Title: Assessment of efficient machine learning algorithms for enhancing road safety and predicting accident severity

Authors: Akshi Bharadwaj; Sudesh Kumar; Pawan Singh

Addresses: School of Mathematics, Statistics and Computational Science, Central University of Rajasthan, Ajmer-305817, Rajasthan, India ' Department of Computer Science, Indira Gandhi National Tribal University, Amarkantak, Madhya Pradesh-484887, India ' School of Mathematics, Statistics and Computational Science, Central University of Rajasthan, Ajmer-305817, Rajasthan, India

Abstract: In response to the pressing need for road safety enhancement, this study explores the implementation of machine learning (ML) methods to forecast the severity of accidents. The research aims to uncover the intricate factors underlying accidents and provide actionable insights for proactive measures. Utilising algorithms including random forest, support vector classification (SVC), XGBoost, balanced bagging, and voting classifier, the study evaluates performance using standard metrics such as F1 score, recall, precision, and accuracy. Notably, the soft voting classifier, comprising XGBoost, balanced bagging, and gradient boosting, emerges as the leading model with an accuracy rate of 85.7%, demonstrating its efficacy in accident severity prediction.

Keywords: accident predictions; machine learning; ML; ITS; XGBoost; random forest.

DOI: 10.1504/IJWET.2025.151156

International Journal of Web Engineering and Technology, 2025 Vol.20 No.4, pp.381 - 403

Received: 16 Apr 2024
Accepted: 17 Nov 2024
Published online: 15 Jan 2026 *

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