  Ground Delay Programs (GDPs) have been widely used to resolve excessive
demand-capacity imbalances at arrival airports by shifting foreseen airborne
delay to pre-departure ground delay. While offering clear safety and efficiency
benefits, GDPs may also create additional delay because of imperfect execution
and uncertainty in predicting arrival airport capacity. This paper presents a
methodology for measuring excess delay resulting from individual GDPs and
investigates factors that influence excess delay using regularized regression
models. We measured excess delay for 1210 GDPs from 33 U.S. airports in 2019.
On a per-restricted flight basis, the mean excess delay is 35.4 min with std of
20.6 min. In our regression analysis of the variation in excess delay, ridge
regression is found to perform best. The factors affecting excess delay include
time variations during gate out and taxi out for flights subject to the GDP,
program rate setting and revisions, and GDP time duration.
