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.

通过正规化回归模型，本文测量了1210个地面延误计划中产生的过度延误，并研究了影响过度延误的因素。结果显示每个受限航班的平均过度延误为35.4分钟，标准差为20.6分钟。影响过度延误的因素包括航班出港和离港期间的时间变化、计划速率设定和修订以及延误计划的持续时间。

GDP 产生的过度滞后：测量和因果分析