# Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks
## 1 Introduction
Graph neural-network-based traffic flow prediction methods aggregate traffic flow information from neighboring nodes within a local spatial range, thereby predicting the potential information of nodes in the traffic subnetwork. Nevertheless, relying solely on spatial information to predict traffic flow states is not rigorous and can lead to a significant loss of temporal information. To address this limitation, researchers have extended their efforts by incorporating temporal prediction learning methods into graph neural networks [17]. By leveraging techniques such as gate units and recurrent neural networks (RNN), they have introduced \"spatiotemporal graph\" models, which effectively capture both the spatial and temporal features of traffic flow prediction [18; 19]. These spatiotemporal graph models take into account both temporal patterns and the spatial correlation structure of traffic entities. They can effectively capture the underlying meanings and interactions of nodes and edges within complex systems, resulting in superior prediction performance in traffic flow prediction. In recent years, numerous excellent spatiotemporal graph models have been proposed and applied in the field of urban traffic prediction. Representative works include Multivariate Time Series Forecasting with Graph Neural Networks (MTGNN) [20], Diffusion Convolutional Recurrent Neural Networks (DCRNN) [21], Spatiotemporal Graph Convolutional Networks (STGCN) [18], and others. However, the application traffic flow prediction methods in the maritime domain still faces two major challenges. Firstly, there is a scarcity of public datasets that can be directly used for analysis. The process of constructing maritime traffic flow datasets is arduous and complex. Secondly, the maritime traffic flow prediction scenarios do not possess the same traffic network structures as urban settings, rendering it difficult to directly apply spatiotemporal graph methods developed for urban traffic flow prediction [22]. Existing port flow prediction methods typically focus on single-port scenarios and do not fully consider the characteristics of port networks. They fail to consider the patterns of flow changes from the perspective of the overall port distribution structure, resulting in a relative scarcity of research in multi-port scenarios [23]. The scarcity of maritime traffic datasets stems primarily from the extensive geographical distribution, intricate structural arrangements, and challenges in regulating the maritime networks comprising ports across diverse countries. The widespread adoption of the Automatic Identification System (AIS) has significantly addressed the data gap in the maritime domain. The AIS is an onboard broadcast response system that has been gradually deployed on international vessels since 2002 and now has achieved widespread coverage through satellite networks. Vessels equipped with AIS devices regularly transmit navigational status data such as position points, speed, heading, and identity to ground-based stations and satellite receivers [24]. This enables data exchange between vessels and assists in navigation. Despite the development of AIS, there are significant challenges related to the noise contamination in the raw AIS data, which limits their application in maritime traffic systems [25]. In this study, we address this issue by combining AIS data with spatial information from various ports worldwide and employing various big data processing techniques, similar to [26]. Through these approaches, we are able to effectively filter out noisy AIS data to the maximum extent possible and reconstruct realistic maritime traffic flow scenarios. As a result, we have constructed a multi-port flow dataset that can assist in conducting in-depth research on ship traffic flow prediction and validating the effectiveness of models under fair conditions.