Detecting anomalous traffic behaviors with seasonal deep Kalman filter graph convolutional neural networks

Yanshen Sun, Yen-Cheng Lu, Kaiqun Fu, Fanglan Chen

Abstract

Anomaly detection over traffic data is crucial for transportation management and abnormal behavior identification. An anomaly in real-world scenarios usually causes abnormal observations for multiple detectors in an extended period. However, existing anomaly detection methods overly leverage the single or isolated feature interdependent contextual information in anomalies, inevitably dropping the detection performance. In this paper, we propose S-DKFN (Seasonal Deep Kalman Filter Network), to identify abnormal patterns with a long duration and wide coverage. S-DKFN models traffic data with a graph and simultaneously investigates the spatial and temporal features to hunt abnormal behaviors. Specifically, a dilation temporal convolutional network (TCN) is used to merge the multi-granular seasonal features and a graph convolution network (GCN) to extract spatial features. The outputs of TCN and GCN are then fed to long-short term models (LSTM) and merged by Kalman filters for denoising. An encoder-decoder module is introduced to predict traffic attributes with seasonal features. The mean squared errors (MSE) of the predictions are considered the anomaly scores. Experimental results on two real-world datasets show that our proposed S-DKFN framework outperforms the state-of-the-art baseline methods in detecting anomalies with long-duration and wide-coverage, especially its ability to detect accidents.