Ship Trajectory Prediction Based on the TTCN-Attention-GRU Model
As shipping continues to play an increasingly important role in world trade, there are consequently a large number of ships at sea at any given time, posing a risk to maritime traffic safety. Therefore, the tracking and monitoring of ships at sea has gradually attracted the attention of scholars. Sh...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/12/2556 |
| _version_ | 1797595185561993216 |
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| author | Zu Lin Weiqi Yue Jie Huang Jian Wan |
| author_facet | Zu Lin Weiqi Yue Jie Huang Jian Wan |
| author_sort | Zu Lin |
| collection | DOAJ |
| description | As shipping continues to play an increasingly important role in world trade, there are consequently a large number of ships at sea at any given time, posing a risk to maritime traffic safety. Therefore, the tracking and monitoring of ships at sea has gradually attracted the attention of scholars. Ship trajectory prediction comprises an important aspect of ship tracking and monitoring. Trajectory prediction describes the forecasting of a ship’s future trajectory over a period of time through use of historical trajectory information of the ship, so as to predict the sailing dynamics of the ship in advance. Accurate trajectory prediction can help maritime regulatory authorities improve supervision efficiency and reduce collisions between ships. Temporal Convolutional Network (TCN) offers good time memory ability and has shown better performance in time series prediction in recent years. Ship trajectory sequence belongs to the category of time series. Thus, in this paper, we introduce TCN into the field of ship trajectory prediction and improve on it, and propose Tiered-TCN (TTCN). The attention mechanism is a way to help neural networks learn data features by highlighting features that have a greater impact on predicted values. Gate Recurrent Unit (GRU) is an important variant of Recurrent Neural Networks (RNN), which bears a strong nonlinear fitting ability. In this paper, TTCN, attention mechanism and GRU network are integrated to construct a hybrid model for trajectory prediction, which is referred to as TTCN-Attention-GRU (TTAG). By optimizing the advantages of each module, the prediction effect is achieved with high precision. The experimental results show that the TTAG model is superior to all the baseline models presented in this paper. |
| first_indexed | 2024-03-11T02:33:08Z |
| format | Article |
| id | doaj.art-d8c84581bcd14ac8b3fa3f57efd52dcc |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T02:33:08Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-d8c84581bcd14ac8b3fa3f57efd52dcc2023-11-18T10:07:27ZengMDPI AGElectronics2079-92922023-06-011212255610.3390/electronics12122556Ship Trajectory Prediction Based on the TTCN-Attention-GRU ModelZu Lin0Weiqi Yue1Jie Huang2Jian Wan3School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaAs shipping continues to play an increasingly important role in world trade, there are consequently a large number of ships at sea at any given time, posing a risk to maritime traffic safety. Therefore, the tracking and monitoring of ships at sea has gradually attracted the attention of scholars. Ship trajectory prediction comprises an important aspect of ship tracking and monitoring. Trajectory prediction describes the forecasting of a ship’s future trajectory over a period of time through use of historical trajectory information of the ship, so as to predict the sailing dynamics of the ship in advance. Accurate trajectory prediction can help maritime regulatory authorities improve supervision efficiency and reduce collisions between ships. Temporal Convolutional Network (TCN) offers good time memory ability and has shown better performance in time series prediction in recent years. Ship trajectory sequence belongs to the category of time series. Thus, in this paper, we introduce TCN into the field of ship trajectory prediction and improve on it, and propose Tiered-TCN (TTCN). The attention mechanism is a way to help neural networks learn data features by highlighting features that have a greater impact on predicted values. Gate Recurrent Unit (GRU) is an important variant of Recurrent Neural Networks (RNN), which bears a strong nonlinear fitting ability. In this paper, TTCN, attention mechanism and GRU network are integrated to construct a hybrid model for trajectory prediction, which is referred to as TTCN-Attention-GRU (TTAG). By optimizing the advantages of each module, the prediction effect is achieved with high precision. The experimental results show that the TTAG model is superior to all the baseline models presented in this paper.https://www.mdpi.com/2079-9292/12/12/2556trajectory predictiondeep learningtemporal convolutional networkattention mechanismgate recurrent unit |
| spellingShingle | Zu Lin Weiqi Yue Jie Huang Jian Wan Ship Trajectory Prediction Based on the TTCN-Attention-GRU Model Electronics trajectory prediction deep learning temporal convolutional network attention mechanism gate recurrent unit |
| title | Ship Trajectory Prediction Based on the TTCN-Attention-GRU Model |
| title_full | Ship Trajectory Prediction Based on the TTCN-Attention-GRU Model |
| title_fullStr | Ship Trajectory Prediction Based on the TTCN-Attention-GRU Model |
| title_full_unstemmed | Ship Trajectory Prediction Based on the TTCN-Attention-GRU Model |
| title_short | Ship Trajectory Prediction Based on the TTCN-Attention-GRU Model |
| title_sort | ship trajectory prediction based on the ttcn attention gru model |
| topic | trajectory prediction deep learning temporal convolutional network attention mechanism gate recurrent unit |
| url | https://www.mdpi.com/2079-9292/12/12/2556 |
| work_keys_str_mv | AT zulin shiptrajectorypredictionbasedonthettcnattentiongrumodel AT weiqiyue shiptrajectorypredictionbasedonthettcnattentiongrumodel AT jiehuang shiptrajectorypredictionbasedonthettcnattentiongrumodel AT jianwan shiptrajectorypredictionbasedonthettcnattentiongrumodel |