{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T22:29:26Z","timestamp":1775600966154,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T00:00:00Z","timestamp":1723593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T00:00:00Z","timestamp":1723593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772087"],"award-info":[{"award-number":["61772087"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2022JJ30621"],"award-info":[{"award-number":["2022JJ30621"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s40747-024-01601-1","type":"journal-article","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T08:02:01Z","timestamp":1723622521000},"page":"8179-8196","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["TARGCN: temporal attention recurrent graph convolutional neural network for traffic prediction"],"prefix":"10.1007","volume":"10","author":[{"given":"He","family":"Yang","sequence":"first","affiliation":[]},{"given":"Cong","family":"Jiang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6962-8475","authenticated-orcid":false,"given":"Yun","family":"Song","sequence":"additional","affiliation":[]},{"given":"Wendong","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Zelin","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Xinke","family":"Bai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,14]]},"reference":[{"issue":"6","key":"1601_CR1","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)","volume":"129","author":"BM Williams","year":"2003","unstructured":"Williams BM, Hoel LA (2003) Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results[J]. J Transp Eng 129(6):664\u2013672","journal-title":"J Transp Eng"},{"issue":"1","key":"1601_CR2","first-page":"136","volume":"10","author":"Z Lu","year":"2016","unstructured":"Lu Z, Zhou C, Wu J et al (2016) Integrating granger causality and vector auto-regression for traffic prediction of largescale WLANs [J]. KSII Trans Internet Inf Syst 10(1):136\u2013151","journal-title":"KSII Trans Internet Inf Syst"},{"issue":"4","key":"1601_CR3","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1109\/TITS.2004.837813","volume":"5","author":"CH Wu","year":"2004","unstructured":"Wu CH, Ho JM, Lee DT (2004) Travel-time prediction with support vector regression [J]. IEEE Trans Intell Transp Syst 5(4):276\u2013281","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"1","key":"1601_CR4","first-page":"22","volume":"22","author":"JWC Van Lint","year":"2012","unstructured":"Van Lint JWC, Van Hinsbergen C (2012) Short-term traffic and travel time prediction models[J]. Artificial Intelligence Applications to Critical Transportation Issues 22(1):22\u201341","journal-title":"Artificial Intelligence Applications to Critical Transportation Issues"},{"key":"1601_CR5","first-page":"802","volume":"28","author":"X Shi","year":"2015","unstructured":"Shi X, Chen Z, Wang H et al (2015) Convolutional LSTM network: A machine learning approach for precipitation nowcasting[J]. Adv Neural Inf Process Syst 28:802\u2013810","journal-title":"Adv Neural Inf Process Syst"},{"key":"1601_CR6","doi-asserted-by":"crossref","unstructured":"Zhang J, Zheng Y, Qi D. Deep spatio-temporal residual networks for citywide crowd flows prediction[C]. AAAI conference on artificial intelligence (AAAI). 2017,31(1).","DOI":"10.1609\/aaai.v31i1.10735"},{"issue":"10","key":"1601_CR7","doi-asserted-by":"publisher","first-page":"3913","DOI":"10.1109\/TITS.2019.2906365","volume":"20","author":"S Guo","year":"2019","unstructured":"Guo S, Lin Y, Li S et al (2019) Deep spatial\u2013temporal 3D convolutional neural networks for traffic data forecasting[J]. IEEE Trans Intell Transp Syst 20(10):3913\u20133926","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"9","key":"1601_CR8","doi-asserted-by":"publisher","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","volume":"21","author":"L Zhao","year":"2019","unstructured":"Zhao L, Song Y, Zhang C et al (2019) T-GCN: A temporal graph convolutional network for traffic prediction[J]. IEEE Trans Intell Transp Syst 21(9):3848\u20133858","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1601_CR9","unstructured":"Yaguang Li, Rose Yu, Cyrus Shahabi, et al. 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting [C]. International Conference on Learning Representations (ICLR), 2018."},{"key":"1601_CR10","first-page":"17804","volume":"33","author":"L Bai","year":"2020","unstructured":"Bai L, Yao L, Li C et al (2020) Adaptive graph convolutional recurrent network for traffic forecasting[J]. Adv Neural Inf Process Syst 33:17804\u201317815","journal-title":"Adv Neural Inf Process Syst"},{"issue":"01","key":"1601_CR11","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1609\/aaai.v33i01.3301922","volume":"33","author":"S Guo","year":"2019","unstructured":"Guo S, Lin Y, Feng N et al (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting[C]. AAAI conference on artificial intelligence (AAAI) 33(01):922\u2013929","journal-title":"AAAI conference on artificial intelligence (AAAI)"},{"key":"1601_CR12","unstructured":"Hochreiter S, Bengio Y, Frasconi P, et al. Gradient flow in recurrent nets: the difficulty of learning long-term dependencies[J]. Wiley-IEEE Press, 2001: 237\u2013243."},{"key":"1601_CR13","first-page":"5998","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need[J]. Adv Neural Inf Process Syst 30:5998\u20136008","journal-title":"Adv Neural Inf Process Syst"},{"issue":"1","key":"1601_CR14","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1609\/aaai.v35i1.16088","volume":"35","author":"K Guo","year":"2021","unstructured":"Guo K, Hu Y, Sun Y et al (2021) Hierarchical graph convolution networks for traffic forecasting[C]. AAAI Conference on Artificial Intelligence (AAAI) 35(1):151\u2013159","journal-title":"AAAI Conference on Artificial Intelligence (AAAI)"},{"key":"1601_CR15","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). 2016: 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"issue":"01","key":"1601_CR16","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.1609\/aaai.v33i01.33011020","volume":"33","author":"Z Lin","year":"2019","unstructured":"Lin Z, Feng J, Lu Z et al (2019) Deepstn+: Context-aware spatial-temporal neural network for crowd flow prediction in metropolis[C]. AAAI conference on artificial intelligence (AAAI) 33(01):1020\u20131027","journal-title":"AAAI conference on artificial intelligence (AAAI)"},{"issue":"01","key":"1601_CR17","doi-asserted-by":"publisher","first-page":"5668","DOI":"10.1609\/aaai.v33i01.33015668","volume":"33","author":"H Yao","year":"2019","unstructured":"Yao H, Tang X, Wei H et al (2019) Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction[C]. AAAI conference on artificial intelligence (AAAI) 33(01):5668\u20135675","journal-title":"AAAI conference on artificial intelligence (AAAI)"},{"issue":"3","key":"1601_CR18","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1109\/TKDE.2019.2891537","volume":"32","author":"J Zhang","year":"2019","unstructured":"Zhang J, Zheng Y, Sun J et al (2019) Flow prediction in spatio-temporal networks based on multitask deep learning[J]. IEEE Trans Knowl Data Eng 32(3):468\u2013478","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1601_CR19","doi-asserted-by":"crossref","unstructured":"Yu B, Yin H, and Zhu Z. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting[C]. International Joint Conference on Artificial Intelligence (IJCAI),2018: 3634\u20133640.","DOI":"10.24963\/ijcai.2018\/505"},{"issue":"01","key":"1601_CR20","doi-asserted-by":"publisher","first-page":"3656","DOI":"10.1609\/aaai.v33i01.33013656","volume":"33","author":"X Geng","year":"2019","unstructured":"Geng X, Li Y, Wang L et al (2019) Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting[C]. AAAI conference on artificial intelligence (AAAI) 33(01):3656\u20133663","journal-title":"AAAI conference on artificial intelligence (AAAI)"},{"key":"1601_CR21","first-page":"3844","volume":"29","author":"M Defferrard","year":"2016","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering[J]. Adv Neural Inf Process Syst 29:3844\u20133852","journal-title":"Adv Neural Inf Process Syst"},{"key":"1601_CR22","unstructured":"Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks[J]. arXiv preprint arXiv:1609.02907, 2016."},{"issue":"01","key":"1601_CR23","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1609\/aaai.v34i01.5477","volume":"34","author":"C Zheng","year":"2020","unstructured":"Zheng C, Fan X, Wang C et al (2020) GMAN: A graph multi-attention network for traffic prediction[C]. AAAI Conference on Artificial Intelligence (AAAI) 34(01):1234\u20131241","journal-title":"AAAI Conference on Artificial Intelligence (AAAI)"},{"issue":"01","key":"1601_CR24","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1609\/aaai.v34i01.5438","volume":"34","author":"C Song","year":"2020","unstructured":"Song C, Lin Y, Guo S et al (2020) Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting[C]. AAAI Conference on Artificial Intelligence (AAAI) 34(01):914\u2013921","journal-title":"AAAI Conference on Artificial Intelligence (AAAI)"},{"issue":"5","key":"1601_CR25","doi-asserted-by":"publisher","first-page":"4189","DOI":"10.1609\/aaai.v35i5.16542","volume":"35","author":"M Li","year":"2021","unstructured":"Li M, Zhu Z (2021) Spatial-temporal fusion graph neural networks for traffic flow forecasting[C]. AAAI conference on artificial intelligence (AAAI) 35(5):4189\u20134196","journal-title":"AAAI conference on artificial intelligence (AAAI)"},{"key":"1601_CR26","unstructured":"Pascanu R, Mikolov T and Bengio Y. On the difficulty of training recurrent neural networks[C]. International conference on machine learning (ICML), 2013: 1310\u20131318."},{"key":"1601_CR27","unstructured":"Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions[J]. arXiv preprint arXiv:1511.07122, 2015."},{"issue":"2","key":"1601_CR28","doi-asserted-by":"publisher","first-page":"102","DOI":"10.3390\/ijgi11020102","volume":"11","author":"Y Wang","year":"2022","unstructured":"Wang Y, Jing C (2022) Spatiotemporal Graph Convolutional Network for Multi-Scale Traffic Forecasting[J]. ISPRS Int J Geo Inf 11(2):102","journal-title":"ISPRS Int J Geo Inf"},{"key":"1601_CR29","unstructured":"Jiang J, Han C, Zhao W X, et al. Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow"},{"key":"1601_CR30","doi-asserted-by":"crossref","unstructured":"Ji J, Wang J, Huang C, et al. Spatio-temporal self-supervised learning for traffic flow prediction[C]\/\/Proceedings of the AAAI conference on artificial intelligence. 2023, 37(4): 4356\u20134364.","DOI":"10.1609\/aaai.v37i4.25555"},{"key":"1601_CR31","doi-asserted-by":"crossref","unstructured":"Song Y, Bai X, Fan W, et al. MSSTN: a multi-scale spatio-temporal network for traffic flow prediction[J]. International Journal of Machine Learning and Cybernetics, 2024: 1\u201315.","DOI":"10.1007\/s13042-023-02067-2"},{"key":"1601_CR32","doi-asserted-by":"crossref","unstructured":"Zeng H, Cui Q, Huang X H, et al. STTD: spatial-temporal transformer with double recurrent graph convolutional cooperative network for traffic flow prediction[J]. Cluster Computing, 2024: 1\u201321.","DOI":"10.1007\/s10586-024-04583-4"},{"key":"1601_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105179","volume":"114","author":"Q Zhang","year":"2022","unstructured":"Zhang Q, Yin C, Chen Y et al (2022) IGCRRN: Improved Graph Convolution Res-Recurrent Network for spatio-temporal dependence capturing and traffic flow prediction[J]. Eng Appl Artif Intell 114:105179","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"1601_CR34","doi-asserted-by":"publisher","first-page":"96","DOI":"10.3141\/1748-12","volume":"1748","author":"C Chen","year":"2001","unstructured":"Chen C, Petty K, Skabardonis A et al (2001) Freeway performance measurement system: mining loop detector data[J]. Transp Res Rec 1748(1):96\u2013102","journal-title":"Transp Res Rec"},{"key":"1601_CR35","doi-asserted-by":"publisher","first-page":"15026","DOI":"10.1007\/s10489-022-03224-w","volume":"52","author":"Q Ni","year":"2022","unstructured":"Ni Q, Zhang M (2022) STGMN: A gated multi-graph convolutional network framework for traffic flow prediction[J]. Appl Intell 52:15026\u201315039","journal-title":"Appl Intell"},{"key":"1601_CR36","doi-asserted-by":"crossref","unstructured":"Xiong, L., Hu, Z., Yuan, X. et al. Multi-scale attention graph convolutional recurrent network for traffic forecasting[J].Cluster Comput 2024,27, 3277\u20133291","DOI":"10.1007\/s10586-023-04140-5"},{"key":"1601_CR37","doi-asserted-by":"crossref","unstructured":"Zhibo Xing, Mingxia Huang, Wentao Li, Dan Peng. Spatial linear transformer and temporal convolution network for traffic flow prediction[J].Scientific Reports. 2024;14(1):1\u201312.","DOI":"10.1038\/s41598-024-54114-9"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01601-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01601-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01601-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T22:16:48Z","timestamp":1729117008000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01601-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,14]]},"references-count":37,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["1601"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01601-1","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,14]]},"assertion":[{"value":"5 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2024","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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}