# Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix
## 5 Experiment and Analysis
### Experimental Results Analysis
The computational complexity of the graph convolution operation mainly depends on the number of nodes and the structure of the adjacency matrix, while the adaptive adjacency matrix is used in ASTGCN, the model can dynamically adjust the number of adjacency matrices through the learning process, which reduces the number of nodes that need to be processed in the traditional graph convolution network and further reduces the computational complexity. In addition, although the spatio-temporal attention mechanism of the model increases the computational complexity compared to other models, this allows the model to better capture the correlation between ship trajectory data, which improves the prediction accuracy and generalization ability. In summary, the ASTGCN model not only performs well in prediction accuracy, but its interpretability also provides us with the ability to deeply observe the dependencies between feature nodes and the patterns in the space-time dimension. This interpretability not only enhances the reliability of model prediction but also enables us to accurately understand the internal structure of vessel trajectory data in order to provide a more reliable and accurate basis for offshore vessel management decisions. To verify the application effect of the model in real scenarios, we developed a trajectory visual analysis platform, as shown in Figure 8, and integrated this trajectory prediction framework. In this platform, users can realize trajectory prediction for any number of steps by setting the number of prediction steps. Figure 9 shows the visualization of the prediction results of the model on straight and curved trajectory. Figure 7: Heatmaps of the attention matrices in the temporal and spatial dimensions during the trajectory prediction process of ASTGCN. (**a**) Heatmap of the attention matrix in the temporal dimension; (**b**) Heatmap of the attention matrix in the spatial dimension. Figure 8: Visual analytics platform interface. (**a**) Selection box of the underlying map; (**b**) Ship type filter; (**c**) Operation box of GIS (measurement, screening, marking); (**d**) Multi-functional menu bar, the functions of ship trajectory data include ship trajectory data loading items, screening of the time range of ship activities and screening of ship types; the functions of calculation and visualization include the algorithm selection of trajectory segmentation, trajectory clustering and representative trajectory display; (**e**) Corresponding to the visualization results of trajectory data loaded according to the filtering conditions as well as the prediction results; (**f**) Interface navigation bar for switching the interface. Figure 9: Visualization of the effect of trajectory prediction with a step size of 5. The arrows indicate the direction of trajectory travel, the localization icon represents the predicted trajectory point, the solid line represents the actual trajectory of the ship, and the dotted line represents the driving path of the real trajectory in the validation set.