Abstract
Motorcycle crashes impose a substantial public safety burden in low- and middle-income countries, yet conventional injury severity models often overlook gender-related heterogeneity in crash mechanisms. This study proposes a three-layer hierarchical explainable machine-learning framework to investigate motorcycle injury occurrence and injury severity among motorcyclists on Thai national highways using 14,464 crash records from the Highway Accident Information Management System (HAIMS) during 2017–2022. The framework comprises an injury occurrence model, a population-level injury severity model, and gender-stratified injury severity submodels for male and female riders. Ensemble machine-learning algorithms are optimized using Bayesian hyperparameter optimization and class-imbalance resampling, while SHapley Additive exPlanations (SHAP) enhanced model interpretability. CatBoost achieved the best predictive performance in three of the four prediction models, whereas Random Forest performed best for the male-specific model. SHAP analysis revealed distinct gender-specific injury severity mechanisms. Fatal injury pathways among male riders are primarily associated with heavy-truck interactions, nighttime travel, rainy-season conditions, and head-on collisions, whereas female riders are more strongly characterized by moderate injury pathways involving passenger-car interactions, daytime exposure, and mixed urban traffic environments. Because all female observations in the dataset are injury cases, these findings should be interpreted as severity-conditional rather than reflecting overall injury risk. The results demonstrate that gender-stratified modeling provides important analytical value by revealing heterogeneity masked by the pooled models, as pooled models mask important heterogeneity in injury severity mechanisms. The proposed framework provides an interpretable basis for evidence-based motorcycle safety management by supporting targeted interventions within a 6Es framework, thereby contributing to more effective road safety policies in Thailand and other motorcycle-dependent low- and middle-income countries.