Linearly-Solvable Mean-Field Approximation for Multi-Team Road Traffic Games

We study the traffic routing game among a large number of selfish drivers over a traffic network. We consider a specific scenario where the strategic drivers can be classified into teams, where drivers in the same team have identical payoff functions. An incentive mechanism is considered to mitigate...

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Bibliographic Details
Published inProceedings of the IEEE Conference on Decision & Control pp. 1243 - 1248
Main Authors Pedram, Ali Reza, Tanaka, Takashi
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2019
Online AccessGet full text
ISSN2576-2370
DOI10.1109/CDC40024.2019.9029579

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Summary:We study the traffic routing game among a large number of selfish drivers over a traffic network. We consider a specific scenario where the strategic drivers can be classified into teams, where drivers in the same team have identical payoff functions. An incentive mechanism is considered to mitigate congestion, where each driver is subject to dynamic tax penalties. We explore a special case in which the tax is affine in the logarithm of the number of drivers selecting the same route from each team. It is shown via a mean-field approximation that a Nash equilibrium in the limit of a large population can be found by linearly solvable algorithms.
ISSN:2576-2370
DOI:10.1109/CDC40024.2019.9029579