# A ResNet1D-AttLSTM-Based Approach for Ship Trajectory Classification
## 4 Conclusions and Future Work  
This paper presents an innovative ship trajectory classification model. The proposed model combines the advantages of ResNet1D and LSTM for local multi-level feature detection and learning long-term dependencies, respectively. It also employs self-attention mechanisms to enhance the understanding of critical information, thereby generating high-integrity spatiotemporal motion feature representations of ship trajectories, effectively improving the performance of ship trajectory classification. Experimental results show that the proposed ResNet1D-AttLSTM model outperforms other models that rely on single  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Accuracy (\%)** \\ \hline ResNet1D-AttLSTM & 89.7 \\ FishNet [31] & 81.4 \\ pGRU [32] & 82.2 \\ CNN-LSTM [34] & 84.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of classification accuracy among different models.  
Figure 7: Comparison of accuracy among four models on five categories of vessel trajectory classification.  
feature sets or lack an integrated attention mechanism in terms of accuracy. On a real AIS trajectory dataset, the overall classification accuracy of this model reached 89.7%, with particularly notable performance in the identification of towing vessel and tanker trajectories. However, there is room for improvement in the classification of fishing boat and sailing yacht trajectories.  
Future research directions include the following:  
1. Expanding the training dataset: Increase the diversity and volume of training data, especially for less represented vessel types like fishing and sailing boats, to improve classification accuracy and robustness.
2. Integrating contextual features: Incorporate additional discriminative features such as operational type and weather conditions into the model to better capture the context of vessel movements, thereby improving classification performance.
3. Refining data preprocessing techniques: Optimize preprocessing methods, considering the periodic nature of certain navigational data (e.g., longitude and COG), to ensure that these steps more accurately reflect actual navigation scenarios.
4. Optimizing model architecture: Explore various optimization strategies for the ResNet1D-AttLSTM architecture, including the testing of different activation functions or the introduction of partitioned activation mechanisms, to potentially achieve even better model performance.
5. Enhancing classification system diversity: Expand the scope of the classification system to include a wider range of vessel types, making it adaptable to diverse maritime environments and specific application scenarios.
6. Generalizing to other domains: Investigate the applicability of the developed methods to other domains, such as urban traffic management, to validate the generalizability and adaptability of the proposed techniques.  
Conceptualization, F.L. and B.F.; methodology, F.L. and B.F.; software, J.K. and Y.L.; validation, J.K.; formal analysis, J.K. and F.L.; investigation, J.K.; resources, B.F.; data curation, Y.L.; writing--original draft preparation, J.K.; writing--review and editing, F.L.; visualization, Y.L.; supervision, F.L.; project administration, F.L. and B.F.; funding acquisition, B.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We gratefully acknowledge [PERSON] for his technical assistance in resolving theoretical doubts during this research.  
The authors declare no conflicts of interest.