## 2 Related Work
### Ship Behavior Learning Method  
Based on current research, ship behavior learning within fairways can be categorized into grid-based, vector-based, and statistical ship behavior learning methods. The basic idea of grid-based methods for learning ship behavior within fairways is to divide the study area into grids, map the ship traffic flow data onto these grids, and then learn the traffic patterns. [PERSON] et al. (2018) proposed creating a grid-based ship traffic database and introduced the problem of trajectory interpolation. They stored dynamic and static ship information in the gridded database, significantly reducing the problem size. Their research primarily applied to ship traffic display and traffic information statistics. [PERSON] et al. (2018) described ship traffic patterns based on grids and used the developed model for ship traffic anomaly detection. [PERSON] et al. (2018) designed the Lattice-Based DBSCAN clustering algorithm, which clusters grids based on their density (the number of trajectory points within a grid). This approach effectively removes noise and irregular ship traffic data, facilitating the learning of ship traffic patterns. The learned models were then applied to ship traffic prediction. Grid-based ship traffic pattern learning effectively reduces the problem size and allows various attributes (such as speed distribution, heading distribution, and ship length distribution, and more) to be stored within the grids. However, the grid size generally needs to be determined before the study, and grid-based methods are typically more suitable for smaller areas.  
The basic idea of vector-based methods for learning ship behavior within fairways is to represent ship routes using turning points and the straight lines connecting them. Before extracting ship routes or constructing a maritime traffic network, specific methods are generally required to cluster ship routes and turning points, thereby constructing the traffic network. [PERSON] et al. (2018) developed a method called TREAD (Traffic Route Extraction and Anomaly Detection), which uses DBSCAN clustering to identify route points (such as ports, offshore platforms, and entry and exit points). They used statistical methods to obtain the distribution of parameters like ship types at these route points, learning traffic patterns for trajectory prediction, and anomaly detection. The study also compared traffic patterns derived from different data volumes and suggested the data volume required to obtain reliable traffic patterns. [PERSON] et al. (2018) identified turning areas, ports, offshore platforms, and entry, and exit points to ultimately form ship routes. Vector-based ship traffic mining methods are generally considered effective for handling high-density traffic but are less effective for irregular ship traffic.  
Statistical methods are widely used in maritime traffic learning to describe important maritime traffic parameters. [PERSON] et al. (2018) established a statistical model of ship movement patterns based on density estimation and applied it to anomaly detection and maritime traffic prediction. [PERSON] et al. (2018) set up gate lines in the navigation channel to collect statistics on the distribution of ship positions, speeds, and headings as they pass through, obtaining traffic patterns for modeling and simulating actual maritime traffic. [PERSON] et al. (2018) investigated ship behavior in densely trafficked areas, with their findings applied to identifying collision risks. [PERSON] et al. (2018) applied the Ising model to establish a calculation model for determining ship collision risk values in port waters. This model provides a quantitative analysis of collision risks in port waters. A case study in Qingdao Port demonstrated that the Ising model effectively identified high-risk collision areas, contributing to overall navigation safety in port waters.