TY - JOUR

T1 - The Helsinki bike‐sharing system

T2 - Insights gained from a spatiotemporal functional model

AU - Piter, Andreas Maximilian

AU - Otto, Philipp

AU - Alkhatib, Hamza

N1 - Funding Information: We sincerely thank two anonymous reviewers for their helpful comments and suggestions. We would also like to thank Jouni Kuha for his detailed comments and suggestions, especially concerning the discussion of the specifics of Helsinki.

PY - 2022/7/27

Y1 - 2022/7/27

N2 - 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.

AB - 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.

KW - bike-sharing system

KW - functional data analysis

KW - spatiotemporal statistics

KW - subsampling

UR - http://www.scopus.com/inward/record.url?scp=85128208083&partnerID=8YFLogxK

U2 - 10.1111/rssa.12834

DO - 10.1111/rssa.12834

M3 - Article

VL - 185

SP - 1294

EP - 1318

JO - Journal of the Royal Statistical Society. Series A: Statistics in Society

JF - Journal of the Royal Statistical Society. Series A: Statistics in Society

SN - 0035-9238

IS - 3

ER -