# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
However, due to the intricate dependencies in historical information and the strong influence of spatial correlations, only relying on trajectory information makes it difficult to attain precise predictive outcomes. Moreover, Multi-level Vessel Characteristics, such as vessel attributes and Sailing Location Preferences, also play a crucial role in trajectory prediction. According to ship maneuverability standards [17], course stability and turning ability are crucial metrics for maneuverability, dependent on the block coefficient, which is determined by a vessel's attributes. Variations in a vessel's attributes significantly impact maneuverability, thereby affecting decisions regarding ports, fairways, and routes. Furthermore, Sailing Location Preferences reveal their tendencies toward specific maritime areas, which should receive more attention in predictions. As depicted in Figure 1, two types of vessels exhibit distinct motion trajectories. Compared to trawlers, cargo ships typically have larger volumes and higher block coefficients, resulting in a larger turning radius and poorer course stability. To mitigate the potential risks, cargo ships tend to select broader shipping lanes and fairways, strictly adhering to established schedules to ensure punctual cargo delivery and enhance overall logistical efficiency, resulting in smoother and more regular sailing trajectories. Conversely, trawlers operate within specific fishing areas, constrained by the distribution of fishery resources and relevant regulatory policies, often resulting in irregular and concentrated navigation paths. Therefore, it is crucial to investigate the behavioral patterns of different vessels and conduct tailored predictive analyses based on vessel attributes and operational areas. For this reason, challenges still persist in obtaining more comprehensive characteristics, along with their advisable selection and implementation. Inspired by user personas [18], we incorporated Shallow-level Attributes and Deep-level Features, defining Multi-level Vessel Characteristics to construct a comprehensive vessel portrait. Considering the aforementioned challenges, we propose a vessel trajectory prediction model VEPO-S2S based on the Seq2 Seq architecture, comprising a Multi-level Vessel Trajectory Representation Module (Multi-Rep) and a Feature Fusion and Decoding Module (FFDM). Multi-Rep serves the function of acquiring and expressing features, consisting of two components: the Feature Acquisition Component and the Feature Expression Component. In the Feature Acquisition Component, we first specify the trajectory information that includes the longitude, latitude, speed, course, and sailing distance. Then, Multi-level Vessel Characteristics are defined, covering Shallow-level Attributes (such as the length, width, draft, etc.) as well as Deep-level Features (Sailing Location Preference, Voyage Time Preference, etc.). All of these are acquired through the Feature Acquisition Component and then encoded separately using three independent encoders within the Feature Expression Component. Apart from that, despite the incorporation of trajectory information and vessel characteristics into the model, basic Seq2 Seq models encounter challenges in discerning and leveraging them efficiently. Therefore, it is imperative to select and integrate the trajectory information and vessel characteristics before applying them. To achieve this purpose, we propose the FFDM module that consists of a Portrait Selection Component, Feature Fusion Component, and Multi-head Decoding Component. At first, the Portrait Selection Component discerns the most relevant vessel characteristics for the current prediction environment via analyzing the encoded characteristics. Then, the Feature Fusion Component is designed to merge trajectory information from the Multi-Rep module with relevant vessel characteristics. Finally, the output serves as the input for the Multi-head Decoding Component, which is designed based on the traditional Seq2 Seq decoder.