通过用户级数据揭示移动行为与流量模式的深层关联
Characterizing User Behavior: The Interplay Between Mobility Patterns and Mobile Traffic
- 构建融合流量与移动行为的用户建模框架，捕捉个体动态特征
- 基于130万用户数据验证，可精准推断行为模式匹配
- 提出马尔可夫模型实现双向推理，兼顾隐私与实用性
移动设备已成为记录人类活动的重要工具，扩展数据记录（XDR）为用户行为建模提供了丰富机会，有助于设计个性化数字服务。以往研究多聚焦于聚合流量与移动性分析，常忽视个体层面的洞察。本文提出一种新方法，探索用户层面流量与移动行为间的依赖关系。通过分析涵盖13个维度的流量模式与多种移动特征，深化了对二者交互机制的理解。所提出的用户建模框架在时间维度上整合流量与移动行为，通过用户专属特征保持群体异质性。此外，我们开发了一种马尔可夫模型，可从移动行为推断流量行为，反之亦然，优先考虑显著依赖关系并兼顾隐私保护。基于智利多个省份1,337,719名用户的为期一周的XDR数据集，验证了该方法的鲁棒性与适用性，证明其在不同城市环境中准确推断用户行为并匹配移动与流量轮廓的能力。
原文摘要 · Abstract (English)
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.
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