{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T10:18:48Z","timestamp":1775384328849,"version":"3.50.1"},"reference-count":28,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","license":[{"start":{"date-parts":[[2016,7,1]],"date-time":"2016-07-01T00:00:00Z","timestamp":1467331200000},"content-version":"vor","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":["61370175"],"award-info":[{"award-number":["61370175"]}],"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":[[2016,7]]},"DOI":"10.1109\/tits.2016.2515105","type":"journal-article","created":{"date-parts":[[2016,2,19]],"date-time":"2016-02-19T14:09:20Z","timestamp":1455890960000},"page":"2014-2019","source":"Crossref","is-referenced-by-count":77,"title":["High-Order Gaussian Process Dynamical Models for Traffic Flow Prediction"],"prefix":"10.1109","volume":"17","author":[{"given":"Jing","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiliang","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2006.888603"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2006.869623"},{"key":"ref12","first-page":"1898","article-title":"Neural network multitask learning for traffic flow forecasting","author":"jin","year":"0","journal-title":"Proc IEEE Int Joint Conf Neural Netw"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/1543834.1543984"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2013.2247040"},{"key":"ref15","author":"rasmussen","year":"2006","journal-title":"gaussian process for machine learning"},{"key":"ref16","author":"bishop","year":"2006","journal-title":"Patten Recognition and Machine Learning"},{"key":"ref17","author":"boyle","year":"2007","journal-title":"Gaussian Processes for Regression and Optimisation"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3141\/2165-08"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2010.2093575"},{"key":"ref28","first-page":"1047","article-title":"Revisiting Gaussian process dynamical models","author":"zhao","year":"0","journal-title":"Proc 24th Int Joint Conf Artif Intell"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2010.2060218"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1167"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2010.2042807"},{"key":"ref6","author":"williams","year":"1999","journal-title":"Modeling and forecasting vehicular traffic flow as a seasonal stochastic time series process"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/TITS.2010.2043751","article-title":"Building an intellectual highway for its research and development","volume":"11","author":"wang","year":"2010","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(1991)117:2(178)"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/0191-2615(84)90002-X"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2009.2028149"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.1993.318226"},{"key":"ref1","first-page":"208","article-title":"Short-term traffic flow forecasting based on Markov chain model","author":"yu","year":"0","journal-title":"Proc IEEE Intell Veh Symp"},{"key":"ref20","first-page":"329","article-title":"Gaussian process latent variable models for visualisation of high dimensional data","volume":"17","author":"lawrence","year":"2004","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref22","first-page":"238","article-title":"3D people tracking with Gaussian process dynamic models","author":"urtasun","year":"0","journal-title":"Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit"},{"key":"ref21","first-page":"1441","article-title":"Gaussian process dynamical models","volume":"19","author":"wang","year":"2006","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref24","first-page":"1","article-title":"Gaussian process dynamical models for phoneme classification","author":"park","year":"0","journal-title":"Proc Neural Inf Process Syst Workshop Bayesian Nonparametrics Hope Hype"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CoASE.2012.6386511"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2011.08.015"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2012.6288919"}],"container-title":["IEEE Transactions on Intelligent Transportation Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6979\/7499895\/07410096.pdf?arnumber=7410096","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T11:43:45Z","timestamp":1641987825000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7410096\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,7]]},"references-count":28,"journal-issue":{"issue":"7"},"URL":"https:\/\/doi.org\/10.1109\/tits.2016.2515105","relation":{},"ISSN":["1524-9050","1558-0016"],"issn-type":[{"value":"1524-9050","type":"print"},{"value":"1558-0016","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,7]]}}}