Abstract
Civil aviation sector is of great importance for all countries. Besides, demand estimation of air passengers is crucial for every part of air industry. It is used for marketing, determining policies, pricing, making decisions on investments and more. This paper presents an econometric model to estimate air travel demand for 10 major European region countries which have high number of airline passengers by using panel regression model. Countries were determined based on data availability of the variables. Authors tried to determine the effects of independent variables as “purchasing power parities”, “net direct investment”, “population”, “GDP per capita”, “consumer prices (transport)”, “exchange rates”, “commercial aircraft fleet”, “number of tourism enterprises” and “urban population rate” on demand. The model is estimated using panel data for 10 countries over a period from 2009 to 2018. The results also suggest that the countries’ GDP values effect the air passenger demand. Besides countries should make investments in more aircraft fleet size to promote their civil aviation sector which is of great importance for the European economy.
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Albayrak, M.B.K., Özcan, İÇ., Can, R., Dobruszkes, F.: The determinants of air passenger traffic at Turkish airports. J. Air Transp. Manag. 86, 101818 (2020)
Alnıpak, S., Kale, S.: Avrupa Bölgesinde Havayolu Yolcu Talebini Etkileyen Faktörler. Int. Soc. Sci. Stud. J. 7(90), 4948–4957 (2021)
Boonekamp, T., Zuidberg, J., Burghouwt, G.: Determinants of air travel demand: The role of low-cost carriers, ethnic links and aviation-dependent employment. Transp. Res. Part A. 112, 18–28 (2018)
Chang, L.-Y.: International air passenger flows between pairs of APEC countries: a non-parametric regression tree approach. J. Air Transp. Manag. 20, 4–6 (2012)
Cunningham, S.W., Haan, A.R.C.: Long-term forecasting for sustainable development: air travel demand for 2050. Int. J. Environ. Sustain. Dev. 5(3), 297–314 (2006)
Das, A.K., Bardhan, A.K., Fageda, X.: What is driving the passenger demand on new regional air routes in India: a study using the gravity model. Case Stud. Transp. Policy 10, 637–646 (2022)
Demirsoy, C.: Analysis of stimulated domestic air transport demand in Turkey. MSc thesis, Erasmus University, Rotterdam, Netharlands (2012)
Duval, D.T., Schiff, A.: Effect of air services availability on international visitors to New Zealand. J. Air Transp. Manag. 17(3), 175–180 (2011)
EUROSTAT. https://ec.europa.eu/eurostat/data/database (2021). Accessed 01 Feb 2021.
Hurlin, C.: Testing Granger Causality in Heterogeneous Panel Data Models with Fixed Coefficients. University of Orléans, Miméo (2004)
IATA: Below-trend but still solid air passenger growth in 2019. https://www.iata.org/en/iata-repository/publications/economic-reports/air-passenger-monthly---dec-2019/ (2019). Accessed 02 April 2021
IATA: Domestic markets dominated the recovery for another month. https://www.iata.org/en/iata-repository/publications/economic-reports/air-passenger-monthly-analysis---august-2020/ (2020). Accessed 01 April 2021
Jorge-Calderon, J.D.: A demand model for scheduled airline services on international European routes. J. Air Transp. Manag. 3(1), 23–35 (1997)
Karlaftis, M.G., Zografos, K.G., Papastavrou, J.D., Charnes, J.M.: Methodologıcal framework for air-travel demand forecastıng. J. Transp. Eng. 122(2), 96–104 (1996)
Kim, S., Shin, D.H.: Forecasting short-term air passenger demand using big data from search engine queries. Autom. Constr. 70, 98–108 (2016)
Li, T., Baik, H., Trani, A.A.: A method to estimate the historical US air travel demand. J. Adv. Transp. 47, 249–265 (2013)
Maheshwari, A., Davendralingam, N., DeLaurentis, D.: A Comparative Study of Machine Learning Techniques for Aviation Applications. AIAA AVIATION Forum, Aviation Technology, Integration, and Operations Conference, June 25–29, Atlanta, Georgia (2018)
Mazareanu, E.: Revenue of airlines worldwide 2003–2021. https://www.statista.com/statistics/278372/revenue-of-commercial-airlines-worldwide/ (2021). Accessed 05 April 2021
OECD https://stats.oecd.org (2021). Accessed 01 Feb 2021
Pesaran, M.: A simple panel unit root test in the presence of cross section dependence. In: Cambridge Working Papers in Economics, pp. 1–24 (2003)
Salas, E.B.: Revenue of airlines worldwide 2003–2022 https://www.statista.com/statistics/278372/revenue-of-commercial-airlines-worldwide/. (2022)
Samagaio, A., Wolters, M.: Comparative analysis of government forecasts for Lisbon airport. J. Air Transp. Manag. 16(4), 213–217 (2010)
Srisaeng, P., Baxter, G., Richardson, S., Wild, G.: A forecasting tool for predicting Australia’s domestic airline passenger demand using a genetic algorithm. J. Aerosp. Technol. Manag. 7(4), 476–489 (2015)
Stecenko, I.P., Parkhimovich, A.V.: Passenger air transportation market in Europe. Civ. Aviation High Technol. 23(1), 59–70 (2020)
Sulistyowati, R., Suhartono, S., Kuswanto, H.: Hybrid forecasting model to predict air passenger and cargo in Indonesia. Int. Conf. Inform. Commun. Technol. 6–7(March), 442–447 (2018)
Tam, M.L., William, H.K., Lo, H.P.: Modeling air passenger travel behavior on airport ground access mode choices. Transportmetrica 4(2), 135–153 (2008)
Tsui, W.H.K., Balli, H.O., Gilbey, A., Gow, H.: Forecasting of Hong Kong airport’s passenger throughput. Tour. Manag. 42, 62–76 (2014)
Ün, T.: STATA ile Panel Veri Analizi. In S. Güriş, STATA ile Panel Veri Modelleri. İstanbul: Der Yayınları (2015)
Valdes, V.: Determinants of air travel demand in Middle Income Countries. J. Air Transp. Manag. 42, 75–84 (2015)
Wadud, Z.: Simultaneous modeling of passenger and cargo demand at an airport. Transp. Res. Rec. J. Transp. Res. Board 2336, 63–74 (2013)
Wang, J., Liu, X., Ding, J.: Air passenger travel forecasting model based on both dynamical individual behavior and social influence force. J. Algorithms Comput. Technol. 13, 1–9 (2019)
Wei, W., Hansen, M.: An aggregate demand model for air passenger traffic in the hub-and-spoke network. Transp. Res. Part A 40, 841–851 (2006)
Wensveen, J.G.: Air Transportation: A Management Perspective, 7th edn. Ashgate Publishing, Farnham (2011)
WORLDBANK: https://databank.worldbank.org/databases. Accessed 01 Feb 2021
Xinyu, Z., Yafei, Z., Shouyang, W.: A demand forecasting method based on Stochastic frontier analysis and model average: an application in air travel demand forecasting. J. Syst. Sci. Complexity 32, 615–633 (2019)
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Dayioglu, T., Alnipak, S. Dynamic effecting factors of air travel demand: an econometric analysis. Qual Quant 57, 3713–3727 (2023). https://doi.org/10.1007/s11135-022-01457-y
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DOI: https://doi.org/10.1007/s11135-022-01457-y