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Passenger BIBO detection with IoT support and machine learning techniques for intelligent transport systems

dc.contributor.authorŚniegula, Anna
dc.contributor.authorMastalerz, Marcin W.
dc.contributor.authorKwiatkowski, Sławomir
dc.contributor.authorMalinowski, Aleksander
dc.contributor.authorWieczorek, Bartosz
dc.date.accessioned2021-09-14T10:10:06Z
dc.date.available2021-09-14T10:10:06Z
dc.date.issued2020
dc.identifier.citationMarcin W. Mastalerz, Aleksander Malinowski, Sławomir Kwiatkowski, Anna Śniegula, Bartosz Wieczorek, Passenger BIBO detection with IoT support and machine learning techniques for intelligent transport systems, Procedia Computer Science, Volume 176, 2020, Pages 3780-3793, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2020.09.009.pl_PL
dc.identifier.issn1877-0509
dc.identifier.urihttp://hdl.handle.net/11089/39055
dc.description.abstractThe present article discusses the issue of automation of the CICO (Check-In/Check-Out) process for public transport fare collection systems, using modern tools forming part of the Internet of Things, such as Beacon and Smartphone. It describes the concept of an integrated passenger identification model applying machine learning technology in order to reduce or eliminate the risks associated with the incorrect classification of a smartphone user as a vehicle passenger. This will allow for the construction of an intelligent fare collection system, operating in the BIBO (Be-In/Be-Out) model, implementing the "hands-free" and "pay-as-you-go" approach. The article describes the architecture of the research environment, and the implementation of the elaborated model in the Bad.App4 proprietary solution. We also presented the complete process of concept verification under real-life conditions. Research results were described and supplemented with commentary.pl_PL
dc.language.isoenpl_PL
dc.publisherElsevierpl_PL
dc.relation.ispartofseriesProcedia Computer Science;176
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Międzynarodowe*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMachine learningpl_PL
dc.subjectElectronic Toll Collection Systempl_PL
dc.subjectInternet of Thingspl_PL
dc.subjectBeaconpl_PL
dc.subjectSmartphonepl_PL
dc.subjectPrediction Analyticspl_PL
dc.subjectSmart citypl_PL
dc.titlePassenger BIBO detection with IoT support and machine learning techniques for intelligent transport systemspl_PL
dc.typeArticlepl_PL
dc.page.number3780-3793pl_PL
dc.contributor.authorAffiliationUniversity of Lodz, Department of Computer Science in Economics, St. POW 3/5, Lódź 90-255, Polandpl_PL
dc.contributor.authorAffiliationUniversity of Szczecin, Department of Computer Science in Management, Cukrowa 8, Szczecin 71-004, Polandpl_PL
dc.contributor.authorAffiliationBradley University, Department of Electrical and Computer Engineering, 1501 W Bradley Ave, Peoria, IL 61625, USApl_PL
dc.contributor.authorAffiliationAsseco Data Systems S.A., St. Podolska 21, Gdynia 81-321, Polandpl_PL
dc.contributor.authorAffiliationLodz University of Technology, Institute of Information Technology, St. Wólczańska 215, Lodz 90-924, Polandpl_PL
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dc.identifier.doi10.1016/j.procs.2020.09.009
dc.disciplineekonomia i finansepl_PL
dc.disciplineinformatykapl_PL


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