title: Clustering-neural network models for freeway work zone capacity estimation.
abstract: 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.
authors.affiliation: Department of Biomedical Informatics, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, Ohio 43210, USA.
authors.forename: Xiaomo
authors.initials: X
authors.lastname: Jiang
authors.forename: Hojjat
authors.initials: H
authors.lastname: Adeli
mesh_terms: D016000:Cluster Analysis; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010561:Personnel Staffing and Scheduling / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D014887:Weather