Evaluating the Impact of Uncertainty on Airport Surface Operations

Abstract

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.