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arXiv · 1808.05305

Linearly Solvable Mean-Field Road Traffic Games

Takashi Tanaka·Ehsan Nekouei·Karl Henrik Johansson
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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 $\epsilon$-Nash equilibrium of the original game can be found simply by solving a finite-dimensional linear system.

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Takashi Tanaka, Ehsan Nekouei, Karl Henrik Johansson. 2018-08-15. Linearly Solvable Mean-Field Road Traffic Games. https://arxiv.org/abs/1808.05305

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