{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:36:43Z","timestamp":1783611403530,"version":"3.55.0"},"reference-count":54,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Zhejiang Provincial Natural Science Foundation of China","doi-asserted-by":"publisher","award":["LZ23F030007"],"award-info":[{"award-number":["LZ23F030007"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011311","name":"Independent Project of State Key Laboratory of Industrial Control Technology","doi-asserted-by":"publisher","award":["ICT2021A25"],"award-info":[{"award-number":["ICT2021A25"]}],"id":[{"id":"10.13039\/501100011311","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Intell. Transport. Syst."],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1109\/tits.2023.3315693","type":"journal-article","created":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T17:43:13Z","timestamp":1695836593000},"page":"1247-1262","source":"Crossref","is-referenced-by-count":7,"title":["A Transfer Learning-Based Approach to Estimating Missing Pairs of On\/Off Ramp Flows"],"prefix":"10.1109","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0564-0341","authenticated-orcid":false,"given":"Jie","family":"Zhang","sequence":"first","affiliation":[{"name":"Institute of Industry Intelligence and Systems Engineering, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3848-6926","authenticated-orcid":false,"given":"Chunyue","family":"Song","sequence":"additional","affiliation":[{"name":"Institute of Industry Intelligence and Systems Engineering, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyan","family":"Mo","sequence":"additional","affiliation":[{"name":"Institute of Industry Intelligence and Systems Engineering, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shan","family":"Cao","sequence":"additional","affiliation":[{"name":"Institute of Industry Intelligence and Systems Engineering, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2815678"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3141\/2160-07"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s12544-011-0058-1"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/0191-2615(89)90021-0"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2004.03.003"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2307884"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-18320-6_1"},{"issue":"2","key":"ref8","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1109\/TITS.2020.2989365","article-title":"A novel approach to estimating missing pairs of on\/off ramp flows","volume":"22","author":"Kan","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3141\/2099-07"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.23943\/9781400890088"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2091281"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.274"},{"key":"ref14","first-page":"264","article-title":"Instance weighting for domain adaptation in NLP","volume-title":"Proc. 45th Annu. Meeting Assoc. Comput. Linguistics","author":"Jiang"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC48978.2021.9564567"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3141\/1678-22"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(2006)132:2(114)"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3108939"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2021.06.013"},{"issue":"8","key":"ref20","doi-asserted-by":"crossref","first-page":"11688","DOI":"10.1109\/TITS.2021.3106259","article-title":"A physics-informed deep learning paradigm for traffic state and fundamental diagram estimation","volume":"23","author":"Shi","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref21","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.trb.2021.02.007","article-title":"Macroscopic traffic flow modeling with physics regularized Gaussian process: A new insight into machine learning applications in transportation","volume":"146","author":"Yuan","year":"2021","journal-title":"Transp. Res. B, Methodol."},{"key":"ref22","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2022.103772","article-title":"Short-term traffic prediction using physics-aware neural networks","volume":"142","author":"Pereira","year":"2022","journal-title":"Transp. Res. C, Emerg. Technol."},{"key":"ref23","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.trb.2022.11.009","article-title":"A traffic flow dependency and dynamics based deep learning aided approach for network-wide traffic speed propagation prediction","volume":"167","author":"Yang","year":"2023","journal-title":"Transp. Res. B, Methodol."},{"key":"ref24","first-page":"1","article-title":"Physics informed deep learning for traffic state estimation","volume-title":"Proc. IEEE 23rd Int. Conf. Intell. Transp. Syst. (ITSC)","author":"Huang"},{"key":"ref25","first-page":"1206","article-title":"Physics informed deep kernel learning","volume-title":"Proc. 25th Int. Conf. Artif. Intell. Statist.","author":"Wang"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2021.110414"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1177\/03611981211027151"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/IAEAC50856.2021.9390960"},{"key":"ref29","volume-title":"Survey Methods for Transport Planning","author":"Richardson","year":"1995"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.3004555"},{"key":"ref31","first-page":"540","article-title":"Transferring naive Bayes classifiers for text classification","volume-title":"Proc. AAAI","volume":"7","author":"Dai"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0080"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74976-9_23"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015382"},{"key":"ref35","first-page":"153","article-title":"Multi-task Gaussian process prediction","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Williams"},{"key":"ref36","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","volume-title":"Proc. 32nd Int. Conf. Mach. Learn.","author":"Long"},{"key":"ref37","first-page":"608","article-title":"Mapping and revising Markov logic networks for transfer learning","volume-title":"Proc. 22nd Nat. Conf. Artif. Intell.","volume":"7","author":"Mihalkova"},{"key":"ref38","article-title":"Deep domain confusion: Maximizing for domain invariance","author":"Tzeng","year":"2014","journal-title":"arXiv:1412.3474"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2988928"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2020.0284"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.2983763"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1587\/transinf.2018EDP7330"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/72.279188"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/YAC.2016.7804912"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2017.0313"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3094659"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl242"},{"key":"ref48","article-title":"Revisiting batch normalization for practical domain adaptation","author":"Li","year":"2016","journal-title":"arXiv:1603.04779"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/7164790"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2018.09.018"},{"key":"ref51","volume-title":"Caltrans. PeMS","year":"2022"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1201\/b18344-27"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/s00365-006-0663-2"},{"key":"ref54","first-page":"20721","article-title":"DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Sun"}],"container-title":["IEEE Transactions on Intelligent Transportation Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6979\/10419116\/10265742.pdf?arnumber=10265742","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T20:25:16Z","timestamp":1707251116000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10265742\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2]]},"references-count":54,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tits.2023.3315693","relation":{},"ISSN":["1524-9050","1558-0016"],"issn-type":[{"value":"1524-9050","type":"print"},{"value":"1558-0016","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2]]}}}