Machine learning based analysis of travel mode choice for healthcare accessibility in urban and rural areas

Timely access to public healthcare is fundamental human rights and a key measure of social equity. In Thailand, transportation barriers especially in rural and underserved areas continue to restrict equitable access to medical services, reinforcing existing social disparities. This study investigates the determinants of hospital transport service utilization, focusing on the differences in travel behavior between urban and rural populations. A dataset of 1,200 respondents was analyzed using Categorical Boosting (CatBoost), a gradient-boosting machine learning algorithm known for high predictive accuracy and interpretability. The results indicate that The CatBoost model outperformed traditional statistical approaches, namely the Binary Logit Model, in identifying behavioral and contextual determinants of transport use. Key influencing factors included travel time, waiting time, travel cost, and parking fees, alongside demographic attributes such as age, income, and travel frequency. Findings reveal persistent inequities in healthcare accessibility shaped by transportation infrastructure and socioeconomic status. By integrating interpretable machine learning with a social equity perspective, this study demonstrates how data driven insights can inform inclusive and context sensitive health transport policies. The results contribute to global discussions on mobility justice and equitable healthcare access, emphasizing the need for socially responsive interventions to enhance accessibility, efficiency, and well-being across urban and rural communities.