{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T14:18:11Z","timestamp":1783433891293,"version":"3.54.6"},"reference-count":38,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62376059"],"award-info":[{"award-number":["62376059"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Fujian Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["2024J01070"],"award-info":[{"award-number":["2024J01070"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Fujian Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["2023J01531"],"award-info":[{"award-number":["2023J01531"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Future Generation Computer Systems"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.future.2026.108641","type":"journal-article","created":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T23:45:29Z","timestamp":1780616729000},"page":"108641","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["FedTETP: Federated learning with topology enhancement for traffic prediction"],"prefix":"10.1016","volume":"185","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1144-0995","authenticated-orcid":false,"given":"Xing","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunxia","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jixiang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3401-1691","authenticated-orcid":false,"given":"Biao","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fumin","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lyuchao","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2927-1018","authenticated-orcid":false,"given":"Ruihao","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.future.2026.108641_b1","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1049\/iet-its.2016.0208","article-title":"LSTM network: a deep learning approach for short-term traffic forecast","volume":"11","author":"Zhao","year":"2017","journal-title":"IET Intell. Transp. Syst."},{"issue":"7","key":"10.1016\/j.future.2026.108641_b2","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1049\/iet-its.2017.0313","article-title":"Combining weather condition data to predict traffic flow: a gru-based deep learning approach","volume":"12","author":"Zhang","year":"2018","journal-title":"IET Intell. Transp. Syst."},{"issue":"1","key":"10.1016\/j.future.2026.108641_b3","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.future.2026.108641_b4","series-title":"2018 5th International Conference on Information Science and Control Engineering","first-page":"241","article-title":"Graph attention lstm network: a new model for traffic flow forecasting","author":"Wu","year":"2018"},{"key":"10.1016\/j.future.2026.108641_b5","unstructured":"Y. Li, R. Yu, C. Shahabi, Y. Liu, Diffusion convolutional recurrent neural network: data-driven traffic forecasting, in: International Conference on Learning Representations, 2018."},{"key":"10.1016\/j.future.2026.108641_b6","doi-asserted-by":"crossref","unstructured":"Z. Wu, S. Pan, G. Long, J. Jiang, C. Zhang, Graph wavenet for deep spatial-temporal graph modeling, in: Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019, pp. 1907\u20131913.","DOI":"10.24963\/ijcai.2019\/264"},{"issue":"8","key":"10.1016\/j.future.2026.108641_b7","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."},{"issue":"1","key":"10.1016\/j.future.2026.108641_b8","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1109\/TITS.2022.3179391","article-title":"Short-term traffic flow prediction based on graph convolutional networks and federated learning","volume":"24","author":"Xia","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.future.2026.108641_b9","series-title":"KDD","first-page":"1202","article-title":"Cross-node federated graph neural network for spatio-temporal data modeling","author":"Meng","year":"2021"},{"key":"10.1016\/j.future.2026.108641_b10","doi-asserted-by":"crossref","unstructured":"L. Yang, W. Chen, X. He, S. Wei, Y. Xu, Z. Zhou, Y. Tong, FedGTP: exploiting inter-client spatial dependency in federated graph-based traffic prediction, in: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD\u201924, 2024.","DOI":"10.1145\/3637528.3671613"},{"key":"10.1016\/j.future.2026.108641_b11","series-title":"Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting","author":"Yu","year":"2017"},{"issue":"3","key":"10.1016\/j.future.2026.108641_b12","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1061\/(ASCE)0733-947X(1995)121:3(249)","article-title":"Short-term prediction of traffic volume in urban arterials","volume":"121","author":"Hamed","year":"1995","journal-title":"J. Transp. Eng."},{"issue":"3","key":"10.1016\/j.future.2026.108641_b13","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.1109\/TITS.2013.2262376","article-title":"Predicting taxi-passenger demand using streaming data","volume":"14","author":"Moreira-Matias","year":"2013","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.future.2026.108641_b14","article-title":"A hierarchical framework for interactive behaviour prediction of heterogeneous traffic participants based on graph neural network","author":"Li","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"5","key":"10.1016\/j.future.2026.108641_b15","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.1109\/TITS.2020.2973279","article-title":"Daily traffic flow forecasting through a contextual convolutional recurrent neural network modeling inter-and intra-day traffic patterns","volume":"22","author":"Ma","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.future.2026.108641_b16","series-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016"},{"key":"10.1016\/j.future.2026.108641_b17","series-title":"AAAI","first-page":"922","article-title":"Attention based spatial-temporal graph convolutional networks for traffic flow forecasting","author":"Guo","year":"2019"},{"issue":"9","key":"10.1016\/j.future.2026.108641_b18","doi-asserted-by":"crossref","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","article-title":"T-gcn: a temporal graph convolutional network for traffic prediction","volume":"21","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.future.2026.108641_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2020.102620","article-title":"Learning traffic as a graph: a gated graph wavelet recurrent neural network for network-scale traffic prediction","volume":"115","author":"Cui","year":"2020","journal-title":"Transp. Res. C"},{"key":"10.1016\/j.future.2026.108641_b20","doi-asserted-by":"crossref","unstructured":"Z. Pan, Y. Liang, W. Wang, Y. Yu, Y. Zheng, J. Zhang, Urban traffic prediction from spatio-temporal data using deep meta learning, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, pp. 1720\u20131730.","DOI":"10.1145\/3292500.3330884"},{"key":"10.1016\/j.future.2026.108641_b21","doi-asserted-by":"crossref","unstructured":"C. Zheng, X. Fan, C. Wang, J. Qi, Gman: a graph multi-attention network for traffic prediction, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 2020, pp. 1234\u20131241.","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"10.1016\/j.future.2026.108641_b22","doi-asserted-by":"crossref","unstructured":"C. Jiang, C. Han, W.X. Zhao, J. Wang, Pdformer: propagation delay-aware dynamic long-range transformer for traffic flow prediction, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, 2023, pp. 4365\u20134373.","DOI":"10.1609\/aaai.v37i4.25556"},{"key":"10.1016\/j.future.2026.108641_b23","series-title":"Advances and open problems in federated learning","author":"Kairouz","year":"2019"},{"issue":"3","key":"10.1016\/j.future.2026.108641_b24","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","article-title":"Federated learning: challenges, methods, and future directions","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"issue":"2","key":"10.1016\/j.future.2026.108641_b25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3298981","article-title":"Federated machine learning: concept and applications","volume":"10","author":"Yang","year":"2019","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"10.1016\/j.future.2026.108641_b26","series-title":"Effective and efficient cross-city traffic knowledge transfer: a privacy-preserving perspective","author":"Zeng","year":"2025"},{"issue":"7","key":"10.1016\/j.future.2026.108641_b27","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1007\/s10994-025-06795-0","article-title":"Fedflow: a personalized federated learning framework for passenger flow prediction","volume":"114","author":"Rocco di Torrepadula","year":"2025","journal-title":"Mach. Learn."},{"key":"10.1016\/j.future.2026.108641_b28","unstructured":"S.P. Karimireddy, S. Kale, M. Mohri, S.J. Reddi, S.U. Stich, A.T. Suresh, Scaffold: stochastic controlled averaging for federated learning, in: Proceedings of the 37th International Conference on Machine Learning, 2020."},{"key":"10.1016\/j.future.2026.108641_b29","series-title":"Artificial Intelligence and Statistics","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"key":"10.1016\/j.future.2026.108641_b30","first-page":"429","article-title":"Federated optimization in heterogeneous networks","author":"Li","year":"2020","journal-title":"Proc. Mach. Learn. Syst."},{"key":"10.1016\/j.future.2026.108641_b31","series-title":"2019 International Joint Conference on Neural Networks","first-page":"1","article-title":"Learning private neural language modeling with attentive aggregation","author":"Ji","year":"2019"},{"issue":"12","key":"10.1016\/j.future.2026.108641_b32","doi-asserted-by":"crossref","first-page":"8464","DOI":"10.1109\/TII.2021.3055283","article-title":"Fastgnn: a topological information protected federated learning approach for traffic speed forecasting","volume":"17","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"7","key":"10.1016\/j.future.2026.108641_b33","doi-asserted-by":"crossref","first-page":"5873","DOI":"10.1007\/s10115-025-02393-7","article-title":"FedTPS: traffic pattern sharing for personalized federated traffic flow prediction","author":"Zhou","year":"2025","journal-title":"Knowl. Inf. Syst."},{"issue":"8","key":"10.1016\/j.future.2026.108641_b34","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.future.2026.108641_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.110175","article-title":"FedAGCN: a traffic flow prediction framework based on federated learning and asynchronous graph convolutional network","volume":"138","author":"Qi","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.future.2026.108641_b36","series-title":"A random matrix approach to differential privacy and structure preserved social network graph publishing","author":"Ahmed","year":"2013"},{"key":"10.1016\/j.future.2026.108641_b37","series-title":"Theory of Motion of the Heavenly Bodies Moving About the Sun in Conic Sections: A Translation of Theoria Motus","author":"Gauss","year":"1809"},{"issue":"1","key":"10.1016\/j.future.2026.108641_b38","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1137\/S1064827595287997","article-title":"A fast and high quality multilevel scheme for partitioning irregular graphs","volume":"20","author":"Karypis","year":"1998","journal-title":"SIAM J. Sci. Comput."}],"container-title":["Future Generation Computer Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167739X2600275X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167739X2600275X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T13:53:30Z","timestamp":1783432410000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167739X2600275X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":38,"alternative-id":["S0167739X2600275X"],"URL":"https:\/\/doi.org\/10.1016\/j.future.2026.108641","relation":{},"ISSN":["0167-739X"],"issn-type":[{"value":"0167-739X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"FedTETP: Federated learning with topology enhancement for traffic prediction","name":"articletitle","label":"Article Title"},{"value":"Future Generation Computer Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.future.2026.108641","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":"108641"}}