Predict marine vessels’ trajectory with machine learning methods
Machine learning techniques have been widely used in various industries to perform forecasting and predictions. One such application that is covered in this project is trajectory prediction of maritime vessels. Information on vessels, such as International Maritime Organisation (IMO) ship identifica...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2017
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| Online Access: | http://hdl.handle.net/10356/71126 |
| _version_ | 1826130471282540544 |
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| author | Cho, Siqi |
| author2 | Huang Guangbin |
| author_facet | Huang Guangbin Cho, Siqi |
| author_sort | Cho, Siqi |
| collection | NTU |
| description | Machine learning techniques have been widely used in various industries to perform forecasting and predictions. One such application that is covered in this project is trajectory prediction of maritime vessels. Information on vessels, such as International Maritime Organisation (IMO) ship identification number, its position, speed over ground (SOG) and course over ground (COG) etc. are broadcasted via automatic identification system (AIS). Making use of the availability of historical AIS data, various machine learning techniques can be applied to make future trajectory prediction based on the vessel’s current motion. The predictions made can be then visualised onto a map for users such as ship captains at sea, and port managements on land to plan the path of the vessel sailing in the port areas. The use of prediction can also be able to spot anomalous vessel’s trajectory which might lead to collision. This could improve port management, traffic efficiency around port and reduce incidents of vessels collision. With safety in mind, bring about this project to design a system, comprises of multiple sub-systems to make prediction using various machine learning algorithms, utilising the predictions made to visualise onto a map. |
| first_indexed | 2024-10-01T07:57:00Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/71126 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:57:00Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | ntu-10356/711262023-07-07T16:09:56Z Predict marine vessels’ trajectory with machine learning methods Cho, Siqi Huang Guangbin School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Machine learning techniques have been widely used in various industries to perform forecasting and predictions. One such application that is covered in this project is trajectory prediction of maritime vessels. Information on vessels, such as International Maritime Organisation (IMO) ship identification number, its position, speed over ground (SOG) and course over ground (COG) etc. are broadcasted via automatic identification system (AIS). Making use of the availability of historical AIS data, various machine learning techniques can be applied to make future trajectory prediction based on the vessel’s current motion. The predictions made can be then visualised onto a map for users such as ship captains at sea, and port managements on land to plan the path of the vessel sailing in the port areas. The use of prediction can also be able to spot anomalous vessel’s trajectory which might lead to collision. This could improve port management, traffic efficiency around port and reduce incidents of vessels collision. With safety in mind, bring about this project to design a system, comprises of multiple sub-systems to make prediction using various machine learning algorithms, utilising the predictions made to visualise onto a map. Bachelor of Engineering 2017-05-15T05:09:13Z 2017-05-15T05:09:13Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/71126 en Nanyang Technological University 33 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Cho, Siqi Predict marine vessels’ trajectory with machine learning methods |
| title | Predict marine vessels’ trajectory with machine learning methods |
| title_full | Predict marine vessels’ trajectory with machine learning methods |
| title_fullStr | Predict marine vessels’ trajectory with machine learning methods |
| title_full_unstemmed | Predict marine vessels’ trajectory with machine learning methods |
| title_short | Predict marine vessels’ trajectory with machine learning methods |
| title_sort | predict marine vessels trajectory with machine learning methods |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/71126 |
| work_keys_str_mv | AT chosiqi predictmarinevesselstrajectorywithmachinelearningmethods |