{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T14:47:42Z","timestamp":1768834062286,"version":"3.49.0"},"reference-count":71,"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\/4.0\/legalcode"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2018YJS052"],"award-info":[{"award-number":["2018YJS052"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000185","name":"Defense Advanced Research Projects Agency","doi-asserted-by":"publisher","award":["W911NF-18-C-0015"],"award-info":[{"award-number":["W911NF-18-C-0015"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1254071"],"award-info":[{"award-number":["IIS-1254071"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009625","name":"Beijing Social Science Fund","doi-asserted-by":"publisher","award":["16JDGLA010"],"award-info":[{"award-number":["16JDGLA010"]}],"id":[{"id":"10.13039\/501100009625","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.2020.2974575","type":"journal-article","created":{"date-parts":[[2020,2,17]],"date-time":"2020-02-17T20:24:27Z","timestamp":1581971067000},"page":"34629-34643","source":"Crossref","is-referenced-by-count":70,"title":["Multi-Lane Short-Term Traffic Forecasting With Convolutional LSTM Network"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4801-5450","authenticated-orcid":false,"given":"Yixuan","family":"Ma","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2738-7749","authenticated-orcid":false,"given":"Zhenji","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4331-1015","authenticated-orcid":false,"given":"Alexander","family":"Ihler","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref71","author":"nau","year":"2005","journal-title":"Introduction to arima Nonseasonal models"},{"key":"ref70","first-page":"1655","article-title":"Deep spatio-temporal residual networks for citywide crowd flows prediction","author":"zhang","year":"2017","journal-title":"Proc 31st AAAI Conf Artif Intell"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.01.015"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1061\/9780784479896.026"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/0191-2615(84)90019-5"},{"key":"ref32","first-page":"222","article-title":"Traffic flow theory","volume":"165","author":"gerlough","year":"1976","journal-title":"Transp Res Board Special Rep"},{"key":"ref31","article-title":"Two-stream multi-channel convolutional neural network (TM-CNN) for multi-lane traffic speed prediction considering traffic volume impact","author":"ke","year":"2019","journal-title":"arXiv 1903 01678"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2909904"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2311123"},{"key":"ref36","first-page":"338","article-title":"Learning deep representation from big and heterogeneous data for traffic accident inference","author":"chen","year":"2016","journal-title":"Proc 13th AAAI Conf Artif Intell"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3390\/s17071501"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/S0191-2615(00)00042-4"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.10.011"},{"key":"ref62","first-page":"1310","article-title":"On the difficulty of training recurrent neural networks","author":"pascanu","year":"2013","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref61","article-title":"Short-term prediction of passenger demand in multi-zone level: Temporal convolutional neural network with multi-task learning","author":"zhang","year":"0","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.03.001"},{"key":"ref64","article-title":"Visualizing and understanding recurrent networks","author":"karpathy","year":"2015","journal-title":"arXiv 1506 02078"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.3390\/s17040818"},{"key":"ref65","article-title":"Statistical methods for estimating speed using single-loop detectors","author":"chen","year":"2002","journal-title":"Proc 82nd Annu Meeting Transp Res Board"},{"key":"ref66","article-title":"Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition","author":"sak","year":"2014","journal-title":"arXiv 1402 1128"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12417"},{"key":"ref67","year":"2019","journal-title":"Mathematica Version 12 0"},{"key":"ref68","first-page":"8","article-title":"Nvidia cuda c programming guide","volume":"120","author":"nvidia","year":"2011","journal-title":"nVidia Corp"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1175\/JCLI-D-18-0355.1"},{"key":"ref2","first-page":"1","article-title":"Analysis of freeway traffic time-series data by using box-jenkins techniques","volume":"722","author":"ahmed","year":"1979","journal-title":"Transp Res Rec"},{"key":"ref1","first-page":"5013","article-title":"Short term traffic prediction models","author":"van hinsbergen","year":"2007","journal-title":"Proc 10th World Congr Intell Transp Syst (ITS)"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1080\/15472450802262281"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1016\/j.sbspro.2013.11.170","article-title":"Short term traffic flow prediction for a non urban highway using artificial neural network","volume":"104","author":"kumar","year":"2013","journal-title":"Procedia - Social and Behavioral Science"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.15837\/ijccc.2017.4.2914"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.03.014"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2345663"},{"key":"ref26","article-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting","author":"yu","year":"2017","journal-title":"arXiv 1709 04875"},{"key":"ref25","article-title":"Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework","author":"wu","year":"2016","journal-title":"arXiv 1612 01022"},{"key":"ref50","article-title":"A deep learning based multitask model for network-wide traffic speed prediction","author":"zhang","year":"0","journal-title":"Neurocomputing"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref59","article-title":"The use of convolutional neural network in relating precipitation to circulation","author":"pan","year":"2017","journal-title":"AGU Fall Meeting Abstracts"},{"key":"ref58","doi-asserted-by":"crossref","first-page":"977","DOI":"10.3390\/w11050977","article-title":"Improving monsoon precipitation prediction using combined convolutional and long short term memory neural network","volume":"11","author":"miao","year":"2019","journal-title":"WATER"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1029\/2018WR024090"},{"key":"ref56","first-page":"69","article-title":"Deep convolutional neural networks for sentiment analysis of short texts","author":"dos santos","year":"2014","journal-title":"Proc 25th Int Conf Comput Linguistics Tech Papers"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390177"},{"key":"ref54","first-page":"568","article-title":"Two-stream convolutional networks for action recognition in videos","author":"simonyan","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.223"},{"key":"ref52","author":"russell","year":"2016","journal-title":"Artificial Intelligence - A Modern Approach"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/S0968-090X(97)82903-8"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3141\/1776-25"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.09.019"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/LISS.2015.7369669"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/LISS.2016.7854526"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2017.02.024"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.15837\/ijccc.2018.2.3034"},{"key":"ref16","article-title":"Attributing crop production in the united states using artificial neural network","author":"ma","year":"2017","journal-title":"AGU Fall Meeting Abstracts"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2004.837813"},{"key":"ref18","first-page":"1041","article-title":"Large scale software test data generation based on collective constraint and weighted combination method","volume":"24","author":"zhang","year":"2017","journal-title":"Tehniki Vjesnik"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(1991)117:2(178)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(1995)121:3(249)"},{"key":"ref3","first-page":"47","article-title":"On forecasting freeway occupancies and volumes (abridgment)","volume":"773","author":"levin","year":"1980","journal-title":"Transp Res Rec"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8667.2010.00681.x"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.02.007"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1016\/j.proeng.2017.04.417","article-title":"Traffic flow prediction using Kalman filtering technique","volume":"187","author":"kumar","year":"2017","journal-title":"Procedia Eng"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.02.005"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.07.003"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/YAC.2016.7804912"},{"key":"ref45","first-page":"132","article-title":"Long short-term memory model for traffic congestion prediction with online open data","author":"chen","year":"2016","journal-title":"Proc IEEE 19th Int Conf Intell Transp Syst (ITSC)"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.07.002"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2016.0208"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/72.279181"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.3141\/1811-04"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/SmartCity.2015.63"},{"key":"ref43","article-title":"Gradient flow in recurrent nets: The difficulty of learning long-term dependencies","author":"hochreiter","year":"2001","journal-title":"A Field Guide to Dynamical Recurrent Networks"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/6287639\/8948470\/9000828-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09000828.pdf?arnumber=9000828","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T18:53:33Z","timestamp":1649444013000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9000828\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":71,"URL":"https:\/\/doi.org\/10.1109\/access.2020.2974575","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}