{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T17:17:10Z","timestamp":1787851030183,"version":"build-2784847793"},"reference-count":32,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2024MF142"],"award-info":[{"award-number":["ZR2024MF142"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014103","name":"Key Technology Research and Development Program of Shandong Province","doi-asserted-by":"publisher","award":["2025CXGC010108"],"award-info":[{"award-number":["2025CXGC010108"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.neucom.2026.133959","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T04:09:45Z","timestamp":1778818185000},"page":"133959","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["Efficient missing traffic flow imputation via normalized spatial-temporal factor autoregression for improving the traffic data reliability"],"prefix":"10.1016","volume":"694","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4246-7544","authenticated-orcid":false,"given":"Zhihao","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianbo","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.133959_bib1","first-page":"1655","article-title":"Deep spatial-temporal residual networks for citywide crowd flows prediction. Proc","volume":"31","author":"Zhang","year":"2017","journal-title":"AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.neucom.2026.133959_bib2","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.111125","article-title":"Fast autoregressive tensor decomposition for online real-time traffic flow prediction","volume":"282","author":"Xu","year":"2023","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.neucom.2026.133959_bib3","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107573","article-title":"Progress and prospects of future urban health status prediction","volume":"129","author":"Xu","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.neucom.2026.133959_bib4","doi-asserted-by":"crossref","first-page":"4485","DOI":"10.1109\/JSYST.2023.3268717","article-title":"Engineering traffic prediction with online data imputation: a graph-theoretic perspective","volume":"17","author":"Yue","year":"2023","journal-title":"IEEE Syst. J."},{"key":"10.1016\/j.neucom.2026.133959_bib5","doi-asserted-by":"crossref","first-page":"34080","DOI":"10.1109\/ACCESS.2023.3264216","article-title":"Missing traffic data imputation for artificial intelligence in intelligent transportation systems: review of methods, limitations, and challenges","volume":"11","author":"Chan","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.133959_bib6","doi-asserted-by":"crossref","first-page":"12301","DOI":"10.1109\/TITS.2021.3113608","article-title":"Low-rank autoregressive tensor imputation for spatial-temporal traffic data imputation","volume":"23","author":"Chen","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.neucom.2026.133959_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2021.103226","article-title":"Scalable low-rank tensor learning for spatial-temporal traffic data imputation","volume":"129","author":"Chen","year":"2021","journal-title":"Transp. Res. Part C. Emerg. Technol."},{"key":"10.1016\/j.neucom.2026.133959_bib8","first-page":"1","article-title":"Static and streaming tucker decomposition for dense tensors","volume":"17","author":"Jang","year":"2023","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"10.1016\/j.neucom.2026.133959_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2020.102673","article-title":"A nonconvex low-rank tensor imputation model for spatial-temporal traffic data imputation","volume":"117","author":"Chen","year":"2020","journal-title":"Transp. Res. Part C. Emerg. Technol."},{"key":"10.1016\/j.neucom.2026.133959_bib10","first-page":"1","article-title":"Multi-scale and multi-direction feature extraction network for hyperspectral and LiDAR classification","volume":"1","author":"Liu","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens"},{"key":"10.1016\/j.neucom.2026.133959_bib11","doi-asserted-by":"crossref","first-page":"16343","DOI":"10.1109\/JIOT.2022.3151238","article-title":"A hybrid data-driven framework for spatial-temporal traffic flow data imputation","volume":"9","author":"Wang","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.neucom.2026.133959_bib12","doi-asserted-by":"crossref","first-page":"7919","DOI":"10.1109\/TITS.2021.3074564","article-title":"Missing data repairs for traffic flow with self-attention generative adversarial imputation net","volume":"23","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.neucom.2026.133959_bib13","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1109\/TBDATA.2022.3154097","article-title":"STGAN: spatial-temporal generative adversarial network for traffic data imputation","volume":"9","author":"Yuan","year":"2022","journal-title":"IEEE Trans. Big Data"},{"key":"10.1016\/j.neucom.2026.133959_bib14","article-title":"A low-rank and sparse enhanced Tucker decomposition approach for tensor imputation","volume":"465","author":"Pan","year":"2024","journal-title":"Appl. Math. Comput."},{"key":"10.1016\/j.neucom.2026.133959_bib15","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111637","article-title":"LSTTN: a long-short term transformer-based spatial-temporal neural network for traffic flow forecasting","volume":"293","author":"Luo","year":"2024","journal-title":"Knowl. -Based Syst."},{"key":"10.1016\/j.neucom.2026.133959_bib16","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1038\/s42256-023-00626-4","article-title":"Parameter-efficient fine-tuning of large-scale pre-trained language models","volume":"5","author":"Ding","year":"2023","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.neucom.2026.133959_bib17","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s13253-019-00361-7","article-title":"Comparison of deep neural networks and deep hierarchical models for spatial-temporal data","volume":"24","author":"Wikle","year":"2019","journal-title":"J. Agric. Biol. Environ. Stat."},{"key":"10.1016\/j.neucom.2026.133959_bib18","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123619","article-title":"A traffic dynamic operation risk assessment method using driving behaviors and traffic flow Data: an empirical analysis","volume":"249","author":"Yang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.133959_bib19","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102317","article-title":"Comprehensive systematic review of information fusion methods in smart cities and urban environments","volume":"107","author":"Fadhel","year":"2024","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.neucom.2026.133959_bib20","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.neucom.2023.02.017","article-title":"Bidirectional spatial\u2013temporal traffic data imputation via graph attention recurrent neural network","volume":"531","author":"Shen","year":"2023","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133959_bib21","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.102196","article-title":"Spatial-temporal fusion graph convolutional network for traffic flow forecasting","volume":"104","author":"Ma","year":"2024","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.neucom.2026.133959_bib22","doi-asserted-by":"crossref","DOI":"10.1016\/j.spasta.2023.100771","article-title":"A spatial panel autoregressive model specification with inverse quantile separation distances of locations","volume":"57","author":"Bulty","year":"2023","journal-title":"Spat. Stat."},{"key":"10.1016\/j.neucom.2026.133959_bib23","doi-asserted-by":"crossref","DOI":"10.1016\/j.cam.2022.114866","article-title":"Riemannian conjugate gradient descent method for fixed multi rank third-order tensor imputation","volume":"421","author":"Song","year":"2023","journal-title":"J. Comput. Appl. Math."},{"key":"10.1016\/j.neucom.2026.133959_bib24","first-page":"227","article-title":"MFAGCN: Multi-feature based attention graph convolutional network for traffic prediction","volume":"16","author":"Li","year":"2021","journal-title":"Wirel. Algorithms Syst. Appl."},{"key":"10.1016\/j.neucom.2026.133959_bib25","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s41060-023-00412-w","article-title":"Spatial\u2013temporal regularized tensor decomposition method for traffic speed data imputation","volume":"17","author":"Xie","year":"2024","journal-title":"Int. J. Data Sci. Anal."},{"key":"10.1016\/j.neucom.2026.133959_bib26","first-page":"2260","article-title":"ImputeFormer: low rankness-induced transformers for generalizable spatiotemporal imputation","volume":"3","author":"Nie","year":"2024","journal-title":"Proc. ACM SIGKDD Conf. Knowl. Disc. Data Min."},{"key":"10.1016\/j.neucom.2026.133959_bib27","first-page":"4669","article-title":"A traffic flow forecasting model using graph convolutional recurrent neural networks with incomplete data","volume":"12","author":"Sun","year":"2023","journal-title":"Proc. IEEE Conf. Intell. Transp. Syst."},{"key":"10.1016\/j.neucom.2026.133959_bib28","article-title":"KE-STCN: an adaptive multi-scale traffic flow prediction method based on knowledge graph","author":"Cao","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133959_bib29","article-title":"A sparse dynamic graph transformer for traffic flow prediction","author":"Teng","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133959_bib30","article-title":"DSM-STWave: Enhancing traffic flow prediction for both offline and online scenarios","author":"Liang","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133959_bib31","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.130117","article-title":"Urban traffic flow prediction based on multi-spatial-temporal feature fusion","volume":"638","author":"Chen","year":"2025","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.neucom.2026.133959_bib32","first-page":"136","article-title":"A novel perspective on travel demand prediction considering natural environmental and socioeconomic factors","volume":"15","author":"Xu","year":"2022","journal-title":"IEEE Intell. Transp. Syst. Mag."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226013561?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226013561?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T16:19:25Z","timestamp":1787847565000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226013561"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":32,"alternative-id":["S0925231226013561"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133959","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Efficient missing traffic flow imputation via normalized spatial-temporal factor autoregression for improving the traffic data reliability","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133959","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"133959"}}