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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MDPI AG
2025-01-01
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| Series: | Mathematics |
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| author | Bita Ghasemkhani Kadriye Filiz Balbal Kokten Ulas Birant Derya Birant |
| author_facet | Bita Ghasemkhani Kadriye Filiz Balbal Kokten Ulas Birant Derya Birant |
| author_sort | Bita Ghasemkhani |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-174a1d6757aa4e7ea86173b7ed98dd75 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-174a1d6757aa4e7ea86173b7ed98dd752025-01-24T13:40:08ZengMDPI AGMathematics2227-73902025-01-0113231010.3390/math13020310Ordinal Random Tree with Rank-Oriented Feature Selection (ORT-ROFS): A Novel Approach for the Prediction of Road Traffic Accident SeverityBita Ghasemkhani0Kadriye Filiz Balbal1Kokten Ulas Birant2Derya Birant3Graduate School of Natural and Applied Sciences, Dokuz Eylul University, Izmir 35390, TurkeyDepartment of Computer Science, Dokuz Eylul University, Izmir 35390, TurkeyDepartment of Computer Engineering, Dokuz Eylul University, Izmir 35390, TurkeyDepartment of Computer Engineering, Dokuz Eylul University, Izmir 35390, TurkeyRoad 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.https://www.mdpi.com/2227-7390/13/2/310machine learningtraffic accident severity predictionordinal classificationfeature selectionrandom treeroad traffic accident |
| spellingShingle | Bita Ghasemkhani Kadriye Filiz Balbal Kokten Ulas Birant Derya Birant Ordinal Random Tree with Rank-Oriented Feature Selection (ORT-ROFS): A Novel Approach for the Prediction of Road Traffic Accident Severity Mathematics machine learning traffic accident severity prediction ordinal classification feature selection random tree road traffic accident |
| title | Ordinal Random Tree with Rank-Oriented Feature Selection (ORT-ROFS): A Novel Approach for the Prediction of Road Traffic Accident Severity |
| title_full | Ordinal Random Tree with Rank-Oriented Feature Selection (ORT-ROFS): A Novel Approach for the Prediction of Road Traffic Accident Severity |
| title_fullStr | Ordinal Random Tree with Rank-Oriented Feature Selection (ORT-ROFS): A Novel Approach for the Prediction of Road Traffic Accident Severity |
| title_full_unstemmed | Ordinal Random Tree with Rank-Oriented Feature Selection (ORT-ROFS): A Novel Approach for the Prediction of Road Traffic Accident Severity |
| title_short | Ordinal Random Tree with Rank-Oriented Feature Selection (ORT-ROFS): A Novel Approach for the Prediction of Road Traffic Accident Severity |
| title_sort | ordinal random tree with rank oriented feature selection ort rofs a novel approach for the prediction of road traffic accident severity |
| topic | machine learning traffic accident severity prediction ordinal classification feature selection random tree road traffic accident |
| url | https://www.mdpi.com/2227-7390/13/2/310 |
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