We analyze the behavior of a large number of strategic drivers traveling er an urban traffic network using the mean-field game framework. We sume an incentive mechanism for congestion mitigation under which each iver selecting a particular route is charged a tax penalty that is fine in the logarithm of the number of agents selecting the same ute. We show that the mean-field approximation of such a rge-population dynamic game leads to the so-called linearly solvable rkov decision process, implying that an open-loop epsilon-Nash uilibrium of the original game can be found simply by solving a nite-dimensional linear system.