DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model

Zhu, Yuanshao1,2; Ye, Yongchao1; Zhang, Shiyao1; Zhao, Xiangyu2; Yu, James J. Q.3

2023

37th Conference on Neural Information Processing Systems (NeurIPS)

1049-5258

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023)

DEC 10-16, 2023

null,New Orleans,LA

10010 NORTH TORREY PINES RD, LA JOLLA, CALIFORNIA 92037 USA

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)

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.

第一

英语

CPCI-S

Stable Support Plan Program of Shenzhen Natural Science Fund[20220815111111002] ; Research Impact Fund[R1015-23] ; APRC -CityU New Research Initiatives[9610565] ; (Start-up Grant for New Faculty of City University of Hong Kong)["CityU -HKIDS","9360163"] ; Hong Kong ITC Innovation and Technology Fund Midstream Research Programme for Universities Project[No.ITS/034/22MS] ; Hong Kong Environmental and Conservation Fund[88/2022] ; SIRG CityU Strategic Interdisciplinary Research Grant["7020046","7020074"]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Information Systems

WOS:001220818800005

Web of Science

1.Southern Univ Sci & Technol, Shenzhen, Peoples R China
2.City Univ Hong Kong, Hong Kong, Peoples R China
3.Univ York, York, N Yorkshire, England

Zhu, Yuanshao,Ye, Yongchao,Zhang, Shiyao,et al. DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model[C]. 10010 NORTH TORREY PINES RD, LA JOLLA, CALIFORNIA 92037 USA:NEURAL INFORMATION PROCESSING SYSTEMS (NIPS),2023.