Linearly Solvable Mean-Field Road Traffic Games
Optimization and Control
2018-08-17 v1
Abstract
We analyze the behavior of a large number of strategic drivers traveling over an urban traffic network using the mean-field game framework. We assume an incentive mechanism for congestion mitigation under which each driver selecting a particular route is charged a tax penalty that is affine in the logarithm of the number of agents selecting the same route. We show that the mean-field approximation of such a large-population dynamic game leads to the so-called linearly solvable Markov decision process, implying that an open-loop -Nash equilibrium of the original game can be found simply by solving a finite-dimensional linear system.
Keywords
Cite
@article{arxiv.1808.05305,
title = {Linearly Solvable Mean-Field Road Traffic Games},
author = {Takashi Tanaka and Ehsan Nekouei and Karl Henrik Johansson},
journal= {arXiv preprint arXiv:1808.05305},
year = {2018}
}