{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T20:56:01Z","timestamp":1770843361138,"version":"3.50.1"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2015,2,1]],"date-time":"2015-02-01T00:00:00Z","timestamp":1422748800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2015,2,1]],"date-time":"2015-02-01T00:00:00Z","timestamp":1422748800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"name":"North Portugal Regional Operational Programme"},{"name":"National Strategic Reference Framework (NSRF)"},{"name":"European Regional Development Fund (ERDF)"},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia (FCT)"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2015,2]]},"DOI":"10.1016\/j.neucom.2014.08.072","type":"journal-article","created":{"date-parts":[[2014,10,5]],"date-time":"2014-10-05T16:23:48Z","timestamp":1412526228000},"page":"428-439","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":38,"special_numbering":"PB","title":["Improving the accuracy of long-term travel time prediction using heterogeneous ensembles"],"prefix":"10.1016","volume":"150","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2471-2833","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Mendes-Moreira","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5475-1382","authenticated-orcid":false,"given":"Al\u00edpio M\u00e1rio","family":"Jorge","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8880-6241","authenticated-orcid":false,"given":"Jorge","family":"Freire de Sousa","sequence":"additional","affiliation":[]},{"given":"Carlos","family":"Soares","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2014.08.072_bib1","doi-asserted-by":"crossref","unstructured":"B. Babcock, S. Babu, M. Datar, R. Motwani, J. Widom, Models and issues in data stream systems, in: PODS, 2002.","DOI":"10.1145\/543614.543615"},{"key":"10.1016\/j.neucom.2014.08.072_bib2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"10.1016\/j.neucom.2014.08.072_bib3","series-title":"Classification and Regression Trees","author":"Breiman","year":"1984"},{"key":"10.1016\/j.neucom.2014.08.072_bib4","doi-asserted-by":"crossref","unstructured":"R. Caruana, A. Niculescu-Mozil, G. Crew, A. Ksikes, Ensemble selection from libraries of models, in: International Conference on Machine Learning, 2004.","DOI":"10.1145\/1015330.1015432"},{"key":"10.1016\/j.neucom.2014.08.072_bib5","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1061\/(ASCE)0733-9488(2002)128:4(225)","article-title":"Urban transit scheduling","volume":"128","author":"Ceder","year":"2002","journal-title":"J. Urban Plan. Dev."},{"key":"10.1016\/j.neucom.2014.08.072_bib6","first-page":"913","article-title":"Creating bus timetables with maximal synchronization","volume":"35","author":"Ceder","year":"2001","journal-title":"Transp. Res. Part A"},{"key":"10.1016\/j.neucom.2014.08.072_bib7","doi-asserted-by":"crossref","unstructured":"G.P. Coelho, F.J. Von Zuben, The influence of the pool of candidates on the performance of selection and combination techniques in ensembles, in: International Joint Conference on Neural Networks, 2006, pp. 10588\u201310595.","DOI":"10.1109\/IJCNN.2006.247243"},{"key":"10.1016\/j.neucom.2014.08.072_bib8","unstructured":"T.G. Dias, A new approach to the bus driver scheduling problem using multiobjective genetic algorithms (Ph.D), 2005."},{"key":"10.1016\/j.neucom.2014.08.072_bib9","series-title":"International Workshop on Multiple Classifier Systems","first-page":"174","article-title":"Dynamic classifier selection by adaptive k-nearest neighbourhood rule","author":"Didaci","year":"2004"},{"key":"10.1016\/j.neucom.2014.08.072_bib10","doi-asserted-by":"crossref","DOI":"10.1145\/2523813","article-title":"A survey on concept drift adaptation","volume":"46","author":"Gama","year":"2014","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.neucom.2014.08.072_bib11","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1109\/TEVC.2005.844158","article-title":"Cooperative coevolution of artificial neural network ensembles for pattern classification","volume":"9","author":"Garc\u00eda-Pedrajas","year":"2005","journal-title":"IEEE Trans. Evol. Comput."},{"key":"10.1016\/j.neucom.2014.08.072_bib12","doi-asserted-by":"crossref","unstructured":"G. Giacinto, F. Roli, Adaptive selection of image classifiers, in: International Conference on Image Analysis and Processing, Springer, Florence, Italy, 1997, pp. 38\u201345.","DOI":"10.1007\/3-540-63507-6_182"},{"key":"10.1016\/j.neucom.2014.08.072_bib13","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1109\/34.506411","article-title":"Discriminant adaptive nearest neighbor classification","volume":"18","author":"Hastie","year":"1996","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2014.08.072_bib14","unstructured":"G. Klunder, P. Baas, F.O.D. Beek, A Long-term Travel Time Prediction Algorithm Using Historical Data, Technical Report. TNO, 2007."},{"key":"10.1016\/j.neucom.2014.08.072_bib15","first-page":"231","article-title":"Neural network ensembles, cross validation, and active learning","volume":"7","author":"Krogh","year":"1995","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2014.08.072_bib16","unstructured":"H.R. Louren\u00e7o, J.P. Paix\u00e3o, R. Portugal, The Crew-Scheduling Module in the GIST System, Technical Report UPF Economics, Working Paper No. 547. Universitat Pompeu, Spain, 2001."},{"key":"10.1016\/j.neucom.2014.08.072_bib17","unstructured":"J. Mendes-Moreira, Travel time prediction for the planning of mass transit companies: a machine learning approach (Ph.D. thesis), 2008."},{"key":"10.1016\/j.neucom.2014.08.072_bib18","series-title":"6th International Conference on Machine Learning and Data Mining","first-page":"191","article-title":"Ensemble learning","author":"Mendes-Moreira","year":"2009"},{"key":"10.1016\/j.neucom.2014.08.072_bib19","doi-asserted-by":"crossref","first-page":"427","DOI":"10.3233\/IDA-2012-0532","article-title":"Comparing state-of-the-art regression methods for long term travel time prediction","volume":"16","author":"Mendes-Moreira","year":"2012","journal-title":"Intell. Data Anal."},{"key":"10.1016\/j.neucom.2014.08.072_bib20","doi-asserted-by":"crossref","DOI":"10.1145\/2379776.2379786","article-title":"Ensemble approaches for regression","volume":"45","author":"Mendes-Moreira","year":"2012","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.neucom.2014.08.072_bib21","series-title":"International Workshop on Artificial Intelligence and Statistics","article-title":"Dynamical selection of learning algorithms","author":"Merz","year":"1996"},{"key":"10.1016\/j.neucom.2014.08.072_bib22","unstructured":"C.J. Merz, Classification and regression by combining models (Ph.D. thesis), 1998."},{"key":"10.1016\/j.neucom.2014.08.072_bib23","unstructured":"C.J. Merz, M.J. Pazzani, Combining neural network regression estimates with regularized linear weights, in: M. Mozer, M.I. Jordan, T. Petsche (Eds.), Advances in Neural Information Processing Systems, vol. 9, 1996, pp. 564\u2013570."},{"key":"10.1016\/j.neucom.2014.08.072_bib24","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/S0925-2312(03)00431-4","article-title":"The support vector machine under test","volume":"55","author":"Meyer","year":"2003","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2014.08.072_bib25","doi-asserted-by":"crossref","unstructured":"S. Puuronen, V. Terziyan, A. Tsymbal, A dynamic integration algorithm for an ensemble of classifiers, in: International Symposium on Methodologies for Intelligent Systems, Springer, 1999, pp. 592\u2013600.","DOI":"10.1007\/BFb0095148"},{"key":"10.1016\/j.neucom.2014.08.072_bib26","unstructured":"R Development Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2011. URL: \u3008http:\/\/www.R-project.org\/\u3009. ISBN 3-900051-07-0."},{"key":"10.1016\/j.neucom.2014.08.072_bib27","doi-asserted-by":"crossref","unstructured":"M. Robnik-S\u02d8ikonja, Improving random forests, in: European Conference on Machine Learning, Springer, Poznan, Poland, 2004, pp. 359\u2013370.","DOI":"10.1007\/978-3-540-30115-8_34"},{"key":"10.1016\/j.neucom.2014.08.072_bib28","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1023\/A:1025667309714","article-title":"Theoretical and empirical analysis of relieff and rrelieff","volume":"53","author":"Robnik-S\u02d8ikonja","year":"2003","journal-title":"Mach. Learn."},{"key":"10.1016\/j.neucom.2014.08.072_bib29","doi-asserted-by":"crossref","unstructured":"F. Roli, G. Giacinto, G. Vernazza, Methods for designing multiple classifier systems, in: International Workshop on Multiple Classifier Systems, Springer, 2001, pp. 78\u201387.","DOI":"10.1007\/3-540-48219-9_8"},{"key":"10.1016\/j.neucom.2014.08.072_bib30","doi-asserted-by":"crossref","unstructured":"N. Rooney, D. Patterson, S. Anand, A. Tsymbal, Dynamic integration of regression models, in: International Workshop on Multiple Classifier Systems, Springer, 2004, pp. 164\u2013173.","DOI":"10.1007\/978-3-540-25966-4_16"},{"key":"10.1016\/j.neucom.2014.08.072_bib31","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1109\/TITS.2010.2090521","article-title":"Travel time prediction using floating car data applied to logistics planning","volume":"12","author":"Simroth","year":"2011","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"10.1016\/j.neucom.2014.08.072_bib32","doi-asserted-by":"crossref","first-page":"647","DOI":"10.2116\/analsci.24.647","article-title":"Random subspace regression ensemble for near-infrared spectroscopic calibration of tobacco samples","volume":"24","author":"Tan","year":"2008","journal-title":"Anal. Sci."},{"key":"10.1016\/j.neucom.2014.08.072_bib33","first-page":"419","article-title":"Combining estimators using non-constant weighting functions","volume":"7","author":"Tresp","year":"1995","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2014.08.072_bib34","doi-asserted-by":"crossref","unstructured":"A. Tsymbal, M. Pechenizkiy, P. Cunningham, Dynamic Integration with Random Forests, Technical Report TCD-CS-2006-23, The University of Dublin, Trinity College, 2006.","DOI":"10.1007\/11871842_82"},{"key":"10.1016\/j.neucom.2014.08.072_bib35","doi-asserted-by":"crossref","unstructured":"A. Tsymbal, S. Puuronen, Bagging and boosting with dynamic integration of classifiers, in: Principles of Data Mining and Knowledge Discovery, Springer, 2000, pp. 116\u2013125.","DOI":"10.1007\/3-540-45372-5_12"},{"key":"10.1016\/j.neucom.2014.08.072_bib36","series-title":"Urban Transit: Operations, Planning, and Economics","author":"Vuchic","year":"2005"},{"key":"10.1016\/j.neucom.2014.08.072_bib37","doi-asserted-by":"crossref","unstructured":"H. Wang, W. Fan, P.S. Yu, J. Han, Mining concept-drifting data streams using ensemble classifiers, in: ACM International Conference on Knowledge Discovery and Data Mining, 2003.","DOI":"10.1145\/956755.956778"},{"key":"10.1016\/j.neucom.2014.08.072_bib38","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1109\/TKDE.2004.29","article-title":"Multistrategy ensemble learning","volume":"16","author":"Webb","year":"2004","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2014.08.072_bib39","unstructured":"J. Wichard, C. Merkwirth, M. Ogorzalek, Building ensembles with heterogeneous models, in: Course of the International School on Neural Nets, Salerno, Italy, 2003."},{"key":"10.1016\/j.neucom.2014.08.072_bib40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1613\/jair.346","article-title":"Improved heterogeneous distance functions","volume":"6","author":"Wilson","year":"1997","journal-title":"J. Artif. Intell. Res."},{"key":"10.1016\/j.neucom.2014.08.072_bib41","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","article-title":"Stacked generalization","volume":"5","author":"Wolpert","year":"1992","journal-title":"Neural Netw."},{"key":"10.1016\/j.neucom.2014.08.072_bib42","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1109\/34.588027","article-title":"Combination of multiple classifiers using local accuracy estimates","volume":"19","author":"Woods","year":"1997","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231214012351?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231214012351?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T18:18:33Z","timestamp":1760379513000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231214012351"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,2]]},"references-count":42,"alternative-id":["S0925231214012351"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2014.08.072","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2015,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Improving the accuracy of long-term travel time prediction using heterogeneous ensembles","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2014.08.072","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"Copyright \u00a9 2014 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}