# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 5 Experiment and Result Analysis
### Analysis of Vessel Trajectory Restoration Results  
#### 5.4.1 Analysis of Model Restoration Results  
In the Seq2 Seq-based vessel trajectory restoration model, the choice of encoder and decoder affects the trajectory restoration effect. To systematically evaluate and compare the repair effects of different model architectures, this study constructs Seq2 Seq models with different structures. These include the basic Seq2 Seq (R2R) model and the LSTM2 LSTM (L2L), BILSTM2 BILSTM (BL2 BLL), GRU2 GRU (G2G), and BIGRU2 BIGRU (BG2 BG) models in which the encoder and decoder are LSTM, BILSTM, GRU, and BIGRU units, respectively. Also, this study selected typical RNN, LSTM, BILSTM, and GRU recurrent network models as baseline models for comparison experiments. The MSEs of different model training phases are counted by the training input data, and the trend of the loss function is observed, as shown in Figure 11. As can be seen from Figure 11, the baseline models show some upward and downward fluctuations in the value of the loss function within the first 10 rounds, but the overall trend decreases. This phenomenon indicates that the model is in the learning stage at this stage. Then, the curve of the loss function gradually flattens out and no longer shows a significant decrease; this phenomenon indicates that the model has converged and that the model training is complete. As shown in Figure 11, the Seq2 Seq-based models converge slightly slower and the LOSS curve stabilizes after 25 rounds. This shows that the model architecture of Seq2 Seq is more complex and the increase in parameters leads to an increase in training time in the same environment. This complexity may stem from the fact that the Seq2 Seq models need to optimize both the encoder and decoder parameters simultaneously, while the baseline models only need to optimize a single parameter, which may increase the training time but may also lead to better prediction performance. Figure 10: AIC and BIC information criteria values for latitude and longitude features: (**a**) the value of LAT; (**b**) the value of LON. The trained model performed vessel trajectory restoration on the test set, and the results of the evaluation metrics of the restoration performance are shown in Table 4. From Table 4, it can be seen that the restoration model based on the Seq2 Seq structure has smaller values of the performance metrics compared with the baseline model, which confirms the superior performance of the Seq2 Seq model in the restoration task. Further comparing the restoration results of different encoder models under the Seq2 Seq structure, some of the trajectory restoration models are shown in Figure 12. As a result of Table 4 and Figure 12, it can be observed that the BG2 BG model exhibits optimal performance in all performance metrics. Specifically, it has the lowest MAE, RMSE, FD, and AED values, which highlights the excellent performance of the BG2 BG model in the trajectory restoration task, which not only has the highest restoration precision and restoration accuracy but also has a higher similarity between the restored trajectory and the real trajectory and has the smallest average Euclidean distance. The excellent performance of the BG2 BG model is attributed to its complex model structure and efficient feature extraction capability, which allows it to capture and restore vessel trajectories more accurately. To explore the generalization ability and robustness of the trajectory restoration model in depth, we selected AIS data from an area in the Gulf of Mexico (with a latitude range from 26.00 N to 28.00 N and a longitude range from 90.10 W to 96.50 W) as a dataset. This dataset has wider coverage compared to the experimental dataset and is intended to provide a more comprehensive assessment of model performance. We conducted robustness experiments using this dataset and compared the various indicators of model performance.