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Research on Urban Road Traffic Network Pinning Control Based on Feedback Control

Author

Listed:
  • Guimin Gong

    (College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China)

  • Wenhong Lv

    (College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China)

  • Qi Wang

    (College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China)

Abstract

The development and application of pinning control methods create conditions for traffic area control, and the objective of possessing global control of the road network is achieved by controlling a small number of intersections in the road network. Based on this, an urban road network pinning control strategy is designed in this paper. Firstly, this paper establishes the state equation of the urban road traffic network according to the characteristics of traffic flow, and proposes an associated state equation for road sections and key intersections. Secondly, by adjusting the signal timing scheme of key intersections as the target of pinning control, it can restrain the road network to achieve the state with the minimum difference between the actual flow and the desired flow on each road section. At the same time, considering the dynamic nature of traffic flow and the fact that the flow rate on the road section changes continuously, a feedback control mechanism is established in order to determine the threshold value at which each road section enters the congestion state. In addition, when the flow rate of a road section exceeds its threshold value to reach the congestion state, the signal timing scheme of the key intersection needs to be adjusted again to ensure that the flow rate on the road section is always lower than the threshold value at which it enters the congestion state. The results show that the average delay time and average stopping time of the road network are reduced by 35.03s and 18.37s, respectively, compared with the original control scheme, proving that the control strategy can effectively reduce congestion and improve the operational efficiency of the road network.

Suggested Citation

  • Guimin Gong & Wenhong Lv & Qi Wang, 2023. "Research on Urban Road Traffic Network Pinning Control Based on Feedback Control," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9631-:d:1172069
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    References listed on IDEAS

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    1. Chiou, Suh-Wen, 2018. "A traffic-responsive signal control to enhance road network resilience with hazmat transportation in multiple periods," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 105-118.
    2. Rusul Abduljabbar & Hussein Dia & Sohani Liyanage & Saeed Asadi Bagloee, 2019. "Applications of Artificial Intelligence in Transport: An Overview," Sustainability, MDPI, vol. 11(1), pages 1-24, January.
    3. Wang, Xiao Fan & Chen, Guanrong, 2002. "Pinning control of scale-free dynamical networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 310(3), pages 521-531.
    4. Wang, Shuzhan & Zhang, Ziye & Lin, Chong & Chen, Jian, 2021. "Fixed-time synchronization for complex-valued BAM neural networks with time-varying delays via pinning control and adaptive pinning control," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    5. Zhang, Lele & Garoni, Timothy M & de Gier, Jan, 2013. "A comparative study of Macroscopic Fundamental Diagrams of arterial road networks governed by adaptive traffic signal systems," Transportation Research Part B: Methodological, Elsevier, vol. 49(C), pages 1-23.
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