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A reinforcement learning scheme for the equilibrium of the in-vehicle route choice problem based on congestion game

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  • Zhou, Bo
  • Song, Qiankun
  • Zhao, Zhenjiang
  • Liu, Tangzhi

Abstract

In this paper, the Bush–Mosteller (B-M) reinforcement learning (RL) scheme is introduced to model the route choice behaviors of the travelers in traffic networks, who aim to seek the optimal travel routes that minimize their individual travel time. The optimal route choice strategy is presented by the Nash equilibrium of the congestion game. By constructing a novel potential function, the congestion game is transformed into the traffic assignment problem (TAP). Then, a distributed algorithm based on B-M RL scheme is devised to solve the TAP. Under some mild conditions, the B-M RL solution method is proven to converge almost surely to the optimal solution of the TAP. A numerical experiment is conducted based on the Nguyen–Dupuis network, the experimental results not only demonstrate the effectiveness of the theoretical analysis, but also show that the B-M RL-based solution method outperforms several existing solution methods.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:apmaco:v:371:y:2020:i:c:s0096300319308872
    DOI: 10.1016/j.amc.2019.124895
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    References listed on IDEAS

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    1. Fisk, Caroline, 1980. "Some developments in equilibrium traffic assignment," Transportation Research Part B: Methodological, Elsevier, vol. 14(3), pages 243-255, September.
    2. Li, Zhibin & Jia, Danyang & Guo, Hao & Geng, Yini & Shen, Chen & Wang, Zhen & Li, Xuelong, 2019. "The effect of multigame on cooperation in spatial network," Applied Mathematics and Computation, Elsevier, vol. 351(C), pages 162-167.
    3. Xu, Hongli & Lou, Yingyan & Yin, Yafeng & Zhou, Jing, 2011. "A prospect-based user equilibrium model with endogenous reference points and its application in congestion pricing," Transportation Research Part B: Methodological, Elsevier, vol. 45(2), pages 311-328, February.
    4. Agryzkov, Taras & Tortosa, Leandro & Vicent, Jose F., 2019. "A variant of the current flow betweenness centrality and its application in urban networks," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 600-615.
    5. Cheng, Yuan & Zheng, Xiaoping, 2018. "Emergence of cooperation during an emergency evacuation," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 485-494.
    6. Angelelli, E. & Arsik, I. & Morandi, V. & Savelsbergh, M. & Speranza, M.G., 2016. "Proactive route guidance to avoid congestion," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 1-21.
    7. Tanimoto, Jun & Nakamura, Kousuke, 2016. "Social dilemma structure hidden behind traffic flow with route selection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 92-99.
    8. 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.
    9. Xu, Hedong & Fan, Suohai & Tian, Cunzhi & Xiao, Xinrong, 2019. "Evolutionary investor sharing game on networks," Applied Mathematics and Computation, Elsevier, vol. 340(C), pages 138-145.
    10. Sang Nguyen & Clermont Dupuis, 1984. "An Efficient Method for Computing Traffic Equilibria in Networks with Asymmetric Transportation Costs," Transportation Science, INFORMS, vol. 18(2), pages 185-202, May.
    11. Papageorgiou, Markos, 1990. "Dynamic modeling, assignment, and route guidance in traffic networks," Transportation Research Part B: Methodological, Elsevier, vol. 24(6), pages 471-495, December.
    12. Kang, Zengxin & Zhang, Lei & Li, Kun, 2019. "An improved social force model for pedestrian dynamics in shipwrecks," Applied Mathematics and Computation, Elsevier, vol. 348(C), pages 355-362.
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    2. 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.
    3. Sun, Wenjun & Zhu, Changfeng & Li, Hui, 2022. "Evolutionary game of emergency logistics path selection under bounded rationality," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    4. Tan, Lihua & Li, Chuandong & Huang, Junjian & Huang, Tingwen, 2021. "Output feedback leader-following consensus for nonlinear stochastic multiagent systems: The event-triggered method," Applied Mathematics and Computation, Elsevier, vol. 395(C).
    5. 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.

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