Ordinal regression and explainable machine learning reveal key determinants of urban bus service quality in Dhaka
This study evaluated urban bus service quality in Dhaka using a SERVQUAL questionnaire survey administered to 500 passengers across five major bus routes. Researchers modeled passenger satisfaction and dissatisfaction using Ordinal Logistic Regression alongside Random Forest and Extreme Gradient Boosting classifiers, achieving classification accuracies of 0.71 and 0.70 respectively. SHAP analysis identified that the largest service gaps and drivers of dissatisfaction relate to safety concerns, unprofessional staff behavior, scheduling, and poor vehicle maintenance.
Why it matters — It identifies the specific psychological and operational dimensions—specifically assurance and empathy—that drive passenger dissatisfaction, moving beyond general service ratings to show transit operators exactly which soft and hard service elements must be corrected to halt the shift toward private vehicles.
Caveat: The findings are based on a relatively small sample of 500 respondents restricted to five specific routes in a single city, which may not fully represent the diverse transit experiences across the entire metropolitan area or other developing urban centers.
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