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

TL;DR

This paper introduces a decentralized multi-agent reinforcement learning framework for large-scale mixed traffic control, addressing the challenge of managing complex networks with both human-driven and robotic vehicles under varying origin-destination patterns.

Contribution

It presents a novel RL-based approach for large-scale mixed traffic management that considers real-world OD variations and intersection control methods.

Findings

Effective reduction in vehicle waiting times in simulations

Demonstrates feasibility of large-scale multi-agent RL for traffic control

Addresses the impact of OD pattern variability on traffic management

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?