SYSTEMATIC LITERATURE REVIEW: K-MEAN CLUSTERING IN AIR TRANSPORT
การทบทวนวรรณกรรมอย่างมีระบบ: การจัดกลุ่มแบบเคมีนในการขนส่งทางอากาศ
คำสำคัญ:
Air transport, Clustering, K-mean, Literature reviewบทคัดย่อ
This research aims to study application of K-mean clustering in air transport and find variable use in each K-mean clustering purpose. Total 495 articles from Thai and international database after articles are Systematic reviews and Meta-Analyses, there are 24 articles remaining. Result found, K-mean clustering was applied in air cargo, air taxi, air traffic management, airline, airport, and vertiport along with variable use in each K-mean clustering purpose as follow, for exploring accident/risk such as personal, equipment, management, and environment. For business operation such as fuel, load factor, available seat mile, and number of passengers. For cargo transport such as total cargo throughput, freighter aircraft movement, and international cargo. For predictive aircraft delay such as actual departure time, actual arrival time, schedule departure time, and schedule arrival time. For flight operation such as velocity, heading, barometric altitude, daily arrival traffic, and daily departure traffic. For passenger movement such as passenger movement per month and passenger per year. In addition, we found interesting topic in air taxi and vertiport for future research.
เอกสารอ้างอิง
Airports council international. (2025). The trusted authority on air travel demand insights.
https://aci.aero/2025/02/26/the-trusted-authority-on-air-travel-demand-insights/
Assef, F. M., Steiner, M. T., & Lima, E. P. (2022). A review of clustering techniques for waste management.
Heliyon, 8(2022), 1-13.
Bajpai, N., Paik, J. H., & Sarkar, S. (2025). Balanced seed selection for K-means clustering with
determinantal point process. Pattern Recognition, 164(2025). 1-11.
Buatoom, U., Kongprawechnon, W., & Theeramukong, T. (2020). Document clustering using k-means with
term weighting as similarity-based constraints. Symmetry, 12(6), 1-25.
Budd, T., Sanchez, P. S., Halpern, N., Mwesiumo, D., & Brathen S. (2021). An assessment of air passenger
confidence a year into the COVID-19 crisis: A segmentation analysis of passengers in Norway.
Journal of Transport Geography, 96(2021), 1-12.
Campuzano, D. P., Andrada, L. R., Ortega, P. M., & Lazaro, A. L. (2022). Visualizing the historical COVID-19
shock in the US airline industry: A Data Mining approach for dynamic market surveillance. Journal of
Air Transport Management, 101(2022), 1-12.
Chandra, A., Hazra, S., Verma, A. (2024). The integration on en route flow optimization, complex network
clustering, and rule-based approach to airspace sub-sectorization for enhance air traffic monitoring.
Journal of the Air Transport Research Society, 3(2024), 1-16.
Chaudhry, M., Shafi, I., Mahnoor, M., Vargas, D., Thompson, E., & Ashraf, I. (2023). A systematic literature
review on identifying patterns using unsupervised clustering algorithms: A data mining perspective.
Symmetry, 15(1679). 1-44.
Chitra, J., & Heikal, J. (2024). Customer segmentation using the K-Means Clustering algorithm in Foreign
Banks in Indonesia. Indonesia Accounting Research Journal, 11(4), 230–241.
Devos, J., Hopkins, D., Hickman, R., & Schwanen, T. (2024.) Tackling the academic air travel dependency.
An analysis of the (in) consistency between academics’ travel behavior and their attitudes. Global
Environmental Change, 88(2024), 1-11.
Ersoz, C. & Ucler, C. (2024). Airline business model evolution: A K-mean clustering analyze. In International
Data Science and Statistic Congress, 15-17 October, Ankara, Turkey.
Gao, Y. (2021). What is the busiest time at an airport? Clustering U.S. hub airports based on
passenger movements. Journal of Transport Geography, 90(2021). 1-12.
Gaon, T., Gabay, Y., & Weiss C. M. (2025). Optimizing electric vehicle routing efficiency using k-
means clustering and genetic algorithms. Future Internet, 17(3), 97-116.
Geske A. M., Herold, D. M., & Kummer, S. (2024). Artificial intelligence as a driver of efficiency in air
passenger transport: A systematic literature review and future research avenues. Journal of the Air
Transport Research Society, 3(2024), 1-13.
Google AI. (2025). Gemini (Version 2.5) [Large language model].
Govindan, K., Kannan, D., Jorgensen, T. B., & Nielsen, T. S. (2022). Supply Chain 4.0 performance
measurement: A systematic literature review, framework development, and empirical evidence.
Transportation Research Part E, 164(2022), 1-41.
Gupta, M. (2025). What is Unsupervised Learning?
https://www.geeksforgeeks.org/machine-learning/unsupervised-learning/
Hirabayashi, H., Brown, M., Takeichi, N. (2024). Study of a representative wind selection method using track
data to evaluate Pacific flight operations. Journal of Air Transport Management, 119(2024). 1-14.
IBM. (2021). What is machine learning?
https://www.ibm.com/think/topics/machine-learning.
Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., & Heming, J. (2023). K-means clustering
algorithms: A comprehensive review, variants analysis, and advances in the era of big data.
Information Sciences, 662(2023), 178-210.
Jsoncrew. (2025). Top countries by coding skills: ranking the best nations for coding.
https://jsoncrew.com/blog/top-countries-by-coding-skills
Kamsing, P., Cao, C., Boonpook, W., Boonprong, S., Xu, M., & Boonsrimuang, P. (2025). Artificial neural
network for air pollutant concentration predictions based on aircraft trajectories over Suvarnabhumi
international airport. Atmosphere, 16(366), 1-21.
Kavallieratos, G. & Katsikas, S. (2023). An exploratory analysis of the last frontier: A systematic literature
review of cybersecurity in space. International Journal of Critical Infrastructure Protection, 43(2023),
-15.
Kong, D., Duan, M., Shang, R. & Li, Y. (2022). Clustering analysis of airport traffic similar days affected by
epidemic based on Hi‐K‐means. Academic Journal of Science and Technology, 4(3), 155-161.
Kotzampasakis, M. (2025). Maritime emissions trading in the EU: Systematic literature review and
policy assessment. Transport Policy, 165(2025), 28-41.
Liu, Y., Hansen, M., Ball, M. O., & Lovell, D. J. (2021). Causal analysis of flight en route inefficiency.
Transportation Research Part B, 151(2021), 91-115.
Magdalina, A. & Bouzaima, M. (2021). An empirical investigation of European airline business models:
Classification and hybridization. Journal of Air Transport Management, 93(2021), 1-11.
Marta, Z. (2017). Cluster analysis of World's airports on the basis of number of passengers handled (Case
study examining the impact of significant events). Analyses, 97(1), 74-88.
Mayer, R. (2016). Airport classification based on cargo characteristic. Journal of Transport Geography,
(2016), 53-65.
Metcalf, C. (2025). Business operations: What they are and how to improve them.
https://www.indeed.com/career-advice/career-development/business-operations
Migdadi, Y. K. (2019). Identifying the effective taxonomies of airline green operations strategy. Management
of Environmental Quality: An International Journal, 31(1), 146-166.
Passarella, R., Noor, T. M., Arsalan, O., & Adenan, M. S. (2024). Anomaly detection in commercial aircraft
landing at SSK II airport using clustering method. Aerospace Traffic and Safety, 1(2024), 141-154.
Petit, V. & Ribeiro, M. (2025). Multi-objective vertiport location optimization for a middle-mile package
delivery framework: Case study in the South Holland Region. Journal of Air Transport Management,
(2025), 1-19.
Priy, S. (2025). Clustering in machine learning.
https://www.geeksforgeeks.org/machine-learning/clustering-in-machine-learning/
Puente, B. & Lange, A. (2025). Characterizing global air cargo: A study profiling air cargo
operations worldwide. Journal of Transport Geography, 127(2025), 1-21.
Ray, S. (2025). Top 10 machine learning algorithms in 2025.
https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/
Renold, M., Vollenweider, J., Mijovic, N., Kuljanin, J., & Kalic, M. (2023). Methodological framework for a
deeper understanding of airline profit cycles in the context of disruptive exogenous impacts, Journal
of Air Transport Management, 106(2023), 1-13.
Rix, J. & Ingham, N. (2021). The impact of education selection according to notions of intelligence: A
systematic literature review. International Journal of Educational Research Open, 2(2021). 1-9.
Ruiz, J. G., Cruzade, D. P., & Sanchez, D. G. (2025). Cluster of symptoms in kidney failure: A systematic
review. Heliyon, 11(2025), 1-14.
Senthilnathan, V. P., Singaravelu, M., Rajendran S., & Srinivas S. (2025). A clustering-metaheuristic-
simulation approach to determine air taxi operating site location. Transportation Research
Interdisciplinary Perspectives, 29(2025). 1-9.
Singh, J. & Singh D. (2024). A comprehensive review of clustering techniques in artificial intelligence for
knowledge discovery: Taxonomy, challenges, applications and future prospects. Advanced
Engineering Informatics, 62(2024), 1-40.
Transportation & logistic international. (2025). Aviation Milestones in 2025: passenger traffic, revenues, and
profits. https://tlimagazine.com/news/aviation-milestones-in-2025-passenger-traffic-revenues-and-
profits/
Urban, M., Klemm, M., Ploetner, K. O., & Hornung, M. (2018). Airline categorization by applying the business
model canvas and clustering algorithms. Journal of Air Transport Management, 71(2018). 175-192.
Wang, R., Yan, H., Kang, R., & Feng, X. (2025). Evaluation and classification of accident-inducing
and risk propagation in airport apron networks. IEEE access, 13(2025). 66,238-66,249
Wei, X., Li, Y., Shang, R., Ruan, C., & Xing, J. (2023). Airport cluster delay prediction based on TS-BiLSTM-
Attention. Aerospace, 10(580). 1-16.
Wendel, L., Albers, S., & Dewulf, W. (2025). Towards a typology of airline vertical strategies. Journal of Air
Transport Management, 127(2025), 1-16.
Zambrano, B., Salvador, F., & Cevallos, R. (2021). Approaches of predictive and clustering methods used in
emergency events: A Systematic Literature Review. XLVII Latin American Computing Conference
(CLEI). (p.1-8). IEEE.
Zarei, M. (2025). Literature review and systematic review: What you need to know?
https://www.litmaps.com/articles/literature-review-and-systematic-review-what-you-need-to-know
Zebua, R., Heroza, R., Adrian, M., & Atrinawati, L. (2022). Determination of Discounts Using K-Means
Clustering with RFM Models in Retail Business. Jurnal CoreIT. 8(1). 1-10.
Zhang, X., Zhao, S., & Mei, H. (2022). Analysis of airport risk propagation in Chinese air
transport network. Journal of Advanced Transportation, 2022, 1-14.
เผยแพร่แล้ว
รูปแบบการอ้างอิง
ฉบับ
ประเภทบทความ
สัญญาอนุญาต
ลิขสิทธิ์ (c) 2026 วารสารบริหารธุรกิจศรีนครินทรวิโรฒ

อนุญาตภายใต้เงื่อนไข Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
ลิขสิทธิ์ของบทความที่ได้รับการตีพิมพ์ในารสารบริหารธุรกิจศรีนครินทรวิโรฒ เป็นของวารสาร โดยผู้เขียนยินยอมโอนสิทธิ์ในการเผยแพร่และจัดพิมพ์บทความให้แก่วารสาร เมื่อบทความได้รับการตอบรับเพื่อตีพิมพ์ วารสารมีสิทธิ์ในการจัดพิมพ์ เผยแพร่ และจัดเก็บบทความในรูปแบบสิ่งพิมพ์และสื่ออิเล็กทรอนิกส์
ทางวารสารอนุญาตให้นำเนื้อหาไปใช้เพื่อประโยชน์ทางการศึกษาและการวิจัยที่ไม่แสวงหากำไรได้ โดยต้องอ้างอิงแหล่งที่มาอย่างถูกต้องครบถ้วน การนำไปใช้ ดัดแปลง เผยแพร่ซ้ำ หรือใช้ในเชิงพหาณิชย์ ต้องได้รับอนุญาตเป็นลายลักษณ์อักษรจากวารสารก่อน