Short Term Forecast of Container Throughput: New Variables Application for the Port of Douala
An accurate container throughput forecast is vital for any port. Since overall improvements in port performance and competitiveness can be derailed by port bottlenecks, ports need to find leverage to identify and prioritize measures to improve weak key performance indicators (KPIs) to attain growth...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/9/7/720 |
| _version_ | 1827600865958035456 |
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| author | Penn Collins Awah Hyungsik Nam Sihyun Kim |
| author_facet | Penn Collins Awah Hyungsik Nam Sihyun Kim |
| author_sort | Penn Collins Awah |
| collection | DOAJ |
| description | An accurate container throughput forecast is vital for any port. Since overall improvements in port performance and competitiveness can be derailed by port bottlenecks, ports need to find leverage to identify and prioritize measures to improve weak key performance indicators (KPIs) to attain growth opportunities. Prior studies had modeled container throughput from socio-economic and growth projection factors. This study aims to provide a practical method for forecasting the optimal container throughput a port can physically handle/attract given a certain level of terminal operation efficiency through random forest (RF) and multilayer perceptron (MLP) models. The study variables are derived from the port operations dimension and include ship turnaround time, vessel draft, container dwell time, berth productivity, container storage capacity, and custom declaration time. Evaluations are made based on the R-squared, NRMSE, MAE and MAPE. Model comparison is deduced with seven competing models in container throughput forecasting. The findings indicate that the RF model is a potential candidate for forecasting the engineering optimal throughput of Douala port. Model interpretation is provided through feature importance and partial dependence plots. The findings from this study will help reduce uncertainty and provide leverage for port management to spot bottlenecks and engage in better port planning and development projects which will strengthen their international competitive advantage. |
| first_indexed | 2024-03-09T04:44:09Z |
| format | Article |
| id | doaj.art-9fa7761b82384b03be806cec313e6def |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T04:44:09Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-9fa7761b82384b03be806cec313e6def2023-12-03T13:17:36ZengMDPI AGJournal of Marine Science and Engineering2077-13122021-06-019772010.3390/jmse9070720Short Term Forecast of Container Throughput: New Variables Application for the Port of DoualaPenn Collins Awah0Hyungsik Nam1Sihyun Kim2Department of Logistics, Korea Maritime and Ocean University, Busan 49112, KoreaDepartment of Logistics, Korea Maritime and Ocean University, Busan 49112, KoreaDepartment of Logistics, Korea Maritime and Ocean University, Busan 49112, KoreaAn accurate container throughput forecast is vital for any port. Since overall improvements in port performance and competitiveness can be derailed by port bottlenecks, ports need to find leverage to identify and prioritize measures to improve weak key performance indicators (KPIs) to attain growth opportunities. Prior studies had modeled container throughput from socio-economic and growth projection factors. This study aims to provide a practical method for forecasting the optimal container throughput a port can physically handle/attract given a certain level of terminal operation efficiency through random forest (RF) and multilayer perceptron (MLP) models. The study variables are derived from the port operations dimension and include ship turnaround time, vessel draft, container dwell time, berth productivity, container storage capacity, and custom declaration time. Evaluations are made based on the R-squared, NRMSE, MAE and MAPE. Model comparison is deduced with seven competing models in container throughput forecasting. The findings indicate that the RF model is a potential candidate for forecasting the engineering optimal throughput of Douala port. Model interpretation is provided through feature importance and partial dependence plots. The findings from this study will help reduce uncertainty and provide leverage for port management to spot bottlenecks and engage in better port planning and development projects which will strengthen their international competitive advantage.https://www.mdpi.com/2077-1312/9/7/720container throughput forecastingport operationsport attractivenessDouala port |
| spellingShingle | Penn Collins Awah Hyungsik Nam Sihyun Kim Short Term Forecast of Container Throughput: New Variables Application for the Port of Douala Journal of Marine Science and Engineering container throughput forecasting port operations port attractiveness Douala port |
| title | Short Term Forecast of Container Throughput: New Variables Application for the Port of Douala |
| title_full | Short Term Forecast of Container Throughput: New Variables Application for the Port of Douala |
| title_fullStr | Short Term Forecast of Container Throughput: New Variables Application for the Port of Douala |
| title_full_unstemmed | Short Term Forecast of Container Throughput: New Variables Application for the Port of Douala |
| title_short | Short Term Forecast of Container Throughput: New Variables Application for the Port of Douala |
| title_sort | short term forecast of container throughput new variables application for the port of douala |
| topic | container throughput forecasting port operations port attractiveness Douala port |
| url | https://www.mdpi.com/2077-1312/9/7/720 |
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