<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

Machine Learning Techniques for Fatal Accident Prediction

Batna 2 University, Faculty of Technology

Laboratory of Automation and Manufacturing, Industrial Engineering Department

University of Putra Malaysia, Faculty of Engineering

Department of Chemical and Environmental Engineering, Safety Engineering Interest Group

Department of Construction, Installations and Structures

ACC JOURNAL

This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.

{"article-title":"Machine Learning Techniques for Fatal Accident Prediction"}

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...

Machine Learning Techniques for Fatal Accident Prediction

Online veröffentlicht: 06. Juli 2024

Seitenbereich: 24 - 49

DOI: https://doi.org/10.2478/acc-2024-0003

Schlüsselwörter
Accident prevention, Machine learning, Traffic safety, Road safety, Accident forecasting, Risk assessment

© 2024 Hanane Zermane et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.

Machine Learning Techniques for Fatal Accident Prediction

Online veröffentlicht: 06. Juli 2024

Seitenbereich: 24 - 49

DOI: https://doi.org/10.2478/acc-2024-0003

SchlüsselwörterAccident prevention, Machine learning, Traffic safety, Road safety, Accident forecasting, Risk assessment

© 2024 Hanane Zermane et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.

Schlüsselwörter
Accident prevention, Machine learning, Traffic safety, Road safety, Accident forecasting, Risk assessment