Overview of clustering ship trajectories based on AIS data using machine learning methods

The table shows an overview and comparison of research on different trajectory clustering methods in water transport based on Automatic Identification System (AIS) data using machine learning. Trajectory clustering is a process of grouping similar trajectories together based on their spatial and temporal characteristics. In the context of maritime traffic, trajectory clustering involves grouping vessel trajectories that have similar movement patterns, such as speed, direction, and location. This process helps to identify common traffic patterns and can be used to extract traffic routes between ports, route planning, traffic management and anomaly detection. Trajectory clustering is achieved by different methods such as K-means clustering, Hierarchical clustering, DBSCAN, OPTICS, Mean-shift clustering, spectral clustering, fuzzy clustering, Douglas and Peucker algorithm etc. The choice of method depends on the specific problem and the characteristics of the data.