Trajectory Clustering

Trajectory clustering aims to group similar movement patterns from data like vehicle routes or pedestrian paths, revealing underlying behavioral modes and improving predictions or anomaly detection. Current research emphasizes efficient algorithms, including those based on distance metrics, tensor models (like Dirichlet Process Multinomial Mixtures), and graph neural networks, to handle high-dimensional and hierarchical data while addressing computational challenges. These advancements have significant implications for various fields, such as improving transportation systems, enhancing autonomous vehicle safety, and enabling more effective monitoring in maritime domains.