This investigation analyzes the determinants of transportation mode selection among elderly populations in Thailand through a comparative approach utilizing both traditional statistical modeling and contemporary machine learning techniques. The research compares the predictive effectiveness of the Multinomial Logistics Regression (MNL) model against advanced algorithmic approaches including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) in forecasting elderly travel behavior. The research utilizes a dataset comprising 1,000 elderly participants distributed across Thailand’s four geographical regions, with data collection conducted via structured questionnaires encompassing demographic factors, journey purposes, frequency of travel, expenditure considerations, and modal preferences. Results indicate that the Random Forest algorithms achieved the highest predictive performance on the comprehensive dataset (99.83% accuracy), while CatBoost demonstrated excellent performance on test data (94%). Shapley Additive Explanations (SHAP) analysis identified transportation expenditure, travel party size, temporal considerations, and economic status as the predominant variables influencing modal selection decisions, with lower-income elderly individuals showing a greater tendency for public transportation utilization relative to their more affluent counterparts. The MNL model revealed transportation cost as the most statistically significant predictor of public transit usage (p < 0.001), indicating that elderly individuals confronted with elevated travel expenses tend to substitute public transportation with private vehicle alternatives. Concurrently, machine learning methodologies demonstrated enhanced capacity to capture complex relationships between predictive factors and exhibited superior predictive accuracy compared to conventional MNL modeling. These findings offer important implications for the formulation of age-sensitive public transportation policies, particularly emphasizing cost reduction strategies and infrastructure enhancements designed to accommodate the specific mobility requirements of elderly populations.

This research studied the relationships of indicators related to the selection of intercity train services in Thailand’s Nakhon Ratchasima province. The goal was to examine the characteristics of the intercity train services and the factors that influence choosing them. The research tool used to collect data consisted of 508 sets of questionnaires completed by samples who had used the intercity train service. The data were analyzed using structural equation modeling to investigate the service quality and customer satisfaction indicators affecting the selection of intercity train services. The study found that the service quality indicators included station, service, staff, and vehicle. The customer satisfaction indicators included station, service, travel promotions, and price satisfaction. However, when the service quality indicators were investigated, they had a direct positive effect on customer satisfaction at the 0.01 level of statistical significance. Passengers are most satisfied with obtaining travel information, such as train timetables, fares, and updated information. The involved agencies can use the study’s findings to determine policies and formulate plans for information management and intercity train service metrics.

This study examines measurement invariance of expectations toward sustainable public transport service quality between urban and rural older adults in Thailand. Using second-order confirmatory factor analysis, data were collected from 1,189 elderly respondents across Thailand's four major regions through face-to-face interviews. The measurement framework incorporated eleven service quality dimensions: nine traditional attributes (Vehicle, Bus Stop, Accessibility, Convenience, Information, Staff, Safety and Security, Reliability, and Affordability) and two extended dimensions (Older's Facilities and Post-Pandemic Prevention). Results demonstrated successful measurement invariance, confirming that the eleven-factor structure operates equivalently across urban and rural contexts. Universal priorities emerged for Convenience, Staff quality, and Reliability, while rural elderly showed elevated importance for Safety and Security. The validation of Older's Facilities and Post-Pandemic Prevention as distinct dimensions establishes empirical support for incorporating age-inclusive design and health protection measures as permanent components of sustainable transport planning, justifying unified national standards while accommodating regional variations for Thailand's aging population.