Single-Agent Reinforcement Learning Model Shows Promise for Regional Traffic Signal Coordination

Global: Single-Agent RL Model Shows Promise for Regional Traffic Signal Coordination

Researchers have introduced a single-agent reinforcement learning (RL) framework designed to manage traffic signals across extensive urban regions, aiming to ease congestion and cut total travel time. The approach was tested using the SUMO traffic simulation platform, where it outperformed a baseline scenario with static signal timings, demonstrating notable reductions in queue lengths and average travel times.

Urban Congestion Context

Rapid urbanization and increasing vehicle ownership have intensified traffic congestion worldwide, adversely affecting residents’ quality of life, environmental conditions, and economic productivity. Conventional traffic signal control methods often struggle to adapt to the dynamic demands of large metropolitan areas.

Model Architecture

Unlike traditional multi‑agent systems that assign independent controllers to each intersection, the proposed model employs a single agent that oversees the entire region. This centralization enables coordinated adjustments to signal phases, allowing the system to address traffic patterns that span multiple intersections.

State and Action Spaces

The state representation captures the current congestion level by recording queue lengths on each road link and the active signal phase at every intersection. The action space operates in two steps: first, the agent selects an intersection, and second, it modifies the phase split for that location, thereby influencing traffic flow downstream.

Reward Functions

Two distinct reward structures were crafted. One rewards the agent for reducing overall congestion, while the other emphasizes minimizing total travel time while still accounting for congestion levels. By combining these objectives, the agent can balance short‑term queue reductions with longer‑term efficiency gains.

Simulation and Results

Experiments conducted in SUMO compared the RL‑driven strategy against a control case with no signal‑timing adjustments. The findings indicated a substantial decrease in vehicle queue lengths across tested scenarios. When both reward components were applied, the average travel time experienced a marked decline, suggesting the model’s capacity to improve traffic conditions holistically.

Implications for Urban Traffic Management

The study provides a proof‑of‑concept for applying single‑agent RL to large‑scale traffic signal coordination, offering a scalable alternative to decentralized methods. If integrated into real‑world traffic management systems, such technology could contribute to smoother flows, lower emissions, and enhanced commuter experiences.

Future Directions

Further research may explore real‑time data integration, robustness to unexpected incidents, and field trials in live traffic environments to validate the model’s performance beyond simulated settings.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.