Modelling public transport trips by radial basis function neural networks

Hilmi Berk Celikoglu*, Hikmet Kerem Cigizoglu

*Bu çalışma için yazışmadan sorumlu yazar

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35 Atıf (Scopus)

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.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)480-489
Sayfa sayısı10
DergiMathematical and Computer Modelling
Hacim45
Basın numarası3-4
DOI'lar
Yayın durumuYayınlandı - Şub 2007

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