Q-Learning Traffic Signal Optimization within Multiple Intersections Traffic Network

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dc.contributor.authorYit Kwong Chin
dc.contributor.authorWei Yeang Kow
dc.contributor.authorWei Leong Khong
dc.contributor.authorMin Keng Tan
dc.contributor.authorKenneth Tze Kin Teo
dc.date.accessioned2023-06-06T07:21:49Z
dc.date.available2023-06-06T07:21:49Z
dc.date.issued2012
dc.descriptionModelling, Simulation & Computing Laboratory, Material & Mineral Research Unit School of Engineering and Information Technology Universiti Malaysia Sabah Kota Kinabalu, Malaysia msclab@ums.edu.my, ktkteo@ieee.org Keywords – Reinforcement learning; Q-Learning; Traffic networks; Traffic signal timing plan management; Multi-agents systems
dc.description.abstractTraffic 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. QLearning 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.
dc.identifier.urihttps://digitallibrary.peninsulacollege.edu.my/handle/123456789/336
dc.language.isoen
dc.titleQ-Learning Traffic Signal Optimization within Multiple Intersections Traffic Network
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