Prediction and factor determination for driver injury severity using machine learning model
Transportation Letters, cilt.18, sa.6, ss.1360-1374, 2026 (SCI-Expanded, SSCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 18 Sayı: 6
- Basım Tarihi: 2026
- Doi Numarası: 10.1080/19427867.2026.2626462
- Dergi Adı: Transportation Letters
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Compendex, INSPEC
- Sayfa Sayıları: ss.1360-1374
- Anahtar Kelimeler: Driver injury severity, machine learning, SHAP interpretability, time-of-day variation, two-wheeler involvement, urban traffic crashes
- Süleyman Demirel Üniversitesi Adresli: Evet
Özet
This study presents a machine learning-based framework for predicting driver injury severity (DIS) in urban traffic crashes, using ten years (2011–2020) of police-reported data from Imphal, India. Focusing on morning and nighttime accidents, six ML models were trained and evaluated based on accuracy, precision–recall analysis, including AUC-PR (area under the Precision–Recall curve), recall (sensitivity) for the fatal class, and precision for the fatal class, under multiple train/test splits and cross-validation schemes. The best-performing models—Random Forest for morning crashes and LightGBM for night—were identified and further analyzed using SHAP-based sensitivity methods to determine key predictors. Results revealed that model performance and variable impact were sensitive to training ratio and cross-validation strategy. Two-wheeler involvement and narrow-road conditions were among the most significant factors. Overall, the study contributes to data-driven traffic injury modeling and highlights the potential of explainable artificial intelligence (XAI) as a decision-support tool for improving urban transport safety management.