As an emerging mobility service, bike-sharing has become increasingly popular around the world. A critical question in planning and designing bike-sharing services is to know how different factors, such as land-use and built environment, affect bike-sharing demand. Most research investigated this problem from a holistic view using regression models, where assume the factor coefficients are spatially homogeneous. However, ignoring the local spatial effects of different factors is not tally with facts. Therefore, we develop a regression model with spatially varying coefficients to investigate how land use, social-demographic, and transportation infrastructure affect the bike-sharing demand at different stations to address this problem. Unlike existing geographically weighted models, we define station-specific regression and use a graph structure to encourage nearby stations to have similar coefficients. Using the bike-sharing data from the BIXI service in Montreal, we showcase the spatially varying patterns in the regression coefficients and highlight more sensitive areas to the marginal change of a specific factor. The proposed model also exhibits superior out-of-sample prediction power compared with traditional machine learning models and geostatistical models.

作为一种新兴的出行服务，自行车共享在世界各地变得越来越流行。规划和设计共享自行车服务中的一个关键问题是要了解不同的因素（例如土地使用和建筑环境）如何影响共享自行车的需求。大多数研究使用回归模型从整体角度研究了这个问题，其中假设因子系数在空间上是同质的。但是，忽略不同因素的局部空间影响并不符合事实。因此，我们开发了一个具有空间变化系数的回归模型，以研究土地使用，社会人口和交通基础设施如何影响不同车站的自行车共享需求，以解决此问题。与现有的地理加权模型不同，我们定义了特定于站点的回归，并使用图结构鼓励附近的站点具有相似的系数。利用蒙特利尔BIXI服务提供的自行车共享数据，我们展示了回归系数的空间变化模式，并突出显示了对特定因素的边际变化更为敏感的区域。与传统的机器学习模型和地统计模型相比，该模型还具有出色的样本外预测能力。