Synthetic Trajectory

Synthetic trajectory generation focuses on creating artificial movement data, addressing limitations in collecting real-world trajectory data due to privacy concerns or data scarcity. Current research emphasizes developing generative models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), diffusion models, and transformer-based architectures, to produce realistic and diverse trajectories that accurately reflect real-world patterns while preserving privacy. These advancements have significant implications for various fields, improving the training of autonomous systems, enhancing urban planning and traffic management simulations, and enabling more robust analysis of human mobility and other dynamic systems.