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A simplified forecasting model for the estimation of container traffic in seaports at a national level – the case of Poland

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Comprehensive forecasting of future volumes of container traffic in seaports is important when it comes to port development, including investments, especially in relation to costly transport infrastructure (e.g. new terminals). The aim of this article is to present a specific, simplified model of demand forecasting for container traffic in seaports as well as to give a practical verification of the model in the Polish seaport sector. The model consists of relevant indexes of containerisation (values, dynamics) referring to the macroeconomic characteristics of the country of cargo origin as well as destination-predictor variables (e.g. population, foreign trade, gross domestic product). This method will facilitate the evaluation of three basic segments of the container market: foreign trade services, maritime transit flows and land transit flows. International comparisons of indexes (benchmarking) as well as extrapolations of future changes can support this prediction process. A practical implementation of this research has enabled us to calculate that the total container volume in Poland will be approximately 4.69 – 4.87 million TEU by the year 2023.
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  • Gdynia Maritime University, Gdynia, Poland
Bibliografia
  • [1] Chen, S. H. and Chen, J. N. (2010) ‘Forecasting container throughputs at ports using genetic programming’, Expert Systems with Applications. Elsevier Ltd, 37(3), pp. 2054–2058. doi: 10.1016/j.eswa.2009.06.054.
  • [2] Chou, C. C., Chu, C. W. and Liang, G. S. (2008) ‘A modified regression model for forecasting the volumes of Taiwan’s import containers’, Mathematical and Computer Modelling, 47(9–10), pp. 797–807. doi: 10.1016/j.mcm.2007.05.005.
  • [3] Darabi, S. and Suljevic, M. (2015) ‘Forecasting Process for Predicting Container Volumes in the Shipping Industry’.
  • [4] Diaz, R., Talley, W. and Tulpule, M. (2011) ‘Forecasting empty container volumes’, Asian Journal of Shipping and Logistics, 27(2), pp. 217–236. doi: 10.1016/S20925212(11)80010-6.
  • [5] Gokkus, Ü., Sinan Yildirim, M. and Akoglu, K. (2015) ‘Prediction of the Container Traffic in a Seaport Stockyard Using Genetic Algorithm’, 7(03), pp. 9–15.
  • [6] Gökkuş, Ü., Yildirim, M. S. and Aydin, M. M. (2017) ‘Estimation of Container Traffic at Seaports by Using Several Soft Computing Methods: A Case of Turkish Seaports’, Discrete Dynamics in Nature and Society, 2017. doi: 10.1155/2017/2984853.
  • [7] Gosasang, V., Chandraprakaikul, W. and Kiattisin, S. (2011) ‘A comparison of traditional and neural networks forecasting techniques for container throughput at bangkok port’, Asian Journal of Shipping and Logistics, 27(3), pp. 463–482. doi: 10.1016/S2092-5212(11)80022-2.
  • [8] Huang, A. et al. (2015) ‘An interval knowledge based forecasting paradigm for container throughput prediction’, Procedia Computer Science. Elsevier Masson SAS, 55(Itqm), pp. 1381–1389. doi: 10.1016/j.procs.2015.07.126.
  • [9] Huang, A., Qiao, H. and Wang, S. (2014) ‘Forecasting container throughputs with domain knowledge’, Procedia Computer Science. Elsevier Masson SAS, 31(Itqm), pp. 648–655. doi: 10.1016/j.procs.2014.05.312.
  • [10] Iannone, R. et al. (2016) ‘Proposal for a flexible discrete event simulation model for assessing the daily operation decisions in a Ro-Ro terminal’, Simulation Modelling Practice and Theory. Elsevier B.V., 61, pp. 28–46. doi: 10.1016/j.simpat.2015.11.005.
  • [11] Jensen, M. (2014) Forecasting Container Cargo Throughput in Ports, Erasmus University Rotterdam.
  • [12] Kotcharat, P. (2016) ‘The Maritime Commons: Digital Repository of the World A forecasting model for container throughput: empirical research for Laem Chabang Port , Thailand Kingdom of Thailand’.
  • [13] KRILE, S., MAIOROV, N. and FETISOV, V. (2018) ‘Forecasting the Operational Activities of the Sea Passenger Terminal Using Intelligent Technologies’, Transport Problems, 13(1), pp. 27–36. doi: 10.21307/tp.2018.13.1.3.
  • [14] Lappalainen, A. (2013) ‘Scenario-based traffic forecast for routes between the penta ports in 2020’, Publication from the Centre for Maritime Studies, University of Turku, A65
  • [15] Peng, W. Y. and Chu, C. W. (2009) ‘A comparison of univariate methods for forecasting container throughput volumes’, Mathematical and Computer Modelling. Elsevier Ltd, 50(7–8), pp. 1045–1057. doi: 10.1016/j.mcm.2009.05.027.
  • [16] Population projection at national level (2015-2080) (2019) Eurostat, https://ec.europa.eu/eurostat/data/database.
  • [17] Rahman, N. S. F. A., Muridan, M. and Najib, A. F. A. (2015) ‘A Maritime Forecasting Method for Analysing the Total Cargo Handling at Johor Port Berhad from 2013 to 2020’, 6(3), pp. 187–193.
  • [18] Rashed, Y. et al. (2018) ‘A combined approach to forecast container throughput demand: Scenarios for the Hamburg-Le Havre range of ports’, Transportation Research Part A: Policy and Practice. Elsevier, 117(July 2016), pp. 127–141. doi: 10.1016/j.tra.2018.08.010.
  • [19] Statistical Yearbook of Maritime Economy (2018) Statistic Poland. Statistical Office in Szczecin, Warsaw/Szczecin.
  • [20] World Economic Outlook Database, October 2018 (2018) IMF, https://www.imf.org/external/pubs/ft/weo/2018/02/weodata/in dex.aspx.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-e30e909d-c125-4a87-ae1d-7620ca5cf812
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