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An analysis of Brazilian flight delays based on frequent patterns

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  • Sternberg, Alice
  • Carvalho, Diego
  • Murta, Leonardo
  • Soares, Jorge
  • Ogasawara, Eduardo

Abstract

In this paper we applied data indexing techniques combined with association rules to unveil hidden patterns of flight delays. Considering Brazilian flight data and guided by six research questions related to causes, moments, differences, and relationships between airports and airlines, we evaluated and quantified all attributes that may lead to delays, showing not only the main patterns, but also their chances of occurrence in the entire network, in each airport and airline. We observed that Brazilian flight system has difficulties to recover from previous delays and when operating under adverse meteorological conditions, delays occurrences may increase up to 216%.

Suggested Citation

  • Sternberg, Alice & Carvalho, Diego & Murta, Leonardo & Soares, Jorge & Ogasawara, Eduardo, 2016. "An analysis of Brazilian flight delays based on frequent patterns," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 95(C), pages 282-298.
    • Handle: RePEc:eee:transe:v:95:y:2016:i:c:p:282-298
    DOI: 10.1016/j.tre.2016.09.013
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    Cited by:

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    5. Nermin Zijadić & Emir Ganić & Matija Bračić & Igor Štimac, 2022. "Impact of Aircraft Delays on Population Noise Exposure in Airport’s Surroundings," IJERPH, MDPI, vol. 19(15), pages 1-20, July.
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    7. Yu, Bin & Guo, Zhen & Asian, Sobhan & Wang, Huaizhu & Chen, Gang, 2019. "Flight delay prediction for commercial air transport: A deep learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 203-221.

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