| Title | A Deep Reinforcement Learning Approach to Traffic Signal Control |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Razack, Aquib Junaid, Ajith, Vysyakh, Gupta, Rajiv |
| Conference Name | 2021 IEEE Conference on Technologies for Sustainability (SusTech) |
| Keywords | Analytical models, Conferences, control systems, coupled congestion control, Deep Learning, pubcrawl, reinforcement learning, Resiliency, Roads, Scalability, sustainable development, traffic lights control, Traffic Management, Urban areas |
| Abstract | Traffic Signal Control using Reinforcement Learning has been proved to have potential in alleviating traffic congestion in urban areas. Although research has been conducted in this field, it is still an open challenge to find an effective but low-cost solution to this problem. This paper presents multiple deep reinforcement learning-based traffic signal control systems that can help regulate the flow of traffic at intersections and then compares the results. The proposed systems are coupled with SUMO (Simulation of Urban MObility), an agent-based simulator that provides a realistic environment to explore the outcomes of the models. |
| DOI | 10.1109/SusTech51236.2021.9467450 |
| Citation Key | razack_deep_2021 |
A Deep Reinforcement Learning Approach to Traffic Signal Control