Traffic Accidents Classification and Injury Severity Prediction

Abstract

Traffic accidents constitutes the first cause of death and injury in many developed countries. However, traffic accidents information and data provided by public organisms can be exploited to classify these accidents according to their type and severity, and consequently try to build predictive model. Detecting and identifying injury severity in traffic accidents in real time is primordial for speeding post-accidents protocols as well as developing general road safety policies. This article presents a case study of traffic accidents classification and severity prediction in Spain. Raw data are from Spanish traffic agency covering a period of six years ranging from 2011 to 2015. To this end, are compared three different machine learning classification techniques, such as Gradient Boosting Trees, Deep Learning and Naïve Bayes.