Liang, Yu; Wu, Dalei
College of Engineering and Computer Science
University of Tennessee at Chattanooga
Place of Publication
Traffic congestion reduces productivity and harms the environment. Enhancing traffic signal control and public transportation are effective solutions. However, prior research has limitations stemming from the absence of real-time reliable data. Recent computer vision systems have made collecting traffic data easier. This thesis explores leveraging these data sources to enhance existing traffic signal controls (TSCs) and citywide transit simulations. For TSC, a comprehensive framework that facilitates rapid prototyping of reinforcement learning (RL) and an automatic feature engineering method are proposed. Additionally, RL techniques are implemented to a digital twin of Chattanooga smart corridor. Regarding transit simulations, a toolkit for calibrating large-scale simulations and an efficient solution for simulating changes in transit system settings are developed. Finally, we delve into a fundamental question of optimization for training neural networks and demonstrate that a novel approach using Neuroevolution outperforms Gradient Descent methods.
I would like to express my sincere gratitude to my advisor, Dr. Mina Sartipi, for her unwavering support and guidance throughout my time as a Master's student. I am also grateful to the UTC professors who offered courses that have enhanced my knowledge in Computer Science. Additionally, I would like to thank the SimCenter for providing computing resources that have been essential to my research. Finally, I kindly thank to the committee members -- Dr. Dalei Wu, Dr. Yu Liang, and Dr. Mina Sartipi for their valuable comments and suggestions.
M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science.
Electronic traffic controls; Reinforcement learning; Urban transportation--Computer simulation
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Tran, Viet Toan, "Improving traffic management efficiency through reinforcement learning-based traffic signal control and citywide transit simulation" (2023). Masters Theses and Doctoral Dissertations.