{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T20:09:40Z","timestamp":1780344580852,"version":"3.54.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2015,8,5]],"date-time":"2015-08-05T00:00:00Z","timestamp":1438732800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"the Young Teachers Subsidy Scheme in 2013 of the Shangqiu Normal University","award":["2013GGJS12"],"award-info":[{"award-number":["2013GGJS12"]}]},{"name":"the basic and frontier technology research projects of Henan Province","award":["132300410203"],"award-info":[{"award-number":["132300410203"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2016,10]]},"DOI":"10.1007\/s00521-015-1991-z","type":"journal-article","created":{"date-parts":[[2015,8,4]],"date-time":"2015-08-04T07:48:33Z","timestamp":1438674513000},"page":"1969-1980","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Research on prediction of traffic flow based on dynamic fuzzy neural networks"],"prefix":"10.1007","volume":"27","author":[{"given":"Haitao","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2015,8,5]]},"reference":[{"issue":"4","key":"1991_CR1","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/S0968-090X(02)00009-8","volume":"10","author":"B Smith","year":"2002","unstructured":"Smith B (2002) Comparison of parametric and nonparametric models for traffic flow forecasting. Transp Res Part C 10(4):303\u2013321","journal-title":"Transp Res Part C"},{"issue":"3","key":"1991_CR2","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1109\/TITS.2004.833765","volume":"5","author":"J Rice","year":"2004","unstructured":"Rice J (2004) A simple and effective method for predicting travel times on freeways. IEEE Trans Intell Transp Syst 5(3):200\u2013207","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"11","key":"1991_CR3","doi-asserted-by":"crossref","first-page":"3066","DOI":"10.1088\/0957-0233\/17\/11\/027","volume":"17","author":"P Giovanni","year":"2006","unstructured":"Giovanni P (2006) A processing architecture for associative short-term memory in electronic noses. Meas Sci Technol 17(11):3066\u20133072","journal-title":"Meas Sci Technol"},{"key":"1991_CR4","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.eiar.2014.06.004","volume":"49","author":"N Vladimir","year":"2014","unstructured":"Vladimir N (2014) Comparison of classical statistical methods and artificial neural network in traffic noise prediction. Environ Impact Assess Rev 49:24\u201330","journal-title":"Environ Impact Assess Rev"},{"issue":"7","key":"1991_CR5","doi-asserted-by":"crossref","first-page":"2936","DOI":"10.1109\/TVT.2011.2158866","volume":"60","author":"G Marfia","year":"2010","unstructured":"Marfia G, Roccetti M (2010) Vehicular congestion detection and short-term forecasting: a new model with results. IEEE Trans Veh Technol 60(7):2936\u20132948","journal-title":"IEEE Trans Veh Technol"},{"issue":"3","key":"1991_CR6","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.trc.2005.04.007","volume":"13","author":"EI Vlahogianni","year":"2005","unstructured":"Vlahogianni EI, Karlaftis MG, Golias JC (2005) Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach. Transp Res Part C 13(3):211\u2013234","journal-title":"Transp Res Part C"},{"issue":"30","key":"1991_CR7","first-page":"277","volume":"1","author":"MT Li","year":"2013","unstructured":"Li MT, Yuan J (2013) WT-AOSVR models for short-time traffic flow prediction. Comput Appl Softw 1(30):277\u2013280","journal-title":"Comput Appl Softw"},{"issue":"3","key":"1991_CR8","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1061\/(ASCE)0733-947X(1995)121:3(249)","volume":"121","author":"M Hamed","year":"1995","unstructured":"Hamed M, Al-Masaeid H (1995) Short-term prediction of traffic volume in urban arterials. J Transp Eng (ASCE) 121(3):249\u2013254","journal-title":"J Transp Eng (ASCE)"},{"issue":"1","key":"1991_CR9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0191-2615(84)90002-X","volume":"18","author":"I Okutani","year":"1984","unstructured":"Okutani I, Stephanedes YJ (1984) Dynamic prediction of traffic volume through Kalman filtering theory. Transp Res B 18(1):1\u201311","journal-title":"Transp Res B"},{"issue":"1\u20132","key":"1991_CR10","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s00170-004-2340-z","volume":"28","author":"WC Hong","year":"2006","unstructured":"Hong WC, Pai PF (2006) Predicting engine reliability by support vector machines. Int J Adv Manuf Technol 28(1\u20132):154\u2013161","journal-title":"Int J Adv Manuf Technol"},{"key":"1991_CR11","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1007\/s00521-010-0456-7","volume":"21","author":"WC Hong","year":"2012","unstructured":"Hong WC (2012) Application of seasonal SVR with chaotic immune algorithm in traffic flow forecasting. Neural Comput Appl 21:583\u2013593","journal-title":"Neural Comput Appl"},{"issue":"10","key":"1991_CR12","first-page":"1160","volume":"41","author":"WB Zhang","year":"2007","unstructured":"Zhang WB, Hu HZ, Liu WJ (2007) traffic flow forecast based on type-2 fuzzy logic approach. J Xi\u2019an Jiaotong Univ 41(10):1160\u20131163","journal-title":"J Xi\u2019an Jiaotong Univ"},{"issue":"5","key":"1991_CR13","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/j.trc.2007.11.003","volume":"16","author":"D Loukas","year":"2008","unstructured":"Loukas D, Theodore T, Antony S (2008) Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow. Transp Res Part C 16(5):554\u2013573","journal-title":"Transp Res Part C"},{"issue":"1","key":"1991_CR14","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/713930748","volume":"7","author":"B Abdulhai","year":"2002","unstructured":"Abdulhai B, Porwal H, Recker W (2002) Short term traffic-flow forecasting using neuro-genetic algorithms. J Intell Transp Syst Technol Plann Oper 7(1):3\u201341","journal-title":"J Intell Transp Syst Technol Plann Oper"},{"key":"1991_CR15","doi-asserted-by":"crossref","first-page":"190","DOI":"10.3141\/1802-21","volume":"1802","author":"B Park","year":"2002","unstructured":"Park B (2002) Hybrid neuro-fuzzy application in short term freeway traffic volume predicting. Transp Res Rec 1802:190\u2013196","journal-title":"Transp Res Rec"},{"key":"1991_CR16","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1016\/j.sbspro.2013.08.076","volume":"96","author":"L Zhang","year":"2013","unstructured":"Zhang L, Liu QC, Yang WC (2013) An improved k-nearest neighbor model for short-term traffic flow prediction. Procedia\u2014Soc Behav Sci 96:653\u2013662","journal-title":"Procedia\u2014Soc Behav Sci"},{"key":"1991_CR17","doi-asserted-by":"crossref","unstructured":"Zhang C, Sun S, Yu G (2004) Short-term traffic flow forecasting using expanded Bayesian network for incomplete data. In: Proceedings international symposium on neural networks, Dalian, China. Lecture Notes in Computer Science (LNCS 3174), Springer, Berlin","DOI":"10.1007\/978-3-540-28648-6_151"},{"issue":"5","key":"1991_CR18","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.trb.2007.10.003","volume":"42","author":"C Enrique","year":"2008","unstructured":"Enrique C, Jose MM, Santos SC (2008) Predicting traffic flow using Bayesian networks. Transp Res Part B 42(5):482\u2013509","journal-title":"Transp Res Part B"},{"key":"1991_CR19","unstructured":"Hodge V, Jackson T, Austin J (2012) A binary neural network framework for attribute selection and prediction. In: Proceedings 4th international conference on neural computation theory and applications (NCTA 2012), SciTePress, Barcelona, pp 510\u2013515"},{"key":"1991_CR20","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.1007\/s00521-014-1646-5","volume":"25","author":"JH Victoria","year":"2014","unstructured":"Victoria JH, Rajesh K, Jim A et al (2014) Short -term prediction of traffic flow using a binary neural network. Neural Comput Appl 25:1639\u20131655","journal-title":"Neural Comput Appl"},{"issue":"3","key":"1991_CR21","first-page":"1682","volume":"11","author":"Y Hou","year":"2013","unstructured":"Hou Y, Mai YM (2013) Chaotic prediction for traffic flow of improved BP neural network. Indones J Electr Eng 11(3):1682\u20131690","journal-title":"Indones J Electr Eng"},{"issue":"4","key":"1991_CR22","first-page":"316","volume":"23","author":"JJ Lu","year":"2006","unstructured":"Lu JJ, Wang ZQ (2006) Internet traffic data flow forecast by RBF neural network based on phase space reconstruction. Trans Nanjing Univ Aeronaut Astronaut 23(4):316\u2013322","journal-title":"Trans Nanjing Univ Aeronaut Astronaut"},{"issue":"5","key":"1991_CR23","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1111\/j.1467-8667.2007.00488.x","volume":"22","author":"EI Vlahogianni","year":"2007","unstructured":"Vlahogianni EI, Karlaftis MG, Golias JC (2007) Spatio-temporal urban traffic volume forecasting using genetically optimized modular networks. Comput-Aided Civil Infrastruct Eng 22(5):317\u2013325","journal-title":"Comput-Aided Civil Infrastruct Eng"},{"issue":"1","key":"1991_CR24","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1061\/(ASCE)0887-3801(2005)19:1(94)","volume":"19","author":"M Zhong","year":"2005","unstructured":"Zhong M, Sharma S, Lingras P (2005) Short-term traffic prediction on different types of roads with genetically designed regression and time delay neural network models. J Comput Civil Eng 19(1):94\u2013103","journal-title":"J Comput Civil Eng"},{"issue":"2","key":"1991_CR25","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1080\/15472450902858384","volume":"13","author":"EI Vlahogianni","year":"2009","unstructured":"Vlahogianni EI (2009) Enhancing predictions in signalized arterials with information on short-term traffic flow dynamics. J Intell Transp Syst 13(2):73\u201384","journal-title":"J Intell Transp Syst"},{"issue":"3","key":"1991_CR26","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1109\/21.256541","volume":"23","author":"J Roger","year":"1993","unstructured":"Roger J (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665\u2013685","journal-title":"IEEE Trans Syst Man Cybern"},{"issue":"17","key":"1991_CR27","first-page":"4210","volume":"28","author":"WF Zhu","year":"2007","unstructured":"Zhu WF, Zhao SJ (2007) Optimal design of structure for neural networks based on rough sets. Comput Eng Des 28(17):4210\u20134212","journal-title":"Comput Eng Des"},{"issue":"27","key":"1991_CR28","first-page":"1637","volume":"12","author":"H Fu","year":"2010","unstructured":"Fu H, Xu LH, Hu G et al (2010) Traffic flow state-forecasting algorithm based on Sugeno neural fuzzy system. Control Theory Appl 12(27):1637\u20131640","journal-title":"Control Theory Appl"},{"issue":"4","key":"1991_CR29","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1109\/91.940970","volume":"9","author":"MJ Er","year":"2001","unstructured":"Er MJ, Wu SQ, Gao YA (2001) fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks. IEEE Trans Fuzzy Syst 9(4):578\u2013594","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"3","key":"1991_CR30","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0165-0114(01)00034-3","volume":"126","author":"MJ Er","year":"2002","unstructured":"Er MJ, Wu SQ (2002) A fast learning algorithm for parsimonious fuzzy neural system [J]. Fuzzy Sets Syst 126(3):337\u2013351","journal-title":"Fuzzy Sets Syst"},{"key":"1991_CR31","unstructured":"Tan W, Wang YN (2008) Fuzzy neural network control theory and method of chaotic system. Science Press, Beijing, pp 108\u2013150"},{"issue":"1","key":"1991_CR32","first-page":"9","volume":"39","author":"JH Yu","year":"2011","unstructured":"Yu JH, Peng YH (2011) Predictability study of ENSO with mutual information and Cao methods. Meteorol Sci Technol 39(1):9\u201312","journal-title":"Meteorol Sci Technol"},{"issue":"3","key":"1991_CR33","doi-asserted-by":"crossref","first-page":"1576","DOI":"10.7498\/aps.59.1576","volume":"59","author":"SQ Zhang","year":"2010","unstructured":"Zhang SQ, Jia J (2010) Study on the parameters determination for reconstructing phase-space in chaos time series. Acta Phys Sin 59(3):1576\u20131582","journal-title":"Acta Phys Sin"},{"key":"1991_CR34","first-page":"15","volume-title":"The chaotic time series analysis and its application","author":"JH Lv","year":"2001","unstructured":"Lv JH, Lu JN (2001) The chaotic time series analysis and its application. Wuhan University Press, Wuhan, pp 15\u201393"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-1991-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-015-1991-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-1991-z","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T22:59:51Z","timestamp":1653001191000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-015-1991-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,8,5]]},"references-count":34,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2016,10]]}},"alternative-id":["1991"],"URL":"https:\/\/doi.org\/10.1007\/s00521-015-1991-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,8,5]]}}}