Graph Convolutional Networks for traffic anomaly

• URL: http://arxiv.org/abs/2012.13637v1
• Date: Fri, 25 Dec 2020 22:36:22 GMT
• Title: Graph Convolutional Networks for traffic anomaly
• Authors: Yue Hu, Ao Qu, Dan Work
• Abstract summary: 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. To fully capture the spatial and temporal traffic patterns remains a challenge, yet serves a crucial role for effective anomaly detection. 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.
• Score: 4.172516437934823
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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