A Machine Learning Approach for Predicting Road Accidents

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

Identyfikatory

DOI

10.37105/sd.230

• Języki publikacji: EN

Abstrakty

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.

Słowa kluczowe

EN

forecasting; machine learning; road accident; safety

PL

prognozowanie; uczenie maszynowe; wypadek drogowy; bezpieczeństwo

• Wydawca: Centrum Rzeczoznawstwa Budowlanego Sp. z o.o.
• Czasopismo: Safety & Defense
• Rocznik: 2024
• Tom: 2
• Strony: 60--70
• Opis fizyczny: Bibliogr. 15 poz., rys., tab., wykr.

Twórcy

autor

Budzyński Artur

• Cracow University of Economics, Cracov, Poland

• https://orcid.org/0000-0002-5803-6749

autor

Federowicz Mikołaj

• Silesian University of Technology, Gliwice, Poland

autor

Jabłoński Adam

• Silesian University of Technology, Gliwice, Poland

autor

Hasan Wesam

• Silesian University of Technology, Gliwice, Poland

autor

Gorszanów Jan

• Silesian University of Technology, Gliwice, Poland

autor

Nikitishyn Taras

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

Bibliografia

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Uwagi

Na dzień 05.03.2025 link DOI do artykułu nie działał.