Discovering the Hidden Community Structure of Public Transportation Networks

László Hajdu, András Bóta, Miklós Krész, Alireza Khani, Lauren M. Gardner

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)209-231
Number of pages23
JournalNetworks and Spatial Economics
Volume20
Issue number1
DOIs
StatePublished - Mar 1 2020

Bibliographical note

Funding Information:
The authors are grateful to Metropolitan Council of Twin Cities for sharing the activity-based travel demand model with researchers at the University of Minnesota. Any limitations of this study remains the sole responsibility of the authors. L?szl? Hajdu acknowledges the National Research, Development and Innovation Office (NKFIH) for funding the project ?Graph Optimisation and Big Data? (Grant No: SNN-117879) and the support of the EU-funded Hungarian grant EFOP-3.6.3-VEKOP-16-2017-00002. Mikl?s Kr?sz acknowledges the European Commission for funding the InnoRenew CoE project (Grant Agreement #739574) under the Horizon2020 Widespread-Teaming program and the support of the EU-funded Hungarian grant EFOP-3.6.2-16-2017-00015. Andr?s B?ta acknowledges the Olle Engkvist Byggm?stare Foundation.

Funding Information:
The authors are grateful to Metropolitan Council of Twin Cities for sharing the activity-based travel demand model with researchers at the University of Minnesota. Any limitations of this study remains the sole responsibility of the authors. László Hajdu acknowledges the National Research, Development and Innovation Office (NKFIH) for funding the project ”Graph Optimisation and Big Data” (Grant No: SNN-117879) and the support of the EU-funded Hungarian grant EFOP-3.6.3-VEKOP-16-2017-00002. Miklós Krész acknowledges the European Commission for funding the InnoRenew CoE project (Grant Agreement #739574) under the Horizon2020 Widespread-Teaming program and the support of the EU-funded Hungarian grant EFOP-3.6.2-16-2017-00015. András Bóta acknowledges the Olle Engkvist Byggmästare Foundation.

Publisher Copyright:
© 2019, The Author(s).

Keywords

  • Community structure
  • Infrastructure security
  • Network modeling
  • Public transportation

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