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Title	Multi-Spatio-Temporal Fusion Graph Recurrent Network For Traffic Forecasting
ID_Doc	38415
Authors	Zhao W.; Zhang S.; Zhou B.; Wang B.
Year	2023
Published	Engineering Applications of Artificial Intelligence, 124
DOI	http://dx.doi.org/10.1016/j.engappai.2023.106615
Abstract	Accurate traffic forecasting is crucial for smart city development in the new era. However, the intricate spatial and temporal dependencies in traffic data present significant challenges for prediction accuracy. Existing methods often rely on predefined adjacency matrices to capture Spatio-temporal dependencies, which may not adapt well to the dynamic nature of road traffic. To address these challenges, we propose the Multi-Spatio-temporal Fusion Graph Recurrent Network (MSTFGRN). This innovative approach introduces a data-driven method for generating a weighted adjacency matrix, effectively capturing real-time spatial dependencies that are not adequately captured by predefined matrices. The MSTFGRN also incorporates a novel two-way Spatio-temporal fusion operation to learn hidden dependencies between parallel Spatio-temporal relations at different time points. Additionally, a global attention mechanism is integrated into the Spatio-temporal fusion module, enabling the simultaneous capture of global Spatio-temporal dependencies. Through extensive trials on publicly available highway traffic datasets, our method demonstrates state-of-the-art performance compared to alternative baselines. © 2023 Elsevier Ltd
Author Keywords	Predefined adjacency matrices; Spatio-temporal dependencies; Spatio-temporal fusion; Traffic forecasting

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