# Smart Traffic Signals: Comparing MARL and Fixed-Time Strategies

arXiv: https://arxiv.org/abs/2505.14544 · HTML: https://mlsanity.com/paper/2505.14544/

## 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.

## Builds on (most cited first, 10 of 10)

- [Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control](https://mlsanity.com/paper/1903.04527/) (2019)
- [IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control](https://mlsanity.com/paper/2003.05738/) (2020)
- [Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network](https://mlsanity.com/paper/1705.02755/) (2017)
- [Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization](https://mlsanity.com/paper/1909.10651/) (2019)
- [Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning](https://mlsanity.com/paper/2104.09936/) (2021)
- [Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method](https://mlsanity.com/paper/2204.12190/) (2022)
- [Intelligent Traffic Light Control Using Distributed Multi-agent Q Learning](https://mlsanity.com/paper/1711.10941/) (2017)
- [A Comparative Study of Algorithms for Intelligent Traffic Signal Control](https://mlsanity.com/paper/2109.00937/) (2021)
- [Multi-agent Reinforcement Traffic Signal Control based on Interpretable Influence Mechanism and Biased ReLU Approximation](https://mlsanity.com/paper/2403.13639/) (2024)
- [Traffic Light Control with Reinforcement Learning](https://mlsanity.com/paper/2308.14295/) (2023)

## 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.