{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T02:58:06Z","timestamp":1780541886377,"version":"3.54.1"},"reference-count":25,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T00:00:00Z","timestamp":1569888000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T00:00:00Z","timestamp":1569888000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10]]},"DOI":"10.1109\/itsc.2019.8917213","type":"proceedings-article","created":{"date-parts":[[2019,11,29]],"date-time":"2019-11-29T06:11:50Z","timestamp":1575007910000},"page":"2570-2576","source":"Crossref","is-referenced-by-count":16,"title":["Learning Dynamic Graph Embedding for Traffic Flow Forecasting: A Graph Self-Attentive Method"],"prefix":"10.1109","author":[{"given":"Zifeng","family":"Kang","sequence":"first","affiliation":[{"name":"Tsinghua University,Department of Automation,Beijing,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanwen","family":"Xu","sequence":"additional","affiliation":[{"name":"Tsinghua University,Department of Automation,Beijing,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianming","family":"Hu","sequence":"additional","affiliation":[{"name":"Tsinghua University,Department of Automation,Beijing,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Pei","sequence":"additional","affiliation":[{"name":"Beijing National Research Center for Information Science and Technology,Beijing,China,100084"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2888561"},{"key":"ref11","author":"li","year":"2018","journal-title":"Towards binary-valued gates for robust lstm training"},{"key":"ref12","author":"lin","year":"2017","journal-title":"A structured self-attentive sentence embedding"},{"key":"ref13","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"The Annual Conference on Neural Information Processing Systems"},{"key":"ref14","author":"jang","year":"2016","journal-title":"Categorical reparameterization with gumbel-softmax"},{"key":"ref15","author":"maddison","year":"2016","journal-title":"The concrete distribution A continuous relaxation of discrete random variables"},{"key":"ref16","first-page":"11711179","article-title":"Scheduled sampling for sequence prediction with recurrent neural networks","author":"bengio","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"ref17","first-page":"31043112","article-title":"Sequence to sequence learning with neural networks","author":"sutskever","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020571"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2013.2247040"},{"key":"ref4","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","author":"li","year":"2018","journal-title":"Proceedings of International Conference on Learning Representations"},{"key":"ref3","article-title":"Graph attention networks","author":"veli?kovi?","year":"2018","journal-title":"International Conference on Learning Representations"},{"key":"ref6","author":"yu","year":"2018","journal-title":"Spatio-temporal graph convolutional neural network A deep learning framework for traffic forecasting"},{"key":"ref5","author":"wang","year":"2018","journal-title":"Efficient metropolitan traffic prediction based on graph recurrent neural network"},{"key":"ref8","article-title":"Deep spatio-temporal residual networks for citywide crowd flows prediction","author":"zhang","year":"2016"},{"key":"ref7","article-title":"Transportation systems engineering: theory and methods, volume 49","author":"cascetta","year":"2013","journal-title":"Springer Science & Business Media"},{"key":"ref2","first-page":"10251035","article-title":"Inductive representation learning on large graphs","author":"hamilton","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2018.0007"},{"key":"ref1","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2017","journal-title":"International Conference on Learning Representations"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)"},{"key":"ref22","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"chung","year":"2014"},{"key":"ref21","first-page":"865873","article-title":"Traffic flow prediction with big data: A deep learning approach","volume":"16","author":"lv","year":"2015","journal-title":"ITS IEEE Transactions on"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300015015"},{"key":"ref23","article-title":"Fast and accurate deep network learning by exponential linear units (elus)","author":"clevert","year":"2016","journal-title":"International Conference on Learning Representations"},{"key":"ref25","first-page":"249256","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"2010","journal-title":"Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics"}],"event":{"name":"2019 IEEE Intelligent Transportation Systems Conference - ITSC","location":"Auckland, New Zealand","start":{"date-parts":[[2019,10,27]]},"end":{"date-parts":[[2019,10,30]]}},"container-title":["2019 IEEE Intelligent Transportation Systems Conference (ITSC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8907344\/8916833\/08917213.pdf?arnumber=8917213","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T19:56:24Z","timestamp":1774036584000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8917213\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10]]},"references-count":25,"URL":"https:\/\/doi.org\/10.1109\/itsc.2019.8917213","relation":{},"subject":[],"published":{"date-parts":[[2019,10]]}}}