Study of Reinforcement Learning Based Dynamic Traffic Control Mechanism
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.
KeywordsIntelligent 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).
- 2.Choy MC, Srinivasan D, Cheu RL (2003) Cooperative, hybrid agent architecture for real-time traffic signal control. IEEE Trans Syst Man Cybern Part A Syst Hum 33(5):597–607Google Scholar
- 3.Liu G, Zhai R, Pei Y (2007) A calculating method of intersection delay under signal control. In: Proceedings of the 2007 IEEE intelligent transportation systems conference, Seattle, pp 1114–1119Google Scholar
- 4.Bao W, Chen Q, Xu X (2006) An adaptive traffic signal timing scheme for bus priority at isolated intersection. In: Proceedings of the 6th world congress on intelligent control and automation, Dalian, pp 8712–8716Google Scholar
- 5.Srinivasan D, Choy MC (2006) Cooperative multi-agent system for coordinated traffic signal control. IEE Proc Intell Transp Syst 153(1):41–50Google Scholar
- 6.Lee JH, Lee-Kwang H (1999) Distributed and cooperative fuzzy controllers for traffic intersections group. IEEE Trans Syst Man Cybern C Appl Rev 29:263–271Google Scholar
- 7.Mitchell TM (1997) Machine learning. McGraw-Hill, New York. ISBN: 0070428077Google Scholar
- 8.D’Ambrogio A et al (2008) Simulation model building of traffic intersections. Simul Model Pract TheoryGoogle Scholar