Studies based on human mobility, including Bicycle Sharing System analysis, has expanded over the past few years. They aim to give insight of the underlying urban phenomena linked to city dynamics. This paper presents a generative count-series model using adapted Poisson mixtures to automatically analyse and find temporal-based clusters over the Velib' origin-destination flow-data. Such an approach may provide latent factors that reveal how regions of different usage interact over the time. More generally, the proposed methodology can be used to cluster edges of temporal valued-graph with respect to their temporal profiles.