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Sun, X., Zheng, C., Wandelt, S., & Zhang, A. (2024). Airline competition: A comprehensive review of recent research. Journal of the Air Transport Research Society, 100013. Elsevier.
1. Sun, X., Zheng, C., Wandelt, S., & Zhang, A. (2024). Airline competition: A comprehensive review of recent research. Journal of the Air Transport Research Society, 100013. Elsevier.

Belobaba, P., Odoni, A., & Barnhart, C. (2015). The global airline industry. John Wiley & Sons.
2. Belobaba, P., Odoni, A., & Barnhart, C. (2015). The global airline industry. John Wiley & Sons.

International Air Transport Association (2019). Economic performance of the airline industry. Retrieved from https://www.iata.org/whatwedo/Documents/economics/IATA-Economic-Performance-of-the-Industry-end-year-2014-report.pdf.
3. International Air Transport Association (2019). Economic performance of the airline industry. Retrieved from https://www.iata.org/whatwedo/Documents/economics/IATA-Economic-Performance-of-the-Industry-end-year-2014-report.pdf.

Borenstein, S., & Rose, N. L. (1994). Competition and price dispersion in the US airline industry. Journal of Political Economy, 102(4), 653–683. The University of Chicago Press.
4. Borenstein, S., & Rose, N. L. (1994). Competition and price dispersion in the US airline industry. Journal of Political Economy, 102(4), 653–683. The University of Chicago Press.

Sherly Puspha Annabel, L., Ramanan, G., Prakash, R., & Sreenidhi, S. (2023). Machine Learning-Based Approach for Airfare Forecasting. In Proceedings of International Conference on Data Science and Applications: ICDSA 2022, Volume 2 (pp. 901–912). Springer.
5. Sherly Puspha Annabel, L., Ramanan, G., Prakash, R., & Sreenidhi, S. (2023). Machine Learning-Based Approach for Airfare Forecasting. In Proceedings of International Conference on Data Science and Applications: ICDSA 2022, Volume 2 (pp. 901–912). Springer.

Kalampokas, T., Tziridis, K., Kalampokas, N., Nikolaou, A., Vrochidou, E., & Papakostas, G. A. (2023). A holistic approach on airfare price prediction using machine learning techniques. IEEE Access, 11, 46627–46643. IEEE.
6. Kalampokas, T., Tziridis, K., Kalampokas, N., Nikolaou, A., Vrochidou, E., & Papakostas, G. A. (2023). A holistic approach on airfare price prediction using machine learning techniques. IEEE Access, 11, 46627–46643. IEEE.

Xu, X., McGrory, C. A., Wang, Y.-G., & Wu, J. (2021). Influential factors on Chinese airlines’ profitability and forecasting methods. Journal of Air Transport Management, 91, 101969. Elsevier.
7. Xu, X., McGrory, C. A., Wang, Y.-G., & Wu, J. (2021). Influential factors on Chinese airlines’ profitability and forecasting methods. Journal of Air Transport Management, 91, 101969. Elsevier.

Gillen, D., & Morrison, W. G. (2005). The economics of franchise contracts and airport policy. Journal of Air Transport Management, 11(1), 43–48. Elsevier.
8. Gillen, D., & Morrison, W. G. (2005). The economics of franchise contracts and airport policy. Journal of Air Transport Management, 11(1), 43–48. Elsevier.

Xiong, X., Song, X., Kaygorodova, A., Ding, X., Guo, L., & Huang, J. (2023). Aviation and carbon emissions: Evidence from airport operations. Journal of Air Transport Management, 109, 102383. Elsevier.
9. Xiong, X., Song, X., Kaygorodova, A., Ding, X., Guo, L., & Huang, J. (2023). Aviation and carbon emissions: Evidence from airport operations. Journal of Air Transport Management, 109, 102383. Elsevier.

Brueckner, J. K., & Abreu, C. (2017). Airline fuel usage and carbon emissions: Determining factors. Journal of Air Transport Management, 62, 10–17. Elsevier.
10. Brueckner, J. K., & Abreu, C. (2017). Airline fuel usage and carbon emissions: Determining factors. Journal of Air Transport Management, 62, 10–17. Elsevier.

scikit-learn developers (2023). Ordinary Least Squares. Retrieved October 30, 2024, from https://scikit-learn.org/stable/modules/linear_model.html#ordinary-least-squares.
11. scikit-learn developers (2023). Ordinary Least Squares. Retrieved October 30, 2024, from https://scikit-learn.org/stable/modules/linear_model.html#ordinary-least-squares.

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