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Evaluating predictability based on gate-in fuel prediction and cost-to-carry estimation

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  • Kang, Lei
  • Hansen, Mark
  • Ryerson, Megan S.

Abstract

Predictability in the aviation system affects costs to airlines and passengers. We propose a predictability metric based on a flight's gate-in fuel (GIF) which can be directly measured and monetized by aviation stakeholders. We estimate GIF for six major U.S. airlines. Since GIF data are not directly available, we develop an estimation methodology to obtain GIF from pushback weight and fuel burn, including a conversion from passenger to weight payload based on an econometric model. The methodology accounts for aircraft operating empty weight and payload. We find that GIF varies across airlines and time of year, and is highest during the summer period. We monetize GIF through a cost-to-carry analysis as extra fuel loading results in additional fuel burn. Our estimates reveal that, in 2012, airlines spent an additional $59 million to $667 million on carrying GIF, with a total across all six airlines of $1.46 billion.

Suggested Citation

  • Kang, Lei & Hansen, Mark & Ryerson, Megan S., 2018. "Evaluating predictability based on gate-in fuel prediction and cost-to-carry estimation," Journal of Air Transport Management, Elsevier, vol. 67(C), pages 146-152.
    • Handle: RePEc:eee:jaitra:v:67:y:2018:i:c:p:146-152
    DOI: 10.1016/j.jairtraman.2017.11.006
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    References listed on IDEAS

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    1. Zou, Bo & Hansen, Mark, 2012. "Impact of operational performance on air carrier cost structure: Evidence from US airlines," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(5), pages 1032-1048.
    2. Hao, Lu & Hansen, Mark, 2014. "Block time reliability and scheduled block time setting," Transportation Research Part B: Methodological, Elsevier, vol. 69(C), pages 98-111.
    3. Ryerson, Megan S. & Hansen, Mark & Bonn, James, 2014. "Time to burn: Flight delay, terminal efficiency, and fuel consumption in the National Airspace System," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 286-298.
    4. Tobaruela, Gonzalo & Fransen, Peter & Schuster, Wolfgang & Ochieng, Washington Y. & Majumdar, Arnab, 2014. "Air traffic predictability framework – Development, performance evaluation and application," Journal of Air Transport Management, Elsevier, vol. 39(C), pages 48-58.
    5. Kang, Lei & Hansen, Mark, 2017. "Behavioral analysis of airline scheduled block time adjustment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 103(C), pages 56-68.
    6. Milind Sohoni & Yu-Ching Lee & Diego Klabjan, 2011. "Robust Airline Scheduling Under Block-Time Uncertainty," Transportation Science, INFORMS, vol. 45(4), pages 451-464, November.
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    Cited by:

    1. Lei Kang & Mark Hansen, 2021. "Quantile Regression–Based Estimation of Dynamic Statistical Contingency Fuel," Transportation Science, INFORMS, vol. 55(1), pages 257-273, 1-2.
    2. Tang, Nga Yung Agnes & Wu, Cheng-Lung & Tan, David, 2020. "Evaluating the implementation of performance-based fuel uplift regulation for airline operation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 47-61.

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