{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T12:51:13Z","timestamp":1740142273932,"version":"3.37.3"},"reference-count":30,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Ind. Inf."],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1109\/tii.2019.2936507","type":"journal-article","created":{"date-parts":[[2019,8,22]],"date-time":"2019-08-22T19:47:03Z","timestamp":1566503223000},"page":"3997-4006","source":"Crossref","is-referenced-by-count":45,"title":["DeepVM: RNN-Based Vehicle Mobility Prediction to Support Intelligent Vehicle Applications"],"prefix":"10.1109","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8529-5148","authenticated-orcid":false,"given":"Wei","family":"Liu","sequence":"first","affiliation":[]},{"given":"Yozo","family":"Shoji","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref30","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"hinton","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICCSE.2016.7581655"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2015.2421277"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/MVT.2017.2752798"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/APNOMS.2014.6996110"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2010.5461932"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/VNC.2018.8628362"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC.2003.1200666"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ISCC.2015.7405541"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2017.7996756"},{"key":"ref19","first-page":"2618","article-title":"Deeptransport: Prediction and simulation of human mobility and transportation mode at a citywide level","volume":"16","author":"song","year":"0","journal-title":"Proc 25th Int Joint Conf Artif Intell"},{"key":"ref28","first-page":"265","article-title":"Tensorflow: A system for large-scale machine learning","author":"abadi","year":"0","journal-title":"Proc 11th USENIX Conf Operating Syst Des Implementation"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2749459"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2016.02.002"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/WoWMoM.2016.7523576"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2012.03.004"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2017.2690995"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2015.2415512"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2015.2480062"},{"year":"2016","key":"ref2","article-title":"Forecast: Connected car production, worldwide"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2013.118"},{"year":"2015","key":"ref1","article-title":"Study on lte-based v2x services (release 14)"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/2996913.2997016"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2810291"},{"key":"ref21","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":"ref24","first-page":"3111","article-title":"Distributed representations of words and phrases and their compositionality","author":"mikolov","year":"0","journal-title":"Proc 26th Int Conf Advances Neural Inf Process Syst"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2808356"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"}],"container-title":["IEEE Transactions on Industrial Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9424\/9026988\/08809218.pdf?arnumber=8809218","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T16:55:58Z","timestamp":1651078558000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8809218\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6]]},"references-count":30,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tii.2019.2936507","relation":{},"ISSN":["1551-3203","1941-0050"],"issn-type":[{"type":"print","value":"1551-3203"},{"type":"electronic","value":"1941-0050"}],"subject":[],"published":{"date-parts":[[2020,6]]}}}