Airline schedule buffers and flight delays: A discrete model

Brueckner, Jan K.; Czerny, Achim I.; Gaggero, Alberto A.

doi:10.1016/j.ecotra.2021.100218

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Cited by 4 publications

(2 citation statements)

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“…First, in the last years airlines worldwide have increasingly used padding to reduce their delays. This practice consists in extending the announced travel duration beyond the minimum travel time (Baumgarten et al, 2014Forbes et al, 2019;Brueckner et al, 2021a;2021b). Airlines can use padding to better coordinate their banks of arrival flights to an airport and to avoid consumers' claims after delays.…”

Section: Discussionmentioning

confidence: 99%

Airport Dominance, Route Network Design and Flight Delays

Calzada

Fageda

2020

SSRN Journal

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Section: Discussionmentioning

confidence: 99%

Airport Dominance, Route Network Design and Flight Delays

Calzada

Fageda

2020

SSRN Journal

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“…It has been used to add more time to the schedule for multiple types of delay. Brueckner et al utilized the method of airline scheduling buffer choice and studied the shocks influencing flight times [5].…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Aircraft Arrival Delay Due to Weather

Charnvanichborikarn,

Kantanatha

2023

Advances in Transdisciplinary Engineering

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The world has seen a dramatic change in weather patterns caused by global warming. Extreme weather and unpredictable events are occurring more often. As weather is the most unpredictable component of a flight, if there is a slight chance of predictability in what the weather is going to be, it could help with the logistical issues and prevent further delays. Therefore, aircraft arrival prediction could potentially be a solution to improve the efficiency of the operation and the effectiveness of the contingency plans to respond to such events. The objective of this paper was to gain more insights and study the effect of weather on aircraft arrival delay by using random forest classification method and adaboost classification method. The results show that random forest classifier is the most accurate model for the weather delay prediction with the accuracy of 92.98 percent. When comparing F-1 score with adaboost, random forest model also gives higher values for all conditions.

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Probabilistic and explainable tree-based models for rotational reactionary flight delay prediction

Dalmau

2024

CEAS Aeronaut J

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Airline schedule buffers and flight delays: A discrete model

Cited by 4 publications

References 8 publications

Airport Dominance, Route Network Design and Flight Delays

Airport Dominance, Route Network Design and Flight Delays

Prediction of Aircraft Arrival Delay Due to Weather

Probabilistic and explainable tree-based models for rotational reactionary flight delay prediction

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