Mobile phone use while driving (MPWD) is one of the most critical behaviours contributing to distraction related fatalities and injuries. However, MPWD is not well understood among long-haul truck drivers (LHTDs) especially in developing countries like India. The present study aims to develop a prediction model for MPWD and attempts to identify the underlying factors and patterns affecting MPWD among LHTDs travelling across India. A total of 756 valid samples were collected utilising a questionnaire in Salem city, Tamil Nadu, India. Machine learning algorithms including Decision tree, Random Forest, Adaptive Boosting, and Extreme gradient boosting were employed to model MPWD. A split ratio of 70:30 was adopted for training and testing purposes. A 10-fold cross validation procedure was carried out during model development. The analysis results showed that XGBoost demonstrated superior performance than other models (accuracy = 0.82, F1 score =0.8, and AUROC = 0.82). In addition, SHapley Additive explanations (SHAP) were implemented to reveal the importance of predictors contributing to MPWD. The findings of the study showed that type of commodity, pressured delivery, calls received during driving, smoking habits, educational level, and continuous driving duration as major factors contributing to MPWD among LHTDs. SHAP values were also utilised to investigate hidden patterns and interaction effects for major factors affecting MPWD. Parcel and food items coupled with frequent pressured delivery and middle aged LHTDs driving continuously for 5-6 h are some of the interactive risk factors enhancing MPWD. These findings are useful for road safety authorities and truck companies to draft suitable policies and help to introduce effective interventions for reducing MPWD among LHTDs.
