Systematic Analyses of Reinforcement Learning Controllers in Signalized Urban Corridors

2026-04-02

AI Summary

This study addresses the deployment of reinforcement learning controllers in urban arterial signal networks to enhance traffic throughput. It systematically evaluates centralized, fully decentralized, and parameter-sharing decentralized multi-agent reinforcement learning strategies against the classical MaxPressure method in terms of capacity region and average travel time. The work proposes a novel, generalizable parameter-sharing decentralized architecture that enables agents to spontaneously generate coordinated "green wave" effects without explicit communication or coordination. Experimental results demonstrate that the proposed approach not only outperforms baseline methods significantly but also maintains superior performance when transferred to larger, previously unseen road networks, exhibiting both high efficiency and strong generalization capability.

Abstract

In this work, we extend our systematic capacity region perspective to multi-junction traffic networks, focussing on the special case of an urban corridor network. In particular, we train and evaluate centralized, fully decentralized, and parameter-sharing decentralized RL controllers, and compare their capacity regions and ATTs together with a classical baseline MaxPressure controller. Further, we show how the parametersharing controller may be generalised to be deployed on a larger network than it was originally trained on. In this setting, we show some initial findings that suggest that even though the junctions are not formally coordinated, traffic may self organise into `green waves'.

Problem

• Reinforcement Learning
• Traffic Signal Control
• Urban Corridor
• Capacity Region
• Decentralized Control

Innovation

• reinforcement learning
• capacity region
• parameter-sharing decentralized control
• urban corridor
• self-organized green waves