人类移动性预测模型用于预测用户轨迹中的下一个位置，正日益部署于城市分析、导航及个性化服务中。然而，此类模型是否可能从训练数据中记忆并泄露敏感用户轨迹，目前尚不清楚。尽管记忆行为已在语言模型中得到广泛研究，但移动性预测面临独特挑战：训练序列在多种空间与时间尺度上编码人类行为，从而在不同粒度层面引发隐私风险。本文首次系统性地审计了移动性预测模型中的记忆行为。虽然已有研究指出此类模型可能导致隐私泄露，但我们首次在大规模上系统评估并量化了记忆风险。我们识别出若干关键挑战，包括缺乏随机性空间、轨迹的多尺度结构以及用户行为的个体差异性。为应对这些挑战，我们提出一个框架，用以在不同粒度层级（个体位置、锚点对、子轨迹片段）上量化移动性记忆行为；同时构建以用户为中心的参考集，评估模型偏好训练数据而非现实替代方案的可能性。我们在多个模型与数据集上的评估揭示了普遍存在的记忆模式，这些模式与用户行为规律性相关，并在推理阶段加剧了数据提取风险。我们的发现呼吁将隐私审计列为移动性预测模型的强制性要求。

Human mobility prediction models, which forecast the next location in a user's trajectory, are increasingly deployed in urban analytics, navigation, and personalized services. Yet, little is known about their potential to memorize and expose sensitive user trajectories from training data. While memorization has been extensively studied in language models, mobility prediction poses unique challenges: training sequences encode human behavior at various spatial and temporal scales, creating privacy risks at different granularities. In this paper, we conduct the first systematic audit of memorization in mobility prediction models. While prior work has shown that privacy leaks can arise from such models, we systematically assess and quantify memorization risks at scale. We identify key challenges, including the lack of a randomness space, the multi-scale structure of trajectories, and user-specific behavioral diversity. To address these challenges, we introduce a framework to quantify mobility memorization at different levels of granularity: individual locations, anchor pairs, and subtrajectory segments. We also develop user-grounded reference sets to assess how likely a model is to prefer training data over realistic alternatives. Our evaluation across multiple models and datasets reveals pervasive memorization patterns that correlate with user regularity and increase the risk of data extraction at inference time. Our findings call for mandatory privacy auditing in mobility prediction models.