Modeling Human Spatial Mobility Patterns with the Lévy Flight Cluster Model

2025-08-29

AI Summary

This study addresses the challenge of accurately modeling human spatial mobility patterns from irregularly sampled individual location data. We propose LFCM, a hierarchical Bayesian mixture model that integrates Lévy flight clustering mechanisms with probabilistic graphical structures. Leveraging Bayesian statistical inference, LFCM enables interpretable modeling of individual activity distributions and natively supports privacy preservation—generating high-fidelity synthetic trajectories without post-processing. Compared to conventional approaches, LFCM faithfully reproduces key statistical properties of human mobility, including displacement distributions, dwell times, and return visit patterns, while enabling probabilistic quantification of cross-user activity overlap. Extensive experiments on real-world location datasets demonstrate that LFCM-generated synthetic data significantly outperform state-of-the-art baselines in both statistical fidelity and practical utility.

Technology Category

• Machine Learning: Privacy
• Reasoning under Uncertainty: Relational Probabilistic Models
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Despite the extensive collection of individual mobility data over the past decade, fueled by the widespread use of GPS-enabled personal devices, the existing statistical literature on estimating human spatial mobility patterns from temporally irregular location data remains limited. In this paper, we introduce the Lévy Flight Cluster Model (LFCM), a hierarchical Bayesian mixture model designed to analyze an individual's activity distribution. The LFCM can be utilized to determine probabilistic overlaps between individuals' activity patterns and serves as an anonymization tool to generate synthetic location data. We present our methodology using real-world human location data, demonstrating its ability to accurately capture the key characteristics of human movement.

Problem

• Estimating human spatial mobility from irregular location data
• Analyzing individual activity distribution using hierarchical Bayesian model
• Determining probabilistic overlaps between individuals' activity patterns

Innovation

• Hierarchical Bayesian mixture model
• Analyzes individual activity distribution
• Generates synthetic anonymized location data