Linearly Solvable Mean-Field Traffic Routing Games

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

1 Scopus Citations
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Detail(s)

Original languageEnglish
Article number9061051
Pages (from-to)880-887
Journal / PublicationIEEE Transactions on Automatic Control
Volume66
Issue number2
Online published8 Apr 2020
Publication statusPublished - Feb 2021

Abstract

We consider a dynamic traffic routing game over an urban road network involving a large number of drivers in which each driver selecting a particular route is subject to a penalty that is affine in the logarithm of the number of drivers selecting the same route. We show that the mean-field approximation of such a game leads to the so-called linearly solvable Markov decision process, implying that its mean-field equilibrium (MFE) can be found simply by solving a finite-dimensional linear system backward in time. Based on this backward-only characterization, it is further shown that the obtained MFE has the notable property of strong time-consistency. A connection between the obtained MFE and a particular class of fictitious play is also discussed.

Research Area(s)

  • Intelligent transportation systems, multiagent systems, terative learning control mean field games

Citation Format(s)

Linearly Solvable Mean-Field Traffic Routing Games. / Tanaka, Takashi; Nekouei, Ehsan; Pedram, Ali Reza; Johansson, Karl Henrik.

In: IEEE Transactions on Automatic Control, Vol. 66, No. 2, 9061051, 02.2021, p. 880-887.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review