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Understanding patterns of demand is fundamental for fleet management of bikesharing systems. In this paper we analyze data from the Divvy system of thecity of Chicago. We show that the demand of bicycles can be modeled as amultivariate temporal point process, with each dimension corresponding to abike station in the network. The availability of daily replications of theprocess allows nonparametric estimation of the intensity functions, even forstations with low daily counts, and straightforward estimation of pairwisecorrelations between stations. These correlations are then used for clustering,revealing different patterns of bike usage.

Exploring patterns of demand in bike sharing systems via replicated point process models