A Machine Learning Approach for Predicting Road Accidents

• Authors: Artur Budzyński , Mikołaj Federowicz , Adam Jabłoński , Wesam Hasan , Jan Gorszanów , Taras Nikitishyn

Abstracts

EN

Road accidents pose significant challenges to public safety and necessitate proactive measures to mitigate them. This paper introduces a machine-learning approach for predicting road accident incidences, leveraging diverse datasets encompassing traffic patterns, weather conditions, and historical accident records. The proposed model integrates feature engineering techniques to capture the multifaceted nature of variables influencing accidents. Through the application of advanced machine learning algorithms, such as ensemble methods and neural networks, the model aims to discern complex patterns within the data, facilitating accurate predictions of accident likelihood. The study also explores the interpretability of the model outputs, providing insights into the key predictors and their interactions. Validation and performance assessment involve rigorous testing on diverse datasets to ensure the generalizability and robustness of the predictive model. The outcomes of this research hold promise for the development of proactive road safety strategies and the implementation of targeted interventions, ultimately contributing to reducing road accidents and their associated societal impacts.

Keywords

EN

forecasting; machine learning; road accident; safety

PL

prognozowanie; uczenie maszynowe; wypadek drogowy; bezpieczeństwo

• Publisher: Centrum Rzeczoznawstwa Budowlanego Sp. z o.o.
• Journal: Safety & Defense
• Year: 2024
• Volume: 2
• Pages: 60-70

Dates

published

2024

Contributors

author

Artur Budzyński

• Cracow University of Economics, Cracov, Poland

• https://orcid.org/0000000258036749

author

Mikołaj Federowicz

• Silesian University of Technology, Gliwice, Poland

author

Adam Jabłoński

• Silesian University of Technology, Gliwice, Poland

author

Wesam Hasan

• Silesian University of Technology, Gliwice, Poland

author

Jan Gorszanów

• Silesian University of Technology, Gliwice, Poland

author

Taras Nikitishyn

• Academic Secondary School of the Silesian
• University of Technology, Gliwice, Poland

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Identifiers

DOI

10.37105/sd.230

Biblioteka Nauki

58906183