{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T07:18:52Z","timestamp":1784531932825,"version":"3.55.0"},"reference-count":83,"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:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100006785","name":"Google","doi-asserted-by":"publisher","award":["Google Faculty Research Award"],"award-info":[{"award-number":["Google Faculty Research Award"]}],"id":[{"id":"10.13039\/100006785","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DP180102050"],"award-info":[{"award-number":["DP180102050"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DP200102611"],"award-info":[{"award-number":["DP200102611"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["LP180100114"],"award-info":[{"award-number":["LP180100114"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/tkde.2020.3001195","type":"journal-article","created":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T21:29:31Z","timestamp":1591738171000},"page":"1-1","source":"Crossref","is-referenced-by-count":254,"title":["A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges"],"prefix":"10.1109","author":[{"given":"David Alexander","family":"Tedjopurnomo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhifeng","family":"Bao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baihua","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farhana","family":"Choudhury","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. K.","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Australian Government Bureau of Infrastructure, Transport and Regional Economics","volume-title":"Information Sheet 74 - Traffic and congestion cost trends for Australian capital cities"},{"key":"ref2","article-title":"Singapore to freeze car numbers","year":"2018"},{"key":"ref3","first-page":"557","article-title":"","author":"Carnegie","year":"2016","journal-title":"The Cost of Roadway Construction, Operations and Maintenance in New Jersey: Phase 1 Final Report"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3390\/s17071501"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2311123"},{"key":"ref6","article-title":"Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework","author":"Wu"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2018.8489600"},{"key":"ref8","article-title":"Deeptrend: A deep hierarchical neural network for traffic flow prediction","author":"Dai"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.01.005"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2018.07.004"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2815678"},{"key":"ref12","article-title":"Keras","author":"Chollet","year":"2015"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1017\/9781108924238.008"},{"key":"ref14","article-title":"TensorFlow: Large-scale machine learning on heterogeneous systems","author":"Abadi","year":"2015"},{"key":"ref15","first-page":"1","article-title":"Analysis of freeway traffic time series data by using box-jenkins techniques","volume":"773","author":"Ahmed","year":"1979","journal-title":"Transp. Res. Rec."},{"issue":"773","key":"ref16","first-page":"47","article-title":"On forecasting freeway occupancies and volumes (abridgment)","author":"Levin","year":"1980","journal-title":"Transp. Res. Rec."},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3141\/1678-22"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3141\/1776-25"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)0733-947x(2003)129:6(664)"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.3141\/1857-09"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2010.10.004"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3231541.3231544"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2020.102674"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1080\/00031305.1996.10473554"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1080\/0144164042000195072"},{"key":"ref26","first-page":"311","article-title":"The use of neural networks to recognise and predict traffic congestion","volume":"34","author":"Dougherty","year":"1993","journal-title":"Traffic Eng. Control"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2005.04.007"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(2006)132:2(114)"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0887-3801(2005)19:1(94)"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2011.2174051"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)0733-947x(1991)117:2(178)"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbspro.2013.08.076"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.11.002"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.07.069"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICNC.2007.661"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-72393-6_121"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2016.7498298"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300015015"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-55699-4_33"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132893"},{"key":"ref42","article-title":"A first look at music composition using LSTM recurrent neural networks","author":"Eck"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298935"},{"key":"ref44","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","volume-title":"Proc. ICLR","volume":"1707.0","author":"Li"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.03.014"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2016.7795712"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2016.0257"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2016.0208"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2345663"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/SmartCity.2015.63"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2016.7727607"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0061"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.3390\/s17040818"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974973.87"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/DASC-PICom-DataCom-CyberSciTec.2017.194"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219895"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/SDF.2018.8547068"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/ISKE.2017.8258813"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966128"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2017.8317872"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"ref63","article-title":"High-order graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting","volume-title":"Proc. Trans. Res. Board 98th Annu. Meeting","author":"Cui"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1145\/3282834.3282836"},{"key":"ref65","article-title":"Crowd flow prediction by deep spatio-temporal transfer learning","author":"Wang"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.03.001"},{"key":"ref67","article-title":"Modeling spatial-temporal dynamics for traffic prediction","volume":"1803.01254","author":"Yao","year":"2018","journal-title":"CoRR"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC48978.2021.9564998"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330884"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330646"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2888561"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.09.008"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2915364"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1063\/1.5117180"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1680\/pf.40694.0014"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/508"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0024"},{"key":"ref78","first-page":"5998","article-title":"Attention is all you need","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Vaswani"},{"key":"ref79","first-page":"2672","article-title":"Generative adversarial nets","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Goodfellow"},{"key":"ref80","article-title":"Spatial-temporal transformer networks for traffic flow forecasting","author":"Xu"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1177\/0361198118798737"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2857224"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852211"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/4358933\/09112608.pdf?arnumber=9112608","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T22:38:30Z","timestamp":1704839910000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9112608\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":83,"URL":"https:\/\/doi.org\/10.1109\/tkde.2020.3001195","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}