DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model

• URL: http://arxiv.org/abs/2304.11582v2
• Date: Tue, 24 Oct 2023 12:43:04 GMT
• Title: DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model
• Authors: Yuanshao Zhu, Yongchao Ye, Shiyao Zhang, Xiangyu Zhao, and James J.Q. Yu
• Abstract summary: We propose a spatial-temporal probabilistic model for trajectory generation (DiffTraj) The core idea is to reconstruct and synthesize geographic trajectories from white noise through a reverse trajectory denoising process. Experiments on two real-world datasets show that DiffTraj can be intuitively applied to generate high-fidelity trajectories.
• Score: 44.490978394267195
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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