Pedestrian Crash

Pedestrian crashes are a significant public health concern, and research focuses on identifying contributing factors to improve safety. Current studies employ advanced machine learning techniques, such as automated machine learning (AutoML), TabNet, and association rule mining, to analyze large datasets and uncover complex relationships between crash severity and variables like lighting, driver behavior, pedestrian characteristics, and environmental conditions. These analyses provide crucial insights for developing targeted interventions and informing policy decisions aimed at reducing pedestrian fatalities and injuries, ultimately enhancing road safety.