Studying the Quality of Airline Customer Service Using Machine Learning Methods

The article presents the results of using machine learning methods to study data from a special questionnaire that considers the general characteristics of flights, the characteristics of passengers and their opinions on various aspects of the flight. The objective of the study is to identify in experimental data factors that negatively affect passengers’ attitudes towards airline services.

When conducting the study, well-known algorithms were used that are part of free WEKA (Waikato Environment for Knowledge Analysis) software for data analysis and machine learning by University of Waikato (New Zealand), distributed under the GNU GPL license: naïve Bayes classifier; multilayer perceptron using backpropagation algorithm; k-nearest neighbour method (KNN) with adaptive selection of parameters; decision trees – J48 is an open-source Java implementation of the C4.5 algorithm; random forest; logistic regression; adaptive boosting algorithm (AdaBoost); support vector machine – the SMO (Sequential Minimal Optimization) algorithm which is one of the possible implementations of the support vector machine algorithm.

It is shown that the best accurate models reflecting passenger satisfaction with airline services are obtained using random forest algorithms (error on the test sample is of 3,9 %) and a neural network approach (error on the test sample is of 3,7 %). At the same time, these algorithms do not allow us to explicitly identify factors characteristic of air passengers who are dissatisfied with the quality of service. This gap is filled by an algorithm based on the method of structural resonance in multidimensional data (SRMD), which made it possible to identify precise logical rules in the data with high completeness. The resulting logical rules are highly interpretable patterns of passengers who either negatively or neutrally evaluate the airline’s services in general.

1. Dike, S. E., Davis, Z., Abrahams, A., Anjomshoae, A., Ractham, P. Evaluation of passengers’ expectations and satisfaction in the airline industry: an empirical performance analysis of online reviews. Benchmarking: An International Journal, 2024, Vol. 31, Iss. 2, pp. 611–639. DOI: 10.1108/BIJ-09-2021-0563.

2. Fodness, D., Murray, B. Passengers’ expectations of airport service quality. Journal of Services Marketing, 2007, Vol. 21, Iss. 7, pp. 492–506. DOI: 10.1108/08876040710824852.

3. Ban, H.-J., Kim, H.-S. Understanding Customer Experience and Satisfaction through Airline Passengers’ Online Review. Sustainability, 2019, Vol. 11(15), pp. 1–17. DOI: 10.3390/su11154066.

4. Awadh, M. Assessing the Quality of Sustainable Airline Services Utilizing the Multicriteria Decision-Making Approach. Sustainability, 2023, Vol. 15(9), pp. 1–19. DOI: 10.3390/su15097044.

5. Namukasa, J. The influence of airline service quality on passenger satisfaction and loyalty: the case of Uganda airline industry. The TQM Journal, 2013, Vol. 25, Iss. 5, pp. 520–532. DOI: 10.1108/TQM-11-2012-0092.

6. Tahanisaz, S., Shokuhyar, S. Evaluation of passenger satisfaction with service quality: A consecutive method applied to the airline industry. Journal of Air Transport Management, 2020, Vol. 83, pp. 101764. DOI: 10.1016/j.jairtraman.2020.101764.

7. Tsaur, S.-H., Chang, T.-Y., Yen, C.-H. The evaluation of airline service quality by fuzzy MCDM. Tourism Management, 2002, Vol. 23 (2), pp. 107–115. DOI: 10.1016/S0261-5177(01)00050-4.

8. Ayriev, R. S., Kudryashov, M. A. Quality Indices of Public Transportation Services. World of Transport and Transportation, 2018, Vol. 16, Iss. 4 (77), pp. 140–149. DOI: https://doi.org/10.30932/1992-3252-2018-16-4-11.

9. Matantseva, O. Y., Aredova, A. K., Shchegoleva, I. V. Study of the Influence of Factors on Passenger Service Quality and Efficiency of Rolling Stock Use. World of Transport and Transportation, 2022, Vol. 20, Iss. 4 (101), pp. 98–104. DOI: https://doi.org/10.30932/1992-3252-2022-20-4-8.

10. Sokolov, Yu. I. Service Quality Should be Assessed by the Clients Themselves. World of Transport and Transportation, 2015, Vol. 13, Iss. 4 (59), pp. 100–109. [Electronic resource]: https://mirtr.elpub.ru/jour/article/view/490/761. Last accessed 07.01.2024.

11. Duke, V. A. Logical methods of machine learning (tools and practical examples) [Logicheskie metody mashinnogo obucheniya (instrumentalnie sredstva i prakticheskie primery)]. St. Petersburg, Publishing and Printing Association of Higher Educational Institutions, 2020, 248 p. ISBN 978-5-91155-087-5.

12. Frank, E., Hall, M. A., Witten, I. H. The WEKA Workbench. Online Appendix for «Data Mining: Practical Machine Learning Tools and Techniques», Morgan Kaufmann, Fourth Edition, 2016. [Electronic resource]: https://www.scirp.org/reference/referencespapers?referenceid=2477648. Last accessed 07.01.2024.

13. Platt, C. Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. In: Advances in Kernel Methods – Support Vector Learning, ed. by B. Schölkopf and C. J. C. Burges and A. J. Smola. Cambridge, MA, MIT Press. 1999, pp. 185–208. [Electronic resource]: https://www.researchgate.net/publication/2624239_Sequential_Minimal_Optimization_A_Fast_Algorithm_for_Training_Support_Vector_Machines. Last accessed 07.01.2024.

14. Quinlan, J. R. C4.5 Programs for Machine Learning, San Mateo, CA, Morgan Kaufmann, 1992, 302 p. ISBN 978-1558602380.

15. Duke, V. A., Malygin, I. G. Comparison of algorithms for recognition of vehicle types by parameters of their silhouettes. Marine intellectual technologies, 2018, Vol.4, Iss. 4 (42), pp. 197–201. EDN: YXSDNR.

16. Duke, V. A., Malygin, I. G. Comparative study of machine learning algorithms in the problem of predicting the dynamics of bike sharing [Sravnitelnoe issledovanie algoritmov mashinnogo obucheniya v zadache prognozirovaniya dinamiki velosheringa]. Transport: science, technology, management. Scientific information collection, 2023, Iss. 6, pp. 40–45. EDN: BVMMSV.