Effectiveness of TMC AI Applications in Case Studies
Document Type
Report
Publication Date
2022
DOI
10.21949/1521668
Pages
52 pp.
Abstract
This report presents research about the potential for using artificial intelligence (AI) algorithms for highway traffic incident detection, specifically an AI-based incident-detection framework that can leverage large-scale sensor data along with advanced learning algorithms to improve the performance of incident detection. This research project was sponsored by the FHWA’s HRDO. This report may be a useful reference for incident management teams, traffic operators, and those interested in traffic incident detection.
The Turner-Fairbank Highway Research Center (TFHRC) performs advanced research on several areas of transportation technology for the Federal Highway Administration (FHWA). The Office of Operations Research and Development (HRDO) focuses on improving operations-related technology through research, development, and testing.
Rights
"No restrictions. This document is available to the public through the National Technical Information Service, Springfield, VA 22161."
ORCID
0000-0003-2003-9343 (Cetin)
Original Publication Citation
Federal Highway Administration. (2022). Effectiveness of TMC AI applications in case studies (FHWA Publication No. HRT-21-081). U.S. Department of Transportation. https://doi.org/10.21949/1521668
Repository Citation
Federal Highway Administration; Yang, H.; Cetin, M.; Wang, Z.; Huang, Z.; Sudhakar, N.; and Huang, P., "Effectiveness of TMC AI Applications in Case Studies" (2022). Civil & Environmental Engineering Faculty Publications. 82.
https://digitalcommons.odu.edu/cee_fac_pubs/82