The economic evaluation of a new airport investment requires the use of estimated future air
passenger demand.Today it is well known that air passenger demand is basicly dependent on
various socioeconomic factors of the country and the region where the planned airport would
serve. This study is focused on estimating the future air passenger demand for planned
airports in Turkey where the historical air passsenger data is not available.For these
purposses, neural networks and multi-linear regression were used to develop forecasting
models.
As independent variables,twelve socioeconomic parameters are found to be significant and
used in models. The available data for the selected indicators are statistically analysed and it
is observed that most of the data is highly volatile, heteroscedastic and show no definite
patterns. In order to develop more reliable models, various methods like data transformation,
outlier elimination and categorization are applied to the data.Only seven of total twelve
indicators are used as the most significant in the regression model whereas in neural network
approach the best model is achieved when all the twelve indicators are included. Both
models can be used to predict air passenger demand for any future year for Or-Gi and Zafer
Airports and future air passenger demand for similar airports.
Regression and neural models are tested by using various statistical test methods and it is
found that neural network model is superior to regression model for the data used in this
study.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12612933/index.pdf |
| Date | 01 February 2011 |
| Creators | Yazici, Riza Onur |
| Contributors | Acar, Soner Osman |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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