  Traffic congestion anomaly detection is of paramount importance in
intelligent traffic systems. The goals of transportation agencies are two-fold:
to monitor the general traffic conditions in the area of interest and to locate
road segments under abnormal congestion states. Modeling congestion patterns
can achieve these goals for citywide roadways, which amounts to learning the
distribution of multivariate time series (MTS). However, existing works are
either not scalable or unable to capture the spatial-temporal information in
MTS simultaneously. To this end, we propose a principled and comprehensive
framework consisting of a data-driven generative approach that can perform
tractable density estimation for detecting traffic anomalies. Our approach
first clusters segments in the feature space and then uses conditional
normalizing flow to identify anomalous temporal snapshots at the cluster level
in an unsupervised setting. Then, we identify anomalies at the segment level by
using a kernel density estimator on the anomalous cluster. Extensive
experiments on synthetic datasets show that our approach significantly
outperforms several state-of-the-art congestion anomaly detection and diagnosis
methods in terms of Recall and F1-Score. We also use the generative model to
sample labeled data, which can train classifiers in a supervised setting,
alleviating the lack of labeled data for anomaly detection in sparse settings.
