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.

翻译：共享单车系统收集的数据展现出复杂的时空特征。我们以单个站点为分析单元，在考虑站点特异性行为及协变量效应的基础上，对韩国首尔采集的共享单车使用数据进行了分析。为此，我们采用了一种基于多层网络融合Lasso惩罚项的惩罚回归方法。这些融合惩罚项施加于嵌入时空关联关系的网络上，能够在不进行任意数据分割的情况下，捕捉由复杂时空特征所导致的单车使用同质性。在真实数据集上的实验表明，所提出的方法不仅具有竞争力的预测性能，还为数据提供了新的解读视角。