Biblio

Airports are the most resource-constrained components of the air transportation system. This paper addresses the problems of increased flight delays and aircraft fuel consumption through the integrated control of airport arrival and departure operations. Departure operations are modeled using a network abstraction of the airport surface. Published arrival routes to airports are synthesized in order to form a realistic model of arrival airspace. The proposed control framework calculates the optimal times of departure of aircraft from the gates, as a function of the arrival and departure traffic as well as airport characteristics such as taxiway layout and gate capacity. The integrated control formulation is solved using dynamic programming, which allows calculation of policies for real-time implementation. The advantages of the proposed methodology are illustrated using simulations of Boston's Logan International Airport

The runway configuration is the set of the runways at an airport that are used for arrivals and departures at any time. While many factors, including weather, expected demand, environmental considerations, and coordination of flows with neighboring airports, influence the choice of runway configuration, the actual selection decision is made by air traffic controllers in the airport tower. As a result, the capacity of an airport at any time is dependent on the behavior of human decision makers. This paper develops a statistical model to characterize the configuration selection decision process using empirical observations. The proposed approach, based on the discrete-choicemodeling framework, identifies the influence of various factors in terms of the utility function of the decision maker. The parameters of the utility functions are estimated through likelihood maximization. Correlations between different alternatives are captured using a multinomial nested logit model. A key novelty of this study is the quantitative assessment of the effect of inertia, or the resistance to configuration changes, on the configuration selection process. The developed models are used to predict the runway configuration 3 h ahead of time, given operating conditions such as wind, visibility, and demand. Case studies based on data from Newark (EWR) and La-Guardia (LGA) airports show that the proposed model predicts runway configuration choices significantly better than a baseline model that only considers the historical frequencies of occurrence of different configurations.

The runway configuration is a key driver of airport capacity at any time. Several factors, such as weather conditions (wind and visibility), traffic demand, air traffic controller workload, and the coordination of flows with neighboring airports influence the selection of 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 3-hour probabilistic forecast of runway configuration. The proposed approach is illustrated using case studies based on data from LaGuardia (LGA) and San Francisco (SFO) airports, first by assuming perfect knowledge of weather and demand 3-hours in advance, 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 model predicts the correct runway configuration at LGA with an accuracy of 82%, and at SFO with an accuracy of 85%. Given the forecast weather and scheduled demand, the accuracy of correct prediction of the runway configuration 3 hours in advance is 80% for LGA and 82% for SFO.