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Traditional traffic lights control system works on a fixed-time basis, which can't optimize the time of traffic lights according to the change of traffic flow. To address this issue, this paper proposes an approach based on data-driven belief rule base for smart traffic lights. The main idea of this approach is using historical traffic data to predict traffic flow, and then determine traffic lights control strategy in term of predicted traffic flow and road conditions. Utilizing the data-driven belief rule base to forecast traffic flow is proposed firstly. It is worth nothing that the data-driven belief rule base can transform data into rules directly involving neither time-consuming iterative learning procedure nor complicated rule generation mechanisms. Subsequently, a simple but efficient method for controlling traffic lights based on priority is proposed. Then some case studies are provided to illustrate the feasibility and efficiency of the proposed approach. © 2016 IEEE.
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Year: 2016
Volume: 1
Page: 460-463
Language: English
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ESI Highly Cited Papers on the List: 0 Unfold All
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30 Days PV: 2
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