Machine Learning Classification of Customer Perceptions of Public Passenger Transport with a Focus on Ecological and Economic Determinants

Public passenger transport systems increasingly face the challenge of balancing economic efficiency with ecological sustainability, reflecting both policy objectives and passenger expectations. This study examines passenger perceptions of the economic and environmental aspects of public transport services and the factors influencing these perceptions, primarily based on survey data collected in Slovakia. The Slovak dataset was analysed using contingency analysis, namely Chi-square tests of independence, contingency coefficients, and sign scheme, and C5.0 decision tree classification models to identify key determinant of behavioural and attitudinal outcomes. In addition, descriptive comparisons with a complementary Polish sample are provided to illustrate potential differences in preference patterns across national contexts, without formal statistical inference. The results identify key socio-demographic and behavioural factors influencing passenger perceptions and usage patterns in Slovakia, while the complementary Polish sample is used to provide contextual descriptive comparison without formal testing. The study enhances scientific understanding of public transport by exploring the interaction between economic efficiency and ecological sustainability of transport services and provides practical recommendations for the strategic management of transport companies, especially in service modernisation, marketing communication, and support for sustainable mobility. The findings are relevant not only to Slovakia but also to broader European discussions on integrating economic and environmental dimensions into public transport development.