To address the limitation of the traditional Node-Place model in capturing actual passenger-flow vitality and its variation across travel scenarios, this study introduces a vitality dimension and develops a Node-lace-vitality model to identify urban rail transit station-area types and examine their differences across travel scenarios. Taking 211 urban rail transit stations in Xi'an as the sample, this study integrates multi-source data, including rail transit network topology, station facilities, feeder transport services, built environment characteristics, population activities, point of interest (POI) functional facilities, and hourly origin-destination (OD) ridership. An evaluation indicator system covering the three dimensions of node, place, and vitality is established. A combined weighting method is used to determine indicator weights, and a Gaussian mixture model is applied to identify station-area types under workday and National Day holiday scenarios. The transition characteristics of station-area types across different scenarios are then analyzed. The results show that the node, place, and vitality scores of Xi'an rail transit stations generally exhibit a spatial pattern of "high values in the central urban area and low values along peripheral lines". Under the National Day holiday scenario, some stations show a significant increase in passenger flow vitality. The station areas can be classified into five types: hub-integrated stations, station-city coordinated stations, vitality-responsive stations, place-supporting stations, and basic-cultivation stations. Their spatial distribution presents a clear concentric pattern characterized by "central agglomeration and peripheral differentiation". Station-area types remain generally stable between workday and holiday scenarios. Among the 211 stations, 192 stations retain the same type, accounting for 91.00%, while 19 stations experience type transitions, accounting for 9.00%. These transitions mainly occur between place-supporting stations and basic-cultivation stations, and are primarily distributed outside the Second Ring Road. The findings provide a scientific basis for station-area functional optimization, peripheral station cultivation, differentiated development under the Transit-Oriented Development (TOD) framework, and refined rail transit operations management.