Departure Throughput Study for Boston Logan International Airport

We propose a new parametric representation of the departure capacity of airports. In particular, we show how the departure capacity can be represented by the variation of the average departure throughput as a function of arrivals, conditioned on persistent departure demand. We also show how this approach can be extended to quantify the dependence of departure capacity on other parameters such as the fleet mix. The proposed approaches are illustrated through the parametric estimation of the departure capacity of Boston Logan International Airport (BOS)

Design and simulation of airport congestion control algorithms

This paper proposes a stochastic model of runway departures and a dynamic programming algorithm for their control at congested airports. Using a multi-variable state description that includes the capacity forecast, the runway system is modeled as a semi-Markov process. The paper then introduces a queuing system for modeling the controlled departure process that enables the efficient calculation of optimal pushback policies using decomposition techniques. The developed algorithm is simulated at Philadelphia International Airport, and compared to other potential control strategies including a threshold-policy. The algorithm is also shown to effectively adapt to changes in airport departure capacity, maintain runway utilization and efficiently manage congestion.National Science Foundation (U.S.) (Award 0931843

Dynamic Control of Airport Departures: Algorithm Development and Field Evaluation

Surface congestion leads to significant increases in taxi times and fuel burn at major airports. In this paper, we formulate the airport surface congestion management problem as a dynamic control problem. We address two main challenges: the random delay between actuation (at the gate) and the server being controlled (the runway), and the need to develop control strategies that can be implemented in practice by human air traffic controllers. The second requirement necessitates a strategy that periodically updates the rate that departures pushback from their gates. We model the runway system as a semi-Markov process using surface surveillance data. We use this modeling framework to derive optimal pushback policies to control congestion. Finally, we present the results of the real-world implementation and field testing of this control protocol at Boston Logan International Airport.United States. Federal Aviation Administration (United States. Air Force Contract FA8721-05-C-0002

On the Probabilistic Modeling of Runway Inter-departure Times

This paperr examines the validity of the Erlang distribution for runway service times. It uses high-fidelity surface surveillance data, for the first time, to model the probability distributions of runway service times and departure throughput, and to validate the Erlang service time assumption. The paper proposes several potential approaches to determine departure runway service time distributions from empirical data, and compares the results. In particular, it finds that a displaced exponential fit may be a better match to the empirical service time distribution than an Erlang distribution. However, the computational benefits offered by the Erlang service time distribution, its accurate reflection of the means and variances of the empirical service time and throughput distributions, and its ability to represent the tail of the service time distribution, make it attractive for use in queuing models of airport operations.National Science Foundation (U.S.) (Cyber-Physical Systems Award 0931843

Modeling and control of airport departure processes for emissions reduction

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics; and, (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 139-141).Taxiing aircraft contribute significantly to the fuel burn and emissions at airports. This thesis investigates the possibility of reducing fuel burn and emissions from surface operations through a reduction of the taxi times of departing aircraft. Data analysis of the departing traffic in four major US airports provides a comprehensive assessment of the impact of surface congestion on taxi times, fuel burn and emissions. For this analysis two metrics are introduced: one that compares the taxi times to the unimpeded ones and another that evaluates them in terms of their contribution to the airport's throughput. A novel approach is proposed that models the aircraft departure process as a queuing system. The departure taxi (taxi-out) time of an aircraft is represented as a sum of three components: the unimpeded taxi-out time, the time spent in the departure queue, and the congestion delay due to ramp and taxiway interactions. The dependence of the taxi-out time on these factors is analyzed and modeled. The performance of the model is validated through a comparison of its predictions with observed data at Boston's Logan International Airport (BOS). A reduction in taxi times may be achieved through the queue management strategy known as N-Control, which controls the push back process so as to keep the number of departing aircraft on the surface of the airport below a specified threshold. The developed model is used to quantify the impact of N-Control on taxi times, delays, fuel burn and emissions at BOS. Finally, the benefits and implications of N-Control are compared to the ones theoretically achievable from a scheme that controls the takeoff queue of each departing aircraft.by Ioannis Simaiakis.S.M.in Technology and PolicyS.M

Analysis, modeling and control of the airport departure process

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2013.This electronic version was submitted and approved by the author's academic department as part of an electronic thesis pilot project. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from department-submitted PDF version of thesis.Includes bibliographical references (p. 305-313).Increased air traffic demand over the past two decades has resulted in significant increases in surface congestion at major airports in the United States. The overall objective of this thesis is to mitigate the adverse effects of airport surface congestion, including increased taxi-out times, fuel burn, and emissions. The thesis tackles this objective in three steps: The first part deals with the analysis of departure operations and the characterization of airport capacity; the second part develops a new model of the departure process; and the third part of the thesis proposes and tests, both on the field and in simulations, algorithms for the control of the departure process. The characterization and estimation of airport capacity is essential for the successful management of congestion. This thesis proposes a new parametric method for estimating the departure capacity of a runway system, the most constrained element of most airports. The insights gained from the proposed technique are demonstrated through a case study of Boston Logan International Airport (BOS). Subsequently, the methodology is generalized to the study of interactions among the three main airports of the New York Metroplex, namely, John F. Kennedy International Airport (JFK), Newark Liberty International Airport (EWR) and LaGuardia Airport (LGA). The individual capacities of the three airports are estimated, dependencies between their operations are identified, and the capacity of the Metroplex as a whole is characterized. The thesis also identifies opportunities for improving the operational capacity of the Metroplex without significant redesign of the airspace. The proposed methodology is finally used to assess the relationship between route availability during convective weather and the capacity of LGA. The second part of the thesis develops a novel analytical model of the departure process. The modeling procedure includes the estimation of unimpeded taxi-out time distributions, and the development of a stochastic and dynamic queuing model of the departure runway(s), based on the transient analysis of D(t)=Ek(t)=1 queuing systems. The parameters of the runway service process are estimated using operational data. Using the aircraft pushback schedule as input, the model predicts the expected runway schedule and the takeoff times. It also estimates the expected queuing delay and its variance for each light, along with the congestion level of the airport, sizes of the departure queues, and the departure throughput. The model is trained using data from EWR in 2011, and is subsequently used to predict taxi-out times at EWR in 2007 and 2010. The final part of this thesis proposes dynamic programming algorithms for controlling the departure process, given the current operating environment. These algorithms, called Pushback Rate Control protocols, predict the departure throughput of the airport, and recommend a rate at which to release pushbacks from the gate in order to control congestion. The thesis describes the design and field-testing of a variant of Pushback Rate Control at BOS in 2011, and the development of a decision-support tool for its implementation. The analysis shows that during 8 four-hour test periods, fuel use was reduced by an estimated 9 US tons (2,650 US gallons), and taxi-out times were reduced by an average of 5.3 min for the 144 flights that were held at the gate. The thesis concludes with simulations of the Pushback Rate Control protocol at Philadelphia International Airport (PHL), one of the most congested airports in the US, and a discussion of the potential benefits and implementation challenges.by Ioannis Simaiakis.Ph.D

Opportunities for Reducing Surface Emissions through Airport Surface Movement Optimization

Aircraft taxiing on the surface contribute significantly to the fuel burn and emissions at airports. This report is an overview of PARTNER’s Project 21, which tries to identify promising opportunities for surface optimization to reduce surface emissions at airports, estimate the potential benefits of these strategies, and assess the critical implementation barriers that need to be overcome prior to the adoption of these approaches at airports. We also present a new queuing network model of the departure processes at airports that can be used to develop advanced queue management strategies to decrease fuel burn and emissions

A Decision Support Tool for the Pushback Rate Control of Airport Departures

Airport surface congestion control has the potential to mitigate the increase in taxi times and fuel burn at major airports. One possible class of congestion control strategies predicts the departure throughput, and recommends a rate at which to release aircraft pushbacks from the gate. This paper describes the field-testing of these types of strategies at Boston Logan International Airport, focusing on the communication of the suggested rate to the air traffic controller, and additional support for its implementation. Two Android tablet computers were used for the field-tests; one to input the data and the other to display the recommended rate to the air traffic controllers. Two potential decision-support displays were tested: a rate control display that only presented a color-coded suggested pushback rate and a volume control display that provided additional support to the controllers on the number of aircraft that had called-ready and had been released. A survey of controllers showed that they had found the decision-support tool easy to use, especially the additional functionality that is provided by the aircraft volume control display. The field tests were also found to yield significant operational benefits showing that such a congestion control strategy could be effective in practice.United States. Federal Aviation AdministrationNational Science Foundation (U.S.) (Award 0931843

Demonstration of Reduced Airport Congestion Through Pushback Rate Control

Airport surface congestion results in significant increases in taxi times, fuel burn and emissions at major airports. This paper presents the field tests of a control strategy to airport congestion control at Boston Logan International Airport. The approach determines a suggested rate to meter pushbacks from the gate, in order to prevent the airport surface from entering congested states and reduce the time that flights spend with engines on while taxiing to the runway. The field trials demonstrated that significant benefits were achievable through such a strategy: during eight four-hour tests conducted during August and September 2010, fuel use was reduced by an estimated 12,000-15,000 kg (3,900-4,900 US gallons), while aircraft gate pushback times were increased by an average of only 4.3 minutes

An Analytical Queuing Model of Airport Departure Processes for Taxi Out Time Prediction.

This paper presents an analytical model of the aircraft departure process at an airport. The modeling procedure includes the estimation of unimpeded taxi-out time distributions, and the development of a queuing model of the departure runway system based on the transient analysis of D(t)/E k (t)/1 queuing systems. The parameters of the runway service process are estimated using operational data. Using the aircraft pushback schedule as input, the model predicts the expected runway schedule and takeoff times. It also estimates the expected taxi-out time, queuing delay and its variance for each flight, in addition to the congestion level of the airport, sizes of the departure runway queues and the departure throughput. The proposed approach is illustrated using a case study based on Newark Liberty International (EWR) Airport. The model is trained using data from 2011, and is subsequently used to predict taxi-out times in 2007 and 2010. The predictions are compared with actual data to demonstrate the predictive capabilities of the model