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

TL;DR

This paper demonstrates that mean-field approaches fail to accurately model herd behavior in vehicle path switching, highlighting the importance of considering collective driver decisions in traffic optimization.

Contribution

It introduces an exhaustive cavity approach to better analyze un-coordinated driver behavior and herd effects in re-routing games, surpassing mean-field limitations.

Findings

Mean-field cavity approach does not capture herd behavior.

Driver decisions are highly correlated, affecting traffic flow.

The framework is applicable to other multi-player, multi-round games.

Abstract

Coordination of vehicle routes is a feasible way to ease traffic congestions amid a fixed road infrastructure. Nevertheless, even the optimal route configurations are provided to individual drivers, it is hard to achieve as greedy drivers may switch to other routes for a lower individual cost. Recent research uses mean-field cavity approach from the studies of spin glasses to analyze the impact of path switching in optimized transportation networks. However, this method only provides a mean-field approximation, which does not take into account the collective herd behavior in path switching due to un-coordinated individual decisions. In this study, we propose an exhaustive cavity approach to investigate the impact of un-coordinated path switching in a re-routing game and reveal that greedy drivers' decision can be highly correlated which leads to the failure of mean-fielded approaches. Our theoretical results fits 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 of un-coordinated human drivers in suppressing congestions through path coordination.