Abstract
The abstract should summarize the contents of the paper in short terms, i.e. 150–250 words. With the development of modern urban traffic, intersection signal control has become a significant research problem to relieve vehicle congestion. The reinforcement learning algorithm is an essential means to solve complex decision-making problems. Applying reinforcement learning technology to intersection signal control is an insight solution. This paper takes Wuhan Optical Valley special roundabout as an example and presents a Markov decision model suitable for special roundabouts. The incentive mechanism of reinforcement learning is used to adjust the right of way. When the traffic congestion of the green phase is less than the average traffic congestion of the intersection, the green light time is shortened. Otherwise, the average queuing time of vehicles at the intersection will be increased. Simulation results show that this method is superior to the traditional signal switching strategy, and the effectiveness of the model is verified.
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Acknowledgment
This research is supported by Major projects of Technological Innovation in Hubei Province, China (No. 2019ABA101).
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Hu, L., Xu, S., Li, X., Niu, Y. (2022). Traffic Signal Switching Strategy Based on Reinforcement Learning Algorithm. In: Zhang, Z. (eds) 2021 6th International Conference on Intelligent Transportation Engineering (ICITE 2021). ICITE 2021. Lecture Notes in Electrical Engineering, vol 901. Springer, Singapore. https://doi.org/10.1007/978-981-19-2259-6_102
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DOI: https://doi.org/10.1007/978-981-19-2259-6_102
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