Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data
This research introduces an online system for monitoring maritime traffic, aimed at tracking vessels in water routes and predicting their subsequent locations in real time. The proposed framework utilizes an Extract, Transform, and Load (ETL) pipeline to dynamically process AIS data by cleaning, com...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/1/152 |
| _version_ | 1827371676776529920 |
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| author | Atefe Sedaghat Homayoon Arbabkhah Masood Jafari Kang Maryam Hamidi |
| author_facet | Atefe Sedaghat Homayoon Arbabkhah Masood Jafari Kang Maryam Hamidi |
| author_sort | Atefe Sedaghat |
| collection | DOAJ |
| description | This research introduces an online system for monitoring maritime traffic, aimed at tracking vessels in water routes and predicting their subsequent locations in real time. The proposed framework utilizes an Extract, Transform, and Load (ETL) pipeline to dynamically process AIS data by cleaning, compressing, and enhancing it with additional attributes such as online traffic volume, origin/destination, vessel trips, trip direction, and vessel routing. This processed data, enriched with valuable details, serves as an alternative to raw AIS data stored in a centralized database. For user interactions, a user interface is designed to query the database and provide real-time information on a map-based interface. To deal with false or missing AIS records, two methods, dead reckoning and machine learning techniques, are employed to anticipate the trajectory of the vessel in the next time steps. To evaluate each method, several metrics are used, including R squared, mean absolute error, mean offset, and mean offset from the centerline. The functionality of the proposed system is showcased through a case study conducted in the Gulf Intracoastal Waterway (GIWW). Three years of AIS data are collected and processed as a simulated API to transmit AIS records every five minutes. According to our results, the Seq2Seq model exhibits strong performance (0.99 R squared and an average offset of ~1400 ft). However, the second scenario, dead reckoning, proves comparable to the Seq2Seq model as it involves recalculating vessel headings by comparing each data point with the previous one. |
| first_indexed | 2024-03-08T10:44:57Z |
| format | Article |
| id | doaj.art-0e07916c6ed64c58bce3693ea01ead2d |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-08T10:44:57Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-0e07916c6ed64c58bce3693ea01ead2d2024-01-26T17:17:18ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-01-0112115210.3390/jmse12010152Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System DataAtefe Sedaghat0Homayoon Arbabkhah1Masood Jafari Kang2Maryam Hamidi3Department of Industrial and Systems Engineering, Lamar University, Beaumont, TX 77710, USADepartment of Industrial and Systems Engineering, Lamar University, Beaumont, TX 77710, USADepartment of Industrial and Systems Engineering, Lamar University, Beaumont, TX 77710, USADepartment of Industrial and Systems Engineering, Lamar University, Beaumont, TX 77710, USAThis research introduces an online system for monitoring maritime traffic, aimed at tracking vessels in water routes and predicting their subsequent locations in real time. The proposed framework utilizes an Extract, Transform, and Load (ETL) pipeline to dynamically process AIS data by cleaning, compressing, and enhancing it with additional attributes such as online traffic volume, origin/destination, vessel trips, trip direction, and vessel routing. This processed data, enriched with valuable details, serves as an alternative to raw AIS data stored in a centralized database. For user interactions, a user interface is designed to query the database and provide real-time information on a map-based interface. To deal with false or missing AIS records, two methods, dead reckoning and machine learning techniques, are employed to anticipate the trajectory of the vessel in the next time steps. To evaluate each method, several metrics are used, including R squared, mean absolute error, mean offset, and mean offset from the centerline. The functionality of the proposed system is showcased through a case study conducted in the Gulf Intracoastal Waterway (GIWW). Three years of AIS data are collected and processed as a simulated API to transmit AIS records every five minutes. According to our results, the Seq2Seq model exhibits strong performance (0.99 R squared and an average offset of ~1400 ft). However, the second scenario, dead reckoning, proves comparable to the Seq2Seq model as it involves recalculating vessel headings by comparing each data point with the previous one.https://www.mdpi.com/2077-1312/12/1/152online traffic monitoringETL pipelineAIS datavessel trajectory predictiondead reckoningGIWW |
| spellingShingle | Atefe Sedaghat Homayoon Arbabkhah Masood Jafari Kang Maryam Hamidi Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data Journal of Marine Science and Engineering online traffic monitoring ETL pipeline AIS data vessel trajectory prediction dead reckoning GIWW |
| title | Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data |
| title_full | Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data |
| title_fullStr | Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data |
| title_full_unstemmed | Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data |
| title_short | Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data |
| title_sort | deep learning applications in vessel dead reckoning to deal with missing automatic identification system data |
| topic | online traffic monitoring ETL pipeline AIS data vessel trajectory prediction dead reckoning GIWW |
| url | https://www.mdpi.com/2077-1312/12/1/152 |
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