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Measuring safety is a complex task. Roadway crashes are perhaps the best indicators of roadway safety. However, using crash frequency alone overlooks many dangerous situations that might have led to actual crashes. In addition, since crashes are rare events, one must wait years to observe a sufficient number of crashes to be able to measure safety. When it comes to evaluating safety, signalized intersections pose an intricate problem because many vulnerable road users are exposed to vehicular traffic. The primary goal of this research is to monitor interactions between vulnerable road users (i.e., pedestrians and bicyclists) and vehicles in order to proactively measure safety at signalized intersections. This research develops a safety assessment framework using real-world video data from ten signalized intersections in San Diego, California.

Developing a Computer Vision-Based Decision Support System for Intersection Safety Monitoring and Assessment of Vulnerable Road Users