Congestion Mitigation

Congestion mitigation research aims to optimize traffic flow and resource allocation to reduce delays and improve efficiency across various systems, from road networks to power grids. Current efforts focus on developing intelligent control strategies using reinforcement learning, graph neural networks, and large language models to coordinate autonomous vehicles, traffic signals, and even energy distribution. These advancements leverage both simulation and real-world data to improve upon traditional rule-based approaches, offering potential for significant improvements in transportation, energy management, and overall societal well-being. The integration of human factors and personalized approaches is also a growing area of focus, aiming to create more user-friendly and effective congestion-reducing systems.

Papers

October 12, 2024

LSTM-Based Proactive Congestion Management for Internet of Vehicle Networks
Aly Sabri Abdalla, Ahmad Al-Kabbany, Ehab F. Badran, Vuk Marojevic
Network Programming Vehicle Plan Internet of Vehicle Grid Congestion Traffic Congestion Congestion Mitigation

June 30, 2024

Cooperative Advisory Residual Policies for Congestion Mitigation
Aamir Hasan, Neeloy Chakraborty, Haonan Chen, Jung-Hoon Cho, Cathy Wu, Katherine Driggs-Campbell
Autonomous Vehicle Driver Behavior Congestion Mitigation Vehicle Fleet

May 31, 2024

Congestion-Aware Path Re-routing Strategy for Dense Urban Airspace
Sajid Ahamed Mohammed Abdul, Prathyush P Menon, Debasish Ghose
Low Altitude Airspace Congestion Mitigation Urban Airspace Aircraft System Traffic Management

March 13, 2024

LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments
Maonan Wang, Aoyu Pang, Yuheng Kan, Man-On Pun, Chung Shue Chen, Bo Huang
Language Model Traffic Signal Control Traffic Management Urban Traffic Congestion Mitigation Real Time Traffic

October 17, 2023

Combat Urban Congestion via Collaboration: Heterogeneous GNN-based MARL for Coordinated Platooning and Traffic Signal Control
Xianyue Peng, Hang Gao, Hao Wang, H. Michael Zhang
Reinforcement Learning Multi Agent Reinforcement Learning Traffic Signal Control Traffic Flow Platoon Control Heterogeneous Setting Congestion Mitigation Traffic Flow Theory

August 1, 2023

PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems
Aamir Hasan, Neeloy Chakraborty, Haonan Chen, Jung-Hoon Cho, Cathy Wu, Katherine Driggs-Campbell
Driver Behavior Intelligent Driving Congestion Mitigation Driver Model Residual Policy

May 16, 2023

Deep Reinforcement Learning to Maximize Arterial Usage during Extreme Congestion
Ashutosh Dutta, Milan Jain, Arif Khan, Arun Sathanur
Deep Reinforcement Learning Traffic Congestion Congestion Mitigation

February 17, 2023

Towards Co-operative Congestion Mitigation
Aamir Hasan, Neeloy Chakraborty, Cathy Wu, Katherine Driggs-Campbell
Simulation Environment Driving Policy Traffic Congestion Congestion Mitigation

February 15, 2023

Reinforcement Learning Based Power Grid Day-Ahead Planning and AI-Assisted Control
Anton R. Fuxjäger, Kristian Kozak, Matthias Dorfer, Patrick M. Blies, Marcel Wasserer
External Control Power Grid Power System Congestion Mitigation

February 6, 2023

Traffic Shaping and Hysteresis Mitigation Using Deep Reinforcement Learning in a Connected Driving Environment
Rami Ammourah, Alireza Talebpour
Reinforcement Learning Deep Reinforcement Learning Multi Agent Deep Reinforcement Learning Connected Vehicle Hysteresis Compensation Congestion Mitigation Traffic Shaping

November 10, 2022

Robust N-1 secure HV Grid Flexibility Estimation for TSO-DSO coordinated Congestion Management with Deep Reinforcement Learning
Zhenqi Wang, Sebastian Wende-von Berg, Martin Braun
Deep Reinforcement Learning Power Grid Optimal Power Flow Grid Congestion Congestion Mitigation

October 28, 2022

Credit-Based Congestion Pricing: Equilibrium Properties and Optimal Scheme Design
Devansh Jalota, Jessica Lazarus, Alexandre Bayen, Marco Pavone
Optimal Design Equilibrium State Congestion Game Congestion Mitigation Congestion Pricing

June 28, 2022

Learning energy-efficient driving behaviors by imitating experts
Abdul Rahman Kreidieh, Zhe Fu, Alexandre M. Bayen
LeArning Abstract Imitation Learning Expert Knowledge Non Local Dense Traffic Congestion Mitigation Efficient Driving

June 5, 2022

Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approach
Xiaohui Liu, Sean Qian, Hock-Hai Teo, Wei Ma
Causal Inference Estimation Task Traffic Flow Congestion Mitigation Urban Road Network

May 2, 2022

Coordination-free Multi-robot Path Planning for Congestion Reduction Using Topological Reasoning
Xiaolong Wang, Alp Sahin, Subhrajit Bhattacharya
Autonomous Vehicle Multi Robot Robot Communication Replanning Strategy Congestion Mitigation Topology Reasoning Communication Free

December 3, 2021

Learning a Robust Multiagent Driving Policy for Traffic Congestion Reduction
Yulin Zhang, William Macke, Jiaxun Cui, Daniel Urieli, Peter Stone
LeArning Abstract Autonomous Vehicle Traffic Flow Traffic Network Congestion Mitigation Multi Agent Autonomous Driving

Congestion Mitigation

Papers

LSTM-Based Proactive Congestion Management for Internet of Vehicle Networks

Cooperative Advisory Residual Policies for Congestion Mitigation

Congestion-Aware Path Re-routing Strategy for Dense Urban Airspace

LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments

Combat Urban Congestion via Collaboration: Heterogeneous GNN-based MARL for Coordinated Platooning and Traffic Signal Control

PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems

Deep Reinforcement Learning to Maximize Arterial Usage during Extreme Congestion

Towards Co-operative Congestion Mitigation

Reinforcement Learning Based Power Grid Day-Ahead Planning and AI-Assisted Control

Traffic Shaping and Hysteresis Mitigation Using Deep Reinforcement Learning in a Connected Driving Environment

Robust N-1 secure HV Grid Flexibility Estimation for TSO-DSO coordinated Congestion Management with Deep Reinforcement Learning

Credit-Based Congestion Pricing: Equilibrium Properties and Optimal Scheme Design

Learning energy-efficient driving behaviors by imitating experts

Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approach

Coordination-free Multi-robot Path Planning for Congestion Reduction Using Topological Reasoning

Learning a Robust Multiagent Driving Policy for Traffic Congestion Reduction