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Spatial-temporal synchronous graphsage for traffic prediction

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Abstract

The application of intelligent transportation systems (ITSs) relies heavily on accurate traffic prediction, which hinges on effectively capturing spatial-temporal features. Current methodologies often address spatial and temporal dependencies separately, which limits their ability to synchronize modeling efforts. Moreover, existing graph convolutional network (GCN) approaches primarily support transductive learning and fall short in inductive tasks. To address these challenges, this paper introduces a novel spatial-temporal synchronous GraphSAGE (STS-GraphSAGE) model for traffic prediction. By integrating spatial and temporal correlations into a unified graph structure, STS-GraphSAGE achieves synchronous learning of these dependencies. Specifically, we introduce the Spearman correlation coefficient to compensate for the spatial adjacency matrix, facilitating the construction of an inclusive spatial graph. Coupled with a causal temporal graph, this forms a spatial-temporal synchronous graph that is capable of capturing intricate dependencies across both dimensions. Furthermore, our model employs multiple STS-GraphSAGE layers equipped with attention mechanisms to inductively aggregate spatial-temporal features from neighboring nodes. Extensive experiments on real-world datasets validate the effectiveness of STS-GraphSAGE, which significantly outperforms state-of-the-art baselines in traffic prediction tasks.

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Data Availability

Data generated during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 62476145; in part by the Humanity and Social Science Foundation of Ministry of Education of China under Grant 24YJAZH126; in part by the 6th “333 Talents” Technology Research and Development Talent Foundation of Jiangsu Province; in part by the Transportation Technology and Achievement Transformation Foundation of Jiangsu Province under Grant 2024G01; in part by the Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant KYCX23_3396.

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Correspondence to Quan Shi.

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Yu, X., Bao, Y. & Shi, Q. Spatial-temporal synchronous graphsage for traffic prediction. Appl Intell 55, 82 (2025). https://doi.org/10.1007/s10489-024-05970-5

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