Probabilistic Airport Capacity Prediction Incorporating Weather Forecast Uncertainty

Abstract

This paper introduces a stochastic analytical model for generating probabilistic airport capacity predictions for strategic traffic flow planning. The model extends previous research on airport capacity estimation by explicitly integrating the impact of terminal weather and its uncertainty. In particular, it ingests different types of weather forecast inputs, including deterministic forecasts, deterministic forecasts with forecast error models, and ensemble forecasts, to produce probabilistic distributions of predicted arrival and departure capacity for each runway configuration at the airport. The paper briefly introduces the formulation of a mathematical model and weather data sources supported by a proof-of-concept prototype implementation. It also introduces a methodology for validating probabilistic airport capacity predictions and results of validation studies at Hartsfield-Jackson Atlanta International Airport. These results are compared with standard airport benchmark capacities and actual observed throughputs.