{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:23:16Z","timestamp":1773771796340,"version":"3.50.1"},"reference-count":59,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFE0112300"],"award-info":[{"award-number":["2022YFE0112300"]}],"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":["U22A2098"],"award-info":[{"award-number":["U22A2098"]}],"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":["62271411"],"award-info":[{"award-number":["62271411"]}],"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":["U22B2036"],"award-info":[{"award-number":["U22B2036"]}],"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":["61976181"],"award-info":[{"award-number":["61976181"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004806","name":"Fok Ying Tong Education Foundation","doi-asserted-by":"publisher","award":["171105"],"award-info":[{"award-number":["171105"]}],"id":[{"id":"10.13039\/501100004806","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2022JM-325"],"award-info":[{"award-number":["2022JM-325"]}]},{"name":"Tencent Foundation"},{"name":"Xplorer Prize"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Intell. Transport. Syst."],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1109\/tits.2024.3365179","type":"journal-article","created":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T19:28:12Z","timestamp":1709062092000},"page":"11241-11255","source":"Crossref","is-referenced-by-count":23,"title":["Regularized Spatial\u2013Temporal Graph Convolutional Networks for Metro Passenger Flow Prediction"],"prefix":"10.1109","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5865-2285","authenticated-orcid":false,"given":"Chao","family":"Gao","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an, China"}]},{"given":"Hao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Southwest University, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7495-3440","authenticated-orcid":false,"given":"Jiajin","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8182-2852","authenticated-orcid":false,"given":"Zhen","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0253-3882","authenticated-orcid":false,"given":"Xianghua","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2924-946X","authenticated-orcid":false,"given":"Xuelong","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence (TeleAI), China Telecom Corporation Ltd., Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3072743"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3001195"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2902405"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.01.043"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00212"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3446344"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3390\/s17040818"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2891537"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01154"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01502"},{"key":"ref11","first-page":"895","article-title":"TNT: Target-driven trajectory prediction","volume-title":"Proc. Conf. Robot Learn.","author":"Zhao"},{"key":"ref12","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Kipf"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref14","first-page":"980","article-title":"Global filter networks for image classification","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Rao"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411874"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2995855"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)0733-947x(2003)129:6(664)"},{"issue":"3","key":"ref18","first-page":"82","article-title":"A summary of traffic flow forecasting methods","volume":"21","author":"Liu","year":"2004","journal-title":"J. Highway Transport. Res. Develop."},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/0191-2615(84)90002-X"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2017.7969326"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2004.837813"},{"issue":"2","key":"ref22","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":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.573"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01240-3_25"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1209"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1159"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013656"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330877"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301485"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3112977"},{"key":"ref32","article-title":"Short-term traffic flow forecasting with spatial\u2013temporal correlation in a hybrid deep learning framework","author":"Wu","year":"2016","journal-title":"arXiv:1612.01022"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2018.03.002"},{"key":"ref34","first-page":"1","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Li"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2935152"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC45102.2020.9294700"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16542"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00262"},{"key":"ref42","first-page":"841","article-title":"N-GCN: Multi-scale graph convolution for semi-supervised node classification","volume-title":"Proc. 35th Uncertainty Artif. Intell. Conf.","author":"Abu-El-Haija"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/630"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3040772"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.07.007"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/s41019-020-00151-z"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3002718"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.11.032"},{"key":"ref50","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref51","first-page":"1","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Veli\u010dkovi\u0107"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2021.3087905"},{"key":"ref53","first-page":"1","article-title":"Spectral networks and locally connected networks on graphs","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Bruna"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1606.09375"},{"key":"ref55","first-page":"1","article-title":"Neural machine translation by jointly learning to align and translate","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Bahdanau"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/264"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3019497"},{"key":"ref58","first-page":"1","article-title":"R-Drop: Regularized dropout for neural networks","volume-title":"Proc. Adv. Neura Inf. Process. Syst.","author":"Wu"},{"key":"ref59","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","author":"Li","year":"2017","journal-title":"arXiv:1707.01926"}],"container-title":["IEEE Transactions on Intelligent Transportation Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6979\/10659279\/10452257.pdf?arnumber=10452257","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,31]],"date-time":"2024-08-31T04:32:42Z","timestamp":1725078762000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10452257\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9]]},"references-count":59,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tits.2024.3365179","relation":{},"ISSN":["1524-9050","1558-0016"],"issn-type":[{"value":"1524-9050","type":"print"},{"value":"1558-0016","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9]]}}}