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Title	Deep Q-Learning Approach For Congestion Problem In Smart Cities
ID_Doc	18026
Authors	Faqir N.; En-Nahnahi N.; Boumhidi J.
Year	2020
Published	4th International Conference on Intelligent Computing in Data Sciences, ICDS 2020
DOI	http://dx.doi.org/10.1109/ICDS50568.2020.9268709
Abstract	Traffic congestion is a critical problem in urban area. In this study, our objective is the control of traffic lights in an urban environment, in order to avoid traffic jams and optimize vehicle traffic; we aim to minimize the total waiting time. Our system is based on a new paradigm, which is deep reinforcement learning; it can automatically learn all the useful characteristics of traffic data and develop a strategy optimizing adaptive traffic light control. Our system is coupled to a microscopic simulator based on agents (Simulation of Urban MObility-SUMO) providing a synthetic but realistic environment in which the exploration of the results of potential regulatory actions can be carried out. © 2020 IEEE.
Author Keywords	Adaptive systems; Traffic control; traffic optimization; urban mobility

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