Trajectory Similarity

Trajectory similarity research focuses on developing efficient and accurate methods for comparing movement patterns represented as trajectories, with key objectives being improved computational speed and enhanced representation of complex spatiotemporal relationships. Current research emphasizes deep learning approaches, including convolutional neural networks (CNNs), graph neural networks (GNNs), and contrastive learning methods, to learn robust trajectory representations and overcome limitations of traditional distance metrics. These advancements have significant implications for various applications, such as traffic management, anomaly detection in maritime transport, location-based services, and autonomous driving, by enabling more effective analysis of movement data.