Safety diagnosis of vehicle-bicycle interactions using computer vision systems : a case study in Vancouver, B.C.

Active road users such as cyclists are usually subject to an elevated risk of collision. Therefore, there is a need for efficient techniques for evaluating the safety of active road users. Traditional road safety analysis has often been conducted using historical collision records. However, limitations associated with collision data have motivated the development of complementary proactive techniques for road safety analysis. Recently, there has been significant interest in using traffic conflicts to analyze safety which has been strengthened by the availability of automated traffic conflict analysis tools. This thesis demonstrates two applications of automated road safety analysis techniques using traffic conflicts. The first application is a safety diagnosis of a major intersection in Vancouver, British Columbia, with bicycle and pedestrian safety issues. Automated video-based computer vision techniques are used to extract and analyze data from the video footage. Traffic conflict indicators, such as time to collision and post-encroachment time, are used to assess conflicts along the intersection to identify safety problems based on the frequency and severity of conflicts. Different spatial and temporal non-conforming behavior patterns are also analyzed. The diagnosis revealed that the Burrard Bridge’s access and exit ramps are the main sources of conflicts at the intersection and their design encouraged many non-conforming behavior patterns. It can be expected that removing both ramps will address a significant amount of safety problems. The second application covers detailed analysis of cyclist yielding behavior at the same intersection. Cyclist yielding behavior is evaluated by analyzing vehicle and bicycle yielding rates in two bicycle crossings with different rules of priority. Compliance with traffic regulations is also studied by looking at how intersections actually operate in contrast to the formal traffic rules. Results showed that bicycle yielding rates can change significantly depending on the crossing’s configuration and legal right-of-way. Low bicycle yielding rates in combination with consistent but relatively low vehicle yielding rates can present a safety problem: understanding cyclist yielding behavior can enable engineers to design and build safer intersections which are consistent with road users’ expectations, and to develop more realistic models of traffic behavior, safety, and operations.Applied Science, Faculty ofCivil Engineering, Department ofGraduat