A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A hybrid prediction model of vessel trajectory based on attention mechanism and CNN-GRU
https://orcid.org/0000-0001-5465-9730
With the increase in global shipping volumes and the complexity of maritime transport systems, vessel trajectory prediction serves an important tool in improving maritime safety. However, most existing vessel trajectory prediction methods focus on a single feature and unable fuse high-dimensional features. To solve these problems, CNN-GRU model with a hybrid attention mechanism (AM) is proposed based on Automatic Identification System (AIS) data. First convolutional neural network (CNN) is proposed to extract the spatio-temporal information of the trajectory data. Then a gated recurrent unit (GRU) is designed to extract the temporal relationship of the trajectories. Finally, AM is introduced to learn the deep-level features and predict the vessel trajectories. To validate the effectiveness of the model, experiments are conducted on three real AIS datasets. In comparison with other models, the method has a high trajectory prediction accuracy.
A hybrid prediction model of vessel trajectory based on attention mechanism and CNN-GRU
https://orcid.org/0000-0001-5465-9730
With the increase in global shipping volumes and the complexity of maritime transport systems, vessel trajectory prediction serves an important tool in improving maritime safety. However, most existing vessel trajectory prediction methods focus on a single feature and unable fuse high-dimensional features. To solve these problems, CNN-GRU model with a hybrid attention mechanism (AM) is proposed based on Automatic Identification System (AIS) data. First convolutional neural network (CNN) is proposed to extract the spatio-temporal information of the trajectory data. Then a gated recurrent unit (GRU) is designed to extract the temporal relationship of the trajectories. Finally, AM is introduced to learn the deep-level features and predict the vessel trajectories. To validate the effectiveness of the model, experiments are conducted on three real AIS datasets. In comparison with other models, the method has a high trajectory prediction accuracy.
A hybrid prediction model of vessel trajectory based on attention mechanism and CNN-GRU
https://orcid.org/0000-0001-5465-9730
With the increase in global shipping volumes and the complexity of maritime transport systems, vessel trajectory prediction serves an important tool in improving maritime safety. However, most existing vessel trajectory prediction methods focus on a single feature and unable fuse high-dimensional features. To solve these problems, CNN-GRU model with a hybrid attention mechanism (AM) is proposed based on Automatic Identification System (AIS) data. First convolutional neural network (CNN) is proposed to extract the spatio-temporal information of the trajectory data. Then a gated recurrent unit (GRU) is designed to extract the temporal relationship of the trajectories. Finally, AM is introduced to learn the deep-level features and predict the vessel trajectories. To validate the effectiveness of the model, experiments are conducted on three real AIS datasets. In comparison with other models, the method has a high trajectory prediction accuracy.
A hybrid prediction model of vessel trajectory based on attention mechanism and CNN-GRU
Cen, Jian (author) / Li, JiaXi (author) / Liu, Xi (author) / Chen, JiaHao (author) / Li, HaiSheng (author) / Huang, WeiSheng (author) / Zeng, LinZhe (author) / Kang, JunXi (author) / Ke, Silin (author)
2024-11-01
Article (Journal)
Electronic Resource
English
Prediction of Ship Trajectory in Nearby Port Waters Based on Attention Mechanism Model
| DOAJ | 2023
Short-term Inland Vessel Trajectory Prediction with Encoder-Decoder Models
| HENRY – Federal Waterways Engineering and Research Institute (BAW) | 2022
A Hydrological Data Prediction Model Based on LSTM with Attention Mechanism
| DOAJ | 2023
TBM Cutterhead Load Prediction Model Based on the Two-Stage Attention Mechanism
| Springer Verlag | 2024