This paper deals with a data mining approach applied on Bike Sharing System Origin-Destination data, but part of the proposed methodology can be used to analyze other modes of transport that similarly generate Dynamic Origin-Destination (OD) matrices. The transportation network investigated in this paper is the Vélib’ Bike Sharing System (BSS) system deployed in Paris since 2007. An approach based on Latent Dirichlet Allocation (LDA), that extracts the main features of the spatio-temporal behavior of the BSS is introduced in this paper. Such approach aims to summarize the behavior of the system by extracting few OD-templates, interpreted as typical and temporally localized demand profiles. The spatial analysis of the obtained templates can be used to give insights into the system behavior and the underlying urban phenomena linked to city dynamics.