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Re-routing game: The inadequacy of mean-field approach in modeling the herd behavior in path switching

Research output: Contribution to journal › Article › peer-review

Abstract

Coordination of vehicle routes is a feasible way to ease traffic congestion issues amid a fixed road infrastructure. Nevertheless, even when optimal route configurations are provided to individual drivers, it is hard to achieve due to the fact that greedy drivers may switch to other routes to lower individual costs. Recent research uses a mean-field cavity approach from spin glass studies to analyze the impact of path switching in optimized transportation networks. However, this method only provides a mean-field approximation, which does not account for the collective herd behavior in path switching due to uncoordinated individual decisions. In this study, we propose an exhaustive cavity approach to investigate the impact of uncoordinated path switching in a re-routing game and reveal that greedy drivers’ decisions can be highly correlated, leading to the failure of mean-field approaches. Our theoretical results fit well with simulations, and our developed framework can be generalized to analyze other games with multiple players and rounds. Our results shed light on the impact of herd behavior among uncoordinated human drivers in suppressing congestion through path coordination.
Original languageEnglish
Article number130266
Number of pages11
JournalPhysica A: Statistical Mechanics and its Applications
Volume658
Early online date2 Dec 2024
DOIs
Publication statusPublished - 15 Jan 2025

Funding

This work is fully supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (Projects No. GRF 18304316, GRF 18301217, GRF 18301119 and GRF 18300623), the Dean’s Research Fund of the Faculty of Liberal Arts and Social Sciences, The Education University of Hong Kong, Hong Kong Special Administrative Region, China (Projects No: FLASS/DRF 04418, FLASS/DRF 04624, and FLASS/DRF 04847), the Research Development Office Internal Research Grant, The Education University of Hong Kong, Hong Kong Special Administrative Region, China (Projects No. RG67 2018-2019R R4015 and No. RG31 2020-2021R R4152). H.F.P acknowledges the support by the European Union Horizon 2020 research and innovation program under Grant No. 964877 (project NEU-CHiP).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

Keywords

  • Herd behavior
  • Mean-field cavity approach
  • Route optimization
  • Traffic congestion
  • Transportation networks

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