Exploring patterns of demand in bike sharing systems via replicated point process models
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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 variable 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 the correlations between stations. These correlations are then used for clustering, which reveal different patterns of demand.
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