Deep Learning-Based Ship Speed Prediction for Intelligent Maritime Traffic Management
Improving maritime operations planning and scheduling can play an important role in enhancing the sector’s performance and competitiveness. In this context, accurate ship speed estimation is crucial to ensure efficient maritime traffic management. This study addresses the problem of ship speed predi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/1/191 |
| _version_ | 1797440288568901632 |
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| author | Sara El Mekkaoui Loubna Benabbou Stéphane Caron Abdelaziz Berrado |
| author_facet | Sara El Mekkaoui Loubna Benabbou Stéphane Caron Abdelaziz Berrado |
| author_sort | Sara El Mekkaoui |
| collection | DOAJ |
| description | Improving maritime operations planning and scheduling can play an important role in enhancing the sector’s performance and competitiveness. In this context, accurate ship speed estimation is crucial to ensure efficient maritime traffic management. This study addresses the problem of ship speed prediction from a Maritime Vessel Services perspective in an area of the Saint Lawrence Seaway. The challenge is to build a real-time predictive model that accommodates different routes and vessel types. This study proposes a data-driven solution based on deep learning sequence methods and historical ship trip data to predict ship speeds at different steps of a voyage. It compares three different sequence models and shows that they outperform the baseline ship speed rates used by the VTS. The findings suggest that deep learning models combined with maritime data can leverage the challenge of estimating ship speed. The proposed solution could provide accurate and real-time estimations of ship speed to improve shipping operational efficiency, navigation safety and security, and ship emissions estimation and monitoring. |
| first_indexed | 2024-03-09T12:05:59Z |
| format | Article |
| id | doaj.art-bb0a8e48b92e4c9dadfe5a28748c8e57 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T12:05:59Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-bb0a8e48b92e4c9dadfe5a28748c8e572023-11-30T22:58:26ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-01-0111119110.3390/jmse11010191Deep Learning-Based Ship Speed Prediction for Intelligent Maritime Traffic ManagementSara El Mekkaoui0Loubna Benabbou1Stéphane Caron2Abdelaziz Berrado3Équipe AMIPS, Ecole Mohammadia d’Ingénieurs, Mohammed V University in Rabat, Rabat 10090, MoroccoDépartement Sciences de la Gestion, Université du Québec à Rimouski, Lévis, QC G6V 0A6, CanadaXpert Solutions Technologiques Inc., Lévis, QC G6W 1J6, CanadaÉquipe AMIPS, Ecole Mohammadia d’Ingénieurs, Mohammed V University in Rabat, Rabat 10090, MoroccoImproving maritime operations planning and scheduling can play an important role in enhancing the sector’s performance and competitiveness. In this context, accurate ship speed estimation is crucial to ensure efficient maritime traffic management. This study addresses the problem of ship speed prediction from a Maritime Vessel Services perspective in an area of the Saint Lawrence Seaway. The challenge is to build a real-time predictive model that accommodates different routes and vessel types. This study proposes a data-driven solution based on deep learning sequence methods and historical ship trip data to predict ship speeds at different steps of a voyage. It compares three different sequence models and shows that they outperform the baseline ship speed rates used by the VTS. The findings suggest that deep learning models combined with maritime data can leverage the challenge of estimating ship speed. The proposed solution could provide accurate and real-time estimations of ship speed to improve shipping operational efficiency, navigation safety and security, and ship emissions estimation and monitoring.https://www.mdpi.com/2077-1312/11/1/191shippingmachine learningsequence modelingartificial intelligencedigitalizationnavigation data |
| spellingShingle | Sara El Mekkaoui Loubna Benabbou Stéphane Caron Abdelaziz Berrado Deep Learning-Based Ship Speed Prediction for Intelligent Maritime Traffic Management Journal of Marine Science and Engineering shipping machine learning sequence modeling artificial intelligence digitalization navigation data |
| title | Deep Learning-Based Ship Speed Prediction for Intelligent Maritime Traffic Management |
| title_full | Deep Learning-Based Ship Speed Prediction for Intelligent Maritime Traffic Management |
| title_fullStr | Deep Learning-Based Ship Speed Prediction for Intelligent Maritime Traffic Management |
| title_full_unstemmed | Deep Learning-Based Ship Speed Prediction for Intelligent Maritime Traffic Management |
| title_short | Deep Learning-Based Ship Speed Prediction for Intelligent Maritime Traffic Management |
| title_sort | deep learning based ship speed prediction for intelligent maritime traffic management |
| topic | shipping machine learning sequence modeling artificial intelligence digitalization navigation data |
| url | https://www.mdpi.com/2077-1312/11/1/191 |
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