Discovering patterns and detecting anomalies in individual travel behavior is a crucial problem in both research and practice. In this paper, we address this problem by building a probabilistic framework to model individual spatiotemporal travel behavior data (e.g., trip records and trajectory data). We develop a two-dimensional latent Dirichlet allocation (LDA) model to characterize the generative mechanism of spatiotemporal trip records of each traveler. This model introduces two separate factor matrices for the spatial dimension and the temporal dimension, respectively, and use a two-dimensional core structure at the individual level to effectively model the joint interactions and complex dependencies. This model can efficiently summarize travel behavior patterns on both spatial and temporal dimensions from very sparse trip sequences in an unsupervised way. In this way, complex travel behavior can be modeled as a mixture of representative and interpretable spatiotemporal patterns. By applying the trained model on future/unseen spatiotemporal records of a traveler, we can detect her behavior anomalies by scoring those observations using perplexity. We demonstrate the effectiveness of the proposed modeling framework on a real-world license plate recognition (LPR) data set. The results confirm the advantage of statistical learning methods in modeling sparse individual travel behavior data. This type of pattern discovery and anomaly detection applications can provide useful insights for traffic monitoring, law enforcement, and individual travel behavior profiling.

发现个人旅行行为的模式和检测异常是研究和实践中的一个关键问题。在本文中，我们通过建立一个概率框架来模拟个人时空旅行行为数据（例如，旅行记录和轨迹数据）来解决这个问题。我们开发了一个二维潜在狄利克雷分配 (LDA) 模型来表征每个旅行者时空旅行记录的生成机制。该模型分别为空间维度和时间维度引入了两个独立的因子矩阵，并在个体层面使用二维核心结构来有效地模拟联合相互作用和复杂依赖关系。该模型可以以无监督的方式从非常稀疏的旅行序列中有效地总结空间和时间维度上的旅行行为模式。通过这种方式，可以将复杂的旅行行为建模为具有代表性和可解释性的时空模式的混合。通过将经过训练的模型应用于旅行者未来/不可见的时空记录，我们可以通过使用困惑度对这些观察进行评分来检测她的行为异常。我们在现实世界的车牌识别 (LPR) 数据集上证明了所提出的建模框架的有效性。结果证实了统计学习方法在建模稀疏的个人出行行为数据方面的优势。这种类型的模式发现和异常检测应用程序可以为交通监控、执法、