Abstract
Values of departure and arrival runway occupancy time and takeoff distance are commonly used parameters for many airport and air transportation simulation models. However, these data are typically found either aggregated by weight or wake class or are otherwise unavailable. Furthermore, field collection of these data can be prohibitively time-consuming and expensive. To fill this data gap, predictive models of departure runway occupancy time, takeoff distance, and arrival runway occupancy time are presented. Through a comparison of multiple frameworks, it is found that the Bayesian modeling framework offers the strongest predictive performance. Using hierarchical Bayesian regression, the models developed cover 85 individual aircraft types and address the considerable variability present in the arrival and departure performances of different aircraft for clear weather conditions.
Published Version
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