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dc.contributor.authorMonechi, B
dc.contributor.authorGravino, P
dc.contributor.authorDi Clemente, R
dc.contributor.authorServedio, VDP
dc.date.accessioned2020-01-29T09:50:28Z
dc.date.issued2018-09-17
dc.description.abstractRailways are a key infrastructure for any modern country. The reliability and resilience of this peculiar transportation system may be challenged by different shocks such as disruptions, strikes and adverse weather conditions. These events compromise the correct functioning of the system and trigger the spreading of delays into the railway network on a daily basis. Despite their importance, a general theoretical understanding of the underlying causes of these disruptions is still lacking. In this work, we analyse the Italian and German railway networks by leveraging on the train schedules and actual delay data retrieved during the year 2015. We use these data to infer simple statistical laws ruling the emergence of localized delays in different areas of the network and we model the spreading of these delays throughout the network by exploiting a framework inspired by epidemic spreading models. Our model offers a fast and easy tool for the preliminary assessment of the effectiveness of traffic handling policies, and of the railway network criticalities.en_GB
dc.description.sponsorshipJohn Templeton Foundationen_GB
dc.description.sponsorshipAustrian Research Promotion Agency FFGen_GB
dc.description.sponsorshipNewton International Fellowshipen_GB
dc.description.sponsorshipThe Royal Societyen_GB
dc.description.sponsorshipThe British Academyen_GB
dc.description.sponsorshipAcademy of Medical Sciencesen_GB
dc.identifier.citationVol. 7, article 35en_GB
dc.identifier.doi10.1140/epjds/s13688-018-0160-x
dc.identifier.grantnumber51663en_GB
dc.identifier.grantnumber857136en_GB
dc.identifier.grantnumberNF170505en_GB
dc.identifier.urihttp://hdl.handle.net/10871/40626
dc.language.isoenen_GB
dc.publisherEDP Sciences with SpringerOpen and Società Italiana di Fisicaen_GB
dc.rights© The Author(s) 2018. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en_GB
dc.subjectComplex systemsen_GB
dc.subjectNetworksen_GB
dc.subjectDelay dynamicsen_GB
dc.subjectModellingen_GB
dc.subjectSpreadingen_GB
dc.titleComplex delay dynamics on railway networks from universal laws to realistic modellingen_GB
dc.typeArticleen_GB
dc.date.available2020-01-29T09:50:28Z
dc.descriptionThis is the final version. Available from EDP Sciences via the DOI in this record.en_GB
dc.descriptionThe datasets supporting the conclusions of this article are included within the article (and its additional files).en_GB
dc.identifier.journalEPJ Data Scienceen_GB
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2018-08-30
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2018-08-30
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2020-01-29T09:44:26Z
refterms.versionFCDVoR
refterms.dateFOA2020-01-29T09:50:32Z
refterms.panelBen_GB
refterms.depositExceptionpublishedGoldOA


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© The Author(s) 2018. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Except where otherwise noted, this item's licence is described as © The Author(s) 2018. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.