Modelling public transport trips by radial basis function neural networks
type: Item
instance of: scholarly article
title: Modelling public transport trips by radial basis function neural networks
zbMATH Open document ID: 1170.90344
author: Hilmi Berk Celikoglu
author: Hikmet Kerem Cigizoglu
published in: Mathematical and Computer Modelling
publication date: 2009-06-23T00:00:00Z
review text: Artificial neural networks (ANNs) are one of the recently explored advanced technologies, which show promise in the area of transportation engineering. The presented study used two different ANN algorithms, feed forward back-propagation (FFBP) and radial basis function (RBF), for the purpose of daily trip flow forecasting. The ANN predictions were quite close to the observations as reflected in the selected performance criteria. The selected stochastic model performance was quite poor compared with ANN results. It was seen that the RBF neural network did not provide negative forecasts in contrast to FFBP applications. Besides, the local minima problem faced by some FFBP algorithms was not encountered in RBF networks.
Mathematics Subject Classification ID: 90B20
Mathematics Subject Classification ID: 41A63
zbMATH DE Number: 5568271
zbMATH Keywords: Public transportation
zbMATH Keywords: Artificial neural networks
zbMATH Keywords: Feed-forward back-propagation algorithm
zbMATH Keywords: Radial basis function algorithm
zbMATH Keywords: Simulation
MaRDI profile type: Publication
full work available at URL: j.mcm.2006.07.002
OpenAlex ID: W1990670850
cites work: Optimal neural networks architectures for the flow-density relationships of traffic models
cites work: A method for the solution of certain non-linear problems in least squares
cites work: An Algorithm for Least-Squares Estimation of Nonlinear Parameters
cites work: Approximation by superpositions of a sigmoidal function
cites work: Learning representations by back-propagating errors
cites work: Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
cites work: Q4411108
cites work: Q3847819
DOI: 10.1016/J.MCM.2006.07.002
description: scientific article; zbMATH DE number 5568271