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Association Analysis Between Airline Destinations

Year 2021, Volume: 25 Issue: 3, 1260 - 1289, 28.09.2021
https://doi.org/10.53487/ataunisosbil.930684

Abstract

Abstract: This study aims to contribute to the strategic decisions of airline companies in determining of their destinations. Using the data of 617 airlines, it was tried to investigate whether there are rules of association between the companies' destinations. Through the analysis made with two different data sets, the rules of association were sought considering both all the destinations and the foreign destinations of the companies. In the analysis of all destinations, 197 rules could be produced with 90% and above confidence level. In the analysis between the companies' foreign destinations, 200 rules with 100% confidence level could be produced. It is presented as a suggestion that airlines can benefit from these rules in their destination planning.

References

  • Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings of the 20th international conference on very large data bases, 1994 Santiago, Chile. Citeseer (pp.487–499).
  • Baker, R. S.J.d. (2010), Data Mining for Education, To appear in McGaw, B., Peterson, P., Baker, E. (Eds.) International Encyclopedia of Education (3rd edition). Oxford, UK: Elsevier.
  • Barrett, S. D. (2004). How do the demands for airport services differ between full-service carriers and low-cost carriers?. Journal of air transport management, 10(1), 33-39.
  • Belobaba, P., Odoni, A., & Barnhart, C. (Eds.). (2015). The global airline industry. John Wiley & Sons.
  • Belton, V., & Stewart, T. (2002). Multiple criteria decision analysis: an integrated approach. Springer Science & Business Media.
  • Bieger, T., & Wittmer, A. (2006). Air transport and tourism—Perspectives and challenges for destinations, airlines and governments. Journal of air transport management, 12(1), 40-46.
  • Bose, I., Chun, L. A.,Yue, L. V. W., Ines, L. H. W. and Helen, W. O. L. (2009), “Business Data Warehouse: The Case of Wal-Mart”, Data Mining Applications for Empowering Knowledge Societies, Ed. Hakikur Rahman, Information Science Reference, pp.189-198
  • Calder, S. (2003). No Frills: The truth behind the low-cost revolution in the skies. Virgin Pub.
  • Chang, Y. C., & Lee, N. (2010). A Multi-Objective Goal Programming airport selection model for low-cost carriers’ networks. Transportation Research Part E: Logistics and Transportation Review, 46(5), 709-718.
  • Chang, Y. C., Lee, W. H., & Wu, C. H. (2019). Airline new route selection using compromise programming-The case of Taiwan-Europe. Journal of Air Transport Management, 76, 10-20.
  • Chang, Y. C., Woon, H. K., Yen, J. R., & Hsu, C. J. (2017). A destination selection model for long haul routes–The case of Taiwan-US. Journal of Air Transport Management, 64, 60-67.
  • Deveci, M., Demirel, N. Ç., & Ahmetoğlu, E. (2017). Airline new route selection based on interval type-2 fuzzy MCDM: A case study of new route between Turkey-North American region destinations. Journal of Air Transport Management, 59, 83-99.
  • Doganis, R. (2006). The airline business. Psychology Press.
  • Dožić, S. (2019). Multi-criteria decision making methods: Application in the aviation industry. Journal of Air Transport Management, 79, 101683.
  • Fayyad, Usama, Gregory Piatetsky-Shapiro, and Padhraic Smyth (1996). From data mining to knowledge discovery in databases, AI magazine 17.3:37-54.
  • Francis, G., Fidato, A., & Humphreys, I. (2003). Airport–airline interaction: the impact of low-cost carriers on two European airports. Journal of Air Transport Management, 9(4), 267-273.
  • Gillen, D., & Lall, A. (2004). Competitive advantage of low-cost carriers: some implications for airports. Journal of Air Transport Management, 10(1), 41-50.
  • Giudici, P. & Figini S. (2008). “Applied Data Mining For Busıness and Industry”, A John Wiley and Sons, Ltd., Publication. 2008 90-91.
  • Hand David; Mannila, Heikki & Smyth, Padhraic (2001). Principles of Data Mining, e-book, MIT Press, Cambridge
  • Hand, D., Mannila H. and Smyth ,P.(2001), Principles of Data Mining, The MIT Press, London
  • Hsu, C. I., & Wen, Y. H. (2002). Reliability evaluation for airline network design in response to fluctuation in passenger demand. Omega, 30(3), 197-213.
  • Humphreys, I., & Francis, G. (2002). Policy issues and planning of UK regional airports. Journal of Transport Geography, 10(4), 249-258.
  • İnan, O. (2003). Veri Madenciliği, Master's Thesis, Selçuk Üniversitesi, Fen Bilimleri Enstitüsü, Konya
  • Jaillet, P., Song, G., & Yu, G. (1996). Airline network design and hub location problems. Location science, 4(3), 195-212.
  • Jain, Y. K., Yadav, V. K. and Panday, G. S., (2011), “An Efficient Association Rule Hiding Algorithm for Privacy Preserving Data Mining”, International Journal On Computer Science And “Engineering, Vol. 3 No. 7, p. 2792-2798.
  • Janic, M., & Reggiani, A. (2002). An application of the multiple criteria decision making (MCDM) analysis to the selection of a new hub airport. European Journal of Transport and Infrastructure Research, 2(2).
  • Kantardzic, Mehmed , (2003). Data Mining: Concepts, Models, Methods, and Algorithms, John Wiley & Sons J. B. Speed Scientific School, University of Louisville IEEE Computer Society
  • Kotegawa, T. (2012). Analyzing the evolutionary mechanisms of the air transportation system-of-systems using Network Theory and machine learning algorithms (Doctoral dissertation, Purdue University).
  • Kotsiantis,S.& Kanellopoulos D. (2006). Association Rules Mining: A Recent Overview, GESTS International Transactions on Computer Science and Engineering, Vol.32 (1), pp. 71-82
  • Lawton, T. C. (2017). Cleared for take-off: Structure and strategy in the low fare airline business. Routledge.
  • Lederer, P. J., & Nambimadom, R. S. (1998). Airline network design. Operations Research, 46(6), 785-804.
  • Lee, J. K., Yoo, K. E., & Song, K. H. (2016). A study on travelers' transport mode choice behavior using the mixed logit model: A case study of the Seoul-Jeju route. Journal of Air Transport Management, 56, 131-137.
  • Lu, H. A., & Liu, R. R. (2014). Market opportunity analysis and evaluation of the expansion of air transport services across the Taiwan Strait. Journal of Air Transport Management, 37, 10-19.
  • Margahny, M. H. and Shakour, A.(2006). Fast Algorıthm For Mining Association Rules, Journal of Engineering Sciences, Assiut University, Vol. 34, No. 1, pp. 79-87
  • Martı́n, J. C., & Román, C. (2004). Analyzing competition for hub location in intercontinental aviation markets. Transportation Research Part E: Logistics and Transportation Review, 40(2), 135-150.
  • Nahar J., Imam T., Tickle K. S., Chen Y. P. (2013). ‘‘Association Rule Mining to Detect Factors Which Contribute To Heart Disease in Males and Females’’. Expert Systems with Applications 40 (2013) 1086-1093. doi:https://doi.org/10.1016/j.eswa.2012.08.028.
  • Nisbet, R., Elder, J., and Miner, G. (2009). Handbook of Statistical Analysis and Data Mining Applications, Elsevier Inc, Burlington.
  • O’Kelly, M. E., & Bryan, D. L. (1998). Hub location with flow economies of scale. Transportation Research Part B: Methodological, 32(8), 605-616.
  • O'kelly, M. E. (1987). A quadratic integer program for the location of interacting hub facilities. European journal of operational research, 32(3), 393-404.
  • Parack, S., Zahid, Z., & Merchant, F. (2012). ‘‘Application of data mining in educational databases for predicting academic trends and patterns’’. 2012 IEEE International Conference on Technology Enhanced Education (ICTEE) (2012) 1-4. doi: 10.1109/ICTEE.2012.6208617.
  • Rokach, Lior and Maimon, Oded (2008), Data Mining with Decision Trees, World Scientific, New Jersey
  • Scheers, J. (2001). Attractıng ınvestors to european regıonal aırports: what are the prerequısıtes?. International Airport Review.
  • Sha, Z., Moolchandani, K. A., Maheshwari, A., Thekinen, J., Panchal, J., & DeLaurentis, D. A. (2015). Modeling airline decisions on route planning using discrete choice models. In 15th AIAA Aviation Technology, Integration, and Operations Conference (p. 2438).
  • Sha, Z., Moolchandani, K., Panchal, J. H., & DeLaurentis, D. A. (2016). Modeling Airlines’ Decisions on City-Pair Route Selection Using Discrete Choice Models. Journal of Air Transportation, 63-73.
  • Song, K., Lewe, J. H., & Mavris, D. N. (2014). A Multi-Tier Evolution Model of Air Transportation Networks. In 14th AIAA Aviation Technology, Integration, and Operations Conference (p. 3267).
  • Taş, Y. (2018). Birliktelik Kuralları Madenciliği ve Bir Uygulama, Yüksek Lisans Tezi, Sivas Cumhuriyet Üniversitesi, Sivas 2018.
  • Teodorović, D., Kalić, M., & Pavković, G. (1994). The potential for using fuzzy set theory in airline network design. Transportation Research Part B: Methodological, 28(2), 103-121.
  • Warnock-Smith, D., & Potter, A. (2005). An exploratory study into airport choice factors for European low-cost airlines. Journal of Air Transport Management, 11(6), 388-392.
  • Webb, G. I. (2003). ‘‘Association Rules’’. Ed. Ye N. The Handbook Of Data Mining. (2003) 27-28. New Jersey.
  • Yılmaz, A. K., Malagas, K. N., Nikitakos, N., & Bal, H. T. (2018). Modeling regional routes with Greek airlines for flight operations to AOE airport. Aircraft Engineering and Aerospace Technology.
  • Zanakis, S. H., Solomon, A., Wishart, N., & Dublish, S. (1998). Multi-attribute decision making: A simulation comparison of select methods. European journal of operational research, 107(3), 507-529.

Association Analysis Between Airline Destinations

Year 2021, Volume: 25 Issue: 3, 1260 - 1289, 28.09.2021
https://doi.org/10.53487/ataunisosbil.930684

Abstract

Öz: Bu çalışma ile havayolu şirketlerinin varış noktalarını belirleme konusundaki stratejik kararlarına katkı sağlamak amaçlanmıştır. 617 havayolu şirketine ait veriler kullanılarak, şirketlerin varış noktaları arasında birliktelik kurallarının olup olmadığı araştırılmaya çalışılmıştır. İki farklı veri seti ile yapılan analizlerle hem tüm varış noktaları, hem de şirketlerin yurt dışı varış noktaları dikkate alınarak birliktelik kuralları aranmıştır. Tüm varış noktaları ilgili analizde %90 ve üzeri güven seviyesinde 197 kural üretilebilmiştir. Şirketlerin yurt dışı varış noktaları arasındaki analizde ise %100 güven seviyende 200 kural üretilebilmiştir. Havayolu şirketlerinin varış noktaları planlamalarında, bu kurallardan yararlanabilecekleri, bir öneri olarak sunulmuştur.

References

  • Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings of the 20th international conference on very large data bases, 1994 Santiago, Chile. Citeseer (pp.487–499).
  • Baker, R. S.J.d. (2010), Data Mining for Education, To appear in McGaw, B., Peterson, P., Baker, E. (Eds.) International Encyclopedia of Education (3rd edition). Oxford, UK: Elsevier.
  • Barrett, S. D. (2004). How do the demands for airport services differ between full-service carriers and low-cost carriers?. Journal of air transport management, 10(1), 33-39.
  • Belobaba, P., Odoni, A., & Barnhart, C. (Eds.). (2015). The global airline industry. John Wiley & Sons.
  • Belton, V., & Stewart, T. (2002). Multiple criteria decision analysis: an integrated approach. Springer Science & Business Media.
  • Bieger, T., & Wittmer, A. (2006). Air transport and tourism—Perspectives and challenges for destinations, airlines and governments. Journal of air transport management, 12(1), 40-46.
  • Bose, I., Chun, L. A.,Yue, L. V. W., Ines, L. H. W. and Helen, W. O. L. (2009), “Business Data Warehouse: The Case of Wal-Mart”, Data Mining Applications for Empowering Knowledge Societies, Ed. Hakikur Rahman, Information Science Reference, pp.189-198
  • Calder, S. (2003). No Frills: The truth behind the low-cost revolution in the skies. Virgin Pub.
  • Chang, Y. C., & Lee, N. (2010). A Multi-Objective Goal Programming airport selection model for low-cost carriers’ networks. Transportation Research Part E: Logistics and Transportation Review, 46(5), 709-718.
  • Chang, Y. C., Lee, W. H., & Wu, C. H. (2019). Airline new route selection using compromise programming-The case of Taiwan-Europe. Journal of Air Transport Management, 76, 10-20.
  • Chang, Y. C., Woon, H. K., Yen, J. R., & Hsu, C. J. (2017). A destination selection model for long haul routes–The case of Taiwan-US. Journal of Air Transport Management, 64, 60-67.
  • Deveci, M., Demirel, N. Ç., & Ahmetoğlu, E. (2017). Airline new route selection based on interval type-2 fuzzy MCDM: A case study of new route between Turkey-North American region destinations. Journal of Air Transport Management, 59, 83-99.
  • Doganis, R. (2006). The airline business. Psychology Press.
  • Dožić, S. (2019). Multi-criteria decision making methods: Application in the aviation industry. Journal of Air Transport Management, 79, 101683.
  • Fayyad, Usama, Gregory Piatetsky-Shapiro, and Padhraic Smyth (1996). From data mining to knowledge discovery in databases, AI magazine 17.3:37-54.
  • Francis, G., Fidato, A., & Humphreys, I. (2003). Airport–airline interaction: the impact of low-cost carriers on two European airports. Journal of Air Transport Management, 9(4), 267-273.
  • Gillen, D., & Lall, A. (2004). Competitive advantage of low-cost carriers: some implications for airports. Journal of Air Transport Management, 10(1), 41-50.
  • Giudici, P. & Figini S. (2008). “Applied Data Mining For Busıness and Industry”, A John Wiley and Sons, Ltd., Publication. 2008 90-91.
  • Hand David; Mannila, Heikki & Smyth, Padhraic (2001). Principles of Data Mining, e-book, MIT Press, Cambridge
  • Hand, D., Mannila H. and Smyth ,P.(2001), Principles of Data Mining, The MIT Press, London
  • Hsu, C. I., & Wen, Y. H. (2002). Reliability evaluation for airline network design in response to fluctuation in passenger demand. Omega, 30(3), 197-213.
  • Humphreys, I., & Francis, G. (2002). Policy issues and planning of UK regional airports. Journal of Transport Geography, 10(4), 249-258.
  • İnan, O. (2003). Veri Madenciliği, Master's Thesis, Selçuk Üniversitesi, Fen Bilimleri Enstitüsü, Konya
  • Jaillet, P., Song, G., & Yu, G. (1996). Airline network design and hub location problems. Location science, 4(3), 195-212.
  • Jain, Y. K., Yadav, V. K. and Panday, G. S., (2011), “An Efficient Association Rule Hiding Algorithm for Privacy Preserving Data Mining”, International Journal On Computer Science And “Engineering, Vol. 3 No. 7, p. 2792-2798.
  • Janic, M., & Reggiani, A. (2002). An application of the multiple criteria decision making (MCDM) analysis to the selection of a new hub airport. European Journal of Transport and Infrastructure Research, 2(2).
  • Kantardzic, Mehmed , (2003). Data Mining: Concepts, Models, Methods, and Algorithms, John Wiley & Sons J. B. Speed Scientific School, University of Louisville IEEE Computer Society
  • Kotegawa, T. (2012). Analyzing the evolutionary mechanisms of the air transportation system-of-systems using Network Theory and machine learning algorithms (Doctoral dissertation, Purdue University).
  • Kotsiantis,S.& Kanellopoulos D. (2006). Association Rules Mining: A Recent Overview, GESTS International Transactions on Computer Science and Engineering, Vol.32 (1), pp. 71-82
  • Lawton, T. C. (2017). Cleared for take-off: Structure and strategy in the low fare airline business. Routledge.
  • Lederer, P. J., & Nambimadom, R. S. (1998). Airline network design. Operations Research, 46(6), 785-804.
  • Lee, J. K., Yoo, K. E., & Song, K. H. (2016). A study on travelers' transport mode choice behavior using the mixed logit model: A case study of the Seoul-Jeju route. Journal of Air Transport Management, 56, 131-137.
  • Lu, H. A., & Liu, R. R. (2014). Market opportunity analysis and evaluation of the expansion of air transport services across the Taiwan Strait. Journal of Air Transport Management, 37, 10-19.
  • Margahny, M. H. and Shakour, A.(2006). Fast Algorıthm For Mining Association Rules, Journal of Engineering Sciences, Assiut University, Vol. 34, No. 1, pp. 79-87
  • Martı́n, J. C., & Román, C. (2004). Analyzing competition for hub location in intercontinental aviation markets. Transportation Research Part E: Logistics and Transportation Review, 40(2), 135-150.
  • Nahar J., Imam T., Tickle K. S., Chen Y. P. (2013). ‘‘Association Rule Mining to Detect Factors Which Contribute To Heart Disease in Males and Females’’. Expert Systems with Applications 40 (2013) 1086-1093. doi:https://doi.org/10.1016/j.eswa.2012.08.028.
  • Nisbet, R., Elder, J., and Miner, G. (2009). Handbook of Statistical Analysis and Data Mining Applications, Elsevier Inc, Burlington.
  • O’Kelly, M. E., & Bryan, D. L. (1998). Hub location with flow economies of scale. Transportation Research Part B: Methodological, 32(8), 605-616.
  • O'kelly, M. E. (1987). A quadratic integer program for the location of interacting hub facilities. European journal of operational research, 32(3), 393-404.
  • Parack, S., Zahid, Z., & Merchant, F. (2012). ‘‘Application of data mining in educational databases for predicting academic trends and patterns’’. 2012 IEEE International Conference on Technology Enhanced Education (ICTEE) (2012) 1-4. doi: 10.1109/ICTEE.2012.6208617.
  • Rokach, Lior and Maimon, Oded (2008), Data Mining with Decision Trees, World Scientific, New Jersey
  • Scheers, J. (2001). Attractıng ınvestors to european regıonal aırports: what are the prerequısıtes?. International Airport Review.
  • Sha, Z., Moolchandani, K. A., Maheshwari, A., Thekinen, J., Panchal, J., & DeLaurentis, D. A. (2015). Modeling airline decisions on route planning using discrete choice models. In 15th AIAA Aviation Technology, Integration, and Operations Conference (p. 2438).
  • Sha, Z., Moolchandani, K., Panchal, J. H., & DeLaurentis, D. A. (2016). Modeling Airlines’ Decisions on City-Pair Route Selection Using Discrete Choice Models. Journal of Air Transportation, 63-73.
  • Song, K., Lewe, J. H., & Mavris, D. N. (2014). A Multi-Tier Evolution Model of Air Transportation Networks. In 14th AIAA Aviation Technology, Integration, and Operations Conference (p. 3267).
  • Taş, Y. (2018). Birliktelik Kuralları Madenciliği ve Bir Uygulama, Yüksek Lisans Tezi, Sivas Cumhuriyet Üniversitesi, Sivas 2018.
  • Teodorović, D., Kalić, M., & Pavković, G. (1994). The potential for using fuzzy set theory in airline network design. Transportation Research Part B: Methodological, 28(2), 103-121.
  • Warnock-Smith, D., & Potter, A. (2005). An exploratory study into airport choice factors for European low-cost airlines. Journal of Air Transport Management, 11(6), 388-392.
  • Webb, G. I. (2003). ‘‘Association Rules’’. Ed. Ye N. The Handbook Of Data Mining. (2003) 27-28. New Jersey.
  • Yılmaz, A. K., Malagas, K. N., Nikitakos, N., & Bal, H. T. (2018). Modeling regional routes with Greek airlines for flight operations to AOE airport. Aircraft Engineering and Aerospace Technology.
  • Zanakis, S. H., Solomon, A., Wishart, N., & Dublish, S. (1998). Multi-attribute decision making: A simulation comparison of select methods. European journal of operational research, 107(3), 507-529.
There are 51 citations in total.

Details

Primary Language English
Journal Section Makaleler
Authors

Ali Rıza İnce 0000-0003-4653-3091

Publication Date September 28, 2021
Published in Issue Year 2021 Volume: 25 Issue: 3

Cite

APA İnce, A. R. (2021). Association Analysis Between Airline Destinations. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 25(3), 1260-1289. https://doi.org/10.53487/ataunisosbil.930684

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