A video-based methodology for extracting microscopic data and evaluating safety countermeasures at intersections using surrogate safety indicators

To address the shortcomings of the current literature and to improve the microscopic data collection tools for non-motorized road users, this thesis presents an automated methodology to classify road users in traffic videos – this methodology is complementary to existing object-tracking tools. The methodology is tested and validated using a large dataset from signalized intersections with high mixed traffic in Montreal, Canada. Road users are classified into three main categories: pedestrian, cyclist, and motor vehicle, with an overall accuracy of over 95 %. The proposed methodology is capable not only of counting the movements of the different road users (generating exposure measures), but also provides microscopic data separately for each road user type for safety analysis. As a result, performing automated surrogate safety studies becomes possible for facilities with mixed motorized and non-motorized traffic. As part of this thesis, the relationship between the surrogate safety measure used in this research, post encroachment time, and the historical accident data has been investigated and shows promising correlation. Using several hours of video recorded from a sample of signalized intersections in Montreal, and analyzed using the proposed techniques, the safety effects of two types of bicycle infrastructure, cycle tracks and bicycle boxes, have been investigated. The results show that based on the interactions between cyclists and turning vehicles, having a cycle track on the right side of the road is safer than not having a cycle track or than having a cycle track on the left side of the road. Also the study on the safety of bicycle boxes at intersections reveals that this type of bicycle facility is associated with a significant reduction in the severity of interactions (increase in post encroachment time) between cyclists and vehicles.


  • English

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Filing Info

  • Accession Number: 01604777
  • Record Type: Publication
  • Source Agency: ARRB
  • Files: ATRI
  • Created Date: Jul 18 2016 4:45PM