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To tackle the challenges mentioned above, we propose a novel unified spatial\u2013temporal regression framework named Generalized Spatial\u2013Temporal Regression Graph Convolutional Transformer (GSTRGCT) that extends panel model in spatial econometrics and combines it with deep neural networks to effectively model non-stationary relationships of spatial\u2013temporal regression. Considering the coupling of existing deep spatial\u2013temporal networks, we introduce the tensor decomposition to explicitly decompose the panel model into a tensor product of spatial regression on the spatial hyper-plane and temporal regression on the temporal hyper-plane. On the spatial hyper-plane, we present dynamic adaptive spatial weight network (DASWNN) to capture the global and local spatial correlations. Specifically, DASWNN adopts spatial weight neural network (SWNN) to learn the semantic global spatial correlation and dynamically adjusts the local changing spatial correlation by multiplying between spatial nodes embedding. On the temporal hyper-plane, we introduce the Auto-Correlation attention mechanism to capture the period-based temporal dependence. Extensive experiments on the two real-world traffic datasets show that GSTRGCT consistently outperforms other competitive methods with an average of 62% and 59% on predictive performance.<\/jats:p>","DOI":"10.1007\/s40747-024-01578-x","type":"journal-article","created":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T09:02:16Z","timestamp":1723280536000},"page":"7943-7964","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Generalized spatial\u2013temporal regression graph convolutional transformer for traffic forecasting"],"prefix":"10.1007","volume":"10","author":[{"given":"Lang","family":"Xiong","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7150-2161","authenticated-orcid":false,"given":"Liyun","family":"Su","sequence":"additional","affiliation":[]},{"given":"Shiyi","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Xiangjing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Tong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,10]]},"reference":[{"key":"1578_CR1","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.inffus.2022.11.019","volume":"92","author":"G Zheng","year":"2023","unstructured":"Zheng G, Chai WK, Duanmu JL, Katos V (2023) Hybrid deep learning models for traffic prediction in large-scale road networks. 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