A Comparative Study on Car Ownership Modeling by Applying Fuzzy Linear Regression and Artificial Neural Network: Case Study of IRAN
Koosha Rafiee, Ali Azadeh and Amir-Mohammad Zohrevand
The 2010 Summer Computer Simulation Conference (SCSC 10)
Ottawa, Canada, July 11-14, 2010
This paper models car ownership in Iran based on the data in a period of years 1980 to 2007 by artificial neural network (ANN) and Fuzzy Linear Regression (FLR). The car ownership is mainly affected by purchasing power of the customers, social and demographic factors, the car ownership model has a multi variable form. To explain the effect of these factors, ANN and FLR models are applied. The major reason for applying fuzzy concept and ANN is to overcome the interrelation problem associated with the independent variables. In this study, average family size; total population; urban population; urbanization rate; gross national product per capita; gasoline price; total length of road are considered as the independent variables and numbers of registered car is considered as response variable. Eight Fuzzy Linear Regression models are tested. In addition, each train method of artificial neural network release a different result, that leads to compare the train function based on the mean absolute percentage error (MAPE). ANN provides better estimation than FLR in Iran.
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