Behavioral analysis of airline scheduled block time adjustment
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DOI: 10.1016/j.tre.2017.04.004
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Cited by:
- Brueckner, Jan K. & Czerny, Achim I. & Gaggero, Alberto A., 2021.
"Airline mitigation of propagated delays via schedule buffers: Theory and empirics,"
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- Jan K. Brueckner & Achim I. Czerny & Alberto A. Gaggero, 2019. "Airline Mitigation of Propagated Delays: Theory and Empirics on the Choice of Schedule Buffers," CESifo Working Paper Series 7875, CESifo.
- Yu, Bin & Guo, Zhen & Asian, Sobhan & Wang, Huaizhu & Chen, Gang, 2019. "Flight delay prediction for commercial air transport: A deep learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 203-221.
- 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.
- 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).
- 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.
- Jan K. Brueckner & Achim I. Czerny & Alberto A. Gaggero, 2020. "Airline Schedule Buffers and Flight Delays: A Discrete Model," CESifo Working Paper Series 8545, CESifo.
- 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.
- Jan K. Brueckner & Achim I. Czerny & Alberto A. Gaggero, 2021. "Airline Delay Propagation: A Simple Method for Measuring Its Extent and Determinants," CESifo Working Paper Series 9369, CESifo.
- 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).
- Wen, Xin & Sun, Xuting & Sun, Yige & Yue, Xiaohang, 2021. "Airline crew scheduling: Models and algorithms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
- 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).
- Kim, Myeonghyeon & Bae, Jiheon, 2021. "Modeling the flight departure delay using survival analysis in South Korea," Journal of Air Transport Management, Elsevier, vol. 91(C).
- 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).
- Prince, Jeffrey T. & Simon, Daniel H., 2024. "Optimal promises: A general framework with application to airline schedule times," Journal of Air Transport Management, Elsevier, vol. 118(C).
- 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).
- 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.
- 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.
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Keywords
Scheduled block time; Utility maximization; Revealed preference; Mixed logit model; Marginal rate of substitution;All these keywords.
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