Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2024, Cilt: 42 Sayı: 2, 555 - 565, 30.04.2024

Öz

Kaynakça

  • REFERENCES
  • [1] Green RK. Airports and economic development. Real Estate Economics 2007;35:91–112. [CrossRef]
  • [2] Bilotkach V. Are airports engines of economic development? A dynamic panel data approach. Urban Studies 2015;52:1577–1593. [CrossRef]
  • [3] Brida JG, Rodriguez-Brindis MA, Lanzilotta B, Rodriguez-Collazo S. Testing linearity in the long-run relationship between economic growth and passenger air transport in Mexico. Int J Transport Econ 2016;43.
  • [4] Chi J, Baek J. Dynamic relationship between air transport demand and economic growth in the United States: A new look. Transport Policy 2013;29:257–260. [CrossRef]
  • [5] Abrahams M. A service quality model of air travel demand: an empirical study. Transport Res A Gener 1983;17:385–393. [CrossRef] [6] Kulendran N, Wilson K. Is there a relationship between international trade and international travel? Appl Econ 2000;32:1001–1009. [CrossRef]
  • [7] Gallet CA, Doucouliagos H. The income elasticity of air travel: A meta-analysis. Ann Tourism Res 2014;49:141–155. [CrossRef]
  • [8] Abrate G, Viglia G, García JS, Forgas-Coll S. Price Competition within and between Airlines and High-Speed Trains: The Case of the Milan-Rome Route. Tourism Econ 2016;22:311–323. [CrossRef]
  • [9] Hakim MM, Merkert R. Econometric evidence on the determinants of air transport in South Asian countries. Transport Policy 2019;83:120–126. [CrossRef]
  • [10] Kim S, Shin DH. Forecasting short-term air passenger demand using big data from search engine queries. Automat Construct 2016;70:98–108. [CrossRef]
  • [11] Kurdel P, Sedláčková AN, Novák A. Analysis of using time series method for prediction of number of passengers at the airport. J KONBiN 2020;50:203–216. [CrossRef]
  • [12] Bermúdez JD, Segura JV, Vercher E. Holt–Winters forecasting: an alternative formulation applied to UK air passenger data. J Appl Stat 2007;34:1075–1090. [CrossRef]
  • [13] Wang M, Song H. Air travel demand studies: a review. J China Tourism Res 2010;6:29–49. [CrossRef]
  • [14] Wang S, Gao Y. A literature review and citation analyses of air travel demand studies published between 2010 and 2020. J Air Transport Manag 2021;97:102135. [CrossRef]
  • [15] Banerjee N, Morton A, Akartunalı K. Passenger demand forecasting in scheduled transportation. Eur J Oper Res 2019;285:797810. [CrossRef]
  • [16] Wang M, Song H. Air travel demand studies: a review. J China Tourism Resh 2010;6:29–49. [CrossRef]
  • [17] Pitfield D. Predicting air-transport demand. Environ Plan A 1993;25:459–466. [CrossRef]
  • [18] Gelhausen MC, Berster P, Wilken D. A new direct demand model of long-term forecasting air passengers and air transport movements at German airports. J Air Transport Manag 2018;71:140–152. [CrossRef]
  • [19] Karlaftis MG. Demand forecasting in regional airports: dynamic Tobit models with GARCH errors. Sitraer 2008;7:100–111.
  • [20] Ming W, Bao Y, Hu Z, Xiong T. Multistep-ahead air passengers traffic prediction with hybrid ARIMA-SVMs models. Sci World J 2014;2014. [CrossRef]
  • [21] Carson RT, Cenesizoglu T, Parker R. Forecasting (aggregate) demand for US commercial air travel. Int J Forecast 2011;27:923–941. [CrossRef]
  • [22] Xiao Y, Liu JJ, Xiao J, Hu Y, Bu H, Wang S. Application of multiscale analysis-based intelligent ensemble modeling on airport traffic forecast. Transport Lett 2015;7:73–79. [CrossRef]
  • [23] Xiong H, Fan C, Chen H, Yang Y, Antwi CO, Fan X. A novel approach to air passenger index prediction: Based on mutual information principle and support vector regression blended model. SAGE Open 2022;12:215824402110711. [CrossRef]
  • [24] Tang H, Yu J, Lin B, Geng Y, Wang Z, Chen X, et al. Airport terminal passenger forecast under the impact of COVID-19 outbreaks: A case study from China. J Build Eng 2023;65:105740. [CrossRef]
  • [25] Jin F, Li Y, Sun S, Li H. Forecasting air passenger demand with a new hybrid ensemble approach. J Air Transport Manag 2020;83. [CrossRef]
  • [26] Abed SY, Ba-Fail AO, Jasimuddin SM. An econometric analysis of international air travel demand in Saudi Arabia. J Air Transport Manag 2001;7:143–148. [CrossRef]
  • [27] Aderamo AJ. Demand for air transport in Nigeria. J Econ 2010;1:23–31. [CrossRef]
  • [28] Baikgaki OA. Determinants of domestic air passenger demand in the republic of South Africa. Doctorial Thesis. 2014. [CrossRef]
  • [29] Sivrikaya O, Tunç E. Demand forecasting for domestic air transportation in Turkey. Open Transport J 2013;7. [CrossRef]
  • [30] Naghawi H, Alobeidyeen A, Abdel-Jaber M. Econometric modeling for international passenger air travel demand in Jordan. Jordan J Civil Eng 2019;13.
  • [31] Xiao Y, Liu Y, Liu JJ, Xiao J, Hu Y. Oscillations extracting for the management of passenger flows in the airport of Hong Kong. Transport A Transport Sci 2016;12:65–79. [CrossRef]
  • [32] Franses PH. Seasonality, non-stationarity and the forecasting of monthly time series. Int J Forecast 1991;7:199–208. [CrossRef]
  • [33] Karlaftis MG, Zografos KG, Papastavrou JD, Charnes JM. Methodological framework for air-travel demand forecasting. J Transport Eng 1996;122:96–104. [CrossRef]
  • [34] Karlaftis MG, Vlahogianni EI. Statistical methods versus neural networks in transportation research: Differences, similarities and some insights. Transport Res C Emerg Technol 2011;19:387399. [CrossRef]Çelik R. Correcting double outward box distributed residuals by WCEV. Commun Stat Theory Methods 2017;46:9566–9590. [CrossRef]
  • [35] Çelik R. Correcting double outward box distributed residuals by WCEV. Commun Stat Theory Methods 2017;46:9566–9590.
  • [36] Çelik R. Stabilizing heteroscedasticity for butterfly-distributed residuals by the weighting absolute centered external variable. J Appl Stat 2015;42:705–721. [CrossRef]
  • [37] Çelik R. RCEV heteroscedasticity test based on the studentized residuals. Commun Stat Theory Methods 2019;48:3258–3268. [CrossRef]
  • [38] Deveci M, Demirel NÇ, Ahmetoğlu E. Airline new route selection based on interval type-2 fuzzy MCDM: A case study of new route between Turkey-North American region destinations. J Air Transport Manag 2017;59:83–99. [CrossRef]
  • [39] Draper NR, Smith H. Applied regression analysis. vol. 326. Hoboken: John Wiley & Sons; 1998. [CrossRef]
  • [40] Gujarati DN. Basic econometrics. India: Tata McGraw-Hill Education; 2009. [CrossRef]
  • [41] Carapeto M, Holt W. Testing for heteroscedasticity in regression models. J Appl Stat 2003;30:13–20.
  • [42] Bischoff W, Heck B, Howind J, Teusch A. A procedure for estimating the variance function of linear models and for checking the appropriateness of estimated variances: a case study of GPS carrier-phase observations. J Geodesy 2006;79:694–704. [CrossRef]
  • [43] Carroll RJ, Ruppet D. Transformation and Weighting in Regression. New York: Chapman and Hall; 1988. [CrossRef]
  • [44] Son N, Kim M. A study on robust regression estimators in heteroscedastic error models. J Korean Data Inform Sci Soc 2017;28:1191–204. [CrossRef]
  • [45] Ott RL, Longnecker M. An Introduction to Statistical Methods and Data Analysis. 6th ed. Australia; United States: Brooks/Cole; 2008. [CrossRef]
  • [46] Montgomery DC. Introduction to statistical quality control. 6th ed. Hoboken, N.J: Wiley; 2009.
  • [47] Sukparungsee S, Areepong Y, Taboran R. Exponentially weighted moving average—Moving average charts for monitoring the process mean. PLoS One 2020;15:e0228208.

A new modelling approach for air transportation: A case study for total number of air passengers per month

Yıl 2024, Cilt: 42 Sayı: 2, 555 - 565, 30.04.2024

Öz

Modeling the number of air passengers correctly is essential for management policy in the global world. Based on seasonality (depending on the season of the year), data about the number of air passengers are heteroscedastic. Heteroscedasticity violates “Homoscedastici-ty” which is one of the central assumptions of linear regression analysis. In this study, a new weighting approach called “Weighting Absolute Centered External Variable” (WCEV) is ap-plied to the Turkish total monthly air passenger’s data to obtain correct statistical inference and forecasting. Besides scatter plot months vs. studentized residuals, the homoscedastici-ty assumption is checked with the studentized RCEV test as well. Consequently, the WCEV method is shown superior performance against multiple linear regressions and exponential weighted moving average (EWMA) methods. The study also provides insights into the sea-sonal patterns of air passenger demand in Turkey, with passenger mobility increasing in the last quarter of each year and the lowest demand in January and February. This information can be used to optimize airport and airplane maintenance schedules and increase capacity during peak months.

Kaynakça

  • REFERENCES
  • [1] Green RK. Airports and economic development. Real Estate Economics 2007;35:91–112. [CrossRef]
  • [2] Bilotkach V. Are airports engines of economic development? A dynamic panel data approach. Urban Studies 2015;52:1577–1593. [CrossRef]
  • [3] Brida JG, Rodriguez-Brindis MA, Lanzilotta B, Rodriguez-Collazo S. Testing linearity in the long-run relationship between economic growth and passenger air transport in Mexico. Int J Transport Econ 2016;43.
  • [4] Chi J, Baek J. Dynamic relationship between air transport demand and economic growth in the United States: A new look. Transport Policy 2013;29:257–260. [CrossRef]
  • [5] Abrahams M. A service quality model of air travel demand: an empirical study. Transport Res A Gener 1983;17:385–393. [CrossRef] [6] Kulendran N, Wilson K. Is there a relationship between international trade and international travel? Appl Econ 2000;32:1001–1009. [CrossRef]
  • [7] Gallet CA, Doucouliagos H. The income elasticity of air travel: A meta-analysis. Ann Tourism Res 2014;49:141–155. [CrossRef]
  • [8] Abrate G, Viglia G, García JS, Forgas-Coll S. Price Competition within and between Airlines and High-Speed Trains: The Case of the Milan-Rome Route. Tourism Econ 2016;22:311–323. [CrossRef]
  • [9] Hakim MM, Merkert R. Econometric evidence on the determinants of air transport in South Asian countries. Transport Policy 2019;83:120–126. [CrossRef]
  • [10] Kim S, Shin DH. Forecasting short-term air passenger demand using big data from search engine queries. Automat Construct 2016;70:98–108. [CrossRef]
  • [11] Kurdel P, Sedláčková AN, Novák A. Analysis of using time series method for prediction of number of passengers at the airport. J KONBiN 2020;50:203–216. [CrossRef]
  • [12] Bermúdez JD, Segura JV, Vercher E. Holt–Winters forecasting: an alternative formulation applied to UK air passenger data. J Appl Stat 2007;34:1075–1090. [CrossRef]
  • [13] Wang M, Song H. Air travel demand studies: a review. J China Tourism Res 2010;6:29–49. [CrossRef]
  • [14] Wang S, Gao Y. A literature review and citation analyses of air travel demand studies published between 2010 and 2020. J Air Transport Manag 2021;97:102135. [CrossRef]
  • [15] Banerjee N, Morton A, Akartunalı K. Passenger demand forecasting in scheduled transportation. Eur J Oper Res 2019;285:797810. [CrossRef]
  • [16] Wang M, Song H. Air travel demand studies: a review. J China Tourism Resh 2010;6:29–49. [CrossRef]
  • [17] Pitfield D. Predicting air-transport demand. Environ Plan A 1993;25:459–466. [CrossRef]
  • [18] Gelhausen MC, Berster P, Wilken D. A new direct demand model of long-term forecasting air passengers and air transport movements at German airports. J Air Transport Manag 2018;71:140–152. [CrossRef]
  • [19] Karlaftis MG. Demand forecasting in regional airports: dynamic Tobit models with GARCH errors. Sitraer 2008;7:100–111.
  • [20] Ming W, Bao Y, Hu Z, Xiong T. Multistep-ahead air passengers traffic prediction with hybrid ARIMA-SVMs models. Sci World J 2014;2014. [CrossRef]
  • [21] Carson RT, Cenesizoglu T, Parker R. Forecasting (aggregate) demand for US commercial air travel. Int J Forecast 2011;27:923–941. [CrossRef]
  • [22] Xiao Y, Liu JJ, Xiao J, Hu Y, Bu H, Wang S. Application of multiscale analysis-based intelligent ensemble modeling on airport traffic forecast. Transport Lett 2015;7:73–79. [CrossRef]
  • [23] Xiong H, Fan C, Chen H, Yang Y, Antwi CO, Fan X. A novel approach to air passenger index prediction: Based on mutual information principle and support vector regression blended model. SAGE Open 2022;12:215824402110711. [CrossRef]
  • [24] Tang H, Yu J, Lin B, Geng Y, Wang Z, Chen X, et al. Airport terminal passenger forecast under the impact of COVID-19 outbreaks: A case study from China. J Build Eng 2023;65:105740. [CrossRef]
  • [25] Jin F, Li Y, Sun S, Li H. Forecasting air passenger demand with a new hybrid ensemble approach. J Air Transport Manag 2020;83. [CrossRef]
  • [26] Abed SY, Ba-Fail AO, Jasimuddin SM. An econometric analysis of international air travel demand in Saudi Arabia. J Air Transport Manag 2001;7:143–148. [CrossRef]
  • [27] Aderamo AJ. Demand for air transport in Nigeria. J Econ 2010;1:23–31. [CrossRef]
  • [28] Baikgaki OA. Determinants of domestic air passenger demand in the republic of South Africa. Doctorial Thesis. 2014. [CrossRef]
  • [29] Sivrikaya O, Tunç E. Demand forecasting for domestic air transportation in Turkey. Open Transport J 2013;7. [CrossRef]
  • [30] Naghawi H, Alobeidyeen A, Abdel-Jaber M. Econometric modeling for international passenger air travel demand in Jordan. Jordan J Civil Eng 2019;13.
  • [31] Xiao Y, Liu Y, Liu JJ, Xiao J, Hu Y. Oscillations extracting for the management of passenger flows in the airport of Hong Kong. Transport A Transport Sci 2016;12:65–79. [CrossRef]
  • [32] Franses PH. Seasonality, non-stationarity and the forecasting of monthly time series. Int J Forecast 1991;7:199–208. [CrossRef]
  • [33] Karlaftis MG, Zografos KG, Papastavrou JD, Charnes JM. Methodological framework for air-travel demand forecasting. J Transport Eng 1996;122:96–104. [CrossRef]
  • [34] Karlaftis MG, Vlahogianni EI. Statistical methods versus neural networks in transportation research: Differences, similarities and some insights. Transport Res C Emerg Technol 2011;19:387399. [CrossRef]Çelik R. Correcting double outward box distributed residuals by WCEV. Commun Stat Theory Methods 2017;46:9566–9590. [CrossRef]
  • [35] Çelik R. Correcting double outward box distributed residuals by WCEV. Commun Stat Theory Methods 2017;46:9566–9590.
  • [36] Çelik R. Stabilizing heteroscedasticity for butterfly-distributed residuals by the weighting absolute centered external variable. J Appl Stat 2015;42:705–721. [CrossRef]
  • [37] Çelik R. RCEV heteroscedasticity test based on the studentized residuals. Commun Stat Theory Methods 2019;48:3258–3268. [CrossRef]
  • [38] Deveci M, Demirel NÇ, Ahmetoğlu E. Airline new route selection based on interval type-2 fuzzy MCDM: A case study of new route between Turkey-North American region destinations. J Air Transport Manag 2017;59:83–99. [CrossRef]
  • [39] Draper NR, Smith H. Applied regression analysis. vol. 326. Hoboken: John Wiley & Sons; 1998. [CrossRef]
  • [40] Gujarati DN. Basic econometrics. India: Tata McGraw-Hill Education; 2009. [CrossRef]
  • [41] Carapeto M, Holt W. Testing for heteroscedasticity in regression models. J Appl Stat 2003;30:13–20.
  • [42] Bischoff W, Heck B, Howind J, Teusch A. A procedure for estimating the variance function of linear models and for checking the appropriateness of estimated variances: a case study of GPS carrier-phase observations. J Geodesy 2006;79:694–704. [CrossRef]
  • [43] Carroll RJ, Ruppet D. Transformation and Weighting in Regression. New York: Chapman and Hall; 1988. [CrossRef]
  • [44] Son N, Kim M. A study on robust regression estimators in heteroscedastic error models. J Korean Data Inform Sci Soc 2017;28:1191–204. [CrossRef]
  • [45] Ott RL, Longnecker M. An Introduction to Statistical Methods and Data Analysis. 6th ed. Australia; United States: Brooks/Cole; 2008. [CrossRef]
  • [46] Montgomery DC. Introduction to statistical quality control. 6th ed. Hoboken, N.J: Wiley; 2009.
  • [47] Sukparungsee S, Areepong Y, Taboran R. Exponentially weighted moving average—Moving average charts for monitoring the process mean. PLoS One 2020;15:e0228208.
Toplam 47 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Biyokimya ve Hücre Biyolojisi (Diğer)
Bölüm Research Articles
Yazarlar

Reşit Çelik 0000-0003-0833-0947

Hasan Aykut Karaboğa 0000-0001-8877-3267

İbrahim Demir

Erdal Gül 0000-0003-0626-0148

Yayımlanma Tarihi 30 Nisan 2024
Gönderilme Tarihi 23 Aralık 2022
Yayımlandığı Sayı Yıl 2024 Cilt: 42 Sayı: 2

Kaynak Göster

Vancouver Çelik R, Karaboğa HA, Demir İ, Gül E. A new modelling approach for air transportation: A case study for total number of air passengers per month. SIGMA. 2024;42(2):555-6.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/