{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T21:18:23Z","timestamp":1781644703173,"version":"3.54.5"},"reference-count":37,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2017YFB1201202"],"award-info":[{"award-number":["2017YFB1201202"]}]},{"DOI":"10.13039\/100010743","name":"Scientific Research Foundation for Advanced Talents of Nanjing Forestry University","doi-asserted-by":"publisher","award":["163106041"],"award-info":[{"award-number":["163106041"]}],"id":[{"id":"10.13039\/100010743","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3024224","type":"journal-article","created":{"date-parts":[[2020,9,15]],"date-time":"2020-09-15T20:52:24Z","timestamp":1600203144000},"page":"170742-170753","source":"Crossref","is-referenced-by-count":9,"title":["Short-Term Passenger Flow Prediction for Urban Rail Stations Using Learning Network Based on Optimal Passenger Flow Information Input Algorithm"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7343-9258","authenticated-orcid":false,"given":"Bo","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mao","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenjun","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1688-6067","authenticated-orcid":false,"given":"Yan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiangsheng","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9086-7622","authenticated-orcid":false,"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2016.2549282"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.2478\/v10117-011-0021-1"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2016.2643005"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2988030"},{"key":"ref37","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/S0191-2615(03)00015-8"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2303146"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.07.012"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/S0968-090X(02)00009-8"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3846\/16484142.2016.1212734"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2937114"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/0191-2615(84)90002-X"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3923\/itj.2012.1508.1512"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/S0968-090X(00)00039-5"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2574840"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2311123"},{"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.2017.08.001"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2017.10.016"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.5198\/jtlu.2018.1286"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2941987"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2011.2158001"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.tra.2014.09.008"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2958378"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtrangeo.2014.03.013"},{"key":"ref8","first-page":"26","article-title":"An improved model of urban rail transit access passenger flow forecasting considering weather impact","volume":"7","author":"lin","year":"2014","journal-title":"Urban Public Transport"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s11116-017-9768-0"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijproman.2014.07.005"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3141\/1644-14"},{"key":"ref1","first-page":"9","article-title":"Statistics and analysis of urban rail transit operation in the world","volume":"32","author":"han","year":"2019","journal-title":"Urban Rapid Rail Transit"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.4028\/www.scientific.net\/AMM.505-506.1023"},{"key":"ref22","first-page":"2071","article-title":"Survey of unstable gradients in deep neural network training","volume":"29","author":"chen","year":"2018","journal-title":"J Softw"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2011.06.009"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2016.0208"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2964680"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09197667.pdf?arnumber=9197667","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:56:53Z","timestamp":1639771013000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9197667\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3024224","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}