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Urban areas provide us with a treasure trove of available data capturingalmost every aspect of a population's life. This work focuses on mobility dataand how it will help improve our understanding of urban mobility patterns.Readily available and sizable farecard data captures trips in a publictransportation network. However, such data typically lacks temporal modalitiesand as such the task of inferring trip semantic, station function, and userprofile is quite challenging. As existing approaches either focus onstation-level or user-level signals, they are prone to overfitting and generateless credible and insightful results. To properly learn such characteristicsfrom trip data, we propose a Collective Learning Framework through LatentRepresentation, which augments user-level learning with collective patternslearned from station-level signals. This framework uses a novel, so-calledTidal-Regularized Non-negative Matrix Factorization method, which incorporatesdomain knowledge in the form of temporal passenger flow patterns in genericNon-negative Matrix Factorization. To evaluate our model performance, a userstability test based on the classical Rand Index is introduced as a metric tobenchmark different unsupervised learning models. We provide a qualitativeanalysis of the station functions and user profiles for the Washington D.C.metro and show how our method supports spatiotemporal intra-city mobilityexploration.

Station-to-User Transfer Learning: Towards Explainable User Clustering  Through Latent Trip Signatures Using Tidal-Regularized Non-Negative Matrix  Factorization