Show simple item record

dc.contributor.authorŚniegula, Anna
dc.contributor.authorMastalerz, Marcin W.
dc.contributor.authorKwiatkowski, Sławomir
dc.contributor.authorMalinowski, Aleksander
dc.contributor.authorWieczorek, Bartosz
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,
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.relation.ispartofseriesProcedia Computer Science;176
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Międzynarodowe*
dc.subjectMachine learningpl_PL
dc.subjectElectronic Toll Collection Systempl_PL
dc.subjectInternet of Thingspl_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.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
dc.referencesM. A. Ali, H. E. Abd El Munim, A. H. Yousef, and S. Hammad. (2018). “A deep learning approach for vehicle detection”. In 2018 13th International Conference on Computer Engineering and Systems (ICCES), pages 98-102. doi: 10.1109/ICCES.2018.8639313.pl_PL
dc.referencesAndroid Developers, Detect when users start or end an activity. (02.03.2020).pl_PL
dc.referencesAsseco Data Systems S.A. “Nowatorska koncepcja poboru opłat i rozliczania usług miejskich w ramach SmartCity”. (10.02.2020).pl_PL
dc.referencesL. Atzori, A. Iera, G. Morabito "The internet of things: A survey. Computer networks", 54 (15) (2010), pp. 2787-2805, 2010. Oct 28pl_PL
dc.referencesR. A. R. Bedruz, A. Fernando and others. (2018) “Vehicle classification using AKAZE and feature matching approach and artificial neural network”. In TENCON 2018 - 2018 IEEE Region 10 Conference, pages 1824-1827. IEEE. ISBN 78-1-5386-5457-6. doi: 10.1109/TENCON.2018.8650119. URL
dc.referencesP. T. Blythe. (2004) “Improving public transport ticketing through smart cards”. Proceedings of the ICE-Municipal Engineer, vol. 157, 1, pp. 47-54, 2004.pl_PL
dc.referencesA. Caraglui, B.C. Del, P. Nijkamp "Smart Cities in Europe" Journal of Urban Technology, 18 (2011), pp. 65-82 2011pl_PL
dc.referencesR. N. Celso, Z. B. Ting and others. (2018). “Twostep vehicle classification system for traffic monitoring in the Philippines”. In TENCON 018 - 2018 IEEE Region 10 Conference, pages 2028-2033. IEEE. ISBN 978-1-5386-5457-6. doi: 0.1109/TENCON.2018.8650420.pl_PL
dc.referencesForbes Technology Council. (2016). “11 New Technology Trends To Watch”, Forbes, 09-08-2016, (20-01-2020).pl_PL
dc.referencesS. Garg, P. Singh and others. (2014) “Smartphone based vehicle classification and its applications in developing region”. In MobiQuitous. doi: 10.4108/icst.mobiquitous.2014.257982.pl_PL
dc.referencesGWT-TUD GmbH. (2009). “Research Project Be-In-Be-Out Payment Systems for Public Transport”, Dresden, July 2009, (20-12-2020).pl_PL
dc.referencesC. Jobanputra, J. Bavishi, N. Doshi "Human Activity Recognition: A Survey." Procedia Computer Science, 155 (2019), pp. 698-703
dc.referencesS. Kanarachos, S.R.G. Christopoulos, A. Chroneos "Smartphones as an integrated platform for monitoring driver behaviour: The role of sensor fusion and connectivity" Transportation Research Part C: Emerging Technologies, 95 (2016), pp. 867-882, October 2018pl_PL
dc.referencesM. Lin, X. Zhao. (2019). “Application research of neural network in vehicle target recognition and classification”. pages 5-8. doi: 10.1109/icitbs.2019.00010.pl_PL
dc.referencesN. Mallat, M. Rossi and others "An empirical investigation of mobile ticketing service adoption in public transportation" Personal and Ubiquitous Computing, 12 (1) (2008), pp. 57-65 2008pl_PL
dc.referencesM.W. Mastalerz, A. Malinowski and others "Use of IoT Solutions for Creating a BeIn/BeOut Model of an Electronic Fare Collection System for Public Transport - a Research Model", Task Publishing, Gdansk University of Technology (2019), pp. 85-91 Gdansk 2019pl_PL
dc.referencesT. McDaniel, F. Haendler, (1993) “Advanced rf cards for fare collection”. Telesystems Conference. Commercial Applications and Dual-UseTechnology, Conference Proceedings., National, Jun 1993, pp. 31-35.pl_PL
dc.referencesW. Narzt, S. Mayerhofer and others. (2015). “Be-in/be-out with Bluetooth low energy: Implicit ticketing for public transportation systems, Intelligent Transportation Systems (ITSC)”, 2015 IEEE 18th International Conference on. IEEE, 2015, pp. 1551- 1556.pl_PL
dc.referencesH. Omrani "Predicting travel mode of individuals by machine learning", 10 (2015), pp. 840-849, ISSN2352-1465. doi: 10.1016/j.trpro.2015.09.037. URLpl_PL
dc.referencesW. Posdorfer, W. Maalej "Towards context-aware surveys using bluetooth beacons" Procedia Computer Science, 83 (2016), pp. 42-49pl_PL
dc.referencesE. A. Roxas, R. R. P. Vicerra and others. (2018). “Multi-scale vehicle classification using different machine learning models”. In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM), pages 1-5. IEEE. ISBN 978-1-5386-7767-4. doi: 10.1109/HNICEM.2018.8666378.pl_PL
dc.referencesC. Sarkar, J.J. Treurniet and others "SEAT: Secure Energy-Efficient Automated Public Transport Ticketing System" IEEE Trans. on Green Commun. Netw., 2 (2018), pp. 222-233
dc.referencesJ. Shah, J. Kothari, N. Doshi. (2019.) “A Survey of Smart City infrastructure via Case study on New York”. Procedia Computer Science, The 10th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN-2019) / The 9th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH-2019) / Affiliated Workshops 160, 702-705.
dc.referencesG. Singh, D. Bansal, S. Sofat "A smartphone based technique to monitor driving behavior using DTW and crowdsensing" Pervasive and Mobile Computing, 40 (2017), pp. 56-70, (September 2017)pl_PL
dc.referencesM. Simoncini, L. Taccari and others "Vehicle classification from low-frequency GPS data with recurrent neural networks" Transportation Research Part C: Emerging Technologies, 91 (2018), pp. 176-191
dc.referencesD. Vakula, B. Raviteja. (2017) “Smart public transport for smart cities” in: 2017 International Conference on Intelligent Sustainable Systems (ICISS). Presented at the 2017 International Conference on Intelligent Sustainable Systems (ICISS), IEEE, Palladam, pp. 805-810.
dc.referencesS. van Cranenburgh, A. Alwosheel "An artificial neural network based approach to investigate travellers’ decision rules" Transportation Research Part C: Emerging Technologies, 98 (2019), pp. 152-166
dc.referencesE.I. Vlahogianni, E.N. Barmpounakis "Driving analytics using smartphones: Algorithms, comparisons and challenges" Transportation Research Part C: Emerging Technologies, 79 (2017), pp. 196-206
dc.referencesR. Widmann, S. Grünberger and others. (2012). “System integration of nfc ticketing into an existing public transport infrastructure”, Near Field Communication (NFC), 2012 4th International Workshop on. IEEE, 2012, pp. 13-18.pl_PL
dc.referencesM.H. Wirtz, J. Klähr. (2018) “Smartphone based in/out ticketing systems: a new generation of ticketing in public transport and its performance testing”. Presented at the URBAN TRANSPORT 2018, Seville, Spain, pp. 351-359.
dc.disciplineekonomia i finansepl_PL

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivatives 4.0 Międzynarodowe
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Międzynarodowe