Ship Trajectory Prediction: An Integrated Approach Using ConvLSTM-Based Sequence-to-Sequence Model
Maritime transportation is one of the major contributors to the development of the global economy. To ensure its safety and reduce the occurrence of a maritime accident, intelligent maritime monitoring and ship behavior identification have been drawing much attention from industry and academia, amon...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/8/1484 |
| _version_ | 1797584226327986176 |
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| author | Wenxiong Wu Pengfei Chen Linying Chen Junmin Mou |
| author_facet | Wenxiong Wu Pengfei Chen Linying Chen Junmin Mou |
| author_sort | Wenxiong Wu |
| collection | DOAJ |
| description | Maritime transportation is one of the major contributors to the development of the global economy. To ensure its safety and reduce the occurrence of a maritime accident, intelligent maritime monitoring and ship behavior identification have been drawing much attention from industry and academia, among which, the accurate prediction of ship trajectory is one of the key questions. This paper proposed a trajectory prediction model integrating the Convolutional LSTM (ConvLSTM) and Sequence to Sequence (Seq2Seq) models to facilitate simultaneous extraction of temporal and spatial features of ship trajectories, thereby enhancing the accuracy of prediction. Firstly, the trajectories are preprocessed using kinematic-based anomaly removal and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to improve the data quality for the training process of trajectory prediction. Secondly, the ConvLSTM-based Seq2seq model is designed to extract temporal and spatial features of the ship trajectory and improve the performance of long-time prediction. Finally, by using real AIS data, the proposed model is compared with the Seq2Seq and Bidirectional LSTM based on attention mechanism (Bi-Attention-LSTM) models to verify its effectiveness. The experimental results demonstrate that the proposed model achieves excellent performance in predicting turning trajectories, good predictive accuracy on straight line motions, and greater improvement in prediction accuracy compared to the other two benchmark models. Overall, the proposed model represents a promising contribution to improving ship trajectory prediction accuracy and may enhance the safety and quality of ship navigation in complex and volatile marine environments. |
| first_indexed | 2024-03-10T23:49:09Z |
| format | Article |
| id | doaj.art-0a285648f53c4e2daf3bab12ab75df40 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T23:49:09Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-0a285648f53c4e2daf3bab12ab75df402023-11-19T01:44:41ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-07-01118148410.3390/jmse11081484Ship Trajectory Prediction: An Integrated Approach Using ConvLSTM-Based Sequence-to-Sequence ModelWenxiong Wu0Pengfei Chen1Linying Chen2Junmin Mou3School of Navigation, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Navigation, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Navigation, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Navigation, Wuhan University of Technology, Wuhan 430063, ChinaMaritime transportation is one of the major contributors to the development of the global economy. To ensure its safety and reduce the occurrence of a maritime accident, intelligent maritime monitoring and ship behavior identification have been drawing much attention from industry and academia, among which, the accurate prediction of ship trajectory is one of the key questions. This paper proposed a trajectory prediction model integrating the Convolutional LSTM (ConvLSTM) and Sequence to Sequence (Seq2Seq) models to facilitate simultaneous extraction of temporal and spatial features of ship trajectories, thereby enhancing the accuracy of prediction. Firstly, the trajectories are preprocessed using kinematic-based anomaly removal and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to improve the data quality for the training process of trajectory prediction. Secondly, the ConvLSTM-based Seq2seq model is designed to extract temporal and spatial features of the ship trajectory and improve the performance of long-time prediction. Finally, by using real AIS data, the proposed model is compared with the Seq2Seq and Bidirectional LSTM based on attention mechanism (Bi-Attention-LSTM) models to verify its effectiveness. The experimental results demonstrate that the proposed model achieves excellent performance in predicting turning trajectories, good predictive accuracy on straight line motions, and greater improvement in prediction accuracy compared to the other two benchmark models. Overall, the proposed model represents a promising contribution to improving ship trajectory prediction accuracy and may enhance the safety and quality of ship navigation in complex and volatile marine environments.https://www.mdpi.com/2077-1312/11/8/1484trajectory predictiontrajectory clusteringAISconvolutional LSTM networksequence-to-sequence (Seq2Seq)deep learning |
| spellingShingle | Wenxiong Wu Pengfei Chen Linying Chen Junmin Mou Ship Trajectory Prediction: An Integrated Approach Using ConvLSTM-Based Sequence-to-Sequence Model Journal of Marine Science and Engineering trajectory prediction trajectory clustering AIS convolutional LSTM network sequence-to-sequence (Seq2Seq) deep learning |
| title | Ship Trajectory Prediction: An Integrated Approach Using ConvLSTM-Based Sequence-to-Sequence Model |
| title_full | Ship Trajectory Prediction: An Integrated Approach Using ConvLSTM-Based Sequence-to-Sequence Model |
| title_fullStr | Ship Trajectory Prediction: An Integrated Approach Using ConvLSTM-Based Sequence-to-Sequence Model |
| title_full_unstemmed | Ship Trajectory Prediction: An Integrated Approach Using ConvLSTM-Based Sequence-to-Sequence Model |
| title_short | Ship Trajectory Prediction: An Integrated Approach Using ConvLSTM-Based Sequence-to-Sequence Model |
| title_sort | ship trajectory prediction an integrated approach using convlstm based sequence to sequence model |
| topic | trajectory prediction trajectory clustering AIS convolutional LSTM network sequence-to-sequence (Seq2Seq) deep learning |
| url | https://www.mdpi.com/2077-1312/11/8/1484 |
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