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A model for projecting flight delays during irregular operation conditions

Author

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  • Abdelghany, Khaled F.
  • S. Shah, Sharmila
  • Raina, Sidhartha
  • Abdelghany, Ahmed F.

Abstract

On-time performance of airlines schedule is key factor in maintaining current customer satisfaction and attracting new ones. This requires management of the different operation resources (crew/aircraft) to ensure their on-time readiness for each flight in the planned schedule. However, flight schedules are often subjected to irregularity. In particular, weather accounts for nearly 75% of system delays. Due to the tight connection among airlines resources, these delays could dramatically propagate over time and space unless the proper recovery actions are taken. This paper presents a model which projects flight delays and alerts for possible future breaks during irregular operation conditions. The results of applying the model at the operation control center of a major airlines company in the US are presented.

Suggested Citation

  • Abdelghany, Khaled F. & S. Shah, Sharmila & Raina, Sidhartha & Abdelghany, Ahmed F., 2004. "A model for projecting flight delays during irregular operation conditions," Journal of Air Transport Management, Elsevier, vol. 10(6), pages 385-394.
    • Handle: RePEc:eee:jaitra:v:10:y:2004:i:6:p:385-394
    DOI: 10.1016/j.jairtraman.2004.06.008
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    References listed on IDEAS

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    1. Ahmed Abdelghany & Goutham Ekollu & Ram Narasimhan & Khaled Abdelghany, 2004. "A Proactive Crew Recovery Decision Support Tool for Commercial Airlines During Irregular Operations," Annals of Operations Research, Springer, vol. 127(1), pages 309-331, March.
    2. Gang Yu & Michael Argüello & Gao Song & Sandra M. McCowan & Anna White, 2003. "A New Era for Crew Recovery at Continental Airlines," Interfaces, INFORMS, vol. 33(1), pages 5-22, February.
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    Cited by:

    1. Abdel-Aty, Mohamed & Lee, Chris & Bai, Yuqiong & Li, Xin & Michalak, Martin, 2007. "Detecting periodic patterns of arrival delay," Journal of Air Transport Management, Elsevier, vol. 13(6), pages 355-361.
    2. Abdelghany, Khaled & Abdelghany, Ahmed & Niznik, Tim, 2007. "Managing severe airspace flow programs: The Airlines’ side of the problem," Journal of Air Transport Management, Elsevier, vol. 13(6), pages 329-337.
    3. Kim, Myeonghyeon & Park, Sunwook, 2021. "Airport and route classification by modelling flight delay propagation," Journal of Air Transport Management, Elsevier, vol. 93(C).
    4. Li, Qiang & Jing, Ranzhe, 2021. "Characterization of delay propagation in the air traffic network," Journal of Air Transport Management, Elsevier, vol. 94(C).
    5. Wu, Cheng-Lung, 2005. "Inherent delays and operational reliability of airline schedules," Journal of Air Transport Management, Elsevier, vol. 11(4), pages 273-282.
    6. Wong, Jinn-Tsai & Tsai, Shy-Chang, 2012. "A survival model for flight delay propagation," Journal of Air Transport Management, Elsevier, vol. 23(C), pages 5-11.
    7. Jane Lee & Lavanya Marla & Alexandre Jacquillat, 2020. "Dynamic Disruption Management in Airline Networks Under Airport Operating Uncertainty," Transportation Science, INFORMS, vol. 54(4), pages 973-997, July.
    8. Kim, Myeonghyeon & Choi, Yuri & Song, Ki Han, 2019. "Identification model development for proactive response on irregular operations (IROPs)," Journal of Air Transport Management, Elsevier, vol. 75(C), pages 1-8.
    9. Birolini, Sebastian & Jacquillat, Alexandre, 2023. "Day-ahead aircraft routing with data-driven primary delay predictions," European Journal of Operational Research, Elsevier, vol. 310(1), pages 379-396.
    10. Ren, Pan & Li, Lishuai, 2018. "Characterizing air traffic networks via large-scale aircraft tracking data: A comparison between China and the US networks," Journal of Air Transport Management, Elsevier, vol. 67(C), pages 181-196.
    11. Kim, Myeonghyeon & Bae, Jiheon, 2021. "Modeling the flight departure delay using survival analysis in South Korea," Journal of Air Transport Management, Elsevier, vol. 91(C).
    12. Bojia Ye & Bo Liu & Yong Tian & Lili Wan, 2020. "A Methodology for Predicting Aggregate Flight Departure Delays in Airports Based on Supervised Learning," Sustainability, MDPI, vol. 12(7), pages 1-13, April.
    13. 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.
    14. Suau-Sanchez, Pere & Burghouwt, Guillaume, 2011. "The geography of the Spanish airport system: spatial concentration and deconcentration patterns in seat capacity distribution, 2001–2008," Journal of Transport Geography, Elsevier, vol. 19(2), pages 244-254.
    15. Abdelghany, Khaled F. & Abdelghany, Ahmed F. & Ekollu, Goutham, 2008. "An integrated decision support tool for airlines schedule recovery during irregular operations," European Journal of Operational Research, Elsevier, vol. 185(2), pages 825-848, March.
    16. Abdelghany, Ahmed & Guzhva, Vitaly S. & Abdelghany, Khaled, 2023. "The limitation of machine-learning based models in predicting airline flight block time," Journal of Air Transport Management, Elsevier, vol. 107(C).
    17. Rodríguez-Sanz, à lvaro & Comendador, Fernando Gómez & Valdés, Rosa Arnaldo & Pérez-Castán, Javier A., 2018. "Characterization and prediction of the airport operational saturation," Journal of Air Transport Management, Elsevier, vol. 69(C), pages 147-172.
    18. Tsegai O. Yhdego & An-Tsun Wei & Gordon Erlebacher & Hui Wang & Miguel G. Tejada, 2023. "Analyzing the Impacts of Inbound Flight Delay Trends on Departure Delays Due to Connection Passengers Using a Hybrid RNN Model," Mathematics, MDPI, vol. 11(11), pages 1-24, May.
    19. Y. X. Lee & Z. W. Zhong, 2016. "A study of the relationship between adverse weather conditions and flight delay," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 2(4), pages 112-117.

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