{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:36:34Z","timestamp":1791916594545,"version":"4.3.4"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T00:00:00Z","timestamp":1683590400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T00:00:00Z","timestamp":1683590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-15511-w","type":"journal-article","created":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T21:41:28Z","timestamp":1683754888000},"page":"61761-61776","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Nonnegative low-rank tensor completion method for spatiotemporal traffic data"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2383-3174","authenticated-orcid":false,"given":"Yongmei","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingfu","family":"Tuo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongmei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangnan","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengyin","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,9]]},"reference":[{"key":"15511_CR1","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.patrec.2019.02.018","volume":"122","author":"SM Atif","year":"2019","unstructured":"Atif SM, Qazi S, Gillis N (2019) Improved SVD-based initialization for nonnegative matrix factorization using low-rank correction. Pattern Recogn Lett 122:53\u201359","journal-title":"Pattern Recogn Lett"},{"issue":"5","key":"15511_CR2","doi-asserted-by":"publisher","first-page":"2466","DOI":"10.1109\/TIP.2017.2672439","volume":"26","author":"JA Bengua","year":"2017","unstructured":"Bengua JA, Phien HN, Tuan HD et al (2017) Efficient tensor completion for color image and video recovery: Low-rank tensor train[J]. IEEE Trans Image Process 26(5):2466\u20132479","journal-title":"IEEE Trans Image Process"},{"key":"15511_CR3","first-page":"7","volume":"11","author":"R Bin","year":"2016","unstructured":"Bin R, Li S, Jian Z, Yang C, Huachun T (2016) Using Tensor Completion Method to Achieving Better Coverage of Traffic State Estimation from Sparse Floating Car Data.[J]. PLoS One 11:7","journal-title":"PLoS One"},{"key":"15511_CR4","first-page":"1","volume":"99","author":"X Chen","year":"2021","unstructured":"Chen X, Sun L (2021) Bayesian Temporal Factorization for Multidimensional Time Series Prediction[J]. IEEE Trans Pattern Anal Mach Intell 99:1","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"15511_CR5","doi-asserted-by":"publisher","first-page":"95903","DOI":"10.1109\/ACCESS.2019.2929189","volume":"7","author":"B Chen","year":"2019","unstructured":"Chen B, Sun T, Zhou Z et al (2019) Nonnegative tensor completion via low- rank Tucker decomposition: model and algorithm[J]. IEEE Access 7:95903\u201395914","journal-title":"IEEE Access"},{"key":"15511_CR6","doi-asserted-by":"publisher","first-page":"102673","DOI":"10.1016\/j.trc.2020.102673","volume":"117","author":"X Chen","year":"2020","unstructured":"Chen X, Yang J, Sun L (2020) A nonconvex low-rank tensor completion model for spatiotemporal traffic data imputation[J]. Transp Res Part C: Emerg Technol 117:102673","journal-title":"Transp Res Part C: Emerg Technol"},{"key":"15511_CR7","first-page":"103226","volume":"129","author":"X Chen","year":"2021","unstructured":"Chen X, Lei M, Saunier N et al (2021) Low-rank autoregressive tensor completion for spatiotemporal traffic data imputation[J]. IEEE Trans Intell Transp Syst 129:103226","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"15511_CR8","doi-asserted-by":"crossref","unstructured":"Gillis N (2020) Nonnegative matrix factorization[M]. Society for Industrial and Applied Mathematics","DOI":"10.1137\/1.9781611976410"},{"key":"15511_CR9","doi-asserted-by":"crossref","unstructured":"Guo X, Yao Q, Kwok J (2017) Efficient sparse low-rank tensor completion using the Frank-Wolfe algorithm[C]\/\/Proceedings of the AAAI Conference on Artificial Intelligence. 31(1)","DOI":"10.1609\/aaai.v31i1.10886"},{"key":"15511_CR10","unstructured":"Jingnan (2017) The static image of a road section in http:\/\/tianqi.moji.com\/liveview\/picture\/72181397[DB]"},{"issue":"3","key":"15511_CR11","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1137\/07070111X","volume":"51","author":"TG Kolda","year":"2009","unstructured":"Kolda TG, Bader BW (2009) Tensor decompositions and applications. SIAM Rev 51(3):455\u2013500","journal-title":"SIAM Rev"},{"key":"15511_CR12","doi-asserted-by":"publisher","first-page":"63188","DOI":"10.1109\/ACCESS.2020.2984588","volume":"8","author":"Q Li","year":"2020","unstructured":"Li Q, Tan H, Wu Y et al (2020) Traffic flow prediction with missing data imputed by tensor completion methods[J]. IEEE Access 8:63188\u201363201","journal-title":"IEEE Access"},{"issue":"1","key":"15511_CR13","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1109\/TPAMI.2012.39","volume":"35","author":"J Liu","year":"2013","unstructured":"Liu J, Musialski P, Wonka P et al (2013) Tensor completion for estimating missing values in visual data[J]. IEEE Trans Pattern Anal Mach Intell 35(1):208\u2013220","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"15511_CR14","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.neucom.2020.01.009","volume":"387","author":"C Liu","year":"2020","unstructured":"Liu C, Shan H, Chen C (2020) Tensor p-shrinkage nuclear norm for low-rank tensor completion[J]. Neurocomputing 387:255\u2013267","journal-title":"Neurocomputing"},{"key":"15511_CR15","doi-asserted-by":"crossref","unstructured":"Morison G (2020) Sure based truncated tensor nuclear norm regularization for low rank tensor completion[C]\/\/2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021: 2001\u20132005. Saeedi T, Rezghi M. A novel enriched version of truncated nuclear norm regularization for matrix completion of inexact observed data[J]. IEEE Transactions on Knowledge and Data Engineering","DOI":"10.23919\/Eusipco47968.2020.9287726"},{"key":"15511_CR16","doi-asserted-by":"publisher","first-page":"103737","DOI":"10.1016\/j.trc.2022.103737","volume":"141","author":"T Nie","year":"2022","unstructured":"Nie T, Qin G, Sun J (2022) Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns[J]. Transp Res Part C: Emerg Technol 141:103737","journal-title":"Transp Res Part C: Emerg Technol"},{"issue":"5","key":"15511_CR17","doi-asserted-by":"publisher","first-page":"1546","DOI":"10.1109\/TPAMI.2019.2956046","volume":"43","author":"J Pan","year":"2019","unstructured":"Pan J, Gillis N (2019) Generalized separable nonnegative matrix factorization[J]. IEEE Trans Pattern Anal Mach Intell 43(5):1546\u20131561","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"11","key":"15511_CR18","doi-asserted-by":"publisher","first-page":"1956","DOI":"10.1016\/j.neucom.2010.06.031","volume":"74","author":"AH Phan","year":"2011","unstructured":"Phan AH, Cichocki A (2011) Extended HALS algorithm for nonnegative Tucker decomposition and its applications for multiway analysis and classification[J]. Neurocomputing 74(11):1956\u20131969","journal-title":"Neurocomputing"},{"key":"15511_CR19","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.patrec.2015.05.019","volume":"63","author":"H Qiao","year":"2015","unstructured":"Qiao H (2015) New SVD based initialization strategy for non-negative matrix factorization. Pattern Recogn Lett 63:71\u201377","journal-title":"Pattern Recogn Lett"},{"key":"15511_CR20","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.physa.2015.09.105","volume":"446","author":"B Ran","year":"2016","unstructured":"Ran B, Tan H, Yuankai W, Jin PJ (2016) Tensor based missing traffic data completion with spatial\u2013temporal correlation[J]. Phys A: Stat Mech Appl 446:54\u201363","journal-title":"Phys A: Stat Mech Appl"},{"key":"15511_CR21","doi-asserted-by":"crossref","unstructured":"Sinha TK, Naram J, Kumar P (2022) Nonnegative Low-Rank Tensor Completion via Dual Formulation with Applications to Image and Video Completion. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","DOI":"10.1109\/WACV51458.2022.00412"},{"key":"15511_CR22","unstructured":"Sj\u00f6lund J, Bankestad M (2022) Graph-based Neural Acceleration for Nonnegative Matrix Factorization[J]. arXiv preprint arXiv:2202.00264"},{"key":"15511_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2020.102791","volume":"70","author":"Y Song","year":"2020","unstructured":"Song Y, Li J, Chen X et al (2020) An efficient tensor completion method via truncated nuclear norm[J]. J Vis Commun Image Represent 70:102791","journal-title":"J Vis Commun Image Represent"},{"key":"15511_CR24","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.trc.2012.12.007","volume":"28","author":"H Tan","year":"2013","unstructured":"Tan H, Feng G, Feng J et al (2013) A tensor-based method for missing traffic data completion[J]. Transp Res Part C Emerg Technol 28:15\u201327","journal-title":"Transp Res Part C Emerg Technol"},{"key":"15511_CR25","doi-asserted-by":"publisher","first-page":"2431","DOI":"10.1016\/j.sbspro.2013.08.272","volume":"96","author":"H Tan","year":"2013","unstructured":"Tan H, Yuankai W, Feng G, Wang W, Ran B (2013) A New Traffic Prediction Method based on Dynamic Tensor Completion[J]. Procedia Soc Behav Sci 96:2431\u20132442","journal-title":"Procedia Soc Behav Sci"},{"issue":"5","key":"15511_CR26","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1089\/big.2020.0074","volume":"8","author":"Y Tsitsikas","year":"2020","unstructured":"Tsitsikas Y, Papalexakis EE (2020) NSVD: Normalized Singular Value Deviation Reveals Number of Latent Factors in Tensor Decomposition[J]. Big Data 8(5):412\u2013430","journal-title":"Big Data"},{"issue":"3","key":"15511_CR27","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/BF02289464","volume":"31","author":"LR Tucker","year":"1966","unstructured":"Tucker LR (1966) Some mathematical notes on three-mode factor analysis[J]. Psychometrika 31(3):279\u2013311","journal-title":"Psychometrika"},{"key":"15511_CR28","doi-asserted-by":"crossref","unstructured":"Wahlberg B, Boyd S, Annergren M et al (2012) An ADMM Algorithm for a Class of Total Variation Regularized Estimation Problems[C]. 83\u201388","DOI":"10.3182\/20120711-3-BE-2027.00310"},{"key":"15511_CR29","doi-asserted-by":"crossref","unstructured":"Wang X et al (2019) A Probabilistic Tensor Factorization Approach to Detect Anomalies in Spatiotemporal Traffic Activities. 2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE","DOI":"10.1109\/ITSC.2019.8917169"},{"key":"15511_CR30","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.neucom.2013.04.049","volume":"129","author":"Q Wu","year":"2014","unstructured":"Wu Q, Zhang L, Cichocki A (2014) Multifactor sparse feature extraction using convolutive nonnegative tucker decomposition[J]. Neurocomputing 129:17\u201324","journal-title":"Neurocomputing"},{"key":"15511_CR31","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.trc.2018.11.003","volume":"98","author":"A Xc","year":"2019","unstructured":"Xc A, Zh A, Ls B (2019) A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation[J]. Transp Res Part C: Emerg Technol 98:73\u201384","journal-title":"Transp Res Part C: Emerg Technol"},{"key":"15511_CR32","unstructured":"Xinyu C et al (2018) Urban Traffic Speed Dataset of Guangzhou, China. https:\/\/github.com\/sysuits\/urban-traffic-speed-dataset-Guangzhou[DB]"},{"issue":"1","key":"15511_CR33","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s12532-014-0074-y","volume":"7","author":"Y Xu","year":"2015","unstructured":"Xu Y (2015) Alternating proximal gradient method for sparse nonnegative Tucker decomposition[J]. Math Program Comput 7(1):39\u201370","journal-title":"Math Program Comput"},{"issue":"12","key":"15511_CR34","doi-asserted-by":"publisher","first-page":"4704","DOI":"10.1109\/TITS.2019.2941649","volume":"20","author":"M Xu","year":"2019","unstructured":"Xu M et al (2019) Anomaly detection in road networks using sliding-window tensor factorization. IEEE Trans Intell Transp Syst 20(12):4704\u20134713","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"15511_CR35","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.neucom.2019.06.054","volume":"364","author":"W Yin","year":"2019","unstructured":"Yin W, Ma Z (2019) LE & LLE Regularized Nonnegative Tucker Decomposition for clustering of high dimensional datasets[J]. Neurocomputing 364:77\u201394","journal-title":"Neurocomputing"},{"key":"15511_CR36","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1016\/j.apm.2019.02.001","volume":"70","author":"YB Zheng","year":"2019","unstructured":"Zheng YB, Huang TZ, Ji TY et al (2019) Low-rank tensor completion via smooth matrix factorization[J]. Appl Math Model 70:677\u2013695","journal-title":"Appl Math Model"},{"issue":"12","key":"15511_CR37","doi-asserted-by":"publisher","first-page":"4990","DOI":"10.1109\/TIP.2015.2478396","volume":"24","author":"G Zhou","year":"2015","unstructured":"Zhou G, Cichocki A, Zhao Q et al (2015) Efficient nonnegative tucker decompositions: Algorithms and uniqueness[J]. IEEE Trans Image Process 24(12):4990\u20135003","journal-title":"IEEE Trans Image Process"},{"key":"15511_CR38","doi-asserted-by":"crossref","unstructured":"Zhou W et al (2019) A multi-source-based coupled tensors completion algorithm for incomplete traffic data imputation. 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE","DOI":"10.1109\/WCSP.2019.8927918"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15511-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-15511-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15511-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T10:13:33Z","timestamp":1719483213000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-15511-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,9]]},"references-count":38,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["15511"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-15511-w","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,9]]},"assertion":[{"value":"17 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 April 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}