Study of Reinforcement Learning Based Dynamic Traffic Control Mechanism

  • Zheng Zhang
  • Seung Jun Baek
  • Duck Jin Lee
  • Kil To Chong
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 240)


A traffic signal control mechanism is proposed to improve the dynamic response performance of a traffic flow control system in an urban area. The necessary sensor networks are installed in the roads and on the roadside upon which reinforcement learning is adopted as the core algorithm for this mechanism. A traffic policy can be planned online according to the updated situations on the roads based on all the information from the vehicles and the roads. The optimum intersection signals can be learned automatically online. An intersection control system is studied as an example of the mechanism using Q-learning based algorithm and simulation results showed that the proposed mechanism can improve traffic efficiently more than a traditional signaling system.


Intelligent transportation system Cooperative vehicle-highway systems Reinforcement learning Traffic control mechanism Intersection signal control 



This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2012-038978) and (No. 2012-0002434).


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Copyright information

© Springer Science+Business Media Dordrecht(Outside the USA) 2013

Authors and Affiliations

  • Zheng Zhang
    • 1
  • Seung Jun Baek
    • 2
  • Duck Jin Lee
    • 3
  • Kil To Chong
    • 2
    • 4
  1. 1.Department of Mechanical EngineeringXian Jiaotong UniversityXianPeoples Republic of China
  2. 2.Department of Electronics EngineeringJeonbuk National UniversityJeonjuRepublic of Korea
  3. 3.Department of Mechanical EngineeringJeonbuk National UniversityJeonjuRepublic of Korea
  4. 4.Advanced Research Center for Electronics and InformationJeonbuk National UniversityJeonjuRepublic of Korea

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