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 inarXiv.org
Main Authors Pedram, Ali Reza, Tanaka, Takashi
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 02.10.2019
Subjects
Algorithms
Approximation
Economic models
Mathematical analysis
Route selection
Traffic congestion
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ISSN2331-8422

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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.
Bibliography:content type line 50
SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
ISSN:2331-8422