Modelling public transport trips by radial basis function neural networks


Celikoglu H. B., CIGIZOGLU H. K.

MATHEMATICAL AND COMPUTER MODELLING, cilt.45, ss.480-489, 2007 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 45
  • Basım Tarihi: 2007
  • Doi Numarası: 10.1016/j.mcm.2006.07.002
  • Dergi Adı: MATHEMATICAL AND COMPUTER MODELLING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.480-489
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

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. (c) 2006 Elsevier Ltd. All rights reserved.