  Pervasive integration of GPS-enabled devices and data acquisition
technologies has led to an exponential increase in GPS trajectory data,
fostering advancements in spatial-temporal data mining research. Nonetheless,
GPS trajectories contain personal geolocation information, rendering serious
privacy concerns when working with raw data. A promising approach to address
this issue is trajectory generation, which involves replacing original data
with generated, privacy-free alternatives. Despite the potential of trajectory
generation, the complex nature of human behavior and its inherent stochastic
characteristics pose challenges in generating high-quality trajectories. In
this work, we propose a spatial-temporal diffusion probabilistic model for
trajectory generation (DiffTraj). This model effectively combines the
generative abilities of diffusion models with the spatial-temporal features
derived from real trajectories. The core idea is to reconstruct and synthesize
geographic trajectories from white noise through a reverse trajectory denoising
process. Furthermore, we propose a Trajectory UNet (Traj-UNet) deep neural
network to embed conditional information and accurately estimate noise levels
during the reverse process. Experiments on two real-world datasets show that
DiffTraj can be intuitively applied to generate high-fidelity trajectories
while retaining the original distributions. Moreover, the generated results can
support downstream trajectory analysis tasks and significantly outperform other
methods in terms of geo-distribution evaluations.
