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Abstract
Airlines face three types of delay that make it difficult to build robust schedules and to support block
time predictability. Block time is the time elapsed from gate departure to gate arrival and refers to
the time when blocks are off the wheels at the departure airport to the time they are back on at the
destination airport. 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. This study presents a methodology based on the case study of the Seattle/Tacoma
International (SEA) and Oakland International airport (OAK) city pair to determine the predictability
of block time. The methodology based on quantile regression models is appropriate for
a skewed distribution where analysts are interested in the impact of selected operational variables
on the conditional mean of block times at given percentiles. Quantile regression provides a measure
of on-time performance based on the percentile results that show the most significance and best fit.