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A Data Mining Approach to Building a Predictive Model of Low-Cost Carriers' Presence in the U.S. Domestic Routes

Canh Nguyen et al · Embry-Riddle Aeronautical University · 2019

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Author / responsible party
Canh Nguyen et al
Publisher
Embry-Riddle Aeronautical University
Year
2019
ISSN
2374-6793
ISSN
2374-6793
Language
English
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<p>The purpose of the study was to build the predictive model of the presence of U.S. low-cost carriers (LCCs) in the domestic network structure. SEMMA (Sample, Explore, Modify, Model, and Assess) schematic in data mining was followed and employed as the primary methodological procedure. Data in the period of 1Q2016-1Q2018 were extracted from the Bureau of Transportation Statistics (DB1B database) and reconstructed to form predictors. Stepwise logistic regression showed a significant predictive performance compared to decision tree technique in terms of fitting measures, which was then used as the concluding model. Significant predictors included: (1) Market concentration positively related with the presence of LCCs, (2) nonstop route associated with the presence of LCCs, (3) market airfare factors negatively related with the presence of LCCs, and (4) origin and destination (O&D) airports being hubs, especially medium hubs, associated with the presence of LCCs. The findings may practically aid network planners in airlines and airports in decision making associated with the presence of LCCs, which ultimately leads to building their more robust and efficient route map.</p>

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APA 7

al, C. N. E. (2019). A Data Mining Approach to Building a Predictive Model of Low-Cost Carriers' Presence in the U.S. Domestic Routes. https://doi.org/10.15394/ijaaa.2019.1354

MLA

al, Canh Nguyen et. "A Data Mining Approach to Building a Predictive Model of Low-Cost Carriers' Presence in the U.S. Domestic Routes." 2019. https://doi.org/10.15394/ijaaa.2019.1354.

Chicago

al, Canh Nguyen et. 2019. "A Data Mining Approach to Building a Predictive Model of Low-Cost Carriers' Presence in the U.S. Domestic Routes.". https://doi.org/10.15394/ijaaa.2019.1354.

Harvard

al, C. N. E. 2019, A Data Mining Approach to Building a Predictive Model of Low-Cost Carriers' Presence in the U.S. Domestic Routes, Embry-Riddle Aeronautical University, available at: https://doi.org/10.15394/ijaaa.2019.1354 [Accessed 6 Aug. 2026].

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Title
A Data Mining Approach to Building a Predictive Model of Low-Cost Carriers' Presence in the U.S. Domestic Routes
Author / contributors
Canh Nguyen et al
Publisher
Embry-Riddle Aeronautical University
Publication year
2019
ISSN
2374-6793
ISSN
2374-6793
Language
English

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Business Administration Management and Operations; Management Sciences and Quantitative Methods; Operations and Supply Chain Management; Business Analytics
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