The Distribution Function of Airport Taxi-Out Times and Selected Applications

Abstract

Except in adverse weather conditions, congestion at large airport hubs appears to be predictable. This paper attempts to translate this predictability into a distribution of taxi-out times, a key component of airport congestion. When scheduled flights are chosen to define the dataset, taxi-out times follow a uniform distribution. This is not only the simplest distribution that inferences can be based on, but also a distribution that can be estimated by simple linear regression leading to very accurate forecasts. But above all, it is an invertible distribution function that can help solve a large class of stochastic optimization problems.

Something old is new again: airline-airport consortia and key stakeholder benefits

“…Moreover, a significant potential benefit of airline-airport consortia is an improvement of on-time flights through the reduction of turn-around and taxi-out times. As stressed by Truong (2011) and Atkin et al (2010) flight delays are a critical problem for the air transport industry with early delays having a cascading effect on total airport operations.…”

Efficient Management of Aircraft Taxiing Phase by Adjusting Speed Through Conflict-Free Routes

“…Mitigating the effect of delays in taxing phase. The work [19] developed a probability distribution function to model taxi times for JFK Airport. [20] Used a regression model to estimate taxi times at Newark Liberty Airport and the paper [21] demonstrated a log-linear regression analysis to estimate taxi time.…”