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Using machine learning to analyze air traffic management actions: Ground delay program case study

Author

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  • Liu, Yulin
  • Liu, Yi
  • Hansen, Mark
  • Pozdnukhov, Alexey
  • Zhang, Danqing

Abstract

We model the impact of weather and arrival demand on ground delay program (GDP) incidence. We use Support Vector Machine (SVM) to analyze how regional convective weather affects GDP incidence and find the impact depends on both distance and direction of convective activity from the airport. We then train and compare the performance of logistic regression (LR) and random forest (RF) in predicting GDP incidence using an SVM-generated regional weather variable, local weather and arrival demand. Generally, RF outperforms LR. Convective weather is the most important factor in predicting GDP incidence at Atlanta International Airport (ATL), while arrival demand has greater impact for the other airports studied. We also examined model transferability across different airports. Lastly, we build GDP duration prediction models to enable a user to assess how long a GDP is likely to continue, if it is in effect in a given hour.

Suggested Citation

  • Liu, Yulin & Liu, Yi & Hansen, Mark & Pozdnukhov, Alexey & Zhang, Danqing, 2019. "Using machine learning to analyze air traffic management actions: Ground delay program case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 80-95.
    • Handle: RePEc:eee:transe:v:131:y:2019:i:c:p:80-95
    DOI: 10.1016/j.tre.2019.09.012
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    References listed on IDEAS

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    1. Avijit Mukherjee & Mark Hansen, 2007. "A Dynamic Stochastic Model for the Single Airport Ground Holding Problem," Transportation Science, INFORMS, vol. 41(4), pages 444-456, November.
    2. Michael O. Ball & Robert Hoffman & Amedeo R. Odoni & Ryan Rifkin, 2003. "A Stochastic Integer Program with Dual Network Structure and Its Application to the Ground-Holding Problem," Operations Research, INFORMS, vol. 51(1), pages 167-171, February.
    3. Avijit Mukherjee & Mark Hansen & Shon Grabbe, 2012. "Ground delay program planning under uncertainty in airport capacity," Transportation Planning and Technology, Taylor & Francis Journals, vol. 35(6), pages 611-628, June.
    4. Michael O. Ball & Robert Hoffman & Avijit Mukherjee, 2010. "Ground Delay Program Planning Under Uncertainty Based on the Ration-by-Distance Principle," Transportation Science, INFORMS, vol. 44(1), pages 1-14, February.
    5. Kan Chang & Ken Howard & Rick Oiesen & Lara Shisler & Midori Tanino & Michael C. Wambsganss, 2001. "Enhancements to the FAA Ground-Delay Program Under Collaborative Decision Making," Interfaces, INFORMS, vol. 31(1), pages 57-76, February.
    6. Diao, Xudong & Chen, Chun-Hsien, 2018. "A sequence model for air traffic flow management rerouting problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 110(C), pages 15-30.
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    Cited by:

    1. Chunzheng Wang & Minghua Hu & Lei Yang & Zheng Zhao, 2021. "Prediction of air traffic delays: An agent-based model introducing refined parameter estimation methods," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-22, April.
    2. 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.
    3. Woo, Young-Bin & Moon, Ilkyeong, 2021. "Scenario-based stochastic programming for an airline-driven flight rescheduling problem under ground delay programs," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    4. Bolić, Tatjana & Castelli, Lorenzo & Corolli, Luca & Scaini, Giovanni, 2021. "Flexibility in strategic flight planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    5. Xiangning Dong & Xuhao Zhu & Minghua Hu & Jie Bao, 2023. "A Methodology for Predicting Ground Delay Program Incidence through Machine Learning," Sustainability, MDPI, vol. 15(8), pages 1-19, April.
    6. Rott, Julian & König, Fabian & Häfke, Hannes & Schmidt, Michael & Böhm, Markus & Kratsch, Wolfgang & Krcmar, Helmut, 2023. "Process Mining for resilient airport operations: A case study of Munich Airport’s turnaround process," Journal of Air Transport Management, Elsevier, vol. 112(C).

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