基于深度表示学习的手机定位数据日常活动模式建模
Modelling daily activity patterns from mobile phone location data via deep representation learning
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中文总结 AI 辅助
本研究提出活动链编码器（ACE），一种自监督深度表示学习方法，结合预训练城市嵌入和Transformer建模手机定位数据中的日常活动链，无需活动标签即可识别伦敦六个工作日活动模式组，为从无标签移动数据中提取行为模式提供了整体路径。
中文摘要 AI 辅助
被动收集的手机定位数据提供了大规模、纵向的人类移动观测，但并未直接揭示活动目的。到访地点的功能特征提供了有用的情境信息，然而其与活动目的之间的关系仍不确定，尤其是在混合用途的城市环境中。我们将活动模式挖掘概念化为表示、聚类和解释的集成过程，并为表示阶段提出了活动链编码器（ACE）。ACE是一种自监督模型，结合了预训练的城市嵌入与访问时间和持续时间，并使用Transformer对停留的序列组织进行建模。它通过掩码活动建模和身份引导的对比学习进行训练，无需确定性的活动目的标签。学习到的日常表示被聚合成用户级别的画像，进行聚类，并通过时间-功能模式和基于人口普查的人口统计背景进行解释。应用于伦敦的手机应用定位数据，并与三种代表性方法进行比较，ACE支持识别六个不同的工作日活动模式组，这些组以不同的日常节奏、城市功能背景和人口统计关联为特征。这些互补的证据形式进一步支持了基于经验的活动模式人物画像的发展，为从无标签的手机定位数据中推导出行为上有意义的人群模式建立了一条整体路径。本研究中开发的整个分析流程的源代码可在此https URL公开获取。
英文摘要
Passively collected mobile phone location data provide large-scale, longitudinal observations of human mobility but do not directly reveal activity purposes. The functional characteristics of visited locations offer useful contextual information, yet their relationship with activity purpose remains uncertain, particularly in mixed-use urban environments. We conceptualise activity pattern mining as an integrated process of representation, clustering, and interpretation, and propose the Activity Chain Encoder (ACE) for the representation stage. ACE is a self-supervised model that combines pre-trained urban embeddings with visit timing and duration and uses a Transformer to model the sequential organisation of stays. It is trained using masked activity modelling and identity-guided contrastive learning without requiring deterministic activity purpose labels. Learned daily representations are aggregated into user-level profiles, clustered, and interpreted through temporal-functional patterns and Census-derived demographic context. Applied to mobile phone app location data from London and compared with three representative methods, ACE supports the identification of six differentiated weekday activity-pattern groups characterised by distinct daily rhythms, urban functional contexts, and demographic associations. These complementary forms of evidence further support the development of empirically grounded activity-pattern personas, establishing a holistic route for deriving behaviourally meaningful population patterns from unlabelled mobile phone location data. The source code for the entire analytical pipeline developed in this study is publicly available at https://github.com/xlwang233/ACE.
发表机构
- University College London（伦敦大学学院）
- Wuhan University（武汉大学）
- The Chinese University of Hong Kong（香港中文大学）
- The Alan Turing Institute（艾伦·图灵研究所）
机构由 AI 辅助整理，请以论文原文为准。
