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A survival model for flight delay propagation

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  • Wong, Jinn-Tsai
  • Tsai, Shy-Chang

Abstract

This paper examines flight delay propagation involving a Taiwanese domestic airline. The Cox proportional hazards model is used to develop departure and arrival delay models that show how flight delay propagation can be formulated through repeated chain effects in aircraft rotations. The hazard ratios obtained provide measures of the chances of recovering from flight delays under a variety of situations and the effects that individual contributing factors of flight delays have on airline schedule reliability.

Suggested Citation

  • Wong, Jinn-Tsai & Tsai, Shy-Chang, 2012. "A survival model for flight delay propagation," Journal of Air Transport Management, Elsevier, vol. 23(C), pages 5-11.
    • Handle: RePEc:eee:jaitra:v:23:y:2012:i:c:p:5-11
    DOI: 10.1016/j.jairtraman.2012.01.016
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    Cited by:

    1. 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.
    2. 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.
    3. Li, Qiang & Jing, Ranzhe, 2021. "Characterization of delay propagation in the air traffic network," Journal of Air Transport Management, Elsevier, vol. 94(C).
    4. Chen, Shenwen & Du, Wenbo & Liu, Runran & Cao, Xianbin, 2023. "Finding spatial and temporal features of delay propagation via multi-layer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 614(C).
    5. Wu, Cheng-Lung & Law, Kristie, 2019. "Modelling the delay propagation effects of multiple resource connections in an airline network using a Bayesian network model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 62-77.
    6. Li, Chi & Mao, Jianfeng & Li, Lingyi & Wu, Jingxuan & Zhang, Lianmin & Zhu, Jianyu & Pan, Zibin, 2024. "Flight delay propagation modeling: Data, Methods, and Future opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    7. 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).
    8. Gurtner, Gérald & Cook, Andrew, 2021. "The hidden cost of uncertainty for airspace users," Journal of Air Transport Management, Elsevier, vol. 91(C).
    9. Birolini, Sebastian & Jacquillat, Alexandre, 2023. "Day-ahead aircraft routing with data-driven primary delay predictions," European Journal of Operational Research, Elsevier, vol. 310(1), pages 379-396.
    10. 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).
    11. 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.
    12. Chen, Gong & Fricke, Hartmut & Okhrin, Ostap & Rosenow, Judith, 2024. "Flight delay propagation inference in air transport networks using the multilayer perceptron," Journal of Air Transport Management, Elsevier, vol. 114(C).
    13. 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).
    14. Kapolke, Manu & Fürstenau, Norbert & Heidt, Andreas & Liers, Frauke & Mittendorf, Monika & Weiß, Christian, 2016. "Pre-tactical optimization of runway utilization under uncertainty," Journal of Air Transport Management, Elsevier, vol. 56(PA), pages 48-56.
    15. 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).
    16. Rodríguez-Sanz, à lvaro & Comendador, Fernando Gómez & Valdés, Rosa Arnaldo & Pérez-Castán, Javier A., 2018. "Characterization and prediction of the airport operational saturation," Journal of Air Transport Management, Elsevier, vol. 69(C), pages 147-172.
    17. Zhe Zheng & Wenbin Wei & Bo Zou & Minghua Hu, 2020. "How Late Does Your Flight Depart? A Quantile Regression Approach for a Chinese Case Study," Sustainability, MDPI, vol. 12(24), pages 1-16, December.
    18. Tsegai O. Yhdego & An-Tsun Wei & Gordon Erlebacher & Hui Wang & Miguel G. Tejada, 2023. "Analyzing the Impacts of Inbound Flight Delay Trends on Departure Delays Due to Connection Passengers Using a Hybrid RNN Model," Mathematics, MDPI, vol. 11(11), pages 1-24, May.
    19. van Schilt, Isabelle M. & van Kalker, Jonna & Lefter, Iulia & Kwakkel, Jan H. & Verbraeck, Alexander, 2024. "Buffer scheduling for improving on-time performance and connectivity with a multi-objective simulation–optimization model: A proof of concept for the airline industry," Journal of Air Transport Management, Elsevier, vol. 115(C).
    20. Malandri, Caterina & Mantecchini, Luca & Reis, Vasco, 2019. "Aircraft turnaround and industrial actions: How ground handlers' strikes affect airport airside operational efficiency," Journal of Air Transport Management, Elsevier, vol. 78(C), pages 23-32.
    21. Keji Wei & Vikrant Vaze, 2018. "Modeling Crew Itineraries and Delays in the National Air Transportation System," Transportation Science, INFORMS, vol. 52(5), pages 1276-1296, October.

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