| Title | Deep Q-learning Approach for Congestion Problem In Smart Cities |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Faqir, Nada, En-Nahnahi, Noureddine, Boumhidi, Jaouad |
| Conference Name | 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS) |
| Keywords | Adaptive systems, convolutional neural networks, coupled congestion control, delays, Green products, pubcrawl, reinforcement learning, Resiliency, Roads, Scalability, Tools, Traffic Control, traffic optimization, Turning, urban mobility |
| 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. |
| DOI | 10.1109/ICDS50568.2020.9268709 |
| Citation Key | faqir_deep_2020 |
Deep Q-learning Approach for Congestion Problem In Smart Cities