Trajectory Datasets

Trajectory datasets, encompassing the recorded movements of individuals, vehicles, or other entities, are crucial for understanding and predicting mobility patterns. Current research focuses on developing robust methods for anomaly detection within these datasets, often employing neural collaborative filtering, temporal point processes, and transformer models to analyze both kinematic features (how entities move) and semantic features (the purpose of movement). These advancements are vital for improving applications ranging from traffic management and public safety to personalized location-based services and the development of safer autonomous vehicles, particularly by addressing challenges like data sparsity, privacy concerns, and the need for explainable AI.