A model for port throughput forecasting using Bayesian estimation
Capacity plays a crucial role in a port’s competitive position and the growth of its market share. An investment decision to provide new port capacity should be supported by a growing demand for port services. However, port demand is volatile and uncertain in an increasingly competitive market envir...
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ftpubmed:oai:pubmedcentral.nih.gov:7968410 2023-05-15T16:50:10+02:00 A model for port throughput forecasting using Bayesian estimation Eskafi, Majid Kowsari, Milad Dastgheib, Ali Ulfarsson, Gudmundur F. Stefansson, Gunnar Taneja, Poonam Thorarinsdottir, Ragnheidur I. 2021-03-17 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7968410/ https://doi.org/10.1057/s41278-021-00190-x en eng Palgrave Macmillan UK http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7968410/ http://dx.doi.org/10.1057/s41278-021-00190-x © The Author(s), under exclusive licence to Springer Nature Limited 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Marit Econ Logist Original Article Text 2021 ftpubmed https://doi.org/10.1057/s41278-021-00190-x 2021-03-21T01:49:35Z Capacity plays a crucial role in a port’s competitive position and the growth of its market share. An investment decision to provide new port capacity should be supported by a growing demand for port services. However, port demand is volatile and uncertain in an increasingly competitive market environment. Also, forecasting models themselves are associated with epistemic uncertainty due to model and parameter uncertainties. This paper applies a Bayesian statistical method to forecast the annual throughput of the multipurpose Port of Isafjordur in Iceland. Model uncertainties are thus taken into account, while parameter uncertainties are handled by selecting influencing macroeconomic variables based on mutual information analysis. The presented model has an adaptive capability as new information becomes available. Our method results in a range of port throughput forecasts, in addition to a point estimate, and it also accounts for epistemic uncertainty, thus increasing the reliability of forecasts. Our results provide support for informed decision-making in capacity planning and management. Our forecasts show a constant linear growth of containerized throughput the period 2020–2025. Noncontainerized throughput declines rapidly over the same period. Text Iceland PubMed Central (PMC) Maritime Economics & Logistics 23 2 348 368 |
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Original Article Eskafi, Majid Kowsari, Milad Dastgheib, Ali Ulfarsson, Gudmundur F. Stefansson, Gunnar Taneja, Poonam Thorarinsdottir, Ragnheidur I. A model for port throughput forecasting using Bayesian estimation |
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Original Article |
description |
Capacity plays a crucial role in a port’s competitive position and the growth of its market share. An investment decision to provide new port capacity should be supported by a growing demand for port services. However, port demand is volatile and uncertain in an increasingly competitive market environment. Also, forecasting models themselves are associated with epistemic uncertainty due to model and parameter uncertainties. This paper applies a Bayesian statistical method to forecast the annual throughput of the multipurpose Port of Isafjordur in Iceland. Model uncertainties are thus taken into account, while parameter uncertainties are handled by selecting influencing macroeconomic variables based on mutual information analysis. The presented model has an adaptive capability as new information becomes available. Our method results in a range of port throughput forecasts, in addition to a point estimate, and it also accounts for epistemic uncertainty, thus increasing the reliability of forecasts. Our results provide support for informed decision-making in capacity planning and management. Our forecasts show a constant linear growth of containerized throughput the period 2020–2025. Noncontainerized throughput declines rapidly over the same period. |
format |
Text |
author |
Eskafi, Majid Kowsari, Milad Dastgheib, Ali Ulfarsson, Gudmundur F. Stefansson, Gunnar Taneja, Poonam Thorarinsdottir, Ragnheidur I. |
author_facet |
Eskafi, Majid Kowsari, Milad Dastgheib, Ali Ulfarsson, Gudmundur F. Stefansson, Gunnar Taneja, Poonam Thorarinsdottir, Ragnheidur I. |
author_sort |
Eskafi, Majid |
title |
A model for port throughput forecasting using Bayesian estimation |
title_short |
A model for port throughput forecasting using Bayesian estimation |
title_full |
A model for port throughput forecasting using Bayesian estimation |
title_fullStr |
A model for port throughput forecasting using Bayesian estimation |
title_full_unstemmed |
A model for port throughput forecasting using Bayesian estimation |
title_sort |
model for port throughput forecasting using bayesian estimation |
publisher |
Palgrave Macmillan UK |
publishDate |
2021 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7968410/ https://doi.org/10.1057/s41278-021-00190-x |
genre |
Iceland |
genre_facet |
Iceland |
op_source |
Marit Econ Logist |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7968410/ http://dx.doi.org/10.1057/s41278-021-00190-x |
op_rights |
© The Author(s), under exclusive licence to Springer Nature Limited 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
op_doi |
https://doi.org/10.1057/s41278-021-00190-x |
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Maritime Economics & Logistics |
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23 |
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2 |
container_start_page |
348 |
op_container_end_page |
368 |
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1766040348646178816 |