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

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 stati...

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Bibliographic Details
Published in:Journal of the Royal Statistical Society Series C: Applied Statistics 2019-04, Vol.68 (3), p.585-602
Main Authors: Gervini, Daniel, Khanal, Manoj
Format: Article
Language:English
Subjects:
Bicycles
Clustering
Demand
Fleet management
Functional canonical correlation
Functional principal components
Hierarchical clustering
Parameter estimation
Poisson process
Spline smoothing
Stations
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Summary: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.
ISSN:0035-9254
1467-9876
DOI:10.1111/rssc.12322