2023/11/14 by Shaila Jamal, K. Bruce Newbold, Darren M. Scott · 1 voice
Engineering · Social Sciences · Medicine · #Traffic and Road Safety #Urban Transport and Accessibility #Injury Epidemiology and Prevention
paper · pdf · doi:10.32866/001c.89416
openalex publication_date 2023/11/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Using the random forest classification technique, this study explored the role of different factors such as demography, pedestrian and drivers’ conditions, collision characteristics, road characteristics, and weather in predicting pedestrian injury severity from automobile-related collisions in Toronto. Spatial information was incorporated in the models to capture spatial autocorrelation. The results revealed the importance of spatial information in predicting pedestrian injury severity. Other important predictors of pedestrian injury severity include aggressive driving, driver’s conditions (e.g., inattentive, slowly stopping, driving properly, failing to yield right of way), pedestrian conditions (e.g., normal, inattentive) and dark lighting conditions.