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Distributed Regret Matching Algorithm for a Dynamic Route Guidance

  • Tai-Yu Ma
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 296)

Abstract

This paper proposes a distributed self-learning algorithm based on the regret matching process in games for a dynamic route guidance. We incorporate a user’s past routing experiences and en-route traffic information into their optimal route guidance learning. The numerical study illustrates that the proposed self-guidance method can effectively reduce the travel times and delays of guided users in congested situation.

Keywords

game multi-agent distributed learning route guidance Nash Equilibrium 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.CEPS/INSTEADEsch-sur-AlzetteLuxembourg

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