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

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  • Daniel Gervini
  • Manoj Khanal

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.

Suggested Citation

  • Daniel Gervini & Manoj Khanal, 2019. "Exploring patterns of demand in bike sharing systems via replicated point process models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(3), pages 585-602, April.
    • Handle: RePEc:bla:jorssc:v:68:y:2019:i:3:p:585-602
    DOI: 10.1111/rssc.12322
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    Cited by:

    1. Agostino Torti & Marika Arena & Giovanni Azzone & Piercesare Secchi & Simone Vantini, 2022. "Bridge closure in the road network of Lombardy: a spatio-temporal analysis of the socio-economic impacts," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 901-923, October.
    2. Jiaqing Sun & Yulin He & Jiantong Zhang, 2023. "A Cluster-Then-Route Framework for Bike Rebalancing in Free-Floating Bike-Sharing Systems," Sustainability, MDPI, vol. 15(22), pages 1-33, November.
    3. Wang, Yi-Jia & Kuo, Yong-Hong & Huang, George Q. & Gu, Weihua & Hu, Yaohua, 2022. "Dynamic demand-driven bike station clustering," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    4. Andreas Piter & Philipp Otto & Hamza Alkhatib, 2022. "The Helsinki bike‐sharing system—Insights gained from a spatiotemporal functional model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1294-1318, July.

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