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

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dc.contributor.authorSun, Yanshenen
dc.contributor.authorLu, Yen-Chengen
dc.contributor.authorFu, Kaiqunen
dc.contributor.authorChen, Fanglanen
dc.contributor.authorLu, Chang-Tienen
dc.date.accessioned2023-05-17T14:39:34Zen
dc.date.available2023-05-17T14:39:34Zen
dc.date.issued2022-09en
dc.description.abstractAnomaly 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 detec-tion 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 mod-ule 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.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1016/j.jksuci.2022.05.017en
dc.identifier.eissn2213-1248en
dc.identifier.issue8en
dc.identifier.urihttp://hdl.handle.net/10919/115088en
dc.identifier.volume34en
dc.language.isoenen
dc.publisherElsevieren
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectTraffic forecastingen
dc.subjectSpatiotemporal fusionen
dc.subjectMulti -granular seasonal featureen
dc.subjectGraph neural networken
dc.subjectAnomaly detectionen
dc.titleDetecting anomalous traffic behaviors with seasonal deep Kalman filter graph convolutional neural networksen
dc.title.serialJournal of King Saud University-Computer and Information Sciencesen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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