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Title	Pstcgcn: Principal Spatio-Temporal Causal Graph Convolutional Network For Traffic Flow Prediction
ID_Doc	43638
Authors	Yang S.; Wu Q.; Li Z.; Wang K.
Year	2025
Published	Neural Computing and Applications, 37, 20
DOI	http://dx.doi.org/10.1007/s00521-024-10591-7
Abstract	Traffic prediction is indispensable for constructing transportation networks in smart cities. Due to the complex spatio-temporal correlations of traffic data, this task presents challenges. Recent studies mainly use graph neural networks to simulate complex spatio-temporal relationships through fixed or adaptive graphs. While fixed graphs may not adapt to data drift caused by changes in road network structures, adaptive graphs overlook critical information of the original roads. To address this challenge, we propose a principal spatio-temporal causal graph convolutional network (PSTCGCN) to accurately predict traffic flow. In response to the data drift problem, we introduce a data-driven semi-principal generated graph embedding (SPGGE) that first extracts the principal features of the original roads to model the spatio-temporal sequence distribution and then remodels the data after drift through adaptive transformation. Traffic flow data, while showcasing fundamental spatial relationships, also exhibit temporal dynamics. We propose an effective temporal causal convolution component comprising SPGGE, graph convolution, both local and global causal learning models to jointly learn short-term and long-term spatio-temporal correlations. PSTCGCN was evaluated using two actual highway datasets, PEMS03 and PEMS07, achieving a notable improvement of 6.12% in RMSE on PEMS03 compared to STGATRGN. Our code is available at https://github.com/OvOYu/PSTCGCN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
Author Keywords	Data drift; Graph convolution network; Principal component analysis; Traffic flow prediction

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