{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T06:33:01Z","timestamp":1774679581953,"version":"3.50.1"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71871027"],"award-info":[{"award-number":["71871027"]}],"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":["51978044"],"award-info":[{"award-number":["51978044"]}],"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":[[2021,11]]},"DOI":"10.1109\/tits.2020.3000761","type":"journal-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T20:20:23Z","timestamp":1594239623000},"page":"7004-7014","source":"Crossref","is-referenced-by-count":203,"title":["Deep Learning Architecture for Short-Term Passenger Flow Forecasting in Urban Rail Transit"],"prefix":"10.1109","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0763-9344","authenticated-orcid":false,"given":"Jinlei","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Feng","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5780-4312","authenticated-orcid":false,"given":"Zhiyong","family":"Cui","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0321-7988","authenticated-orcid":false,"given":"Yinan","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Yadi","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.05.039"},{"key":"ref38","article-title":"Neural machine translation by jointly learning to align and translate","author":"bahdanau","year":"2014","journal-title":"arXiv 1409 0473"},{"key":"ref33","first-page":"630","article-title":"Identity mappings in deep residual networks","author":"he","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref32","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"2010","journal-title":"Proc 13th Int Conf Artif Intell Statist"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi8060243"},{"key":"ref37","first-page":"1","article-title":"Deeper insights into graph convolutional networks for semi-supervised learning","author":"li","year":"2018","journal-title":"Proc 32nd AAAI Conf Artif Intell"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00936"},{"key":"ref35","article-title":"A comprehensive survey on graph neural networks","author":"wu","year":"2019","journal-title":"arXiv 1901 00596"},{"key":"ref34","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2016","journal-title":"arXiv 1609 02907"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12417"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.03.014"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/YAC.2016.7804912"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2907739"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/8392592"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105620"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3390\/s17040818"},{"key":"ref17","first-page":"1","article-title":"DeepTC: ConvLSTM network for trajectory prediction of tropical cyclone using spatiotemporal atmospheric simulation data","author":"kim","year":"2018","journal-title":"Proc NIPS Workshop Spatiotemporal"},{"key":"ref18","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":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.01.027"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2950416"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-76837-1_80"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2935152"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-36546-1_21"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2017.02.005"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/SmartWorld.2018.00041"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ISKE.2017.8258756"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/3189238"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1063\/1.5039099"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/WiCom.2008.1064"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s12205-017-1016-9"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1080\/0144164042000195072"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref45","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","author":"xingjian","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref22","first-page":"1","article-title":"Deep spatio-temporal residual networks for citywide crowd flows prediction","author":"zhang","year":"2017","journal-title":"Proc 21st AAAI Conf Artif Intell"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.3390\/app9040615"},{"key":"ref42","first-page":"265","article-title":"Tensorflow: A system for large-scale machine learning","author":"abadi","year":"2016","journal-title":"Proc USENIX Symp on Operating System Design and Implementation"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2869768"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.03.001"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.01.015"},{"key":"ref44","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2020.01.010"},{"key":"ref43","first-page":"1","author":"norusis","year":"1993","journal-title":"SPSS for Windows Base System User's Guide Release 6 0"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2020.02.013"}],"container-title":["IEEE Transactions on Intelligent Transportation Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6979\/9599518\/09136910.pdf?arnumber=9136910","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T21:37:44Z","timestamp":1645479464000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9136910\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11]]},"references-count":45,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tits.2020.3000761","relation":{},"ISSN":["1524-9050","1558-0016"],"issn-type":[{"value":"1524-9050","type":"print"},{"value":"1558-0016","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11]]}}}