# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 4 Definitions and Problem Statement
### Vessel Portrait Construction
#### 4.2.1 Label System Construction  
As shown in Figure 3, we established a label system based on Shallow-level Attributes and Deep-level Features. The Shallow-level Attributes included a series of fundamental attributes of a vessel (such as the length, width, draft, and type). These attributes significantly impact the maneuvering performance of vessels. According to ship maneuverability standards [17], both course stability and turning ability are pivotal indicators of maneuverability and are affected by the ship's block coefficient. The block coefficient is defined as the displacement of a ship divided by the product of its length, width, and draft. Moreover, different types of ships have different block coefficients due to variations in the shape of their underwater hulls. For vessels of the same displacement, ships with smaller block coefficients (such as container ships) exhibit better course stability but poorer turning ability than those with larger block coefficients (such as tankers). Therefore, they require wider navigational fairways to reduce the risk of collisions with other vessels. In addition, these attributes (length, width, draft, and type) also play a crucial role in the selection of fairways, ports, and routes. According to coastal engineering manuals [49], the fairway width is typically two to five times the ship's breadth. Vessels must consider both the width and depth when navigating to ensure safety and efficiency. In port selection, according to the PIANC [50], large vessels need to choose ports with sufficient berth and maneuvering space to ensure safe berthing. In route planning, vessels must consider their turning radius and draft, choosing suitable routes to avoid the risk of grounding or collision. Consequently, these attributes are crucial for feasibility and must be thoroughly considered to ensure more accurate predictions of different vessels.  
To model trajectory information and Multi-level Vessel Characteristics more effectively, we take into account not only Shallow-level Attributes but also Deep-level Features. The Deep-level Features are defined as the Sailing Location Preference, the Voyage Time Preference, and the Anchoring Time Preference. The Sailing Location Preference reflects the behavioral pattern of the ship. For example, container ships engaged in liner shipping, typically operate on fixed routes and within port areas for cargo handling and transport [51]. The fixed routes and regular schedules of liner shipping ensure logistics timeliness, reducing losses and enhancing revenue. Meanwhile, trawlers primarily operate in specific fishing areas [52], where their Sailing Location Preferences are influenced by the distribution of fisheries resources. Unlike liner shipping, trawlers have a more flexible navigation pattern, often adjusting their fishing locations based on the season, to comply with regulatory constraints and to increase revenue. This preference provides a more comprehensive understanding of vessel behavior and improves the accuracy of trajectory prediction. Regarding the Voyage Time Preference and Anchoring Time Preference, container ships tend to minimize the anchorage time [53], strictly adhering to schedules to optimize operational efficiency. This operational mode not only ensures the timely transportation of goods but also helps to reduce the operating costs. In contrast, trawlers' Voyage Time Preferences are more influenced by fisheries management regulations and market demands. This temporal information contributes to a deeper understanding of vessel behavior patterns and empowers prediction models to precisely capture fluctuations in vessel movements over time.