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Clustering the Vélib ’ origin-destinations flows by means of Poisson mixture models
BibTeX
@MISC{R_clusteringthe,
author = {Andry R and Latifa Oukhellou},
title = {Clustering the Vélib ’ origin-destinations flows by means of Poisson mixture models},
year = {}
}
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Abstract
Abstract. Studies based on human mobility, including Bicycle Sharing System analysis, has expanded over the past few years. They aim to give insight of the underlying urban phenomena linked to city dynamics. This paper presents a generative count-series model using adapted Poisson mixtures to automatically analyse and find temporal-based clusters over the Vélib ’ origin-destination flow-data. Such an approach may provide latent factors that reveal how regions of different usage interact over the time. More generally, the proposed methodology can be used to cluster edges of temporal valued-graph with respect to their temporal profiles. 1
Keyphrases
origin-destinations flow poisson mixture model human mobility different usage interact temporal profile bicycle sharing system analysis past year ve lib origin-destination flow-data generative count-series model temporal-based cluster temporal valued-graph poisson mixture latent factor city dynamic underlying urban phenomenon
