Traffic Anomaly Detection based on Spatio-Temporal Hypergraph Convolution Neural Networks

Jiangtao Feng, Yong Zhang, Xinglin Piao, Yongli Hu and Baocai Yin

Physica A: Statistical Mechanics and its Applications, 2024, vol. 646, issue C

Abstract: Traffic Anomaly Detection (TAD) is an important and difficult task in Intelligent Transportation Systems (ITS) . Traffic anomaly events are sparse in both spatial and temporal spaces, posing a challenge to the performance of model. Moreover, a single traffic anomaly event can impact multiple road sections in the neighborhood, further undermining the accuracy of TAD. In this paper, we propose a new TAD method based on spatio-temporal hypergraph convolutional neural network. Specifically, we adopt a spatial–temporal augmentation approach for traffic data. This will enhance the performance of detecting sparse anomalies. Meanwhile, we introduce a hypergraph learning method to model the road network. This could capture the spreading features of anomalies for better detection results. Additionally, we design a dynamic hypergraph construction method to extract the evolving relationships of road segments. The proposed model evaluation on the Beijing (SE-BJ) dataset for TAD reveals superior performance compared to state-of-the-art ones.

Keywords: Traffic anomaly detection; Data sparsity; Data augmentation; Hypergraph learning; Dynamic hypergraph construction; Hypergraph convolution (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:646:y:2024:i:c:s037843712400400x

DOI: 10.1016/j.physa.2024.129891

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