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Objective When mining travel characteristics from urban rail transit travel trajectory data, passenger travel activity patterns are found closely related to the stations near their residence and workplace. This is reflected in their travel trajectories by fixed boarding/alighting routes and concentrated entry/exit stations. Meanwhile, passenger travel trajectories associated with residence and workplace exhibit certain fluctuations, and the travel data strongly related to workplace-residence often differ significantly from other travel data. To accurately extract passenger travel trajectory characteristics, it is essential to clarify the residence-workplace relationship within urban rail transit passenger travel data. Method A mechanism for mining travel characteristics and classifying passengers based on residence-workplace relationship is proposed. The preliminary workplace-residence judgments are formed by identifying the hotspots of passenger trajectory activities related to residence, workplace and social activity locations. Then, the spatio-temporal patterns strongly associated with workplace-residence are explored. A K-means clustering algorithm extracted on the basis of passenger travel trajectory characteristic values is used to cluster passengers according to their residence-workplace judgment, resulting in a passenger classification method that precisely matches the passenger travel characteristics. Result & Conclusion Simulation validation results indicate that passengers with clearly defined residence-workplace relationship exhibit four distinct travel patterns of two-point, two-point-two-line, three-point, and multi-point. These travel pattern classification results make up a crucial technical foundation for constructing detailed passenger profiles and developing intelligent security check strategies.