibliometric Analysis Based on CiteSpace," Sustainability, MDPI, vol. 16(22), pages 1-45, November.
    18. Biao Yin & Monica Menendez & Kaidi Yang, 2021. "Joint Optimization of Intersection Control and Trajectory Planning Accounting for Pedestrians in a Connected and Automated Vehicle Environment," Sustainability, MDPI, vol. 13(3), pages 1-25, January.
    19. Lu Liu & Caihong Li & Yi Yang & Jianzhou Wang, 2024. "Short-Term Traffic Flow Forecasting Based on a Novel Combined Model," Sustainability, MDPI, vol. 16(23), pages 1-25, November.

    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:jsusta:v:17:y:2025:i:10:p:4492-:d:1656128. 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.