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Smart city article details

Title	Intelligent Traffic Signal Control System Using Deep Q-Network
ID_Doc	32620
Authors	Joo H.; Lim Y.
Year	2021
Published	Proceedings of the 3rd IEEE Eurasia Conference on IOT, Communication and Engineering 2021, ECICE 2021
DOI	http://dx.doi.org/10.1109/ECICE52819.2021.9645679
Abstract	Traffic congestion is one of the common urban problems caused by increased traffic. Traffic congestion accelerates environmental pollution by wasting drivers' time and fuel and generating more fumes. Therefore, traffic congestion is an important issue to be solved. Currently, as technologies develop, a smart city that efficiently manages data information collected is in the spotlight. The smart transportation system utilizes the infrastructure and network built in the smart city to analyze traffic flow and control traffic in real-time. Accordingly, traffic congestion can be effectively alleviated. This paper proposes a smart traffic signal control system using a Deep Q-network (DQN), a type of reinforcement learning. The proposed algorithm distributes the optimal green signal time by collecting and learning information about the intersection situation. The proposed algorithm is designed to improve the performance in terms of throughput. As a result, the number of waiting vehicles also decreased. To validate the algorithm, we evaluate the performance in various traffic scenarios. © 2021 IEEE.
Author Keywords	deep Q-network; intelligent traffic signal control; reinforcement learning; single intersection; throughput

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