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

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

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 sin...

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Bibliographic Details
Main Authors: Yanshen Sun, Yen-Cheng Lu, Kaiqun Fu, Fanglan Chen, Chang-Tien Lu
Format: Article
Language:English
Published: Elsevier 2022-09-01
Series:Journal of King Saud University: Computer and Information Sciences
Subjects:
Traffic forecasting
Spatiotemporal fusion
Multi-granular seasonal feature
Graph neural network
Anomaly detection
Online Access:http://www.sciencedirect.com/science/article/pii/S1319157822001665

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http://www.sciencedirect.com/science/article/pii/S1319157822001665

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