Prediction of Airline Ticket Price Using Machine Learning Method

Yıl 2024, Cilt: 9 Sayı: 2, 205 - 218, 19.11.2024

Hüseyin Korkmaz

https://doi.org/10.26650/JTL.2024.1486696

Öz

Airline ticket pricing is a complex and dynamic process influenced by various factors, including demand fluctuations, seasonal variations, and competitive strategies. Accurate price prediction is crucial for both airlines, to maximize revenue, and customers, to secure the best deals. Traditional methods often fall short of capturing the intricate and rapidly changing patterns of airfare pricing. With the advent of machine learning algorithms, there is a growing potential to enhance the accuracy and reliability of ticket price predictions. This paper aims to predict ticket prices based on airline flight data using ML algorithms and to compare the performance of ML algorithms. The secondary objective of this paper is to identify the main factors affecting airline ticket prices. The flight and ticket price datasets of THY and PGS that were obtained from open-access sources are used in this paper. The final dataset consists of 962 records for three months from June 1st, 2022 to August 30th, 2022 and includes 19 different variables. Statistical tests and ML algorithms were applied to the final dataset. This paper compares various ML models to predict airline ticket prices, considering performance metrics such as MAE, MSE, RMSE, and R2 during training and test phases. According to the model training and test results, the best algorithm is GPR with R2: 0.86 (training) and R2: 0.90 (test). The findings are consistent with existing literature, further validating the superior efficacy of certain models in specific contexts and demonstrating significant progress in the field. This paper contributes to the literature by comparing the effectiveness of various machine learning algorithms in predicting airline ticket prices, providing new and valuable insights into model performance and key price-determining factors.

Anahtar Kelimeler

Price Prediction, Ticket Price, Airfare Price, Machine Learning, Intelligent Transportation Systems

Kaynakça

• Abdella, J. A., Zaki, N., Shuaib, K., & Khan, F. (2021). Airline ticket price and demand prediction: A survey. Journal of King Saud University -Computer and Information Sciences, 33(4), 375-391. https://doi.Org/10.1016/j.jksuci.2019.02.001 google scholar
• Aliberti, A., Xin, Y., Viticchie, A., Macii, E., & Patti, E. (2023). Comparative analysis of neural networks techniques to fore-cast Airfare Prices. 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), 1023-1029. https://doi.org/10.1109/COMPSAC57700.2023.00157 google scholar
• Barrett, G. B. (2000). The Coefficient of Determination: Understanding r squared and R squared. The Mathematics Teacher, 93(3), 230-234. google scholar
• Chen, T., Yin, H., Chen, H., Wu, L., Wang, H., Zhou, X., & Li, X. (2018). TADA: Trend Alignment with Dual-Attention Multi-task Recurrent Neural Networks for Sales Prediction. 2018 IEEE International Conference on Data Mining (ICDM), 49-58. https://doi.org/10.1109/ICDM.2018.00020 google scholar
• Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Routledge. google scholar
• Deng, T. (2024). International flight fare prediction and analysis of factors impacting flight fare. Theoretical and Natural Science, 31(1), 329-335. https://doi.org/10.54254/2753-8818/31/20241079 google scholar
• Di Bucchianico, A. (2008). Coefficient of determination (R 2). Encyclopedia of Statistics in Quality and Reliability. google scholar
• Farrar, D. E., & Glauber, R. R. (1967). Multicollinearity in Regression Analysis: The Problem Revisited. The Review of Economics and Statistics, 49(1), 92. https://doi.org/10.2307/1937887 google scholar
• Gibbons, J. D. (1997). Nonparametric methods for quantitative analysis. Journal of the Operational Research Society, 48(8), 853. google scholar
• Gordiievych, A., & Shubin, I. (2015). Forecasting of airfare prices using time series. 2015 Information Technologies in Innovation Business Conference (ITIB), 68-71. https://doi.org/10.1109/ITIB.2015.7355055 google scholar
• Groves, W., & Gini, M. (2015). On Optimizing Airline Ticket Purchase Timing. ACM Transactions on Intelligent Systems and Technology, 7(1), 1-28. https://doi.org/10.1145/2733384 google scholar
• Gui, G., Liu, F., Sun, J., Yang, J., Zhou, Z., & Zhao, D. (2020). Flight Delay Prediction Based on Aviation Big Data and Machine Learning. IEEE Transactions on Vehicular Technology, 69(1), 140-150. https://doi.org/10.1109/TVT.2019.2954094 google scholar
• Howell, D. C. (1992). Statistical methods for psychology. PWS-Kent Publishing Co. google scholar
• Huang, H.-C. (2013). A Hybrid Neural Network Prediction Model of Air Ticket Sales. TELKOMNIKA Indonesian Journal of Electrical Engineering, 11(11). https://doi.org/10.11591/telkomnika.v11i11.2762 google scholar
• Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688. https://doi.org/10.1016/j.ijforecast.2006.03.001 google scholar
• Janssen, T., Dijkstra, T., Abbas, S., & van Riel, A. C. (2014). A linear quantile mixed regression model for prediction of airline ticket prices. Radboud University, 3. google scholar
• 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. https://doi.org/10.1109/ACCESS.2023.3274669 google scholar
• Karataş, G. (2021). The Effects of Normalization and Standardization an Internet of Things Attack Detection. European Journal of Science and Technology, 29, 187-192. https://doi.org/10.31590/ejosat.1017427 google scholar
• Kumar, A. (2023). Airline Price Prediction Using XGBoost Hyper-parameter Tuning (pp. 239-248). https://doi.org/10.1007/978-3-031-28183-9_17 google scholar
• Lai, G., Chang, W.-C., Yang, Y., & Liu, H. (2018). Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks. The 41st Inter-national ACM SIGIR Conference on Research & Development in Information Retrieval, 95-104. https://doi.org/10.1145/3209978.3210006 google scholar
• Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen, E., & Winkler, R. (1982). The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting, 1(2), 111-153. https://doi.org/10.1002/for.3980010202 google scholar
• Miles, J., & Banyard, P. (2007). Understanding and using statistics in psychology: A practical introduction. google scholar
• Nevitt, J., & Hancock, G. R. (2000). Improving the Root Mean Square Error of Approximation for Nonnormal Conditions in Structural Equation Modeling. The Journal of Experimental Education, 68(3), 251-268. https://doi.org/10.1080/00220970009600095 google scholar
• Qin, Y., Song, D., Chen, H., Cheng, W., Jiang, G., & Cottrell, G. W. (2017). A Dual-Stage Attention-Based Recurrent Neural Net-work for Time Series Prediction. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2627-2633. https://doi.org/10.24963/ijcai.2017/366 google scholar
• Ren, R., Yang, Y., & Yuan, S. (2014). Prediction of airline ticket price. University of Stanford, 1-5. google scholar
• Sammut, C., & Webb, G. I. (2010). Mean absolute error. Encyclopedia of Machine Learning, 652. google scholar
• Santana, E., Mastelini, S., & Jr., S. (2017). Deep Regressor Stacking for Air Ticket Prices Prediction. Anais Do Simposio Brasileiro de Sistemas de Informaçao (SBSI), 25-31. https://doi.org/10.5753/sbsi.2017.6022 google scholar
• Sherly Puspha Annabel, L., Ramanan, G., Prakash, R., & Sreenidhi, S. (2023). Machine Learning-Based Approach for Airfare Forecasting (pp. 901-912). https://doi.org/10.1007/978-981-19-6634-7_65 google scholar
• Shih, S.-Y., Sun, F.-K., & Lee, H. (2019). Temporal pattern attention for multivariate time series forecasting. Machine Learning, 108(8-9), 1421-1441. https://doi.org/10.1007/s10994-019-05815-0 google scholar
• Tziridis, K., Kalampokas, Th., Papakostas, G. A., & Diamantaras, K. I. (2017). Airfare prices prediction using machine learning techniques. 2017 25th European Signal Processing Conference (EUSIPCO), 1036-1039. https://doi.org/10.23919/EUSIPCO.2017.8081365 google scholar
• Wang, T., Pouyanfar, S., Tian, H., Tao, Y., Alonso, M., Luis, S., & Chen, S.-C. (2019). A Framework for Airfare Price Prediction: A Machine Learning Approach. 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI), 200-207. https://doi.org/10.1109/IRI.2019.00041 google scholar
• Wohlfarth, T., Clemencon, S., Roueff, F., & Casellato, X. (2011). A Data-Mining Approach to Travel Price Forecasting. 2011 10th International Conference on Machine Learning and Applications and Workshops, 84-89. https://doi.org/10.1109/ICMLA.2011.11 google scholar
• Yujing, D., Zhihao, W., & Youfang, L. (2020). Flight passenger load factors prediction based on RNN using multi-granularity time attention. Computer Engineering, 46(509), 01. google scholar
• Zhao, Z., You, J., Gan, G., Li, X., & Ding, J. (2022). Civil airline fare prediction with a multi-attribute dual-stage attention mechanism. Applied Intelligence, 52(5), 5047-5062. https://doi.org/10.1007/s10489-021-02602-0 google scholar