Diffusion Model for GPS Trajectory Generation

Research output: Working PapersPreprint

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Original languageEnglish
PublisherArchive Books
Number of pages12
Publication statusOnline published - 23 Apr 2023


With the deployment of GPS-enabled devices and data acquisition technology, the massively generated GPS trajectory data provide a core support for advancing spatial-temporal data mining research. Nonetheless, GPS trajectories comprise personal geo-location information, rendering inevitable privacy concerns on plain data. One promising solution to this problem is trajectory generation, replacing the original data with the generated privacy-free ones. However, owing to the complex and stochastic behavior of human activities, generating high-quality trajectories is still in its infancy. To achieve the objective, we propose a diffusion-based trajectory generation (Diff-Traj) framework, effectively integrating the generation capability of the diffusion model and learning from the spatial-temporal features of trajectories. Specifically, we gradually convert real trajectories to noise through a forward trajectory noising process. Then, Diff-Traj reconstructs forged trajectories from the noise by a reverse trajectory denoising process. In addition, we design a trajectory UNet (Traj-UNet) structure to extract trajectory features for noise level prediction during the reverse process. Experiments on two real-world datasets show that Diff-Traj can be intuitively applied to generate high-quality trajectories while retaining the original distribution.

Research Area(s)

  • Diffusion model, Privacy preserving, Spatial-temporal data mining, GPS trajectory

Citation Format(s)

Diffusion Model for GPS Trajectory Generation. / Zhu, Yuanshao; Ye, Yongchao; Zhao, Xiangyu et al.
Archive Books, 2023.

Research output: Working PapersPreprint