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Title	Adaptive Graph Convolution In Spatio-Temporal Synchronous Networks For Traffic Flow Prediction
ID_Doc	6252
Authors	Xiao Q.; Xu Y.
Year	2025
Published	Proceedings of 2024 8th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2024
DOI	http://dx.doi.org/10.1145/3711129.3711285
Abstract	The field of traffic flow prediction is vital for smart city development and traffic management. However, accurately predicting spatio-temporal data is challenging due to its complexity and potential heterogeneity. To address this, a novel model called the spatio-temporal synchronous graph convolutional network (STASCN) has been introduced. This model effectively captures local spatio-temporal relationships and incorporates an adaptive graph learning approach to capture node-specific spatial and temporal correlations in a more focused manner. and also outperforms other methods in terms of prediction accuracy in real-life datasets. © 2024 Copyright held by the owner/author(s).
Author Keywords	Adaptive graph learning; Graph convolutional network; Heterogeneity; Spatial-temporal correlations; Traffic flow prediction

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