Journal article Open Access

Discovering the Hidden Community Structure of Public Transportation Networks

László Hajdu; András Bóta; Miklós Krész; Alezira Khani; Lauren M. Gardner


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    <subfield code="a">Network modeling</subfield>
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    <subfield code="a">Public Transportation</subfield>
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    <subfield code="a">Community Structure</subfield>
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    <subfield code="a">András Bóta</subfield>
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    <subfield code="u">Innorenew CoE, University of Primorska Andrej Marušic Institute, University of Szeged Gyula Juhász Faculty of Education</subfield>
    <subfield code="a">Miklós Krész</subfield>
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    <subfield code="u">Department of Civil, Environmental and Geo- Engineering, University of Minnesota Twin Cities, 500 Pillsbury Drive SE, Minneapolis, MN 55455, USA</subfield>
    <subfield code="a">Alezira Khani</subfield>
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    <subfield code="u">Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia, and Department of Civil Engineering, Johns Hopkins University, 3400 N. Charles St, Baltimore, MD 21218, USA</subfield>
    <subfield code="a">Lauren M. Gardner</subfield>
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    <subfield code="u">University of Szeged Institute of Informatics</subfield>
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    <subfield code="a">László Hajdu</subfield>
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    <subfield code="a">Discovering the Hidden Community Structure of Public Transportation Networks</subfield>
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    <subfield code="a">&lt;p&gt;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 &amp;ldquo;dangerous&amp;rdquo; during an outbreak.&lt;/p&gt;</subfield>
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