Masters Theses
Date of Award
12-2024
Degree Type
Thesis
Degree Name
Master of Science
Major
Computer Engineering
Major Professor
Weizi Li
Committee Members
Jian Liu, Xueping Li, Shuai Li
Abstract
The Beacon study provides insights into urban traffic dynamics during blackouts through the analysis of naturalistic driving data from two intersections in Memphis, TN. We reconstructed the proposed dataset Beacon in three different traffic settings which are (i) unsignalized, (ii) signalized, and (iii) mixed traffic control. We investigate the behavior of traffic at different intersections during traffic light blackout using Beacon dataset. Besides, our findings demonstrate the potential benefits of integrating robot vehicles for traffic management under these conditions, particularly in high-volume conditions. Moreover, the reconstruction of various traffic conditions such as unsignalized, signalized, and mixed showcases the usefulness of Beacon dataset while also revealing areas for improvement in modeling approaches. Our future work will focus on improving traffic simulation accuracy and expanding the dataset for broader applications, enhancing urban traffic resilience.
Recommended Citation
Sarker, Supriya, "Beacon: A Naturalistic Driving Dataset During Blackouts for Benchmarking Traffic Reconstruction and Control. " Master's Thesis, University of Tennessee, 2024.
https://trace.tennessee.edu/utk_gradthes/12865
