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

TL;DR

This paper presents a novel user-level modeling approach that explores the dependency between mobility patterns and mobile traffic, enhancing understanding of their interaction for personalized digital services.

Contribution

It introduces a new framework integrating traffic and mobility behaviors at the user level, with a Markov model for behavior inference, validated on a large Chilean dataset.

Findings

Robust inference of user behavior from mobility and traffic data

Effective matching of mobility and traffic profiles across urban contexts

Preservation of user privacy while modeling behavior

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.