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Tracking the evolution of temporal patterns of usage in bicycle-Sharing systems using nonnegative matrix factorization on multiple sliding windows

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  • Remy Cazabet
  • Pablo Jensen
  • Pierre Borgnat

Abstract

Bicycle-Sharing Systems (BSS) are growing quickly in popularity all over the world. In this article, we propose a method based on Nonnegative Matrix Factorization to study the typical temporal patterns of usage of the BSS of Lyon, France, by studying logs of rentals. First, we show how this approach allows us to understand the spatial and temporal usage of the system. Second, we show how we can track the evolution of these temporal patterns over several years, and how this information can be used to better understand the BSS, but also changes in the city itself, by considering the stations as social sensors.

Suggested Citation

  • Remy Cazabet & Pablo Jensen & Pierre Borgnat, 2018. "Tracking the evolution of temporal patterns of usage in bicycle-Sharing systems using nonnegative matrix factorization on multiple sliding windows," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 22(2), pages 147-161, April.
    • Handle: RePEc:taf:rjusxx:v:22:y:2018:i:2:p:147-161
    DOI: 10.1080/12265934.2017.1336468
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    Cited by:

    1. Yong Gao & Jiajun Liu & Yan Xu & Lan Mu & Yu Liu, 2019. "A Spatiotemporal Constraint Non-Negative Matrix Factorization Model to Discover Intra-Urban Mobility Patterns from Taxi Trips," Sustainability, MDPI, vol. 11(15), pages 1-22, August.

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