Browsing by Author "Min Keng Tan"

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    Agent-Based Optimization for Multiple Signalized Intersections using Q-Learning
    (Faculty of Engineering, Universiti Malaysia Sabah Kota Kinabalu, Malaysia, 2014-12-30) Yit Kwong Chin; Kiam Beng Yeo; Kenneth Tze Kin Teo; Helen Sin Ee Chuo; Min Keng Tan
    Relieving urban traffic congestion has always been an urgent call in a dynamic traffic network. The objective of this research is to control the traffic flow within a traffic network consists of multiple signalized intersections with traffic ramp. The massive traffic network problem is dealt through Q-learning actuated traffic signalization (QLTS), where the traffic phases will be monitored as immediate actions can be taken during congestion to minimize the number of vehicles in queue. QLTS is tested under two cases and has better performance than common fixed-time traffic signalization (FTS). When dealing with the ramp flow, QLTS has flexibility to change the traffic signals according to the traffic conditions and necessity.
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    Q-Learning Traffic Signal Optimization within Multiple Intersections Traffic Network
    (2012) Yit Kwong Chin; Wei Yeang Kow; Wei Leong Khong; Min Keng Tan; Kenneth Tze Kin Teo
    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. 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.