{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T15:55:16Z","timestamp":1778255716422,"version":"3.51.4"},"reference-count":48,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"20","license":[{"start":{"date-parts":[[2022,10,15]],"date-time":"2022-10-15T00:00:00Z","timestamp":1665792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,10,15]],"date-time":"2022-10-15T00:00:00Z","timestamp":1665792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,10,15]],"date-time":"2022-10-15T00:00:00Z","timestamp":1665792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China through the project \u201cConnected vehicle big data drove expressway multiobjective coordinated control fusing deep learning and traffic flow model\u201d","doi-asserted-by":"publisher","award":["71901070"],"award-info":[{"award-number":["71901070"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2022,10,15]]},"DOI":"10.1109\/jiot.2022.3171780","type":"journal-article","created":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T20:29:49Z","timestamp":1651523389000},"page":"20203-20213","source":"Crossref","is-referenced-by-count":53,"title":["A Multi-Attention Tensor Completion Network for Spatiotemporal Traffic Data Imputation"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6485-6041","authenticated-orcid":false,"given":"Xuesong","family":"Wu","sequence":"first","affiliation":[{"name":"College of Civil Engineering, Fuzhou University, Fuzhou, Fujian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3875-8840","authenticated-orcid":false,"given":"Mengyun","family":"Xu","sequence":"additional","affiliation":[{"name":"Intelligent Transport System Research Center, Wuhan University of Technology, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0076-8453","authenticated-orcid":false,"given":"Jie","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Fuzhou University, Fuzhou, Fujian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiongwei","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Fuzhou University, Fuzhou, Fujian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"2181","article-title":"Online time series prediction with missing data","volume-title":"Proc. 32nd Int. Conf. Mach. Learn. (ICML)","volume":"3","author":"Anava"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.14778\/3229863.3229878"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3231541.3231544"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.11.003"},{"key":"ref5","first-page":"3491","article-title":"Fast multivariate spatio-temporal analysis via low rank tensor learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"4","author":"Bahadori"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.03.097"},{"key":"ref7","first-page":"847","article-title":"Temporal regularized matrix factorization for high-dimensional time series prediction","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yu"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3066551"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2015.2507259"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-21763-5_11"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3113608"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2021.103226"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2891760"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2910295"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2020.2984658"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2021.103185"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2020.102620"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16575"},{"issue":"2","key":"ref19","first-page":"113","article-title":"Nearest neighbor imputation for survey data","volume":"16","author":"Chen","year":"2000","journal-title":"J. Official Stat."},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1002\/wics.1341"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5716"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2016.2530312"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390267"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.39"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2020.102673"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.08.067"},{"key":"ref27","first-page":"1027","article-title":"A theoretically grounded application of dropout in recurrent neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Gal"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-24271-9"},{"key":"ref29","first-page":"9052","article-title":"Supplementary materials GAIN: Missing data imputation using generative adversarial nets","volume-title":"Proc. 35th Int. Conf. Mach. Learn. (ICML)","volume":"13","author":"Yoon"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3074564"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106705"},{"key":"ref32","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2020.102671"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3102983"},{"key":"ref36","volume-title":"Dynamic spatiotemporal graph convolutional neural networks for traffic data imputation with complex missing patterns","author":"Liang","year":"2021"},{"key":"ref37","first-page":"1","article-title":"Multi-scale context aggregation by dilated convolutions","volume-title":"Proc. 4th Int. Conf. Learn. Represent. (ICLR)","author":"Yu"},{"key":"ref38","first-page":"1551","article-title":"Language modeling with gated convolutional networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn. (ICML)","volume":"2","author":"Dauphin"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref40","first-page":"1","article-title":"Graph attention networks","volume-title":"Proc. 6th Int. Conf. Learn. Represent. (ICLR)","author":"Veli\u010dkovi\u0107"},{"key":"ref41","first-page":"5999","article-title":"Attention is all you need","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Vaswani"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2017.10.023"},{"key":"ref43","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC.2007.799"},{"key":"ref45","volume-title":"Introduction to Multi-Modal Transportation Planning: Principles and Practices","year":"2020"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"ref47","first-page":"5244","article-title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Li"},{"key":"ref48","first-page":"1","article-title":"Reformer: The Efficient Transformer","volume-title":"Proc. ICLR Submission","volume":"3","author":"Kitaev"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6488907\/9913253\/09766153.pdf?arnumber=9766153","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T21:49:09Z","timestamp":1705960149000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9766153\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,15]]},"references-count":48,"journal-issue":{"issue":"20"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2022.3171780","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,15]]}}}