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Distributed Q-learning Controller for a Multi-Intersection Traffic Network

  • Sahar AraghiEmail author
  • Abbas Khosravi
  • Douglas Creighton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9489)

Abstract

This paper proposes a Q-learning based controller for a network of multi intersections. According to the increasing amount of traffic congestion in modern cities, using an efficient control system is demanding. The proposed controller designed to adjust the green time for traffic signals by the aim of reducing the vehicles’ travel delay time in a multi-intersection network. The designed system is a distributed traffic timing control model, applies individual controller for each intersection. Each controller adjusts its own intersection’s congestion while attempt to reduce the travel delay time in whole traffic network. The results of experiments indicate the satisfied efficiency of the developed distributed Q-learning controller.

Keywords

Traffic Congestion Traffic Signal Green Time Reinforcement Learning Method Traffic Control System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sahar Araghi
    • 1
    Email author
  • Abbas Khosravi
    • 1
  • Douglas Creighton
    • 1
  1. 1.Center for Intelligent Systems Research (CISR)Deakin UniversityWaurn PondsAustralia

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