A Resilient Intelligent Traffic Signal Control Scheme for Accident Scenario at Intersections via Deep Reinforcement Learning

Zahra Zeinaly, Mahdi Sojoodi () and Sadegh Bolouki
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Zahra Zeinaly: Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran 14115-111, Iran
Mahdi Sojoodi: Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran 14115-111, Iran
Sadegh Bolouki: Department of Mechanical Engineering, Polytechnique Montréal, Montreal, QC H3T 1J4, Canada

Sustainability, 2023, vol. 15, issue 2, 1-26

Abstract: Deep reinforcement learning methods have shown promising results in the development of adaptive traffic signal controllers. Accidents, weather conditions, or special events all have the potential to abruptly alter the traffic flow in real life. The traffic light must take immediate and appropriate action based on a reasonable understanding of the environment. In this way, traffic congestion would be prevented. In this paper, we develop a reliable controller for such a highly dynamic environment and investigate the resilience of these controllers to a variety of environmental disruptions, such as accidents. In this method, the agent is provided with a complete understanding of the environment by discretizing the intersection and modifying the state space. The proposed algorithm is independent of the location and time of accidents. If the location of the accident changes, the agent does not need to be retrained. The agent is trained using deep Q-learning and experience replay. The model is evaluated in the traffic microsimulator SUMO. The simulation results demonstrate that the proposed method is effective at shortening queues when there is disruption.

Keywords: traffic signal control; accident; deep reinforcement learning; traffic safety (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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