Smart Traffic Signals: Comparing MARL and Fixed-Time Strategies

TL;DR

This paper compares multi-agent reinforcement learning (MARL) and fixed-time strategies for traffic signal control, showing MARL's potential to significantly reduce vehicle wait times and improve traffic flow in simulated urban environments.

Contribution

It introduces a decentralized MARL approach for traffic signal coordination and demonstrates its effectiveness over traditional fixed-time methods in a realistic simulation.

Findings

MARL reduces average vehicle wait times

MARL improves overall traffic throughput

Statistically significant performance gains over fixed-time control

Abstract

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.