Adaptive Traffic Signal Control

Adaptive traffic signal control (ATSC) aims to optimize traffic flow and safety by dynamically adjusting signal timings based on real-time conditions. Current research heavily utilizes reinforcement learning (RL), often employing deep neural networks like Deep Q-Networks (DQNs) and Transformers, to learn optimal control policies, addressing challenges like partial observability and multi-agent coordination in complex networks. This field is significant for improving urban mobility, reducing congestion and emissions, and enhancing intersection safety, with ongoing work focusing on robustness to cyberattacks and resource-constrained deployments.

Papers

September 16, 2024

Mitigating Partial Observability in Adaptive Traffic Signal Control with Transformers
Xiaoyu Wang, Ayal Taitler, Scott Sanner, Baher Abdulhai
Reinforcement Learning Transformer Megatron Decepticons Partial Observability Traffic Signal Control Adaptive Traffic Signal Control

August 28, 2024

Adaptive Traffic Signal Control Using Reinforcement Learning
Muhammad Tahir Rafique, Ahmed Mustafa, Hasan Sajid
Reinforcement Learning Reinforcement Learning Algorithm Traffic Scenario Traffic Signal Control Traffic Network Adaptive Traffic Signal Control

August 1, 2024

Adaptive traffic signal safety and efficiency improvement by multi objective deep reinforcement learning approach
Shahin Mirbakhsh, Mahdi Azizi
High Efficiency Traffic Signal Control Adaptive Traffic Signal Control Multi Objective Deep Reinforcement Learning

July 31, 2024

Adaptive Transit Signal Priority based on Deep Reinforcement Learning and Connected Vehicles in a Traffic Microsimulation Environment
Dickness Kwesiga, Angshuman Guin, Michael Hunter
Reinforcement Learning Deep Reinforcement Learning Traffic Management Connected Vehicle Adaptive Traffic Signal Control Microscopic Traffic Simulation

July 24, 2024

MoveLight: Enhancing Traffic Signal Control through Movement-Centric Deep Reinforcement Learning
Junqi Shao, Chenhao Zheng, Yuxuan Chen, Yucheng Huang, Rui Zhang
Traffic Signal Control Adaptive Traffic Signal Control Urban Traffic Management

May 29, 2024

Exploring the impact of traffic signal control and connected and automated vehicles on intersections safety: A deep reinforcement learning approach
Amir Hossein Karbasi, Hao Yang, Saiedeh Razavi
Global Impact Vehicle Plan Traffic Signal Control Deep Reinforcement Learning Approach Adaptive Traffic Signal Control Intersection Safety

March 11, 2024

RL-MSA: a Reinforcement Learning-based Multi-line bus Scheduling Approach
Yingzhuo Liu
Learning Based Positive Reinforcement Adaptive Traffic Signal Control Rl Msa

January 16, 2024

CycLight: learning traffic signal cooperation with a cycle-level strategy
Gengyue Han, Xiaohan Liu, Xianyue Peng, Hao Wang, Yu Han
Deep Reinforcement Learning Two Phase Adaptive Traffic Signal Control

October 19, 2023

Deep Reinforcement Learning-based Intelligent Traffic Signal Controls with Optimized CO2 emissions
Pedram Agand, Alexey Iskrov, Mo Chen
Transportation Network Adaptive Traffic Signal Control Signalized Intersection CO2 Emission Tabular Q Learning

June 5, 2023

A Novel Multi-Agent Deep RL Approach for Traffic Signal Control
Shijie Wang, Shangbo Wang
Multi Agent Deep Reinforcement Learning Traffic Signal Control DQN Agent Adaptive Traffic Signal Control Centralized Reinforcement Learning

April 20, 2023

SocialLight: Distributed Cooperation Learning towards Network-Wide Traffic Signal Control
Harsh Goel, Yifeng Zhang, Mehul Damani, Guillaume Sartoretti
Reinforcement Learning Multi Agent Reinforcement Learning Adaptive Traffic Signal Control Centralized Reinforcement Learning Cooperative Intelligent

April 5, 2023

HumanLight: Incentivizing Ridesharing via Human-centric Deep Reinforcement Learning in Traffic Signal Control
Dimitris M. Vlachogiannis, Hua Wei, Scott Moura, Jane Macfarlane
Traffic Signal Control Adaptive Traffic Signal Control Ridesharing Platform

November 26, 2022

A Critical Review of Traffic Signal Control and A Novel Unified View of Reinforcement Learning and Model Predictive Control Approaches for Adaptive Traffic Signal Control
Xiaoyu Wang, Scott Sanner, Baher Abdulhai
Reinforcement Learning Predictive Control Traffic Signal Control Unified View Control Design Transportation Network Adaptive Traffic Signal Control Control Algorithm

November 20, 2022

SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control
Wenlu Du, Junyi Ye, Jingyi Gu, Jing Li, Hua Wei, Guiling Wang
Reinforcement Learning Traffic Signal Control Residual Reinforcement Learning Adaptive Traffic Signal Control

October 31, 2022

Reinforcement Learning based Cyberattack Model for Adaptive Traffic Signal Controller in Connected Transportation Systems
Muhammad Sami Irfan, Mizanur Rahman, Travis Atkison, Sagar Dasgupta, Alexander Hainen
Reinforcement Learning Threat Model Connected Vehicle Adaptive Traffic Signal Control

October 16, 2022

Connection-Based Scheduling for Real-Time Intersection Control
Hsu-Chieh Hu, Joseph Zhou, Gregory J. Barlow, Stephen F. Smith
Intersection Scenario Adaptive Traffic Signal Control Scheduling Heuristic Traffic Signal

October 6, 2022

Lyapunov Function Consistent Adaptive Network Signal Control with Back Pressure and Reinforcement Learning
Chaolun Ma, Bruce Wang, Zihao Li, Ahmadreza Mahmoudzadeh, Yunlong Zhang
Reinforcement Learning Lyapunov Function Traffic Signal Control Adaptive Traffic Signal Control Heterogeneous Traffic Traffic Flow Theory

October 2, 2022

Economic-Driven Adaptive Traffic Signal Control
Shan Jiang, Yufei Huang, Mohsen Jafari, Mohammad Jalayer
Traffic Signal Control Adaptive Traffic Signal Control Vehicle Flow

August 1, 2022

Model-based graph reinforcement learning for inductive traffic signal control
François-Xavier Devailly, Denis Larocque, Laurent Charlin
Task Transferability Graph Reinforcement Learning Adaptive Traffic Signal Control Road Topology Dynamic Traffic Traffic Light Control

May 1, 2022

TinyLight: Adaptive Traffic Signal Control on Devices with Extremely Limited Resources
Dong Xing, Qian Zheng, Qianhui Liu, Gang Pan
Deep Reinforcement Learning Smart Device lightWeight Network Adaptive Traffic Signal Control Limited Resource

Adaptive Traffic Signal Control

Papers

Mitigating Partial Observability in Adaptive Traffic Signal Control with Transformers

Adaptive Traffic Signal Control Using Reinforcement Learning

Adaptive traffic signal safety and efficiency improvement by multi objective deep reinforcement learning approach

Adaptive Transit Signal Priority based on Deep Reinforcement Learning and Connected Vehicles in a Traffic Microsimulation Environment

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

Exploring the impact of traffic signal control and connected and automated vehicles on intersections safety: A deep reinforcement learning approach

RL-MSA: a Reinforcement Learning-based Multi-line bus Scheduling Approach

CycLight: learning traffic signal cooperation with a cycle-level strategy

Deep Reinforcement Learning-based Intelligent Traffic Signal Controls with Optimized CO2 emissions

A Novel Multi-Agent Deep RL Approach for Traffic Signal Control

SocialLight: Distributed Cooperation Learning towards Network-Wide Traffic Signal Control

HumanLight: Incentivizing Ridesharing via Human-centric Deep Reinforcement Learning in Traffic Signal Control

A Critical Review of Traffic Signal Control and A Novel Unified View of Reinforcement Learning and Model Predictive Control Approaches for Adaptive Traffic Signal Control

SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control

Reinforcement Learning based Cyberattack Model for Adaptive Traffic Signal Controller in Connected Transportation Systems

Connection-Based Scheduling for Real-Time Intersection Control

Lyapunov Function Consistent Adaptive Network Signal Control with Back Pressure and Reinforcement Learning

Economic-Driven Adaptive Traffic Signal Control

Model-based graph reinforcement learning for inductive traffic signal control

TinyLight: Adaptive Traffic Signal Control on Devices with Extremely Limited Resources