Service bottlenecks are a key barrier to building a resilient public transport system. In this paper, we propose a new approach to automatically extract the role of a station in dynamical public transport flow networks based on the emerging role discovery method in network science. The term “role” in this study refers to the distinctive position or function that a station plays within the public transport flow network. Using smart card data from Nanjing public bikesharing agencies, we first construct dynamical public transport flow networks with notions of dynamical graph and edge. We then develop a dynamical algorithm to recursively compute the structural flow characteristics of nodes in passenger flow networks. Non-negative Matrix Factorization is conducted to extract the role memberships from the derived structural feature matrix and interpret each role in terms of measurements with practical values. The network hubs and potential service bottlenecks are then identified based on their operating characteristics and dynamics. Furthermore, the day-to-day and within-day role dynamics of public transport stations over time are unveiled. The results contribute to a better understanding of the interplay between stations in the network, and the identification of roles provides insight for public transport agencies to improve service resilience.

中文翻译：

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识别公共自行车共享流网络中的服务瓶颈

服务瓶颈是建设有弹性的公共交通系统的主要障碍。在本文中，我们基于网络科学中新兴的角色发现方法，提出了一种自动提取动态公共交通流网络中车站角色的新方法。本研究中的“角色”一词是指车站在公共交通流量网络中所扮演的独特地位或功能。利用南京公共自行车共享机构的智能卡数据，我们首先利用动态图和边的概念构建动态公共交通流量网络。然后，我们开发了一种动态算法来递归计算客流网络中节点的结构流特征。进行非负矩阵分解以从派生的结构特征矩阵中提取角色隶属关系，并根据具有实际值的测量来解释每个角色。然后根据其运行特征和动态来识别网络中心和潜在的服务瓶颈。此外，随着时间的推移，公共交通站点的日常和内部角色动态也被揭示。结果有助于更好地了解网络中车站之间的相互作用，并且角色的识别为公共交通机构提供了提高服务弹性的见解。