{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T05:21:33Z","timestamp":1773120093048,"version":"3.50.1"},"reference-count":14,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2004,6]]},"abstract":"<jats:p>Two neural network models, called clustering-RBFNN and clustering-BPNN models, are created for estimating the work zone capacity in a freeway work zone as a function of seventeen different factors through judicious integration of the subtractive clustering approach with the radial basis function (RBF) and the backpropagation (BP) neural network models. The clustering-RBFNN model has the attractive characteristics of training stability, accuracy, and quick convergence. The results of validation indicate that the work zone capacity can be estimated by clustering-neural network models in general with an error of less than 10%, even with limited data available to train the models. The clustering-RBFNN model is used to study several main factors affecting work zone capacity. The results of such parametric studies can assist work zone engineers and highway agencies to create effective traffic management plans (TMP) for work zones quantitatively and objectively.<\/jats:p>","DOI":"10.1142\/s0129065704001954","type":"journal-article","created":{"date-parts":[[2004,7,6]],"date-time":"2004-07-06T09:27:27Z","timestamp":1089106047000},"page":"147-163","source":"Crossref","is-referenced-by-count":35,"title":["CLUSTERING-NEURAL NETWORK MODELS FOR FREEWAY WORK ZONE CAPACITY ESTIMATION"],"prefix":"10.1142","volume":"14","author":[{"given":"XIAOMO","family":"JIANG","sequence":"first","affiliation":[{"name":"Departments of Biomedical Informatics, Civil and Environmental Engineering and Geodetic Science and Neuroscience, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, Ohio 43210, USA"}]},{"given":"HOJJAT","family":"ADELI","sequence":"additional","affiliation":[{"name":"Departments of Biomedical Informatics, Civil and Environmental Engineering and Geodetic Science and Neuroscience, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, Ohio 43210, USA"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1111\/0885-9507.00219"},{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1016\/0096-3003(94)90134-1"},{"key":"rf3","volume-title":"Machine Learning \u2014 Neural Networks, Genetic Algorithms, and Fuzzy Sets","author":"Adeli H.","year":"1995"},{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(2003)129:5(484)"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(2000)126:6(464)"},{"key":"rf6","doi-asserted-by":"crossref","DOI":"10.1201\/9781482267686","volume-title":"Construction Scheduling, Cost Optimization, and Management \u2014 A New Model Based on Neurocomputing and Object Technologies","author":"Adeli H.","year":"2001"},{"key":"rf7","doi-asserted-by":"publisher","DOI":"10.1201\/9781315214764"},{"key":"rf9","doi-asserted-by":"crossref","first-page":"267","DOI":"10.3233\/IFS-1994-2306","volume":"2","author":"Chiu S.","journal-title":"J. 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