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Multi-Agent-Based Data-Driven Distributed Adaptive Cooperative Control in Urban Traffic Signal Timing

Author

Listed:
  • Haibo Zhang

    (School of Electrical & Control Engineering, North China University of Technology, Beijing 100144, China)

  • Xiaoming Liu

    (School of Electrical & Control Engineering, North China University of Technology, Beijing 100144, China)

  • Honghai Ji

    (School of Electrical & Control Engineering, North China University of Technology, Beijing 100144, China)

  • Zhongsheng Hou

    (School of Automation, Qingdao University, Qingdao 266071, China)

  • Lingling Fan

    (School of Automation, Beijing Information Science & Technology University, Beijing 100192, China)

Abstract

Data-driven intelligent transportation systems (D 2 ITSs) have drawn significant attention lately. This work investigates a novel multi-agent-based data-driven distributed adaptive cooperative control (MA-DD-DACC) method for multi-direction queuing strength balance with changeable cycle in urban traffic signal timing. Compared with the conventional signal control strategies, the proposed MA-DD-DACC method combined with an online parameter learning law can be applied for traffic signal control in a distributed manner by merely utilizing the collected I/O traffic queueing length data and network topology of multi-direction signal controllers at a single intersection. A Lyapunov-based stability analysis shows that the proposed approach guarantees uniform ultimate boundedness of the distributed consensus coordinated errors of queuing strength. The numerical and experimental comparison simulations are performed on a VISSIM-VB-MATLAB joint simulation platform to verify the effectiveness of the proposed approach.

Suggested Citation

  • Haibo Zhang & Xiaoming Liu & Honghai Ji & Zhongsheng Hou & Lingling Fan, 2019. "Multi-Agent-Based Data-Driven Distributed Adaptive Cooperative Control in Urban Traffic Signal Timing," Energies, MDPI, vol. 12(7), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1402-:d:221936
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    Cited by:

    1. Rongquan Zhang & Saddam Aziz & Muhammad Umar Farooq & Kazi Nazmul Hasan & Nabil Mohammed & Sadiq Ahmad & Nisrine Ibadah, 2021. "A Wind Energy Supplier Bidding Strategy Using Combined EGA-Inspired HPSOIFA Optimizer and Deep Learning Predictor," Energies, MDPI, vol. 14(11), pages 1-22, May.
    2. Yue Zhou & Hussein Obeid & Salah Laghrouche & Mickael Hilairet & Abdesslem Djerdir, 2020. "A Disturbance Rejection Control Strategy of a Single Converter Hybrid Electrical System Integrating Battery Degradation," Energies, MDPI, vol. 13(11), pages 1-19, June.

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