  Data collected from a bike-sharing system exhibit complex temporal and
spatial features. We analyze shared-bike usage data collected in Seoul, South
Korea, at the level of individual stations while accounting for
station-specific behavior and covariate effects. For this, we adopt a penalized
regression approach with a multilayer network fused Lasso penalty. These fusion
penalties are imposed on networks which embed spatio-temporal linkages, and
capture the homogeneity in bike usage that is attributed to intricate
spatio-temporal features without arbitrarily partitioning the data. On the
real-life datasets, we demonstrate that the proposed approach yields
competitive predictive performance and provides a new interpretation of the
data.
