# Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix
###### Abstract  
This study proposes a vessel position prediction method using attention spatiotemporal graph convolutional networks, which addresses the issue of low prediction accuracy due to less consideration of inter-feature dependencies in current vessel trajectory prediction methods. First, the method cleans the vessel trajectory data and uses the Time-ratio trajectory compression algorithm to compress the trajectory data, avoiding data redundancy and providing feature points for vessel trajectories. Second, the Spectral Temporal Graph Neural Network (StemGNN) extracts the correlation matrix that describes the relationship between multiple variables as a priori matrix input to the prediction model. Then the vessel trajectory prediction model is constructed, and the attention mechanism is added to the spatial and temporal dimensions of the trajectory data based on the spatio-temporal graph convolutional network at the same time as the above operations are performed on different time scales. Finally, the features extracted from different time scales are fused through the full connectivity layer to predict the future trajectories. Experimental results show that this method achieves higher accuracy and more stable prediction results in trajectory prediction. The attention-based spatio-temporal graph convolutional networks effectively capture the spatio-temporal correlations of the main features in vessel trajectories, and the spatio-temporal attention mechanism and graph convolution have certain interpretability for the prediction results. a +
Footnote †: journal: Medical  
During the process of using AIS trajectory data for trajectory prediction, there are several key steps involved, including data preprocessing and trajectory prediction model construction [7; 8]. The data preprocessing stage involves steps such as data parsing, cleaning and extraction, and trajectory compression, intending to select valid information and provide an accurate and complete data foundation for subsequent analysis. Subsequently, in the trajectory prediction step, deep learning techniques like recurrent neural networks (RNNs) play a crucial role [8]. This technique constructs ship trajectory prediction models to dig deeper into the patterns and relationships hidden in AIS trajectory data; when dealing with large amounts of data with multiple attributes and strong spatiotemporal correlations, it shows high efficiency and accuracy. Since trajectory data has multiple attributes, large data volume, and strong spatiotemporal correlation, vessel trajectory prediction models must have the ability to capture non-linear relationships between multiple variables. In response to the above difficulties, this study proposes a trajectory prediction framework based on Attention Spatio-Temporal Graph Convolutional Network (ASTGCN). The primary objective is to accurately capture the intricate interrelationships among variables inherent in vessel trajectory data. Through comparative analysis with existing models, we demonstrate the significance of considering feature dependencies in improving trajectory prediction accuracy. Moreover, we validate the efficacy of ASTGCN with a correlation matrix derived from StemGNN in enhancing vessel trajectory prediction. The proposed method adopts a multi-step approach. First, the Time-Ratio (TR) trajectory compression algorithm is used to extract key feature points from vessel trajectories. Second, to capture the non-linear relationships between the data more accurately, a self-attention mechanism is used to construct the prior matrix input of the model, which learns the inherent connections and dependencies between the data. Simultaneously, attention mechanisms are applied in both time and space dimensions to assign higher weights to the main features in the data, helping the model to focus on the key information that has a greater impact on the prediction results, thus improving the accuracy of the prediction.