Open AccessApplication of MLP and RBF Methods in Prediction of Travelling within thecity
Author(s) -
Mehdi Nosrati,
Mohammad Hoseini,
Alireza Shirmarz,
Abbas Mirzaei Somarin,
Nayereh Hoseininia,
Morteza Barari
Publication year - 2016
Publication title -
bulletin de la société royale des sciences de liège
Language(s) - English
Resource type - Journals
ISSN - 1783-5720
DOI - 10.25518/0037-9565.6125
Subject(s) - radial basis function , computer science , multilayer perceptron , subway station , perceptron , service (business) , artificial intelligence , function (biology) , operations research , artificial neural network , transport engineering , engineering , business , marketing , evolutionary biology , biology
Forecasting ofTravelling within the city demand is necessary for the correct operations of subway stations. This includes the provision of station security, management and better service to passengers will be. In this paper we are compared multilayer perceptron(MLP) and Radial Basis Function (RBF) models together for prediction of travelling within the city. The models are trained and assessed on dataset of Aliabad subway station in Tehran.