Study of reinforcement learning based dynamic traffic control mechanism

Abstract

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