# Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks
## 5 Conclusions  
In this study, a comprehensive dataset of real port traffic and a port topology structure were constructed using global AIS data, Lloyd's ship archive, and port geospatial data. To explore different modeling approaches, a variety of comparative models were reproduced using an open-source spatiotemporal graph model training framework [32].  
Subsequently, a novel fine-grained multi-port traffic flow prediction spatiotemporal graph network model called GAGW was designed and implemented. This new model aimed to address the challenge of imbalanced temporal and spatial hidden features. Drawing inspiration from gate-based structures, the GAGW model processed temporal and spatial features in parallel, departing from the traditional sequential \"time-space-time\" processing pattern. The model incorporated distinct mapping functions for temporal and spatial feature vectors, placing greater emphasis on the influence of temporal features on prediction outcomes while reducing the sensitivity to spatial features.  
To evaluate the effectiveness of the GAGW model, comparative experiments were conducted using maritime datasets, special monthly datasets, and urban road datasets. The results demonstrated the reliability and robustness of the proposed model. The GAGW model achieved advanced levels of prediction performance and robustness, among other evaluation metrics.  
Overall, this research contributes to the field of port traffic flow prediction by introducing a novel spatiotemporal graph model that effectively handles imbalanced temporal and spatial features. The proposed GAGW model demonstrates superior prediction performance and adaptability, making it a promising approach for multi-port traffic forecasting applications.  
Although the new framework has demonstrated the inequality of spatiotemporal features in experiments, there is room for further improvement in the construction of dynamic port graph structures. The proposed method for dynamic graph construction solely considers the spatial correlation between ports based on ship traffic, without incorporating the objective geographical relationships that exist between ports. In future work, the paper aims to focus on constructing flow datasets that encompass multi-port scenarios of practical significance, such as Europe and coastal regions of China.  
Additionally, this paper only utilized AIS source data from the year 2019, which may be considered insufficient in capturing the full variability in port traffic patterns at a daily level. In future research, the study intends to incorporate AIS data from multiple consecutive years to augment the volume of data available for model training.  
In summary, the future research endeavors of this paper will concentrate on two main aspects: optimizing the design of dynamic graph structures and refining the strategies for the processing of temporal layers in the model.  
Conceptualization, Y.L. and Z.L.; methodology, Z.L. and Q.M.; software, W.H., Z.W. and W.X.; validation, Y.L., Z.L. and Q.M.; formal analysis, Q.M.; investigation, Y.Y. and Y.C.; resources, Y.L. and P.W.; data curation, Z.L., W.H. and Z.W.; writing--original draft preparation, Z.L.; writing--review and editing, Y.L. and Q.M.; visualization, W.X.; supervision, Y.L. and P.W.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China: [Grant Number 71804059]; Natural Science Foundation of Fujian Province: [Grant Number 2021]01821]; Shanghai Science and Technology Committee: [Grant Number 18 DZ1206300]; the National Key Research and Development Program of China: [Grant Number 2018 YFC1407400].  
Not applicable.  
The data used to support the findings of this study are included within the article.  
The authors declare no conflict of interest.