Analyzing Factors Contributing to Pedestrian Fatalities Using Predictive Models

Abstract

Pedestrian fatalities are a significant issue worldwide, particularly in the United States. These fatalities stem from various causes, including distracted driving, speeding, inadequate road maintenance, and poor visibility due to weather conditions. The risk also varies heavily by area and population density, with high-traffic and high-speed regions often prioritizing vehicle flow over pedestrian safety. The National Highway Traffic Safety Administration (NHSTA) collects extensive accident data involving pedestrians. Using this dataset, we aim to build a classification model to predict the likelihood of one or more fatalities in a traffic accident given key factors like road conditions, weather, time of day, speed and more. The goal of this research is to leverage predictive modeling to identify high-risk situations and develop intervention or fast reaction strategies. By combining NHTSA data with machine learning techniques, this study enhances our understanding of critical risk factors. Furthermore, it explores the potential for practical applications, such as improved road safety tools, safer urban planning, and real-time alert systems.