Committee Chair

Sartipi, Mina

Committee Member

Liang, Yu; Wu, Dalei

Department

Dept. of Computer Science and Engineering

College

College of Engineering and Computer Science

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

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.

Acknowledgments

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.

Degree

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.

Date

8-2023

Subject

Electronic traffic controls; Reinforcement learning; Urban transportation--Computer simulation

Keyword

reinforcement learning; intelligent transportation systems; traffic signal control; citywide transit simulation

Document Type

Masters theses

DCMI Type

Text

Extent

x, 83 leaves

Language

English

Rights

http://rightsstatements.org/vocab/InC/1.0/

License

http://creativecommons.org/licenses/by/4.0/

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