Feasibility of Computer Vision-Based Safety Evaluations Case Study of a Signalized Right-Turn Safety Treatment

Traditional road safety analysis has often been undertaken with historical collision records. However, limitations on the quality and completeness of collision data gave rise to surrogate ways of measuring safety, especially the traffic conflict technique. Traditionally, traffic conflict techniques have relied on field observations, which have some reliability and repeatability problems. Therefore, successfully automating conflict detection with data extracted from video sensors could have considerable benefits for traffic safety studies. Before-and-after safety evaluations could greatly benefit from automated analysis of traffic conflicts, and the main objective of this paper is to demonstrate the use of this analysis technique for such evaluations. A right-turn safety improvement was implemented at an intersection in Edmonton, Alberta, Canada, in 2009 to mitigate the high rate of rear-end and merging collisions. The right-turn ramp was closed, and all right-turning vehicles were brought to the right-turn lane at the intersection, where a No-Right-Turn-on-Red sign was installed. In this study, video sensors were the primary source of conffict data. The video data were analyzed and traffic conflicts were measured with an automated traffic safety tool. The distributions of the calculated conflict indicators before and after the treatment showed a considerable reduction in the frequency and severity of traffic conflicts. This result suggests significant positive changes in rear-end, merging, and total conflicts. The results of this study show the potential benefit of adopting automated conflict analysis for before-and-after safety studies.