Prevendo a gravidade de acidentes rodoviários no Brasil: a influência do ambiente e características do veículo

Abstract

This work supports the problematization of the rate of traffic accidents due to the high 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. This fact has motivated some researchers to engage in analysis and prediction regarding the severity of accidents. 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 from the FIPE table 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. Through applying classification algorithms and machine learning explainability techniques, we aim to acquire relevant knowledge related to this dataset, contributing to understanding and prevention of 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.