# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 5 Experiments
### Ablation Study
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **First** & **Second** & **Third** & **Fourth** & **Fifth** \\ \hline Kalman & \(3.42\times 10^{-4}\) & \(4.62\times 10^{-4}\) & \(8.22\times 10^{-4}\) & \(11.52\times 10^{-4}\) & \(14.29\times 10^{-4}\) \\ VAR & \(1.38\times 10^{-4}\) & \(5.30\times 10^{-4}\) & \(122.49\times 10^{-4}\) & - & - \\ ARIMA & \(4.83\times 10^{-4}\) & \(9.38\times 10^{-4}\) & \(14.05\times 10^{-4}\) & \(18.79\times 10^{-4}\) & \(23.65\times 10^{-4}\) \\ LSTM & \(0.68\times 10^{-4}\) & \(1.76\times 10^{-4}\) & \(3.19\times 10^{-4}\) & \(4.93\times 10^{-4}\) & \(6.99\times 10^{-4}\) \\ BiLSTM & \(2.37\times 10^{-4}\) & \(2.38\times 10^{-4}\) & \(4.22\times 10^{-4}\) & \(6.00\times 10^{-4}\) & \(7.84\times 10^{-4}\) \\ GRU & \(\mathbf{0.59\times 10^{-4}}\) & \(\mathbf{1.48\times 10^{-4}}\) & \(2.75\times 10^{-4}\) & \(4.20\times 10^{-4}\) & \(5.86\times 10^{-4}\) \\ BiGRU & \(2.42\times 10^{-4}\) & \(2.84\times 10^{-4}\) & \(4.00\times 10^{-4}\) & \(4.91\times 10^{-4}\) & \(5.20\times 10^{-4}\) \\ LSTM-LSTM & \(1.11\times 10^{-4}\) & \(2.06\times 10^{-4}\) & \(3.21\times 10^{-4}\) & \(4.52\times 10^{-4}\) & \(5.98\times 10^{-4}\) \\ BiLSTM-LSTM & \(1.39\times 10^{-4}\) & \(2.41\times 10^{-4}\) & \(3.61\times 10^{-4}\) & \(4.89\times 10^{-4}\) & \(6.32\times 10^{-4}\) \\ GRU-GRU & \(0.80\times 10^{-4}\) & \(1.66\times 10^{-4}\) & \(2.76\times 10^{-4}\) & \(4.02\times 10^{-4}\) & \(5.42\times 10^{-4}\) \\ BiGRU-GRU & \(1.16\times 10^{-4}\) & \(2.23\times 10^{-4}\) & \(3.65\times 10^{-4}\) & \(5.06\times 10^{-4}\) & \(6.60\times 10^{-4}\) \\ Transformer & \(1.28\times 10^{-4}\) & \(1.72\times 10^{-4}\) & \(2.50\times 10^{-4}\) & \(3.50\times 10^{-4}\) & \(4.67\times 10^{-4}\) \\ METO-S2S & \(2.22\times 10^{-4}\) & \(2.30\times 10^{-4}\) & \(2.71\times 10^{-4}\) & \(3.49\times 10^{-4}\) & \(4.62\times 10^{-4}\) \\ Ours & \(0.83\times 10^{-4}\) & \(1.51\times 10^{-4}\) & \(\mathbf{2.34\times 10^{-4}}\) & \(\mathbf{3.37\times 10^{-4}}\) & \(\mathbf{4.57\times 10^{-4}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The quantitative analysis results of each 4. \(w\)_/o k3_. Delete the Anchoring Time Preference from the Multi-level Vessel Trajectory Representation Module (see Section 4.3.1);
5. \(w\)_/o p_s_. Delete the Portrait Selection Component from the Feature Fusion and Decoding Module (see Section 4.3.2);
6. \(w\)_/o f_f_. Delete the Feature Fusion Component from the Feature Fusion and Decoding Module (see Section 4.3.2);
7. \(w\)_/o muti_d_. Delete the Multi-head Decoder Component from the Feature Fusion and Decoding Module, using a single GRU for decoding instead, which does not receive trajectory information separately (see Section 4.3.2). The results are shown in Table 9. We evaluated the performance using the RMSE, MAE, ADE, and FDE. The results indicate that the deletion of the Portrait Selection Component had the most significant impact on the RMSE metric, decreasing it from \(3.17\times 10^{-4}\) to \(4.48\times 10^{-4}\). This indicates that the Portrait Selection Component plays a crucial role in the model. This is because the Portrait Selection Component is responsible for selecting the vessel characteristics that are most suitable for the current environment. When the Portrait Selection Component is removed, the model's performance significantly declines. Additionally, it is observed that the deletion of the Multi-head Decoder Component had the least impact, with a decrease from \(3.17\times 10^{-4}\) to \(3.32\times 10^{-4}\). This is because enhancing the decoder does not affect the overall structure. The model still has the ability to select and learn how to use the corresponding characteristics to generate accurate predictions. However, reinforcing the decoder leads to a slight improvement in model performance. Consistent with the above analysis, the MAE, ADE, and FDE evaluation metrics exhibited similar trends. After removing various innovations, the performance of the model decreased.