Intelligent Traffic Light

Intelligent traffic light systems aim to optimize traffic flow and reduce congestion by dynamically adjusting signal timings based on real-time traffic conditions. Current research heavily utilizes deep reinforcement learning (DRL), employing various architectures like Deep Q-Networks (DQNs) and Proximal Policy Optimization (PPO), often incorporating multi-agent approaches and model-based meta-learning to improve efficiency and scalability. These advancements offer the potential for significant improvements in urban mobility, leading to reduced fuel consumption, emissions, and travel times, while also providing valuable insights into complex multi-agent control problems.

Papers

December 16, 2024

Artificial Intelligence in Traffic Systems
Ritwik Raj Saxena
Artificial Intelligence Autonomous Vehicle Traffic Management Traffic Management System Intelligent Traffic Intelligent Traffic Light

September 18, 2023

Deep Reinforcement Learning for the Joint Control of Traffic Light Signaling and Vehicle Speed Advice
Johannes V. S. Busch, Robert Voelckner, Peter Sossalla, Christian L. Vielhaus, Roberto Calandra, Frank H. P. Fitzek
Deep Reinforcement Learning Traffic Management System Joint Control Traffic Light Control Speed Advisory Intelligent Traffic Light

June 13, 2023

DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback
Junfan Lin, Yuying Zhu, Lingbo Liu, Yang Liu, Guanbin Li, Liang Lin
Deep Reinforcement Learning Traffic Signal Control Efficient Control Heterogeneous Traffic Traffic Control Feedback Annotation Intelligent Traffic Light

February 4, 2023

Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systems
Xiao-Yang Liu, Ming Zhu, Sem Borst, Anwar Walid
Deep Reinforcement Learning Deep Q Network Road Network Intelligent Transportation System Traffic Light Control Intelligent Traffic Light

March 8, 2022

Multi-Agent Broad Reinforcement Learning for Intelligent Traffic Light Control
Ruijie Zhu, Lulu Li, Shuning Wu, Pei Lv, Yafai Li, Mingliang Xu
Multi Agent Deep Reinforcement Learning Single Agent Reinforcement Learning Intelligent Traffic Light

December 27, 2021

Intelligent Traffic Light via Policy-based Deep Reinforcement Learning
Yue Zhu, Mingyu Cai, Chris Schwarz, Junchao Li, Shaoping Xiao
Reinforcement Learning Proximal Policy Optimization Intelligent Traffic Light

November 15, 2021

ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Control
Xingshuai Huang, Di Wu, Michael Jenkin, Benoit Boulet
Reinforcement Learning Meta Reinforcement Learning Traffic Signal Control Traffic Light Control Intelligent Traffic Light