Exploring Q-Learning Optimization in Traffic Signal Timing Plan Management

dc.contributor.author	Yit, Kwong Chin
dc.contributor.author	Lai, Kuan Lee
dc.contributor.author	Nurmin Bolong
dc.contributor.author	Soo, Siang Yang
dc.contributor.author	Tze, Kin Teo (Kenneth)
dc.date.accessioned	2023-06-06T03:28:09Z
dc.date.available	2023-06-06T03:28:09Z
dc.date.issued	2011
dc.description.abstract	Traffic congestions often occur within the entire traffic network of the urban areas due to the increasing of traffic demands by the outnumbered vehicles on road. The problem may be solved by a good traffic signal timing plan, but unfortunately most of the timing plans available currently are not fully optimized based on the on spot traffic conditions. The incapability of the traffic intersections to learn from their past experiences has cost them the lack of ability to adapt into the dynamic changes of the traffic flow. The proposed Qlearning approach can manage the traffic signal timing plan more effectively via optimization of the traffic flows. Qlearning gains rewards from its past experiences including its future actions to learn from its experience and determine the best possible actions. The proposed learning algorithm shows a good valuable performance that able to improve the traffic signal timing plan for the dynamic traffic flows within a traffic network.
dc.identifier.uri	https://digitallibrary.peninsulacollege.edu.my/handle/123456789/326
dc.language.iso	en
dc.title	Exploring Q-Learning Optimization in Traffic Signal Timing Plan Management
dc.type	Article

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