Decision Modeling Framework to Minimize Arrival Delays from Ground Delay Programs

Abstract

Convective weather and other constraints create uncertainty in air transportation, leading to costly delays. A Ground Delay Program (GDP) is a strategy to mitigate these effects. Systematic decision support can increase GDP efficacy, reduce delays, and minimize direct operating costs. In this study we construct a decision analysis (DA) model combining a decision tree and Bayesian belief network. Through a case study of LaGuardia Airport, the DA model demonstrates that larger GDP scopes including more flights in the program, hourly rates between 30-34 operations, and lead times greater than two hours trigger the fewest delays, a savings monetized up to $1,850 per flight. Furthermore, when convective weather is predicted, forecast weather confidences and scheduled traffic remain the same level or greater nearly 70% of the time, supporting more strategic decision making. Thus, the DA model enables quantification of uncertainties and insights on causal relationships, providing support for future GDP decisions.