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The Helsinki bike‐sharing system

Insights gained from a spatiotemporal functional model

authored by
Andreas Maximilian Piter, Philipp Otto, Hamza Alkhatib
Abstract

Understanding the usage patterns for bike-sharing systems is essential in terms of supporting and enhancing operational planning for such schemes. Studies have demonstrated how factors such as weather conditions influence the number of bikes that should be available at bike-sharing stations at certain times during the day. However, the influences of these factors usually vary over the course of a day, and if there is good temporal resolution, there could also be significant effects only for some hours/minutes (rush hours, the hours when shops are open and so forth). Thus, in this paper, an analysis of Helsinki's bike-sharing data from 2017 is conducted that considers full temporal and spatial resolutions. The station hire data are analysed in a spatiotemporal functional setting, where the number of bikes at a station is defined as a continuous function of the time of day. For this completely novel approach, we apply a functional spatiotemporal hierarchical model to investigate the effect of environmental factors and the magnitude of the spatial and temporal dependence. Challenges in computational complexity are faced using a Monte Carlo subsampling approach. The results show the necessity of splitting the bike-sharing stations into two clusters based on the similarity of their spatiotemporal functional observations in order to model the station hire data of Helsinki's bike-sharing system effectively.

Organisation(s)
Institute of Photogrammetry and GeoInformation (IPI)
Institute of Cartography and Geoinformatics
Geodetic Institute
Type
Article
Journal
Journal of the Royal Statistical Society. Series A: Statistics in Society
Volume
185
Pages
1294-1318
No. of pages
25
ISSN
0035-9238
Publication date
27.07.2022
Publication status
Published
Peer reviewed
Yes
ASJC Scopus subject areas
Statistics and Probability, Social Sciences (miscellaneous), Economics and Econometrics, Statistics, Probability and Uncertainty
Electronic version(s)
https://doi.org/10.1111/rssa.12834 (Access: Open)