{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:16:34Z","timestamp":1760058994897,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T00:00:00Z","timestamp":1747612800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chinese Academy of Surveying and Mapping Basic Research Fund Program","award":["AR2204","2024-ZJ-927"],"award-info":[{"award-number":["AR2204","2024-ZJ-927"]}]},{"name":"Basic Research Program of Qinghai Province","award":["AR2204","2024-ZJ-927"],"award-info":[{"award-number":["AR2204","2024-ZJ-927"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Accurate forecasting of city-level large-scale traffic flow is crucial for efficient traffic management and effective transport planning. However, previously proposed traffic flow prediction methods model dynamic spatial correlations across entire traffic networks, leading to high computational complexity, elevated memory usage, and model overfitting. Therefore, a novel grid partition-based dynamic spatial\u2013temporal graph convolutional network was developed in this study to capture correlations within a large-scale traffic network. It includes the following: a dynamic graph convolution module to divide the traffic network into grid regions and thereby effectively capture the local spatial dependencies inherent in large-scale traffic topologies, an attention-based dynamic graph convolutional network to capture the local spatial correlations within each region, a global spatial dependency aggregation module to model inter-regional correlation weights using sequence similarity methods and comprehensively reflect the overall state of the traffic network, and multi-scale gated convolutions to capture both long- and short-term temporal correlations across varying time ranges. The performance of the proposed model was compared with that of different baseline models using two large-scale real-world datasets; the proposed model significantly outperformed the baseline models, demonstrating its potential effectiveness in managing large-scale traffic networks.<\/jats:p>","DOI":"10.3390\/ijgi14050207","type":"journal-article","created":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T11:54:26Z","timestamp":1747655666000},"page":"207","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Grid Partition-Based Dynamic Spatial\u2013Temporal Graph Convolutional Network for Large-Scale Traffic Flow Forecasting"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3308-1825","authenticated-orcid":false,"given":"Lifeng","family":"Gao","sequence":"first","affiliation":[{"name":"Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, Chinese Academy of Surveying and Mapping (CASM), Beijing 100036, China"},{"name":"Chinese Academy of Surveying and Mapping (CASM), Beijing 100036, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liujia","family":"Chen","sequence":"additional","affiliation":[{"name":"National Center for Public Credit and Geospatial Information, Beijing 100045, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2195-9252","authenticated-orcid":false,"given":"Agen","family":"Qiu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, Chinese Academy of Surveying and Mapping (CASM), Beijing 100036, China"},{"name":"Chinese Academy of Surveying and Mapping (CASM), Beijing 100036, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9218-2293","authenticated-orcid":false,"given":"Qinglian","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, Chinese Academy of Surveying and Mapping (CASM), Beijing 100036, China"},{"name":"Chinese Academy of Surveying and Mapping (CASM), Beijing 100036, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianlong","family":"Wang","sequence":"additional","affiliation":[{"name":"Changjiang Spatial Information Technology Engineering Co., Ltd., Wuhan 430010, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cai","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, Chinese Academy of Surveying and Mapping (CASM), Beijing 100036, China"},{"name":"Chinese Academy of Surveying and Mapping (CASM), Beijing 100036, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geli","family":"Ou\u2019er","sequence":"additional","affiliation":[{"name":"Geomatics Technology and Application Key Laboratory of Qinghai Province, Xining 810001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1109\/TSMCC.2011.2161577","article-title":"Computational intelligence in urban traffic signal control: A survey","volume":"42","author":"Zhao","year":"2011","journal-title":"IEEE Trans. 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