Estimating a Regional Airport Air Passenger Demand Using an Artificial Neural Network Approach: The Case of Huahin Airport, Thailand
Loading...
Date
2022-03-20
ISBN
Journal Title
Journal ISSN
Volume Title
Resource Type
Article
Publisher
LAMBERT Academic Pubishing
Journal Title
Estimating a Regional Airport Air Passenger Demand Using an Artificial Neural Network Approach: The Case of Huahin Airport, Thailand
Recommended by
Abstract
Abstract: Artificial neural networks (ANNs) are a promising modelling approach for predicting
an airport’s air passenger demand. The study proposed and empirically tested an artificial
neural network model to predict the annual passenger demand for Huahin Airport, a regional
and tourist focused airport located in Thailand. The ANN input variables included Thailand’s
population size, Thailand’s real GDP, world jet fuel prices, Thailand total passengers carried,
Thailand’s tourist numbers and Thailand’s unemployment rates. The data were trained using
the Levenberg-Marquandt back-propagation algorithm. The ANN comprises eight neurons
in the hidden layer and one neuron in the output layer. 80 per cent of the data was used in
the training phase with the remaining data divided into validation (10 per cent) and testing
(10 per cent) phases. The proposed ANN provided very accurate prediction values. The
coefficient of determination R value of model was around 0.995, and the mean absolute
percentage error (MAPE) of the final ANN model was 13.27%. The study found that the four
key determinants of Huahin Airport annual air passenger demand were Thailand population
size, the commencement of AirAsia services at Huahin Airport, Thailand’s tourist numbers,
and Thailand’s real GDP.