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

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

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...

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Bibliographic Details
Main Authors: Bita Ghasemkhani, Kadriye Filiz Balbal, Kokten Ulas Birant, Derya Birant
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Mathematics
Subjects:
machine learning
traffic accident severity prediction
ordinal classification
feature selection
random tree
road traffic accident
Online Access:https://www.mdpi.com/2227-7390/13/2/310
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