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Robust and resilient equilibrium routing mechanism for traffic congestion mitigation built upon correlated equilibrium and distributed optimization

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  • Ning, Yuqiang
  • Du, Lili

Abstract

With the rapid development of wireless communication, mobile computing, and GPS technologies, drivers’ route decisions nowadays rely more on navigation services, such as Google or Waze. However, these navigation services don't always come with improved traffic conditions. Individual drivers often make independent and selfish route decisions that are not systematically favorable and thus often result in severe congestions. This study aims to alleviate such problems by exploiting the information gaps between individuals and the central planner (CP). Specifically, we develop a correlated equilibrium routing mechanism (CeRM) for the CP, which drives a group of vehicles’ route choices to an equilibrium with a systematically optimal traffic condition while still satisfying individuals’ selfish nature. Participating drivers would only be better off by following the suggested routing guidance than navigating on their best responses to real-time traffic information. The CeRM is modeled as a nonconvex and nonlinear program involving a large-scale of users. A distributed Augmented Lagrangian algorithm (D-AL) is developed to efficiently solve the CeRM to provide online real-time navigation service, taking advantage of the on-board computation resources of individual vehicles. Considering the D-AL relies on the wireless communications between vehicles and the CP, we proved the convergence robustness of the D-AL against random communication failures and derived the convergence rate upper bound as a function of the communication failure probability. It is noticed that the convergence rate of the D-AL degrades dramatically as the communication failure probability increases, which hampers the applicability of implementing the CeRM in practice. To improve the solution algorithm's resilience in the computation performance, we further designed and proved an acceleration scheme aided D-AL (aD-AL) to expedite the convergence rate under the high likelihood of communication failures. Numerical experiments conducted on the Sioux Falls city network confirmed the D-AL's convergence properties, robustness against random communication failures, and the resilience of the aD-AL to solve the CeRM. The experiments also show that the CeRM results in better system performance (have less system cost) compared with the existing Independent Routing (IR) mechanism and user-oriented Equilibrium Routing (uoER) mechanism.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:transb:v:168:y:2023:i:c:p:170-205
    DOI: 10.1016/j.trb.2022.12.006
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    1. Small, Kenneth A & Rosen, Harvey S, 1981. "Applied Welfare Economics with Discrete Choice Models," Econometrica, Econometric Society, vol. 49(1), pages 105-130, January.
    2. Mariska van Essen & Tom Thomas & Eric van Berkum & Caspar Chorus, 2016. "From user equilibrium to system optimum: a literature review on the role of travel information, bounded rationality and non-selfish behaviour at the network and individual levels," Transport Reviews, Taylor & Francis Journals, vol. 36(4), pages 527-548, July.
    3. Guo, Xiaolei & Yang, Hai, 2010. "Pareto-improving congestion pricing and revenue refunding with multiple user classes," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 972-982, September.
    4. Liu, Yixuan & Whinston, Andrew B., 2019. "Efficient real-time routing for autonomous vehicles through Bayes correlated equilibrium: An information design framework," Information Economics and Policy, Elsevier, vol. 47(C), pages 14-26.
    5. Jin Y. Yen, 1971. "Finding the K Shortest Loopless Paths in a Network," Management Science, INFORMS, vol. 17(11), pages 712-716, July.
    6. 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.
    7. Small, Kenneth A., 2001. "The Value of Pricing," University of California Transportation Center, Working Papers qt0rm449sx, University of California Transportation Center.
    8. Herbert A. Simon, 1955. "A Behavioral Model of Rational Choice," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 69(1), pages 99-118.
    9. Rey, David & Dixit, Vinayak V. & Ygnace, Jean-Luc & Waller, S. Travis, 2016. "An endogenous lottery-based incentive mechanism to promote off-peak usage in congested transit systems," Transport Policy, Elsevier, vol. 46(C), pages 46-55.
    10. 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.
    11. Wu, Di & Yin, Yafeng & Lawphongpanich, Siriphong, 2011. "Pareto-improving congestion pricing on multimodal transportation networks," European Journal of Operational Research, Elsevier, vol. 210(3), pages 660-669, May.
    12. Dirk Van Amelsfort & Michiel Bliemer, 2005. "Valuation of uncertainty in travel time and arrival time - some findings from a choice experiment," ERSA conference papers ersa05p721, European Regional Science Association.
    13. Hai Yang, 1999. "System Optimum, Stochastic User Equilibrium, and Optimal Link Tolls," Transportation Science, INFORMS, vol. 33(4), pages 354-360, November.
    14. 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).
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