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. The station hire data are 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 Monte Carlo subsampling 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.

中文翻译：

---

赫尔辛基共享单车系统——从时空函数模型中获得的见解

了解自行车共享系统的使用模式对于支持和加强此类计划的运营规划至关重要。研究表明，天气条件等因素如何影响自行车共享站在一天中的特定时间应提供的自行车数量。然而，这些因素的影响通常在一天中变化，如果有良好的时间分辨率，也可能仅在几个小时/分钟内产生显着影响（高峰时间、商店营业时间等） . 因此，在本文中，对赫尔辛基 2017 年的共享单车数据进行了分析，并考虑了完整的时间和空间分辨率。车站租用数据在时空功能设置中进行分析，其中一个车站的自行车数量被定义为一天中时间的连续函数。对于这种全新的方法，我们应用功能时空分层模型来研究环境因素的影响以及空间和时间依赖性的大小。使用蒙特卡洛二次抽样方法面临计算复杂性方面的挑战。结果表明，为了有效地模拟赫尔辛基共享单车系统的站点租用数据，有必要根据其时空功能观测的相似性将共享单车站点分成两个集群。我们应用功能时空分层模型来研究环境因素的影响以及时空依赖性的大小。使用蒙特卡洛二次抽样方法面临计算复杂性方面的挑战。结果表明，为了有效地模拟赫尔辛基共享单车系统的站点租用数据，有必要根据其时空功能观测的相似性将共享单车站点分成两个集群。我们应用功能时空分层模型来研究环境因素的影响以及时空依赖性的大小。使用蒙特卡洛二次抽样方法面临计算复杂性方面的挑战。结果表明，为了有效地模拟赫尔辛基共享单车系统的站点租用数据，有必要根据其时空功能观测的相似性将共享单车站点分成两个集群。