Investigating the application of deep learning to identify pedestrian collision-prone zones

The main objective of this study is to understand the factors that contribute to the frequency of both the total pedestrian-vehicle collisions and collisions that involve pedestrian violations and identify collision-prone areas. The two Full Bayes (FB) macro-level models were applied to historical collision records of the City of Hamilton to identify the collision-prone zones and the key factors that contribute to collision occurrence in TAZs. …