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Using Degree Constrained Gravity Null-Models to understand the structure of journeys' networks in Bicycle Sharing Systems

Rémy Cazabet
Pierre Borgnat
Pablo Jensen

Abstract

Bicycle Sharing Systems are now ubiquitous in large cities around the world. In most of these systems, journeys' data can be extracted , providing rich information to better understand it. Recent works have used network based-machine learning, and in particular space-corrected node clustering, to analyse such datasets. In this paper, we show that spatial-null models used in previous methods have a systematic bias, and we propose a degree-contrained null-model to improve the results. We finally apply the proposed method on the BSS of a city.
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Dates and versions

hal-01500352 , version 1 (03-04-2017)

Identifiers

  • HAL Id : hal-01500352 , version 1

Cite

Rémy Cazabet, Pierre Borgnat, Pablo Jensen. Using Degree Constrained Gravity Null-Models to understand the structure of journeys' networks in Bicycle Sharing Systems. ESANN 2017 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2017, Bruges, Belgium. ⟨hal-01500352⟩
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