Ordinal Random Tree with Rank-Oriented Feature Selection (ORT-ROFS): A Novel Approach for the Prediction of Road Traffic Accident Severity

Bita Ghasemkhani, Kadriye Filiz Balbal, Kokten Ulas Birant and Derya Birant ()
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Bita Ghasemkhani: Graduate School of Natural and Applied Sciences, Dokuz Eylul University, Izmir 35390, Turkey
Kadriye Filiz Balbal: Department of Computer Science, Dokuz Eylul University, Izmir 35390, Turkey
Kokten Ulas Birant: Department of Computer Engineering, Dokuz Eylul University, Izmir 35390, Turkey
Derya Birant: Department of Computer Engineering, Dokuz Eylul University, Izmir 35390, Turkey

Mathematics, 2025, vol. 13, issue 2, 1-26

Abstract: Road traffic accident severity prediction is crucial for implementing effective safety measures and proactive traffic management strategies. Existing methods often treat this as a nominal classification problem and use traditional feature selection techniques. However, ordinal classification methods that account for the ordered nature of accident severity (e.g., slight < serious < fatal injuries) in feature selection still need to be investigated thoroughly. In this study, we propose a novel approach, the Ordinal Random Tree with Rank-Oriented Feature Selection (ORT-ROFS), which utilizes the inherent ordering of class labels both in the feature selection and prediction stages for accident severity classification. The proposed approach enhances the model performance by separately determining feature importance based on severity levels. The experiments demonstrated the effectiveness of ORT-ROFS with an accuracy of 87.19%. According to the results, the proposed method improved prediction accuracy by 10.81% over state-of-the-art studies on average on different train–test split ratios. In addition, it achieved an average improvement of 4.58% in accuracy over traditional methods. These findings suggest that ORT-ROFS is a promising approach for accurate accident severity prediction, supporting road safety planning and intervention strategies.

Keywords: machine learning; traffic accident severity prediction; ordinal classification; feature selection; random tree; road traffic accident; crash injury severity; traffic management; mathematics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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