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Distributed coordinated in-vehicle online routing using mixed-strategy congestion game

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  • Du, Lili
  • Han, Lanshan
  • Li, Xiang-Yang

Abstract

This study proposes a coordinated online in-vehicle routing mechanism for smart vehicles with real-time information exchange and portable computation capabilities. The proposed coordinated routing mechanism incorporates a discrete choice model to account for drivers’ behavior, and is implemented by a simultaneously-updating distributed algorithm. This study shows the existence of an equilibrium coordinated routing decision for the mixed-strategy routing game and the convergence of the distributed algorithm to the equilibrium routing decision, assuming individual smart vehicles are selfish players seeking to minimize their own travel time. Numerical experiments conducted based on Sioux Falls city network indicate that the proposed distributed algorithm converges quickly under different smart vehicle penetrations, thus it possesses a great potential for online applications. Moreover, the proposed coordinated routing mechanism outperforms traditional independent selfish-routing mechanism; it reduces travel time for both overall system and individual vehicles, which represents the core idea of Intelligent Transportation Systems (ITS).

Suggested Citation

  • Du, Lili & Han, Lanshan & Li, Xiang-Yang, 2014. "Distributed coordinated in-vehicle online routing using mixed-strategy congestion game," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 1-17.
    • Handle: RePEc:eee:transb:v:67:y:2014:i:c:p:1-17
    DOI: 10.1016/j.trb.2014.05.003
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Zhou, Bo & Song, Qiankun & Zhao, Zhenjiang & Liu, Tangzhi, 2020. "A reinforcement learning scheme for the equilibrium of the in-vehicle route choice problem based on congestion game," Applied Mathematics and Computation, Elsevier, vol. 371(C).
    2. Rinaldi, Marco & Tampère, Chris M.J., 2015. "An extended coordinate descent method for distributed anticipatory network traffic control," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 107-131.
    3. Du, Lili & Gong, Siyuan, 2016. "Stochastic Poisson game for an online decentralized and coordinated parking mechanism," Transportation Research Part B: Methodological, Elsevier, vol. 87(C), pages 44-63.
    4. Baiocchi, Andrea, 2016. "Analysis of timer-based message dissemination protocols for inter-vehicle communications," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 105-134.
    5. Le Vine, Scott & Polak, John, 2016. "A novel peer-to-peer congestion pricing marketplace enabled by vehicle-automation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 483-494.
    6. Wang, Hua & Meng, Qiang & Chen, Shukai & Zhang, Xiaoning, 2021. "Competitive and cooperative behaviour analysis of connected and autonomous vehicles across unsignalised intersections: A game-theoretic approach," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 322-346.
    7. Ning, Yuqiang & Du, Lili, 2023. "Robust and resilient equilibrium routing mechanism for traffic congestion mitigation built upon correlated equilibrium and distributed optimization," Transportation Research Part B: Methodological, Elsevier, vol. 168(C), pages 170-205.
    8. Pi, Xidong & Qian, Zhen (Sean), 2017. "A stochastic optimal control approach for real-time traffic routing considering demand uncertainties and travelers’ choice heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 710-732.
    9. Du, Lili & Han, Lanshan & Chen, Shuwei, 2015. "Coordinated online in-vehicle routing balancing user optimality and system optimality through information perturbation," Transportation Research Part B: Methodological, Elsevier, vol. 79(C), pages 121-133.
    10. Le Zhang & Lijing Lyu & Shanshui Zheng & Li Ding & Lang Xu, 2022. "A Q-Learning-Based Approximate Solving Algorithm for Vehicular Route Game," Sustainability, MDPI, vol. 14(19), pages 1-14, September.
    11. Liu, Siyuan & Qu, Qiang, 2016. "Dynamic collective routing using crowdsourcing data," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 450-469.

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