Evaluation of Near-Miss Crashes Using a Video-Based Tool

Vision-based trajectory data provide useful information on the trajectories of roadway users (vehicles, pedestrians, bicycles) that could be used for analyzing roadway safety and users’ interactions. Several companies offer vision-based software that promise to identify near-miss crashes between vehicles, pedestrians and vehicles, or bicycles and vehicles, by estimating surrogate measures for near-misses through the trajectory data. However, the accuracy of these methods in terms of predicting near-misses has not been evaluated. Therefore, the objective of this research is to install a commonly-used video-based software, and evaluate its accuracy of predicting near-misses at a busy signalized intersection in the Lawrence, KS area. The main objective of this research is to evaluate a video-based tool offered by “Brisk Synergies” in terms of its accuracy of predicting near-misses at signalized intersections. The findings of this study will help KDOT and local transportation partners to decide whether to invest or not in video-based tools at signalized intersections.

• Supplemental Notes:
  • Final report is pending.

Language

• English

Project

• Status: Active
• Funding: $71500
• Contract Numbers:

  K-TRAN: KU-21-4

  RE-0816-01

  C2171

• Sponsor Organizations:

  Kansas Department of Transportation

  Eisenhower State Office Building
  700 SW Harrison Street
  Topeka, KS  United States  66603-3754
• Performing Organizations:

  University of Kansas Center for Research, Incorporated

  2291 Irving Hill Drive, Campus West
  Lawrence, KS  United States  66045
• Principal Investigators:

  Schrock, Steven

  Kondyli, Alexandra

• Start Date: 20200701
• Expected Completion Date: 20231130
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Accuracy; Machine vision; Mathematical prediction; Near crashes; Signalized intersections; Software; Trajectory; Video
• Identifier Terms: Kansas Department of Transportation
• Geographic Terms: Kansas
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Safety and Human Factors;

Filing Info

• Accession Number: 01760107
• Record Type: Research project
• Source Agency: Kansas Department of Transportation
• Contract Numbers: K-TRAN: KU-21-4, RE-0816-01, C2171
• Files: RIP, STATEDOT
• Created Date: Dec 15 2020 3:29PM