Human mobility appears highly diverse, yet much of a person's daily mobility can be explained by a small set of recurring behavioral templates, such as commuting, school-centered activities, caregiving, nightlife, or errand patterns. We present \texttt{IBAD} (\underline{I}nterpretable \underline{B}ehavioral \underline{A}nomaly \underline{D}etection), a framework that learns interpretable daily mobility templates and represents each individual as a distribution over mixtures of these templates. Rather than focusing on specific locations, IBAD characterizes activities that individuals perform across locations. This approach first discovers global behavioral templates using Latent Dirichlet Allocation (LDA), then employs a hierarchical self-supervised model to learn normal behavior of individuals from their soft behavioral templates. We also introduce a \emph{splicing benchmark} that creates controlled behavioral mismatches between an individual's historical profile and injected mobility patterns. Experiments on real-world and synthetic datasets show that daily behavior can be effectively decomposed into a small number of interpretable templates. Crucially, we show that the learned behavioral archetypes \emph{transfer} across distinct geographic and demographic contexts. Furthermore, IBAD maintains a robust competitive performance across all settings. For reproducibility purposes, the code is accessible at ~\href{https://github.com/USC-InfoLab/IBAD}{https://github.com/USC-InfoLab/IBAD}.

翻译：人类移动行为呈现出高度多样性，但个体的日常移动模式大多可归因于少量重复性行为模板，例如通勤、以学校为中心的活动、照护、夜生活或差事模式。本文提出\texttt{IBAD}（\underline{I}nterpretable \underline{B}ehavioral \underline{A}nomaly \underline{D}etection，可解释行为异常检测）框架，该框架可学习可解释的日常移动模板，并将每个个体表示为这些模板的混合分布。IBAD并不聚焦于特定地点，而是刻画个体在不同地点执行的活动特征。该方法首先利用潜在狄利克雷分配（LDA）发现全局行为模板，随后采用 hierarchical 自监督模型，基于个体的软行为模板学习其正常行为模式。我们还引入了一种\emph{拼接基准测试}方法，通过将注入的移动模式与个体的历史行为画像进行受控拼接，生成行为失配样本。在真实与合成数据集上的实验表明，日常行为可有效分解为少量可解释模板。关键的是，我们证明了所学到的行为原型具有跨不同地理与人口统计背景的\emph{迁移}能力。此外，IBAD在所有实验设定下均保持稳健的竞争性能。为确保可复现性，代码已开源至~\href{https://github.com/USC-InfoLab/IBAD}{https://github.com/USC-InfoLab/IBAD}。