Machine Learning Techniques for Fatal Accident Prediction

Zermane Hanane (), Zermane Abderrahim () and Tohir Mohd Zahirasri Mohd ()
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Zermane Hanane: Batna 2 University, Faculty of Technology, Laboratory of Automation and Manufacturing, Industrial Engineering Department, Constantine Street 53, Fésdis, Batna 05078, Algeria
Zermane Abderrahim: University of Putra Malaysia, Faculty of Engineering, Department of Chemical and Environmental Engineering, Safety Engineering Interest Group, Karjalankatu 3, 43400 Serdang, Selangor, Malaysia
Tohir Mohd Zahirasri Mohd: University of Putra Malaysia, Faculty of Engineering, Department of Chemical and Environmental Engineering, Safety Engineering Interest Group, Karjalankatu 3, 43400 Serdang, Selangor, Malaysia

ACC Journal, 2024, vol. 30, issue 1, 24-49

Abstract: Ensuring public safety on our roads is a top priority, and the prevalence of road accidents is a major concern. Fortunately, advances in machine learning allow us to use data to predict and prevent such incidents. Our study delves into the development and implementation of machine learning techniques for predicting road accidents, using rich datasets from Catalonia and Toronto Fatal Collision. Our comprehensive research reveals that ensemble learning methods outperform other models in most prediction tasks, while Decision Tree and K-NN exhibit poor performance. Additionally, our findings highlight the complexity involved in predicting various aspects of crashes, as the Stacking Regressor shows variability in its performance across different target variables. Overall, our study provides valuable insights that can significantly contribute to ongoing efforts to reduce accidents and their consequences by enabling more accurate predictions.

Keywords: Accident prevention; Machine learning; Traffic safety; Road safety; Accident forecasting; Risk assessment (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:accjnl:v:30:y:2024:i:1:p:24-49:n:1003

DOI: 10.2478/acc-2024-0003

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