Abstract
AbstractFor many years, researchers have been using statistical tools to estimate parameters of macroeconomic models. Forecasting plays a major role in logistic planning and it is an essential analytical tool in countries’ air traffic strategies. In recent years, researchers are developing new techniques for estimation. In particular, this research focuses on the application of smoothing techniques and estimation of air traffic volume. In this study four air traffic indicators including total passenger traffic, total cargo traffic, total flight traffic and commercial flight traffic were used for forecasting. Also seasonal effects of these parameters were investigated. As analysis tools, classical time series forecasting methods such as moving averages, exponential smoothing, Brown’s single parameter linear exponential smoothing, Brown’s second-order exponential smoothing, Holt’s two parameter linear exponential smoothing and decomposition methods applied to air traffic volume data between January 2007 and May 2013. The study focuses mainly on the applicability of Traditional Time Series Analysis (Smoothing & Decomposition Techniques). To facilitate the presentation, an empirical example is developed to forecast Turkey’s four important air traffic parameters. Time Series statistical theory and methods are used to select an adequate technique, based on residual analysis.
Highlights
Forecasting is the center tool of the planning and control processes
Air transport is an important part of logistic sector
There are not sufficient logistics researches about air traffic forecasting
Summary
Forecasting is the center tool of the planning and control processes. The objective of forecasting is to provide information that can be used to evaluate and clarify the effects of uncertainty about the future. We apply forecasting techniques to the air traffic data for the future of the air logistics services industry till the year 2023. International Airport during the years 2004–2007 [10] Their results indicate that population, air freight rate and three dummy variables, including the regional economic bloc of the ‘‘Chinese Circle”(an informal partnership between Hong Kong, Macao, Taiwan and mainland China), the Open Sky Agreements and long established colonial links, are key determinants of international air cargo flows from/to Taiwan. Onder and Hasgul (2009) used traditional time series analysis and Box Jenkins’ models and artificial neural network forecasting method to forecast international tourism arrivals to Turkey for 2008-2010 based on data period 1986-2007 [14] They found that Winter’s seasonal exponential smoothing technique and artificial neural networks are two successful estimator methods for regarding monthly time series data. Carson et al (2011) analyze whether it is better to forecast air travel demand using aggregate data at a national level, or to aggregate the forecasts derived for individual airports using airport-specific data [3]
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