Impact of Off-Block Time Uncertainty on the Control of Airport Surface Operations

Abstract

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.

Comparative Analysis of Departure Metering at United States and European Airports

“…The optimal control approach determines hold times by explicitly optimizing the queue lengths on the airport surface instead of using a rule-based heuristic. We have presented the optimal control approach and applied it for CLT in our earlier work [27]. We briefly discuss the approach again here for the sake of clarity.…”

Modeling Aircraft Departure at a Runway Using a Time-Varying Fluid Queue

“…A key operation for airports is aircraft departure management, and in particular "departure metering", which assigns suitable holds for departure aircraft at their gates [2,3]. The goal of departure metering is to reduce the departure queue at runway entry points while maintaining the runway throughput [4]. As far as the authors are aware, there have been few studies characterizing stochastic features in the aircraft departure process.…”

“…To predict aircraft waiting times at a departure queue, several studies propose queuing models to analyze the taxi-out process of the departure air traffic [4,8,9]. In Reference [8], the authors represent a single departure runway queue using a D(t)/E k (t)/1 queuing model assuming that the aircraft arrive at the queue according to a time-dependent schedule and that the service rate follows a time-dependent Erlang distribution.…”

“…The expected queue length is determined using the flow conservation principle. These queuing networks were used to estimate taxi-out times at three case study airports in the United States [4]. The results showed that the taxi-out times of 60% to 70% flights were estimated within an error margin of 5 min.…”

Deep Reinforcement Learning Based Airport Departure Metering

“…Moreover, control actions, that are derived from DM control policies, are based on model parameters-which are often fixed, fail to handle large airside environment perturbations. Not surprisingly, benefits realised due to these methods have reduced significantly under uncertainty [12], [13].…”