Citation

Vadhwani D, Thakor D. Int. J. Crashworthiness 2023; 28(3): 299-305.

Copyright

(Copyright © 2023, Informa - Taylor and Francis Group)

DOI

10.1080/13588265.2022.2075101

PMID

unavailable

Abstract

Road crash are common cause for fatal deaths. Lives of human suffers during severe road crashes. Public safety in road crash is the main and national goal for world. To find the cause of death in the crash is always the motivation for transportation agency. In the crash if vehicle damage is found then the extent of severity to driver and other passengers in vehicle crash is analyzed. So prediction of extent of damage in vehicle during crash is one of the crucial task. The extent of damaged is deformity in vehicle is considered as the multiclassification problem. The deformity or amount of damage occurred in vehicle is found during crash is predicted using FARS 2018 dataset by machine learning algorithms in this research study. Different machine learning models like Random Forest, Multinomial logistic regression, Naïve Bayes Classifier, Extra Trees and XGB Classifier, Neural networks, XGB with Bayesian and Optimised XGBoost are applied on vehicle database to predict the extent of damage, i.e. deformity in vehicle. The improved XGBoost machine learning method performs better in terms of performances metrics like accuracy, F1-score and loss.

Language: en

Keywords

accuracy; crash severity; loss; machine learning; Vehicle deformity; XGBoost