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arXiv · 1908.09440

Role Detection in Bicycle-Sharing Networks Using Multilayer Stochastic Block Models

Jane Carlen·Jaume de Dios Pont·Cassidy Mentus·Shyr-Shea Chang·Stephanie Wang·Mason A. Porter
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Abstract

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.

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Jane Carlen, Jaume de Dios Pont, Cassidy Mentus, Shyr-Shea Chang, Stephanie Wang, Mason A. Porter. 2021-08-12. Role Detection in Bicycle-Sharing Networks Using Multilayer Stochastic Block Models. https://arxiv.org/abs/1908.09440

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