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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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    2. Li, Chi & Mao, Jianfeng & Li, Lingyi & Wu, Jingxuan & Zhang, Lianmin & Zhu, Jianyu & Pan, Zibin, 2024. "Flight delay propagation modeling: Data, Methods, and Future opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    3. Song, Cen & Guo, Jingquan & Zhuang, Jun, 2020. "Analyzing passengers’ emotions following flight delays- a 2011–2019 case study on SKYTRAX comments," Journal of Air Transport Management, Elsevier, vol. 89(C).
    4. Weiwei Wu & Haoyu Zhang & Tao Feng & Frank Witlox, 2019. "A Network Modelling Approach to Flight Delay Propagation: Some Empirical Evidence from China," Sustainability, MDPI, vol. 11(16), pages 1-16, August.
    5. Kenan, Nabil & Jebali, Aida & Diabat, Ali, 2018. "The integrated aircraft routing problem with optional flights and delay considerations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 355-375.
    6. 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.
    7. Alderighi, Marco & Gaggero, Alberto A., 2018. "Flight cancellations and airline alliances: Empirical evidence from Europe," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 90-101.
    8. 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.
    9. Khan, Waqar Ahmed & Chung, Sai-Ho & Eltoukhy, Abdelrahman E.E. & Khurshid, Faisal, 2024. "A novel parallel series data-driven model for IATA-coded flight delays prediction and features analysis," Journal of Air Transport Management, Elsevier, vol. 114(C).

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