<p>Smart card data (SCD) from Automatic Fare Collection (AFC) systems provide fine-grained insights into urban mobility rhythms. Yet most existing studies focus on static ridership, with limited attention to the relationship between station-level travel rhythms and the surrounding built environment. This study develops a spatiotemporal and data-driven framework to classify urban rail transit (URT) stations and examine their land-use determinants. Using two weeks of AFC data from 128 stations in Nanjing, China, a two-stage approach is implemented. First, a Gaussian Mixture Model (GMM) is applied to cluster stations based on weekday ridership profiles, yielding six distinct categories: residential oriented, employment oriented, hub comprehensive, spatial mismatched, predominantly residential mixed use, and predominantly employment mixed use. These categories reveal a clear transition from mixed and hub functions in the city center to residential-dominated stations in suburban areas. Second, a Random Parameter Logit (RPL) model is employed to assess the influence of socio-demographic, land-use, and amenity variables, capturing heterogeneity more effectively than the conventional Multinomial Logit (MNL) model. Results highlight the decisive roles of population density, housing prices, and employment land in shaping employment- and hub-oriented stations, while community-oriented facilities, such as healthcare and daily services, exert stronger effects on residential-oriented stations. These findings enrich theoretical understanding of station heterogeneity and provide empirical evidence for transit-oriented development (TOD), land-use coordination, and multimodal integration. The proposed framework is transferable to other rapidly urbanizing cities, offering practical guidance for building efficient and sustainable URT systems.</p>