论文标题
通过多层网络Fused Lasso捕获自行车共享系统中的使用模式
Capturing usage patterns in bike sharing system via multilayer network fused Lasso
论文作者
论文摘要
从自行车共享系统收集的数据具有复杂的时间和空间特征。我们分析在单个站点的三个大城市中收集的共享自行车使用数据，这考虑了特定于车站的行为和协变量效应。为此，我们采用了多层网络融合套索惩罚的惩罚回归方法。这些融合惩罚是在嵌入时空链接的网络上施加的，并捕获了自行车使用中的同质性，这归因于复杂的时空特征而无需任意分区数据。在现实生活中，我们证明了所提出的方法会产生竞争性的预测性能，并提供了对数据的新解释。
Data collected from a bike-sharing system exhibit complex temporal and spatial features. We analyze shared-bike usage data collected in three large cities at the level of individual stations, 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.
