Monitoring and Assessing Traffic Safety at Signalized Intersections Using Live Video Images
Signalized intersections represent the most hazard spots on a roadway network. Road users are required to be alert and timely process and respond to a variety of information at signalized intersections, including traffic signal indications and changes, signage, pavement marking, road conditions, and a mix of various road users in conflict. Traditional road safety diagnosis has been conducted in a reactive manner based on crashes that had occurred. However, to effectively reduce and eventually eliminate crashes, proactive approaches are needed. Following this direction, traffic conflict events have been collected more frequently and used as a surrogate safety measure for traffic crashes. The goal of Vision Zero would only be possible if the inconsequential event data, such as traffic conflicts, can be objectively and systematically collected and effectively utilized to diagnose and improve road safety such that consequential crash events can be prevented. In this study, the art of deep learning, multiple objects detection and tracking were explored and tested in the domain of traffic conflict monitoring and assessing. As a result, an artificial intelligence (AI) enhanced computational system was developed to automate the detection and quantification of traffic conflict events as they occur in real time using traffic monitoring cameras currently installed by transportation agencies.
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Corporate Authors:
Kennesaw State University
Georgia Pavement and Traffic Research Center
Marietta, GA United StatesGeorgia Department of Transportation
Office of Performance-Based Management and Research
Forest Park, GA United States GA 30297-2534Federal Highway Administration
1200 New Jersey Avenue, SE
Washington, DC United States 20590 -
Authors:
- Yang, Jidong J
- Wang, Ying
- Hung, Chih-Cheng
- Publication Date: 2018-10
Language
- English
Media Info
- Media Type: Digital/other
- Edition: Final Report
- Features: Figures; Photos; References; Tables;
- Pagination: 78p
Subject/Index Terms
- TRT Terms: Artificial intelligence; Automatic incident detection; Image analysis; Signalized intersections; Traffic conflicts; Traffic safety; Traffic surveillance; Video
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Safety and Human Factors;
Filing Info
- Accession Number: 01688519
- Record Type: Publication
- Report/Paper Numbers: RP 14-29
- Contract Numbers: 0013527
- Files: NTL, TRIS, ATRI, USDOT, STATEDOT
- Created Date: Dec 17 2018 10:26AM