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.

本文提出基于扩散模型的轨迹生成框架 Diff-Traj，并通过轨迹 UNet (Traj-UNet) 结构提取轨迹特征，使其能视为高质量的轨迹生成应用程序，并保留原始分布。