Density-based Clustered Airline Network Carriers and Efficiency Comparison with Robust DEA
DOI:
https://doi.org/10.37241/jatss.2025.138Keywords:
aviation, aircraft, network carriers, data envelopment analysis, k-means clustering, unsupervised machine learningAbstract
Introduction: This research examines the concept of a network carrier, specifically its structure. In the idea of network carriers, the primary structure is constructed as a service carrier that applies a high comfort level and high ticket fares, and the secondary structure is built as a low-cost carrier that applies a low comfort level and low ticket fares for its customers. In this research, the three concepts of airline restructuring are explained in detail.
Method: Robust Data Envelopment Analysis (DEA) methodology is used to analyze network carriers by two outputs and four inputs. The outputs are: passengers carried (PC) and passenger kilometres flown (PKF). The inputs are: passenger tonne kilometers (PTK) performed in thousands, available seat kilometers (ASK), passenger load factor (PLF), and kilometres flown (KF) in thousands.
Results or Findings: These parameters are used to assess the efficiency and consistency of efficiency levels among identified airlines that employ a network carrier strategy. The results of the efficiency analyses show that the majority of airlines exhibit minor time-related drops in their efficiency while the two low-cost carriers which are the top performers keep on doing so, and the grouping of firms into three categories -better, slightly good, and worst- based on efficiency levels and stability is done using k-means clustering and the coefficient of variation.
Discussion or Conclusion: This research employs Data Envelopment Analysis to evaluate the efficiency and consistency of airlines operating as network carriers, offering insights into the effectiveness of this strategic approach.
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