Predicting Block Time: An Application of Quantile Regression

Airlines face three types of delay that make it difficult to build robust schedules and to support block time predictability. These delays can be induced (i.e., ground delays), propagated, or stochastic. With capacity constrained at major airports and regulators facing greater public pressure to alleviate congestion and tarmac delays, aviation practitioners have renewed their interest in the predictability of block time, that is, the time elapsed from gate departure to gate arrival. This study presents a methodology based on the case study of the Seattle/Tacoma International and Oakland International airport city pair to determine a block time. This methodology based on quantile regression models is appropriate for skewed distribution where analysts are interested in the impact of selected operational covariates on the conditional mean of block times at given percentiles.

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 Record created 2017-04-01, last modified 2020-10-28

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