  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. Moreover, the data are available at a very
high frequency. Hence, the station hire data is 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
bootstrapping 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. The estimated functional
influences of the proposed factors are different for the two clusters.
Moreover, the estimated parameters reveal high random effects in the data that
are not explained by the mean of the process. In this random-effects model, the
temporal autoregressive parameter dominates the spatial dependence.
