Predictive analysis of injury severity of person across angle crashes using machine learning models

Abstract

Human injury in a vehicle crash is a critical subject of analysis. The injury severity of a person in crashes helps the transportation agency to determine crash conditions. This will in turn helps road safety manager or engineer to implement the counter measures and to improve and enhance the level of safety at the roadside. In this research study, the injury severity of a person in crash analysis across angles is analyzed using various machine learning models. For the injury severity prediction 2018 Fatality Analysis Reporting System (FARS) NHTSA (National Highway Traffic Safety Administration) dataset of United States is used. The person injury severity is predicted with the help of machine learning models like Multinomial logistic regression, Naive Bayes Classifier, Random Forest, Extra Trees, XGB Classifier and optimized XGBoost model. It is observed that optimized XGBoost method performs better than other machine learning models in terms of performance metrics like Accuracy, Error rate, Cohen-kappa-score, Loss and Misclassified samples.

Keywords:

• Injury severity
• multinomial LR
• Random forest
• Extra trees
• Naive Bayes
• XGB classifier
• optimized XGBoost

Data availability

In this research study, the National Highway Traffic Safety Administration FARS 2018 dataset is used to evaluate the performance of machine learning models across angle crashes. The dataset is freely accessed online: ‘https://www.nhtsa.gov/file-downloads?p=nhtsa/downloads/FARS/2018/’.

Disclosure statement

No potential conflict of interest was reported by the authors.