Smart Traffic Signals: Comparing MARL and Fixed-Time Strategies
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Abstract (arXiv)
Urban traffic congestion, particularly at intersections, significantly affects travel time, fuel consumption, and emissions. Traditional fixed-time signal control systems often lack the adaptability to effectively manage dynamic traffic patterns. This study explores the application of multi-agent reinforcement learning (MARL) to optimize traffic signal coordination across multiple intersections within a simulated environment. A simulation was developed to model a network of interconnected intersections with randomly generated vehicle flows to reflect realistic traffic variability. A decentralized MARL controller was implemented in which each traffic signal operates as an autonomous agent, making decisions based on local observations and information from neighboring agents. Performance was evaluated against a baseline fixed-time controller using metrics such as average vehicle wait time and overall throughput. The MARL approach demonstrated statistically significant improvements, including reduced average waiting times and improved throughput. These findings suggest that MARL-based dynamic control strategies hold substantial promise to improve urban traffic management efficiency. More research is recommended to address the challenges of scalability and real-world implementation.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 59 references that are in the map. First posted on arXiv 20 May 2025.
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10 of 59 references.
References · 10
- 2019Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Controlcited by 164
- 2020IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Controlcited by 22
- 2017Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Networkcited by 16
- 2019Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimizationcited by 12
- 2021Network-wide traffic signal control optimization using a multi-agent deep reinforcement learningcited by 12
- 2022Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Methodcited by 10
- 2017Intelligent Traffic Light Control Using Distributed Multi-agent Q Learningcited by 7
- 2021A Comparative Study of Algorithms for Intelligent Traffic Signal Controlcited by 3
- 2024Multi-agent Reinforcement Traffic Signal Control based on Interpretable Influence Mechanism and Biased ReLU Approximationcited by 2
- 2023Traffic Light Control with Reinforcement Learningcited by 1
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- 2024Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion
- 2025A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control
- 2024PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal Control
- 2025Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning
- 2025Multi-Agent Reinforcement Learning in Intelligent Transportation Systems: A Comprehensive Survey
- 2025Convergence of Multiagent Learning Systems for Traffic control
- 2026A Robust and Efficient Multi-Agent Reinforcement Learning Framework for Traffic Signal Control
- 2025Toward Dependency Dynamics in Multi-Agent Reinforcement Learning for Traffic Signal Control