共享单车系统的时空函数模型——基于赫尔辛基市的案例
A Spatiotemporal Functional Model for Bike-Sharing Systems -- An Example based on the City of Helsinki
AI总结：
本文针对赫尔辛基共享单车数据提出时空函数层次模型，将站点车辆数定义为时间连续函数，发现按时空相似性划分两聚类并分析环境因素影响能更有效建模，且时间自回归效应主导空间依赖。
摘要：
了解共享单车系统的使用模式对于支持和增强此类计划的运营规划至关重要。已有研究表明，天气条件等因素如何影响一天中特定时段共享单车站点应配备的可用车辆数。然而，这些因素的影响通常在一天中不断变化，如果具备良好的时间分辨率，可能仅在部分小时/分钟（如高峰时段、商店营业时间等）内存在显著效应。因此，本文对赫尔辛基市2017年的共享单车数据进行了分析，考虑了完整的时间和空间分辨率。此外，该数据以极高频提供。因此，站点租赁数据在时空函数框架下进行分析，其中站点的车辆数被定义为一天中时间的连续函数。对于这一全新方法，我们应用了函数时空层次模型来研究环境因素的效应以及空间和时间依赖性的强度。通过自助法解决了计算复杂性方面的挑战。结果表明，有必要根据站点时空函数观测的相似性将其划分为两个聚类，以有效建模赫尔辛基共享单车系统的租赁数据。所提出因素的估计函数影响在两个聚类中存在差异。此外，估计参数揭示了数据中存在较高的随机效应，这些效应无法由过程的均值解释。在此随机效应模型中，时间自回归参数主导了空间依赖性。
英文摘要：
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. Moreover, the data are available at a very high frequency. Hence, the station hire data is 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 bootstrapping 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. The estimated functional influences of the proposed factors are different for the two clusters. Moreover, the estimated parameters reveal high random effects in the data that are not explained by the mean of the process. In this random-effects model, the temporal autoregressive parameter dominates the spatial dependence.
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