MoveLight: Enhancing Traffic Signal Control through Movement-Centric Deep Reinforcement Learning

TL;DR

MoveLight leverages movement-centric deep reinforcement learning with lane-level control to optimize traffic signals, significantly reducing congestion and improving traffic flow in urban environments.

Contribution

The paper introduces MoveLight, a novel deep reinforcement learning system that employs movement-centric data and lane-level control for adaptive traffic signal management.

Findings

Significant reduction in queue length and delay.

Improved traffic throughput across multiple levels.

Effective scalability demonstrated on real-world datasets.

Abstract

This paper introduces MoveLight, a novel traffic signal control system that enhances urban traffic management through movement-centric deep reinforcement learning. By leveraging detailed real-time data and advanced machine learning techniques, MoveLight overcomes the limitations of traditional traffic signal control methods. It employs a lane-level control approach using the FRAP algorithm to achieve dynamic and adaptive traffic signal control, optimizing traffic flow, reducing congestion, and improving overall efficiency. Our research demonstrates the scalability and effectiveness of MoveLight across single intersections, arterial roads, and network levels. Experimental results using real-world datasets from Cologne and Hangzhou show significant improvements in metrics such as queue length, delay, and throughput compared to existing methods. This study highlights the transformative potential of deep reinforcement learning in intelligent traffic signal control, setting a new standard for sustainable and efficient urban transportation systems.