{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T05:40:08Z","timestamp":1723873208638},"reference-count":60,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1016\/j.neucom.2020.10.091","type":"journal-article","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T02:30:27Z","timestamp":1604975427000},"page":"138-149","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":10,"special_numbering":"C","title":["Nonrecurrent traffic congestion detection with a coupled scalable Bayesian robust tensor factorization model"],"prefix":"10.1016","volume":"430","author":[{"given":"Qin","family":"Li","sequence":"first","affiliation":[]},{"given":"Huachun","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Zhuxi","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Yuankai","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4380-7766","authenticated-orcid":false,"given":"Linhui","family":"Ye","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2020.10.091_b0005","article-title":"Recurring and non-recurring congestion: Causes, impacts, and solutions","author":"McGroarty","year":"2010","journal-title":"Neihoff Urban Studio\u2013W10"},{"key":"10.1016\/j.neucom.2020.10.091_b0010","doi-asserted-by":"crossref","first-page":"118","DOI":"10.3141\/1856-12","article-title":"Measuring recurrent and nonrecurrent traffic congestion","volume":"1856","author":"Skabardonis","year":"2003","journal-title":"Transp. Res. Rec., J. Transp. Res. Board"},{"year":"2010","series-title":"Travel time reliability: Making it there on time, all the time","author":"Fha","key":"10.1016\/j.neucom.2020.10.091_b0015"},{"issue":"2","key":"10.1016\/j.neucom.2020.10.091_b0020","first-page":"70","article-title":"Traffic Speed Prediction Under Non-Recurrent Congestion: Based on LSTM Method and BeiDou Navigation Satellite System Data","volume":"11","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Sys."},{"issue":"4","key":"10.1016\/j.neucom.2020.10.091_b0025","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1080\/15472450.2014.1000456","article-title":"Accuracy Analysis of Freeway Traffic Speed Estimation Based on the Integration of Cellular Probe System and Loop Detector","volume":"19","author":"Zhang","year":"2015","journal-title":"J. Intell. Transp. Sys.: Technology, Planning, and Operations"},{"key":"10.1016\/j.neucom.2020.10.091_b0030","doi-asserted-by":"crossref","unstructured":"H. Tan, Q. Li, Y. Wu, B. Ran, B. Liu, \u201cTensor Recovery Based Nonrecurrent Traffic Congestion Recognition,\u201d in CICTP, Beijing, China, 2015, pp. 591-603.","DOI":"10.1061\/9780784479292.054"},{"issue":"6","key":"10.1016\/j.neucom.2020.10.091_b0035","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1109\/JAS.2017.7510469","article-title":"Expressway traffic flow model study based on different traffic rules","volume":"5","author":"Zeng","year":"2018","journal-title":"IEEE\/CAA J. Autom. Sinica"},{"issue":"3","key":"10.1016\/j.neucom.2020.10.091_b0040","first-page":"87","article-title":"Traffic congestion identification method of urban expressway","volume":"6","author":"Jiang","year":"2006","journal-title":"Jour. Tra. Transp. Eng."},{"key":"10.1016\/j.neucom.2020.10.091_b0045","doi-asserted-by":"crossref","first-page":"158","DOI":"10.29007\/cxkb","article-title":"Recurrent and Non-recurrent Congestion Based GridlockDetection on Chula-SSS Urban Road Network","volume":"62","author":"Mon","year":"2019","journal-title":"EPiC Series in Computing"},{"issue":"5","key":"10.1016\/j.neucom.2020.10.091_b0050","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.1109\/TITS.2014.2305334","article-title":"Detecting Road Traffic Events by Coupling Multiple Timeseries With a Nonparametric Bayesian Method","volume":"15","author":"Yang","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Sys."},{"issue":"1","key":"10.1016\/j.neucom.2020.10.091_b0055","first-page":"2773","article-title":"Tensor decompositions for learning latent variable models","volume":"5","author":"Anandkumar","year":"2014","journal-title":"J. Mach. Learn. Res"},{"key":"10.1016\/j.neucom.2020.10.091_b0060","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.trc.2017.10.023","article-title":"Spatial-temporal traffic speed patterns discovery and incomplete data recovery via SVD-combined tensor decomposition","volume":"86","author":"Chen","year":"2018","journal-title":"Transp. Res. part C: Emerg. Technol."},{"key":"10.1016\/j.neucom.2020.10.091_b0065","first-page":"374","article-title":"Travel time estimation of a path using sparse trajectories","author":"Wang","year":"2014","journal-title":"Proc. 20th ACM SIGKDD"},{"key":"10.1016\/j.neucom.2020.10.091_b0070","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.trc.2018.11.003","article-title":"A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation","volume":"vo.98","author":"Chen","year":"2019","journal-title":"Transp. Res. part C: Emerg. Technol."},{"key":"10.1016\/j.neucom.2020.10.091_b0075","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.trc.2014.08.002","article-title":"Spatio-temporal Clustering for Nonrecurrent Traffic Congestion Detection on Urban Road Networks","volume":"48","author":"Anbaroglu","year":"2014","journal-title":"Transp. Res. C, Emerging Technol."},{"key":"10.1016\/j.neucom.2020.10.091_b0080","unstructured":"H. J. Payne, E. D. Helfenbein, and H. C. Knobel, \u201cDevelopment and testing of incident detection algorithms, Volume 2: Research Methodology and Detailed Results,\u201d Technol. Ser. Corp., Santa Monica, CA, FHWA-RD-76-20 Final Report, Apr. 1976."},{"key":"10.1016\/j.neucom.2020.10.091_b0085","first-page":"52","article-title":"Incident detection: A Bayesian approach","volume":"682","author":"Levin","year":"1978","journal-title":"Transp. Res. Rec., J. Transp. Res. Board"},{"key":"10.1016\/j.neucom.2020.10.091_b0090","unstructured":"C. Dudek, C. Messer, and N. Nuckles (1974), \u201cIncident Detection on Urban Freeways,\u201d Transp. Res. Rec., J. Transp. Res. Board, vol.495, pp.12-24, 1974."},{"key":"10.1016\/j.neucom.2020.10.091_b0095","doi-asserted-by":"crossref","first-page":"112","DOI":"10.3141\/1603-15","article-title":"Incident Detection on an Arterial Roadway","volume":"1603","author":"Cullip","year":"1997","journal-title":"Transp. Res. Rec., J. Transp. Res. Board"},{"key":"10.1016\/j.neucom.2020.10.091_b0100","first-page":"506","article-title":"A calibration process for automatic incident detection algorithms","author":"Cohen","year":"1993","journal-title":"Micro. Transp."},{"year":"1991","series-title":"Automatic incident detection\u2014TRRL algorithms HIOCC and PATERG","author":"Collins","key":"10.1016\/j.neucom.2020.10.091_b0105"},{"key":"10.1016\/j.neucom.2020.10.091_b0110","doi-asserted-by":"crossref","unstructured":"C.P. Chang, Edmond, \u201cFuzzy Systems Based Automatic Freeway Incident Detection,\u201d In Proc. IEEE-SMC, San Ant., TX, USA, 1994, pp.1727-1733.","DOI":"10.1109\/ICSMC.1994.400098"},{"issue":"4","key":"10.1016\/j.neucom.2020.10.091_b0115","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1080\/19475683.2019.1675760","article-title":"Fuzzy inference approach in traffic congestion detection","volume":"25","author":"Kalinic","year":"2019","journal-title":"Annals of GIS"},{"issue":"2","key":"10.1016\/j.neucom.2020.10.091_b0120","first-page":"397","article-title":"A support vector machine with the tabu search algorithm for freeway incident detection","volume":"24","author":"Yao","year":"2014","journal-title":"Int. Jour. App. Math. and Com. Sci."},{"issue":"1","key":"10.1016\/j.neucom.2020.10.091_b0125","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.physa.2018.10.060","article-title":"SVM and KNN ensemble learning for traffic incident detection","volume":"517","author":"Xiao","year":"2019","journal-title":"Physica A-statistical Mechanics and Its Applications"},{"key":"10.1016\/j.neucom.2020.10.091_b0130","unstructured":"R. Sujatha, R.A. Nithya, S. Subhapradha, S. Srinithibharathi, \u201cDecision Tree Classification for Traffic Congestion Detection Using Data Mining,\u201d Inter. Jour. Eng. and Tech., vol.4, no.2, 2018."},{"issue":"9","key":"10.1016\/j.neucom.2020.10.091_b0135","doi-asserted-by":"crossref","first-page":"2303","DOI":"10.1109\/TITS.2016.2635719","article-title":"Prioritizing influential factors for freeway incident clearance time prediction using the gradient boosting decision trees method","volume":"18","author":"Ma","year":"2017","journal-title":"IEEE Trans Intel. Transp. Sys."},{"key":"10.1016\/j.neucom.2020.10.091_b0140","first-page":"40","article-title":"February. \u201cTraffic accident detection using random forest classifier\u201d, In Proc","volume":"2018","author":"Dogru","year":"2018","journal-title":"IEEE-LT, Jeddah, KSA"},{"key":"10.1016\/j.neucom.2020.10.091_b0145","unstructured":"L. Zhu, F. Guo, R. Krishnan, J. Polak, \u201cThe Use of Convolutional Neural Networks for Traffic Incident Detection at a Network Level,\u201d In Pro. Transportation Research Board 97th Annual Meeting, no. 01657942, 2018."},{"issue":"1","key":"10.1016\/j.neucom.2020.10.091_b0150","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1061\/(ASCE)0733-947X(2002)128:1(21)","article-title":"Comparison of Fuzzy-Wavelet Radial Basis Function Neural Network Freeway Incident Detection Model with California Algorithm","volume":"128","author":"Karim","year":"2002","journal-title":"Jour. Transp. Eng."},{"key":"10.1016\/j.neucom.2020.10.091_b0155","series-title":"An incident detection algorithm using artificial neural networks and traffic information","first-page":"1","author":"Ki","year":"2018"},{"key":"10.1016\/j.neucom.2020.10.091_b0160","unstructured":"M. Motamed, \u201cDeveloping a real-time freeway incident detection model using machine learning techniques,\u201d Ph.D. dissertation, Dept. Civ., Arch., and Env. Eng., Texas. Univ., Austin, TX, USA, 2016."},{"key":"10.1016\/j.neucom.2020.10.091_b0165","unstructured":"J. Sarath, G. Kiran, L. Leo, (2011, Oct.). Non-Recurring Congestion Study.Maricopa Association of Governments, USA. [online]. Available: http:\/\/www.azmag.gov\/Projects\/Project.asp?CMSID=1108."},{"key":"10.1016\/j.neucom.2020.10.091_b0170","first-page":"589","article-title":"Traffic Congestion Detection using Whale Optimization Algorithm and Multi-Support Vector Machine","volume":"vol 7, no. 6C2","author":"Sony","year":"2019","journal-title":"Inter. Jour. Recent Tech. Eng."},{"key":"10.1016\/j.neucom.2020.10.091_b0175","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.trc.2012.12.007","article-title":"A tensor-based method for missing traffic data completion","volume":"28","author":"Tan","year":"2013","journal-title":"Transp. Res. C, Emerging Technol."},{"issue":"4","key":"10.1016\/j.neucom.2020.10.091_b0180","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1142\/S0129065707001159","article-title":"Nonnegative tensor factorization for continuous EEG classification","volume":"17","author":"Lee","year":"2007","journal-title":"Int. J. Neu. Sys."},{"key":"10.1016\/j.neucom.2020.10.091_b0185","doi-asserted-by":"crossref","unstructured":"Y. Li, J. Yan, Y. Zhou, and J. Yang, \u201cOptimum subspace learning and error correction for tensors,\u201d in Proc. CV\u2013ECCV, 2010, pp.790-803.","DOI":"10.1007\/978-3-642-15558-1_57"},{"issue":"3","key":"10.1016\/j.neucom.2020.10.091_b0190","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1137\/07070111X","article-title":"Tensor decompositions and applications","volume":"51","author":"Kolda","year":"2009","journal-title":"Siam Review"},{"issue":"20","key":"10.1016\/j.neucom.2020.10.091_b0195","doi-asserted-by":"crossref","first-page":"5423","DOI":"10.1109\/TSP.2016.2586759","article-title":"Smooth parafac decomposition for tensor completion","volume":"64","author":"Yokota","year":"2016","journal-title":"IEEE Trans. Sig. Process."},{"key":"10.1016\/j.neucom.2020.10.091_b0200","doi-asserted-by":"crossref","unstructured":"H. Wu, Xin Luo, M. Zhou, \u201cAdvancing non-negative latent factorization of tensors with diversified regularization,\u201d IEEE Trans. Services Comp., DOI: 10.1109\/TSC.2020.2988760, Early Access, 2020.","DOI":"10.1109\/TSC.2020.2988760"},{"issue":"2","key":"10.1016\/j.neucom.2020.10.091_b0205","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1109\/TPAMI.2013.201","article-title":"Bayesian nonparametric models formultiway data analysis","volume":"37","author":"Xu","year":"2013","journal-title":"IEEE Trans. PAMI."},{"issue":"6","key":"10.1016\/j.neucom.2020.10.091_b0210","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1007\/s12559-018-9565-x","article-title":"Knowledge Base Completion by Variational Bayesian Neural Tensor Decomposition","volume":"10","author":"He","year":"2018","journal-title":"Cognitive Computation"},{"issue":"9","key":"10.1016\/j.neucom.2020.10.091_b0215","doi-asserted-by":"crossref","first-page":"1751","DOI":"10.1109\/TPAMI.2015.2392756","article-title":"Bayesian CP factorization of incomplete tensors with automatic rank determination","volume":"37","author":"Zhao","year":"2015","journal-title":"IEEE trans. PAMI"},{"key":"10.1016\/j.neucom.2020.10.091_b0225","unstructured":"M. Zhang, C. Ding, \u201cRobust Tucker tensor decomposition for effective image representation,\u201d in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Sydney, NSW, Australia, Dec., pp. 2448\u20132455."},{"issue":"9","key":"10.1016\/j.neucom.2020.10.091_b0235","first-page":"1933","article-title":"Robust low-rank tensor recovery with regularized redescending M-estimator","volume":"27","author":"Yang","year":"2016","journal-title":"IEEE trans. NNLS"},{"key":"10.1016\/j.neucom.2020.10.091_b0240","first-page":"1502","article-title":"Graph based tensor recovery for accurate internet anomaly detection","author":"Xie","year":"2018","journal-title":"Pro. ICCC"},{"key":"10.1016\/j.neucom.2020.10.091_b0245","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.neucom.2018.11.012","article-title":"Noisy low-tubal-rank tensor completion","author":"Wang","year":"2019","journal-title":"Neurocomputing"},{"issue":"4","key":"10.1016\/j.neucom.2020.10.091_b0250","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1109\/TNNLS.2015.2423694","article-title":"Bayesian robust tensor factorization for incomplete multiway data","volume":"27","author":"Zhao","year":"2016","journal-title":"IEEE trans. Neu. Net. Lea. Sys."},{"key":"10.1016\/j.neucom.2020.10.091_b0255","unstructured":"M. Zhang, Y. Gao, C. Sun, M. Blumenstein, \u201cRobust Tensor Decomposition for Image Representation Based on Generalized Correntropy,\u201d arXiv preprint arXiv:2005.04605, 2020."},{"key":"10.1016\/j.neucom.2020.10.091_b0260","doi-asserted-by":"crossref","DOI":"10.1155\/2014\/763469","article-title":"Robust Missing Traffic Flow Imputation Considering Nonnegativity and Road-capacity","volume":"2014","author":"Tan","year":"2014","journal-title":"Mathematical Problems in Engineering"},{"key":"10.1016\/j.neucom.2020.10.091_b0265","doi-asserted-by":"crossref","first-page":"97","DOI":"10.3141\/2489-11","article-title":"Freeway short-term travel time prediction based on dynamic tensor completion","volume":"2489","author":"Tan","year":"2015","journal-title":"Transp. Res. Rec., J. Transp. Res. Board"},{"issue":"8","key":"10.1016\/j.neucom.2020.10.091_b0270","first-page":"1","article-title":"Short-Term Traffic Prediction Based on Dynamic Tensor Completion","volume":"17","author":"Tan","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Sys."},{"issue":"5","key":"10.1016\/j.neucom.2020.10.091_b0275","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TCYB.2019.2903736","article-title":"Temporal Pattern-aware QoS Prediction via Biased Non-negative Latent Factorization of Tensors","volume":"50","author":"Luo","year":"2020","journal-title":"IEEE Trans. Cybernetics"},{"key":"10.1016\/j.neucom.2020.10.091_b0280","doi-asserted-by":"crossref","DOI":"10.1155\/2013\/164810","article-title":"Traffic volume data outlier recovery via tensor model","volume":"2013","author":"Tan","year":"2013","journal-title":"Mathematical Problems in Engineering"},{"issue":"1","key":"10.1016\/j.neucom.2020.10.091_b0285","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1109\/TPAMI.2012.39","article-title":"Tensor completion for estimating missing values in visual data","volume":"35","author":"Liu","year":"2013","journal-title":"IEEE Trans Pattern Ana. Mach. Intell."},{"issue":"2","key":"10.1016\/j.neucom.2020.10.091_b0295","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1093\/biomet\/asr013","article-title":"Sparse Bayesian infinite factor models","volume":"98","author":"Bhattacharya","year":"2011","journal-title":"Biometrika"},{"key":"10.1016\/j.neucom.2020.10.091_b0300","unstructured":"S. Zhe, Z. Xu, X. Chu, Q. Yuan (Alan), and Y. Park, \u201cScalable Nonparametric Multiway Data Analysis,\u201d in Proc. JMLR- AISTATS, San Diego, CA, USA, 2015."},{"year":"2012","series-title":"Machine Learning: A Probabilistic Perspective","author":"Murphy","key":"10.1016\/j.neucom.2020.10.091_b0305"},{"issue":"10","key":"10.1016\/j.neucom.2020.10.091_b0310","first-page":"902","article-title":"Adaptive estimator design for unstable output error systems: a test problem and traditional system identification based analysis","volume":"229","author":"Tutsoy","year":"2015","journal-title":"Proc. Ins. Mech. Eng., Part I: J. Sys. Con. Eng."},{"key":"10.1016\/j.neucom.2020.10.091_b0315","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.trc.2015.08.016","article-title":"Local ramp metering with distant downstream bottlenecks: A comparative study","volume":"62","author":"Kan","year":"2016","journal-title":"Transp. Res. Part C"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231220317045?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231220317045?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T05:18:20Z","timestamp":1723871900000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231220317045"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3]]},"references-count":60,"alternative-id":["S0925231220317045"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2020.10.091","relation":{},"ISSN":["0925-2312"],"issn-type":[{"type":"print","value":"0925-2312"}],"subject":[],"published":{"date-parts":[[2021,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Nonrecurrent traffic congestion detection with a coupled scalable Bayesian robust tensor factorization model","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2020.10.091","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}