Learning to Generate Pseudo Personal Mobility

TL;DR

This paper introduces GeoAvatar, a novel deep generative model that creates realistic pseudo personal mobility data by capturing individual heterogeneity while preserving privacy, outperforming existing methods.

Contribution

The paper presents GeoAvatar, a new individual-based human mobility generator that models heterogeneity, incorporates demographic data, and offers interpretability, advancing privacy-preserving mobility data synthesis.

Findings

Generated high-quality heterogeneous mobility data.

Outperformed mechanism-based and deep learning approaches.

Maintained privacy without accessing personal data.

Abstract

The importance of personal mobility data is widely recognized in various fields. However, the utilization of real personal mobility data raises privacy concerns. Therefore, it is crucial to generate pseudo personal mobility data that accurately reflects real-world mobility patterns while safeguarding user privacy. Nevertheless, existing methods for generating pseudo mobility data, such as mechanism-based and deep-learning-based approaches, have limitations in capturing sufficient individual heterogeneity. To address these gaps, taking pseudo-person(avatar) as ground-zero, a novel individual-based human mobility generator called GeoAvatar has been proposed - which considers individual heterogeneity in spatial and temporal decision-making, incorporates demographic characteristics, and provides interpretability. Our method utilizes a deep generative model to simulate heterogeneous individual life patterns, a reliable labeler for inferring individual demographic characteristics, and a Bayesian approach for generating spatial choices. Through our method, we have achieved the generation of heterogeneous individual human mobility data without accessing individual-level personal information, with good quality - we evaluated the proposed method based on physical features, activity patterns, and spatial-temporal characteristics, demonstrating its good performance, compared to mechanism-based modeling and black-box deep learning approaches. Furthermore, this method maintains extensibility for broader applications, making it a promising paradigm for generating human mobility data.