{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T17:18:11Z","timestamp":1785863891825,"version":"3.56.0"},"reference-count":37,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-19-CE22-0010"],"award-info":[{"award-number":["ANR-19-CE22-0010"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.neucom.2026.132712","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T16:26:44Z","timestamp":1768580804000},"page":"132712","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["Federated dynamic modeling and learning for spatiotemporal data forecasting"],"prefix":"10.1016","volume":"672","author":[{"given":"Thien","family":"Pham","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9658-9179","authenticated-orcid":false,"given":"Angelo","family":"Furno","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5894-3103","authenticated-orcid":false,"given":"Fa\u00efcel","family":"Chamroukhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Latifa","family":"Oukhellou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.132712_bib0005","author":"Acar"},{"key":"10.1016\/j.neucom.2026.132712_bib0010","series-title":"Advances in Neural Information Processing Systems","first-page":"17804","article-title":"Adaptive graph convolutional recurrent network for traffic forecasting","author":"Bai","year":"2020"},{"key":"10.1016\/j.neucom.2026.132712_bib0015","author":"Casella"},{"key":"10.1016\/j.neucom.2026.132712_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.126662","article-title":"Temporal metrics based aggregated graph convolution network for traffic forecasting","volume":"556","author":"Chen","year":"2023","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.132712_bib0025","doi-asserted-by":"crossref","first-page":"185136","DOI":"10.1109\/ACCESS.2020.3027375","article-title":"Dynamic spatio-temporal graph-based cnns for traffic flow prediction","volume":"8","author":"Chen","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.132712_bib0030","series-title":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","first-page":"234","article-title":"Temporal graph convolutional networks for traffic speed prediction considering external factors","author":"Ge","year":"2019"},{"key":"10.1016\/j.neucom.2026.132712_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2024.142581","article-title":"Dynamic spatial\u2013temporal model for carbon emission forecasting","volume":"463","author":"Gong","year":"2024","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.neucom.2026.132712_bib0040","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1109\/TITS.2019.2963722","article-title":"Optimized graph convolution recurrent neural network for traffic prediction","volume":"22","author":"Guo","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.neucom.2026.132712_bib0045","first-page":"922","article-title":"Attention based spatial-temporal graph convolutional networks for traffic flow forecasting","volume":"33","author":"Guo","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.neucom.2026.132712_bib0050","doi-asserted-by":"crossref","first-page":"13116","DOI":"10.1109\/JSEN.2022.3176016","article-title":"Dstgcn: Dynamic spatial-temporal graph convolutional network for traffic prediction","volume":"22","author":"Hu","year":"2022","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.neucom.2026.132712_bib0055","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.future.2024.04.052","article-title":"Dynamic multi-scale spatial\u2013temporal graph convolutional network for traffic flow prediction","volume":"158","author":"Hu","year":"2024","journal-title":"Futur. Gener. Comput. Syst."},{"key":"10.1016\/j.neucom.2026.132712_bib0060","series-title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20","first-page":"2355","article-title":"Lsgcn: Long short-term traffic prediction with graph convolutional networks","author":"Huang","year":"2020"},{"key":"10.1016\/j.neucom.2026.132712_bib0065","series-title":"International Conference on Learning Representations","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2017"},{"key":"10.1016\/j.neucom.2026.132712_bib0070","article-title":"Federated learning: Strategies for improving communication efficiency","volume":"abs\/1610.05492","author":"Konecn\u00fd","year":"2016","journal-title":"ArXiv"},{"key":"10.1016\/j.neucom.2026.132712_bib0075","doi-asserted-by":"crossref","first-page":"4009","DOI":"10.1109\/TITS.2023.3325936","article-title":"Multimodal transport demand forecasting via federated learning","volume":"25","author":"Li","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.neucom.2026.132712_bib0080","author":"Li"},{"key":"10.1016\/j.neucom.2026.132712_bib0085","author":"Li"},{"key":"10.1016\/j.neucom.2026.132712_bib0090","doi-asserted-by":"crossref","first-page":"7751","DOI":"10.1109\/JIOT.2020.2991401","article-title":"Privacy-preserving traffic flow prediction: A federated learning approach","volume":"7","author":"Liu","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.neucom.2026.132712_bib0095","series-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","first-page":"1025","article-title":"Spatiotemporal adaptive gated graph convolution network for urban traffic flow forecasting","author":"Lu","year":"2020"},{"key":"10.1016\/j.neucom.2026.132712_bib0100","series-title":"International Conference on Artificial Intelligence and Statistics","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2016"},{"key":"10.1016\/j.neucom.2026.132712_bib0105","author":"Reddi"},{"key":"10.1016\/j.neucom.2026.132712_bib0110","doi-asserted-by":"crossref","first-page":"119607","DOI":"10.1109\/ACCESS.2022.3221970","article-title":"Fed-ntp: A federated learning algorithm for network traffic prediction in vanet","volume":"10","author":"Sepasgozar","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.132712_bib0115","doi-asserted-by":"crossref","first-page":"8227","DOI":"10.1109\/ACCESS.2022.3144112","article-title":"Network traffic prediction model considering road traffic parameters using artificial intelligence methods in vanet","volume":"10","author":"Sepasgozar","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.132712_bib0120","article-title":"Short-term demand prediction of shared bikes based on lstm network","volume":"12","author":"Shi","year":"2023","journal-title":"Electronics"},{"key":"10.1016\/j.neucom.2026.132712_bib0125","doi-asserted-by":"crossref","first-page":"16654","DOI":"10.1109\/TITS.2021.3094659","article-title":"A short-term traffic flow prediction model based on an improved gate recurrent unit neural network","volume":"23","author":"Shu","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.neucom.2026.132712_bib0130","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"914","article-title":"Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting","author":"Song","year":"2020"},{"key":"10.1016\/j.neucom.2026.132712_bib0135","series-title":"Advances in Neural Information Processing Systems","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.neucom.2026.132712_bib0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122381","article-title":"Dynamic spatial\u2013temporal graph convolutional recurrent networks for traffic flow forecasting","volume":"240","author":"Xia","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.132712_bib0145","series-title":"2017 IEEE 42nd Conference on Local Computer Networks (LCN)","first-page":"261","article-title":"A sequence learning model with recurrent neural networks for taxi demand prediction","author":"Xu","year":"2017"},{"key":"10.1016\/j.neucom.2026.132712_bib0150","author":"Yu"},{"key":"10.1016\/j.neucom.2026.132712_bib0155","doi-asserted-by":"crossref","first-page":"8738","DOI":"10.1109\/TITS.2022.3157056","article-title":"Fedstn: Graph representation driven federated learning for edge computing enabled urban traffic flow prediction","volume":"24","author":"Yuan","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.neucom.2026.132712_bib0160","series-title":"IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","first-page":"1","article-title":"Fedtse: Low-cost federated learning for privacy-preserved traffic state estimation in iov","author":"Yuan","year":"2022"},{"key":"10.1016\/j.neucom.2026.132712_bib0165","first-page":"451","article-title":"Multi-task federated learning for traffic prediction and its application to route planning","author":"Zeng","year":"2021","journal-title":"IEEE Intell. Veh. Symp."},{"key":"10.1016\/j.neucom.2026.132712_bib0170","doi-asserted-by":"crossref","first-page":"3259","DOI":"10.1109\/TITS.2023.3324962","article-title":"Federated learning in intelligent transportation systems: Recent applications and open problems","volume":"25","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.neucom.2026.132712_bib0175","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s40649-019-0069-y","article-title":"Graph convolutional networks: a comprehensive review","volume":"6","author":"Zhang","year":"2019","journal-title":"Comput. Soc. Netw."},{"key":"10.1016\/j.neucom.2026.132712_bib0180","author":"Zheng"},{"key":"10.1016\/j.neucom.2026.132712_bib0185","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1007\/s10618-022-00903-7","article-title":"Graph convolutional networks for traffic forecasting with missing values","volume":"37","author":"Zuo","year":"2023","journal-title":"Data Min. Knowl. Discov."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226001098?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226001098?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T15:42:01Z","timestamp":1778514121000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226001098"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":37,"alternative-id":["S0925231226001098"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.132712","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Federated dynamic modeling and learning for spatiotemporal data forecasting","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.132712","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"132712"}}