# Linearly Solvable Mean-Field Road Traffic Games

arXiv: https://arxiv.org/abs/1808.05305 · HTML: https://mlsanity.com/paper/1808.05305/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2018.

## Built on by (most cited first, 2 of 2)

- [On Imitation in Mean-field Games](https://mlsanity.com/paper/2306.14799/) (2023)
- [Learning Correlated Equilibria in Mean-Field Games](https://mlsanity.com/paper/2208.10138/) (2022)

## Abstract (arXiv)

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 ε-Nash equilibrium of the original game can be found simply by solving a finite-dimensional linear system.