{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T01:16:27Z","timestamp":1777598187303,"version":"3.51.4"},"reference-count":32,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018AAA0101400"],"award-info":[{"award-number":["2018AAA0101400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61936006"],"award-info":[{"award-number":["61936006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012709","name":"Alibaba-Zhejiang University Joint Institute of Frontier Technologies","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012709","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2019.2958380","type":"journal-article","created":{"date-parts":[[2020,4,13]],"date-time":"2020-04-13T15:47:18Z","timestamp":1586792838000},"page":"122285-122293","source":"Crossref","is-referenced-by-count":8,"title":["Dual Graph for Traffic Forecasting"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4021-1083","authenticated-orcid":false,"given":"Long","family":"Wei","sequence":"first","affiliation":[{"name":"State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6059-0300","authenticated-orcid":false,"given":"Zhengxu","family":"Yu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2341-2978","authenticated-orcid":false,"given":"Zhongming","family":"Jin","sequence":"additional","affiliation":[{"name":"Alibaba Damo Academy, Alibaba Group, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7604-1410","authenticated-orcid":false,"given":"Liang","family":"Xie","sequence":"additional","affiliation":[{"name":"State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5735-2910","authenticated-orcid":false,"given":"Jianqiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Alibaba Damo Academy, Alibaba Group, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9817-4065","authenticated-orcid":false,"given":"Deng","family":"Cai","sequence":"additional","affiliation":[{"name":"State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3821-5125","authenticated-orcid":false,"given":"Xiaofei","family":"He","sequence":"additional","affiliation":[{"name":"State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8232-5049","authenticated-orcid":false,"given":"Xian-Sheng","family":"Hua","sequence":"additional","affiliation":[{"name":"Alibaba Damo Academy, Alibaba Group, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Traffic flow theory and control","author":"Drew","year":"1968"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.01.005"},{"issue":"1287","key":"ref3","article-title":"Adaptive forecasting of freeway traffic congestion","author":"Davis","year":"1990","journal-title":"Transp. Res. Rec."},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2345663"},{"key":"ref5","article-title":"DeepTrend: A deep hierarchical neural network for traffic flow prediction","author":"Dai","year":"2017","journal-title":"arXiv:1707.03213"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"ref7","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Int. Conf. Learn. Represent. (ICLR)","author":"Kipf"},{"key":"ref8","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","author":"Li","year":"2017","journal-title":"arXiv:1707.01926"},{"key":"ref9","article-title":"How powerful are graph neural networks?","author":"Xu","year":"2018","journal-title":"arXiv:1810.00826"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2010.10.004"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-72393-6_121"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2015.2457240"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3390\/s17040818"},{"key":"ref14","article-title":"A dynamic model for traffic flow prediction using improved DRN","author":"Tan","year":"2018","journal-title":"arXiv:1805.00868"},{"key":"ref15","article-title":"Deep stacked bidirectional and unidirectional lstm recurrent neural network for network-wide traffic speed prediction","volume-title":"Proc. 6th Int. Workshop Urban Comput.","author":"Cui"},{"key":"ref16","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sutskever"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/SmartCity.2015.63"},{"key":"ref18","article-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting","author":"Yu","year":"2017","journal-title":"arXiv:1709.04875"},{"key":"ref19","article-title":"Dynamic Spatio-temporal graph-based CNNs for traffic prediction","author":"Wang","year":"2019","journal-title":"arXiv:1812.02019"},{"key":"ref20","article-title":"A comprehensive survey on graph neural networks","author":"Wu","year":"2019","journal-title":"arXiv:1901.00596"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1606.09375"},{"key":"ref22","article-title":"FastGCN: Fast learning with graph convolutional networks via importance sampling","author":"Chen","year":"2018","journal-title":"arXiv:1801.10247"},{"key":"ref23","first-page":"4558","article-title":"Adaptive sampling towards fast graph representation learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Huang"},{"key":"ref24","article-title":"Simplifying graph convolutional networks","author":"Wu","year":"2019","journal-title":"arXiv:1902.07153"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-04167-0_33"},{"key":"ref26","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref27","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2017","journal-title":"arXiv:1710.10903"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.330"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220096"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/2611567"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.3141\/1748-12"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10820123\/08928590.pdf?arnumber=8928590","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,26]],"date-time":"2025-07-26T06:28:00Z","timestamp":1753511280000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8928590\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":32,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2958380","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}