Exploring Public Health Perspectives on Travel Behavior Using a Machine Learning Approach: Thailand Case Study

Hospital transport services represent a vital alternative for addressing inequities in access to medical care, particularly in countries where public transportation systems are inadequate, such as Thailand. This approach enables equitable and widespread access to healthcare services for residents in underserved areas. The objective of this study is to analyze the factors influencing the choice of hospital transport travel mode by comparing various machine learning algorithms. The findings reveal that the categorical boosting model outperformed the other models across all performance metrics. The model results indicate that waiting time, travel time, travel cost, and comfortability significantly influence the decision to use hospital transport services. Furthermore, demographic data analysis highlights critical factors such as age, gender, income, travel frequency, occupation, and time of travel, all of which significantly affect the choice of hospital transport service. To maximize the practical implications of this study, policy recommendations and implementation strategies are proposed to support decision-makers in promoting equitable travel options and eliminating barriers to fair access to healthcare services.