Optimization of Airport Terminal-Area Air Traffic Operations under Uncertain Weather Conditions ...

158 Pages·2011·5.94 MB·English

Optimization of Airport Terminal-Area Air Traffic
Operations under Uncertain Weather Conditions
by
Diana Michalek Pfeil
B.A., University of California at Berkeley (2004)
Submitted to the Sloan School of Management
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Operations Research
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
June 2011
c Massachusetts Institute of Technology 2011. All rights reserved.
(cid:13)
Author ..............................................................
Sloan School of Management
April 26, 2011
Certified by..........................................................
Hamsa Balakrishnan
Assistant Professor of Aeronautics and Astronautics
Thesis Supervisor
Accepted by.........................................................
Dimitris Bertsimas
Co-Directory, Operations Research Center
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Optimization of Airport Terminal-Area Air Traffic
Operations under Uncertain Weather Conditions
by
Diana Michalek Pfeil
Submitted to the Sloan School of Management
on April 26, 2011, in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy in Operations Research
Abstract
Convective weather is responsible for large delays and widespread disruptions in
the U.S. National Airspace System, especially during summer. Although Air Traffic
Flow Management algorithms exist to schedule and route traffic in the face of disrup-
tions, they require reliable forecasts of airspace capacity. However, there exists a gap
between the spatial and temporal accuracy of aviation weather forecasts (and existing
capacity models) and what these algorithms assume. In this thesis we consider the
problem of integrating currently available convective weather forecasts with air traffic
management in terminal airspace (near airports).
We first demonstrate how raw convective weather forecasts, which provide deter-
ministic predictions of the Vertically Integrated Liquid (the precipitation content in
a column of airspace) can be translated into reliable and accurate probabilistic fore-
casts of whether or not a terminal-area route will be blocked. Given a flight route
through the terminal-area, we apply techniques from machine learning to determine
the probability that the route will be open in actual weather.
This probabilistic route blockage predictor is then used to optimize terminal-area
operations. We develop an integer programming formulation for a 2-dimensional
model of terminal airspace that dynamically moves arrival and departure routes to
maximize expected capacity. Experiments using real weather scenarios on stormy
days show that our algorithms recommend that a terminal-area route be modified
30% of the time, opening up 13% more available routes during these scenarios. The
error rate is low, with only 5% of cases corresponding to a modified route being
blocked while the original route is in fact open. In addition, for routes predicted
to be open with probability 0.95 or greater by our method, 96% of these routes are
indeed open (on average) in the weather that materializes.
Inthefinalpartofthethesiswe consider morerealistic modelsofterminalairspace
routing and structure. We develop an A*-based routing algorithm that identifies 3-D
routes through airspace that adhere to physical aircraft constraints during climb and
descent, are conflict-free, and are likely to avoid convective weather hazards. The
proposed approach is aimed at improving traffic manager decision-making in today’s
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operational environment.
Thesis Supervisor: Hamsa Balakrishnan
Title: Assistant Professor of Aeronautics and Astronautics
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Acknowledgments
My most sincere thank you goes to my advisor Hamsa Balakrishnan, for helping
me stumble upon and define research problems, for honest and helpful feedback, for
supporting and encouraging my research path, and all the proof-reading and input
of research write-ups. It’s rare to find a advisor so energetic and enthusiastic about
research, and I feel fortunate to have been able to soak up the excitement and keep
myself interested and motivated.
Iwouldalsoliketothankmyothertwo thesiscommitteemembers CindyBarnhart
and Amedeo Odoni for their interest, plentiful questions, feedback, and guidance. I
thank Daniel Delahaye for being a fantastic host during my research visit to ENAC,
and Andreas Schulz for advising me during my first year at the ORC. I was lucky
to TA for Arnie Barnett, and am thankful for his advice and insights into life and
statistics, and for his incredible sense of humor.
Obtaining data is (unfortunately) one of the greatest challenges of doing applied
research. I thank Marilyn Wolfson and the MIT Lincoln Lab Weather Sensing group
for kindly providing me with access to CIWS weather data. In particular I’d like to
thank Rich DeLaura and Mike Matthews for answering my (plentiful) questions, for
the informative and helpful conversations, and for their enthusiasm for my research.
I’m thankful to (the faculty, students, staff, and environment at) the ORC for
the passion and love I developed for all things operations research. We really have
a fascinating, fun, and almost all-encompassing field, and it’s been a thrill to learn
about some of my now-favorite topics such as primal-dual algorithms.
To students who became friends at my two homes at MIT, the ORC and ICAT
labs: thank you for your advice, research (and life) discussions, encouragement, and
senseofhumor. ThanksinparticulartoJasonAcimovic, DougFearing,ClaireCizaire,
Ioannis Simaiakis, Nikolas Pyrgiotis, and Lishuai Li for your friendship. To friends
outside the ORC, I’d especially like to thank Lisa Messeri, Abby Spinak, and Michael
Baym for the climbing and outdoor adventures, which rank among my best times
during grad school.
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Finally, I’d like to thank my family: my mother Lubica Michalek for always
believing in my abilities and decisions, and calling constantly to talk; my father Peter
Michalek for always emphasizing my education, and encouraging and supporting my
love of math; I could not ask for more brave and loving parents. To my little sista
Lillian Michalek, for her love and growing friendship. I’d also like to thank my
parents-in-law Garry and Kathy Pfeil for their generosity and kindness.
Most importantly, I’d like to thank my husband and favorite collaborator Greg
Pfeil for among countless other things, being there for me throughout my PhD, from
the stresses of studying for quals and generals to the elation of having research pub-
lished and defending my thesis. Thank you for your cheer, sense of humor, and love.
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Contents
1 Introduction 17
1.1 The causes and impacts of air traffic delay . . . . . . . . . . . . . . . 18
1.2 Background and Related Literature . . . . . . . . . . . . . . . . . . . 20
1.2.1 The National Airspace System . . . . . . . . . . . . . . . . . . 20
1.2.2 Air Traffic Flow Management Models . . . . . . . . . . . . . . 23
1.2.3 Dynamic Airspace Configuration . . . . . . . . . . . . . . . . 24
1.2.4 Convective Weather Forecasts . . . . . . . . . . . . . . . . . . 25
1.2.5 Validation of Aviation Weather Forecasts . . . . . . . . . . . . 26
1.2.6 Capacity Estimation . . . . . . . . . . . . . . . . . . . . . . . 27
1.2.7 Integration of aviation weather forecasts with ATFM . . . . . 28
1.3 Contributions of thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 28
1.3.1 Probabilistic model of route robustness . . . . . . . . . . . . 29
1.3.2 Model for terminal-area dynamic airspace . . . . . . . . . . . 30
1.3.3 Identification of realistic 3D airspace trajectories for tactical
planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
1.3.4 Validation with actual weather conditions . . . . . . . . . . . 32
1.4 Organization of thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2 Convective weather forecasts for traffic flow management 33
2.1 Lincoln Lab convective weather forecast . . . . . . . . . . . . . . . . 33
2.2 Pixel-based evaluation of CIWS forecast . . . . . . . . . . . . . . . . 35
2.2.1 Evaluation of skill scores . . . . . . . . . . . . . . . . . . . . . 35
2.2.2 Distribution of actual weather given forecast . . . . . . . . . . 36
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2.3 Forecast objectives from an operational perspective . . . . . . . . . . 39
2.4 Model for a route-based forecast . . . . . . . . . . . . . . . . . . . . . 41
2.4.1 Definition of open route . . . . . . . . . . . . . . . . . . . . . 41
2.4.2 Terminal airspace setup . . . . . . . . . . . . . . . . . . . . . 41
2.4.3 Dynamic weather grid . . . . . . . . . . . . . . . . . . . . . . 42
2.4.4 Identifying robust routes through terminal airspace . . . . . . 44
2.5 Generation of data sets . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.5.1 Selection of weather scenarios . . . . . . . . . . . . . . . . . . 44
2.5.2 Selection of routes . . . . . . . . . . . . . . . . . . . . . . . . 45
2.5.3 Details of route dataset . . . . . . . . . . . . . . . . . . . . . . 49
2.5.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3 Prediction of robust routes through terminal airspace 55
3.1 Features for route blockage prediction . . . . . . . . . . . . . . . . . . 56
3.2 Identifying robust routes using individual features . . . . . . . . . . . 58
3.2.1 The conditional probability of route blockage . . . . . . . . . . 58
3.2.2 Comparison of results across features . . . . . . . . . . . . . . 60
3.2.3 Results across flight direction and planning horizon . . . . . . 62
3.3 Feature selection using mutual information . . . . . . . . . . . . . . . 64
3.4 Classification algorithms for route availability . . . . . . . . . . . . . 66
3.4.1 Introduction to supervised learning and classification . . . . . 67
3.4.2 Classification training objectives . . . . . . . . . . . . . . . . . 68
3.4.3 Classification of route blockage . . . . . . . . . . . . . . . . . 69
3.5 Classification Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
3.5.1 Results for Ensemble of SVMs Classifier . . . . . . . . . . . . 72
3.5.2 Sensitivity of classifier to inner radius R . . . . . . . . . . . . 74
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3.5.3 Sensitivity of classifier to wiggle room B . . . . . . . . . . . . 75
3.5.4 Results for Weighted Random Forest classifier . . . . . . . . . 77
3.5.5 Two more classifiers . . . . . . . . . . . . . . . . . . . . . . . 78
3.6 Probabilistic prediction of route availability . . . . . . . . . . . . . . 79
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3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4 Dynamic Reconfiguration of Terminal Airspace 85
4.1 Motivation for dynamic terminal airspace . . . . . . . . . . . . . . . . 86
4.1.1 Terminal airspace structure and air traffic constraints . . . . . 86
4.1.2 Terminal airspace operations during convective weather . . . . 87
4.1.3 Comparison of terminal and enroute Dynamic Airspace Config-
uration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.2 Terminal-area DAC model setup . . . . . . . . . . . . . . . . . . . . . 91
4.2.1 Terminal airspace sectorization model . . . . . . . . . . . . . . 91
4.2.2 Route robustness model . . . . . . . . . . . . . . . . . . . . . 92
4.3 Model 1: Dynamic terminal routes . . . . . . . . . . . . . . . . . . . 93
4.3.1 Algorithm Description . . . . . . . . . . . . . . . . . . . . . . 93
4.3.2 Algorithm Analysis . . . . . . . . . . . . . . . . . . . . . . . . 94
4.3.3 Stability of route selection . . . . . . . . . . . . . . . . . . . . 98
4.4 Model 2: Dynamic terminal routes with renegotiation of sector bound-
aries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
4.4.1 Integer programming formulation . . . . . . . . . . . . . . . . 101
4.4.2 Computation of model solution . . . . . . . . . . . . . . . . . 105
4.4.3 Analysis of results for fixed parameter settings . . . . . . . . . 106
4.4.4 Analysis of results as objective function varies . . . . . . . . . 111
4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
5 3D routing in terminal airspace 115
5.1 Constraints for realistic terminal airspace trajectories . . . . . . . . . 116
5.2 Blank slate design of terminal airspace . . . . . . . . . . . . . . . . . 117
5.2.1 Previous Research . . . . . . . . . . . . . . . . . . . . . . . . 117
5.2.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . 118
5.2.3 Solution Approach . . . . . . . . . . . . . . . . . . . . . . . . 118
5.2.4 IP formulation for selection of optimal terminal fixes . . . . . 119
5.2.5 A∗–1: algorithm for 3D conflict-free route identification . . . . 121
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5.3 Results for blank slate terminal design . . . . . . . . . . . . . . . . . 123
5.3.1 Chicago airspace configuration . . . . . . . . . . . . . . . . . . 124
5.3.2 Demand data . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
5.3.3 Altitude profiles . . . . . . . . . . . . . . . . . . . . . . . . . . 126
5.3.4 Results: optimal terminal fixes . . . . . . . . . . . . . . . . . 128
5.3.5 Results: conflict-free 3D routes . . . . . . . . . . . . . . . . . 130
5.4 3D routes through convective weather . . . . . . . . . . . . . . . . . . 132
5.4.1 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
5.4.2 A∗–2: Further modifications to the A∗ algorithm . . . . . . . . 133
5.4.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . 134
5.4.4 Caveats and future directions . . . . . . . . . . . . . . . . . . 136
5.4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
6 Conclusions 139
6.1 Review of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
6.2 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
A Interviews with Air Traffic Controllers 145
B Pseudocode for 3D route planning 149
B.1 Details of the A* algorithm . . . . . . . . . . . . . . . . . . . . . . . 149
B.2 Pseudocode for A∗–2: IS SINK and PRUNE . . . . . . . . . . . . . . 150
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