Applying Transformers to Naval Route Prediction
This work studies the application of the transformer technology, usually used for natural language processing tasks, in the realm of maritime route prediction. We use as dataset the reports from the Automatic Identification System which contain the recent historic coordinate of most ships. Then we i...
- Autores:
-
Gómez Zapata, Camilo
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2024
- Institución:
- Universidad de Antioquia
- Repositorio:
- Repositorio UdeA
- Idioma:
- eng
- OAI Identifier:
- oai:bibliotecadigital.udea.edu.co:10495/45988
- Acceso en línea:
- https://hdl.handle.net/10495/45988
- Palabra clave:
- Deep learning (Machine learning)
Aprendizaje profundo (Aprendizaje automático)
Spatial data mining
Minería de datos espaciales
Ships - Automatic identification systems
Barcos - Sistemas de identificación automática
Maritime transport
Transporte marítimo
Machine learning
Aprendizaje automático
Artificial intelligence
Inteligencia artificial
Neural networks
Redes neuronales
Naval route prediction
Predicción de Rutas Navales
http://aims.fao.org/aos/agrovoc/c_49834
http://aims.fao.org/aos/agrovoc/c_27064
http://aims.fao.org/aos/agrovoc/c_37467
http://id.loc.gov/authorities/subjects/sh90001937
http://id.loc.gov/authorities/subjects/sh2021006947
http://id.loc.gov/authorities/subjects/sh2019001855
http://id.loc.gov/authorities/subjects/sh2003001275
http://vocabularies.unesco.org/thesaurus/concept11744
ODS 9: Industria, innovación e infraestructura. Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-sa/4.0/
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Applying Transformers to Naval Route Prediction |
| title |
Applying Transformers to Naval Route Prediction |
| spellingShingle |
Applying Transformers to Naval Route Prediction Deep learning (Machine learning) Aprendizaje profundo (Aprendizaje automático) Spatial data mining Minería de datos espaciales Ships - Automatic identification systems Barcos - Sistemas de identificación automática Maritime transport Transporte marítimo Machine learning Aprendizaje automático Artificial intelligence Inteligencia artificial Neural networks Redes neuronales Naval route prediction Predicción de Rutas Navales http://aims.fao.org/aos/agrovoc/c_49834 http://aims.fao.org/aos/agrovoc/c_27064 http://aims.fao.org/aos/agrovoc/c_37467 http://id.loc.gov/authorities/subjects/sh90001937 http://id.loc.gov/authorities/subjects/sh2021006947 http://id.loc.gov/authorities/subjects/sh2019001855 http://id.loc.gov/authorities/subjects/sh2003001275 http://vocabularies.unesco.org/thesaurus/concept11744 ODS 9: Industria, innovación e infraestructura. Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación |
| title_short |
Applying Transformers to Naval Route Prediction |
| title_full |
Applying Transformers to Naval Route Prediction |
| title_fullStr |
Applying Transformers to Naval Route Prediction |
| title_full_unstemmed |
Applying Transformers to Naval Route Prediction |
| title_sort |
Applying Transformers to Naval Route Prediction |
| dc.creator.fl_str_mv |
Gómez Zapata, Camilo |
| dc.contributor.advisor.none.fl_str_mv |
Rueda Muñoz, Edgar Alberto |
| dc.contributor.author.none.fl_str_mv |
Gómez Zapata, Camilo |
| dc.contributor.jury.none.fl_str_mv |
Pachón Contreras, Leonardo Augusto Salinas Jiménez, Hernán David |
| dc.subject.lcsh.none.fl_str_mv |
Deep learning (Machine learning) Aprendizaje profundo (Aprendizaje automático) Spatial data mining Minería de datos espaciales Ships - Automatic identification systems Barcos - Sistemas de identificación automática |
| topic |
Deep learning (Machine learning) Aprendizaje profundo (Aprendizaje automático) Spatial data mining Minería de datos espaciales Ships - Automatic identification systems Barcos - Sistemas de identificación automática Maritime transport Transporte marítimo Machine learning Aprendizaje automático Artificial intelligence Inteligencia artificial Neural networks Redes neuronales Naval route prediction Predicción de Rutas Navales http://aims.fao.org/aos/agrovoc/c_49834 http://aims.fao.org/aos/agrovoc/c_27064 http://aims.fao.org/aos/agrovoc/c_37467 http://id.loc.gov/authorities/subjects/sh90001937 http://id.loc.gov/authorities/subjects/sh2021006947 http://id.loc.gov/authorities/subjects/sh2019001855 http://id.loc.gov/authorities/subjects/sh2003001275 http://vocabularies.unesco.org/thesaurus/concept11744 ODS 9: Industria, innovación e infraestructura. Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación |
| dc.subject.unesco.none.fl_str_mv |
Maritime transport Transporte marítimo |
| dc.subject.agrovoc.none.fl_str_mv |
Machine learning Aprendizaje automático Artificial intelligence Inteligencia artificial Neural networks Redes neuronales |
| dc.subject.proposal.eng.fl_str_mv |
Naval route prediction |
| dc.subject.proposal.spa.fl_str_mv |
Predicción de Rutas Navales |
| dc.subject.agrovocuri.none.fl_str_mv |
http://aims.fao.org/aos/agrovoc/c_49834 http://aims.fao.org/aos/agrovoc/c_27064 http://aims.fao.org/aos/agrovoc/c_37467 |
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http://id.loc.gov/authorities/subjects/sh90001937 http://id.loc.gov/authorities/subjects/sh2021006947 http://id.loc.gov/authorities/subjects/sh2019001855 http://id.loc.gov/authorities/subjects/sh2003001275 |
| dc.subject.unescouri.none.fl_str_mv |
http://vocabularies.unesco.org/thesaurus/concept11744 |
| dc.subject.ods.none.fl_str_mv |
ODS 9: Industria, innovación e infraestructura. Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación |
| description |
This work studies the application of the transformer technology, usually used for natural language processing tasks, in the realm of maritime route prediction. We use as dataset the reports from the Automatic Identification System which contain the recent historic coordinate of most ships. Then we implement a methodology to discretize the dataset by splitting the world into a grid and splitting the data into routes, so that we can train a model to predict the next element of the route. And finally we perform the training on a subset of the full dataset, where we achieve a 68 % accuracy in the top 25% of ships in the validation dataset. |
| publishDate |
2024 |
| dc.date.issued.none.fl_str_mv |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2025-05-19T15:15:37Z |
| dc.type.none.fl_str_mv |
Trabajo de grado - Pregrado |
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http://purl.org/coar/resource_type/c_7a1f |
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http://purl.org/redcol/resource_type/TP |
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Text |
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http://purl.org/coar/version/c_b1a7d7d4d402bcce |
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info:eu-repo/semantics/bachelorThesis |
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info:eu-repo/semantics/draft |
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http://purl.org/coar/resource_type/c_7a1f |
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draft |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/10495/45988 |
| url |
https://hdl.handle.net/10495/45988 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.none.fl_str_mv |
Capobianco, Samuele et al. (2021). “Deep learning methods for vessel trajectory prediction based on recurrent neural networks”. In: IEEE Transactions on Aerospace and Electronic Systems 57.6, pp. 4329–4346. Nguyen, Duong and Ronan Fablet (2024). “A Transformer Network With Sparse Augmented Data Representation and Cross Entropy Loss for AIS-Based Vessel Trajectory Prediction”. In: IEEE Access 12, pp. 21596–21609. doi: 10.1109/ACCESS.2024.3349957. Ou, Ziqiang and Jianjun Zhu (2008). “AIS database powered by GIS technology for maritime safety and security”. In: The Journal of Navigation 61.4, pp. 655–665. Park, Jinwan, Jungsik Jeong, and Youngsoo Park (2021). “Ship trajectory prediction based on bi-LSTM using spectral-clustered AIS data”. In: Journal of marine science and engineering 9.9, p. 1037. Radford, Alec et al. (2019). “Language models are unsupervised multitask learners”. In: OpenAI blog 1.8, p. 9. Tu, Enmei et al. (2017). “Exploiting AIS data for intelligent maritime navigation: A comprehensive survey from data to methodology”. In: IEEE Transactions on Intelligent Transportation Systems 19.5, pp. 1559–1582. Vaswani, Ashish et al. (2017). “Attention is All You Need”. In: Advances in Neural Information Processing Systems. 30, pp. 5998–6008. Volkova, Tamara A, Yulia E Balykina, and Alexander Bespalov (2021). “Predicting ship trajectory based on neural networks using AIS data”. In: Journal of Marine Science and Engineering 9.3, p. 254. Wang, Congcong, Paul Nulty, and David Lillis (Dec. 2020). “A Comparative Study on Word Embeddings in Deep Learning for Text Classification”. In: pp. 37–46. doi: 10.1145/3443279.3443304. |
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Attribution-NonCommercial-ShareAlike 4.0 International |
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22 páginas |
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Universidad de Antioquia |
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Física |
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Instituto de Física |
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Medellín, Colombia |
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Facultad de Ciencias Exactas y Naturales |
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Campus Medellín - Ciudad Universitaria |
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Universidad de Antioquia |
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Universidad de Antioquia |
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Rueda Muñoz, Edgar AlbertoGómez Zapata, CamiloPachón Contreras, Leonardo AugustoSalinas Jiménez, Hernán David2025-05-19T15:15:37Z2024https://hdl.handle.net/10495/45988This work studies the application of the transformer technology, usually used for natural language processing tasks, in the realm of maritime route prediction. We use as dataset the reports from the Automatic Identification System which contain the recent historic coordinate of most ships. Then we implement a methodology to discretize the dataset by splitting the world into a grid and splitting the data into routes, so that we can train a model to predict the next element of the route. And finally we perform the training on a subset of the full dataset, where we achieve a 68 % accuracy in the top 25% of ships in the validation dataset.If you would like access to the Python code or the data used, please contact the author via email at camilo.gomez9@udea.edu.coPregradoFísico22 páginasapplication/pdfengUniversidad de AntioquiaFísicaInstituto de FísicaMedellín, ColombiaFacultad de Ciencias Exactas y NaturalesCampus Medellín - Ciudad Universitariahttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://purl.org/coar/access_right/c_abf2Deep learning (Machine learning)Aprendizaje profundo (Aprendizaje automático)Spatial data miningMinería de datos espacialesShips - Automatic identification systemsBarcos - Sistemas de identificación automáticaMaritime transportTransporte marítimoMachine learningAprendizaje automáticoArtificial intelligenceInteligencia artificialNeural networksRedes neuronalesNaval route predictionPredicción de Rutas Navaleshttp://aims.fao.org/aos/agrovoc/c_49834http://aims.fao.org/aos/agrovoc/c_27064http://aims.fao.org/aos/agrovoc/c_37467http://id.loc.gov/authorities/subjects/sh90001937http://id.loc.gov/authorities/subjects/sh2021006947http://id.loc.gov/authorities/subjects/sh2019001855http://id.loc.gov/authorities/subjects/sh2003001275http://vocabularies.unesco.org/thesaurus/concept11744ODS 9: Industria, innovación e infraestructura. Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovaciónApplying Transformers to Naval Route PredictionTrabajo de grado - Pregradohttp://purl.org/coar/resource_type/c_7a1fhttp://purl.org/redcol/resource_type/TPTexthttp://purl.org/coar/version/c_b1a7d7d4d402bcceinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/draftCapobianco, Samuele et al. (2021). “Deep learning methods for vessel trajectory prediction based on recurrent neural networks”. In: IEEE Transactions on Aerospace and Electronic Systems 57.6, pp. 4329–4346.Nguyen, Duong and Ronan Fablet (2024). “A Transformer Network With Sparse Augmented Data Representation and Cross Entropy Loss for AIS-Based Vessel Trajectory Prediction”. In: IEEE Access 12, pp. 21596–21609. doi: 10.1109/ACCESS.2024.3349957.Ou, Ziqiang and Jianjun Zhu (2008). “AIS database powered by GIS technology for maritime safety and security”. In: The Journal of Navigation 61.4, pp. 655–665.Park, Jinwan, Jungsik Jeong, and Youngsoo Park (2021). “Ship trajectory prediction based on bi-LSTM using spectral-clustered AIS data”. In: Journal of marine science and engineering 9.9, p. 1037.Radford, Alec et al. (2019). “Language models are unsupervised multitask learners”. In: OpenAI blog 1.8, p. 9.Tu, Enmei et al. (2017). “Exploiting AIS data for intelligent maritime navigation: A comprehensive survey from data to methodology”. In: IEEE Transactions on Intelligent Transportation Systems 19.5, pp. 1559–1582.Vaswani, Ashish et al. (2017). “Attention is All You Need”. In: Advances in Neural Information Processing Systems. 30, pp. 5998–6008.Volkova, Tamara A, Yulia E Balykina, and Alexander Bespalov (2021). “Predicting ship trajectory based on neural networks using AIS data”. In: Journal of Marine Science and Engineering 9.3, p. 254.Wang, Congcong, Paul Nulty, and David Lillis (Dec. 2020). “A Comparative Study on Word Embeddings in Deep Learning for Text Classification”. In: pp. 37–46. doi: 10.1145/3443279.3443304.PublicationORIGINALGomezCamilo_2024_TransformersNavalPrediction.pdfGomezCamilo_2024_TransformersNavalPrediction.pdfapplication/pdf1753126https://bibliotecadigital.udea.edu.co/bitstreams/f0c1f26e-df2a-453c-a53e-d0bc6d00c624/download985511a41e5ea43058c8345e3bae1207MD51trueAnonymousREADLICENSElicense.txtlicense.txttext/plain; charset=utf-814837https://bibliotecadigital.udea.edu.co/bitstreams/76b79f9d-8f51-4df3-bf7c-0923e8d9ea76/downloadb76e7a76e24cf2f94b3ce0ae5ed275d0MD52falseAnonymousREADCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-81160https://bibliotecadigital.udea.edu.co/bitstreams/c4ba5948-056c-4802-9c19-242c0e7b2875/download5643bfd9bcf29d560eeec56d584edaa9MD53falseAnonymousREADTEXTGomezCamilo_2024_TransformersNavalPrediction.pdf.txtGomezCamilo_2024_TransformersNavalPrediction.pdf.txtExtracted 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