AutoSTDiff: Autoregressive Spatio-Temporal Denoising Diffusion Model for Asynchronous Trajectory Generation
Rongchao Xu, Zhiqing Hong, Guang Wang · Society for Industrial and Applied Mathematics eBooks · 2025
Large-scale trajectory data is crucial for applications like human mobility prediction and pandemic intervention. However, concerns over data privacy have limited access to real-world datasets. Advanced generative models offer a promising alternative for creating synthetic yet realistic trajectory data, but most existing methods focus on synchronous trajectories with fixed time intervals, which fail to capture the complexities of asynchronous trajectories, such as Point of Interest (POI) check-ins with uncertain intervals and varying lengths. To address this gap, we propose AutoSTDiff, a novel Autoregressive Spatio-Temporal denoising Diffusion model for asynchronous trajectory generation. AutoSTDiff includes two key components: (i) a hybrid embedding module that captures comprehensive spatio-temporal patterns considering human behavior and varying trajectory lengths, and (ii) a spatiotemporal diffusion model with a spatial status conversion module and a conditional spatio-temporal generation module for autoregressive trajectory generation. Extensive experiments on two public trajectory datasets show that AutoSTDiff outperforms state-of-the-art models, e.g., an increase of 51.2% and 43.8% on the length and G-rank metrics.