Trajectory Data Mining

Trajectory data mining focuses on extracting meaningful insights from sequences of locations and times, aiming to understand movement patterns of individuals, vehicles, or other entities. Current research emphasizes anomaly detection, often leveraging machine learning models like neural networks (including collaborative filtering and contrastive learning approaches), deep learning, and large language models to identify unusual behaviors or predict travel times. These advancements have significant implications for various fields, including urban planning, public health surveillance, personalized recommendations, and security applications, by enabling more efficient and accurate analysis of complex movement data.