Research Article
BibTex RIS Cite

Year 2025, Volume: 9 Issue: 2, 285 - 294, 28.06.2025
https://doi.org/10.30518/jav.1662031

Abstract

Project Number

-

References

  • Abebe, T. H. (2024). Regression analysis: A theoretical approach. Journal of Statistical and Econometric Methods, 1(40).
  • Altınkeski, B. K., Özyiğit, O., & Çevik, E. (2022). The relationship among eco-friendly technologies, civil aviation and environmental quality: Panel threshold regression analysis. Gaziantep University Journal of Social Sciences, 21(3), 1162–1179. http://dergipark.org.tr/tr/pub/jss
  • Anandhi, P., & Nathiya, E. (2023). Application of linear regression with their advantages, disadvantages, assumptions, and limitations. International Journal of Statistics and Applied Mathematics, 8(6), 133–137.
  • Bahadır, E., Kalender, B., Yumuşak, F. N., & Karaman, G. (2018). A comparative study on modeling analyses: Structural equation modeling and regression. Kesit Akademi Dergisi(16), 410–420.
  • Bulut, Y. (2024). Measuring the determinants of CO2 emissions in Turkey: Regression analysis with an instrumental variable [Master’s thesis, Sakarya University]. Sakarya University Social Sciences Institute.
  • Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?– Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7, 1247–1250.
  • Chung, W. (2012). Using the fuzzy linear regression method to benchmark the energy efficiency of commercial buildings. Applied Energy, 95, 45–49.
  • Daoud, J. I. (2017). Multicollinearity and regression analysis. Journal of Physics: Conference Series, 949(1), 012009.
  • Dinev, T., & Hart, P. (2004). Internet privacy concerns and their antecedents - Measurement validity and a regression model. Behaviour & Information Technology, 23(6), 413–422.
  • Gerede, E. (Ed.). (2015). Airline transportation and economic regulations: Theory and practice in Turkey. General Directorate of Civil Aviation Publications. ISBN: 978-975-493-067-2
  • Hamdan, S., Jouini, O., Cheaitou, A., Jemai, Z., Granberg, T. A., & Josefsson, B. (2022). Air traffic flow management under emission policies: Analyzing the impact of sustainable aviation fuel and different carbon prices. Transportation Research Part A: Policy and Practice, 166, 14–40.
  • He, M., & Zhou, Y. (2016, September). Analysis of power factor of capacitive pulse load in aviation variable frequency power supply system. In Proceedings of the 2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016) (pp. 40–44). Atlantis Press.
  • IATA (International Air Transport Association). (2023). IATA Net Zero Roadmaps: Providing a strategic vision to achieve net zero CO2 emissions by 2050. 79th AGM and World Air Transport Summit, Istanbul, Türkiye, 19–21 June 2022.
  • Kaçar, E., & Kalkan, K. (2025). Investigation of the relationship between aircraft mobility and air quality at airports: Aerosol index and European example. Journal of Aviation Research, 7(1), 56–68.
  • Kaltenecker, S., & Leopold, K. (2022). Flight Levels: A short introduction. Loop Organisationsberatung GmbH.
  • Kang, L., & Hansen, M. (2018). Improving airline fuel efficiency via fuel burn prediction and uncertainty estimation. Transportation Research Part C: Emerging Technologies, 97, 128–146.
  • Karaca, C., & Karacan, H. (2016). Examining factors affecting electricity consumption demand using multiple regression method. Sujest, 4(3), 182–195.
  • Kilic, S. (2013). Linear regression analysis. Journal of Mood Disorders, 3(2), 90.
  • Köse, Y. (2021). Specific financial ratios in the aviation sector: Analysis and evaluation of airline companies in Turkey. Financial Research and Studies Journal, 13(25), 623–636.
  • Maxwell, A. E. (1975). Limitations on the use of the multiple linear regression model. British Journal of Mathematical and Statistical Psychology, 28(1), 51–62.
  • Olden, J. D., & Jackson, D. A. (2000). Torturing data for the sake of generality: How valid are our regression models? Écoscience, 7(4), 501–510.
  • Osborne, J., & Waters, E. (2002). Four assumptions of multiple regression that researchers should always test. Practical Assessment, Research & Evaluation, 8.
  • Öztürk, O. (2023). Comparative analysis of operational, financial, and environmental efficiency of major airlines worldwide [Doctoral dissertation, Hasan Kalyoncu University].
  • Pehlivanoğlu, Y. V. (2013). Introduction to aviation and space sciences. Air Force Academy Press.
  • Pinchemel, Alexandre & Caetano, Mauro & Rossi, Ricardo & Silva, Marco. (2022). Airline’s business performance indicators and their impact on operational efficiency. Brazilian Business Review. 19. 642-665.
  • Reid, C. (2020). Technical appendix: The limitations of regression models.
  • Schisterman, E. F., Vexler, A., Whitcomb, B. W., & Liu, A. (2006). The limitations due to exposure detection limits for regression models. American Journal of Epidemiology, 163(4), 374–383.
  • Seymour, K., Held, M., Georges, G., & Boulouchos, K. (2020). Fuel estimation in air transportation: Modeling global fuel consumption for commercial aviation. Transportation Research Part D: Transport and Environment, 86, 102528.
  • Sezgin, E., & Çelik, Y. (2013). Comparison of data used for loss of data mining methods. In XV. Academic Informatics Conference Proceedings, January 23–25, 2013, Akdeniz University, Antalya. Retrieved from https://www.researchgate.net/publication/348787393
  • STM Defense Technologies Engineering and Trade Inc. (2021). Fuel efficiency in civil aviation: Research report, March 2021.
  • Şahinler, S. (2000). The basic principles of fitting linear regression model by least squares method. MKÜ Journal of Agriculture Faculty, 5(1–2), 57–73. Retrieved from https://www.researchgate.net/profile/Suat- Sahinler/publication/305711004_The_Basic_Principles_of_Fitting_Linear_Regression_Model_By_Least_Squ ares_Method/links/58ac442e92851cf0e3ccd32e/The-Basic-Principles-of-Fitting-Linear-Regression-Model- By-Least-Squares-Method.pdf
  • Şen, Y., & Erdağ, T. (2021). Evaluation of airline transportation sector development stages with PEST analysis: An investigation in the scope of five periods + Covid-19 pandemic process period. TroyAcademy International Journal of Social Sciences, 6(2), 422–461.
  • Tabernier, L., Calvo Fernández, E., Tautz, A., Deransy, R., & Martin, P. (2021). Fuel tankering: Economic benefits and environmental impact for flights up to 1500 NM (full tankering) and 2500 NM (partial tankering). Aerospace, 8(2), 37.
  • Tranmer, M., Murphy, J., Elliot, M., & Pampaka, M. (2020). Multiple linear regression (2nd ed.). Cathie Marsh Institute Working Paper 2020-01. https://hummedia.manchester.ac.uk/institutes/cmist/archive- publications/working-papers/2020/2020-1multiple-linear-regression.pdf

Assessment of Aircraft Fuel Efficiency in Domestic Flights using Multiple Regression Analysis

Year 2025, Volume: 9 Issue: 2, 285 - 294, 28.06.2025
https://doi.org/10.30518/jav.1662031

Abstract

The aviation industry has significantly evolved over the past century, playing a crucial role in global transportation, trade, and tourism. However, its reliance on fossil fuels has raised environmental concerns, necessitating sustainable practices to mitigate carbon emissions. This study examines the relationship between fuel consumption and various operational parameters for the Airbus A321 aircraft, utilizing multiple linear regression analysis to develop a predictive model for fuel efficiency.
The dataset, comprising 110 flight records from Istanbul Airport, includes independent variables such as the number of passengers, flight level, flight distance, average wind speed, airspeed, flight duration, aircraft takeoff weight, and total fuel load. Statistical tests, including normality checks, correlation analysis, and multicollinearity assessments, were conducted to ensure the validity of the model. Findings indicate that flight duration, aircraft takeoff weight, and total fuel load significantly influence fuel consumption, while variables such as flight level and wind speed have negligible effects.
The study highlights the importance of optimizing flight planning, weight management, and fuel policies to enhance operational efficiency and reduce environmental impact. The results provide valuable insights for the aviation industry, supporting data-driven decision-making in fuel efficiency and sustainability efforts. By integrating advanced statistical modeling and strategic operational planning, airlines can achieve cost optimization while promoting environmentally responsible practices.
This research contributes to aviation sustainability by offering a data-driven approach to fuel efficiency analysis, which can inform future innovations in aircraft design, air traffic management, and alternative fuel utilization.

Project Number

-

Thanks

This study is derived from Ertuğrul Metehan SERTDEMİR`s MSc. Thesis titled “İç Hat Uçuşlarda Hava Aracı Yakıt Verimliliğinin Çoklu Doğrusal Regresyon Analizi”.

References

  • Abebe, T. H. (2024). Regression analysis: A theoretical approach. Journal of Statistical and Econometric Methods, 1(40).
  • Altınkeski, B. K., Özyiğit, O., & Çevik, E. (2022). The relationship among eco-friendly technologies, civil aviation and environmental quality: Panel threshold regression analysis. Gaziantep University Journal of Social Sciences, 21(3), 1162–1179. http://dergipark.org.tr/tr/pub/jss
  • Anandhi, P., & Nathiya, E. (2023). Application of linear regression with their advantages, disadvantages, assumptions, and limitations. International Journal of Statistics and Applied Mathematics, 8(6), 133–137.
  • Bahadır, E., Kalender, B., Yumuşak, F. N., & Karaman, G. (2018). A comparative study on modeling analyses: Structural equation modeling and regression. Kesit Akademi Dergisi(16), 410–420.
  • Bulut, Y. (2024). Measuring the determinants of CO2 emissions in Turkey: Regression analysis with an instrumental variable [Master’s thesis, Sakarya University]. Sakarya University Social Sciences Institute.
  • Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?– Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7, 1247–1250.
  • Chung, W. (2012). Using the fuzzy linear regression method to benchmark the energy efficiency of commercial buildings. Applied Energy, 95, 45–49.
  • Daoud, J. I. (2017). Multicollinearity and regression analysis. Journal of Physics: Conference Series, 949(1), 012009.
  • Dinev, T., & Hart, P. (2004). Internet privacy concerns and their antecedents - Measurement validity and a regression model. Behaviour & Information Technology, 23(6), 413–422.
  • Gerede, E. (Ed.). (2015). Airline transportation and economic regulations: Theory and practice in Turkey. General Directorate of Civil Aviation Publications. ISBN: 978-975-493-067-2
  • Hamdan, S., Jouini, O., Cheaitou, A., Jemai, Z., Granberg, T. A., & Josefsson, B. (2022). Air traffic flow management under emission policies: Analyzing the impact of sustainable aviation fuel and different carbon prices. Transportation Research Part A: Policy and Practice, 166, 14–40.
  • He, M., & Zhou, Y. (2016, September). Analysis of power factor of capacitive pulse load in aviation variable frequency power supply system. In Proceedings of the 2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016) (pp. 40–44). Atlantis Press.
  • IATA (International Air Transport Association). (2023). IATA Net Zero Roadmaps: Providing a strategic vision to achieve net zero CO2 emissions by 2050. 79th AGM and World Air Transport Summit, Istanbul, Türkiye, 19–21 June 2022.
  • Kaçar, E., & Kalkan, K. (2025). Investigation of the relationship between aircraft mobility and air quality at airports: Aerosol index and European example. Journal of Aviation Research, 7(1), 56–68.
  • Kaltenecker, S., & Leopold, K. (2022). Flight Levels: A short introduction. Loop Organisationsberatung GmbH.
  • Kang, L., & Hansen, M. (2018). Improving airline fuel efficiency via fuel burn prediction and uncertainty estimation. Transportation Research Part C: Emerging Technologies, 97, 128–146.
  • Karaca, C., & Karacan, H. (2016). Examining factors affecting electricity consumption demand using multiple regression method. Sujest, 4(3), 182–195.
  • Kilic, S. (2013). Linear regression analysis. Journal of Mood Disorders, 3(2), 90.
  • Köse, Y. (2021). Specific financial ratios in the aviation sector: Analysis and evaluation of airline companies in Turkey. Financial Research and Studies Journal, 13(25), 623–636.
  • Maxwell, A. E. (1975). Limitations on the use of the multiple linear regression model. British Journal of Mathematical and Statistical Psychology, 28(1), 51–62.
  • Olden, J. D., & Jackson, D. A. (2000). Torturing data for the sake of generality: How valid are our regression models? Écoscience, 7(4), 501–510.
  • Osborne, J., & Waters, E. (2002). Four assumptions of multiple regression that researchers should always test. Practical Assessment, Research & Evaluation, 8.
  • Öztürk, O. (2023). Comparative analysis of operational, financial, and environmental efficiency of major airlines worldwide [Doctoral dissertation, Hasan Kalyoncu University].
  • Pehlivanoğlu, Y. V. (2013). Introduction to aviation and space sciences. Air Force Academy Press.
  • Pinchemel, Alexandre & Caetano, Mauro & Rossi, Ricardo & Silva, Marco. (2022). Airline’s business performance indicators and their impact on operational efficiency. Brazilian Business Review. 19. 642-665.
  • Reid, C. (2020). Technical appendix: The limitations of regression models.
  • Schisterman, E. F., Vexler, A., Whitcomb, B. W., & Liu, A. (2006). The limitations due to exposure detection limits for regression models. American Journal of Epidemiology, 163(4), 374–383.
  • Seymour, K., Held, M., Georges, G., & Boulouchos, K. (2020). Fuel estimation in air transportation: Modeling global fuel consumption for commercial aviation. Transportation Research Part D: Transport and Environment, 86, 102528.
  • Sezgin, E., & Çelik, Y. (2013). Comparison of data used for loss of data mining methods. In XV. Academic Informatics Conference Proceedings, January 23–25, 2013, Akdeniz University, Antalya. Retrieved from https://www.researchgate.net/publication/348787393
  • STM Defense Technologies Engineering and Trade Inc. (2021). Fuel efficiency in civil aviation: Research report, March 2021.
  • Şahinler, S. (2000). The basic principles of fitting linear regression model by least squares method. MKÜ Journal of Agriculture Faculty, 5(1–2), 57–73. Retrieved from https://www.researchgate.net/profile/Suat- Sahinler/publication/305711004_The_Basic_Principles_of_Fitting_Linear_Regression_Model_By_Least_Squ ares_Method/links/58ac442e92851cf0e3ccd32e/The-Basic-Principles-of-Fitting-Linear-Regression-Model- By-Least-Squares-Method.pdf
  • Şen, Y., & Erdağ, T. (2021). Evaluation of airline transportation sector development stages with PEST analysis: An investigation in the scope of five periods + Covid-19 pandemic process period. TroyAcademy International Journal of Social Sciences, 6(2), 422–461.
  • Tabernier, L., Calvo Fernández, E., Tautz, A., Deransy, R., & Martin, P. (2021). Fuel tankering: Economic benefits and environmental impact for flights up to 1500 NM (full tankering) and 2500 NM (partial tankering). Aerospace, 8(2), 37.
  • Tranmer, M., Murphy, J., Elliot, M., & Pampaka, M. (2020). Multiple linear regression (2nd ed.). Cathie Marsh Institute Working Paper 2020-01. https://hummedia.manchester.ac.uk/institutes/cmist/archive- publications/working-papers/2020/2020-1multiple-linear-regression.pdf
There are 34 citations in total.

Details

Primary Language English
Subjects Aircraft Performance and Flight Control Systems, Aerospace Engineering (Other)
Journal Section Research Article
Authors

Ertuğrul Metehan Sertdemir 0000-0002-4374-6289

Hülya Kaftelen Odabaşı 0000-0003-1111-5420

Ali Altinok 0000-0002-0544-663X

Project Number -
Submission Date March 22, 2025
Acceptance Date June 11, 2025
Publication Date June 28, 2025
Published in Issue Year 2025 Volume: 9 Issue: 2

Cite

APA Sertdemir, E. M., Kaftelen Odabaşı, H., & Altinok, A. (2025). Assessment of Aircraft Fuel Efficiency in Domestic Flights using Multiple Regression Analysis. Journal of Aviation, 9(2), 285-294. https://doi.org/10.30518/jav.1662031

Journal of Aviation - JAV 


www.javsci.com - editor@javsci.com


9210This journal is licenced under a Creative Commons Attiribution-NonCommerical 4.0 İnternational Licence