Forecasting air traffic volumes using smoothing techniques

Uzun yıllardır araştırmacılar makroekonomik modellere ait parametrelerin tahmininde istatistik araçlar kullanırlar. Tahminleme lojistik planlamada önemli bir yere sahiptir ve ülkelerin hava trafik stratejilerinin belirlenmesinde kullanılan bir sayısal yöntemdir. Bu araştırmada özellikle düzgünleştirme tekniklerinin uygulanabilirliği ve hava trafik yoğunluğunun tahminlenmesine odaklanılmıştır. Çalışma kapsamında toplam yolcu trafiği, toplam kargo trafiği, toplam uçak trafiği ve toplam ticari uçak trafiği olmak üzere dört hava trafik yoğunluğu parametresi incelenmiştir. Bunun yanı sıra bu parametrelere ait mevsimsel etkiler tespit edilmiştir. İstatistik analiz araçları olarak hareketli ortalamalar, üstel düzgünleştirme, Brown’ın tek parametreli doğrusal üstel düzgünleştirme yöntemi, Brown’ın ikinci derece üstel düzgünleştirme yöntemi, Holt’un iki parametreli doğrusal üstel düzgünleştirme yöntemi ve zaman serilerinin bileşenlere ayırma yöntemleri gibi klasik zaman serisi yöntemleri Ocak 2007-Mayıs 2013 döneminde gerçeklesen hava trafik yoğunluğu üzerinde uygulanmıştır. Araştırmada klasik zaman serisi yöntemlerinin (Düzgünleştirme ve Ayrıştırma) uygulanabilirliği üzerinde durulmuştur. Uygulamada Türkiye hava trafik yoğunluğuna ait dört parametre kullanılmıştır. Zaman serisi istatistiki altyapısı, metotları ve hata ortalamasından yararlanılarak uygun tekniğin seçimini sağlamıştır.

Hava trafik yoğunluğunun düzgünleştirme yöntemleri ile tahmini

For 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.

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[1] Gracht, H.A. and Darkow, I.L., (2010). Scenarios for the logistics services industry: A Delphi- based analysis for 2025, Int. J. Production Economics 127, 46–59
[2] Charles, M.B., Barnes, P., Ryanb, N. and Clayton, J., (2007). Airport futures: Towards a critique of the aerotropolis model, Futures 39, 1009 – 1028
[3] Carson, R.T., Cenesizoglu, T. and Parker, R., (2011).Forecasting (aggregate) demand for US commercial air travel, International Journal of Forecasting 27, 923–941
[4] China,A.T.H., Tay, J.H., (2001). Developments in air transport: implications on investment decisions, profitability and survival of Asian airlines, Journal of Air Transport Management 7, 319–330
[5] Adrangi, B., Chatrath, A. and Raffiee, K., (2001). The demand of US air transport service: a chaos and nonlinearity investigation, Transportation Research, Part E, 37, 337-353
[6] Jonga, G., Gunnc, H. and Akiva, M.B., (2004). A meta-model for passenger and freight transport in Europe Transport Policy 11, 329–344
[7] Matsumoto, H., (2004). International urban systems and air passenger and cargo flows: some calculations, Journal of Air Transport Management 10, 241–249
[8] Lee, H. S., (2009). The networkability of cities in the international air passenger flows 1992–2004. Journal of Transport Geography 17, 166–175
[9] Hui, G W. L., Hui, Y. V. and Zhang, A., (2004). Analyzing China’s air cargo flows and data. Journal of Air Transport Management 10, 125–135
[10] Hwang, C. C., Shiao, G. C., (2011). Analyzing air cargo flows of international routes: an empirical study of Taiwan Taoyuan International Airport. Journal of Transport Geography 19, 738–744
[11] Mason, K.J., (2005). Observations of fundamental changes in the demand for aviation services. Journal of Air Transport Management 1, 19– 25
[12] Matthiessen, C.W., (2004). International air traffic in the Baltic Sea Area: Hub-gateway status and prospects. Copenhagen in focus. Journal of Transport Geography 12, 197–206
[13] Sengupta, P., Tandale, M., Cheng, V., Menon, P., (2011) .Air Traffic Estimation and Decision Support for Stochastic Flow Management, American Institute of Aeronautics and Astronautics Guidance, Navigation, and Control Conference, 8-11 August 2011, Portland, Oregon
[14] Önder, E., Hasgül, O., 2009. Time Series Analysis with Using Box Jenkins Models and Artificial Neural Network for Forecasting Number of Foreign Visitors. Journal of Institute of Business Administration - Yönetim (20), 62, 62-83
[15] Orhunbilge, N., 1999, Time Series Analysis, Forecasting and Price Index. Istanbul University, School of Business Press, Publication No: 277, 11– 130. (In Turkish)
[16] www.dhmi.gov.tr/istatistik.aspx (17.09.2013)