A novel approach for diagnosing road safety issues using automated computer vision techniques

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Tarek Sayed
Mohamed H Zaki
Jarvis Autey

The use of traffic conflicts for safety diagnosis has been gaining acceptance as a surrogate for collision data analysis. The traffic conflicts approach provided better understanding of collision contributing factors and the failure mechanism that leads to road collisions. This paper demonstrates an automated proactive safety diagnosis approach for vehicles, pedestrian and cyclists using video-based computer vision techniques. Traffic conflicts are automatically detected and several conflict indicators such as Time to collision (TTC) are calculated based on the analysis of the road-user positions in space and time. Additionally, spatial violations are detected based on the non-conformance of road users to travel regulations. Several case studies are described. The first case study deals of the safety analysis of a newly installed bike lane at the southern approach of a heavy volume Bridge in Vancouver, British Columbia. The results showed a high exposure of cyclists to traffic conflicts. Practical solutions to address the safety issues at the location were presented. The second case study deals with a major signalized intersection where concerns were raised regarding the high conflict rate between vehicles and pedestrians as well as an elevated number of traffic violations (i.e., jaywalking) at the intersection. Conflicts and violations were identified and pedestrian safety countermeasures were presented. The third case study deals with a before-after (BA) safety evaluation of a newly proposed design for channelized right-turn lanes at intersections. The new design, termed “Smart Channels”, decreases the angle of the channelized right turn to approximately 70 degrees. The results of the evaluation show that the implementation of the right-turn treatment has resulted in a considerable reduction in the severity and frequency of merging, rear-end, and total conflicts. Overall, the three case studies demonstrate the considerable potential of using video-based computer vision techniques for automated road safety analysis based on traffic conflicts.

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