Road collisions are a worldwide pandemic that can be addressed through the improvement of existing tools for safety analysis. A refined probabilistic framework is presented for the analysis of road-user interactions. In particular, the identification of potential collision points is used to estimate collision probabilities, and their spatial distribution can be visualized. A probabilistic time to collision is introduced, and interactions are grouped into four categories: head-on, rear-end, side, and parallel. The framework is applied to a large data set of video recordings collected in Kentucky that contains more than 300 severe interactions and collisions. The results demonstrate the usefulness of the approach for studying road-user behavior and mechanisms that may lead to collisions.

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Series Transportation Research Record
Saunier, N. (Nicolas), Sayed, T. (Tarek), & Ismail, K. (2010). Large-scale automated analysis of vehicle interactions and collisions. Transportation Research Record. doi:10.3141/2147-06