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.

翻译：全球定位系统（GPS）设备的普及与数据采集技术的融合，催生了GPS轨迹数据的指数级增长，推动了时空数据挖掘研究的发展。然而，GPS轨迹包含个人地理位置信息，直接使用原始数据会引发严重的隐私问题。轨迹生成作为一种有前景的解决方案，通过生成无隐私风险的替代数据来取代原始数据。尽管轨迹生成具有潜力，但人类行为的复杂性及其固有的随机特征给高质量轨迹生成带来了挑战。本文提出一种面向轨迹生成的时空扩散概率模型（DiffTraj），该模型有效结合了扩散模型的生成能力与从真实轨迹中提取的时空特征。其核心思想是通过逆向轨迹去噪过程，从白噪声中重建并合成地理轨迹。此外，我们提出一种轨迹UNet（Traj-UNet）深度神经网络，用于嵌入条件信息并在逆向过程中精确估计噪声水平。两个真实数据集上的实验表明，DiffTraj可直观应用于生成高保真轨迹，同时保留原始分布特征。此外，生成结果可支持下游轨迹分析任务，且在地理分布评估方面显著优于其他方法。