Advances in public transit modeling and smart card technologies can reveal detailed contact patterns of passengers. A natural way to represent such contact patterns is in the form of networks. In this paper we utilize known contact patterns from a public transit assignment model in a major metropolitan city, and propose the development of two novel network structures, each of which elucidate certain aspects of passenger travel behavior. We first propose the development of a transfer network, which can reveal passenger groups that travel together on a given day. Second, we propose the development of a community network, which is derived from the transfer network, and captures the similarity of travel patterns among passengers. We then explore the application of each of these network structures to identify the most frequently used travel paths, i.e., routes and transfers, in the public transit system, and model epidemic spreading risk among passengers of a public transit network, respectively. In the latter our conclusions reinforce previous observations, that routes crossing or connecting to the city center in the morning and afternoon peak hours are the most “dangerous” during an outbreak.

中文翻译：

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发现公共交通网络的隐藏社区结构

公共交通建模和智能卡技术的进步可以揭示乘客的详细联系方式。代表这种联系方式的自然方式是网络形式。在本文中，我们利用了大城市中公共交通分配模型中的已知联系方式，并提出了两种新颖的网络结构的开发，每种结构都阐明了旅客出行行为的某些方面。我们首先建议开发一个转乘网络，该网络可以显示在同一天一起旅行的旅客群体。其次，我们提出了一个社区网络的开发，该社区网络是从转乘网络派生而来的，它捕获了乘客之间旅行方式的相似性。然后，我们探索每种网络结构的应用，以确定最常用的旅行路径，即 公共交通系统中的路线和转乘，并分别模拟了公共交通网络中乘客之间的流行病传播风险。在后一种情况中，我们的结论加强了先前的观察结果，即在爆发高峰时段，在早上和下午高峰时段穿越或连接到市中心的路线是最“危险”的。