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.

• Record URL:
  http://g92018.eos-intl.net/eLibSQL14_G92018_Documents/14-29.pdf
• 
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/66890
• Corporate Authors:

  Kennesaw State University

  Georgia Pavement and Traffic Research Center
  Marietta, GA  United States 

  Georgia Department of Transportation

  Office of Performance-Based Management and Research
  Forest Park, GA  United States  GA 30297-2534

  Federal 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