{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T04:44:38Z","timestamp":1768452278319,"version":"3.49.0"},"reference-count":24,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/100010014","name":"Manchester Metropolitan University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010014","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004144","name":"Liverpool John Moores University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004144","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2019]]},"DOI":"10.1109\/access.2019.2943028","type":"journal-article","created":{"date-parts":[[2019,9,24]],"date-time":"2019-09-24T01:09:01Z","timestamp":1569287341000},"page":"153533-153541","source":"Crossref","is-referenced-by-count":13,"title":["A Novel Online Dynamic Temporal Context Neural Network Framework for the Prediction of Road Traffic Flow"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9519-0043","authenticated-orcid":false,"given":"Zoe","family":"Bartlett","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2491-7473","authenticated-orcid":false,"given":"Liangxiu","family":"Han","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3268-1790","authenticated-orcid":false,"given":"Trung Thanh","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Princy","family":"Johnson","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2017.02.024"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2016.2585575"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"ref13","article-title":"Prediction of road traffic flow based on deep recurrent neural networks","author":"bartlett","year":"0","journal-title":"Proc 5th IEEE Smart World Congr"},{"key":"ref14","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":"ref15","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2017.2686012"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-44781-0_1"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/YAC.2016.7804912"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2016.0208"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3115\/1072133.1072204"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.03.001"},{"key":"ref7","article-title":"Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction","author":"cui","year":"2018","journal-title":"arXiv 1801 02143"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2311123"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCC\/SmartCity\/DSS.2018.00215"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2574840"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-2037"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.03.049"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2747560"},{"key":"ref24","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref23","article-title":"Adam: A Method for Stochastic Optimization","author":"kingma","year":"2014","journal-title":"Proc Int Conf Learn Represent"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8600701\/08846033.pdf?arnumber=8846033","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T19:39:43Z","timestamp":1628624383000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8846033\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2943028","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]}}}