Abstract
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.
Highlights
Introduction and BackgroundNetworks can be used to represent public transportation systems from various unique perspectives
In this work we propose the development of two novel network structures, namely the transfer network, and the community network
In order to build the transfer network we identify the subgraph corresponding to each vehicle trip, detect atomic passenger groups – defined as maximal cliques – on each of the resulting subgraphs of the contact network and connect the atomic passengers groups according to direction of transfer between vehicle trips
Summary
Networks can be used to represent public transportation systems from various unique perspectives. Such detailed travel patterns can be used for various planning objectives including the estimation of the capacity of infrastructure (Wang et al 2011), calculating environmental impact (Carlsson-Kanyama and Linden 1999) or designing surveillance and containment strategies during an epidemic outbreak (Pendyala et al 2012; Rey et al 2016) While these methods allow researchers to map contacts between known individuals, the data collection and processing required to recreate a real-world contact network presents many challenges in terms of accuracy and computational complexity, among other issues such as privacy (Huerta and Tsimring 2002; Hoogendoorn and Bovy 2005; Balcan 2009; Salathe 2010; Funk et al 2010; Nassir et al 2012). The community network is applied to evaluate a diffusion process in a transit network, infectious disease spread among passengers This application complements our previous work (Bota et al 2017a), to identify the components of the public transportation system most vulnerable to a bio-security threat.
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