  Event detection has been an important task in transportation, whose task is
to detect points in time when large events disrupts a large portion of the
urban traffic network. Travel information {Origin-Destination} (OD) matrix data
by map service vendors has large potential to give us insights to discover
historic patterns and distinguish anomalies. However, to fully capture the
spatial and temporal traffic patterns remains a challenge, yet serves a crucial
role for effective anomaly detection. Meanwhile, existing anomaly detection
methods have not well-addressed the extreme data sparsity and high-dimension
challenges, which are common in OD matrix datasets. To tackle these challenges,
we formulate the problem in a novel way, as detecting anomalies in a set of
directed weighted graphs representing the traffic conditions at each time
interval. We further propose \textit{Context augmented Graph Autoencoder}
(\textbf{Con-GAE }), that leverages graph embedding and context embedding
techniques to capture the spatial traffic network patterns while working around
the data sparsity and high-dimensionality issue. Con-GAE adopts an autoencoder
framework and detect anomalies via semi-supervised learning. Extensive
experiments show that our method can achieve up can achieve a 0.1-0.4
improvements of the area under the curve (AUC) score over state-of-art anomaly
detection baselines, when applied on several real-world large scale OD matrix
datasets.
