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Title	Spatial-Temporal Interactive Graph Convolutional Networks For Traffic Forecasting
ID_Doc	52517
Authors	Huang X.; Pan Z.; Zhao G.
Year	2024
Published	2024 4th International Conference on Electronic Information Engineering and Computer Technology, EIECT 2024
DOI	http://dx.doi.org/10.1109/EIECT64462.2024.10866702
Abstract	Traffic flow prediction, as an important component of smart city construction, plays a crucial role in urban development. Capturing the temporal periodicity and spatial heterogeneity of traffic data is the key to accurately predicting traffic flow. In recent years, many studies have used graph neural networks (GNN) to model and overcome the shortcomings of traditional linear models by extracting spatial features. Additionally, by introducing attention mechanisms, they capture the spatial dependencies and temporal dynamic correlations of different road traffics. However, existing methods typically use separate components to capture spatial-temporal correlations, neglecting the dependencies among various dimensions. To address this challenge, we propose a new traffic flow prediction framework, Spatial-Temporal Interactive Graph Convolutional Networks (STIGCN). By introducing a triplet attention mechanism, the model alternates the use of temporal, spatial, and feature dimension attentions to learn both long short-term and spatial-temporal features. This model eliminates the indirect correspondence between dimensions and weights, enhancing feature expression among dimensions. This paper benchmarks the proposed model on two real-world datasets and achieves good prediction performance in comparative experiments with other baseline models. © 2024 IEEE.
Author Keywords	graph convolution; Spatial interaction; spatial-temporal attention; traffic prediction

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