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dc.contributor.authorKayıran, Burhan
dc.contributor.authorSaatçioğlu, Ömür
dc.date.accessioned2022-12-01T12:33:32Z
dc.date.available2022-12-01T12:33:32Z
dc.date.issued2022en_US
dc.identifier.citationKayıran, B. & Saatçioğlu, Ö. (2022). Container Cargo Demand Forecast for Gemlik Bay Ports . Journal of Maritime Transport and Logistics , 3 (2) , 50-60 . DOI: 10.52602/mtl.1126012 https://doi.org/10.52602/mtl.1126012en_US
dc.identifier.issn2757-8119
dc.identifier.urihttps://doi.org/10.52602/mtl.1126012
dc.identifier.urihttps://dergipark.org.tr/en/pub/mtl/issue/71040/1126012
dc.identifier.urihttps://hdl.handle.net/20.500.12508/2367
dc.description.abstractA large part of world trade is carried out by maritime transport. In addition, ports are the only gates of maritime transport to international trade. In this sense, capacity and demand planning for ports, which are an important node of maritime transport, is necessary for both port operators and other logistics enterprises such as maritime trade fleets, foreign trade enterprises that are parties to international trade, intermediary institutions, third, fourth and fifth parties, and government institutions that It provides very important contributions in making efficiency analyzes and creating investment plans for. In this study, as stated above, container cargo demand forecasting analysis was carried out for the ports of Gemlik Bay, which is one of the important centers opened to foreign trade and located in the Marmara Region of the Republic of Turkey, in order to shed light on the capacity and investment plans for the port operators and other parties For this purpose, container handling data for the ports of Gemlik Bay between 2004-2021 were used as the dependent variable (estimate variable) and again for the same years, Turkey's gross national product data, import and export data, population amount and total container amount handled. were considered as independent variables and annual container handling data of Gemlik Bay ports have been forecasted until 2028 by multiple regression analysis method with 93,1% adjusted R square value.en_US
dc.language.isoengen_US
dc.publisherİskenderun Teknik Üniversitesien_US
dc.relation.isversionof10.52602/mtl.1126012en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGemliken_US
dc.subjectPorten_US
dc.subjectContaineren_US
dc.subjectDemand forecasten_US
dc.subject.classificationDenizcilik
dc.titleContainer Cargo Demand Forecast for Gemlik Bay Portsen_US
dc.typearticleen_US
dc.relation.journalDeniz Taşımacılığı ve Lojistiği Dergisien_US
dc.relation.journalJournal of Maritime Transport and Logistics
dc.identifier.volume3en_US
dc.identifier.issue2en_US
dc.identifier.startpage50en_US
dc.identifier.endpage60en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Başka Kurum Yazarıen_US


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