Today, more and more bicycle sharing systems (BSS) are being introduced in big cities. These transportation systems generate sizable transportation data, the mining of which can reveal the underlying urban phenomena linked to city dynamics. This paper presents a statistical model to automatically analyze the trips data of a bike sharing system. The proposed solution partitions (i.e. cluster) the stations according to their usage profiles. To do so, count series describing the stations’ usage through departure/arrival counts per hour throughout the day are built and analyzed. The model for processing these count series is based on Poisson mixtures and introduces a station scaling factor which handles the differences between the stations’ global usage. Differences between weekday and weekend usage are also taken into account. This model identifies the latent factors which shape the geography of trips and the results may thus offer insights into the relationships between station neighborhood type (its amenities, its demographics, etc.) and the generated mobility patterns. In other words, the proposed method brings to light the different functions in different areas which induce specific patterns in BSS data. These potentials are demonstrated through an in-depth analysis of the results obtained on the Paris Velib’ large-scale bike sharing system.