{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T19:41:23Z","timestamp":1768765283399,"version":"3.49.0"},"reference-count":66,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation (NSFC) of China","doi-asserted-by":"crossref","award":["42501584"],"award-info":[{"award-number":["42501584"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Yunnan Key Laboratory of Intelligent Monitoring and Spatio-temporal Big Data Governance of Natural Resources","award":["202449CE340023"],"award-info":[{"award-number":["202449CE340023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Forecasting the spatio-temporal evolutions of cellular traffic is crucial for urban management. However, achieving accurate forecasting is challenging due to \u201ccomplex correlation modeling\u201d and \u201cmodel-blindness\u201d issues. Specifically, cellular traffic is generated within complex urban systems characterized by an intricate structure and human mobility. Existing approaches, often based on proximity or attributes, struggle to learn the latent correlation matrix governing traffic evolution, which limits forecasting accuracy. Furthermore, while substantial knowledge about urban systems can supplement the modeling of correlations, existing methods for integrating this knowledge\u2014typically via loss functions or embeddings\u2014overlook the synergistic collaboration between data and knowledge, resulting in weak model robustness. To address these challenges, we develop a data\u2013knowledge collaborative learning framework termed the knowledge-empowered spatio-temporal neural network (KESTNN). This framework first extracts knowledge triplets representing urban structures to construct a knowledge graph. Representation learning is then conducted to learn the correlation matrix. Throughout this process, data and knowledge are integrated collaboratively via backpropagation, contrasting with the forward feature injection methods typical of existing approaches. This mechanism ensures that data and knowledge directly guide the dynamic updating of model parameters through backpropagation, rather than merely serving as a static feature prompt, thereby fundamentally alleviating the \u201cmodel-blindness\u201d issue. Finally, the optimized matrix is embedded into a forecasting module. Experiments on the Milan dataset demonstrate that the KESTNN exhibits excellent forecast performance, reducing RMSE by up to 23.91%, 16.73%, and 10.40% for 3-, 6-, and 9-step forecasts, respectively, compared to the best baseline.<\/jats:p>","DOI":"10.3390\/ijgi15010043","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T14:46:33Z","timestamp":1768574793000},"page":"43","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Data\u2013Knowledge Collaborative Learning Framework for Cellular Traffic Forecasting via Enhanced Correlation Modeling"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8090-8957","authenticated-orcid":false,"given":"Keyi","family":"An","sequence":"first","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University (CSU), Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiangjun","family":"Li","sequence":"additional","affiliation":[{"name":"Yunnan Key Laboratory of Intelligent Monitoring and Spatio-Temporal Big Data Governance of Natural Resources, Kunming 650093, China"},{"name":"Yunnan Institute of Geology and Mineral Surveying and Mapping Co., Ltd., Kunming 650011, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7505-0764","authenticated-orcid":false,"given":"Kaiqi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University (CSU), Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University (CSU), Changsha 410083, China"},{"name":"Hunan Geospatial Information Engineering and Technology Research Center, Changsha 410119, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yafei","family":"Liu","sequence":"additional","affiliation":[{"name":"Yunnan Key Laboratory of Intelligent Monitoring and Spatio-Temporal Big Data Governance of Natural Resources, Kunming 650093, China"},{"name":"Yunnan Institute of Geology and Mineral Surveying and Mapping Co., Ltd., Kunming 650011, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Senzhang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Central South University (CSU), Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaiyuan","family":"Lei","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University (CSU), Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3610402","article-title":"Where are the (cellular) data?","volume":"56","author":"Amini","year":"2023","journal-title":"ACM Comput. 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