Measuring Airport Efficiency with Fixed Asset Utilization to Minimize Airport Delays

357 Pages·2017·4.49 MB·English

by  Scott D. Widener

University of Miami
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2010-10-22
Measuring Airport Efficiency with Fixed Asset
Utilization to Minimize Airport Delays
Scott D. Widener
University of Miami, [email protected]
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UNIVERSITY OF MIAMI

MEASURING AIRPORT EFFICIENCY WITH FIXED ASSET UTILIZATION TO
MINIMIZE AIRPORT DELAYS

By

Scott D. Widener

A DISSERTATION

Submitted to the Faculty
of the University of Miami
in partial fulfillment of the requirements for
the degree of Doctor of Philosophy

Coral Gables, Florida

December 2010
©2010
Scott D. Widener
All Rights Reserved
UNIVERSITY OF MIAMI

A dissertation submitted in partial fulfillment of
the requirements for the degree of
Doctor of Philosophy

MEASURING AIRPORT EFFICIENCY WITH FIXED ASSET UTILIZATION TO
MINIMIZE AIRPORT DELAYS

Scott D. Widener

Approved:

________________ _________________
Murat Erkoc, Ph.D. Terri A. Scandura, Ph.D.
Assistant Professor of Dean of the Graduate School
Industrial Engineering

________________ _________________
Shihab Asfour, Ph.D. Joseph Sharit, Ph.D.
Professor of Research Professor of
Industrial Engineering Industrial Engineering

________________
Howard Gitlow, Ph.D.
Professor of
Management Science
WIDENER, SCOTT D. (Ph.D., Industrial Engineering)
Measuring Airport Efficiency with Fixed Asset (December 2010)
Utilization to Minimize Airport Delays

Abstract of a dissertation at the University of Miami.

Dissertation supervised by Professor Murat Erkoc.
No. of pages in text. (344)

Deregulation of the airlines in the United States spawned a free-for-all system
which led to a variety of agents within the aviation system all seeking to optimize
their own piece of the aviation system, and the net result was that the aviation
system itself was not optimized in aggregate, frequently resulting in delays.
Research on the efficiency of the system has likewise focused on the individual
agents, primarily focusing on the municipalities in an economic context, and
largely ignoring the consumer. This paper develops the case for a systemic
efficiency measurement which incorporates the interests of the airlines and the
consumers with those of the airport operating municipalities in three different
Data Envelopment Analysis (DEA) models: traditional Charnes-Cooper-Rhodes
and Banker-Charnes-Cooper models, and a Directional Output Distance Function
model, devised and interpreted using quality management principles. These
models were combined to allow the resulting efficiencies of the operating
configurations of the given airport to predict the efficiency of the associated
airport. Based upon regression models, these efficiency measurements can be
used as a diagnostic for improving the efficiency of the entire United States
airspace, on a systemic basis, at the individual airport configuration level. An
example analysis using this diagnostic is derived in the course of the
development and description of the diagnostic and two additional case studies
are presented.
TABLE OF CONTENTS

Page

LIST OF FIGURES………………………………………………………………… v

LIST OF TABLES ........................................................................................... viii

Chapter

1 INTRODUCTION AND LITERATURE REVIEW……………………….. 1
1. Introduction…………………………………………………………..…. 2
2. Contextual Setting……………………………………………………... 9
3. Aviation Efficiency Literature Review………………………………... 15
4. Shaping and Refining the Context………………………………….... 22
5. Creating a Case for a New Paradigm for Efficiency Analysis…...... 35

2 MODEL AND EFFICIENCY ANALYSIS…………………………………. 44
6. A New Model for Efficiency Analysis…………...…………………….. 45
7. Sources of Data and Model Refinement…………...………………… 63
8. Mathematical Framework of the Models for Efficiency Assessment 92

3 APPLICATIONS AND ANALYSES………………………………………. 103
9. Methods and Applications……………………………...……………... 104
10. General Results of the Efficiency Models………………………..…. 127
11. Statistically Modeling Total Airport Results………………………… 146

4 DIAGNOSTIC DEVELOPMENT…………………………………………..165
12. Background for an Airport Diagnostic………………………………...166
13. Existing Diagnostic Shortcomings…………………………………….170
14. Creating a New Airport Operations Diagnostic……………………... 181
15. Creating a New Airport Operations Diagnostic—Total Airport
Candidates……………………………………………………………… 183
16. Creating a New Airport Operations Diagnostic—Configuration
Candidates……………………………………………………………… 202
17. Creating a New Airport Operations Diagnostic—Weighting and
Sorting…………………………………………………………………... 208
18. Creating a New Airport Operations Diagnostic—Selections
Based Upon Policy…………………………………………………..... 213
19. Model Assessment…………………………………………………….. 220

5 CASE STUDIES…………………………………………………………… 227
20. Case Study—Extending the Policy Example at JFK………………..228

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21. Case Study—A Historical Look at Shifting the Efficient Frontier
Using Non-Capacity Investment at ATL……………………………..233

6 CONCLUSIONS…………………………………………………………….247
22. Conclusions……………………………………………………………..248
23. Future Work…………………………………………………………….. 250

REFERENCES…………………………………………………………………….. 252

APPENDIX 1: PASSENGERS BOARDED AT THE TOP 50 U.S.
AIRPORTS……………………………………………………….. 258

APPENDIX 2: THE ASPM 77-OPSNET 45-OEP 35 CONTENT
COMPARISONS…………………………………………………. 261

APPENDIX 3: THE DATA TRACKED IN ASPM……………………………….. 263

APPENDIX 4: THE CARRIERS TRACKED IN ASPM………………………… 268

APPENDIX 5: THE DIRECTIONAL OUTPUT DISTANCE MODEL DATA…. 269

APPENDIX 6: THE BCC MODEL DATA………………………………………... 274

APPENDIX 7: THE CCR MODEL DATA……………………………………….. 279

APPENDIX 8: THE SORTED CONFIGURATION CANDIDATE DATA……... 284

APPENDIX 9: THE REVISED SORTED CONFIGURATION CANDIDATE
DATA……………………………………………………………… 298

APPENDIX 10: THE CUT CONFIGURATION CANDIDATE
CONFIGURATION DATA…….……………………………….. 316

APPENDIX 11: THE WEIGHTED CONFIGURATION CANDIDATE DATA… 332

iv
LIST OF FIGURES

Figure 1: Percentage of Delayed Operations at the OPSNET 45 Airports by
Year……………………………………………………………………… 25
Figure 2: Total Operations Count at the OPSNET 45 Airports by Year……… 25
Figure 3: Satellite View of ATL……………………………………………………. 54
Figure 4: Satellite View of MSP…………………………………………………... 55
Figure 5: Satellite View of BOS…………………………………………………… 56
Figure 6: ATL 26R, 27L, 28 | 26L, 27R (52.2 Percent of Total Operations)…. 59
Figure 7: MSP 12L, 12R | 12L, 12R, 17 (39.4 Percent of Total Operations)… 60
Figure 8: BOS 22L, 27 | 22L, 22R (14.3 Percent of Total Operations)……….. 61
Figure 9: The ASPM Download Interface………………………………………… 65
Figure 10: A Larger View of the Download Box………………………………… 66
Figure 11: The Prepared Download Box………………………………………… 69
Figure 12: Primary Configuration Identification at ATL in 2008……………….. 81
Figure 13: Primary Configuration Identification at MSP in 2008………………. 82
Figure 14: Primary Configuration Identification at BOS in 2008………………. 83
Figure 15: The Download Box Showing Weather Condition Options………… 85
Figure 16: Primary Configurations at ATL in 2008 Revised for VFR-IFR
Distinction………………………………………………………………. 87
Figure 17: Primary Configurations at MSP in 2008 Revised for VFR-IFR
Distinction………………………………………………………………. 88

v
Figure 18: Primary Configurations at BOS in 2008 Revised for VFR-IFR
Distinction………………………………………………………………. 89
Figure 19: The Position of RWY 9L/27R at OAK………………………………. 113
Figure 20: A Simple Input-Output Plot……………………………………………119
Figure 21: Directional Output Distance Model Plot…………………………….. 122
Figure 22: Combined Data for the Directional Output Distance Model………. 129
Figure 23: Combined Data for the BCC Model…………………………………. 131
Figure 24: Combined Data for the CCR Model…………………………………. 134
Figure 25: Combined Data for the Derived Scale Efficiencies………………... 136
Figure 26: BetaK Values of All Primary Configurations from 2000-2008……..141
Figure 27: BCC Values of All Primary Configurations from 2000-2008……….142
Figure 28: CCR Values of All Primary Configurations from 2000-2008……….143
Figure 29: Scale Efficiency Frequencies for the Primary Configurations
2000-2008………………………………………………………………..144
Figure 30: The Residual Plot from the BetaK Regression………………………157
Figure 31: The Residual Plot from the BCC Regression………………………..160
Figure 32: The Residual Plot from the CCR Regression………………………..163
Figure 33: A Schematic of the Diagnostic…………………………………………182
Figure 34: Year-Over-Year Variance Equivalence for Beta-Ks…………………188
Figure 35: Year-Over-Year Variance Equivalence for CCR…………………….189
Figure 36: Graphical Summary of Across Airport Beta-K Variances…………..190
Figure 37: Graphical Summary of Across Airport CCR Variances……………..191
Figure 38: JFK 22L | 22R,31L………………………………………………………230
vi