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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Oubenaalla, Youness
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Computer Science & IT Engineering

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Enhancing traffic flow through multi-agent reinforcement learning for adaptive traffic light duration control Faqir, Nada; Boumhidi, Jaouad; Loqman, Chakir; Oubenaalla, Youness
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp500-515

Abstract

This study addresses urban traffic congestion through deep learning for traffic signal control (TSC). In contrast to previous research on single traffic light controllers, our approach is tailored to the TSC challenge within a network of two intersections. Employing convolutional neural networks (CNN) in a deep Q-network (DQN) model, our method adopts centralized training and distributed execution (CTDE) within a multi-agent reinforcement learning (MARL) framework. The primary aim is to optimize traffic flow in a twointersection setting, comparing outcomes with baseline strategies. Overcoming scalability and partial observability challenges, our approach demonstrates the efficacy of the CTDE-based MARL framework. Experiments using urban mobility simulation (SUMO) exhibit a 68% performance enhancement over basic traffic light control systems, validating our solution across diverse scenarios. While the study focuses on two intersections, it hints at broader applications in complex settings, presenting a promising avenue for mitigating urban traffic congestion. The research underscores the importance of collaboration within MARL frameworks, contributing significantly to the advancement of adaptive traffic signal control (ATSC) in urban environments for sustainable transportation solutions.
Co-Authors Boumhidi, Jaouad Faqir, Nada Loqman, Chakir