{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T14:23:22Z","timestamp":1776435802554,"version":"3.51.2"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2018,4,30]],"date-time":"2018-04-30T00:00:00Z","timestamp":1525046400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61663021"],"award-info":[{"award-number":["No.61663021"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific Research Project in Universities of Gansu","award":["No. 2015B-031"],"award-info":[{"award-number":["No. 2015B-031"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2018,10]]},"DOI":"10.1007\/s10489-018-1181-7","type":"journal-article","created":{"date-parts":[[2018,4,30]],"date-time":"2018-04-30T01:21:41Z","timestamp":1525051301000},"page":"3827-3838","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["A multivariate short-term traffic flow forecasting method based on wavelet analysis and seasonal time series"],"prefix":"10.1007","volume":"48","author":[{"given":"Hong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoming","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minan","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yirong","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,4,30]]},"reference":[{"key":"1181_CR1","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1016\/j.trc.2016.05.008","volume":"68","author":"Y Wang","year":"2016","unstructured":"Wang Y, Geroliminis N, Leclercq L (2016) Recent advances in ITS, traffic flow theory, and network operations. Transp Res C: Emerg Technol 68:507\u2013508","journal-title":"Transp Res C: Emerg Technol"},{"key":"1181_CR2","doi-asserted-by":"publisher","first-page":"1637","DOI":"10.1016\/j.procs.2016.08.211","volume":"96","author":"BCR Rota","year":"2016","unstructured":"Rota BCR, Simic M (2016) Traffic flow optimization on freeways. Procedia Comput Sci 96:1637\u20131646","journal-title":"Procedia Comput Sci"},{"issue":"3","key":"1181_CR3","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1080\/15472450.2016.1147813","volume":"20","author":"Y Zhang","year":"2016","unstructured":"Zhang Y, Zhang Y (2016) A comparative study of three multivariate Short-Term freeway traffic flow forecasting methods with missing data. J Intell Transp Syst 20(3):205\u2013218","journal-title":"J Intell Transp Syst"},{"issue":"2","key":"1181_CR4","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1109\/TITS.2009.2021448","volume":"10","author":"B Ghosh","year":"2009","unstructured":"Ghosh B, Basu B (2009) Multivariate Short-Term Traffic Flow Forecasting Using Time-Series Analysis. IEEE Trans Intell Transp Syst 10(2):246\u2013254","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"6","key":"1181_CR5","first-page":"1","volume":"11","author":"C Dong","year":"2015","unstructured":"Dong C, Shao Z, Xiong C, Zhang H (2015) A spatial-temporal-based state space approach for freeway network traffic flow modelling and prediction. Transportmetrica: A Transport Science 11(6):1\u201314","journal-title":"Transportmetrica: A Transport Science"},{"key":"1181_CR6","doi-asserted-by":"publisher","first-page":"200","DOI":"10.4236\/jtts.2016.64020","volume":"6","author":"X Pang","year":"2016","unstructured":"Pang X, Wang C, Huang G (2016) A short-term traffic flow forecasting method based on a three-layer k-nearest neighbor non-parametric regression algorithm. J Transp Technol 6:200\u2013206","journal-title":"J Transp Technol"},{"key":"1181_CR7","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1016\/j.physa.2016.09.041","volume":"466","author":"A Cheng","year":"2016","unstructured":"Cheng A, Jiang X, Li Y et al. (2016) Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method. Physica A Statistical Mechanics &, Its Applications 466:422\u2013434","journal-title":"Physica A Statistical Mechanics &, Its Applications"},{"key":"1181_CR8","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.proeng.2016.01.234","volume":"137","author":"Y Cong","year":"2016","unstructured":"Cong Y, Wang J, Li X (2016) Traffic flow forecasting by a least squares support vector machine with a fruit fly optimization algorithm. Procedia Eng 137:59\u201368","journal-title":"Procedia Eng"},{"key":"1181_CR9","doi-asserted-by":"crossref","unstructured":"Tang J, Liu F, Zou Y, Zhang W, Wang Y (2017) An improved fuzzy neural network for traffic speed prediction considering periodic characteristic. IEEE Transactions on Intelligent Transportation Systems, 99:1:11","DOI":"10.1109\/TITS.2016.2643005"},{"issue":"C","key":"1181_CR10","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neucom.2014.08.100","volume":"167","author":"F Moretti","year":"2015","unstructured":"Moretti F, Pizzuti S, Panzieri S, Annunziato M (2015) Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling. Neurocomputing 167(C):3\u20137","journal-title":"Neurocomputing"},{"key":"1181_CR11","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s11063-015-9409-6","volume":"43","author":"W Hu","year":"2016","unstructured":"Hu W, Yan L, Liu K, Wand H (2016) A short-term traffic flow forecasting method based on the hybrid PSO-SVR. Neural Process Lett 43:155\u2013172","journal-title":"Neural Process Lett"},{"issue":"5","key":"1181_CR12","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1080\/15472450.2015.1091735","volume":"20","author":"C Wang","year":"2015","unstructured":"Wang C, Ye Z (2015) Traffic flow forecasting based on a hybrid model. J Intell Transp Syst 20(5):428\u2013437","journal-title":"J Intell Transp Syst"},{"issue":"2","key":"1181_CR13","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1109\/TITS.2016.2573306","volume":"18","author":"S Liu","year":"2017","unstructured":"Liu S, Hellendoorn H, Schutter B D (2017) Model predictive control for freeway networks based on Multi-Class traffic flow and emission models. IEEE Trans Intell Transp Syst 18(2):306\u2013320","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"6","key":"1181_CR14","first-page":"8","volume":"66","author":"S Liu","year":"2017","unstructured":"Liu S, Chen W, Chi Q, Yan H (2017) Day-to-day dynamical evolution of network traffic flow with elastic demand. Acta Phys Sin 66(6):8\u201322","journal-title":"Acta Phys Sin"},{"key":"1181_CR15","unstructured":"Ni D (2016) Traffic Flow Theory. In: Ni D (ed) Chapter 24 multiscale traffic flow modeling. Butterworth-Heinemann, Oxford, pp 361\u2013377"},{"key":"1181_CR16","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.trc.2012.08.004","volume":"27","author":"J Wang","year":"2013","unstructured":"Wang J, Shi Q (2013) Short-term traffic speed forecasting hybrid model based on chaos\u2013wavelet analysis-support vector machine theory. Transportation Research Part C: Emerging Technologies 27:219\u2013232","journal-title":"Transportation Research Part C: Emerging Technologies"},{"issue":"2","key":"1181_CR17","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1109\/TITS.2015.2491365","volume":"17","author":"P Lopez-Garcia","year":"2016","unstructured":"Lopez-Garcia P, Onieva E, Osaba E, Masegosa A (2016) A hybrid method for short-term traffic congestion forecasting using genetic algorithms and cross entropy. IEEE Trans Intell Transp Syst 17(2):557\u2013569","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"C","key":"1181_CR18","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neucom.2014.08.100","volume":"167","author":"F Moretti","year":"2015","unstructured":"Moretti F, Pizzuti S, Annunziato M, Annunziato M (2015) Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling. Neurocomputing 167(C):3\u20137","journal-title":"Neurocomputing"},{"issue":"3","key":"1181_CR19","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s521-001-8054-3","volume":"10","author":"H Chen","year":"2001","unstructured":"Chen H, Grant-Muller S, Mussone L, F Montgomery F (2001) A study of hybrid neural network approaches and the effects of missing data on traffic forecasting. Neural Computing &, Applications 10(3):277\u2013286","journal-title":"Neural Computing &, Applications"},{"key":"1181_CR20","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1016\/j.neucom.2016.08.046","volume":"216","author":"J Corr\u00eaa","year":"2016","unstructured":"Corr\u00eaa J, Neto A, J\u00fanior L et al. (2016) Time series forecasting with the WARIMAX-GARCH method. Neurocomputing 216:805\u2013815","journal-title":"Neurocomputing"},{"issue":"3","key":"1181_CR21","first-page":"243","volume":"10","author":"R Paul","year":"2015","unstructured":"Paul R (2015) ARIMAX-GARCH-WAVELET model for forecasting volatile data. Model Assist Stat Appl & Appl 10(3):243\u2013252","journal-title":"Model Assist Stat Appl & Appl"},{"issue":"3","key":"1181_CR22","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1080\/15472450.2016.1147813","volume":"20","author":"Y Zhang","year":"2016","unstructured":"Zhang Y, Zhang Y (2016) A comparative study of three multivariate short-term freeway traffic flow forecasting methods with missing data. J Intell Transp Syst 20(3):205\u2013218","journal-title":"J Intell Transp Syst"},{"key":"1181_CR23","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.amc.2016.07.017","volume":"291","author":"Y Yin","year":"2016","unstructured":"Yin Y, Shang P (2016) Forecasting traffic time series with multivariate predicting method. Appl Math Comput 291:266\u2013278","journal-title":"Appl Math Comput"},{"issue":"4","key":"1181_CR24","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1080\/15598608.2010.10412020","volume":"4","author":"H Ghosh","year":"2010","unstructured":"Ghosh H, Paul R, Prajneshu (2010) Wavelet frequency domain approach for statistical modeling of rainfall time-series data. Journal of Statistical Theory and Practice 4(4):813\u2013825","journal-title":"Journal of Statistical Theory and Practice"},{"issue":"702","key":"1181_CR25","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1002\/qj.2881","volume":"143","author":"M Wenigera","year":"2017","unstructured":"Wenigera M, Kappa F, Friederichsa P (2017) Spatial verification using wavelet transforms: a review. Q J R Meteorol Soc 143(702):120\u2013136","journal-title":"Q J R Meteorol Soc"},{"issue":"3","key":"1181_CR26","first-page":"1","volume":"2016","author":"J Lu","year":"2016","unstructured":"Lu J, Lin H, Ye D, Zhang Y (2016) A new wavelet threshold function and denoising application. Math Probl Eng 2016(3):1\u20138","journal-title":"Math Probl Eng"},{"issue":"3","key":"1181_CR27","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1080\/03610926.2010.529532","volume":"41","author":"M Aminghafari","year":"2012","unstructured":"Aminghafari M, Poggi J (2012) Nonstationary time series forecasting using wavelets and kernel smoothing. Communication in Statistics - Theory Methods 41(3):485\u2013499","journal-title":"Communication in Statistics - Theory Methods"},{"issue":"3","key":"1181_CR28","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s12544-015-0170-8","volume":"7","author":"S Kumar","year":"2015","unstructured":"Kumar S, Vanajakshi L (2015) Short-term traffic flow prediction using seasonal ARIMA model with limited input data. European Transport Research Review 7(3):21","journal-title":"European Transport Research Review"},{"key":"1181_CR29","volume-title":"Machine learning with R - Second Edition.","author":"l Brett","year":"2015","unstructured":"Brett l (2015) Machine learning with R - Second Edition. PACKT Publishing, Birmingham"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10489-018-1181-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-018-1181-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-018-1181-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T10:42:47Z","timestamp":1604227367000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10489-018-1181-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,30]]},"references-count":29,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2018,10]]}},"alternative-id":["1181"],"URL":"https:\/\/doi.org\/10.1007\/s10489-018-1181-7","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,30]]},"assertion":[{"value":"30 April 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}