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Behavioral analysis of airline scheduled block time adjustment

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  • Kang, Lei
  • Hansen, Mark

Abstract

Scheduled block time (SBT) is the time between gate departure and gate arrival assumed by airlines for use in published timetables and operations planning. SBT setting has critical impacts on airlines’ operating cost and on-time performance. Air carriers regularly update their SBTs to respond to changing operating conditions and evolving business strategies. Most existing studies have focused on investigating the impact of SBT on on-time performance or predicting SBT based on historical performance and market characteristics. However, the dynamics of adjusting SBT, which may allow deeper understanding about the trade-offs airlines make between SBT and on-time performance, have been rarely studied. In this paper, we assume that SBT adjustment choices reveal preferences. Based on airlines’ practice in setting SBT, hypothetical SBT scenarios and their corresponding on-time performance profiles are re-constructed to mimic the situations faced by airline schedulers. This enables us to infer how airlines trade-off between SBT, on-time arrivals, and earliness. By using correlated mixed logit models, we find that our five study airlines are willing to increase SBT from 0.38 to 0.54min to increase on-time performance by 1%. We also find that both on-time performance and early arrival are valued by airlines, but the former is considerably more valuable. The estimated models can also be used to predict airlines’ SBT adjustments in response to changes in operational performance.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transe:v:103:y:2017:i:c:p:56-68
    DOI: 10.1016/j.tre.2017.04.004
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    8. 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).
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    10. 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.
    11. 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).
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