{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T11:43:51Z","timestamp":1784893431348,"version":"3.55.0"},"reference-count":77,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["4222021"],"award-info":[{"award-number":["4222021"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21B2038"],"award-info":[{"award-number":["U21B2038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U19B2039"],"award-info":[{"award-number":["U19B2039"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1811463"],"award-info":[{"award-number":["U1811463"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172023"],"award-info":[{"award-number":["62172023"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003213","name":"Research and Deveopment Program of Beijing Municipal Education Commission","doi-asserted-by":"publisher","award":["KZ202210005008"],"award-info":[{"award-number":["KZ202210005008"]}],"id":[{"id":"10.13039\/501100003213","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017616","name":"Beijing Talents Fund","doi-asserted-by":"publisher","award":["2017A24"],"award-info":[{"award-number":["2017A24"]}],"id":[{"id":"10.13039\/501100017616","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Intell. Transport. Syst."],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1109\/tits.2022.3208943","type":"journal-article","created":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T19:33:45Z","timestamp":1664998425000},"page":"23680-23693","source":"Crossref","is-referenced-by-count":136,"title":["Dual Dynamic Spatial-Temporal Graph Convolution Network for Traffic Prediction"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0872-384X","authenticated-orcid":false,"given":"Yanfeng","family":"Sun","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3936-0479","authenticated-orcid":false,"given":"Xiangheng","family":"Jiang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0440-438X","authenticated-orcid":false,"given":"Yongli","family":"Hu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuqing","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Normal University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9828-1028","authenticated-orcid":false,"given":"Kan","family":"Guo","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2677-8342","authenticated-orcid":false,"given":"Boyue","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9803-0256","authenticated-orcid":false,"given":"Junbin","family":"Gao","sequence":"additional","affiliation":[{"name":"Discipline of Business Analytics, The University of Sydney Business School, The University of Sydney, Camperdown, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3121-1823","authenticated-orcid":false,"given":"Baocai","family":"Yin","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1145\/2611567"},{"key":"ref72","first-page":"933","article-title":"Language modeling with gated convolutional networks","author":"dauphin","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013558"},{"key":"ref70","first-page":"1511","article-title":"HyperGCN: A new method for training graph convolutional networks on hypergraphs","author":"yadati","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-04167-0_33"},{"key":"ref77","article-title":"Incrementally improving graph WaveNet performance on traffic prediction","author":"shleifer","year":"2019","journal-title":"arXiv 1912 07390"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.3141\/1748-12"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref75","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","volume":"27","author":"sutskever","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.1993.318226"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2012.03.006"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1080\/21680566.2015.1060582"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2005.1520184"},{"key":"ref37","article-title":"Predicting traffic congestion using recurrent neural networks","author":"zhou","year":"2002","journal-title":"Proc World Congr Intell Transp Syst"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2015.0136"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2005.03.001"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/0967-0661(95)00221-9"},{"key":"ref60","article-title":"Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution","author":"li","year":"2021","journal-title":"arXiv 2104 14917"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-020-07612-8"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/MTITS.2015.7223248"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3043250"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/MITS.2019.2919615"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3054840"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-014-5453-0"},{"key":"ref65","article-title":"Graph neural network for traffic forecasting: A survey","author":"jiang","year":"2021","journal-title":"arXiv 2101 11174"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1007\/s41019-020-00151-z"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.3141\/1836-03"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5758"},{"key":"ref69","first-page":"1","article-title":"Convolution neural networks on graphs with fast localized spectral filtering","author":"defferrard","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref2","first-page":"82","article-title":"A summary of traffic flow forecasting methods","volume":"21","author":"liu","year":"2004","journal-title":"J Highway Transp Res Develop"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.01.005"},{"key":"ref20","first-page":"155","article-title":"Support vector regression machines","volume":"9","author":"drucker","year":"1997","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2014.10.022"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2004.837813"},{"key":"ref24","first-page":"1","article-title":"Support vector regression machines","volume":"9","author":"drucker","year":"1996","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2015.2411675"},{"key":"ref26","first-page":"193","article-title":"Traffic flow prediction using AdaBoost algorithm with random forests as a weak learner","volume":"19","author":"leshem","year":"2007","journal-title":"Proc World Acad Sci Eng Technol"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009715923555"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.113"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-62005-9_7"},{"key":"ref58","first-page":"1","article-title":"Dynamic graph convolution network for traffic forecasting based on latent network of Laplace matrix estimation","volume":"23","author":"guo","year":"2020","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013656"},{"key":"ref55","first-page":"1","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","author":"li","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2963722"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301890"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/264"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/S0968-090X(02)00009-8"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-6419.2010.00637.x"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2016.0208"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8667.2007.00489.x"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2004.03.003"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.02.006"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2006.06.001"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2007.4357755"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2011.2175728"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.07.005"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2011.2178837"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2013.05.012"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.11.002"},{"key":"ref6","first-page":"1","article-title":"Analysis of freeway traffic time-series data by using Box&#x2013;Jenkins techniques","volume":"722","author":"ahmed","year":"1979","journal-title":"Transp Res Rec"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.2307\/2284333"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/S0968-090X(97)82903-8"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1099-131X(199705)16:3<147::AID-FOR652>3.0.CO;2-X"},{"key":"ref49","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"bai","year":"2018","journal-title":"arXiv 1803 01271"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)"},{"key":"ref46","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/YAC.2016.7804912"},{"key":"ref48","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2017","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref47","first-page":"1","article-title":"Spectral networks and deep locally connected networks on graphs","author":"bruna","year":"2014","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2345663"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.3390\/s17071501"}],"container-title":["IEEE Transactions on Intelligent Transportation Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6979\/9972869\/09912360.pdf?arnumber=9912360","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T19:26:28Z","timestamp":1672082788000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9912360\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12]]},"references-count":77,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tits.2022.3208943","relation":{},"ISSN":["1524-9050","1558-0016"],"issn-type":[{"value":"1524-9050","type":"print"},{"value":"1558-0016","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12]]}}}