Reinforcement Learning for Traffic Signal Control: Comparison with Commercial Systems

TL;DR

This paper compares reinforcement learning-based traffic signal control systems with commercial controllers, demonstrating RL's potential to reduce delays and queue lengths in various traffic scenarios.

Contribution

It introduces three RL architectures for traffic signal control and benchmarks them against existing commercial systems, providing a clear comparison and pseudo-code implementations.

Findings

RL systems achieve significantly lower delays than commercial controllers.

RL approaches reduce average queue lengths across different scenarios.

Performance improvements are consistent and notable.

Abstract

Recently, Intelligent Transportation Systems are leveraging the power of increased sensory coverage and computing power to deliver data-intensive solutions achieving higher levels of performance than traditional systems. Within Traffic Signal Control (TSC), this has allowed the emergence of Machine Learning (ML) based systems. Among this group, Reinforcement Learning (RL) approaches have performed particularly well. Given the lack of industry standards in ML for TSC, literature exploring RL often lacks comparison against commercially available systems and straightforward formulations of how the agents operate. Here we attempt to bridge that gap. We propose three different architectures for TSC RL agents and compare them against the currently used commercial systems MOVA, SurTrac and Cyclic controllers and provide pseudo-code for them. The agents use variations of Deep Q-Learning and Actor Critic, using states and rewards based on queue lengths. Their performance is compared in across different map scenarios with variable demand, assessing them in terms of the global delay and average queue length. We find that the RL-based systems can significantly and consistently achieve lower delays when compared with existing commercial systems.