Vessel Trajectory Prediction Based on Automatic Identification System Data: Multi-Gated Attention Encoder Decoder Network
Utilizing time-series data from ship trajectories to forecast their subsequent movement is crucial for enhancing the safety within maritime traffic environments. The application of deep learning techniques, leveraging Automatic Identification System (AIS) data, has emerged as a pivotal area in marit...
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| Published in: | Journal of marine science and engineering Vol. 12; no. 10; p. 1695 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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01.10.2024
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| ISSN: | 2077-1312, 2077-1312 |
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| Abstract | Utilizing time-series data from ship trajectories to forecast their subsequent movement is crucial for enhancing the safety within maritime traffic environments. The application of deep learning techniques, leveraging Automatic Identification System (AIS) data, has emerged as a pivotal area in maritime traffic studies. Within this domain, the precise forecasting of ship trajectories stands as a central challenge. In this study, we propose the multi-gated attention encoder decoder (MGAED) network, a model based on an encoder–decoder structure specialized for predicting ship trajectories in canals. The model employs a long short-term memory network (LSTM) as an encoder, combined with multiple Gated Recurrent Units (GRUs) and an attention mechanism for the decoder. Long-term dependencies in time-series data are captured through GRUs, while the attention mechanism is used to strengthen the model’s ability to capture key information, and a soft threshold residual structure is introduced to handle sparse features, thus enhancing the model’s generalization ability and robustness. The efficacy of our model is substantiated by an extensive evaluation against current deep learning benchmarks. Through comprehensive comparison experiments with existing deep learning methods, our model shows significant improvements in prediction accuracy, with an at least 9.63% reduction in the mean error (MAE) and an at least 20.0% reduction in the mean square error (MSE), providing a new solution to improve the accuracy and efficiency of ship trajectory prediction. |
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| AbstractList | Utilizing time-series data from ship trajectories to forecast their subsequent movement is crucial for enhancing the safety within maritime traffic environments. The application of deep learning techniques, leveraging Automatic Identification System (AIS) data, has emerged as a pivotal area in maritime traffic studies. Within this domain, the precise forecasting of ship trajectories stands as a central challenge. In this study, we propose the multi-gated attention encoder decoder (MGAED) network, a model based on an encoder–decoder structure specialized for predicting ship trajectories in canals. The model employs a long short-term memory network (LSTM) as an encoder, combined with multiple Gated Recurrent Units (GRUs) and an attention mechanism for the decoder. Long-term dependencies in time-series data are captured through GRUs, while the attention mechanism is used to strengthen the model’s ability to capture key information, and a soft threshold residual structure is introduced to handle sparse features, thus enhancing the model’s generalization ability and robustness. The efficacy of our model is substantiated by an extensive evaluation against current deep learning benchmarks. Through comprehensive comparison experiments with existing deep learning methods, our model shows significant improvements in prediction accuracy, with an at least 9.63% reduction in the mean error (MAE) and an at least 20.0% reduction in the mean square error (MSE), providing a new solution to improve the accuracy and efficiency of ship trajectory prediction. |
| Audience | Academic |
| Author | He, Chunlin Zeng, Anping Hu, Longhe Yang, Fan Liu, Yi |
| Author_xml | – sequence: 1 givenname: Fan surname: Yang fullname: Yang, Fan – sequence: 2 givenname: Chunlin surname: He fullname: He, Chunlin – sequence: 3 givenname: Yi surname: Liu fullname: Liu, Yi – sequence: 4 givenname: Anping surname: Zeng fullname: Zeng, Anping – sequence: 5 givenname: Longhe surname: Hu fullname: Hu, Longhe |
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| SubjectTerms | Accuracy AIS data Artificial intelligence Benchmarks Coders Deep learning Efficiency encoder–decoder model Energy consumption Error reduction Forecasting techniques Global positioning systems GPS Identification systems Kalman filters Long short-term memory Machine learning Neural networks Predictions Sea vessels Support vector machines Time series Traffic analysis vessel trajectory prediction |
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| Title | Vessel Trajectory Prediction Based on Automatic Identification System Data: Multi-Gated Attention Encoder Decoder Network |
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