Road collisions represent a worldwide pandemic which can be addressed through the improvement of existing tools for safety analysis. The paper presents a refined probabilistic framework 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 also introduced, and interactions are categorized in four categories: head-on, rear-end, side and parellel. The framework is applied to a large dataset of video recordings containing more than 300 severe interactions and collision collectedin Kentucky. The results demonstrate the usefulness of the approach in studying road user behaviour and mechanisms that may lead to collisions.
Abstract