Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection

Traffic time series anomaly detection has been intensively studied for years because of its potential applications in intelligent transportation. However, classical traffic anomaly detection methods often overlook the evolving dynamic associations between road network nodes, which leads to challenge...

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Bibliographic Details
Published in:Scientific reports Vol. 14; no. 1; pp. 1247 - 14
Main Authors: Ren, Zhiyu, Li, Xiaojie, Peng, Jing, Chen, Ken, Tan, Qushan, Wu, Xi, Shi, Canghong
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 13.01.2024
Nature Publishing Group
Nature Portfolio
Subjects:
639/166/986
639/705/1046
639/705/258
Datasets
Humanities and Social Sciences
multidisciplinary
Neural networks
Nodes
Periodicity
Science
Science (multidisciplinary)
Sensors
Sparsity
Time series
Traffic
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Traffic time series anomaly detection has been intensively studied for years because of its potential applications in intelligent transportation. However, classical traffic anomaly detection methods often overlook the evolving dynamic associations between road network nodes, which leads to challenges in capturing the long-term temporal correlations, spatial characteristics, and abnormal node behaviors in datasets with high periodicity and trends, such as morning peak travel periods. In this paper, we propose a mirror temporal graph autoencoder (MTGAE) framework to explore anomalies and capture unseen nodes and the spatiotemporal correlation between nodes in the traffic network. Specifically, we propose the mirror temporal convolutional module to enhance feature extraction capabilities and capture hidden node-to-node features in the traffic network. Morever, we propose the graph convolutional gate recurrent unit cell (GCGRU CELL) module. This module uses Gaussian kernel functions to map data into a high-dimensional space, and enables the identification of anomalous information and potential anomalies within the complex interdependencies of the traffic network, based on prior knowledge and input data. We compared our work with several other advanced deep-learning anomaly detection models. Experimental results on the NYC dataset illustrate that our model works best compared to other models for traffic anomaly detection.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-51374-3