Q-Learning traffic signal optimization within multiple intersections traffic network

Yit Kwong Chin, Wei Yeang Kow, Wei Leong Khong, Min Keng Tan, Kenneth Tze Kin Teo

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

19 Citations (Scopus)


Traffic flow optimization within traffic networks has been approached through different kinds of methods. One of the methods is to reconfigure the traffic signal timing plan. However, dynamic characteristic of the traffic flow is not able to be resolved by the conventional traffic signal timing plan management. As a result, traffic congestion still remains as an unsolved problem. Thus, in this study, artificial intelligence algorithm has been introduced in the traffic signal timing plan to enable the traffic management systems' learning ability. Q-Learning algorithm acts as the learning mechanism for traffic light intersections to release itself from traffic congestions situation. Adjacent traffic light intersections will work independently and yet cooperate with each other to a common goal of ensuring the fluency of the traffic flows within traffic network. The experimental results show that the Q-Learning algorithm is able to learn from the dynamic traffic flow and optimized the traffic flow.

Original languageEnglish
Title of host publicationProceedings - UKSim-AMSS 6th European Modelling Symposium, EMS 2012
Number of pages6
Publication statusPublished - 2012
Externally publishedYes
EventUKSim-AMSS European Modelling Symposium on Computer Modelling and Simulation (EMS) 2012 - Malta, Malta
Duration: 14 Nov 201216 Nov 2012
Conference number: 6th
https://ieeexplore.ieee.org/xpl/conhome/6409604/proceeding (Proceedings)


ConferenceUKSim-AMSS European Modelling Symposium on Computer Modelling and Simulation (EMS) 2012
Abbreviated titleEMS 2012
Internet address


  • Multi-agents systems
  • Q-Learning
  • Reinforcement learning
  • Traffic networks
  • Traffic signal timing plan management

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