Safety-Prioritized, Reinforcement Learning-Enabled Traffic Flow Optimization in a 3D City-Wide Simulation Environment

TL;DR

This paper introduces a 3D city-wide traffic simulation with reinforcement learning that prioritizes safety, significantly reducing collisions and emissions while improving fuel efficiency, demonstrating feasibility for real-world traffic management.

Contribution

It develops a comprehensive 3D simulation environment with safety-focused reinforcement learning and custom collision modeling, advancing traffic flow optimization methods.

Findings

Over 3x reduction in serious collisions and vehicle collisions

39% improvement in fuel efficiency

88% reduction in carbon emissions

Abstract

Traffic congestion and collisions represent significant economic, environmental, and social challenges worldwide. Traditional traffic management approaches have shown limited success in addressing these complex, dynamic problems. To address the current research gaps, three potential tools are developed: a comprehensive 3D city-wide simulation environment that integrates both macroscopic and microscopic traffic dynamics; a collision model; and a reinforcement learning framework with custom reward functions prioritizing safety over efficiency. Unity game engine-based simulation is used for direct collision modeling. A custom reward enabled reinforcement learning method, proximal policy optimization (PPO) model, yields substantial improvements over baseline results, reducing the number of serious collisions, number of vehicle-vehicle collisions, and total distance travelled by over 3 times the baseline values. The model also improves fuel efficiency by 39% and reduces carbon emissions by 88%. Results establish feasibility for city-wide 3D traffic simulation applications incorporating the vision-zero safety principles of the Department of Transportation, including physics-informed, adaptable, realistic collision modeling, as well as appropriate reward modeling for real-world traffic signal light control towards reducing collisions, optimizing traffic flow and reducing greenhouse emissions.