2019/08/26 by Jane Carlen, Carlen, Jane, Jaume de Dios Pont +9
Engineering · Social Sciences · #Adaptation and Self-Organizing Systems (nlin.AO) #Applications (stat.AP) #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Human Mobility and Location-Based Analysis #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Statistics Theory (math.ST) #Transportation Planning and Optimization #Transportation and Mobility Innovations
paper · pdf · doi:10.48550/arxiv.1908.09440
openalex publication_date 2019/08/26 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
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.