Linearly Solvable Mean-Field Road Traffic Games

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

Detail(s)

Original languageEnglish
Title of host publication2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages283-289
ISBN (Print)9781538665961
Publication statusPublished - Oct 2018
Externally publishedYes

Publication series

NameAnnual Allerton Conference on Communication, Control, and Computing, Allerton

Conference

Title56th Annual Allerton Conference on Communication, Control, and Computing (Allerton 2018)
PlaceUnited States
CityMonticello
Period2 - 5 October 2018

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.

Citation Format(s)

Linearly Solvable Mean-Field Road Traffic Games. / Tanaka, Takashi; Nekouei, Ehsan; Johansson, Karl Henrik.

2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton). Institute of Electrical and Electronics Engineers Inc., 2018. p. 283-289 8636077 (Annual Allerton Conference on Communication, Control, and Computing, Allerton).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review