Application of Machine Learning Predicting Injuries in Traffic Accidents through the Application of Random Forest

Abstract

The objective of this work is to analyze and predict the harmfulness in traffic accidents. To this end, several Random Forest statistical models are created, in which the predictable variable (response/ output variable) is the harmfulness of the accident. Several Random Forest statistical models are created, in which the predictable variable (response/ output variable) is the harmfulness of the accident, while the input variables are the various characteristics of the accident. In addition, these generated models will allow estimating the influence or importance of each of the factors studied (input variables) concerning the harmfulness of road accidents so that it is possible to know in which aspects it is more profitable to work with the objective of reducing mortality from traffic accidents [1]. The input variables that condition this prediction are the various characteristics of the accident. In this regard, the predictive algorithm has an out-of-bag error of 26.55% and an overall accuracy of 74.1%. Meanwhile, the local accuracy of the mildly wounded class is 66.1% compared to 81.4% of the dead and severely wounded class, which, as mentioned, has higher prediction reliability. In addition, these generated models will allow estimating the influence or importance of each of the factors studied (input variables) on the harmfulness of road accidents Finally, it is worth noting the enormous usefulness of the Random Forest machine learning technique, which provides very useful information for possible research or studies that may be carried out. In the specific case of this work, through the use of the R programming language, which in turn presents a wide range of freely accessible utilities and functions with which it may be interesting working, it has generated results of great value for this area of activity, important to society as road safety. it is possible to know in which aspects it is more profitable to work with the objective of reducing mortality from traffic accidents .