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Congestion at major airports worldwide results in increased taxi times, fuel burn, and emissions. Regulating the pushback of aircraft from their gates, also known as departure metering, is a promising approach to mitigating surface congestion. Departure metering algorithms require models of airport surface traffic and knowledge of when a flight would be to be ready for pushback, which is called the earliest off-block time (EOBT). While EOBTs are known to be inaccurate due to several reasons, there has been little prior research on characterizing EOBT uncertainty and its impact on departure metering. We present a new class of queuing network models for the airport surface that are capable of capturing congestion at multiple locations. We demonstrate our modeling approach using operational data from three major U.S. airports: Newark Liberty International Airport, Dallas/Fort Worth International Airport, and Charlotte Douglas International Airport. We analyze the current levels of uncertainty in the EOBT information published by the airlines and conduct a parametric analysis of the reduction in departure metering benefits due to errors in the EOBT information. Our analysis indicates that the current levels of EOBT uncertainty lead to a 50% reduction in benefits at some airports when compared with an ideal case with no EOBT uncertainty. Two approaches to departure metering are considered: the National Aeronautics and Space Administration’s Airspace Technology Demonstration-2 logic and a new optimal control approach. We show that our queuing network models can help design and evaluate both approaches and that the optimal control approach is more effective in accommodating EOBT uncertainty while maintaining runway utilization.

Departure metering has the potential to mitigate airport surface congestion and decrease flight delays. This paper considers several candidate departure metering techniques, including a trajectory-based optimization approach using a node-link model and three aggregate queuebased approaches (a scheduler meant to represent NASA's Airspace Technology Demonstration-2 (ATD-2) logic, an optimal control approach, and a robust control approach). The outcomes of these different approaches are compared for two major airports: Paris Charles De Gaulle airport (CDG) in Europe and Charlotte Douglas International airport (CLT) in the United

Flights spend significantly more time taxiing on the airport surface during periods when the departure demand exceeds airport capacity, resulting in excessive fuel burn. Departure metering by holding aircraft at the gate during periods of congestion has been shown to yield benefits by lowering the taxi-out time. However, an important aspect of this problem that has not been understood well is the impact of uncertainty in departure demand. Recently, some airlines are beginning to publish an expected time that the flights are ready to pushback, which is referred to as Earliest Off-Block Time (EOBT). Tactical decisions for departure metering need to be made with the EOBT information. However, the EOBT published by airlines is often found to deviate from the actual gate out time without departure metering, which represents an error in the EOBT estimate. Hence, it is important to consider errors in EOBT information while analyzing benefits from departure metering.In this paper, we present a queuing network model to predict aircraft taxi-times on the airport surface. The predictions from the queue model are used for departure metering with NASA's ATD-2 logic that is being used in field trials at Charlotte airport. The framework allows us to quantify the reduction in departure metering benefits due to errors in EOBT information. The analysis reveals that the benefits reduce significantly due to EOBT uncertainty which has important implications for future departure metering applications, such as through the Terminal Flight Data Manager (TFDM) platform.

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