Data-driven modeling of the airport runwayconfiguration selection process using maximum likelihooddiscrete-choice models

by Jacob Bryan Avery

Institution: MIT
Year: 2016
Keywords: Aeronautics and Astronautics.
Posted: 02/05/2017
Record ID: 2123012
Full text PDF: http://hdl.handle.net/1721.1/103444


The runway configuration is a key driver of airport capacity at any time. Several factors, such as wind speed, wind direction, visibility, traffic demand, air traffic controller workload, and the coordination of flows with neighboring airports influence the selection of the runway configuration. This paper identifies a discrete-choice model of the configuration selection process from empirical data. The model reflects the importance of various factors in terms of a utility function. Given the weather, traffic demand and the current runway configuration, the model provides a probabilistic forecast of the runway configuration at the next 15-minute interval. This prediction is then extended to obtain the probabilistic forecast of runway configuration on time horizons up to 6 hours. Case studies for Newark (EWR), John F. Kennedy (JFK), LaGuardia (LGA), and San-Francisco (SFO) airports are completed with this approach, first by assuming perfect knowledge of future weather and demand, and then using the Terminal Aerodrome Forecasts (TAFs). The results show that given the actual traffic demand and weather conditions 3 hours in advance, the models predict the correct runway configuration at EWR, JFK, LGA, and SFO with accuracies 79.5%, 63.8%, 81.3% and 82.8% respectively. Given the forecast weather and scheduled demand 3 hours in advance, the models predict the correct runway configuration at EWR, LGA, and SFO with accuracies 78.9%, 78.9% and 80.8% respectively. Finally, the discrete-choice method is applied to the entire New York Metroplex using two different methodologies and is shown to predict the Metroplex configuration with accuracies of 69.0% on a 3 hour prediction horizon. Advisors/Committee Members: Hamsa Balakrishnan (advisor).