International Journal For Multidisciplinary Research

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A Review of Deep Reinforcement Learning for Traffic Signal Control

Author(s) Mahendralal Prajapati, Alok Kumar Upadhyay, Dr. Harshali Patil, Dr. Jyotshna Dongradive
Country India
Abstract Traffic signal control plays a vital role in effectively managing traffic flow and alleviating congestion in urban areas. Traditional methods for controlling traffic signals often rely on fixed timing plans or predefined algorithms, which may not be adaptable to changing traffic conditions. Reinforcement Learning is gaining traction as a favored data-centric method for adapting traffic signal control in intricate urban traffic networks. This article represents a conceptual review of recent studies and techniques that showcase the effectiveness of Deep Reinforcement Learning (DRL) in enhancing the performance of traffic signal control. These improvements include reducing travel time, fuel consumption, and emissions. Additionally, we will delve into different algorithms and learning systems explored in research papers, such as multi-agent reinforcement learning and Deep Q Networks (DQN).
Keywords Deep reinforcement learning, Deep Q-Network (DQN), Intelligent traffic-control system, Adaptive traffic signal control, multi-agent reinforcement learning, Artificial intelligence.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-01-06
Cite This A Review of Deep Reinforcement Learning for Traffic Signal Control - Mahendralal Prajapati, Alok Kumar Upadhyay, Dr. Harshali Patil, Dr. Jyotshna Dongradive - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.11650
DOI https://doi.org/10.36948/ijfmr.2024.v06i01.11650
Short DOI https://doi.org/gtdr7q
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