This study investigates how various urban indicators affect public transportation use. To overcome the limitations of previous studies that typically focused on single indicators, this study develops a multidimensional dataset encompassing urban characteristics, demographic profiles, transport infrastructure, economic conditions, and institutional factors. The analysis covers 17 regions in Korea and 91 cities across 55 countries, drawing on both quantitative and qualitative approaches.
The results for Korean cities reveal that density-based indicators exhibit stronger associations with public transportation use than absolute scale measures. Transit-related infrastructure generally shows a positive relationship with public transportation use, whereas car-oriented infrastructure tends to be negatively associated. Furthermore, the Public Transportation–Car Time Ratio (PCTR) reveals that larger metropolitan areas with faster and more competitive transit systems achieve higher public transportation usage. Qualitative assessments also support these findings, demonstrating that higher service satisfaction is consistently linked to higher levels of public transportation use.
For global cities, PCTR, rail station density, fuel prices, fare levels, and car-restriction policies are significantly related to public transportation use, and the direction of these effects aligns more closely with theoretical expectations than in Korea. This contrast highlights the need for broad comparative analyses to mitigate multicollinearity caused by hierarchical indicator structures within domestic datasets.
A formative PLS-SEM model was developed to capture interactions among indicators, showing that public transportation service attributes, urban characteristics, and alternative mode service conditions jointly determine public transportation use (R² = 0.467). This confirms that usage is not driven by a single factor, but by combined and interacting urban and mobility elements.
Scenario analyses further show that changes in indicators such as station density, PCTR, and aging rate may produce different effects depending on city characteristics, suggesting that uniform infrastructure policies may be less effective than strategies tailored to local contexts. These findings support the development of more customized and structurally informed public transportation policies that contribute to sustainable and city-specific mobility planning.