Using LSTM with Trajectory Point Correlation and Temporal Pattern Attention for Ship Trajectory Prediction

Accurate ship trajectory prediction is crucial for real-time vessel position tracking and maritime safety management. However, existing methods for ship trajectory prediction encounter significant challenges. They struggle to effectively extract long-term and complex spatial–temporal features hidden...

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Veröffentlicht in:Electronics (Basel) 2024-12, Vol.13 (23), p.4705
Hauptverfasser: Zhou, Yi, Guo, Haitao, Lu, Jun, Gong, Zhihui, Yu, Donghang, Ding, Lei
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container_end_page
container_issue 23
container_start_page 4705
container_title Electronics (Basel)
container_volume 13
creator Zhou, Yi
Guo, Haitao
Lu, Jun
Gong, Zhihui
Yu, Donghang
Ding, Lei
description Accurate ship trajectory prediction is crucial for real-time vessel position tracking and maritime safety management. However, existing methods for ship trajectory prediction encounter significant challenges. They struggle to effectively extract long-term and complex spatial–temporal features hidden within the data. Moreover, they often overlook correlations among multivariate dynamic features such as longitude (LON), latitude (LAT), speed over ground (SOG), and course over ground (COG), which are essential for precise trajectory forecasting. To address these pressing issues and fulfill the need for more accurate and comprehensive ship trajectory prediction, we propose a novel and integrated approach. Firstly, a Trajectory Point Correlation Attention (TPCA) mechanism is devised to establish spatial connections between trajectory points, thereby uncovering the local trends of trajectory point changes. Subsequently, a Temporal Pattern Attention (TPA) mechanism is introduced to handle the associations between multiple variables across different time steps and capture the dynamic feature correlations among trajectory attributes. Finally, a Great Circle Route Loss Function (GCRLoss) is constructed, leveraging the perception of the Earth’s curvature to deepen the understanding of spatial relationships and geographic information. Experimental results demonstrate that our proposed method outperforms existing ship trajectory prediction techniques, showing enhanced reliability in multi-step predictions.
doi_str_mv 10.3390/electronics13234705
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Subsequently, a Temporal Pattern Attention (TPA) mechanism is introduced to handle the associations between multiple variables across different time steps and capture the dynamic feature correlations among trajectory attributes. Finally, a Great Circle Route Loss Function (GCRLoss) is constructed, leveraging the perception of the Earth’s curvature to deepen the understanding of spatial relationships and geographic information. 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subjects Accuracy
Algorithms
Correlation
Deep learning
Great circles
Kalman filters
Machine learning
Methods
Multivariate analysis
Neural networks
Real time
Safety management
Ship accidents & safety
Trends
title Using LSTM with Trajectory Point Correlation and Temporal Pattern Attention for Ship Trajectory Prediction
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