Revolutionizing Marine Traffic Management: A Comprehensive Review of Machine Learning Applications in Complex Maritime Systems
This review article explores the applications and impacts of Machine Learning (ML) techniques in marine traffic management and prediction within complex maritime systems. It provides an overview of ML techniques, delves into their practical applications in the maritime sector, and presents an in-dep...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/14/8099 |
| _version_ | 1827734125842268160 |
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| author | Irmina Durlik Tymoteusz Miller Lech Dorobczyński Polina Kozlovska Tomasz Kostecki |
| author_facet | Irmina Durlik Tymoteusz Miller Lech Dorobczyński Polina Kozlovska Tomasz Kostecki |
| author_sort | Irmina Durlik |
| collection | DOAJ |
| description | This review article explores the applications and impacts of Machine Learning (ML) techniques in marine traffic management and prediction within complex maritime systems. It provides an overview of ML techniques, delves into their practical applications in the maritime sector, and presents an in-depth analysis of their benefits and limitations. Real-world case studies are highlighted to illustrate the transformational impact of ML in this field. The article further provides a comparative analysis of different ML techniques and discusses the future directions and opportunities that lie ahead. Despite the challenges, ML’s potential to revolutionize marine traffic management and prediction, driving safer, more efficient, and more sustainable operations, is substantial. This review article serves as a comprehensive resource for researchers, industry professionals, and policymakers interested in the interplay between ML and maritime systems. |
| first_indexed | 2024-03-11T01:21:05Z |
| format | Article |
| id | doaj.art-e6f026933c924fc4a114ada3b4718690 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:21:05Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e6f026933c924fc4a114ada3b47186902023-11-18T18:08:11ZengMDPI AGApplied Sciences2076-34172023-07-011314809910.3390/app13148099Revolutionizing Marine Traffic Management: A Comprehensive Review of Machine Learning Applications in Complex Maritime SystemsIrmina Durlik0Tymoteusz Miller1Lech Dorobczyński2Polina Kozlovska3Tomasz Kostecki4Faculty of Navigation, Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, PolandPolish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, PolandFaculty of Mechatronics and Electrical Engineering, Maritime University of Szczecin, Willowa 2, 71-650 Szczecin, PolandPolish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, PolandA.P. Møller-Mærsk A/S, Esplanaden 50, DK-1098 Copenhagen, DenmarkThis review article explores the applications and impacts of Machine Learning (ML) techniques in marine traffic management and prediction within complex maritime systems. It provides an overview of ML techniques, delves into their practical applications in the maritime sector, and presents an in-depth analysis of their benefits and limitations. Real-world case studies are highlighted to illustrate the transformational impact of ML in this field. The article further provides a comparative analysis of different ML techniques and discusses the future directions and opportunities that lie ahead. Despite the challenges, ML’s potential to revolutionize marine traffic management and prediction, driving safer, more efficient, and more sustainable operations, is substantial. This review article serves as a comprehensive resource for researchers, industry professionals, and policymakers interested in the interplay between ML and maritime systems.https://www.mdpi.com/2076-3417/13/14/8099machine learningmaritime systemsmarine traffic managementpredictive analyticsautonomous vessels |
| spellingShingle | Irmina Durlik Tymoteusz Miller Lech Dorobczyński Polina Kozlovska Tomasz Kostecki Revolutionizing Marine Traffic Management: A Comprehensive Review of Machine Learning Applications in Complex Maritime Systems Applied Sciences machine learning maritime systems marine traffic management predictive analytics autonomous vessels |
| title | Revolutionizing Marine Traffic Management: A Comprehensive Review of Machine Learning Applications in Complex Maritime Systems |
| title_full | Revolutionizing Marine Traffic Management: A Comprehensive Review of Machine Learning Applications in Complex Maritime Systems |
| title_fullStr | Revolutionizing Marine Traffic Management: A Comprehensive Review of Machine Learning Applications in Complex Maritime Systems |
| title_full_unstemmed | Revolutionizing Marine Traffic Management: A Comprehensive Review of Machine Learning Applications in Complex Maritime Systems |
| title_short | Revolutionizing Marine Traffic Management: A Comprehensive Review of Machine Learning Applications in Complex Maritime Systems |
| title_sort | revolutionizing marine traffic management a comprehensive review of machine learning applications in complex maritime systems |
| topic | machine learning maritime systems marine traffic management predictive analytics autonomous vessels |
| url | https://www.mdpi.com/2076-3417/13/14/8099 |
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