GLOSS: Tensor-based anomaly detection in spatiotemporal urban traffic data

Abstract

Anomaly detection in spatiotemporal data is a problem encountered in a variety of applications including urban traffic monitoring. For urban traffic data, anomalies refer to unusual events such as traffic congestion and unexpected crowd gatherings. Detecting these anomalies is challenging due to the scarcity of anomalous events and the dependence of anomaly definition on time and space. Existing spatiotemporal anomaly detection methods cannot preserve the spatial and temporal correlations and do not take the structure of anomalies into account. In this paper, we introduce a temporally regularized, locally consistent, robust low-rank plus sparse tensor model for spatiotemporal anomaly detection. The proposed method takes the spatially sparse and temporally smooth structure of urban anomalies into account by modeling the anomalies as the sparse part of the tensor and minimizing the total variation across the temporal mode of this part. The local consistency of the low-rank part is ensured using a manifold embedding approach. The proposed approach is referred to as Graph Regularized Low-rank plus Temporally Smooth Sparse decomposition (GLOSS) and is evaluated on synthetic and real spatiotemporal urban traffic data. The results illustrate the accuracy and robustness of the proposed method with respect to missing data, noise and anomaly strength.