Integrated allocation and utilization of airport capacity to mitigate air traffic congestion

227 Pages·2015·22.27 MB·English

Integrated Allocation and Utilization of Airport Capacity
to Mitigate Air Traffic Congestion
by
Alexandre Jacquillat
S.M., Applied Mathematics, Ecole Polytechnique (2010)
S.M., Technology and Policy, Massachusetts Institute of Technology (2012)
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Submitted to the Engineering Systems Division
in partial fulfillment of the requirements for the degree of MASSACHUSETT$ INSTITUTE
OF TECHNOLOLGY
Doctor of Philosophy
at the JUL 02 2015
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@ Massachusetts Institute of Technology 2015. All rights reserved.
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Amedeo R. Odoni
Professor of Aeronautics and Astronautics, and Civil and Environmental Engineering
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Acting Director, Engineering Systems Division

Integrated Allocation and Utilization of Airport Capacity
to Mitigate Air Traffic Congestion
by
Alexandre Jacquillat
Submitted to the Engineering Systems Division
on May 18, 2015, in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
Abstract
The combination of air traffic growth and airport capacity limitations has resulted in signifi-
cant congestion throughout the US National Airspace System, which imposes large costs on
the airlines, passengers and society. Absent opportunities for capacity expansion, the miti-
gation of air traffic congestion requires improvements in (i) the utilization of airport capacity
to enhance operating efficiency at the tactical level (i.e., over each day of operations), and/or
(ii) the allocation of airport capacity to the airlines to limit over-capacity scheduling at the
strategic level (i.e., months in advance of the day of operations). This thesis develops an
integrated approach to airport congestion mitigation that jointly optimizes the utilization of
airport capacity and the design of airport capacity allocation mechanisms.
First, we focus on airport capacity utilization. We formulate an original Dynamic Pro-
gramming model that optimizes, at the tactical level, the selection of runway configurations
and the balancing of arrival and departure service rates to minimize congestion costs, for
any given schedule of flights. The model integrates the stochasticity of airport operations
into a dynamic decision-making framework. We implement exact and approximate Dynamic
Programming algorithms that, in combination, enable the real-time implementation of the
model. Results show that optimal policies are path-dependent, i.e., depend on prior decisions
and on the stochastic evolution of the system, and that the model can reduce congestion
costs, compared to advanced heuristics aimed to replicate typical decisions made in practice
and to existing approaches based on deterministic queue dynamics.
Second, we integrate the model of airport capacity utilization into a macroscopic queuing
model of airport congestion. The resulting model quantifies the relationships between flight
schedules, airport capacity and flight delays at the strategic level, while accounting for the
way airport capacity utilization procedures can vary tactically to maximize operating effi-
ciency. Results suggest that the model estimates the average departure queue lengths, the
variability of departure queue lengths and the average arrival and departure delays at the
three major airports in the New York Metroplex relatively well. The application of the model
shows that the strong nonlinearities between flight schedules and flight delays observed in
practice are captured by the model.
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Third, we develop an Integrated Capacity Utilization and Scheduling Model (ICUSM) that
jointly optimizes scheduling interventions for airport capacity allocation at the strategic
level and airport capacity utilization at the tactical level. Scheduling interventions start
with a schedule of flights provided by the airlines, and reschedule a selected set of flights
to reduce imbalances between demand and capacity, while minimizing interference with
airline competitive scheduling. The ICUSM optimizes such interventions, while accounting
for the impact of changes in flight schedules on airport operations. It relies on an original
modeling architecture that integrates a Stochastic Queuing Model of airport congestion, our
Dynamic Programming model of capacity utilization, and an Integer Programming model
of scheduling interventions. We develop an iterative solution algorithm that converges in
reasonable computational times. Results suggest that substantial delay reductions can be
achieved at busy airports through limited changes in airline schedules. It is also shown that
the proposed integrated approach to airport congestion mitigation performs significantly
better than a typical sequential approach where scheduling and operating decisions are made
separately.
Last, we build upon the ICUSM to design, optimize and assess non-monetary mechanisms
for scheduling interventions that ensure inter-airline equity and enable airline collaboration.
Under the proposed mechanism, the airlines would provide their preferred schedules of flights,
their network connections, and the relative scheduling flexibility of their flights to a central
decision-maker, who may then consider scheduling adjustments to reduce anticipated delays.
We develop a lexicographic architecture that optimizes such interventions based on efficiency
(i.e., meeting airline scheduling preferences), equity (i.e., balancing scheduling adjustments
fairly among the airlines), and on-time performance (i.e., mitigating airport congestion)
objectives. Theoretical and computational results suggest that inter-airline equity can be
achieved at no, or small, losses in efficiency, and that accounting for airline scheduling
preferences can significantly improve the outcome of scheduling interventions.
Thesis Supervisor: Amedeo R. Odoni
Title: Professor of Aeronautics and Astronautics, and Civil and Environmental Engineering
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Acknowledgments
This dissertation is the result of an exciting, instructive and mind-opening journey at MIT.
First and foremost, I would like to express my deepest gratitude to my research advisor,
Amedeo Odoni. Amedeo has been a constant source of knowledge, support and guidance
since I arrived at MIT. From start to finish, this research has tremendously benefited from
his expertise in air transportation and in operations research, as well as from his vision, his
openness and his candid encouragements. I am truly thankful for his remarkable dedication
to his students, his continuous feedback, and his numerous edits that greatly improved the
quality of this dissertation. Beyond his research guidance, I am very fortunate to have found
in Amedeo a great mentor who has helped me develop my passions and has made me a better
researcher and a better professional.
I would also like to thank the other members of a unique and interdisciplinary doctoral
committee. I am greatly indebted to Cynthia Barnhart for her invaluable mentoring. Cindy
is one of the busiest persons I know, yet she has always been available when I needed her, she
has continuously guided and advised this research, and she has invested significant amounts
of time in my personal and professional development. Thank you to Mort Webster for
his guidance regarding the strategic directions of this research and the framing of research
questions. I have also been fortunate to work with Vikrant Vaze, who invested a lot of time
to share his broad expertise and invaluable insights to improve the contents of this thesis.
This research has very much benefited from our collaboration, which I hope to sustain in the
future. Finally, thanks to David Gillen for expanding the scope of my thinking by raising
thought-provoking economic questions. I am grateful for David's mentorship and friendship.
This research has benefited from many interactions with industry practitioners who
greatly contributed to the realism and relevance of the models developed in this disser-
tation. I would like to gratefully acknowledge the Federal Aviation Administration, the Port
Authority of New York and New Jersey and the MIT Airline Industry Consortium and its
members for funding or advising this research and for inviting me to share this work.
I am grateful to my colleagues and friends from the International Center for Air Trans-
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portation for many stimulating discussions about aviation and everything else. I would par-
ticularly like to acknowledge Ioannis Simaiakis for helping me develop my research approach
and for providing empirical capacity estimates at several US airports, Nikolas Pyrgiotis for
helping me identify research opportunities, Michael Wittman for many exciting research
conversations in and out of the lab, and Hamsa Balakrishnan for her insightful feedback on
various aspects of this research. Thanks also to Joseph Sussman, Christopher Magee and
Peter Belobaba for providing exciting teaching opportunities. And many thanks to Philippe
Bonnefoy, Vivek Sakhrani and Stephen Zoepf for exciting collaborations which have taught
me a lot about domains I was less familiar with.
I am greatly indebted to MIT's Engineering Systems Division, which has been a fantastic
academic home for me over the past three years. Thanks to Elizabeth Milnes for making
everything possible in E40. To Lita Das, Yinjin Lee, Maite Pena-Alcaraz, Fernando de
Sisternes and Abigail Horn for being amazing office mates and for creating a stimulating and
fun environment on a daily basis, and to Vivek Sakhrani for the frequent visits and spirited
conversations. And to all the members of the vibrant ESD Student Society for the continuous
feedback on this research, and for all the fun in and out of the office. I am very fortunate
to have been part of this extraordinary and diverse community of passionate colleagues and
close friends who have made me a better researcher and a better person.
Finally, I would like to thank my family and my other friends from both sides of the
Atlantic. Special thanks to my parents for their long-standing support and for their commit-
ment to creating the best opportunities for me. And, last but very not least, many thanks
to my wife, my best friend and my roommate, Claire, without whom none of this would
have been possible. Thank you for your understanding and support throughout the tortuous
road that led to this dissertation, for your daily refreshing and contagious laughs, and for
our exciting past and future adventures together.
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Contents
1 Introduction 15
1.1 Congestion in the US National Airspace System . . . . . . . . . . . . . . . . 16
1.1.1 Airport Congestion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.1.2 Airport Congestion Mitigation Interventions . . . . . . . . . . . . . . 19
1.1.3 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
1.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
1.2.1 Models of Airport Congestion . . . . . . . . . . . . . . . . . . . . . . 25
1.2.2 Airport Capacity Utilization . . . . . . . . . . . . . . . . . . . . . . . 27
1.2.3 Airport Demand Management . . . . . . . . . . . . . . . . . . . . . . 28
1.3 Contributions of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
1.3.1 Capacity Utilization under Stochastic Operating Conditions . . . . . 33
1.3.2 An Integrated Model of Airport Congestion . . . . . . . . . . . . . . 35
1.3.3 An Integrated Approach to Scheduling Interventions . . . . . . . . . 36
1.3.4 Equity and Collaboration in Scheduling Interventions . . . . . . . . . 37
1.4 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2 Modeling Framework 41
2.1 Modeling Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.1.1 Airport Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.1.2 Flight Schedules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.2 Congestion Mitigation Interventions . . . . . . . . . . . . . . . . . . . . . . . 45
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2.3 Descriptive Model of Airport Operations . . . 46
2.3.1 Queuing Model of Airport Congestion. 46
2.3.2 A Model of Weather Variations . . . . 49
2.4 Discussion of the Assumptions of the Models . 50
2.5 Experimental Setup . . . . . . . . . . . . . . . 53
3 Capacity Utilization under Operating Stochasticity 59
3.1 Model Formulation . . . . . . . . . . . . . . . . . . . . . 61
3.1.1 Problem Statement . . . . . . . . . . . . . . . . . 61
3.1.2 State Variables . . . . . . . . . . . . . . . . . . . 62
3.1.3 Decision Variables . . . . . . . . . . . . . . . . . 63
3.1.4 Dynamics of the System . . . . . . . . . . . . . . 65
3.1.5 Cost Function . . . . . . . . . . . . . . . . . . . . 69
3.1.6 Dynamic Programming Formulation . . . . . . . . 69
3.2 Solution Algorithm . . . . . . . . . . . . . . . . . . . . . 70
3.2.1 Experimental Setup . . . . . . . . . . . . . . . . . 70
3.2.2 Exact Dynamic Programming Algorithm . . . . . 72
3.2.3 One-Step Look-Ahead Algorithm . . . . . . . . . 73
3.2.4 Evaluation of Performance . . . . . . . . . . . . . 75
3.3 Computational Results . . . . . . . . . . . . . . . . . . . 78
3.3.1 Optimal Policies . . . . . . . . . . . . . . . . . . 78
3.3.2 Frequency of Decisions . . . . . . . . . . . . . . . 80
3.3.3 Sensitivity of Queue Lengths to Model Parameters 86
3.4 Performance Evaluation . . . . . . . . . . . . . . . . . . 88
3.4.1 Comparison of the Optimal Policy to Heuristics 89.
3.4.2 Benefits of the Integration of Queue Stochasticity 92
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 93
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4 Application to Congestion Modeling 97
4.1 Model Formulation ....................... . . . . . . . . . 98
4.1.1 Model Presentation ................... . . . . . . . . . 98
4.1.2 Simplified Control of Service Rates . . . . . . . . . . . . . . . . . . . 100
4.2 Model Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
4.2.1 Measures of Airport On-Time Performance . . . . . . . . . . . . . . . 104
4.2.2 Model of Departure Queue Lengths . . . . . . . . . . . . . . . . . . . 107
4.2.3 Model of Arrival and Departure Delays . . . . . . . . . . . . . . . . . 110
4.2.4 Benefits of the Integrated Approach . . . . . . . . . . . . . . . . . . . 113
4.3 Scheduling and On-time Performance Trends . . . . . . . . . . . . . . . . . . 115
4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
5 Integrated Capacity Utilization and Scheduling 121
5.1 Integrated Capacity Utilization and Scheduling Model . . . . . . . . . . . . . 122
5.1.1 Model Presentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
5.1.2 Model of Scheduling Interventions . . . . . . . . . . . . . . . . . . . . 126
5.1.3 Queue Length Reduction Constraints . . . . . . . . . . . . . . . . . . 130
5.2 Iterative Solution Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
5.2.1 Deterministic Queue Dynamics . . . . . . . . . . . . . . . . . . . . . 131
5.2.2 Collinearity Assumption . . . . . . . . . . . . . . . . . . . . . . . . . 132
5.2.3 A Bi-level Iterative Solution Algorithm . . . . . . . . . . . . . . . . . 133
5.2.4 Size of the Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 137
5.3 Computational Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
5.3.1 Convergence of the Iterative Algorithm . . . . . . . . . . . . . . . . . 138
5.3.2 Optimal Schedules and Delays . . . . . . . . . . . . . . . . . . . . . . 141
5.3.3 Sensitivity of Displacement to Queue Length Targets . . . . . . . . . 145
5.4 Benefits of Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
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6 Inter-airline Equity and Collaboration in Scheduling Interventions 153
6.1 Motivation ....... ................................. 154
6.2 Multi-criteria Modeling Architecture . . . . . . . . . . . . . . . . . . . . 156
6.2.1 Performance Attributes . . . . . . . . . . . . . . . . . . . . . . . . 157
6.2.2 Lexicographic Modeling Approach . . . . . . . . . . . . . . . . . . 159
6.2.3 Solution Architecture . . . . . . . . . . . . . . . . . . . . . . . . . 162
6.3 A Theoretical Discussion on Inter-airline Equity . . . . . . . . . . . . . . 166
6.3.1 Cases of Joint Maximization of Efficiency and Equity . . . . . . . 166
6.3.2 Cases of Efficiency/Equity Trade-off . . . . . . . . . . . . . . . . . 174
6.4 Mechanisms for Airport Scheduling Interventions . . . . . . . . . . . . . 177
6.5 Computational Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
6.5.1 Inter-Airline Equity: The ESM-IN . . . . . . . . . . . . . . . . . . 181
6.5.2 Network Connectivities: The ESM-SN . . . . . . . . . . . . . . . 184
6.5.3 Credit Allocation: The ECSM-SN . . . . . . . . . . . . . . . . . . 188
6.5.4 Sum m ary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190
6.6 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
7 Conclusion 195
7.1 Summary of Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196
... 7.2 Practical Implications. . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
7.3 Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
A Airport Diagrams 207
B Tail Number Reconstruction 211
C Scheduling Interventions at LGA 215
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