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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Veröffentlicht in:Journal of marine science and engineering Jg. 12; H. 10; S. 1695
Hauptverfasser: Yang, Fan, He, Chunlin, Liu, Yi, Zeng, Anping, Hu, Longhe
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.10.2024
Schlagworte:
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
ISSN:2077-1312, 2077-1312
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Zusammenfassung: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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ISSN:2077-1312
2077-1312
DOI:10.3390/jmse12101695