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

Daniel Gervini and Manoj Khanal

Journal of the Royal Statistical Society Series C, 2019, vol. 68, issue 3, 585-602

Abstract: Understanding patterns of demand is fundamental for fleet management of bike sharing systems. We analyse data from the Divvy system of the city of Chicago. We show that the demand for bicycles can be modelled 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 enables non‐parametric 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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:68:y:2019:i:3:p:585-602

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