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