Uncertainty-aware Human Mobility Modeling and Anomaly Detection

Haomin Wen, Shurui Cao, Leman Akoglu

Keywords: uncertaitnty, human mobility, anomaly detection

Abstract:

Given the GPS coordinates of a large collection of human agents over time, how can we model their mobility behavior toward effective anomaly detection (e.g. for bad-actor or malicious behavior detection) without any labeled data?
Human mobility and trajectory modeling have been studied extensively with varying capacity to handle complex input, and performance-efficiency trade-offs. With the arrival of more expressive models in machine learning, we attempt to model GPS data as a sequence of stay-point events, each with a set of characterizing spatiotemporal features, and leverage modern sequence models such as Transformers for un/self-supervised training and inference. Notably, driven by the inherent stochasticity of certain individuals' behavior, we equip our model with aleatoric/data uncertainty estimation. In addition, to handle data sparsity of a large variety of behaviors, we incorporate epistemic/model uncertainty into our model. Together, aleatoric and epistemic uncertainty enable a robust loss and training dynamics, as well as uncertainty-aware decision making in anomaly scoring. Experiments on large expert-simulated datasets with tens of thousands of agents demonstrate the effectiveness of our model against both forecasting and anomaly detection baselines. All code is available at https://anonymous.4open.science/r/mobility-ad.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 10346