Generative Human Trajectory Recovery via Embedding-Space Conditional Diffusion

Recovering human trajectories from incomplete or missing data is crucial for many mobility-based urban applications, e. g. , urban planning, transportation, and location-based services. Existing methods mainly rely on recurrent neural networks or attention mechanisms. Though promising, they encounter limitations in capturing complex spatial-temporal dependencies in low-sampling trajectories. Recently, diffusion models show potential in content generation. However, most of proposed methods are used to generate contents in continuous numerical representations, which cannot be directly adapted to the human location trajectory recovery. In this paper, we introduce a conditional diffusion-based trajectory recovery method, namely, DiffMove. It first transforms locations in trajectories into the embedding space, in which the embedding denoising is performed, and then missing locations are recovered by an embedding decoder. DiffMove not only improves accuracy by introducing high-quality generative methods in the trajectory recovery, but also carefully models the transition, periodicity, and temporal patterns in human mobility. Extensive experiments based on two representative real-world mobility datasets are conducted, and the results show significant improvements (an average of 11% in recall) over the best baselines.

Authors

• Kaijun Liu
• Sijie Ruan
• Liang Zhang
• Cheng Long 0001
• Shuliang Wang 0001
• Liang Yu 0005

Keywords

• Trajectory recovery
• Diffusion model
• Self-supervised learning
• Human mobility