Big data predictions of Seasonal Fluctuations in Marine Traffic (using AIS data) by monitoring idle ships
The current article analyzes big data and makes predictions for the sea freight transport trends. It aims to support the management of sea transport, improve sea logistics services to predict trends in sea freight transport, support the management of sea transport, improve sea logistics services, a...
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| Format: | Article |
| Language: | English |
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University of Economics – Varna
2024-12-01
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| Series: | Business & Management Compass |
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| Online Access: | https://bi.ue-varna.bg/ojs/index.php/bmc/article/view/83 |
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| author | Liliya Mileva |
| author_facet | Liliya Mileva |
| author_sort | Liliya Mileva |
| collection | DOAJ |
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The current article analyzes big data and makes predictions for the sea freight transport trends. It aims to support the management of sea transport, improve sea logistics services to predict trends in sea freight transport, support the management of sea transport, improve sea logistics services, and optimize the organization of transport services. The used data are data from the Automatic identification system (AIS data). The methods used are SQL queries and Excel functions for data extraction and calculations for data to receive results about seasonality and structural change indexes in five years (2019-2023). The study examines seasonality and idle ship indexes for the most commonly used ship types carrying cargo divided into three categories: cargo, tanker, and other types of ships. The results of calculations indicate a pronounced seasonality in the number of idle ships transiting through the Port of Varna, which appears to be directly proportional to the "idle ship" metric. Specifically, a decline in the idle ship index correlates with a decrease in the overall number of vessels. This relationship leads to an important inference: no unnecessary delays occur at the Port of Varna. Instead, any significant delays observed are primarily attributed to an increased number of ships during certain months. By understanding the cyclical patterns and supply-demand dynamics in the sea freight market, logistics providers can make more informed decisions and adapt their strategies accordingly. For the research period, other similar research is absent in the field of big data analysis in maritime traffic.
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| format | Article |
| id | doaj-art-dd81701515f741648b7e7337c08e8b46 |
| institution | Kabale University |
| issn | 3033-0106 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | University of Economics – Varna |
| record_format | Article |
| series | Business & Management Compass |
| spelling | doaj-art-dd81701515f741648b7e7337c08e8b462025-02-11T09:00:19ZengUniversity of Economics – VarnaBusiness & Management Compass3033-01062024-12-0168410.56065/f0wzb375Big data predictions of Seasonal Fluctuations in Marine Traffic (using AIS data) by monitoring idle shipsLiliya Mileva0University of Economics Varna The current article analyzes big data and makes predictions for the sea freight transport trends. It aims to support the management of sea transport, improve sea logistics services to predict trends in sea freight transport, support the management of sea transport, improve sea logistics services, and optimize the organization of transport services. The used data are data from the Automatic identification system (AIS data). The methods used are SQL queries and Excel functions for data extraction and calculations for data to receive results about seasonality and structural change indexes in five years (2019-2023). The study examines seasonality and idle ship indexes for the most commonly used ship types carrying cargo divided into three categories: cargo, tanker, and other types of ships. The results of calculations indicate a pronounced seasonality in the number of idle ships transiting through the Port of Varna, which appears to be directly proportional to the "idle ship" metric. Specifically, a decline in the idle ship index correlates with a decrease in the overall number of vessels. This relationship leads to an important inference: no unnecessary delays occur at the Port of Varna. Instead, any significant delays observed are primarily attributed to an increased number of ships during certain months. By understanding the cyclical patterns and supply-demand dynamics in the sea freight market, logistics providers can make more informed decisions and adapt their strategies accordingly. For the research period, other similar research is absent in the field of big data analysis in maritime traffic. https://bi.ue-varna.bg/ojs/index.php/bmc/article/view/83Marine TrafficLogisticsSea Freight TransportManagement of Sea TrafficStructural Change IndexSeasonality Index of Sea Transport |
| spellingShingle | Liliya Mileva Big data predictions of Seasonal Fluctuations in Marine Traffic (using AIS data) by monitoring idle ships Business & Management Compass Marine Traffic Logistics Sea Freight Transport Management of Sea Traffic Structural Change Index Seasonality Index of Sea Transport |
| title | Big data predictions of Seasonal Fluctuations in Marine Traffic (using AIS data) by monitoring idle ships |
| title_full | Big data predictions of Seasonal Fluctuations in Marine Traffic (using AIS data) by monitoring idle ships |
| title_fullStr | Big data predictions of Seasonal Fluctuations in Marine Traffic (using AIS data) by monitoring idle ships |
| title_full_unstemmed | Big data predictions of Seasonal Fluctuations in Marine Traffic (using AIS data) by monitoring idle ships |
| title_short | Big data predictions of Seasonal Fluctuations in Marine Traffic (using AIS data) by monitoring idle ships |
| title_sort | big data predictions of seasonal fluctuations in marine traffic using ais data by monitoring idle ships |
| topic | Marine Traffic Logistics Sea Freight Transport Management of Sea Traffic Structural Change Index Seasonality Index of Sea Transport |
| url | https://bi.ue-varna.bg/ojs/index.php/bmc/article/view/83 |
| work_keys_str_mv | AT liliyamileva bigdatapredictionsofseasonalfluctuationsinmarinetrafficusingaisdatabymonitoringidleships |