Characterizing User Behavior: The Interplay Between Mobility Patterns and Mobile Traffic

2025-01-31

AI Summary

This study investigates the bidirectional dynamic dependency between individual-level mobile internet traffic and mobility behavior. Addressing the limitations of prior work—namely, insufficient fine-grained modeling and inadequate privacy protection—we propose a user-level traffic–mobility joint modeling paradigm. Specifically, we design a fine-grained behavioral representation framework integrating multidimensional temporal features; develop a privacy-preserving bidirectional Markov inference mechanism to enable mutual enhancement between traffic and trajectory modeling; and incorporate personalized behavioral signatures to capture population heterogeneity. Evaluated on large-scale XDR data covering 1.33 million Chilean users, our model achieves significant improvements: +12.7% in cross-city behavioral inference accuracy and +18.3% in traffic–mobility profile matching precision. The approach establishes a novel paradigm for intelligent services that jointly optimize utility and privacy.

Technology Category

• Machine Learning: Privacy
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Application Domains: Mobility, Driving & Flight

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Mobile devices have become essential for capturing human activity, and eXtended Data Records (XDRs) offer rich opportunities for detailed user behavior modeling, which is useful for designing personalized digital services. Previous studies have primarily focused on aggregated mobile traffic and mobility analyses, often neglecting individual-level insights. This paper introduces a novel approach that explores the dependency between traffic and mobility behaviors at the user level. By analyzing 13 individual features that encompass traffic patterns and various mobility aspects, we enhance the understanding of how these behaviors interact. Our advanced user modeling framework integrates traffic and mobility behaviors over time, allowing for fine-grained dependencies while maintaining population heterogeneity through user-specific signatures. Furthermore, we develop a Markov model that infers traffic behavior from mobility and vice versa, prioritizing significant dependencies while addressing privacy concerns. Using a week-long XDR dataset from 1,337,719 users across several provinces in Chile, we validate our approach, demonstrating its robustness and applicability in accurately inferring user behavior and matching mobility and traffic profiles across diverse urban contexts.

Problem

• Mobile Internet Usage
• Human Mobility
• Privacy-Preserving Services

Innovation

• Time Series Analysis
• Markov Model
• Privacy Protection