{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T18:49:41Z","timestamp":1788202181356,"version":"build-2803163510"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41571397"],"award-info":[{"award-number":["41571397"]}],"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":["41501442"],"award-info":[{"award-number":["41501442"]}],"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":["41871364"],"award-info":[{"award-number":["41871364"]}],"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":["51678077"],"award-info":[{"award-number":["51678077"]}],"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":["71871224"],"award-info":[{"award-number":["71871224"]}],"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":["41771492"],"award-info":[{"award-number":["41771492"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Intell. Transport. Syst."],"published-print":{"date-parts":[[2020,9]]},"DOI":"10.1109\/tits.2019.2935152","type":"journal-article","created":{"date-parts":[[2019,8,22]],"date-time":"2019-08-22T16:19:35Z","timestamp":1566490775000},"page":"3848-3858","source":"Crossref","is-referenced-by-count":3031,"title":["T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction"],"prefix":"10.1109","volume":"21","author":[{"given":"Ling","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujiao","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0016-2902","authenticated-orcid":false,"given":"Yu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4503-6643","authenticated-orcid":false,"given":"Pu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1173-6593","authenticated-orcid":false,"given":"Haifeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1126\/science.aam6960"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1038\/nature24270"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2011.2175728"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2013.2247040"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.3141\/1678-22"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of Go with deep neural networks and tree search","volume":"529","author":"silver","year":"2016","journal-title":"Nature"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/S0968-090X(01)00004-3"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1023\/B:STCO.0000035301.49549.88"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ACC.2013.6580568"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2015.09.001"},{"key":"ref27","first-page":"410","article-title":"Interval prediction for traffic time series using local linear predictor","author":"sun","year":"2004","journal-title":"Proc 7th Int IEEE Conf Intell Transp Syst"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/S0968-090X(97)82903-8"},{"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","first-page":"18","article-title":"Dynamic modeling of urban transportation networks and analysis of its travel behaviors","volume":"2","author":"huang","year":"2005","journal-title":"Chinese Journal of Management"},{"key":"ref20","first-page":"309","article-title":"Traffic velocity distributions for different spacings","volume":"51","author":"qi","year":"2011","journal-title":"J Tsinghua Univ Sci Technol"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-18320-6_7"},{"key":"ref21","first-page":"185","article-title":"Impacts of traffic management measures on urban network microscopic fundamental diagram","volume":"13","author":"xu","year":"2013","journal-title":"J Transp Syst Eng Inf Technol"},{"key":"ref24","first-page":"562","article-title":"Short-term traffic volume intelligent hybrid forecasting model and its application","volume":"31","author":"xiangjie","year":"2011","journal-title":"System Engineering &#x2014;Theory & Practice"},{"key":"ref23","first-page":"118","article-title":"Urban road traffic speed estimation for missing probe vehicle data based on multiple linear regression model","author":"shan","year":"2013","journal-title":"Proc IEEE Int Conf Intell Transp Syst"},{"key":"ref26","first-page":"22","article-title":"Short-term traffic and travel time prediction models","volume":"22","author":"van lint","year":"2012","journal-title":"Artif Intell Appl Critical Transp"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1080\/0144164042000195072"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/72.279181"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref53","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"chung","year":"2014","journal-title":"arXiv 1412 3555"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref10","first-page":"178","article-title":"Short-term traffic flow forecasting based on K-nearest neighbors non-parametric regression","volume":"24","author":"zhang","year":"2009","journal-title":"J Syst Eng"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2006.869623"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1111\/0885-9507.00154"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2311123"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/YAC.2016.7804912"},{"key":"ref14","first-page":"1655","article-title":"Deep spatio-temporal residual networks for citywide crowd flows prediction","author":"zhang","year":"2017","journal-title":"Proc 31st AAAI Conf Artif Intell"},{"key":"ref15","article-title":"Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework","author":"wu","year":"2016","journal-title":"arXiv 1612 01022"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2787696"},{"key":"ref17","first-page":"3844","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","author":"defferrard","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2013.10.010"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2013.03.004"},{"key":"ref4","first-page":"128","article-title":"Spatial and temporal characteristics for congested traffic on urban expressway","volume":"38","author":"dong","year":"2012","journal-title":"J Beijing Univ Technol"},{"key":"ref3","first-page":"73","article-title":"Synthesis of short&#x2013;term traffic flow forecasting research progress","volume":"10","author":"yuan","year":"2012","journal-title":"Urban China in Transition"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(1995)121:3(249)"},{"key":"ref5","first-page":"1","article-title":"Analysis of freeway traffic time-series data by using Box-Jenkins techniques","author":"ahmed","year":"1979","journal-title":"Transp Res Rec"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2004.837813"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/0191-2615(84)90002-X"},{"key":"ref49","article-title":"Spectral networks and locally connected networks on graphs","author":"bruna","year":"2013","journal-title":"arXiv 1312 6203"},{"key":"ref9","first-page":"19","article-title":"Research on methods of short-term traffic forecasting based on support vector regression","volume":"30","author":"yao","year":"2006","journal-title":"J Beijing Jiaotong Univ"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.3390\/s17071501"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2017.10.016"},{"key":"ref48","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","author":"li","year":"2017","journal-title":"arXiv 1707 01926"},{"key":"ref47","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2016","journal-title":"arXiv 1609 02907"},{"key":"ref42","doi-asserted-by":"crossref","first-page":"4148","DOI":"10.1109\/TVT.2018.2883046","article-title":"Robust hierarchical deep learning for vehicular management","volume":"68","author":"qi","year":"2019","journal-title":"IEEE Trans Veh Technol"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.03.030"},{"key":"ref44","first-page":"865","article-title":"Traffic flow prediction with big data: A deep learning approach","volume":"16","author":"lv","year":"2015","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.3141\/1811-04"}],"container-title":["IEEE Transactions on Intelligent Transportation Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6979\/9179974\/08809901.pdf?arnumber=8809901","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T12:02:46Z","timestamp":1651060966000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8809901\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9]]},"references-count":53,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tits.2019.2935152","relation":{},"ISSN":["1524-9050","1558-0016"],"issn-type":[{"value":"1524-9050","type":"print"},{"value":"1558-0016","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9]]}}}