Latest revision as of 19:25, 1 February 2021

Abstract

Historical data are used to build two types of models that predict Ground Delay Program implementation decisions and produce insights into how and why those decisions are made. More specifically, behavioral cloning and inverse reinforcement learning models are built that predict hourly Ground Delay Program implementation at Newark Liberty International and San Francisco International airports. Data available to the models include actual and scheduled air traffic metrics and observed and forecasted weather conditions. The developed random forest models are substantially better at predicting hourly Ground Delay Program implementation for these airports than the developed inverse reinforcement learning models. However, all of the models struggle to predict the initialization and cancellation of Ground Delay Programs. The structure of the models are also investigated in order to gain insights into Ground Delay Program implementation decision making. Notably, characteristics of both types of model suggest that...

Original document

The different versions of the original document can be found in:

• http://pdfs.semanticscholar.org/4469/d86042c8d530b19c873f26091279e6708655.pdf

• http://www.aviationsystemsdivision.arc.nasa.gov/publications/2015/bloem_JAIS_GDP_analytics_BC_IRL_AAM.pdf

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DOIS: 10.2514/1.i010304 10.2514/6.2014-2026