Discovering the hidden community structure of public transportation networks

Recent advances in transit modeling can provide detailed contact patterns for passengers of public transportation networks. A natural way to represent such patterns is in the form of graphs. In this paper we analyze the public transportation network of a major metropolitan area from a unique perspective. We present a novel network structure to identify and track the relationships between passenger groups traveling on different vehicles, allowing us to discover the community structure of the users of the transportation network. The proposed network structure can be used to identify the most frequently used travel paths taken by these communities, but more importantly it provides a means to explore possibly hidden contacts of a selected passenger. We explore one application of this feature by identifying parts of the public transportation system most susceptible to spreading infectious diseases.