{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T18:12:36Z","timestamp":1778695956885,"version":"3.51.4"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Key Talent Project of Gansu Provinc"},{"name":"Major Science and Technology Special Program of Gansu Provinc"},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of Chin","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-026-08554-x","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:24:27Z","timestamp":1778693067000},"update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual-attention dynamic graph\u2013hypergraph convolutional network for traffic flow forecasting"],"prefix":"10.1007","volume":"82","author":[{"given":"Hong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linbiao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Hou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,13]]},"reference":[{"issue":"24","key":"8554_CR1","doi-asserted-by":"publisher","first-page":"21217","DOI":"10.1109\/JIOT.2023.3283611","volume":"10","author":"Y Miao","year":"2023","unstructured":"Miao Y, Bai X, Cao Y, Liu Y, Dai F, Wang F, Qi L, Dou W (2023) A novel short-term traffic prediction model based on SVD and Arima with blockchain in industrial internet of things. IEEE Internet Things J 10(24):21217\u201321226","journal-title":"IEEE Internet Things J"},{"key":"8554_CR2","doi-asserted-by":"crossref","unstructured":"Nguyen H-AT, Nguyen H-D, Do T-H (2022) An application of vector autoregressive model for analyzing the impact of weather and nearby traffic flow on the traffic volume. In: 2022 RIVF International Conference on Computing and Communication Technologies (RIVF). IEEE, pp 328\u2013333","DOI":"10.1109\/RIVF55975.2022.10013894"},{"issue":"3","key":"8554_CR3","doi-asserted-by":"publisher","first-page":"2716","DOI":"10.1007\/s10489-024-05291-7","volume":"54","author":"P Bikram","year":"2024","unstructured":"Bikram P, Das S, Biswas A (2024) Attentive graph structure learning embedded in deep spatial-temporal graph neural network for traffic forecasting. Appl Intell 54(3):2716\u20132749","journal-title":"Appl Intell"},{"key":"8554_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.129473","volume":"297","author":"W Wu","year":"2025","unstructured":"Wu W, Zhou N, Liang X, Gui W, Yang C, Liu Y (2025) TCAC-transformer: a fast convolutional transformer with temporal-channel attention for efficient industrial fault diagnosis. Expert Syst Appl 297:129473","journal-title":"Expert Syst Appl"},{"issue":"28","key":"8554_CR5","doi-asserted-by":"publisher","first-page":"21181","DOI":"10.1007\/s00521-023-08831-3","volume":"35","author":"W Zhu","year":"2023","unstructured":"Zhu W, Sun Y, Yi X, Wang Y, Liu Z (2023) A correlation information-based spatiotemporal network for traffic flow forecasting. Neural Comput Appl 35(28):21181\u201321199","journal-title":"Neural Comput Appl"},{"key":"8554_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2025.116898","volume":"199","author":"A Ali","year":"2025","unstructured":"Ali A, Naeem HY, Sharafian A, Qiu L, Wu Z, Bai X (2025) Dynamic multi-graph spatio-temporal learning for citywide traffic flow prediction in transportation systems. Chaos Solitons Fract 199:116898","journal-title":"Chaos Solitons Fract"},{"key":"8554_CR7","first-page":"11463","volume":"39","author":"L Cao","year":"2025","unstructured":"Cao L, Wang B, Jiang G, Yu Y, Dong J (2025) Spatiotemporal-aware trend-seasonality decomposition network for traffic flow forecasting. Proc AAAI Conf Artif Intell 39:11463\u201311471","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"8554_CR8","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"},{"issue":"7","key":"8554_CR9","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1007\/s10586-025-05123-4","volume":"28","author":"H Zhang","year":"2025","unstructured":"Zhang H, Yi M, Zhang X (2025) Traffic flow forecasting based on dynamic graph convolution and triplet attention. Clust Comput 28(7):425","journal-title":"Clust Comput"},{"key":"8554_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.120938","volume":"677","author":"S Zhang","year":"2024","unstructured":"Zhang S, Yu W, Zhang W (2024) Interactive dynamic diffusion graph convolutional network for traffic flow prediction. Inf Sci 677:120938","journal-title":"Inf Sci"},{"key":"8554_CR11","doi-asserted-by":"crossref","unstructured":"Yang Y, Huang C, Xia L, Liang Y, Yu Y, Li C (2022) Multi-behavior hypergraph-enhanced transformer for sequential recommendation. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp 2263\u20132274","DOI":"10.1145\/3534678.3539342"},{"issue":"12","key":"8554_CR12","doi-asserted-by":"publisher","first-page":"23680","DOI":"10.1109\/TITS.2022.3208943","volume":"23","author":"Y Sun","year":"2022","unstructured":"Sun Y, Jiang X, Hu Y, Duan F, Guo K, Wang B, Gao J, Yin B (2022) Dual dynamic spatial-temporal graph convolution network for traffic prediction. IEEE Trans Intell Transp Syst 23(12):23680\u201323693","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"2","key":"8554_CR13","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/s10707-024-00527-7","volume":"29","author":"W Zhao","year":"2025","unstructured":"Zhao W, Yuan G, Bing R, Lu R, Shen Y (2025) Periodicity aware spatial-temporal adaptive hypergraph neural network for traffic forecasting. GeoInformatica 29(2):201\u2013232","journal-title":"GeoInformatica"},{"key":"8554_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11831-025-10286-9","volume":"32","author":"G Fan","year":"2025","unstructured":"Fan G, Sabri AQM, Rahman SSA, Pan L, Rahardja S (2025) Emerging trends in graph neural networks for traffic flow prediction: a survey. Arch Comput Methods Eng 32:1\u201345","journal-title":"Arch Comput Methods Eng"},{"key":"8554_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109670","volume":"142","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 142:109670","journal-title":"Pattern Recogn"},{"key":"8554_CR16","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1016\/j.ins.2022.05.127","volume":"607","author":"Y Wang","year":"2022","unstructured":"Wang Y, Jing C, Xu S, Guo T (2022) Attention based spatiotemporal graph attention networks for traffic flow forecasting. Inf Sci 607:869\u2013883","journal-title":"Inf Sci"},{"issue":"10","key":"8554_CR17","doi-asserted-by":"publisher","first-page":"5388","DOI":"10.1109\/TKDE.2023.3333824","volume":"36","author":"G Jin","year":"2023","unstructured":"Jin G, Liang Y, Fang Y, Shao Z, Huang J, Zhang J, Zheng Y (2023) Spatio-temporal graph neural networks for predictive learning in urban computing: a survey. IEEE Trans Knowl Data Eng 36(10):5388\u20135408","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8554_CR18","doi-asserted-by":"crossref","unstructured":"Shao Z, Zhang Z, Wang F, Xu Y (2022) Pre-training enhanced spatial-temporal graph neural network for multivariate time series forecasting. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp 1567\u20131577","DOI":"10.1145\/3534678.3539396"},{"key":"8554_CR19","doi-asserted-by":"crossref","unstructured":"Yin N, Feng F, Luo Z, Zhang X, Wang W, Luo X, Chen C, Hua X-S (2022) Dynamic hypergraph convolutional network. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, pp 1621\u20131634","DOI":"10.1109\/ICDE53745.2022.00167"},{"key":"8554_CR20","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.aej.2024.10.022","volume":"111","author":"X Fan","year":"2025","unstructured":"Fan X, Qi K, Wu D, Xie H, Qu Z, Ren C (2025) MGHCN: multi-graph structures and hypergraph convolutional networks for traffic flow prediction. Alex Eng J 111:221\u2013237","journal-title":"Alex Eng J"},{"key":"8554_CR21","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30"},{"key":"8554_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120203","volume":"227","author":"Q Ren","year":"2023","unstructured":"Ren Q, Li Y, Liu Y (2023) Transformer-enhanced periodic temporal convolution network for long short-term traffic flow forecasting. Expert Syst Appl 227:120203","journal-title":"Expert Syst Appl"},{"key":"8554_CR23","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.7717\/peerj-cs.1484","volume":"9","author":"W Zhao","year":"2023","unstructured":"Zhao W, Zhang S, Wang B, Zhou B (2023) Spatio-temporal causal graph attention network for traffic flow prediction in intelligent transportation systems. PeerJ Comput Sci 9:1484","journal-title":"PeerJ Comput Sci"},{"key":"8554_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106207","volume":"173","author":"W Ju","year":"2024","unstructured":"Ju W, Fang Z, Gu Y, Liu Z, Long Q, Qiao Z, Qin Y, Shen J, Sun F, Xiao Z et al (2024) A comprehensive survey on deep graph representation learning. Neural Netw 173:106207","journal-title":"Neural Netw"},{"key":"8554_CR25","unstructured":"Lan S, Ma Y, Huang W, Wang W, Yang H, Li P (2022) DSTAGNN: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting. In: International Conference on Machine Learning. PMLR, pp 11906\u201311917"},{"key":"8554_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106044","volume":"121","author":"Y Bao","year":"2023","unstructured":"Bao Y, Huang J, Shen Q, Cao Y, Ding W, Shi Z, Shi Q (2023) Spatial-temporal complex graph convolution network for traffic flow prediction. Eng Appl Artif Intell 121:106044","journal-title":"Eng Appl Artif Intell"},{"key":"8554_CR27","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.ins.2023.03.093","volume":"634","author":"Y Bao","year":"2023","unstructured":"Bao Y, Liu J, Shen Q, Cao Y, Ding W, Shi Q (2023) PKET-GCN: prior knowledge enhanced time-varying graph convolution network for traffic flow prediction. Inf Sci 634:359\u2013381","journal-title":"Inf Sci"},{"key":"8554_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.120648","volume":"671","author":"J Xiao","year":"2024","unstructured":"Xiao J, Long B (2024) A multi-channel spatial-temporal transformer model for traffic flow forecasting. Inf Sci 671:120648. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.ins.2024.120648","journal-title":"Inf Sci"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08554-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/article\/10.1007\/s11227-026-08554-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08554-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:24:32Z","timestamp":1778693072000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/10.1007\/s11227-026-08554-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,13]]},"references-count":28,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["8554"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/s11227-026-08554-x","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,13]]},"assertion":[{"value":"21 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 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":"414"}}