Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation
The prediction of vessel maritime navigation has become an exciting topic in the last years, especially considering economics, commercial exchange, and security. In addition, vessel monitoring requires better systems and techniques that help enterprises and governments to protect their interests. Sp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/5/2429 |
| _version_ | 1797542054526451712 |
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| author | Rogelio Bautista-Sánchez Liliana Ibeth Barbosa-Santillan Juan Jaime Sánchez-Escobar |
| author_facet | Rogelio Bautista-Sánchez Liliana Ibeth Barbosa-Santillan Juan Jaime Sánchez-Escobar |
| author_sort | Rogelio Bautista-Sánchez |
| collection | DOAJ |
| description | The prediction of vessel maritime navigation has become an exciting topic in the last years, especially considering economics, commercial exchange, and security. In addition, vessel monitoring requires better systems and techniques that help enterprises and governments to protect their interests. Specifically, the prediction of vessel movements is essential for safety and tracking. However, the applications of prediction techniques have a high cost related to computational efficiency and low resource saving. This article presents a sample method to select historical data on vessel-specific routes to optimize the computational performance of the prediction of vessel positions and route estimation in real-time. These historical navigation data can help to estimate a complete path and perform vessel position predictions through time. This Select Best AIS Data in Prediction Vessel Movements and Route Estimation (PreMovEst) method works in a Vessel Traffic Service database to save computational resources when predictions or route estimations are executed. This article discusses AIS data and the artificial neural network. This work aims to present a prediction model that correctly predicts the physical movement in the route. It supports path planning for the Vessel Traffic Service. After testing the method, the results obtained for route estimation have a precision of 76.15%, and those for vessel position predictions through time have an accuracy of 81.043%. |
| first_indexed | 2024-03-10T13:24:13Z |
| format | Article |
| id | doaj.art-7afb1fc38dd84fcca0b4254cc0d03fdc |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T13:24:13Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7afb1fc38dd84fcca0b4254cc0d03fdc2023-11-21T09:45:43ZengMDPI AGApplied Sciences2076-34172021-03-01115242910.3390/app11052429Method for Select Best AIS Data in Prediction Vessel Movements and Route EstimationRogelio Bautista-Sánchez0Liliana Ibeth Barbosa-Santillan1Juan Jaime Sánchez-Escobar2Data Mining Engineering Group, Guadalajara 44630, MexicoDepartment of Computer Science, University of Guadalajara, Guadalajara 44100, MexicoCentro de Enseñanza Técnica Industrial, Guadalajara 44638, MexicoThe prediction of vessel maritime navigation has become an exciting topic in the last years, especially considering economics, commercial exchange, and security. In addition, vessel monitoring requires better systems and techniques that help enterprises and governments to protect their interests. Specifically, the prediction of vessel movements is essential for safety and tracking. However, the applications of prediction techniques have a high cost related to computational efficiency and low resource saving. This article presents a sample method to select historical data on vessel-specific routes to optimize the computational performance of the prediction of vessel positions and route estimation in real-time. These historical navigation data can help to estimate a complete path and perform vessel position predictions through time. This Select Best AIS Data in Prediction Vessel Movements and Route Estimation (PreMovEst) method works in a Vessel Traffic Service database to save computational resources when predictions or route estimations are executed. This article discusses AIS data and the artificial neural network. This work aims to present a prediction model that correctly predicts the physical movement in the route. It supports path planning for the Vessel Traffic Service. After testing the method, the results obtained for route estimation have a precision of 76.15%, and those for vessel position predictions through time have an accuracy of 81.043%.https://www.mdpi.com/2076-3417/11/5/2429select AIS dataroute estimationneural networks |
| spellingShingle | Rogelio Bautista-Sánchez Liliana Ibeth Barbosa-Santillan Juan Jaime Sánchez-Escobar Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation Applied Sciences select AIS data route estimation neural networks |
| title | Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation |
| title_full | Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation |
| title_fullStr | Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation |
| title_full_unstemmed | Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation |
| title_short | Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation |
| title_sort | method for select best ais data in prediction vessel movements and route estimation |
| topic | select AIS data route estimation neural networks |
| url | https://www.mdpi.com/2076-3417/11/5/2429 |
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