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.


  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Figures; Photos; References; Tables;
  • Pagination: 78p

Subject/Index Terms

Filing Info

  • Accession Number: 01688519
  • Record Type: Publication
  • Report/Paper Numbers: RP 14-29
  • Contract Numbers: 0013527
  • Created Date: Dec 17 2018 10:26AM