Data Mining Application in Air Transportation – a Case of Turkish Airlines

Renata Pisarek, Musab Talha Akpinar, Abdulkadir Hızıroglu


The paper presents an exemplification of data mining techniques in aviation industry on the basis of Turkish Airlines. The purpose of the paper is to present application of data mining on the selected operational data, concerning international flight passenger baggage data, in year 2015.  Examined were differences in passenger and flight profiles. Firstly, two-steps approach allowed defining the number of clusters. Secondly, K-means clustering were applied to divide data into a certain number of clusters representing the different areas of consumption. Results can contribute to higher efficiency in decision making regarding destination offer and fleet management.


data mining, K-means, airlines, air transport, Turkish Airlines

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Akerkar R., Analytics on big aviation data: turning data into insights. International Journal of Computer Science and Applications, (2014)/11, pp. 116-127.

Ayhan S., Pesce J., Comitz P., Sweet D., Bliesner S., Gerberick G., Predictive analytics with aviation big data. Integrated Communications, Navigation and Surveillance Conference (ICNS), IEEE, 2013, pp. 1-13.

Bazargan M., Airline operations and scheduling. Ashgate, Routledge 2016.

Boeing Current Market Outlook 2014-2034, Seattle 2015.

De Luca M., Abbondati F., Pirozzi M., Žilionienė D., Preliminary Study on Runway Pavement Friction Decay Using Data Mining, "Transportation Research Procedia", 14 (2016), pp. 3751-3760.

Han J., Kamber M., Data mining: concepts and techniques, the Morgan Kaufmann Series in data management systems, Elsevier, San Francisco 2006.

Huang J. Y., Kao Y. C., Lu W. C., Shieh J. C. P., An inductive literature review of REITs by data mining methods, [in:] Applied Engineering Sciences: Proceedings of the 2014 AASRI International Conference on Applied Engineering Sciences, CRC Press. Hollywood, USA, 2014, Vol. 1.

IATA: Annual Review 2016, 72nd Annual General Meeting, June 2016 -

Kim, K., Park, O. J., Yun, S., Yun, H., What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management, "Technological Forecasting and Social Change", 123 (20170, pp. 362-369.

Li L., Hansman R. J., Palacios R., Welsch R., Anomaly detection via a Gaussian Mixture Model for flight operation and safety monitoring, "Transportation Research Part C: Emerging Technologies", 64 (2016), pp. 45-57.

Pagels D. A., Aviation Data Mining, "Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal", 2 (2015)/1, Article 3, pp. 1-6.

Philbin A., Traffic growth and airline profitability were highlights of air transport in 2016 -

Ravizza S., Chen J., Atkin J. A., Stewart P., Burke E. K., Aircraft taxi time prediction: comparisons and insights, "Applied Soft Computing", 14 (2014), pp. 397-406.

Smith L. D., Ehmke J. F., A mathematical programming technique for matching time-stamped records in logistics and transportation systems, "Transportation Research Part C: Emerging Technologies", 69 (2016), pp. 375-385.

Sudhir K. B., Data mining applications in transportation engineering, "Transport", 18 (2003)/5, pp. 216-223.

Tanguy L., Tulechki N., Urieli A., Hermann E., Raynal C., Natural language processing for aviation safety reports: from classification to interactive analysis, "Computers in Industry", 78 (2016), pp. 80-95.

Tripathi S. S., The Process of Analytics. Learn Business Analytics in Six Steps Using SAS and R. Springer Verlag, Berlin 2016.

Vercellis C., Business intelligence: data mining and optimization for decision making. John Wiley & Sons, Chichester 2010.

Zanin M., Network analysis reveals patterns behind air safety events, "Physica A: Statistical Mechanics and its Applications", 401 (2014), pp. 201-206.


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