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Discovering patterns and detecting anomalies in individual travel behavior isa crucial problem in both research and practice. In this paper, we address thisproblem by building a probabilistic framework to model individualspatiotemporal travel behavior data (e.g., trip records and trajectory data).We develop a two-dimensional latent Dirichlet allocation (LDA) model tocharacterize the generative mechanism of spatiotemporal trip records of eachtraveler. This model introduces two separate factor matrices for the spatialdimension and the temporal dimension, respectively, and use a two-dimensionalcore structure at the individual level to effectively model the jointinteractions and complex dependencies. This model can efficiently summarizetravel behavior patterns on both spatial and temporal dimensions from verysparse trip sequences in an unsupervised way. In this way, complex travelbehavior can be modeled as a mixture of representative and interpretablespatiotemporal patterns. By applying the trained model on future/unseenspatiotemporal records of a traveler, we can detect her behavior anomalies byscoring those observations using perplexity. We demonstrate the effectivenessof the proposed modeling framework on a real-world license plate recognition(LPR) data set. The results confirm the advantage of statistical learningmethods in modeling sparse individual travel behavior data. This type ofpattern discovery and anomaly detection applications can provide usefulinsights for traffic monitoring, law enforcement, and individual travelbehavior profiling.

Routine pattern discovery and anomaly detection in individual travel behavior