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Title	Sdsinet: A Spatiotemporal Dual-Scale Interaction Network For Traffic Prediction
ID_Doc	47493
Authors	Yang S.; Wu Q.
Year	2025
Published	Applied Soft Computing, 173
DOI	http://dx.doi.org/10.1016/j.asoc.2025.112892
Abstract	Accurate traffic forecasting is essential for smart city development. However, existing spatiotemporal modeling methods often face significant challenges, including limitations in handling complex temporal dependencies, capturing multiscale spatial relationships, and modeling the interaction between temporal and spatial features. These challenges arise due to the reliance on extended historical data, fixed adjacency matrices, and the lack of dynamic spatiotemporal interaction modeling. To address these issues, we propose the Spatiotemporal Dual-Scale Interaction Network (SDSINet). SDSINet introduces an implicit temporal information enhancement method that embeds temporal identity information into feature representations, reducing the computational overhead and improving the modeling of global temporal features. Additionally, SDSINet integrates a dynamic multiscale spatial modeling approach that combines adaptive and scale-specific graphs, enabling the model to capture both local and global spatial dependencies. Furthermore, SDSINet incorporates a dual-scale spatiotemporal interaction learning framework that captures short-term and long-term temporal dependencies as well as multiscale spatial correlations. Extensive experiments on real-world datasets – traffic flow (PeMS04), speed (PeMSD7(M)), and demand (NYCBike Drop-off/Pick-up) – demonstrate that SDSINet outperforms existing state-of-the-art methods in prediction accuracy and computational efficiency. Notably, SDSINet achieves a 14.03% reduction in MAE on the NYCBike Drop-off dataset compared to AFDGCN, setting a new benchmark for traffic forecasting. © 2025
Author Keywords	Graph convolutional network; Interactive learning; Spatiotemporal dependencies; Traffic prediction

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