mlsanityData to 14 Oct 2026

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

arXiv 2407.17303 ↗First posted 24 Jul 2024cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 14 references that are in the map. First posted on arXiv 24 Jul 2024.

Builds on

6 of 14 references.

References · 6

  1. 2019CoLight: Learning Network-level Cooperation for Traffic Signal Controlcited by 138
  2. 2019Learning Phase Competition for Traffic Signal Controlcited by 87
  3. 2019A Survey on Traffic Signal Control Methodscited by 79
  4. 2018Deep Reinforcement Learning for Traffic Light Control in Vehicular Networkscited by 52
  5. 2017Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Networkcited by 16
  6. 2018Distributed traffic light control at uncoupled intersections with real-world topology by deep reinforcement learningcited by 1

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  1. 2024MTLight: Efficient Multi-Task Reinforcement Learning for Traffic Signal Control
  2. 2026Balancing Efficiency and Fairness in Traffic Light Control through Deep Reinforcement Learning
  3. 2024Applying Reinforcement Learning to Optimize Traffic Light Cycles
  4. 2024Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion
  5. 2024X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learner
  6. 2024MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctions
  7. 2026LightSim: A Lightweight Cell Transmission Model Simulator for Traffic Signal Control Research
  8. 2023Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL Approach