Heterogeneous Mixed Traffic Control and Coordination

TL;DR

This paper demonstrates that integrating robot vehicles with reinforcement learning at heterogeneous intersections significantly reduces waiting times and improves traffic flow, despite some environmental trade-offs.

Contribution

It introduces a novel approach using RL and real-world data to optimize mixed traffic control with high RV penetration rates at complex intersections.

Findings

Waiting times reduced by up to 86% and 91%.

Less frequent vehicles benefit most from control strategies.

Traffic efficiency improves despite increased emissions.

Abstract

Urban intersections with diverse vehicle types, from small cars to large semi-trailers, pose significant challenges for traffic control. This study explores how robot vehicles (RVs) can enhance heterogeneous traffic flow, particularly at unsignalized intersections where traditional methods fail during power outages. Using reinforcement learning (RL) and real-world data, we simulate mixed traffic at complex intersections with RV penetration rates ranging from 10% to 90%. Results show that average waiting times drop by up to 86% and 91% compared to signalized and unsignalized intersections, respectively. We observe a "rarity advantage," where less frequent vehicles benefit the most (up to 87%). Although CO2 emissions and fuel consumption increase with RV penetration, they remain well below those of traditional signalized traffic. Decreased space headways also indicate more efficient road usage. These findings highlight RVs' potential to improve traffic efficiency and reduce environmental impact in complex, heterogeneous settings.