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Block time reliability and scheduled block time setting

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  • Hao, Lu
  • Hansen, Mark

Abstract

While in ground transportation the concept of reliability has been extensively studied, there is little literature in air transportation. Scheduled block time (SBT) setting is a crucial part in airlines’ scheduling. Interviews with an airline and relevant work in ground transportation have shown that SBT and the historical block time distribution, reflecting block time reliability, have a close relationship. This paper investigates how the change in actual block time distribution will affect SBT and system performance. Firstly this relationship is studied with empirical data and multiple regression models. The distribution of the historical block time for a flight is depicted by the difference between every 10th percentiles. We found that gate delay plays a minor role in setting SBT and that SBTs have decreasing sensitivity to historical flight times toward the right tail of the distribution. To specifically link SBT setting with the flight’s on-time performance, a SBT adjustment model is further developed. Poor on-time performance leads to increased SBT in the next year. With the behavior model results showing that both the median block time and the “inner right tail” of the distribution affect SBT setting, an impact study is conducted to validate these impacts with historical data. The impact of historical block time distribution on SBT is validated with real data in year 2006–2008 and 2009–2011. Furthermore, by studying the flight performance difference based on different changes in SBT, we conclude that ignoring the impact on SBT changes when considering potential benefits of improved block time distribution could lead to inaccurate results.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transb:v:69:y:2014:i:c:p:98-111
    DOI: 10.1016/j.trb.2014.08.008
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    4. 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).
    5. Brueckner, Jan K. & Czerny, Achim I. & Gaggero, Alberto A., 2021. "Airline schedule buffers and flight delays: A discrete model," Economics of Transportation, Elsevier, vol. 26.
    6. Brueckner, Jan K. & Czerny, Achim I. & Gaggero, Alberto A., 2022. "Airline delay propagation: A simple method for measuring its extent and determinants," Transportation Research Part B: Methodological, Elsevier, vol. 162(C), pages 55-71.
    7. Liu, Ke & Zheng, Zhe & Zou, Bo & Hansen, Mark, 2023. "Airborne flight time: A comparative analysis between the U.S. and China," Journal of Air Transport Management, Elsevier, vol. 107(C).
    8. Kafle, Nabin & Zou, Bo, 2016. "Modeling flight delay propagation: A new analytical-econometric approach," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 520-542.
    9. Abdelghany, Ahmed & Abdelghany, Khaled & Guzhva, Vitaly S., 2024. "Schedule-level optimization of flight block times for improved airline schedule planning: A data-driven approach," Journal of Air Transport Management, Elsevier, vol. 115(C).
    10. Li, Max Z. & Ryerson, Megan S., 2019. "Reviewing the DATAS of aviation research data: Diversity, availability, tractability, applicability, and sources," Journal of Air Transport Management, Elsevier, vol. 75(C), pages 111-130.
    11. Eufrásio, Ana Beatriz R. & Eller, Rogéria A.G. & Oliveira, Alessandro V.M., 2021. "Are on-time performance statistics worthless? An empirical study of the flight scheduling strategies of Brazilian airlines," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    12. 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).
    13. 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.
    14. 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.
    15. Wang, Yanjun & Zhou, Ying & Hansen, Mark & Chin, Christopher, 2019. "Scheduled block time setting and on-time performance of U.S. and Chinese airlines—A comparative analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 825-843.
    16. Gillen, David & Hasheminia, Hamed, 2016. "Measuring reliability of transportation networks using snapshots of movements in the network – An analytical and empirical study," Transportation Research Part B: Methodological, Elsevier, vol. 93(PB), pages 808-824.

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