Highlights • The impact of different features in the severity of vehicle–pedestrian collisions is examined. • The mixed effect of traffic enforcement cameras in the severity of such collisions is analyzed. • A Linear Discriminant Analysis model outperforms other ML models for predicting such collisions in Medellín. • Age of the victim, heavy vehicles and illumination are associated to pedestrian deaths. • Motorcycles, densely populated locations, and quality of vertical signals are associated to pedestrian injuries.

Abstract Purpose: One of the leading causes of violent fatalities around the world is road traffic collisions, and pedestrians are among the most vulnerable road users with respect to such incidents. Since walking is highly promoted in urban areas to alleviate motor-vehicle externalities, it is paramount to understand the causes associated with vehicle–pedestrian collisions and their severity to provide safe environments. Although traffic enforcement cameras can address vehicle-vehicle collisions, little is known about their effectiveness with respect to vehicle–pedestrian incidents. Methodology: In this study, we trained a set of machine learning models to forecast if a vehicle–pedestrian collision will turn into an injury or fatality, and the most suitable model was used to investigate the contributing features associated with such events with emphasis on the impact of traffic enforcement cameras. In addition to traffic enforcement camera proximity, features associated with the collision, weather, vehicle, victim, and infrastructure are included in the model to reduce unobserved heterogeneity. Results: Results show that a Linear Discriminant Analysis model surpasses other machine learning models considering the evaluation metrics. Results reveal that the age and gender of the victim, the involvement of larger vehicles in the collision, and the quality of the illumination are the causes associated with pedestrian fatalities. On the other hand, involvement of motorcycles and collisions that occurred in densely populated locations are the causes associated with pedestrian injuries. Conclusions: This investigation demonstrates how to articulate machine learning into a vehicle–pedestrian crash analysis to understand the direction and magnitude of covariates in the corresponding severity outcome. Furthermore, it highlights the remarkable effect that traffic enforcement cameras and other features have on vehicle–pedestrian crash severity. These results provide actionable guidance for educational campaigns, enhanced traffic engineering, and infrastructure improvements that could be implemented in the analyzed region to provide safer transportation.