Distributed regret matching algorithm for a dynamic route guidance

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

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.
Original languageEnglish
Title of host publicationAgent and Multi-Agent Systems: Technologies and Applications
Subtitle of host publicationAdvances in Intelligent Systems and Computing
PublisherSpringer
Pages107-116
Number of pages10
ISBN (Print)978-3-319-07650-8
DOIs
Publication statusPublished - 2014

Publication series

NameAdvances in Intelligent Systems and Computing book series (AISC)
Volume296

Keywords

  • Nash equilibrium
  • distributed learning
  • game
  • multiagent
  • route guidance

Cite this