Prediction and factor determination for driver injury severity using machine learning model

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.