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Assessing strategic flight schedules at an airport using machine learning-based flight delay and cancellation predictions

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  • Lambelho, Miguel
  • Mitici, Mihaela
  • Pickup, Simon
  • Marsden, Alan

Abstract

To mitigate air traffic demand-capacity imbalances, large European airports implement strategic flight schedules, where flights are assigned arrival/departure slots several months prior to execution. We propose a generic assessment of such strategic schedules using predictions about arrival/departure flight delays and cancellations. We demonstrate our approach for strategic flight schedules in the period 2013–2018 at London Heathrow Airport. Together with the development of dedicated strategic flight schedule optimization models, our proposed approach supports an integrated strategic flight schedule assessment, where schedules are evaluated with respect to flight delays and cancellations.

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  • Lambelho, Miguel & Mitici, Mihaela & Pickup, Simon & Marsden, Alan, 2020. "Assessing strategic flight schedules at an airport using machine learning-based flight delay and cancellation predictions," Journal of Air Transport Management, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:jaitra:v:82:y:2020:i:c:s0969699719302303
    DOI: 10.1016/j.jairtraman.2019.101737
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    References listed on IDEAS

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    6. Konstantinos G. Zografos & Michael A. Madas & Konstantinos N. Androutsopoulos, 2017. "Increasing airport capacity utilisation through optimum slot scheduling: review of current developments and identification of future needs," Journal of Scheduling, Springer, vol. 20(1), pages 3-24, February.
    7. Pellegrini, Paola & Bolić, Tatjana & Castelli, Lorenzo & Pesenti, Raffaele, 2017. "SOSTA: An effective model for the Simultaneous Optimisation of airport SloT Allocation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 99(C), pages 34-53.
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    Cited by:

    1. Sismanidou, Athina & Tarradellas, Joan & Suau-Sanchez, Pere, 2022. "The uneven geography of US air traffic delays: Quantifying the impact of connecting passengers on delay propagation," Journal of Transport Geography, Elsevier, vol. 98(C).
    2. Liu, Wenjing & Zhao, Qiuhong & Delahaye, Daniel, 2022. "Research on slot allocation for airport network in the presence of uncertainty," Journal of Air Transport Management, Elsevier, vol. 104(C).
    3. Wang, Chunzheng & Hu, Minghua & Yang, Lei & Zhao, Zheng, 2022. "Improving the spatial-temporal generalization of flight block time prediction: A development of stacking models," Journal of Air Transport Management, Elsevier, vol. 103(C).
    4. Ziming Wang & Chaohao Liao & Xu Hang & Lishuai Li & Daniel Delahaye & Mark Hansen, 2022. "Distribution Prediction of Strategic Flight Delays via Machine Learning Methods," Sustainability, MDPI, vol. 14(22), pages 1-14, November.
    5. Guo, Zhen & Hao, Mengyan & Yu, Bin & Yao, Baozhen, 2022. "Detecting delay propagation in regional air transport systems using convergent cross mapping and complex network theory," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    6. Truong, Dothang, 2021. "Using causal machine learning for predicting the risk of flight delays in air transportation," Journal of Air Transport Management, Elsevier, vol. 91(C).
    7. Jiang, Yirui & Tran, Trung Hieu & Williams, Leon, 2023. "Machine learning and mixed reality for smart aviation: Applications and challenges," Journal of Air Transport Management, Elsevier, vol. 111(C).

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