{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T06:16:09Z","timestamp":1783318569425,"version":"3.54.6"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T00:00:00Z","timestamp":1783296000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T00:00:00Z","timestamp":1783296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Key teaching research project of Anhui Education Alliance","award":["2023jyxm1270"],"award-info":[{"award-number":["2023jyxm1270"]}]},{"name":"Traditional professional transformation and upgrading projects","award":["2023zygzts088"],"award-info":[{"award-number":["2023zygzts088"]}]},{"name":"Key Project of Anhui Province University Scientific Research","award":["2024AH052860"],"award-info":[{"award-number":["2024AH052860"]}]},{"name":"Scientific Research Start-up Fund Project of Chaohu University","award":["KYQD-2024005"],"award-info":[{"award-number":["KYQD-2024005"]}]},{"name":"Chaohu University Talent Project","award":["KYQD-2023072"],"award-info":[{"award-number":["KYQD-2023072"]}]},{"name":"Key Construction Discipline of Chaohu University","award":["kj22zdjsxk01, kj22xjzz01"],"award-info":[{"award-number":["kj22zdjsxk01, kj22xjzz01"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-026-08684-2","type":"journal-article","created":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T05:49:26Z","timestamp":1783316966000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FGSNN: a FusionGraphSAGE with neural networks for traffic flow prediction"],"prefix":"10.1007","volume":"82","author":[{"given":"Yanan","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongmei","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuchao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunbiao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weicai","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,6]]},"reference":[{"issue":"1","key":"8684_CR1","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1109\/MVT.2009.935537","volume":"5","author":"G Dimitrakopoulos","year":"2010","unstructured":"Dimitrakopoulos G, Demestichas P (2010) Intelligent transportation systems. IEEE Veh Technol Mag 5(1):77\u201384","journal-title":"IEEE Veh Technol Mag"},{"issue":"3","key":"8684_CR2","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s12544-011-0055-4","volume":"3","author":"G Yannis","year":"2011","unstructured":"Yannis G, Antoniou C, Papadimitriou E (2011) Autoregressive nonlinear time-series modeling of traffic fatalities in Europe. Eur Transp Res Rev 3(3):113\u2013127","journal-title":"Eur Transp Res Rev"},{"key":"8684_CR3","doi-asserted-by":"crossref","unstructured":"Mai T, Ghosh B, Wilson S (2014) Short-term traffic-flow forecasting with auto-regressive moving average models. Proceedings of the Institution of Civil Engineers-transport Thomas Telford Ltd 167:232\u2013239","DOI":"10.1680\/tran.12.00012"},{"issue":"3","key":"8684_CR4","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s12544-015-0170-8","volume":"7","author":"SV Kumar","year":"2015","unstructured":"Kumar SV, Vanajakshi L (2015) Short-term traffic flow prediction using seasonal arima model with limited input data. Eur Transp Res Rev 7(3):21","journal-title":"Eur Transp Res Rev"},{"key":"8684_CR5","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1016\/j.ins.2022.06.090","volume":"608","author":"G Lin","year":"2022","unstructured":"Lin G, Lin A, Gu D (2022) Using support vector regression and k-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient. Inf Sci 608:517\u2013531","journal-title":"Inf Sci"},{"issue":"2","key":"8684_CR6","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.trb.2004.03.003","volume":"39","author":"Y Wang","year":"2005","unstructured":"Wang Y, Papageorgiou M (2005) Real-time freeway traffic state estimation based on extended kalman filter: a general approach. Transp Res Part B Methodol 39(2):141\u2013167","journal-title":"Transp Res Part B Methodol"},{"issue":"3","key":"8684_CR7","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1080\/15472450.2020.1742121","volume":"24","author":"H Cui","year":"2020","unstructured":"Cui H, Yuan G, Liu N, Xu M, Song H (2020) Convolutional neural network for recognizing highway traffic congestion. J Intell Transp Syst 24(3):279\u2013289","journal-title":"J Intell Transp Syst"},{"issue":"2","key":"8684_CR8","doi-asserted-by":"publisher","first-page":"1888","DOI":"10.4249\/scholarpedia.1888","volume":"8","author":"S Grossberg","year":"2013","unstructured":"Grossberg S (2013) Recurrent neural networks. Scholarpedia 8(2):1888","journal-title":"Scholarpedia"},{"key":"8684_CR9","doi-asserted-by":"crossref","unstructured":"Graves A (2012) Supervised sequence labelling with recurrent neural networks. pp 37\u201345 (Long short-term memory)","DOI":"10.1007\/978-3-642-24797-2_4"},{"issue":"1","key":"8684_CR10","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2020) A comprehensive survey on graph neural networks. IEEE trans neural netw learn syst 32(1):4\u201324","journal-title":"IEEE trans neural netw learn syst"},{"key":"8684_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2023.128913","volume":"623","author":"S He","year":"2023","unstructured":"He S, Luo Q, Du R, Zhao L, He G, Fu H, Li H (2023) Stgc-gnns: a gnn-based traffic prediction framework with a spatial-temporal granger causality graph. Physica A 623:128913","journal-title":"Physica A"},{"key":"8684_CR12","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems 30"},{"issue":"10","key":"8684_CR13","doi-asserted-by":"publisher","first-page":"3325","DOI":"10.1109\/LCOMM.2021.3098557","volume":"25","author":"Q Liu","year":"2021","unstructured":"Liu Q, Li J, Lu Z (2021) St-tran: Spatial-temporal transformer for cellular traffic prediction. IEEE Commun Lett 25(10):3325\u20133329","journal-title":"IEEE Commun Lett"},{"key":"8684_CR14","doi-asserted-by":"crossref","unstructured":"Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. Proceedings of the 27th International Joint Conference on Artificial Intelligence. pp 3634\u20133640","DOI":"10.24963\/ijcai.2018\/505"},{"key":"8684_CR15","doi-asserted-by":"crossref","unstructured":"Guo S, Lin Y, Feng N, Song C, Wan H (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proceedings of the AAAI Conference on Artificial Intelligence. pp 922\u2013929","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"8684_CR16","unstructured":"Li Y, Yu R, Shahabi C, Liu Y (2018) Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In: Proceedings of the 6th International Conference on Learning Representations, 2018:1\u201316"},{"key":"8684_CR17","doi-asserted-by":"crossref","unstructured":"Fang Z, Long Q, Song G, Xie K (2021) Spatial-temporal graph ode networks for traffic flow forecasting. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 364\u2013373","DOI":"10.1145\/3447548.3467430"},{"key":"8684_CR18","doi-asserted-by":"crossref","unstructured":"Zheng C, Fan X, Wang C, Qi J (2020) Gman: A graph multi-attention network for traffic prediction. Proceedings of the AAAI Conference on Artificial Intelligence. pp 1234\u20131241","DOI":"10.1609\/aaai.v34i01.5477"},{"issue":"7","key":"8684_CR19","doi-asserted-by":"publisher","first-page":"7645","DOI":"10.1109\/TITS.2024.3362145","volume":"25","author":"A Liu","year":"2024","unstructured":"Liu A, Zhang Y (2024) Spatial-temporal dynamic graph convolutional network with interactive learning for traffic forecasting. IEEE Trans Intell Transp Syst 25(7):7645\u20137660","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"2","key":"8684_CR20","doi-asserted-by":"publisher","first-page":"2003","DOI":"10.1109\/TNNLS.2022.3186103","volume":"35","author":"Z Wu","year":"2022","unstructured":"Wu Z, Zheng D, Pan S, Gan Q, Long G, Karypis G (2022) Traversenet: unifying space and time in message passing for traffic forecasting. IEEE Trans Neural Netw Learn Syst 35(2):2003\u20132013","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"8684_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106941","volume":"183","author":"J Fan","year":"2025","unstructured":"Fan J, Weng W, Chen Q, Wu H, Wu J (2025) PDG2Seq: Periodic dynamic graph to sequence model for traffic flow prediction. Neural Netw 183:106941","journal-title":"Neural Netw"},{"key":"8684_CR22","doi-asserted-by":"crossref","unstructured":"Wang X, Ma Y, Wang Y, Jin W, Wang X, Tang J, Jia C, Yu J (2020) Traffic flow prediction via spatial temporal graph neural network. In: Proceedings of the Web Conference 2020:1082\u20131092","DOI":"10.1145\/3366423.3380186"},{"key":"8684_CR23","doi-asserted-by":"crossref","unstructured":"Wu Z, Pan S, Long G, Jiang J, Zhang C (2019) Graph wavenet for deep spatial-temporal graph modeling. Proceedings of the International Joint Conference on Artificial Intelligence. pp 1907\u20131913","DOI":"10.24963\/ijcai.2019\/264"},{"key":"8684_CR24","first-page":"17804","volume":"33","author":"L Bai","year":"2020","unstructured":"Bai L, Yao L, Li C, Wang X, Wang C (2020) Adaptive graph convolutional recurrent network for traffic forecasting. Adv Neural Inf Process Syst 33:17804\u201317815","journal-title":"Adv Neural Inf Process Syst"},{"issue":"1","key":"8684_CR25","first-page":"1","volume":"17","author":"F Li","year":"2023","unstructured":"Li F, Feng J, Yan H, Jin G, Yang F, Sun F, Jin D, Li Y (2023) Dynamic graph convolutional recurrent network for traffic prediction: benchmark and solution. ACM Trans Knowl Discov Data 17(1):1\u201321","journal-title":"ACM Trans Knowl Discov Data"},{"key":"8684_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109670","volume":"132","author":"W Weng","year":"2023","unstructured":"Weng W, Fan J, Wu H, Hu Y, Tian H, Zhu F, Wu J (2023) A decomposition dynamic graph convolutional recurrent network for traffic forecasting. Pattern Recogn 132:109670","journal-title":"Pattern Recogn"},{"issue":"11","key":"8684_CR27","doi-asserted-by":"publisher","first-page":"20681","DOI":"10.1109\/TITS.2022.3173689","volume":"23","author":"J Huang","year":"2022","unstructured":"Huang J, Luo K, Cao L, Wen Y, Zhong S (2022) Learning multiaspect traffic couplings by multirelational graph attention networks for traffic prediction. IEEE Trans Intell Transp Syst 23(11):20681\u201320695","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"3","key":"8684_CR28","first-page":"82","volume":"21","author":"J Liu","year":"2004","unstructured":"Liu J, Guan W (2004) A summary of traffic flow forecasting methods. J Highw Transp Res Dev 21(3):82\u201385","journal-title":"J Highw Transp Res Dev"},{"key":"8684_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v027.i04","volume":"27","author":"B Pfaff","year":"2008","unstructured":"Pfaff B (2008) Var, svar and svec models: Implementation within r package vars. J Stat Softw 27:1\u201332","journal-title":"J Stat Softw"},{"key":"8684_CR30","doi-asserted-by":"crossref","unstructured":"Li M, Zhu Z (2021) Spatial-temporal fusion graph neural networks for traffic flow forecasting. Proceedings of the AAAI Conference on Artificial Intelligence. pp 4189\u20134196","DOI":"10.1609\/aaai.v35i5.16542"},{"key":"8684_CR31","unstructured":"Chen Y, Segovia I, Gel YR (2021) Z-GCNETs: Time zigzags at graph convolutional networks for time series forecasting. International Conference on Machine Learning. pp 1684\u20131694"},{"key":"8684_CR32","doi-asserted-by":"crossref","unstructured":"Song C, Lin Y, Guo S, Wan H (2020) Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting. Proceedings of the AAAI Conference on Artificial Intelligence. pp 914\u2013921","DOI":"10.1609\/aaai.v34i01.5438"},{"issue":"11","key":"8684_CR33","doi-asserted-by":"publisher","first-page":"5415","DOI":"10.1109\/TKDE.2021.3056502","volume":"34","author":"S Guo","year":"2021","unstructured":"Guo S, Lin Y, Wan H, Li X, Cong G (2021) Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting. IEEE Trans Knowl Data Eng 34(11):5415\u20135428","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"11","key":"8684_CR34","doi-asserted-by":"publisher","first-page":"22386","DOI":"10.1109\/TITS.2021.3102983","volume":"23","author":"H Yan","year":"2021","unstructured":"Yan H, Ma X, Pu Z (2021) Learning dynamic and hierarchical traffic spatiotemporal features with transformer. IEEE Trans Intell Transp Syst 23(11):22386\u201322399","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"8684_CR35","doi-asserted-by":"publisher","first-page":"69638","DOI":"10.52202\/075280-3050","volume":"36","author":"K Yi","year":"2023","unstructured":"Yi K, Zhang Q, Fan W, He H, Hu L, Wang P, An N, Cao L, Niu Z (2023) FourierGNN: Rethinking multivariate time series forecasting from a pure graph perspective. Adv Neural Inf Process Syst 36:69638\u201369660","journal-title":"Adv Neural Inf Process Syst"},{"key":"8684_CR36","doi-asserted-by":"crossref","unstructured":"Chen L, Chen L, Wang H (2025) Spatiotemporal multi-view trend-aware network for traffic flow prediction. Knowl Based Syst , 115002","DOI":"10.1016\/j.knosys.2025.115002"},{"key":"8684_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109575","volume":"139","author":"H Chi","year":"2025","unstructured":"Chi H, Lu Y, Xie C, Ke W, Chen B (2025) Spatio-temporal attention based collaborative local-global learning for traffic flow prediction. Eng Appl Artif Intell 139:109575","journal-title":"Eng Appl Artif Intell"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08684-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-026-08684-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08684-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T05:49:42Z","timestamp":1783316982000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-026-08684-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,6]]},"references-count":37,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["8684"],"URL":"https:\/\/doi.org\/10.1007\/s11227-026-08684-2","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,6]]},"assertion":[{"value":"7 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"554"}}