Graph Attention Neural Network Using Reinforcement Learning for Mixed Traffic Control

Author

Zachary SublettFollow

Date of Award

8-2025

Degree Type

Thesis

Degree Name

Master of Science

Major

Computer Science

Major Professor

Wezi Li

Committee Members

Wezi Li, Audris Mockus, Catherine D. Schuman

Abstract

We look at the problem of unsignalized mixed traffic control in large urban areas and propose a method for topology independence that should allow for a more generalizable approach to this problem. We will utilize a Graph Attention Network (GAT) for feature extraction and a Dueling Deep Q-Network (DQN) for autonomous vehicle control. to optimize real world traffic flow data from a benchmark dataset. SUMO (Simulation of Urban Mobility) was used to conduct the experiments.

Recommended Citation

Sublett, Zachary, "Graph Attention Neural Network Using Reinforcement Learning for Mixed Traffic Control. " Master's Thesis, University of Tennessee, 2025.
https://trace.tennessee.edu/utk_gradthes/14505