Air Transportation Demand Estimation in Turkey by Arima Models

Abstract

The air transportation, which is gradually become more important and widespread in our country, is as the day goes on getting more attention and more demand in domestic ights except for using just for internal ights. In terms of knowing the demand for air transportation, institutions and companies determine of the strategy, pricing policy and infrastructure work. In this study, under the framework of ARIMA models which were used in univariate time series analysis and based on the internal dynamics of air transport between 1991-2008, monthly number of passengers forecasts for the year 2009 are carried out and reliability of the forecasts evaluated.

Keywords

• Time series,air transport,demand estimation,ARIMA models

Türkiye’de Hava Ulaşım Talebinin Arıma Modelleri ile Tahmin Edilmesi

Abstract

Ülkemizdeki ulaşım ağları içerisinde gittikçe önem kazanan ve yaygınlaşan havayolu ulaşımı, dış hatlar diye nitelenen ülkeler arası ulaşımda ilk tercih edilen ulaşım yolu olmasının yanında, iç hatlar diye nitelenen şehirlerarası yolculukta da geçmişe nazaran çok daha fazla ilgi görmekte ve havayolu ulaşımına talep gün geçtikçe artmaktadır. Havayolu ulaşım talebinin bilinmesi, kurumlar ve şirketler için strateji belirleme, yat politikalarını oluşturma ve altyapı çalışmaları açısından büyük bir avantaj sağlayacaktır. Bu çalışmada, tek değişkenli zaman serileri analizlerinde kullanılan ARIMA modelleri çerçevesinde, 1991–2008 yılları arasındaki süre zarfında, havayolu ulaşımının kendi dinamiklerinden hareketle 2009 yılı için aylık yolcu sayılarının tahmin edilmesi gerçekleştirilmiş ve elde edilen tahminlerin güvenilirliği değerlendirilmiştir. 

Keywords

• Zaman serileri,havayolu ulaşımı,talep tahmini,ARIMA modelleri

References

1. Abdel-Aty, M., Abdelwahab, H., 2004. Analysis and prediction of traf c fatalities resulting from angle collisions including the effect of vehicles’ con guration and compatibility, Accident Analysis and Prevention 36: 457–469.
2. Ahmed, M.S., Cook, A.R., 1979. Analysis of freeway traf c time-series data by using Box–Jenkins techniques”, Transportation Research Board 722: 1–9.
3. Anonim,, 2008. “SHGM 2008-2012 Stratejik Plan”, Ankara,.
4. Babcock, M.W., Lu, X., 2002. Forecasting inland waterway grain traf c, Transportation Research Part E: Logistics and Transportation Review 38 (1): 65–74.
5. Box, G.E.P. and Jenkins G.M., 1970. Time Series Analysis Forecasting and Control, Holden Day Inc., 185 p, San Fransisco.
6. Cho, V., 2003. A comparison of three different approaches to tourist arrival forecasting, Tourism Management 24 (3): 323–330.
7. Çodur, M.Y., 2012. Tra k Kaza Tahmin Modelleri: Erzurum İli Çevre Karayolları İçin Uygulamalar, Doktora Tezi, Atatürk Üniversitesi, Fen Bilimler Enstitüsü.
8. Davis, G.A., Niham, N.L., Hamed, M.M., Jacobson, L.N., 1991. Adaptive forecasting of freeway traf c congestion, Transportation Research Record 1287: 29–33.

9. Enders, W., 1995. Applied Time Series Analysis, John Wiley and Sons Inc., 236 p, New York.
10. Enders, W., 2003. Applied Time Series Analysis, John Wiley and Sons, Inc., Second Edition, 277 p, New York.
11. Erel, A., 2001. Ulaşım Planlaması I ve II Basılmamış Ders Notları, İstanbul.
12. Garrido, R.A., Mahmassani, H.S., 1998. Forecasting short-term freight transportation demand: Poisson STARMA model, Transportation Research Record 1645: 8–16.
13. Godfrey, G.A., Powell, W.B., 2000. Adaptive estimation of daily demands with complex calendar effects for freight transportation, Transportation Research Part B: Methodological 24: 451–469.
14. Gokhale, S., Khare, M., 2004. A review of deterministic, stochastic and hybrid vehicular exhaust emission models, International Journal of Transport Management 2 (2), 59–74.
15. Grif ths, W.E., Hill C.R., and Judge G. G., 1992. Learning and Practicing Econometrics”, John Wiley and Sons Inc., 670 p, New York.
16. Grif ths, W.E., 1993. Learning and Practicing Econometrics, John Wiley and Sons Inc., 224 p, New York.
17. Gujarati, D., 2004. Basic Econometrics, The McGraw Hill Companies, 796 p, USA.
18. Hamed, M.M., Al-Masaeid, H.R., Bani Said, Z.M., 1995. Short- term prediction of traf c volume in urban arterials, ASCE Journal of Transportation Engineering 121 (3), 249–254.
19. Jeong, R., Rilett, L.R. 2005., “Prediction model of bus arrival time for real-time applications”, Journal Transportation Research Record 1927: 195–204
20. Kutlar, A., 2000. Ekonometrik Zaman serileri:Teori ve Uygulama, Gazi Kitapevi, 429 s, Ankara. Levin, M., and Tsao, Y.D., “On forecasting freeway occupancies and volumes”, Transportation Research Record 773: 47–49.
21. Masten, S.V., Hagge, R.A. 2004, “Evaluation of California’s graduated driver licensing program”, Journal of Safety Research 35 (5): 523–535.
22. McCollister, G.M., Wilson, K.R., 1957. Linear stochastic models for forecasting daily maxima and hourly concentrations of air pollutants, Atmospheric Environment 9 (4): 417–423.
23. Pit eld, D.E., 1980. The impact on traf c, market shares and concentration of airline alliances on selected European. US routes, Journal of Air Transport Management 13 (4). 192–202.
24. Polhemus, N.W., 1980. The construction and use of continuous autoregressive models for traf c indices, Transportation Research Part B: Methodological 14 (3). 271–279.
25. Poo, J.M.R., 2003. Computer Aided Introduction to Econometrics, Springer Verlag GmbH & Co.KG, 226 p, New York.
26. Quinn, T., Kenny G., Meyler A., 1998. Forecasting Irish In ation Using ARIMA Models, Central Bank of Ireland, 3/RT, 17 p, Ireland,.
27. Raeside, R., White, D., 2004 Predicting casualty numbers in Great Britain, Transportation Research Record 1897: 142– 147.
28. Sevüktekin, M., Nargeleçekenler M., 2005. Zaman Serileri Analizi, Nobel Basımevi, 336 s, Ankara.
29. Sevüktekin, M. Nargeleçekenler M., 2007. Ekonometrik Zaman Serileri Analizi E Views Uygulamalı, Nobel Basımevi, 491 s, Ankara.
30. Sharma, P., Khare, M., 2000 . Real-time prediction of extreme ambient carbon monoxide concentrations due to vehicular exhaust emissions using univariate linear stochastic models, Transportation Research Part D: Transport and Environment 5 (1): 59–69.
31. Stathopoulos, A., Karlaftis, M.G., 2001. Spectral and cross- spectral analysis of urban traf c ow, In: Proceedings, Institute of Electrical and Electronics Engineers (IEEE) 4th International Conference on Intelligent Transportation Systems, Oakland, CA, USA.
32. Stathopoulos, A., Karlaftis, M.G., 2003. A multivariate state- space approach for urban traf c ow modeling and prediction, Transportation Research Part C 11 (2): 121– 135.
33. Subaşı, D.B. 2005. En asyonun ARIMA Modelleri İle Tahminlenmesi:1994–2005 Türkiye Uygulaması, Yüksek Lisans Tezi, Dumlupınar Üniversitesi, Sosyal Bilimler Enstitüsü, İktisat Anabilim Dalı, Kütahya.
34. Van den Bossche, F.A.M., Wets, G., Brijs, T., 2007. Analysis of road risk by age and gender category: time series approach, Transportation Research Record 2019: 7–14.
35. Vandaele, W., 1983. Applied Time Series and Box-Jenkins Models, Academic Press Inc, 193 p, Florida.
36. Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C., 2006. Statistical methods for detecting nonlinearity and non-stationarity in univariate short-term time-series of traf c volume, Transportation Research Part C: Emerging Technologies 14 (5): 351–367.
37. Voas, R.B., DeYoung, D.J., 2002. Vehicle action: effective policy for controlling drunk and other high-risk drivers?, Accident Analysis & Prevention 34 (3): 263–270.
38. Williams, B.M., Hoel, L.A., 2003. Modeling and forecasting vehicular traf c ow as a seasonal ARIMA process: theoretical basis and empirical results, ASCE Journal of Transportation Engineering 129 (6): 664–672.
39. Yayla , N., 2002. Karayolu Mühendisliği, İstanbul.