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Proactive Coordination of Traffic Guidance and Signal Control for a Divergent Network

Author

Listed:
  • Yaming Guo

    (Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
    Department of Civil Engineering, Tsinghua University, Beijing 100080, China)

  • Ke Zhang

    (Department of Civil Engineering, Tsinghua University, Beijing 100080, China)

  • Xiqun Chen

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China)

  • Meng Li

    (Department of Civil Engineering, Tsinghua University, Beijing 100080, China)

Abstract

In the realm of transportation system optimization, enhancing overall performance through the proactive coordination of traffic guidance and signal control in a divergent network can tackle the challenges posed by traffic congestion and inefficiency. Thus, we propose an innovative approach to first allow the information on variable message signs (VMS) that deviates from estimated travel times. This proactive approach guides drivers towards optimal routes from a system-wide perspective, such as minimizing vehicle hours traveled. The deviation is constrained both by the lower bound of drivers’ long-term compliance rate and the upper bound of the favored traffic signal operation. The proposed approach coordinates the traffic guidance system with the signal control system. The traffic signal control system sets the upper limit for information deviation in the traffic guidance system, while the traffic guidance system provides demand predictions for the traffic signal control system. Overall, the objective function of the approach is the network-level performance of all users. We gauge traveler satisfaction as a measure of system credibility, using both a route choice module and a satisfaction degree module established through stated preference surveys. Numerical results demonstrate that proactive-coordinated (PC) strategies outperform reactive-coordinated (RC), proactive-independent (PI), and reactive-independent (RI) strategies by improving the system performance, meanwhile keeping the system trustworthy. Under the normal traffic scenario, the PC strategy reduces total travel time by approximately 10%. Driver satisfaction with the PC strategy increases from a baseline of 76% to 95%. Moreover, in scenarios with sudden changes in either traffic demand or supply, e.g., accidents or large events, the proactive guidance strategy is more flexible and can potentially improve more from the system perspective.

Suggested Citation

  • Yaming Guo & Ke Zhang & Xiqun Chen & Meng Li, 2023. "Proactive Coordination of Traffic Guidance and Signal Control for a Divergent Network," Mathematics, MDPI, vol. 11(20), pages 1-19, October.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:20:p:4262-:d:1258425
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    References listed on IDEAS

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    1. Poulopoulou, Maria & Spyropoulou, Ioanna, 2019. "Active traffic management in urban areas: Is it effective for professional drivers? The case of variable message signs," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 412-423.
    2. Binshuang Zheng & Zhengqiang Hong & Junyao Tang & Meiling Han & Jiaying Chen & Xiaoming Huang, 2023. "A Comprehensive Method to Evaluate Ride Comfort of Autonomous Vehicles under Typical Braking Scenarios: Testing, Simulation and Analysis," Mathematics, MDPI, vol. 11(2), pages 1-23, January.
    3. Rodriguez-Roman, Daniel & Ritchie, Stephen G., 2020. "Surrogate-based optimization for multi-objective toll design problems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 485-503.
    4. Boriana Vatchova & Yordanka Boneva, 2023. "Design of Fuzzy and Conventional Controllers for Modeling and Simulation of Urban Traffic Light System with Feedback Control," Mathematics, MDPI, vol. 11(2), pages 1-11, January.
    5. Laurens Bliek, 2022. "A Survey on Sustainable Surrogate-Based Optimisation," Sustainability, MDPI, vol. 14(7), pages 1-19, March.
    6. Yin, Yafeng & Yang, Hai, 2003. "Simultaneous determination of the equilibrium market penetration and compliance rate of advanced traveler information systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(2), pages 165-181, February.
    7. Huang, Ruqing & Han, Lee D. & Huang, Zhongxiang, 2022. "A new network equilibrium flow model: User-equilibrium with quantity adjustment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    8. Lo, Hong K. & Tung, Yeou-Koung, 2003. "Network with degradable links: capacity analysis and design," Transportation Research Part B: Methodological, Elsevier, vol. 37(4), pages 345-363, May.
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