Airport passenger forecast with time series: case of study Samsun Çarşamba Airport

Year 2021, , 2103 - , 15.07.2021

Tahsin Baykal , Fatih Ergezer , Serdal Terzi

https://doi.org/10.53635/jit.958682

Cited By: 1

Abstract

This study, it is aimed to estimate the number of airport passengers expected to be in the future with time series analysis. For this purpose, the total number of domestic and international passengers between January 2007 and May 2021 belonging to Samsun Çarşamba Airport was used. First, the normality test was applied to the data and it was observed that the data complied with the normal distribution. Using data from January 2007 to June 2018, the total number of passengers between July 2018 and May 2021 was estimated by time series. The estimated results were compared with the actual results and the R2 value was 0.628. Then, with all data between January 2007 and May 2021, the total number of passengers expected to occur between June 2021 and March 2025 was estimated.

Keywords

Time Series, Transportation, Airline Transportation, Airline Transportation Planning

References

• Tortum, A., Gözcü, O., & Çodur, M. Y. (2014). Türkiye’de Hava Ulaşım Talebinin Arıma Modelleri ile Tahmin Edilmesi. Journal of the Institute of Science and Technology, 4(2), 39-54.
• Erel, A., (2001). Transportation Planning I and II Unpublished Lecture Notes. İstanbul.
• Yayla , N., (2002). Highway Engineering. İstanbul, Birsen Press Inc.
• Çodur, M.Y., (2012). Traffic accident prediction models: applications for surrounding highways of Erzurum. PhD Thesis, Atatürk University, Graduate School of Natural and Applied Sciences.
• Atay, M., Eroğlu, Y. & Seçkiner, S. U. (2019). Demand Forecasting of 3. Istanbul Grand Airport via Artificial Neural Networks and Adaptive Neuro Fuzzy Inference Systems for Optimization of Domestic Aircraft Fleet of Turkish Airlines. Endüstri Mühendisliği, 30(2), 141-156.
• Efendigil, T. & Eminler, Ö. E. (2017). The Importance of Demand Estimation in the Aviation Sector: A Model to Estimate Airline Passenger Demand. Journal of Yaşar University, 12, 14-30.
• Önder, E., & Kuzu, S. (2014). Forecasting air traffic volumes using smoothing techniques. Journal of Aeronautics and space Technologies, 7(1), 65-85.
• Gujarati, D., (2004). Basic Econometrics. USA, The McGraw Hill Companies.
• Vandaele, W., (1983). Applied Time Series and Box-Jenkins Models. Florida Academic Press Inc.
• Bayazıt, M. (1996). Probability Methods in Civil Engineering (1. Edition). İstanbul: Istanbul Technical University Civil Engineering Press.
• Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2013). Multivariate Data Analysis. Essex, England.