A framework of boundary collision data aggregation into neighbourhoods

A large portion of the total number of motor collisions can be boundary collisions; therefore, exaggerated or underestimated numbers for boundary collisions aggregated into neighbourhoods may hamper road safety analyses and management. In this paper, we propose a systematic framework for boundary collision aggregation. First, an entropy-based histogram thresholding method is utilized to determine the boundary zone size and identify boundary collisions. Next, the collision density probability distribution is then established, based on the collisions in each neighbourhood. Last, an effective boundary collision aggregation method, called the collision density ratio (CDR), is used to aggregate boundary collisions into neighbourhoods. The proposed framework is applied to collision data in the City of Edmonton for a case study. The experimental results show that the proposed entropy-based histogram thresholding method can identify boundary collision with the high precision and recall, and the proposed CDR method is more effective than the existing methods, the half-to-half ratio method and the one-to-one ratio method, to aggregate boundary collisions into neighbourhoods.