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dc.contributor.authorOzuysal, Mustafa
dc.contributor.authorTayfur, Gokmen
dc.contributor.authorTanyel, Serhan
dc.date.accessioned2021-02-12T18:52:14Z
dc.date.available2021-02-12T18:52:14Z
dc.date.issued2012
dc.identifier.issn0353-5320
dc.identifier.urihttps://doi.org/10.7307/ptt.v24i1.264
dc.identifier.urihttps://hdl.handle.net/11147/10565
dc.descriptionOzuysal, Mustafa/0000-0002-3276-3075; TAYFUR, GOKMEN/0000-0001-9712-4031en_US
dc.description.abstractPassenger flow estimation of transit systems is essential for new decisions about additional facilities and feeder lines. For increasing the efficiency of an existing transit line, stations which are insufficient for trip production and attraction should be examined first. Such investigation supports decisions for feeder line projects which may seem necessary or futile according to the findings. In this study, passenger flow of a light rail transit (LRT) system in Izmir, Turkey is estimated by using multiple regression and feed-forward back-propagation type of artificial neural networks (ANN). The number of alighting passengers at each station is estimated as a function of boarding passengers from other stations. It is found that ANN approach produced significantly better estimations specifically for the low passenger attractive stations. In addition, ANN is found to be more capable for the determination of trip-attractive parts of LRT lines.en_US
dc.description.sponsorshipTUBITAK, The Scientific and Technological Research Council of TurkeyTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)en_US
dc.description.sponsorshipThe authors would like to thank the personnel of Izmir Metro Inc., Ilgaz Candemir, Emre Oral and Nurten Caliskan for providing the data of the study. Besides, Mustafa Ozuysal appreciates TUBITAK, The Scientific and Technological Research Council of Turkey for doctorate scholarship.en_US
dc.language.isoengen_US
dc.publisherSVENCILISTE U ZAGREBU, FAKULTET PROMETNIH ZNANOSTIen_US
dc.relation.isversionof10.7307/ptt.v24i1.264en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectlight rail transiten_US
dc.subjectmultiple regressionen_US
dc.subjectartificial neural networksen_US
dc.subjectpublic transportationen_US
dc.titlePASSENGER FLOWS ESTIMATION OF LIGHT RAIL TRANSIT (LRT) SYSTEM IN IZMIR, TURKEY USING MULTIPLE REGRESSION AND ANN METHODSen_US
dc.typearticleen_US
dc.typearticleen_US
dc.relation.journalPromet-Traffic & Transportationen_US
dc.contributor.departmentIzmir Isntitute of Technologyen_US
dc.identifier.volume24en_US
dc.identifier.issue1en_US
dc.identifier.startpage1en_US
dc.identifier.endpage14en_US
dc.identifier.wosWOS:000301566300001
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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