Origin-Destination Pattern Effects on Large-Scale Mixed Traffic Control via Multi-Agent Reinforcement Learning

2025/05/19 by Fan, Muyang, Songyang Liu, Liu, Songyang +4 · 5 citations
Social Sciences · #Computers and Society (cs.CY) #FOS: Computer and information sciences #Multiagent Systems (cs.MA) #Transportation Planning and Optimization

paper · pdf · doi:10.48550/arxiv.2505.13543

openalex publication_date 2025/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Traffic congestion remains a major challenge for modern urban transportation, diminishing both efficiency and quality of life. While autonomous driving technologies and reinforcement learning (RL) have shown promise for improving traffic control, most prior work has focused on small-scale networks or isolated intersections. Large-scale mixed traffic control, involving both human-driven and robotic vehicles, remains underexplored. In this study, we propose a decentralized multi-agent reinforcement learning framework for managing large-scale mixed traffic networks, where intersections are controlled either by traditional traffic signals or by robotic vehicles. We evaluate our approach on a real-world network of 14 intersections in Colorado Springs, Colorado, USA, using average vehicle waiting time as the primary measure of traffic efficiency. We are exploring a problem that has not been sufficiently addressed: Is large-scale Multi-Agent Traffic Control (MTC) still feasible when facing time-varying Origin-Destination (OD) patterns?

Citations

• Heterogeneous Mixed Traffic Control and Coordination
• CARL: Congestion-Aware Reinforcement Learning for Imitation-based Perturbations in Mixed Traffic Control
• LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic Simulation
• EnduRL: Enhancing Safety, Stability, and Efficiency of Mixed Traffic Under Real-World Perturbations Via Reinforcement Learning
• Large-scale Mixed Traffic Control Using Dynamic Vehicle Routing and Privacy-Preserving Crowdsourcing
• Can ChatGPT Enable ITS? The Case of Mixed Traffic Control via Reinforcement Learning
• Mixed Traffic Control and Coordination from Pixels
• Multi-Agent Broad Reinforcement Learning for Intelligent Traffic Light Control
• ADAPS: Autonomous Driving Via Principled Simulations
• Using Reinforcement Learning with Partial Vehicle Detection for Intelligent Traffic Signal Control
• Rainbow: Combining Improvements in Deep Reinforcement Learning
• Congested Traffic States in Empirical Observations and Microscopic Simulations
• Analyzing Emissions and Energy Efficiency at Unsignalized Real-world Intersections Under Mixed Traffic Control

Cited by

• Learning to Design City-scale Transit Routes
• Elevation Aware 2D/3D Co-simulation Framework for Large-scale Traffic Flow and High-fidelity Vehicle Dynamics
• Multi-Objective Reinforcement Learning for Large-Scale Mixed Traffic Control
• Thinking While Driving: A Concurrent Framework for Real-Time, LLM-Based Adaptive Routing
• Analyzing Collision Rates in Large-Scale Mixed Traffic Control via Multi-Agent Reinforcement Learning

Related

• Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning
• Neighbor-Aware Reinforcement Learning for Mixed Traffic Optimization in Large-scale Networks
• Robust Single-Agent Reinforcement Learning for Regional Traffic Signal Control Under Demand Fluctuations
• Smart Traffic Signals: Comparing MARL and Fixed-Time Strategies
• Analyzing Behaviors of Mixed Traffic via Reinforcement Learning at Unsignalized Intersections