In urban spatial networks, there is an interdependency between neighborhood roles and the transportation methods between neighborhoods. In this paper, we classify docking stations in bicycle-sharing networks to gain insight into the human mobility patterns of three major United States cities. We propose novel time-dependent stochastic block models (SBMs), with degree-heterogeneous blocks and either mixed or discrete block membership, which classify nodes based on their time-dependent activity patterns. We apply these models to (1) detect the roles of bicycle-sharing docking stations and (2) describe the traffic within and between blocks of stations over the course of a day. Our models successfully uncover work, home, and other districts; they also reveal activity patterns in these districts that are particular to each city. Our work has direct application to the design and maintenance of bicycle-sharing systems, and it can be applied more broadly to community detection in temporal and multilayer networks with heterogeneous degrees.
翻译:在城市空间网络中,邻里作用和邻里运输方法之间互为依存。在本文中,我们在自行车共享网络中对对对接站进行分类,以深入了解美国三大城市的人类流动模式。我们提出了新的基于时间的随机区块模型(SBMs),其中含有程度异质区块以及混合或离散区块成员,根据时间不同活动模式对节点进行分类。我们运用这些模型(1) 检测自行车共享对接站的作用,(2) 描述一天期间车块内部和之间的交通。我们的模型成功地揭示了工作、住宅和其他地区;还揭示了每个城市特有的这些地区的活动模式。我们的工作直接应用于自行车共享系统的设计和维护,可以更广泛地应用于时间和多层网络中的社区探测。