Predicting Highway Accident Severity in Brazil: Environmental Factors and Vehicle Features

This work supports the problematization of the high rate of traffic accidents due to the increased use of automobiles for utility or transport in Brazil. Every year, the lives of approximately 1.3 million people are interrupted due to traffic accidents worldwide. In this regard, this study proposes the use of data mining as an approach to analyze and explore datasets of traffic accidents that occurred on Brazilian highways between the years 2017 and 2022, as provided by the Federal Highway Police. Additionally, vehicle price data were included, allowing for a more comprehensive analysis that also considers the financial value of the vehicles. The goal is to assess the predictive capability of classification models regarding the severity of accidents, focusing on vehicle characteristics and environmental factors. By applying classification algorithms and machine learning explainability techniques, we acquired relevant knowledge regarding the studied data, contributing to understanding and preventing accidents. As a result, the attributes related to vehicle characteristics had a more positive impact on the predictive capability of the models when compared to the attributes describing the environment and other variables.