Abstract
Traffic signal control (TSC) is an important problem that has been interested by many researchers and urban managers. Simulating and optimizing TSC for real-time control system is investigated recently with development by the Internet of things (IoT). The new model integrating Multi-agent system, geographic information system (GIS), and reinforcement learning to optimize TSC is proposed in this paper. The proposed simulation is minimizing total waiting time. Moreover, the simulation is applied into Ba Dinh ward, Hanoi, Vietnam for a case study.
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Acknowledgment
This research was partially supported by a project of Hanoi University of science and technology, Hanoi, Vietnam and IRD, UPMC Univ Paris 06 UMMISCO.
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Van Dong, H., Quoc Khanh, B., Tran Lich, N., Ngoc Anh, N.T. (2019). Integrating Multi-agent System, Geographic Information System, and Reinforcement Learning to Simulate and Optimize Traffic Signal Control. In: Unger, H., Sodsee, S., Meesad, P. (eds) Recent Advances in Information and Communication Technology 2018. IC2IT 2018. Advances in Intelligent Systems and Computing, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-319-93692-5_15
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DOI: https://doi.org/10.1007/978-3-319-93692-5_15
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