Fleet Type Planning for Private Air Transport After Covid-19

Year 2023, Volume: 11 Issue: 2, 475 - 485, 23.06.2023

Raziye Kılıç , Özge Albayrak Ünal , Burak Erkayman

https://doi.org/10.29109/gujsc.1178375

Abstract

The global impact of the epidemic COVID-19 has done great damage to air transport. Demand for airline transportation has declined for reasons such as quarantine practices by countries, curfews, the economic recession, and the transfer of meetings to digital platforms. This situation has also led to a change in individuals' preferences for air transport. The most striking change in air transport is the tendency of individuals to private air transport privately to minimize the health risks that may arise from personal contacts. Individuals who avoid commercial air transport where public transportation is has transitioned private air transport. For these reasons, an forecasting study was conducted in this study so that a private airline company can provide accurate flight plans in the future. For the forecast study, the number of aircraft types for 2022 was determined by obtaining data on the number of aircraft by passenger capacity, the number of flights, and the number of passengers for 2019-2021 from the airline company. In the forecasting study, the models with the highest accuracy value were selected from the machine learning models. The results provided important information about the company's future fleet planning.

Keywords

: Private airline transport, Fleet type planning, Machine learning, Support vector machine, Gaussian process regression

References

• Meriç, S., COVID-19’UN DÜNYA ve TÜRK SİVİL HAVACILIK SEKTÖRÜNE EKONOMİK ETKİLERİ. Atlas Journal, 2021. 7(40): p. 1699-1710.
• Adeniran, A. and M. Stephens, The dynamics for evaluating forecasting methods for international air passenger demand in Nigeria. Journal of tourism & hospitality, 2018. 7(4): p. 1-11.
• Atay, M., Y. Eroğlu, and S. Ulusam Seçkiner, YAPAY SİNİR AĞLARI VE ADAPTİF NÖROBULANIK SİSTEMLER İLE 3. İSTANBUL HAVALİMANI TALEP TAHMİNİ VE TÜRK HAVA YOLLARI İÇ HAT FİLO OPTİMİZASYONU. Journal of Industrial Engineering (Turkish Chamber of Mechanical Engineers), 2019. 30(2).
• Efendigil, T. and Ö.E. Eminler, Havacılık sektöründe talep tahminin önemi: Yolcu talebi üzerine bir tahmin modeli. Yaşar Üniversitesi E-Dergisi, 2017. 12: p. 14-30.
• Jiang, X., L. Zhang, and X.M. Chen, Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China. Transportation Research Part C: Emerging Technologies, 2014. 44: p. 110-127.
• Sun, Y., B. Leng, and W. Guan, A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing, 2015. 166: p. 109-121.
• Jafari, N., The chaos on US domestic airline passenger demand forecasting caused by COVID-19. International Journal of Business Forecasting and Marketing Intelligence, 2022. 7(3): p. 241-258.
• Marie-Sainte, S.L., T. Saba, and S. Alotaibi, Air passenger demand forecasting using particle swarm optimization and firefly algorithm. Journal of Computational and Theoretical Nanoscience, 2019. 16(9): p. 3735-3743.
• Dursun, Ö.O. and S. Toraman, Uzun Kısa Vadeli Bellek Yöntemi ile Havayolu Yolcu Tahmini. Journal of Aviation, 2021. 5(2): p. 241-248.
• Pandit, P.K. and M.A. Akhtar Hasin. Business model of aircraft fleet planning using ANN. in The Road to a Digitalized Supply Chain Management: Smart and Digital Solutions for Supply Chain Management. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 25. 2018. Berlin: epubli GmbH.
• Wild, G., et al. Machine Learning for Air Transport Planning and Management. in AIAA AVIATION 2022 Forum. 2022.
• Mishra, N. and S. Silakari, Predictive analytics: a survey, trends, applications, oppurtunities & challenges. International Journal of Computer Science and Information Technologies, 2012. 3(3): p. 4434-4438.
• Kumar, V. and M. Garg, Predictive analytics: a review of trends and techniques. International Journal of Computer Applications, 2018. 182(1): p. 31-37.
• Indriasari, E., et al. Application of Predictive Analytics at Financial Institutions: A Systematic Literature Review. in 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI). 2019. IEEE.
• Sharma, N., R. Sharma, and N. Jindal, Machine learning and deep learning applications-a vision. Global Transitions Proceedings, 2021. 2(1): p. 24-28.
• Zhang, L., et al., A review of machine learning in building load prediction. Applied Energy, 2021. 285: p. 116452.
• Jordan, M.I. and T.M. Mitchell, Machine learning: Trends, perspectives, and prospects. Science, 2015. 349(6245): p. 255-260.
• Nguyen, G., et al., Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey. Artificial Intelligence Review, 2019. 52(1): p. 77-124.
• Quan, Q., et al., Research on water temperature prediction based on improved support vector regression. Neural Computing and Applications, 2020: p. 1-10.
• Zhang, F. and L.J. O'Donnell, Support vector regression, in Machine Learning. 2020, Elsevier. p. 123-140.
• Çoban, F. and L. Demir, Yapay Sinir Ağları ve Destek Vektör Regresyonu ile Talep Tahmini: Gıda İşletmesinde Bir Uygulama. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 2021. 23(67): p. 327-338.
• Dewangan, C.L., S. Singh, and S. Chakrabarti, Combining forecasts of day-ahead solar power. Energy, 2020. 202: p. 117743.
• Fairbrother, J., et al., GaussianProcesses. jl: a Nonparametric Bayes package for the Julia Language. arXiv preprint arXiv:1812.09064, 2018.
• Liu, K., et al., Gaussian process regression with automatic relevance determination kernel for calendar aging prediction of lithium-ion batteries. IEEE Transactions on Industrial Informatics, 2019. 16(6): p. 3767-3777.
• Mehmet, A. and G.A. Doğansoy, Makine öğrenmesi ve derin öğrenme yöntemleri kullanılarak e-perakende sektörüne yönelik talep tahmini. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 2022. 37(3): p. 1325-1340.
• Suau-Sanchez, P., A. Voltes-Dorta, and N. Cugueró-Escofet, An early assessment of the impact of COVID-19 on air transport: Just another crisis or the end of aviation as we know it? Journal of Transport Geography, 2020. 86: p. 102749.
• Wild, P., F. Mathys, and J. Wang, Impact of political and market-based measures on aviation emissions and passenger behaviors (a Swiss case study). Transportation Research Interdisciplinary Perspectives, 2021. 10: p. 100405.
• Zhang, L., et al., The impact of COVID-19 on airline passenger travel behavior: An exploratory analysis on the Chinese aviation market. Journal of Air Transport Management, 2021. 95: p. 102084.
• Ozbilen, B., K.M. Slagle, and G. Akar, Perceived risk of infection while traveling during the COVID-19 pandemic: Insights from Columbus, OH. Transportation Research Interdisciplinary Perspectives, 2021. 10: p. 100326.
• Sobieralski, J.B. and S. Mumbower, Jet-setting during COVID-19: Environmental implications of the pandemic induced private aviation boom. Transportation Research Interdisciplinary Perspectives, 2022. 13: p. 100575.