{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T13:18:24Z","timestamp":1770470304419,"version":"3.49.0"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2012,10,16]],"date-time":"2012-10-16T00:00:00Z","timestamp":1350345600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2013,11]]},"DOI":"10.1007\/s00521-012-1114-z","type":"journal-article","created":{"date-parts":[[2012,10,17]],"date-time":"2012-10-17T13:06:14Z","timestamp":1350479174000},"page":"1611-1629","source":"Crossref","is-referenced-by-count":33,"title":["Identifying important variables for predicting travel time of freeway with non-recurrent congestion with neural networks"],"prefix":"10.1007","volume":"23","author":[{"given":"Chi-Sen","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mu-Chen","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2012,10,16]]},"reference":[{"issue":"3","key":"1114_CR1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1002\/atr.5670390305","volume":"39","author":"WHK Lam","year":"2005","unstructured":"Lam WHK, Chan KS, Tam ML, Shi JWZ (2005) Short-term travel time forecasts for transport information system in Hong Kong. J Adv Transp 39(3):289\u2013306","journal-title":"J Adv Transp"},{"key":"1114_CR2","doi-asserted-by":"crossref","unstructured":"Chin SM, Franzese O, Greene DL, Hwang HL, Gibson RC (2004) Temporary loss of freeway capacity and impacts on performance: phase 2. Report No. ORLNL\/TM-2004\/209, Oak Ridge National Laboratory","DOI":"10.2172\/885576"},{"key":"1114_CR3","doi-asserted-by":"crossref","first-page":"118","DOI":"10.3141\/1856-12","volume":"1856","author":"A Skabardonis","year":"2003","unstructured":"Skabardonis A, Varaiya P, Petty KF (2003) Measuring recurrent and nonrecurrent traffic congestion. Transp Res Record 1856:118\u2013124","journal-title":"Transp Res Record"},{"issue":"8","key":"1114_CR4","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1016\/j.ssci.2005.04.004","volume":"43","author":"LY Chang","year":"2005","unstructured":"Chang LY (2005) Analysis of freeway accident frequencies: negative binomial regression versus artificial neural network. Saf Sci 43(8):541\u2013557","journal-title":"Saf Sci"},{"issue":"3","key":"1114_CR5","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1016\/j.ress.2008.08.005","volume":"94","author":"TV Santosh","year":"2009","unstructured":"Santosh TV, Srivastava A, Sanyasi Rao VVS, Ghosh AK, Kushwaha HS (2009) Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks. Reliab Eng Syst Saf 94(3):759\u2013762","journal-title":"Reliab Eng Syst Saf"},{"issue":"3","key":"1114_CR6","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/S0304-3800(02)00257-0","volume":"160","author":"M Gevrey","year":"2003","unstructured":"Gevrey M, Dimopoulos I, Lek S (2003) Review and comparison of methods to study the contribution of variables in Artificial Neural Network models. Ecol Model 160(3):249\u2013264","journal-title":"Ecol Model"},{"issue":"1","key":"1114_CR7","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.aap.2009.08.005","volume":"42","author":"Y Chung","year":"2010","unstructured":"Chung Y (2010) Development of an accident duration prediction model on the Korean Freeway Systems. Accid Anal Prev 42(1):282\u2013289","journal-title":"Accid Anal Prev"},{"issue":"4","key":"1114_CR8","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1016\/j.aap.2006.12.017","volume":"39","author":"CH Wei","year":"2007","unstructured":"Wei CH, Lee Y (2007) Sequential forecast of incident duration using Artificial Neural Network models. Accid Anal Prev 39(4):944\u2013954","journal-title":"Accid Anal Prev"},{"issue":"1","key":"1114_CR9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0965-8564(00)00034-3","volume":"36","author":"RW Hall","year":"2002","unstructured":"Hall RW (2002) Incident dispatching, clearance and delay. Transp Res Part A Policy Pract 36(1):1\u201316","journal-title":"Transp Res Part A Policy Pract"},{"issue":"6","key":"1114_CR10","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(608)","volume":"129","author":"SIJ Chien","year":"2003","unstructured":"Chien SIJ, Kuchipudi CM (2003) Dynamic travel time prediction with real-time and historic data. J Transp Eng 129(6):608\u2013616","journal-title":"J Transp Eng"},{"issue":"2","key":"1114_CR11","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/S0968-090X(03)00004-4","volume":"11","author":"A Stathopoulos","year":"2003","unstructured":"Stathopoulos A, Karlaftis MG (2003) A multivariate state space approach for urban traffic flow modeling and prediction. Transp Res Part C Emerg Technol 11(2):121\u2013135","journal-title":"Transp Res Part C Emerg Technol"},{"issue":"3","key":"1114_CR12","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1080\/03081060500120340","volume":"28","author":"M Zhong","year":"2005","unstructured":"Zhong M, Sharma S, Lingras P (2005) Refining genetically designed models for improved traffic prediction on rural roads. Transp Plan Technol 28(3):213\u2013236","journal-title":"Transp Plan Technol"},{"key":"1114_CR13","first-page":"1","volume":"722","author":"MS Hamed","year":"1979","unstructured":"Hamed MS, Cook AR (1979) Analysis of freeway traffic time-series data by using Box\u2013Jenkins techniques. Transp Res Record 722:1\u20139","journal-title":"Transp Res Record"},{"issue":"3","key":"1114_CR14","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1061\/(ASCE)0733-947X(1995)121:3(249)","volume":"121","author":"MM Hamed","year":"1995","unstructured":"Hamed MM, Al-Masaeid HR, Said ZM (1995) Short-term prediction of traffic volume in urban arterials. J Transp Eng 121(3):249\u2013254","journal-title":"J Transp Eng"},{"issue":"5","key":"1114_CR15","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0968-090X(97)82903-8","volume":"4","author":"MV Voort Der","year":"1996","unstructured":"Der Voort MV, Dougherty M, Watson S (1996) Combining Kohonen maps with ARIMA time series models to forecast traffic flow. Transp Res Part C Emerg Technol 4(5):307\u2013318","journal-title":"Transp Res Part C Emerg Technol"},{"key":"1114_CR16","doi-asserted-by":"crossref","first-page":"39","DOI":"10.3141\/1651-06","volume":"1651","author":"B Park","year":"1998","unstructured":"Park B, Messer CJ, Urbanik TII (1998) Short-term traffic volume forecasting using radial basis function neural network. Transp Res Record 1651:39\u201347","journal-title":"Transp Res Record"},{"key":"1114_CR17","doi-asserted-by":"crossref","first-page":"179","DOI":"10.3141\/1678-22","volume":"1678","author":"S Lee","year":"1999","unstructured":"Lee S, Fambro D (1999) Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting. Transp Res Record 1678:179\u2013188","journal-title":"Transp Res Record"},{"issue":"3, Part 2","key":"1114_CR18","doi-asserted-by":"crossref","first-page":"6164","DOI":"10.1016\/j.eswa.2008.07.069","volume":"36","author":"M Castro-Neto","year":"2009","unstructured":"Castro-Neto M, Jeong Y-S, Jeong M-K, Han LD (2009) Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Syst Appl 36(3, Part 2):6164\u20136173","journal-title":"Expert Syst Appl"},{"issue":"4","key":"1114_CR19","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1109\/TITS.2004.837813","volume":"5","author":"CH Wu","year":"2004","unstructured":"Wu CH, Wei CC, Su DC, Chan MH, Ho JM (2004) Travel time prediction with support vector regression. IEEE Trans Intell Transp Syst 5(4):276\u2013281","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"2","key":"1114_CR20","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/BF00167127","volume":"9","author":"NL Nihan","year":"1980","unstructured":"Nihan NL, Holmesland KO (1980) Use of the box and Jenkins time series technique in traffic forecasting. Transportation 9(2):125\u2013143","journal-title":"Transportation"},{"issue":"4","key":"1114_CR21","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.trb.2007.08.005","volume":"42","author":"J Yeon","year":"2008","unstructured":"Yeon J, Elefteriadou L, Lawphongpanich S (2008) Travel time estimation on a freeway using Discrete Time Markov Chains. Transp Res Part B Methodol 42(4):325\u2013338","journal-title":"Transp Res Part B Methodol"},{"issue":"6","key":"1114_CR22","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1061\/(ASCE)0733-947X(1999)125:6(515)","volume":"125","author":"D Park","year":"1999","unstructured":"Park D, Rilett L, Han G (1999) Spectral basis neural networks for realtime travel time forecasting. J Transp Eng 125(6):515\u2013523","journal-title":"J Transp Eng"},{"issue":"5\u20136","key":"1114_CR23","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.trc.2005.03.001","volume":"13","author":"JWC Lint Van","year":"2005","unstructured":"Van Lint JWC, Hoogendoorn SP, Van Zuylen HJ (2005) Accurate freeway travel time prediction with state-space neural networks under missing data. Transp Res Part C Emerg Technol 13(5\u20136):347\u2013369","journal-title":"Transp Res Part C Emerg Technol"},{"issue":"6","key":"1114_CR24","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1007\/s11116-005-0219-y","volume":"32","author":"S Innamaa","year":"2005","unstructured":"Innamaa S (2005) Short-term prediction of travel time using neural networks on an interurban freeway. Transportation 32(6):649\u2013669","journal-title":"Transportation"},{"issue":"3\u20134","key":"1114_CR25","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/S0968-090X(03)00026-3","volume":"11","author":"X Zhang","year":"2003","unstructured":"Zhang X, Rice JA (2003) Short-term travel time prediction. Transp Res Part C Emerg Technol 11(3\u20134):187\u2013210","journal-title":"Transp Res Part C Emerg Technol"},{"issue":"1","key":"1114_CR26","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/TITS.2008.915649","volume":"9","author":"JWC Lint Van","year":"2008","unstructured":"Van Lint JWC (2008) Online learning solutions for freeway travel time prediction. Trans Intell Transp Syst 9(1):38\u201347","journal-title":"Trans Intell Transp Syst"},{"issue":"6","key":"1114_CR27","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1016\/j.trc.2010.10.005","volume":"19","author":"X Fei","year":"2011","unstructured":"Fei X, Lu CC, Liu K (2011) A Bayesian dynamic linear model approach for real-time short term freeway travel time prediction. Transp Res Part C Emerg Technol 19(6):1306\u20131318","journal-title":"Transp Res Part C Emerg Technol"},{"key":"1114_CR28","doi-asserted-by":"crossref","first-page":"45","DOI":"10.3141\/1836-07","volume":"1836","author":"S Ishak","year":"2003","unstructured":"Ishak S, Kotha P, Alecsandru C (2003) Optimization of dynamic neural network performance for short-term traffic prediction. Transp Res Record 1836:45\u201356","journal-title":"Transp Res Record"},{"issue":"4","key":"1114_CR29","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1061\/(ASCE)0733-947X(2004)130:4(452)","volume":"130","author":"S Ishak","year":"2004","unstructured":"Ishak S, Alecsandru C (2004) Optimizing traffic prediction performance of neural networks under various topological input, and traffic condition settings. J Transp Eng 130(4):452\u2013465","journal-title":"J Transp Eng"},{"key":"1114_CR30","doi-asserted-by":"crossref","first-page":"17","DOI":"10.3141\/1879-03","volume":"1879","author":"H Xiao","year":"2004","unstructured":"Xiao H, Sun H, Ran B, Oh Y (2004) Special factor adjustment model using fuzzy-neural network in traffic prediction. Transp Res Record 1879:17\u201323","journal-title":"Transp Res Record"},{"key":"1114_CR31","doi-asserted-by":"crossref","unstructured":"Vanajakshi L, Rilett LR (2004) A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed. 2004 IEEE Intell Veh Symp 194\u2013199","DOI":"10.1109\/IVS.2004.1336380"},{"issue":"2","key":"1114_CR32","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/S0377-2217(00)00125-9","volume":"131","author":"H Dia","year":"2001","unstructured":"Dia H (2001) An object-oriented neural network approach to short-term traffic forecasting. Eur J Oper Res 131(2):253\u2013261","journal-title":"Eur J Oper Res"},{"key":"1114_CR33","author":"A Najah","year":"2012","unstructured":"Najah A, El-Shafie A, Karim OA, El-Shafie AH (2012) Application of artificial neural networks for water quality prediction. Neural Comput Appl. doi: 10.1007\/s00521-012-0940-3","journal-title":"Neural Comput Appl"},{"issue":"1","key":"1114_CR34","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s00521-011-0605-7","volume":"21","author":"M Reza Peyghami","year":"2012","unstructured":"Reza Peyghami M, Khanduzi R (2012) Predictability and forecasting automotive price based on a hybrid train algorithm of MLP neural network. Neural Comput Appl 21(1):125\u2013132","journal-title":"Neural Comput Appl"},{"issue":"3\u20134","key":"1114_CR35","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/S0968-090X(03)00020-2","volume":"11","author":"F Yuan","year":"2003","unstructured":"Yuan F, Cheu RL (2003) Incident detection using support vector machines. Transp Res Part C Emerg Technol 11(3\u20134):309\u2013328","journal-title":"Transp Res Part C Emerg Technol"},{"issue":"4","key":"1114_CR36","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.jsr.2005.06.013","volume":"36","author":"LY Chang","year":"2005","unstructured":"Chang LY, Chen WC (2005) Data mining of tree-based models to analyze freeway accident frequency. J Saf Res 36(4):365\u2013375","journal-title":"J Saf Res"},{"issue":"9","key":"1114_CR37","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1016\/j.trb.2005.10.002","volume":"40","author":"F Dion","year":"2006","unstructured":"Dion F, Rakha H (2006) Estimating dynamic roadway travel times using automatic vehicle identification data for low sampling rates. Transp Res Part B Methodol 40(9):745\u2013766","journal-title":"Transp Res Part B Methodol"},{"issue":"1","key":"1114_CR38","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.inffus.2010.01.002","volume":"12","author":"ML Tam","year":"2010","unstructured":"Tam ML, Lam William HK (2010) Application of automatic vehicle identification technology for real-time journey time estimation. Information Fusion 12(1):11\u201319","journal-title":"Information Fusion"},{"issue":"1","key":"1114_CR39","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.trc.2008.05.002","volume":"17","author":"X Liu Henry","year":"2009","unstructured":"Liu Henry X, Ma W (2009) A virtual vehicle probe model for time-dependent travel time estimation on signalized arterials. Transp Res Part C Emerg Technol 17(1):11\u201326","journal-title":"Transp Res Part C Emerg Technol"},{"key":"1114_CR40","volume-title":"Automatic vehicle identification model deployment initiative\u2014system design document. Report prepared for TransGuide","author":"R Sw","year":"1998","unstructured":"Sw R (1998) Automatic vehicle identification model deployment initiative\u2014system design document. Report prepared for TransGuide. Texas Department of Transportation, Southwest Research Institute, San Antonio"},{"key":"1114_CR41","unstructured":"Mouskos KC, Niver E, Pignataro LJ, Lee S (1998) Transmit system evaluation. Final Report. Institute for Transportation. New Jersey Institute of Technology, Newark, NJ"},{"key":"1114_CR42","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representation by error propagation. In: Rumelhart DE, McClelland JL, PDP Research Group (eds) Parallel distributed processing: explorations in the microstructure of cognition: foundations, vol 1. MIT Press, Cambridge, pp 318\u2013362"},{"issue":"1","key":"1114_CR43","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/S0957-4174(99)00016-0","volume":"17","author":"A Vellido","year":"1999","unstructured":"Vellido A, Lisboa PJG, Vaughan J (1999) Neural networks in business a survey of applications (1992\u20131998). Expert Syst Appl 17(1):51\u201370","journal-title":"Expert Syst Appl"},{"key":"1114_CR44","volume-title":"Neural networks: a classroom approach","author":"S Kumar","year":"2005","unstructured":"Kumar S (2005) Neural networks: a classroom approach. McGraw-Hill, New York"},{"issue":"3","key":"1114_CR45","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/j.aap.2009.09.022","volume":"42","author":"P Konstantopoulos","year":"2010","unstructured":"Konstantopoulos P, Chapman P, Crundall D (2010) Driver\u2019s visual attention as a function of driving experience and visibility. Using a driving simulator to explore drivers\u2019 eye movements in day, night and rain driving. Accid Anal Prev 42(3):827\u2013834","journal-title":"Accid Anal Prev"},{"issue":"3","key":"1114_CR46","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1016\/j.engappai.2010.11.004","volume":"24","author":"E Mazloumi","year":"2011","unstructured":"Mazloumi E, Rose G, Currie G, Moridpour S (2011) Prediction intervals to account for uncertainties in neural network predictions: methodology and application in bus travel time prediction. Eng Appl Artif Intell 24(3):534\u2013542","journal-title":"Eng Appl Artif Intell"},{"key":"1114_CR47","volume-title":"International and business forecasting methods","author":"CD Lewis","year":"1982","unstructured":"Lewis CD (1982) International and business forecasting methods. Butterworths, London"},{"issue":"2","key":"1114_CR48","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1177\/1094428103251907","volume":"6","author":"KB DeTienne","year":"2003","unstructured":"DeTienne KB, Detienne DH, Joshi SA (2003) Neural networks as statistical tools for business researchers. Organ Res Methods 6(2):236\u2013265","journal-title":"Organ Res Methods"},{"issue":"3","key":"1114_CR49","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.trc.2010.10.004","volume":"19","author":"MG Karlaftis","year":"2011","unstructured":"Karlaftis MG, Vlahogianni EI (2011) Statistical methods versus neural networks in transportation research: differences, similarities and some insights. Transp Res Part C Emerg Technol 19(3):387\u2013399","journal-title":"Transp Res Part C Emerg Technol"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-012-1114-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-012-1114-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-012-1114-z","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,7,4]],"date-time":"2019-07-04T15:19:23Z","timestamp":1562253563000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-012-1114-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,10,16]]},"references-count":49,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2013,11]]}},"alternative-id":["1114"],"URL":"https:\/\/doi.org\/10.1007\/s00521-012-1114-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,10,16]]}}}