A Survey on Traffic Signal Control Methods

Abstract

Traffic signal control is an important and challenging real-world problem, which aims to minimize the travel time of vehicles by coordinating their movements at the road intersections. Current traffic signal control systems in use still rely heavily on oversimplified information and rule-based methods, although we now have richer data, more computing power and advanced methods to drive the development of intelligent transportation. With the growing interest in intelligent transportation using machine learning methods like reinforcement learning, this survey covers the widely acknowledged transportation approaches and a comprehensive list of recent literature on reinforcement for traffic signal control. We hope this survey can foster interdisciplinary research on this important topic.

arXiv comment32 pages

References in corpus (5)

• Learning Multiagent Communication with Backpropagation
• Using a Deep Reinforcement Learning Agent for Traffic Signal Control
• Deep Deterministic Policy Gradient for Urban Traffic Light Control
• Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization
• Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control

Cited by in corpus (17)

• Traffic Signal Optimization on a Square Lattice with Quantum Annealing
• EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System
• GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning
• Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control
• UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Control
• Quantifying the Impact of Non-Stationarity in Reinforcement Learning-Based Traffic Signal Control
• A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN Prediction
• Traffic Signal Cycle Control with Centralized Critic and Decentralized Actors under Varying Intervention Frequencies
• Adaptive Traffic Control with Deep Reinforcement Learning: Towards State-of-the-art and Beyond
• Assessment of Reward Functions in Reinforcement Learning for Multi-Modal Urban Traffic Control under Real-World limitations
• Bayesian Meta-reinforcement Learning for Traffic Signal Control
• Back to Basics: Deep Reinforcement Learning in Traffic Signal Control
• Adaptive Coordination Offsets for Signalized Arterial Intersections using Deep Reinforcement Learning
• Learning to Simulate on Sparse Trajectory Data
• DQN Control Solution for KDD Cup 2021 City Brain Challenge
• A Methodology for the Development of RL-Based Adaptive Traffic Signal Controllers
• Batch-Augmented Multi-Agent Reinforcement Learning for Efficient Traffic Signal Optimization