The Evolution of Passenger Accessibility in the US Airline Industry, 1980-2010
by
James Joseph Jenkins
B.A., Economics and Foreign Affairs
University of Virginia, 2007
Submitted to the Department of Civil and Environmental Engineering
and the Department of Urban Studies and Planning
in partial fulfillment of the requirements for the degrees of
Master of Science in Transportation
MASSCHUETTS NTE
OF TECHNOLOG-Y
and
Master in City Planning
at the
Massachusetts Institute of Technology
September 2011
0 Massachusetts Institute of Technology 2011.
All rights reserved.
Author........
MAR 0 8 2012
LIBRAR IES
ARCHivEs
.......................................................
epart e
of Civil d Environme tal Engineering and Urban Studies and Planning
August 5, 2011
C ertified by .............
.............................
....................................................
Frederick P. Salvucci
Senior Lecturer of Civil and Environmental Engineering
Thesis Supervisor
Accepted by..............
...
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Joseph Ferreira
Chair, MCP Committee
Department of Urban Studies and Planning
Acepted by
....................................
................
.VI.
.....
Hei[di M. Nepf
Chair, Departmental Committee for Graduate Students
Department of Civil and Environmental Engineering
The evolution of passenger accessibility in the U.S. airline industry, 1980-2010
by
James Joseph Jenkins
Submitted to the Department of Civil and Environmental Engineering
and the Department of Urban Studies and Planning on August 5, 2011
in Partial Fulfillment of the Requirements for the Degrees of
Master of Science in Transportation
and
Master in City Planning
ABSTRACT
Since deregulation, passenger air travel and the airline industry as a whole have changed
dramatically. While most previous research has focused on the changes experienced by the
airlines, this thesis seeks to understand the effects of industry changes on passenger accessibility.
This work develops a methodology to quantitatively measure accessibility changes and then
applies the methodology to the Bureau of Transportation Statistics' DB 1B dataset, a 10% sample
of all tickets purchased in the United States. The analysis reviews changes in accessibility based
on 4 measures: a new path quality measure, passenger volume, circuity, and fares at all
continental US Primary and Commercial Service airports.
The results indicate that between 1980 and 2010 accessibility declined in terms of path
quality and circuity, but those declines were offset by accessibility increases in terms of lower
fares which resulted in increased passenger volume. While the primary analysis was aggregated
by FAA airport size classifications, the methodology can also be used at a regional scale. This is
demonstrated through a case study of the 3 largest airports in the Boston region which have
developed symbiotically despite having overlapping catchment areas. The results yield insights
that would otherwise be missed in a higher level analysis and highlight the importance of
regional analysis to understanding passenger accessibility.
The thesis also uses the methodology and data to examine the effects of industry changes
on rural communities by looking at the Essential Air Service program, which subsidizes air
service to rural communities. The analysis found that the program has performed well in terms of
preserving rural air accessibility but that certain program regulations make the program
financially inefficient. Several changes are proposed to improve the program's effectiveness and
value to passengers. Finally, the analysis looks towards the future and the extension of this work
as a foundation for thinking about how to improve passenger accessibility while dealing with the
challenges facing the industry including financial instability, airport congestion, the potential for
partial re-regulation, future changes in the network structure, and potential reduction in EAS
funding.
Thesis Supervisor: Frederick Salvucci
Title: Senior Lecturer, Civil and Environmental Engineering
Thesis Reader: Christopher Zegras
Title: Associate Professor, Urban Studies and Planning
Acknowledgements
This thesis would never have been possible without the help of a number of people who
supported me throughout my time at MIT:
" Fred for your generosity with your time, insight, and wisdom.. .and for all the coffee. I
learned more from your teaching and mentoring than in all my other classes at MIT.
" The MIT Transportation Productivity Project: Mr. Messner, Mr. Shiverick, and
the Speedwell Foundation for your generous financial support. Peter Belobaba for
your feedback on my drafts and for helping me navigate the airline industry. Bill
Swelbar for your insights, data help, and publicity. Kari Hernandez, Robert Powell,
and Paul Lewis for the moral support as we slogged through data and learned about
productivity. Becky Fearing for masterfully guiding and organizing all of us.
" My family for their encouragement and love and for making the long drives to Boston.
* My beautiful wife for more love and support than any man deserves and for your
adventurous spirit, without which I would never have come to MIT.
" God for the innumerable blessings in my life and for giving me the opportunity to
study at MIT.
Contents
1. Introduction
. . . .
1.1. Motivation.
1.2. Research questions
1.3. Methodology
. . .
1.4. Terminology . . . .
1.5.
Structure
. . . . .
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..
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. . . . . . . . . . . . . . . . . . . . .
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.. . . . ... . . . .
.
.-
. . . . . . . . . . . . . . . . . . . . .-
2. History of the US aviation industry, its deregulation, and cha iges in the air
network
2.1. Birth of the US aviation industry and its regulation . . . . . .
2.1.1.
Precursors to deregulation . . . . . . . . . . . . . . ..
2.1.2.
Moving towards deregulation
2.1.3.
Deregulation.... . . . . . . . . . . . . . .
. . . . . . . . . . . . ..
.. . .
2.2. Evolution of the network . . . . . . . . . . . . . . . . . . . . .
2.3. Airports today
. . . . . . . . . . . . . . . . . . . . . . . . ..
2.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . ..
3. A brief review of literature on air connectivity and accessibility
3.1.
Measures of connectivity . . . . . . . . . . . . . . . . . . . . .
3.2. M odel inputs
. . . . . . . . . . . . . . . . . . . . . . . . . . .
Contents
3.3. Detailed review of models . . . . . . . . . . . . . . . . . . . . . . . . . . . .
45
3.3.1.
Weighted connectivity
3.3.2.
Weighted connectivity number
. . . . . . . . . . . . . . . . . . . . .
47
3.3.3.
N etscan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
48
3.3.4.
Capacity and destination importance.......... . . . . .
50
3.3.5.
Airline reservation data based measure . . . . . . . . . . . . . . . . .
52
3.4. C onclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
54
. . . . . . . . . . . . . . . . . . . . . . . . . .
. . ..
4. Data description and methodology
4.1.
55
D ata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2. Data limitations
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3. Review of Quality of Service Models
4.4. Methodology
45
55
57
. . . . . . . . . . . . . . . . . . . . . .
58
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
62
4.4.1.
Inferring connections.......
4.4.2.
Weighting connections.
4.4.3.
O-D market path quality
. . . . . . . . . . . . . . . . . . . . . . . .
65
4.4.4.
Index measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
65
4.4.5.
Passenger based measure . . . . . . . . . . . . . . . . . . . . . . . . .
66
4.4.6.
Destination based measure . . . . . . . . . . . . . . . . . . . . . . . .
67
4.5. C onclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
68
..
......
...............
. .. . . .. .
.. . .. . .
. . ..
62
. . ..
63
5. Data analysis
69
5.1. Path quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
70
5.2.
Changes in passengers
74
5.3.
Changes in destinations served..... . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . .
82
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
86
5.5. Fares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
92
5.4. Circuity.... . . . .
5.6. C onclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
Contents
105
6. A closer look: Airport specific impacts
6.1.
Winners and losers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
6.1.1.
Large hubs
6.1.2.
Medium hubs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
6.1.3.
Sm all hubs
6.1.4.
N on-hubs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
6.1.5.
Commercial Service
6.1.6.
Sum m ary
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
. . . . . . . . . . . . . . . . . . . . . . . . . . . 120
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
6.2. Essential Air Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
6.3. Boston Logan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
6.4. Sum m ary
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
142
7. Conclusion
7.1.
Summary of results and general conclusions...... . . . . . .
.. . .. . . .. . ..
7.2. The future............
. . . .
. . . . ..
142
. . . . . . . ..
149
7.2.1.
Low fares and rising costs . . . . . . . . . . . . . . . . . . . . . . . . 149
7.2.2.
Service to small communities
7.2.3.
Re-regulation
7.2.4.
Network structure
. . . . . . . . . . . . . . . . . . . . . . 154
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
7.3. Future work and final remarks . . . . . . . . . . . . . . . . . . . . . . . . . . 159
A. Sensitivity Analysis
162
B. Fare scatterplots
164
C. Non-hub path quality quartiles
169
D. Non-hub destinations quartiles
172
E. Commercial Service path quality quartiles
175
Contents
F. Commercial Service destinations quartiles
176
G. EAS communities no longer receiving subsidy
177
H. Bibliography
178
List of Figures
2.1. Domestic passengers 1940-2010[6] . . . . . . . . . . . . . . ...
2.2. Eastern Airlines route map (1947) and United Airlines route map (1940)
2.3. Point-to-point (left) Hub-and-spoke (right).....
. . ..
2.4. Load factor 1932-2010[6] . . . . . . . . . . . . . . . . . . .
2.5. Flights needed for point-to-point (left) and hub-and-spoke (right)
2.6. S curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.7. US Primary and Commercial Service airports
. . . . . . .
3.1. Hub centrality . . . . . . . . . . . . . . . .
.
3.2. Two-leg Connectivity . . . . . . . . . . . .
. . . . . . . . . .
41
3.3. Cost-based accessibility from Grubesic and Zook[39]
. . . . . . . . . .
53
Average path quality by airport type . . . . . . . . .
. . . . . . . . . .
71
5.2. Passenger-weighted path quality . . . . . . . . . . . .
. . . . . . . . . .
73
5.3. US domestic passengers 1940-2010 [6] . . . . . . . . .
. . . . . . . . . .
75
5.4. Passengers by airport type (10% sample) . . . . . . .
. . . . . . . . . .
76
5.5.
1980 Lorenz Curve . . . . . . . . . . . . . . . . . . .
. . . . . . . . . .
77
5.6.
Gini Coefficient 1969-1993 [62]
. . . . . . . . . . . .
. . . . . . . . . .
79
5.1.
.
5.7. Equivalent non-stop passengers by airport type (10% sampl e)
5.8. Average number of destinations by airport type . . .
5.9.
.
.
.
.
.
.
.
.
4 1
. . . . . . . .
81
. . . . . . . . . .
83
Average equivalent non-stop destinations by airport type . . .
List of Figures
5.10. Passenger-weighted circuity. . . . . . . . . . . .
..
5.11. Hub flights by carrier 167] . . . . . . . . . . . . . . . .
5.12. Average circuity . . . . . . . . . . . . . . . . . . . . . .
5.13. Average inflation-adjusted fares by airport type . . . .
5.14. Average passenger weighted market distance by airport type
5.15. Large hub distance histograms
. . . . . . . . . . . . .
5.16. Medium hub distance histogram . . . . . . . . . . . . .
5.17. Small hub distance histogram . . . . . . . . . . . . . .
5.18. Non-hub distance histogram . . . . . . . . . . . . . . .
5.19. Commercial Service distance histogram .
5.20. Average fares by mileage (1980-2010)
. . . . ..
. . . . . . . . . .
6.1. Large hub path quality quartiles
. . . . . . . . 107
6.2. Large hub destination quartiles
. . . . . . . . 109
6.3. Large hub accessibility . . . . . . .
. . .
110
6.4. Medium hub path quality quartiles
. . . . . . . . 112
6.5. Medium hubs destinations quartiles
. . . . . . . . 113
6.6. Medium hub accessibility.....
. . . . . . . . 114
6.7. Small hub path quality quartiles
. . . . . . . . 115
6.8. Small hub destination quartiles
. ... . . . . . 116
6.9. Small hub accessibility . . . ...
. . . . . . . . 117
6.10. Non-hub accessibility . . . . . . . .
. . . . . . . . 119
6.11. Commercial Service accessibility.
.
... . . . . . . 120
6.12. US accessibility (2010) . . . . . . .
. . . . . . . . 122
. . . . . . . . . . . .
. . . . . . . . 125
6.14. Boston Logan area map [11] . . . .
. . . . . . . . 134
6.13. EAS airports
6.15. BOS, MHT, PVD passenger share [9 . . . . . . . . . . . . . . . . . . . . . . 136
List of Figures
7.1.
US airline system-wide profit margin 1947-2009 [6]
B.1. Large hub, fares (not inflation adjusted) vs distance
. . . . . . . . . . . . . . 151
. . . . . . . . . . . . . 164
B.2. Medium hub, fares (not inflation adjusted) vs market distance . . . . . . . . 165
B.3. Small hub, fares (not inflation adjusted) vs market distance
. . . . . . . . . 166
B.4. Non-hub, fares (not inflation adjusted) vs market distance . . . . . . . . . . 167
B.5. Commercial Service, fare (not inflation adjusted) vs market distance
C.1. Non-hub path quality quartiles
. . . . 168
. . . . . . . . . . . . . . . . . . . . . . . . . 171
D.1. Non-hub destinations quartiles.... . .
. . . . . . . . . . . . . . . . . . . 174
E.1. Commercial Service path quality quartiles . . . . . . . . . . . . . . . . . . . 175
F.1. Commercial Service destinations quartiles . . . . . . . . . . . . . . . . . . . 176
List of Tables
2.1.
Enplanements by airport type 2009 . . . . . . .
3.1.
Model inputs of reviewed studies, adapted from Burghouwt and Redondi [20]
3.2.
Detail of a subset of model inputs [20] . . . . . . . . . . . . . . . . . . . . .
4.1.
Sample data extract
4.2.
CAB QSI weights [49]
4.3.
Brewer QSI weights [15]
. . . . . . . .
. . . . . . .
. . . . . .
4.4. DIIO QSI weights[45] . . . . . . . .
4.5.
Emrich and Harris QSI weights [35]
4.6.
Primary weights used . . . . . . . .
4.7. Sensitivity analysis (high range)
4.8.
Sensitivity analysis (low range)
5.1.
Average excess miles flown . . . . . . . . . . . . . . . . . . . . .
91
5.2.
Linear regression statistics on O-D markets (inflation adjusted)
98
5.3.
Summary of accessibility measures
6.1.
EAS Airports (as of May 1, 2010) [34]
126
6.2.
Distance to nearest jet service [33]
128
. . . . . . . . . . . . . . . .
.
103
6.3. Average passenger-weighted path quality
129
6.4. Passengers . . . . . . . . . . . . . . . . .
130
List of Tables
6.5. Equivalent non-stop passengers . . . . .
131
6.6. Number of destinations . . . . . . . . . .
131
. . . .
132
Average fares (inflation adjusted) . . . .
132
6.7. Equivalent non-stop destinations
6.8.
6.9. Passengers at BOS, MHT, and PVD
135
6.10. Average passenger-weighted path quality and percentage connecting .
137
6.11. Number of destinations . . . . . . . . . . . . . . . . . . . . . . . . . .
138
6.12. Average fares (inflation adjusted) . . . .
138
Passenger-weighted accessibility averages 1980-2010 . . . . . . . . . . . . .
145
7.2. System total operating expense in cents per available seat mile [31 . . . . .
152
7.1.
A.1. Path Quality sensitivity analysis. . . . . . . . . .
. . . . . . . . . ...
G.1. EAS communities no longer receiving subsidy [34] . . . . . . . . . . . . . .
163
177
1. Introduction
You can't deregulate this industry. You're going to wreck it.
Robert L. Crandall, CEO American Airlines, 1977.
Deregulation will be the greatest thing to happen to the airlines since the jet engine.
-
Richard Ferris, CEO United Airlines, 1976.
While Crandall and Ferris disagreed about how deregulation would affect aviation, they
would almost certainly agree about one thing; the aviation industry has changed dramatically since it was deregulated in 1978. Since then, airlines have experienced large swings
in profitability and have altered many of their operating strategies to adapt to changing
markets including shifts towards hub-and-spoke networks, increased use of discount fares,
greater reliance on regional jets and code sharing, etc. Some analysts believe that these
changes have been tremendously beneficial for consumers.
For example, Morrison and
Winston found that as of 1999, net benefits to travelers from deregulation were in excess
of $20 billion annually when accounting for lower fares and the convenience value of increased frequency.[53] In contrast, Richards claims the lower fares are greatly overstated
and Kuttner argues that increased frequency does not have as large an economic value as
Morrison and Winston suggest.[63, 46] Others question the financial sustainability of lower
1. Introduction
fares which have led to loss of airline profitability and insufficient funding to purchase new
aircraft.
As legacy airlines have attempted to achieve profitability and compete with low cost
carriers (LCCs) they have used discounted fares to attract more passengers and in the hopes
of raising revenues. As Morrison and Winston note, this benefits consumers because they
can travel more inexpensively than before, at least in the short-run. The long-run effects
are less evident. While passengers clearly prefer lower fares, airlines have fared unevenly
using this strategy-manifest in the perpetual profit and loss cyclicality that continues to
grow in amplitude.[8, 91 The airlines experienced a particularly difficult period in the 2000s
with bankruptcies at most of the major legacy carriers that affected creditors, investors,
and airline employees who experienced salary cutbacks, layoffs, and changes in work rules.
Deregulation was also supposed to benefit passengers by increasing competition in the
market. This was to take the form of competition on service quality and, most prominently,
economic competition. When reflecting on deregulation, former Civil Aeronautics Board
(CAB) chairman, Alfred Kahn, suggested that the predicted increased competition from a
free-market has been smaller than expected because of the large economies of scale associated with the shift towards hub-and-spoke networks. A hub-and-spoke network structures
produces a competitive advantage which has allowed incumbent operators to stymie new
entrants.[44, 461 Kahn wrote this in 1988. Since then, LCCs have gained significant market share by adopting different business models than legacy carriers, such as only entering
routes that the LCC views as profitable, which results in the competitive market seen today.
The changing environment has also led to changing service quality for passengers. Kuttner addresses one component of service quality; while acknowledging the increased number
of destinations available as a result of the rise in hubbing by airlines, he notes that this
change came at the cost of greater reliance on connecting itineraries instead of nonstop
and a decreased number of airports with scheduled service.[461 In addition to more connections, load factors, the percentage of seats full, have increased considerably since 2001.
1. Introduction
While higher load factors make it economically feasible for airlines to offer lower fares,
they typically mean a less enjoyable passenger experience, and, as Ben Abda points out,
when airlines are moving roughly the same number of passengers with higher load factors,
it means decreased flight frequency as well as higher prices on last minute travel.[9] The
increased security measures following 9/11 have also diminished the attractiveness of flying.
The hassle of having to arrive at the airport 90 minutes early for domestic flights and the
aggravation of standing in line and being scanned have made air travel less convenient and
have almost certainly lost customers, particularly in the short-haul market where driving,
bus service, or railroads are feasible alternatives. Lastly, the increased utilization of hubbing
may be contributing to increasing congestion at airports and in heavily trafficked airspace
which results in the passenger delay seen in many major airports.
Some of the changes highlighted above were primarily influenced by deregulation, while
others, like profit cycles, are affected not only by the increased competition that deregulation
made possible, but also by rising jet fuel prices and decreased demand brought about by
global business cycles and events like 9/11, SARS, and the H1N1 outbreak. This thesis
makes a contribution to the understanding of these changes from the passenger's perspective
by exploring what the changes have meant for passenger accessibility and connectivity.
1.1. Motivation
Transportation is an integral part of the US economy that facilitates the exchange of goods,
services, and ideas. Changes in the transportation system can add economic value when
they increase accessibility and connectivity. Improvements in accessibility may take the
form of faster movement or less expensive movement.
The aviation industry has been an important component of US and global economic
growth because it has increased the speed and geographic reach of travel options. Ivy, et
al, found that changes in air connectivity had a positive relationship with the professional
employment levels in a metropolitan area.[42] Irwin and Kasarda reached a similar conclu-
1. Introduction
sion finding that hub location decisions by airlines following deregulation created "winners"
and "losers". They particularly note the areas that were "sub-dominant" like Denver and
Nashville before becoming airline hubs have increased their economic strength due to their
new found integration in the aviation network. They note that previous "dominant" cities
like New York, Miami, and Chicago could be losers in the scenario because of a loss in traffic,
but that these airports have become international gateways which has actually strengthened
their dominance and opened the cities' economies to a larger global market.[41]
In addition to facilitating connectivity and economic growth, the aviation industry itself
is a major driver of the US economy. According to the FAA, in the US, aviation made
up 5.2% of the US economy in 2007, had $1.315 trillion in economic impact and employed
11,512,000 people.[2]
When Congress and industry regulators began discussing the future of airline regulation,
it was argued that the competition of a free-market would be a benefit to both consumers
and airlines. Looking back at the more than 30 years since deregulation, much has changed
in the airline industry-there are now low cost carriers, merged legacy carriers, internet
booking, and greater reliance on hub-and-spoke networks. What does all this mean for
passengers? Are more destinations available to passengers? Are the options available more
or less convenient than there were under regulation? Are fares higher or lower?
Certainly many have argued that the shifts towards hub-and-spoke networks and the
greater use of regional jets has made more destinations available, but does the mere presence
of more destinations via hub connections increase accessibility? Is it possible to measure
the trade-offs between more possible destinations via hubs and fewer destinations available
via nonstop flights? This thesis endeavors to answer these types of questions by evaluating
how connectivity and accessibility have changed in the US from 1980 to 2010 and to analyze
what those changes mean from the passenger's perspective. Moreover, the aviation system is
under growing stress with negative profitability, aging fleets, and growing congestion leading
significant voices to call for changes like partial re-regulation, funding cuts to Essential Air
1. Introduction
Service, and changed regulation of slots at key airports.
The goal of this thesis is to
establish a robust methodology for measuring passenger accessibility to ensure that this
critical dimension can be considered in any change to industry structure.
1.2. Research questions
The purpose of this thesis is to infuse the discussion of deregulation and passenger aviation
with the perspective of consumers that is, passengers. To that end, it will differentiate
between the common unit of analysis connectivity (does a feasible connection exist between
airport pairs) and the more passenger oriented measure, accessibility (do passengers make
use of the connections).
The questions posed in this research are:
" What are the different components of air accessibility?
" How has accessibility changed over the three decades comprised in the data points
1980, 1990, 2000, and 2010?
" What are the benefits or disadvantages of observed changes in accessibility?
" Have the benefits accrued equally across the air network or have certain regions or
types of airports benefited more than others? Have some airports lost accessibility?
" What are the future challenges to accessibility from the passenger perspective?
1.3. Methodology
This thesis makes a contribution to the understanding of these questions by analyzing
accessibility through the use of ticketed passenger data. Most previous studies of connectivity, and sometimes accessibility, use published schedule data to construct feasible
origin-destination (O-D) pairs. From this basis they draw conclusions about changes in the
1. Introduction
connectivity and accessibility of domestic air travel. This approach analyzes what passengers could fly, connectivity, not what they actually did fly, accessibility. Instead of following
that approach, this thesis makes use of data from purchased tickets collected by the US
Department of Transportation. The collected data provide information on the number of
destinations from each origin airport, the number of passengers flying in each O-D market (that is the market for travel between the airport pairs that are the beginning of the
itinerary and the final destination), average fares per O-D market, and average number of
connections per O-D market.
Given the richness of the data set, the work is largely based in quantitative analysis
supplemented with qualitative analysis and case studies. Specifically, the thesis makes use
of descriptive statistics to understand changes in accessibility and it develops a model based
on the Civil Aeronautics Board's Quality of Service Index to objectively measure changes
in accessibility over the years studied. This approach will facilitate not only a historical
review, but also tracking of the industry going forward and the analysis of alternative policy
scenarios.
1.4. Terminology
Flights, tickets, itineraries, legs... the terminology of the aviation industry is used rather
casually both in the academic literature and in the popular. Most people have a general
idea of what these things mean, but for the purpose of precision, their definition as used in
this thesis is given here.
" Flights " Itinerary -
flights are a supply variable. Airlines provide flights to passengers.
when a passenger books themselves trip they have created an itinerary.
An itinerary may be made up of a single flight or it may be comprised of multiple
flights.
* Nonstop itinerary -
a non-stop flight is one in which the passenger takes off at their
20
1. Introduction
origin airport and lands at their final destination airport without any intermediate
stops.
" One-stop itinerary -
a one-stop itinerary is one in which a passenger takes off at
their origin airport and makes one intermediate stop along the way and then proceeds
to the destination airport in the same plane. This type of itinerary is still relatively
common on Southwest Airlines flights, but is rarer on most other carriers. One should
be careful to note that this is distinct from a one-connection itinerary.
" One-connection itinerary - distinct from a one-stop flight, a one-connection itinerary
is one in which the passenger departs their origin destination airport and then changes
planes at an intermediate airport before continuing on to the final destination.
" Double-connection itinerary -
the same as a one-connection itinerary except that
the passenger changes planes at two intermediate airports.
" Coupons -
an itinerary is comprised of ticket coupons. On a non-stop or one-stop
flight a passenger's itinerary will have only one coupon. On the other hand, on a
one-connection itinerary they will have two.
" Load factor -
a flight's load factor is the number of seats filled divided by the total
number of seats on the airplane.
" Circuity -
circuity is a measure of the distance flown on an itinerary divided by the
nonstop distance between the origin and destination. Thus a nonstop flight has a
circuity of 1, while a connection itinerary might have a circuity above 2.
1.5. Structure
This thesis is comprised of seven chapters including this one. Chapter 2 provides a brief
overview of the history of the US aviation industry. The chapter covers the foundations of
1. Introduction
the industry and the initial reasons for industry regulation. It then discusses the changes in
the industry that led to its deregulation in 1978 and finally depicts the changes in network
structure that accompanied deregulation and the status of the network today.
Chapter 3 provides a review and critique of existing literature on connectivity. Specifically, it addresses many of the schedule-based methodologies used to analyze connectivity
and explains why these models are not appropriate for analyzing accessibility from the
perspective of passengers.
Chapter 4 introduces the data set used in the analysis and presents the methodology
for calculating quality-weighted changes which are used in this thesis to model changes in
accessibility.
Chapter 5 provides an analysis of the data using the methodology from Chapter 4 and
addresses changes in the number of passengers flying, how the use of connecting itineraries
has changed, and how many destinations are available to passengers. It then depicts changes
in fares paid by passengers. Finally, as a proxy for changes in convenience of flying, the
chapter discusses the way circuity has changed as a result of hub-and-spoke routing. The
data set does not provide flight time information, making it difficult to precisely analyze
changes in travel times, but measures of connections and circuity will help understand how
travel times have changed.
Chapter 6 takes the aggregate statistics from Chapter 5 and examines the airports with
more granularity to understand how individual airports changed and to determine if there
are any geographic patterns in terms of higher or lower accessibility. The chapter then
presents 2 case studies to demonstrate how these measures of accessibility can be used to
examine airports of different sizes: one on Essential Air Service airports and the other on
Boston-Logan and the surrounding airports.
Chapter 7 concludes the thesis by reviewing the key findings, considers the challenges
ahead like rising operating costs in terms of their impact on accessibility, and proposes
areas for further study.
2. History of the US aviation industry, its
deregulation, and changes in the air
network
2.1. Birth of the US aviation industry and its regulation
The US air transportation network has its roots in the mail service as do many other forms
of transportation in the US. 1 In 1918, the US government began using commercial flights
to augment military flights to deliver airmail.[14] This trend continued to grow and 1925
saw the passage of the Kelley Air Mail Act that was designed to encourage the growth
of commercial services to deliver airmail.[22] This led to the emergence of many small
operations working as contractors to the US Postal Service. The industry consolidated into
fewer, larger companies when the Airmail Act of 1930 changed the way the postal service
paid for airmail such that it favored larger planes.[22] As these airlines grew they began to
shift focus towards passenger aviation.
Following an airmail scandal in 1934, Congress gave regulatory control to the Interstate
Commerce Commission (ICC). The ICC's role was short-lived because many airlines experienced financial problems as a result of the ICC's regulatory approach.[59] In response, the
'Going back to Gallatin and Madison, state's rights advocates questioned the constitutionality of federal
involvement in transportation. Since the US Constitution expressly establishes the national interest in a
postal service, federal intervention in transportation has often been based on postal needs. "Post roads"
are an early example; railroads and aviation follow the precedent.
2. History of the US aviation industry, its deregulation, and changes in the air network
Civil Aeronautics Act of 1938 created a separate organization to regulate the US aviation
industry.[14] In 1940, that organization re-named itself the Civil Aeronautics Board (CAB)
and was responsible for regulation of the aviation industry until 1978.
The CAB was born out of Depression era fears that markets lacked the pricing stability
that was necessary for the success of the airline industry. Specifically, there was concern
that "excessive" competition could lead to "unfair or destructive competitive practices" and
that entry into the market should be limited only to when it was warranted due to "public
convenience and necessity."[14, 59] Under the provisions of the act, the CAB was given
regulatory authority over all inter-state operations of US airlines. The result was that the
CAB had the power to determine which airlines could operate which routes and to set fares
for those routes.[56, 40] Once granted approval to operate a route, airlines could neither
add nor abandon a route without the permission of the CAB thus preventing the free entry
and exit in the market that economists would later cite as a reason for deregulation.
In addition to regulating the routes and fares, the CAB also regulated the creation of
new airlines. When the market became regulated all 16 existing airlines were grandfathered
in under the terms of the act. Over the years, the CAB allowed mergers and consolidations
to shrink that number to 11 by 1978, despite receiving 79 applications seeking approval to
create new airlines.[59] This reflected the CAB's approach to using regulation to protect
the financial viability of airlines and limit competition.
Fearing the destructive effects of competition, the CAB generally allowed only one airline
to operate in a market save for the rare occasions when demand merited the addition of
a second airline. The report of the Federal Aviation Commission of 1935 asserted that:
"... . too much competition can be as bad as too little. To allow half a dozen airlines to eke
out a hand-to-mouth existence where there is enough traffic to support one really first-class
service and one alone would be a piece of folly."[via 52] As a result of this approach, the
airlines, facing little competition in the market and a CAB that was generally agreeable to
their requests for fare increases, lacked incentives to innovate or keep costs down because
2. History of the US aviation industry, its deregulation, and changes in the air network
they were guaranteed returns by the CAB. Initially, profits were guaranteed through the
use of cross-subsidies where the CAB would grant operating rights on profitable routes to
supplement losses on others. Later the CAB used what it called the Standard Industry
Fare Level formula that set fares with the goal of a 12% rate-of-return and a target load
factor of 55%.[56, 14] Instead of competing on price as many airlines do today, what little
competition there was in the airline industry was on the basis of service quality, often
through CAB-encouraged introduction of new aircraft.[14]
Despite CAB imposed fares above predicted free-market levels, air travel continued to
grow in popularity throughout the regulated era. Figure 2.1 shows that between 1940 and
1978 annual domestic passenger traffic grew from 2,803,000 to 253,957,000.
Passengers (in 000s)
800000
700000
600000
500000
300000
200000
100000
1940
1950
1960
1970
1980
1990
Figure 2.1.: Domestic passengers 1940-2010[6]
2000
2010
2. History of the US aviation industry, its deregulation, and changes in the air network
2.1.1. Precursors to deregulation
While the CAB had regulatory authority over inter-state aviation, it did not have authority
over intra-state aviation. While most states were too small to have much of an intra-state
market, California had a thriving intra-state airline industry. In 1965, the Los Angeles to
Oakland-San Francisco route was the largest market in the world and provides an interesting
case study in regulated versus free-market fares.[47] The route's service frequency was excellent with 60 round-trip flights Monday-Thursday and 75 round-trips Friday-Sunday.[47]
According to Levine, the market was "characterized by extreme competition" with Pacific
Southwest and United splitting about 70% of the market and Trans World Airlines and
Western Airlines accounting for the rest.[47] The result of minimal regulation and competition was a market that looked and operated differently than the regulated inter-state
markets:
... although the number of passengers traveling by air in the United States
as a whole has increased between the years 1959 and 1964 by approximately 50
percent, the number of travelers passing between Los Angeles and San Francisco
by air has increased by almost 300 percent.
Although the average jet coach
fare level in the United States is approximately 5.5 cents per mile over stages
considerably longer, and hence cheaper to operate, jet coach fare for the 350mile trip from San Francisco to Los Angeles is approximately 3.9 cents per
mile. Although the lowest fares between Boston and Washington, serviced only
by CAB-certificated trunk carriers, is $24.65, Pacific Southwest Airlines, using
the same modern turbo-prop equipment, carries passengers between Los Angeles
and San Francisco, only 59 miles closer together, for $11.43. The jet fare is only
$13.50.[47]
One way that airlines in this market kept fares down was by flying older aircraft that
were less expensive to operate.[47] This stands in contrast to the regulated markets where
2. History of the US aviation industry, its deregulation, and changes in the air network
one of the only ways for airlines to compete was on service quality which typically meant
newer aircraft. Had regulated airlines been allowed to compete on fares, it is likely that
the regulated routes would have seen older aircraft to keep costs down as is the case with
most US airlines' fleets today. As it was the CAB's policies provided little incentive for
controlling costs through the use of older aircraft.
It was cases like this one in California and others in the Florida intra-state aviation
market that eventually led academics and Congress to question whether regulation was in
fact necessary and beneficial to passengers and airlines.
2.1.2. Moving towards deregulation
The landscape of the regulated airline industry began to change in the early 1970s. The
changes began with CAB regulations that sought to ensure industry profitability. In short
succession, the CAB implemented a moratorium on new routes, limited flights in the busiest
markets, set minimum fares for charter flights which had begun to compete with commercial
aviation, and agreed to a general fare increase. All told, these changes raised average fares
by 20% in one year, an unwelcomed outcome against stories like the California example.[59]
The rumblings of deregulation began in 1975 when Massachusetts Senator Edward Kennedy
held hearings on the regulation of the airline industry. His policy entrepreneurship, with
the help of his aide, eventual Supreme Court Justice Breyer,[28] would later become one of
the driving forces behind deregulation. Over the next three years, Congress held a series
of hearings on the aviation industry, many of them focusing on the costs of regulation,
particularly the fare differences between regulated inter-state air travel and unregulated
intra-state travel such as that in California previously noted.2
2
There are a number of reasons for the fare differences observed. First, is the previously noted use of older
aircraft in the intra-state markets. Second, is that the flows between Los Angeles and San Francisco
were fairly well balanced meaning that airlines did not have to fly empty seats in one direction to
accommodate demand in the other.[47 Likewise, there was little seasonality in demand meaning that
airlines did not have to fly partially empty larger planes in a low-season to account for the greater
demand in the high-season.[47] Lastly, between 1950 and 1970 the population of California grew by 88%
while US population grew by only 34% meaning that California airports could be profitable with smaller
2. History of the US aviation industry, its deregulation, and changes in the air network
The disposition of the CAB began to change as well. Once a proponent of regulation
on the grounds that airlines operated as a natural monopoly requiring regulation, the
CAB became more receptive to change as the 1970s progressed and became an outright
proponent of it when noted regulatory economist Alfred Kahn became chair of the CAB in
1977. In 1975, the CAB relaxed restrictions on charter flights allowing them to compete
with commercial aviation. 3
In response to competition from charter airlines, the CAB
allowed airlines to offer restricted tickets with fares discounted as much as 45% below the
rates set by the CAB. When American Airlines discounted fares it saw a 60% increase in
passengers on some routes. Upon seeing these results, Kahn supported this activity and
encouraged other airlines to adopt similar discount strategies.[59]
2.1.3. Deregulation
The combination of these political and economic forces resulted in the Airline Deregulation
Act of 1978 which called for a phasing out regulation by 1983 and abolition of the CAB by
1985. The case for deregulation rested on the results seen from CAB liberalization and the
assumption that a fully deregulated industry, with free entry and exit, would be a benefit
to consumers in terms of quality of service, expansion of service, and fares. 4 [56]
While Congress believed that deregulation would be a powerful tool in improving air
margins because of larger volume. No doubt staffers and experts understood these differences between
the California and US aviation markets; however, these nuances are often lost in policy debates where
the issues are condensed to the high-level issues: high fares vs. low fares, regulated vs. deregulated.
3
In some cases it was less expensive for groups to charter an aircraft since those prices were not regulated,
than to buy commercial tickets.
4
The political climate was such that deregulation of the aviation industry was the primary goal. Nevertheless, there are other approaches that could have achieved similar outcomes without full deregulation.
For example, the field of public transit, recognizing that 5 private bus operators serving the same route is
not practical, has emphasized competition for the market, as opposed to the competition in the market
seen in free markets. Under a competition for the market scenario, as used by Transport for London, for
example, the government identifies specific routes and sets fares and service quality standards and then
allows private operators to bid for the market. In some cases that means bidding for the lowest subsidy
to operate un-profitable routes while in other cases it means bidding for the largest payment to the
government for the profitable routes. Periodically, the routes are put up for re-bid ensuring continual
competition. This is similar to what is done now for EAS markets and could have been a practical
alternative to full deregulation. Another option would have been for the CAB to raise its target load
factor and lower fares, thus changing the dynamic of the regulated industry.
2. History of the US aviation industry, its deregulation, and changes in the air network
service for most communities.
It also recognized that there were some routes that were
only served by airlines because of regulation and the cross-subsidization that occurred with
profitable routes. Once cross-subsidization ceased and airlines were free to enter and exit
the market, service to those communities would cease. In response to fears that this would
unfairly harm those communities, Congress set aside money for what it called Essential Air
Service (EAS). Under this program, service to small communities would likely decline, but
it would not decline below a minimum service level. The plan was for the service to last for
ten years, but the EAS program was renewed for another ten years and subsequently made
permanent.[7] At the beginning the EAS program had a budget of around $100 million,
at one point the budget was as low as $25 million, but it has risen in recent years and in
2010, the subsidy to EAS communities was just over $175 million.[56, 69] As of May 2010,
Essential Air Service still existed in 151 communities, 44 of which are in Alaska. 5
2.2. Evolution of the network
Under the CAB's regulation, the US air network provided mostly point-to-point service.
This network structure developed primarily from the way the CAB authorized service in an
effort to prevent airlines from creating a hubbing effect. [59] The maps in Figure 2.2 from
Eastern Airlines and United Airlines depict typical carrier networks with clearly defined
trunk routes between major city pairs with intermediate stops along the trunk route offering
almost train-like service.
5
The EAS funding is a tax subsidy to the communities receiving service and because EAS serves only
small communities the program has a small constituency making it politically unstable. While the
service is an easy target for cuts, it has nevertheless remained despite Congressional deficit reduction
efforts. However, given the small constituency served, the future of EAS is far from secure.
6
The term "hub" has different contextual meanings in aviation. In the context of hub-and-spoke, it refers
to an airport in which one or more airlines operate waves of flights that are temporally coordinated to
facilitate connections. The FAA uses the term "hub" as a synonym for airports in its size classification.
2. History of the US aviation industry, its deregulation, and changes in the air network
Figure 2.2.: Eastern Airlines route map (1947) and United Airlines route map (1940)
In the 1950s, the CAB began to permit the creation of "local service carriers" to act as
feeders to the trunk-line carriers and provide greater access to the air transportation network
to more, smaller communities. [59] These carriers were distinct from the major trunk-line
carriers and received subsidy payments from the CAB to make the service profitable. The
idea was to bring passengers to larger cities for access to the trunk routes. This was before
the era of code-sharing and connecting banks or "waves" of coordinated flights, making
connecting much less convenient from the passenger's perspective than it is today.
While the CAB attempted to use route awards to construct its desired route structure
without hubs, by 1976, 12 of 15 trunk and local service carriers had created at least one hub
airport, defined as 50 or more average weekday departures.[59] With the implementation of
deregulation and free entry and exit, airlines began to change their network structure in an
effort to increase profits. Typically, this meant discontinuing service in smaller communities
and on other less profitable routes, expansion of less expensive to operate (on a cost per
mile basis) long-haul routes, and increased utilization of hubs. By 1984, 4 major carriers
were operating hubs at two different airports with many more operating hubs at a single
airport. [59]
With hub operations on high-demand, jet-serviced routes as their bread and butter, major
2. History of the US aviation industry, its deregulation, and changes in the air network
carriers began to rely on regional airlines to provide feeder service to their hub airports. 7
Unlike the disconnected "local service carrier" connections under regulation, deregulated
carriers implemented code sharing that made connecting flights more convenient from the
passenger's perspective as they were then able to purchase one ticket for their whole journey
despite traveling on multiple carriers. It also allows carriers to consolidate their operations
around their hubs, lowering costs and strengthening the airline's control at the hub.
The airlines' rush to shift to a hub-and-spoke oriented network structure following deregulation suggests that the airlines saw an opportunity to increase profitability with such
a structure. The first benefit of hubs comes from cost savings resulting from economies
of density. Consider demand for travel to Reagan National (DCA) from Kennedy (JFK),
Boston (BOS), and Burlington, VT (BTV) depicted in Figure 2.3.
In a point-to-point
network, an airline captures the demand from the New York area for a JFK-DCA flight,
likewise for BTV-DCA and BOS-DCA. With a hub-and-spoke network, the airline can aggregate passengers from flights from BOS and BTV through JFK onto one larger plane.
Thus, instead of operating flights from BOS, BTV, and JFK to DCA it can operate a single
flight from JFK with flights from BOS to JFK and BTV to JFK that capture demand for
travel to both JFK and DCA.
7
Regional carriers can often operate at lower costs, allowing major airlines to maintain access to smaller
markets at lower costs.
2. History of the US aviation industry, its deregulation, and changes in the air network
BOS
0
JFK
DCA
F
BTV
DCA
"TV
Figure 2.3.: Point-to-point (left) Hub-and-spoke (right)
This aggregation of demand allows the airlines to achieve higher load factors and to
8
operate fewer, larger planes, both of which achieve cost savings. [38, 66] These increased
load factors are apparent in Figure 2.4 which depicts load factors from 1932 until 2009.
During the regulated era, at the CAB's initiative, load factors were typically kept below
60%, but they have been steadily rising since deregulation as a result of a number of factors
including hubbing, pricing and revenue management strategies, and better operating and
scheduling approaches.
sAll the benefits of hubbing must be considered in light of the increased congestion that hubbing creates.
Mayer and Sinai found that flights departing a concentrated, single airline-dominated hub face 4-7
minutes of additional travel time on average and 1.5-4.5 minute delays for arriving flights at airlinedominated hubs. That said, the authors note two important caveats. First, the delays are primarily
absorbed by the hubbing airline and airlines flying, but not hubbing at the airport, did not experience
delays like those of the hubbing airline. Second, because the hubbing airline is internalizing the delays,
the continuation of hubbing suggests that the network effect benefits exceed the congestion effects.
Airports with lower concentrations, and thus no large hub carriers, like LaGuardia, JFK, Washington
National and O'Hare, may still experience airlines trying to coordinate flights around peak periods. In
those situations the airline only internalizes part of the delay, thus creating a "tragedy of the commons"
situation.[511
2. History of the US aviation industry, its deregulation, and changes in the air network
100%
90%
80%
70%
60%
50%
40%
30% ...................................................
1932
1938
1944
1950
...
1956
1962
1968
1974
1980
,....,
1986
1992
.,.
1998
2004
2010
Figure 2.4.: Load factor 1932-2010[6]
In addition to economies of density, hubs also result in economies of scope.
A hub
facilitates service to more destinations with fewer flights than would be required to provide
service to the same city pair markets with a point-to-point network. Consider the BOS,
BTV, DCA, and JFK example shown again in Figure 2.5. If an airline wanted to provide
service to each airport via a point-to-point network, it would require 10 flights (1 between
each airport in each direction). With a hub-and-spoke network with JFK as a hub, the
airline can serve all the airports with one-connection or better service through 6 flights.
2. History of the US aviation industry, its deregulation, and changes in the air network
BCS
805
6
4
7
.8
JFK
JFK
5
2
3
4
3
DCA
C
9
6
T
BTV
Figure 2.5.: Flights needed for point-to-point (left) and hub-and-spoke (right)
Lastly, hubs benefit airlines because they can create quasi-monopoly conditions for airlines. For example, Graham notes that once an airline gets 40% of the traffic in a market its
market share is likely to continue to rise.[38] A related phenomenon that gives incumbents
a competitive advantage is the "S curve". The "S curve" describes the observation that
flight frequency has a disproportionate impact on market share. This means that if Delta
operates three times as many flights from Atlanta to Chicago as Airtran, Delta can expect
to have more than three times as many passengers as Airtran in that market.[37]
100%
0%
Flight frequency
Figure 2.6.: S curve
2. History of the US aviation industry, its deregulation, and changes in the air network
This means that once an airline has established a good level of service from a hub airport
it is difficult for new carriers to enter that market for two reasons. First, it requires a
large investment in staff, gates, and air planes to provide the level of service required to
compete with the established carrier. Second, the spatial limitations of the airport often
mean there is not capacity or gates to handle the level of service that would be required
for a new entrant to compete. In some cases, this lack of competition has resulted in what
researchers call fortress hubs in which one airline operates the vast majority of the traffic
at an airport making it difficult for new entrants to compete.
A 1993 GAO study found that at airports where an airline has a concentrated market
share, the airlines were able to charge a "hub premium" of up to 34%.[56] Effectively, this
market share benefit of hubbing means that airlines were able to mitigate some of the
competitive pressure that deregulation was supposed to create through the operation of
hubs. The emergence and growth of low cost carriers (LCCs) has changed the dynamics of
fortress hubs because LCCs, while not providing comparable service or network coverage,
are able to compete with the hub carrier by offering lower prices. This is the case even
for what were formerly fortress hubs that do not have LCC service but are within a 1-2
hour drive of another airport with LCC service. The analysis on accessibility presented
later will help to determine whether these changes have increased or decreased passenger
accessibility.
2.3. Airports today
Despite deregulation, the US government still maintains a degree of influence over the airline
industry through federal funding for airports,9 control of air space and air traffic control, and
many regulations on airlines focused on safety among other issues. However, the influence
9In FY-2011 the FAA had a budget of about $3.5B for its Airport Improvement Program which dispersed
funds to airports for improvements, typically 75% project funding for Large and Medium hub airports
and 90% for most others.[58] Airports are also able to fund improvements themselves through the
Passenger Facility Charge (PFC) that is added to fares. As of May 2011, 332 US airports were authorized
to collect a $4.50 PFC.[5] In 2010, PFC collection was just over $2.7B.[5]
2. History of the US aviation industry,its deregulation, and changes in the air network
is too diffuse for federal policy to significantly affect the deregulated environment.
In March 2011, there were 375 primary service airports, 109 commercial service airports,
and over 2,000 general aviation airports. Figure 2.7 shows the location of the continental
US airports in the primary and commercial service classifications. The Federal Aviation
Administration (FAA) classifies airports in these categories on the basis of passenger volume
at the airports.
- Commercial service: are publicly owned airports that have at least 2,500 but no more
than 10,000 passenger boardings each year and receive scheduled passenger service.
- Primary service: are commercial service airports that have more than 10,000 passenger
boardings each year. Within the primary service category the FAA further classifies
airports into hub size: 10
- Large hub: 1% or more of annual US passenger boardings, there are 29 large hub
airports
- Medium hub: At least 0.25% but less than 1% of annual US passenger boardings,
there are 37 medium hub airports
- Small hub: At least 0.05% but less than 0.25% of annual passenger boardings, there
are 71 small hub airports
- Non-hub: Airports qualifying as primary airports that do not fall in the above
categories, there are 239 non-hub airports
1
"The FAA uses the term hub in a different manner than as it applies to the hub-and-spoke discussion.
2. History of the US aviation industry, its deregulation, and changes in the air network
44
*
4
+
4
4
+
1~
*I.
14 '4
4I~
1'4
'4
-
4
4
4
4
-4
-------4
41
4
J-
--
*
Airport Type
+ Largehub
T Mediumhub
( Small hub
-
Non-hub
-
Commercial
Service
t-(
4
44
7.
It
T
'4,
4
+1
~'
2
K~+
4
**1
J+
Figure 2.7.: US Primary and Commercial Service airports
The analysis in this thesis will be confined to the primary and commercial service airports
as they account for the vast majority of all air traffic in the US as Table 2.1 below shows.
Airport classification
Large Hub
Medium Hub
Small Hub
Non-hub
Commercial Service
Percentage of total US enplanements
70%
19%
8%
3%
0.001%
Table 2.1.: Enplanements by airport type 2009
2.4. Conclusion
The US aviation industry has had a turbulent first century, from its inauspicious beginnings
as a mode of transporting the mail to the era of luxury travel during the regulated era to
today's price-driven market. The questions this thesis asks are: what do these changes actu-
2. History of the US aviation industry, its deregulation, and changes in the air network
ally mean for passengers and accessibility, and have those changes been good for passengers
and economic competition?
More importantly, what lies ahead with low fares harming
airline financial stability and probable rising fuel costs and what policies can government
use to improve the welfare of passengers and the financial stability of airlines?
Should
federal monies be directed towards expanding already large hubs or into growing smaller
hubs? Should the FAA or Congress attempt to limit the domination of particular airports
by carriers through mechanisms like expanding the number of slot controlled airports and
periodic slot auctions? This analysis will use data on passenger travel to develop a tool to
help evaluate these questions.11
"There are important policy questions regarding the role for government intervention in mitigating congestion most efficiently through pricing schemes such as increased landing fees more in line with actual
costs and peak pricing for landing fees to spread traffic throughout the day and lessen congestion during
the peaks. This thesis does not directly address congestion in the analysis performed. This issue is
raised here because it is an important component of the passenger's flying experience and is an area
ripe for study by others.
3. A brief review of literature on air
connectivity and accessibility
Much has changed in the aviation industry since deregulation. The common perception
is that deregulation has been beneficial to consumers but there is a paucity of systematic
analysis on the topic. Many have argued that the shifts towards hub-and-spoke networks
and the greater use of regional jets has made more destinations accessible, but does the
presence of more destinations via hub connections increase accessibility? The challenge is
how to measure those changes and equate them to passenger benefit.
This chapter of the thesis reviews and critiques the existing studies that have attempted
to measure changes in the aviation network. As discussed below, some authors have made
use of schedule data to measure connectivity while others have used information from
reservation systems to model connectivity from a particular airport. The motivation of most
previous studies is typically network analysis, not understanding changes in accessibility.
Nevertheless, many of the approaches can be borrowed and applied to capturing accessibility
from the passenger perspective.
The distinction between accessibility and connectivity is important and bears repeating.
As noted previously, in this study connectivity refers to the actual connections between
nodes of a network, e.g. two airports, without regard to fare, level of service, or frequency.
Simply put, if a flight between Boston and Burlington is added where one previously did
not exist, then connectivity has increased.
Accessibility is similar, but refers to a more
passenger oriented perspective. If the Boston to Burlington flight is so expensive that no
3. A brief review of literature on air connectivity and accessibility
one flies it then while connectivity has increased accessibility has not.
This distinction highlights the problems associated with the use of schedule data in
attempting to measure accessibility and makes apparent the need for the research in this
thesis. First, because schedule data only contains information on non-stop flights, it relies
on numerous assumptions, such as the amount of time passengers are willing to wait for
connecting flights, in order to construct individual flight legs into itineraries.
Second,
schedule data reveals the supply side, but says nothing about demand for flights or changes
in travel patterns. By basing the analysis on purchased ticket data, as is proposed here, a
more accurate picture of changes in the air network emerges with respect to accessibility:
where passengers are flying, how much they are paying, and how many of them are flying.
3.1. Measures of connectivity
Most commonly, the literature on connectivity addresses two types of measures: connectivity and hub centrality. Hub centrality, sometimes called hub connectivity, refers to the
number origin-destination pairs that include a stop at a particular intermediate airport.
This measure is often used in network analysis studies and captures the importance of a
particular node in the network. On a basic level, this means that if a larger percentage of
itineraries pass through a particular airport, then the airport's hub centrality has increased.
In Figure 3.1, the hub connectivity is measured from the perspective of the node labeled
"hub" in the middle of the diagram.
Alternatively, connectivity looks at the number of destinations that are reachable from
an airport within a given number of flight legs. Figure 3.2 depicts an example of two-leg
connectivity in which H1, H2, D1, D2, D3, and D4 are all connected to the origin.
While the majority of the literature has developed models from the hub centrality perspective, these measures are focused on airline and airport operations but do not capture
the passenger perspective. [19] In this analysis of the changes in the US air network, the
connectivity measure in Figure 3.2 is a better starting point because it offers an approach
3. A brief review of literatureon air connectivity and accessibility
to understanding the travel environment from the passenger perspective since it considers
the destinations available to passengers at a given airport.
Figure 3.1.: Hub centrality
Figure 3.2.: Two-leg Connectivity
3.
A
brief
review
of
on air
literature
accessibility
and
connectivity
3.2. Model inputs
In constructing measures, most studies make use of similar inputs into their models. The
most common considerations in a connectivity measure are temporal coordination, routing
factor, connection quality, and the number of legs considered for the creation of a viable
journey.[20] Table 3.1 shows the major papers reviewed on connectivity and the inputs
considered in their models.
Name of
Measure
[
Temporal
Studies
Circuity
Coordination
Connection
Number of
Seat
Quality
Coupons
Availability
Fares
No
Yes
No
No
Binary
2
2
No
No
No
No
Yes
Yes
No
Yes
Discrete
Continuous
2
2
No
No
No
No
Yes
Yes
Continuous
2
No
No
Dansei 2006[24]
Yes
Yes
Discrete
2
No
No
Reynolds-Feighan and McLay
2006[61}
No
No
1
Yes
No
Hub Potential
Doganis and Dennis
Dennis 1998[31]
Doganis and Dennis 1989[321;
Connectivity
Dennis 1994[29, 30]
Bootsma Connectivity
WNX (Weighted
number of
connections)
Netscan Connectivity
Units
Bootsma 1997[12]
Burghouwt 2007[16];
Burghouwt and deWit
2004[18]
Burghouwt and Veldius
2006[17]; Matsumoto et al
2008;Veldhius 1997[711;
Veldhius and Kroes 2002[721
WCn (Weighted
number of
connections)
Destination
Importance and
Capacity
Ticketing System
Based
I
I
I
I
Yes
Yes
Unlimited
No
Yes
Grubesic and Zook 2007[39]
I
I
Table 3.1.: Model inputs of reviewed studies, adapted from Burghouwt and Redondi [20]
The temporal coordination of two legs of an itinerary impacts that quality of the overall
journey and the feasibility and attractiveness of particular connection pairs. While studies
that are focused solely on hubbing and temporal coordination perform detailed analysis on
banks of connecting flights, most of the studies examined here follow a cruder approach
of setting minimum and maximum connecting times to determine whether an itinerary is
feasible. The studies typically set minimum connecting times between 45-60 minutes with
maximum connection times around 2-3 hours for domestic itineraries and much longer for
international itineraries. This heavy reliance on assumptions to construct feasible itineraries
is a drawback in trying to understand passenger accessibility because passengers do not
I
3. A brief review of literatureon air connectivity and accessibility
always behave according to assumptions thus making the measure less realistic.
Circuity refers to the distance that passengers have to travel between an origin-destination
pair in excess of the direct great circle (direct flight) distance between the O-D pair. Alternatively, Malighetti, et al[50] defines it as "the ratio between in-flight time and potential
direct flight time". In essence it is a measure of inconvenience as it captures how far out
of the way a passenger must fly on a connecting itinerary as opposed to a non-stop flight
though it is only a partial measure of inconvenience because it does not capture the waiting
time at intermediate airports. A number of studies assume cut-off points for circuity beyond which it is assumed that a passenger would not fly. The typical range is 1.2-1.50.[19]
Again, these assumptions are necessary for the simplification of these models but represent
a weakness compared to using actual flight data, particularly in trying to model connectivity from smaller airports where it could be difficult to reach certain destinations without
exceeding say a 1.25 circuity threshold. For example, in the analysis presented later, of
flights originating in Boise in 2000, 26% of the destinations served required flights with a
circuity above 1.25.
Connection quality is similar to temporal coordination except that instead of serving
as a cut-off point to consider the feasibility of two connecting flights, it is used to weight
measures of connectivity to reflect the quality of a particular connection. For example, in
the Netscan model an airport is assigned connectivity units based on both the the quantity
and quality of an airport's connectivity. In assigning units to a particular flight, waiting
time in the airport between connecting flights is weighted as 2.4 times as onerous to travelers
as flying time. Thus flights with large amounts of time spent waiting will produce a lower
number of connectivity units than flights with longer flight times but shorter wait times.
Others, like the Dansei model, classify connection quality into groups such as high, medium,
and low. Obviously, the judgment about weights for waiting and in-flight time is somewhat
arbitrary and likely varies based on factors such as the quality of the intermediate airport,
time of day, or money saved by waiting longer. Nevertheless, it is a useful approach to
3. A brief review of literatureon air connectivity and accessibility
try to capture the quality of connections because it helps a model move beyond measuring
strict connectivity to incorporate the passenger perspective and move towards accessibility.
The methodology presented in Chapter 4 builds on the idea of connection quality and
weights itineraries based on path quality. A path quality measure is used by many airlines
and consultants, but is not discussed in the literature examined here. The measure is useful
because it can be used as a proxy for connection quality when temporal data is not available.
For example, a path quality measure might weight non-stop flights as a 1.0, one-connection
itineraries as a 0.30, and two-connection itineraries as 0.01.
The models typically assume a maximum of 2 flight legs, i.e. an itinerary with one connection. The likely reason for this constraint is to minimize the computational complexity
of the model, but again such an assumption makes the model less realistic. Such simplifying assumptions are entirely appropriate when confining analysis to large, or even top-100
airports, but when smaller, more rural airports are considered the number of legs can very
often extend beyond 2, particularly with the increased reliance on hub-and-spoke networks.
Minimum
Connection Time
60 minutes
Model
Weighted
Maximum
Connection Time
European Domestic:
Routing Factor
Limit
1.4*flying time
180 minutes
Connectivity
Netscan
Bootsma
None
None
60 minutes
60 minutes
None
European Domestic:
45 minutes
European Domestic:
120 minutes
1.5*distance
60 minutes
90 minutes
None
180 minutes
Connectivity
Weighted
Connectivity
Number
Doganis and Dennis
Connectivity
Table 3.2.: Detail of a subset of model inputs [20]
Two of the studies examined took a different approach to understanding the air network.
Reynolds-Feighan and McLay[61] considers the supply dimension of availableseats (for nonstop flights only). In the author's model connectivity increases if more seats are available.
3. A brief review of literature on air connectivity and accessibility
While some of the other models examined include flight frequency measures, they do not
consider the type of aircraft used or the number of seats. This is an interesting component
of connectivity because it offers some of the passenger perspective, though there are data
drawbacks to this approach which is discussed further in Section 3.3.4.
The model by Grubesic and Zook[39] is unique in that it considers fares in the analysis,
which helps to move beyond a strict connectivity model towards accessibility. The study
did not seek to develop a model or index for analysis across airports but rather compares
the air network across different factors (number of flight legs, flight time, and fares).
3.3. Detailed review of models
This section reviews and critiques some of the most commonly cited measures of connectivity.
3.3.1. Weighted connectivity
Burghouwt and de Wit[18] sought to understand how European airports were coordinated
temporally and to what extent the airports made use of waves of synchronized flights to
improve indirect connections. While much of their work was focused on understanding and
modeling flight banks; they also made use of indirect connectivity measures in modeling
how connectivity changed.
The Burghouwt and de Wit model makes use of pieces of models developed by Bootsma[12]
and Veldhuis[71] to measure indirect connectivity which refers to connections between origin
and destination pairs that are not connected by a non-stop flight. The measure of indirect
connectivity is based on "the number of direct frequencies, the minimum connecting times
and the quality of the connection."[18] From this basis they measure a weighted indirect
connection:
3. A brief review of literatureon air connectivity and accessibility
WI =
2.A*TI+RI
3.4
1
where TI = 1 -
(3.1)
,
Th
withTh > M and TI = 0 if Th > T
RI = 1 - (2.5R - 2.5) and
IDT
DDT
with 1 < R< 1.4 and RI=Oif R> 1.4
WI = weighted indirect connection, TI = transfer index, RI = routing index, Mi,j =
minimum connection time for
j, Th
j at airport
I, Tj = maximum connecting time for connection
= transfer time at the hub, IDT = actual in-flight time of indirect connection, DTT =
estimated in-flight time of a direct connection, R = routing factor. The constraints ensure
that flights exceeding the maximum connection time are excluded from the analysis as are
flights with a routing index about 1.4.
This indirect connection methodology captures the quality of a connection both in terms
of wait time in the airport (TI) and flight time in excess of a non-stop flight(RI). While
the authors acknowledge that there will be wide variation in how passengers value different
types of time, they assume that time waiting is 2.4 times as onerous to passengers as in-flight
time.
Burghouwt and de Wit use this weighted connectivity measure to compare indirect connections at various European airports by summing all the airport's flights
WNX =
E
WIa
(3.2)
Airports a
Given this measure, they classify hubs into 3 connectivity categories. Hubs with a WNX
>2,500 are considered high, 500-2,000 are medium, and <500 are low.
3. A brief review of literatureon air connectivity and accessibility
3.3.2. Weighted connectivity number
Building on the work of Burghouwt and de Wit, Dansei[24] develops a weighted connectivity
ratio to capture the connectivity of airports while also including measures to capture quality.
The Dansei model begins with two matrices, a spatial connectivity matrix and a temporal
connectivity matrix. For flight ij the temporal connectivity matrix is defined as:
Tij = 1 if MCTk 5 td,j -ta,i
Tij = 0.5 if ICTk < td,j
-
< ICTk
ta,i
MACTk
(3.3)
rij = 0 otherwise
where MCTk is the minimum connecting time defined as 45 minutes for continentalcontinental flights, td,j is the departure time of flight
j and
ta,i is the arrival time if flight i,
ICTk is the intermediate connecting time defined as 90 minutes for continental-continental
flights, and MACTk is the maximum acceptable connecting time defined as 120 minutes for
continental-continental flights. The ICTk is used as a mid-point for binning connections
into different quality types.
Similarly, the spatial connectivity matrix is defined as:
Sij =1if DRk 1.20
ig = 0.5 if 1.20 < DRk < 1.50
(3.4)
6 j = 0 otherwise
where DRk is the de-routing index defined as DRk
=
k
where DDk is the direct flight
distance between the origin of i and the destination of j and ID is the indirect flight distance
of flights i and
j.
These two measures are combined to form a weighted connection:wij = rjoij which feeds
into the weighted connectivity ratio:
3. A brief review of literatureon air connectivity and accessibility
(3.5)
WCR= WNc
WNr
where WNc =
Ei E jijn
nana
E
wij
MACT + ICT - 2MCT1
2T
W
WNc represents the number of flights that are flown into the airport during time period
T and WNr accounts for the number of flights that would have been flown under a uniform,
random arrival during time period T with naand nd being the number of arrivals and
departures respectively.
Dansei constructed this model with the intention of understanding the improvements in
connectivity that are associated with schedule coordination over a random arrival structure.
While this end result connectivity measure is useful for measuring network connectivity, it
is less useful in capturing the accessibility picture of airports. What is of more interest
is Danseis method of capturing the quality of connections. Specifically, the use of three
different categories with varying weights to measure temporal connectivity is similar to the
approach for evaluating path quality discussed earlier and the spatial connectivity weighting
could be replicated from the data on purchased tickets.
3.3.3. Netscan
Netscan is a model developed by Veldhuis[71] that has been subsequently used by a number of other authors to examine connectivity. The Netscan model attempts to measure
connectivity by classifying airports based on a quality index measured as:
Quality = 1 -
PTT - NST
MAT - NST
MAXT - NST
PTT = IFT + 3 * TT
(3.6)
(3.7)
3. A brief review of literature on air connectivity and accessibility
MAXT = (3 -. 075 * NST) * NST
(3.8)
Where NST = nonstop travel time, PTT = perceived travel time, MAXT = maximum
perceived travel time. Thus a non-stop flight will have a quality index of 1 while connecting
itineraries will have a quality index between 0 and 1 depending on the connection time.
The end result is that each feasible itinerary has a quality index which is then combined
with the frequency of the flight resulting in connectivity units.
CNU = Frequency * Quality
The connectivity units (CNU) are then summed up across all of an airport's feasible
itineraries to establish a measure of the airport's connectivity. The Netscan model has the
benefit being relatively simple to understand and compute while still capturing the essence
of connectivity from a passenger perspective. While the model does not include an explicit
routing index constraint, the MAXT acts as one to some extent.
The Netscan model is attractive because it captures the temporal aspect of air travel
which is an important factor in accessibility. Nevertheless, the model leaves out a number
of important pieces of accessibility because it is based only on supply data. Specifically,
the Netscan model reflects neither fares nor passenger choices. While the model can depict
changes in connectivity due to an increased number of destinations through connecting
airports, there may be no demand for those airports either due to high fares, lack of capacity,
or lack of demand for the flight. While high fares are an obvious constraint on accessibility
from the passenger perspective, capacity and lack of demand are less obvious concerns with
the Netscan model. Imagine that a flight is added between New York's Kennedy airport
(JFK) and the small, Essential Air Service airport in Rutland, VT (SVR). Looking solely
at supply data, the connectivity of the dozens of airports with non-stop flights to JFK is
increased by the addition of the JFK-SVR flight. In this scenario, a once daily JFK-SVR
flight (probably on a 19 seat prop aircraft) would increase connectivity at JFK by the same
3. A brief review of literatureon air connectivity and accessibility
amount as a new flight from JFK to Los Angeles, CA (LAX) (on a 160+ passenger aircraft)
would. The proposed use of ticket data mitigates these issues by providing both fare and
O-D data for each passenger.
3.3.4. Capacity and destination importance
A second type of research takes a much simpler approach to measuring connectivity. For
example, Reynolds-Feighan and McLay[61] attempt to model accessibility for airports in
the UK to other markets. They note that many of the European low cost carriers like
Ryanair and Easyjet discourage passengers from trying to connect successive flights on the
carrier to reach destinations not served directly by the carrier. For this reason, they suggest
that methods such as those discussed above that use scheduled data to construct flights
may be overstating the accessibility from an airport. The authors propose to measure only
non-stop accessibility. The accessibility measure is based on the number of seats available
and the importance of the destination airport as measured by traffic into the destination
airport. Therefore an airport's accessibility would be measured as:
n
Number seatsij * weight,
Accessi =
(3.9)
j=1
The weight of the airport will vary based on the spatial area of analysis, so if the goal
was to measure accessibility on a regional scale, the weighting would be based on the
destination's regional ranking, likewise a national measure would take a national ranking.
They proposed two measures of weights for the destination airport. The first is based on
ranking in terms of seats available (or available seat miles):
N - (ri -
1)
N
Where N is the number of airports in the area of analysis and ri is the ranking of the
destination airport.
3. A brief review of literature on air connectivity and accessibility
A second weighting was used that took a proportional weighting of the destination airport's seats (or available seat miles). This is measured as:
Ti
TTOP
Where Ti is the traffic through the destination airport and TT
is the traffic through
the busiest airport in the area of analysis.
The authors note that a rank weighting will tend to mask important differences in the
accessibility of airports that are exposed through the use of the proportional weighting.
The reason for this they note is that "because there tend to be only small differences
between the ranks of destination airports in the same region, accessibility measures prepared
using ranking-based weights will be closely correlated to the underlying volumes of seats or
available seat miles that depart from each origin airport."[611
The analysis is interesting in that it attempts to address the earlier noted problems with
some of the models that fail to account for any capacity considerations and count a new
service to Rutland, VT the same as new service to LAX. However, the obvious flaw in the
model is that it does not consider itineraries other than non-stop-though by weighting the
destination airport based on the traffic traveling through the airport the model attempts
to reflect the onward accessibility of the destination airport in the origin airport's measure.
While such an approach may be appropriate in the UK context the authors analyze (where
most flights are non-stop because of the country's smaller size), in the US air network with
heavy reliance on connecting itineraries the measure of only non-stop flights would create
an inaccurate picture of accessibility.
Unfortunately, the model cannot be expanded to include multi-leg itineraries because it
is not possible to attribute capacity of a non-stop leg among O-D markets. For example,
consider two itineraries BOS-JFK-LAX and PHL-JFK-LAX. Both itineraries rely on the
same second leg, JFK-LAX, and it is impossible to attribute the capacity to one or the other
of the itineraries. The use of ticket data proposed here eliminates the need to distribute
3. A brief review of literature on air connectivity and accessibility
capacity by showing how passengers are actually flying.
3.3.5. Airline reservation data based measure
Grubesic and Zook[39] is one of the few studies that attempted to measure accessibility as
defined in this thesis. The authors measure accessibility in the United States based on data
from the WorldSpan Global Distribution System (GDS). The WorldSpan GDS processed
around 65% of all online ticket purchases in 2003. The authors mined data from the internet
in August 2004 for a week of departures in September 2004. Their data encompassed 156
US airports, those with 300 or more arrivals in the month of June 2004. The results allowed
the authors to understand accessibility from the perspective of potential passengers. They
were able to capture the number of destinations available, the time and frequency of flights,
and the prices offered to passengers for flights departing during the one-week period of
analysis.
Based on the data gathered, the authors measured connectivity based on the percentage
departing itineraries that were non-stop, one-connection, or two-connection. They found as
one might expect, that many of the smaller airports relied more on one and two-connection
itineraries for their connectivity while the largest airports were majority non-stop flights.
Grubesic and Zook measure accessibility in two ways, cost-based accessibility and timebased accessibility. The authors rightly note that, in the mind of travelers, cost is often
a primary concern and may limit the passenger choices. 1 The authors map both average
minimum ticket cost, that is the average of the cheapest ticket between an origin-destination
pair over the surveyed week, and the average minimum cost per mile. The authors conclude
that major airports in the center of the US like Chicago O'Hare (ORD) offer the best costbased accessibility in terms of both price and price adjusted for distance. They also find
that moderately accessible national hubs and highly accessible regional hubs often yield
'While cost is a concern to passengers, particularly in the short-run, a point that will be made later in the
thesis is that the low costs seen today are not sustainable from the business perspective of the airlines,
and ultimately their sustainability should be of concern to passengers in the long-run.
3. A brief review of literature on air connectivity and accessibility
good values for travelers when flying a one-stop connection through another well-connected
national hub due to economies of scale and scope. As one might expect, the authors find
the least attractive prices for itineraries originating from spoke airports most likely due to
a larger percentage of trips requiring one or more connecting flights and because of the lack
of volume and competition in those markets.
*
-
Figure 3..: Cost-based accessibility from Grubesic and Zook[39]
The authors' analysis of time-based accessibility does not yield results of any significance.
The authors found that time-based accessibility for the most part decreases along the 8
categories of airport connectivity determined in their cluster analysis because of the need
for larger numbers of connections and smaller, slower aircraft servicing smaller markets.
There are few meaningful insights that can be drawn from the analysis based on results
returned from internet fare searches for one week. The time period is simply too short to
draw any significant conclusions. Furthermore, the analysis shows only the fares that were
offered to potential passengers during the week of analysis and does not account for demand
or prices paid by actual passengers. Nevertheless, the types of information captured in the
analysis are interesting and are useful to consider in analysis of accessibility and connectivity
3. A brief review of literatureon air connectivity and accessibility
using the sample of purchased tickets.
3.4. Conclusion
While using supply data can provide an interesting picture for network analysis, the data do
not provide a good picture of accessibility. Using supply data to create feasible connections
overstates connectivity by assuming the existence of many more feasible itineraries than
will ever be presented to the passenger. Airlines do not supply lists of connections to global
distribution systems (GDS) which power things like internet booking sites. Instead, much
like the models presented here, the GDS uses an algorithm to piece together connections
based on assumed minimum and maximum connection times and then presents the more
appropriate ones to passengers based on their requested departure or arrival times.
So
while there might be 600 ways to get from BOS to SFO thus making it appear in the
models that the two airports are very connected, in reality there are perhaps only 30 or 40
itineraries that most passengers actually fly. Likewise, treating each feasible connection as
an option for passengers without considering fares and other factors presents a flawed picture
of connectivity. This is the particular advantage of using data on purchased tickets; it
depicts a more accurate picture of how passenger accessibility is changing, not just potential
connectivity. The data also maintains an understanding of connectivity because the data
still shows the links that exist in the network (if at least one person flew it), but more
importantly how they fly and where they fly with counts of passengers.
4. Data description and methodology
As Chapter 3 discussed, a number of studies have produced methodologies designed to
measure connectivity. The challenge in applying these connectivity measures to accessibility
is that most of them are not oriented towards the perspective of the passenger's experience,
meaning the models do not always include things like quality measures. This chapter takes
the useful pieces of the models presented earlier to develop a methodology for calculating
passenger-oriented accessibility. The chapter begins by presenting the data used in the
analysis and then proposes an analysis methodology and a series of metrics derived from
the methodology.
4.1. Data
Before the deregulation of the US airline industry, the CAB collected information that it
used to understand demand and competition as part of its regulatory duties. Following
deregulation, with the CAB abolished, the US Department of Transportation (US DOT)
continued to require collection of fairly detailed information about tickets purchased in the
form of the DB1B database reported by the Bureau of Transportation Statistics (BTS). The
database is publicly available from the BTS, though this research makes use of a database
from a private company, Data Base Products, that cleans and organizes the data.
The ticket data is available from 1979 to present on a quarterly basis.1 While small
'While the data are available from 1979, the first data point used here is from 1980 because it has been
reported that the 1979 data is unreliable because many airlines had difficulty reporting in the first year
following deregulation.
4. Data description and methodology
carriers operating only aircraft with fewer than 60 seats are exempt, the database otherwise
contains data from all US airlines.[26] The data is based on coupons (boarding passes)
collected at boarding, not when the ticket is purchased, thereby ensuring that the data
are a sample of actual travel patterns. Prior to 1987, the DB1B contains a 10% sample
of tickets in all markets. Beginning in 1987, the DOT/BTS began collecting 1% of tickets
in major domestic markets and 10% of tickets in all other domestic and US-originating
international markets.[251 Effectively, this means that the collection system gathers every
first-leg coupon, e.g. the coupon that is the first flight of a traveler's itinerary, with a
serial number ending in a 0.[25] In major domestic markets it collects coupons with a serial
number ending in 00. While the collection system only captures data on the first coupon of
an itinerary, that coupon contains information about the rest of the passenger's itinerary.
The database contains information from the collected coupons including:
* Origin - both the originating city as well as specific airport.
Thus the data can be
used for both metropolitan area analysis, say New York City, as well as individual
airport analysis such as JFK or LGA.
" Destination - both destination city and airport
" Number of passengers - reflects the total number of passengers that flew on a particular
origin-destination pair during the aggregation period.
" Number of coupons - provides information on whether a passenger flew a non-stop
or connecting itinerary. For example, an itinerary from Boise to Washington, DC
connecting through Chicago would be made up of 2 coupons while a non-stop Boise
to DC flight would have 1 coupon.
" Non-stop miles - this data is added by the DOT after collection and reflects the
number of non-stop miles between the O-D pair.
" Miles flown - this captures the miles actually flown by the passenger. In conjunction
4. Data description and methodology
with the non-stop miles it can be used to calculate the circuity, the miles flown in
excess of the non-stop miles as a result of a connecting itinerary. In the case of a nonstop flight, the miles flown would be the same as the non-stop miles. For connecting
itineraries, the miles flown will exceed the non-stop miles.
e Gross fare paid - includes not only the fare paid to the airline but also the Federal
Excise Tax and the Passenger Facility Charge.[9]
Table 4.1 displays a snippet of relevant fields from the D1B1 database for Boise (BOI) in
2000:
Origin
Destination
Nonstop miles
Passengers
Coupons
Coupon miles
Fares
BOI
BOI
ABQ
ALB
781
2,121
798
212
1.96
2.265
791,291
495,377
137.036
161.646
Table 4.1.: Sample data extract
In addition to the above, the database contains other data that is not used in this analysis.
The raw ticket-by-ticket data behind the database are also available from BTS. In that form,
the database contains more information about the itinerary including specifics about any
connecting airports, though there is no data regarding flight time or flight frequency. While
greater granularity is available, given that even a 10% sample would contain tens of millions
of individual records for each year; this analysis uses statistics that are aggregated to the
airport origin-destination pairs on an annual basis.
4.2. Data limitations
The database is the most detailed publicly available air travel data in the world[65], and it
provides otherwise unavailable insight into actual travel patterns within the United States.
Nevertheless, it is not without limitations.
For example, because aggregated data are
not available with any greater granularity than quarterly, it precludes detailed seasonality
4. Data description and methodology
analysis.[651 For this analysis, that is not of concern as the methodology will rely on data
aggregated to the annual level. Of greater concern is the sample size in the smallest markets,
such as the small airports serviced by Essential Air Service that may have as few as 10
passengers daily each way.[65] There is no way around this limitation, so results in the
smallest markets must be interpreted with this limitation in mind. Likewise, as noted, some
small regional carriers are not required to report data to the DOT, though this accounts
for such a small percentage of passengers that it should not have an adverse impact on the
analysis.
It is important to clarify what is not included in the database because the data available
dictates the form of the proposed methodology. Specifically, the database does not include
information about the frequency of service between O-D pairs, the wait time at connecting
airports, nor the type of aircraft (and hence capacity) serving a market. As noted, these
are commonly included in previous measures of connectivity and service quality and are
typically taken from the airline schedule data. It is unfortunate not to have those pieces
of data for analysis because it excludes some possible measures.
However, this analysis
somewhat mitigates this lack of information by creating a proxy for measuring service
quality.
4.3. Review of Quality of Service Models
The methodology proposed in this thesis is rooted in a model developed by the CAB called
the Quality of Service Index (QSI). This section explains the QSI and its history and the
next section presents the QSI-based methodology to create a Path Quality Index.
In the 1970s, when the CAB was still regulating the aviation industry, it developed a
model to predict demand for flights and airline market share known as the QSI. The impetus
for the model's creation was the requirement that the CAB conduct a public convenience
and needs test when considering requests for additional service in a particular market.[40]
The CAB needed a way to estimate demand and market share to determine the impact of
4. Data description and methodology
granting new service.
At the time, the CAB was concerned that the industry was caught in a Catch-22 scenario,
sometimes called the ratchet effect, in which quality and fares increased cyclically. As an
airline added new service in a market its costs rose leading the airline to ask permission
to raise fares.
Once the airline was allowed to raise fares it became profitable to offer
more service so the airline would request permission to add service to a market and the
cycle would start again.[40] Played out to its conclusion, this problem is self-limiting based
on demand, but the increasing fares and frequencies represented a problem that the CAB
sought to address.
In response the CAB developed the QSI to calculate the demand from passengers in a
market and to estimate the market share of airlines operating in the same market. The
original QSI model used inputs on frequency, aircraft size, and other factors affecting demand. With the model outputs, the CAB was better equipped to monitor service quality
and make decisions about service levels in particular markets.
When it was used by the CAB, the QSI model was econometrically estimated using
two nine-month periods of data for 141 one-carrier markets and 151 two-carrier markets.
Following deregulation, the model's reach expanded outside of the CAB and into the airline
industry where it is used in making route and frequency decisions in the deregulated market.
As computing power has increased, so have the number of variables in the models, but the
typical form is something like:
QSI = 01*ConnectType+
$2*
PlaneType + P3 * Circuity +04 * Frequency +...
(4.1)
The specific formulation of the model is not of particular interest here, what is important
is the value of the coefficient for connection type because it will serve as the basis for the
path quality measure developed here. The coefficient is useful in the development of an
index for accessibility because it captures how passengers value various connection types.
4. Data description and methodology
The QSI coefficient weights are unitless, but have value in comparison with other QSI
weights. For example, a QSI of 1.0 means that a random passenger prefers that itinerary
four times as much as an itinerary with a QSI of 0.25. The model used by the CAB assigned
the following weights for different schedule/itinerary alternatives:
Stops
QSI Weight
Non-stop
One-stop
Two-stop
Three-or-more stop
1.0
0.55
0.40
0.03
Table 4.2.: CAB QSI weights [49]
These weights were established in a time when carriers operated main-line routes almost
as train service, stopping multiple times between the origin and final destination. Therefore,
the CAB's weights are for multi-leg stop flights not connecting flights meaning passengers
continuing on did not have to change planes. Today this type of service is almost nonexistent save for some Southwest Airlines itineraries.
This makes selecting weights for
connecting flights more appropriate. The weights for stop flights do not directly translate
to connecting flights given the state of the airline industry today with dozens of connecting
itinerary options.
The CAB's weights of 1.0 for non-stop and 0.55 for one-stop imply
that passengers would prefer the availability of 2 one-stop itineraries over a single non-stop
flight. This may have been the case when these measures were developed, a time when flight
frequency was lower. However, today, with connecting opportunities much more common,
it is likely that passenger preference for non-stop flights is higher, meaning lower weights
for connecting itineraries.
As previously mentioned, since deregulation the QSI model has been used throughout the
airline industry. Each user has been left to determine the weights in their use of the model.
The choice of weights by other practitioners provide guidance for selecting appropriate
weights here. In a presentation on the airline industry at MIT former President and CEO
4. Data description and methodology
of Air Canada, Montie Brewer, suggested the weights in Table 4.3. These weights are lower
than those used by the CAB reflecting this new reality of many connecting options in O-D
markets.
Connection Type
QSI Weights
Non-stop
One-stop
Connections
1.0
0.33
0.03
Table 4.3.: Brewer QSI weights [15]
Likewise Jordan Kayloe, Vice President of DIIO, noted that a commonly used industry
model based weights for connection type based on aircraft size and connection type. These
weights are too detailed for the data used here, but nevertheless provide insight about the
relationship between connection types.
I_
_
IQSI
Weight
Regional
Narrow-body
1.00
0.66
0.20
0.10
RJ/Turbo-RJ/Turbo
0.03
0.003
Turbo Prop
0.35
0.08
Average
1.102
0.216
On-line single
Interline single
Wide-body
2.00
0.40
Jet-Jet
0.05
0.005
Mid-body
1.50
0.30
Jet-RJ/Turbo
0.04
0.004
Jet
RJ/Turbo
On-line double
0.002
0.001
0.0015
Interline double
0.0003
0.00015
0.000225
Connection
Non-stop
One-stop
0.04
0.004
Table 4.4.: DIIO QSI weights[45]
Lastly, Emrich and Harris note that QSI is "highly nonlinear". Based on this, the authors
suggest that if you set a non-stop flight as 1.0 then these are the relationships at a minimum:
Connection
QSI Weight
Non-stop
One-stop
One-connection
1.0
0.25
0.125
Table 4.5.: Emrich and Harris QSI weights [35]
4. Data description and methodology
4.4. Methodology
As the previous section demonstrated, there is no consensus on connection weights. Nevertheless, the reviewed connection weights offer a way of thinking about the various types of
connections and the way passenger perceive them. This next section builds on the idea of
the QSI and itinerary preferences to develop a path quality index that captures accessibility.
4.4.1. Inferring connections
In order to understand the level of service in an O-D market, we need to know about the
typical number of flights that passengers take between an O-D pair. Because the data set
does not contain the intermediate stops of an itinerary, only the origin and destination, it is
necessary to make an inference about the number of flights. Aggregated at the O-D level,
the data provide the average number of coupons flown between the O-D pair. The average
coupons can be used to infer the percentage of passengers traveling on different itinerary
types.
For example, the O-D market from BOI to ABQ has an average of 1.961 coupons per
itinerary. Based on this average of 1.961 coupons, we can infer that 96.1% of BOI-ABQ
passengers had a one-connection itinerary and the remaining 3.9% took a one-coupon,
typically nonstop, flight. Written mathematically, this means 1.961 = (1*0.03) + (2 *0.961)
Likewise, the BOI to ALB flight has an average of 2.265 coupons.
Applying the same
logic, the assumption is that 26.5% of passengers flew a two-connection itinerary and the
remaining 73.5% flew a one-connection itinerary, or 2.265 = (3 * 0.265) + (2 * 0.735).
There are two limitations to this approach. First, for markets with average of 2 coupons or
more it assumes that none of the itineraries were non-stop because we can only differentiate
between two connection types from the average number of coupons. This is probably a safe
assumption, though it is possible that some passengers may have flown non-stop and this
approach does not allow for that possibility. Second, this approach prevents differentiation
between non-stop and one-stop flights where a passenger makes an intermediate stop but
4. Data description and methodology
does not change planes and thus has only 1 coupon. However, as noted earlier, one-stop
itineraries are increasingly rare and this should not significantly influence the usability of
this method of inferring connections.
4.4.2. Weighting connections
Once the connection types are inferred, it is necessary to select weights for connection
types. Once weights are selected, these two pieces will be combined to develop measures
that capture accessibility.
Selecting the proper weights is a mixture of art and science. As Coldren notes, "weights
are obtained using statistical techniques and/or an analyst's intuition."[21] This analysis
takes a similar approach, taking cues from those with experience in the QSI models and
incorporating sensitivity analysis. Table 4.6 shows the weights primarily used in this analysis.
Connection Type
Weight
Non-stop
One-connection
Two or more connections
1.0
0.3
0.01
Table 4.6.: Primary weights used
The 1.0 value for non-stop flights is straightforward since the purpose of the weight is
to develop an index. The other weights were chosen to incorporate the guidance from the
reviewed QSI weights and this analyst's intuition. Since the model is designed to measure
changes in accessibility the goal should be, if anything, to underestimate the weights of
connecting flights so that accessibility changes are more apparent.
We can use the roots of the QSI as a market share forecasting tool to understand what
these weights imply. A non-stop weight of 1.0 and a 0.3 for one-connection indicates that,
all else equal, if Delta flies 1 non-stop flight from BOS to LAX, United must provide 3
one-connection itinerary options to roughly split the market.
4. Data description and methodology
Generalized cost is another way of understanding the relationship between QSI weights.
Transport economics frequently uses the idea of generalized cost to measure the impact of
travel on a passenger. The generalized cost of a trip is the sum of the actual cost of travel
and the monetized cost of other non-monetary "costs" such as time spent, risk of delay,
comfort, etc. A QSI of 1.0 for non-stop and 0.125 for one-connection, as Emrich and Harris
suggest, implies that a random passenger would be indifferent to a non-stop flight with a
generalized cost of $1,000 and a one-connection itinerary with a generalized cost of $125.
While this might be true for the relatively price insensitive demand of business travelers,
it is not the case for leisure travelers. The 0.3 weight for one-connections used here implies
that the relationship between non-stop and one-connection is $1,000 to $300.
This highlights the challenge of selecting weights, which is to find a medium between
the preferences of business travelers and leisure travelers. These weights are an attempt
to do that. To further the understanding, some sensitivity analysis was conducted using
the weights in Table 4.7 and Table 4.8. Sensitivity analysis is used to understand the way
the results would change if other weights were used. Specifically, the objective is to look
at things like the magnitude of changes and the directionality of changes with different
weights. The sensitivity analysis here considers both a higher and a lower set of weights
with the results presented in Appendix A.
Connection Type
Weight
Non-stop
One-connection
Two or more connections
1.0
0.4
0.02
Table 4.7.: Sensitivity analysis (high range)
4. Data description and methodology
Connection Type
Weight
Non-stop
One-connection
Two or more connections
1.0
0.2
0.005
Table 4.8.: Sensitivity analysis (low range)
4.4.3. 0-0 market path quality
Once the itinerary types have been inferred and the path quality weights chosen, they can
be combined to create an average path quality value for an O-D market. This takes the
same approach as the earlier discussed QSI measure, but it is not a QSI in the strict sense
of the word, rather it is an index of the path quality for a particular O-D market, hence a
PQI, path quality index.
The PQI for an O-D market can be calculated as follows:
PQI
=
%nonstop*Weightnonstop+%1-connect*Weighti-connect+%2+connect*Weight2+connect
(4.2)
Taking the previous example of BOI to ABQ where it was determined that 3.9% of
passengers flew non-stop and the remaining 96.1% took one-connection itineraries, we would
calculate the PQI as: (0.039 * 1.0) + (0.961 * 0.3) = 0.327.
4.4.4. Index measures
The PQI can be aggregated at the airport level in two ways. First in terms of markets, as
the average across all markets:
Market PQI
OD markets m PQIm
# of markets
(43)
This value is the expected path quality of an O-D market picked at random from all the
4. Data description and methodology
O-D markets from a given airport. Second, as a passenger weighted average:
PassengerPQI =
PQIm * Passengersm
Total Passengers
SOD markets m
(44)
This figure can be interpreted as the PQI of the average random passenger originating
at a given airport.
4.4.5. Passenger based measure
Another way to develop a picture of accessibility from an airport is by looking at the
passenger volume at an airport. The basic intuition is that if across time periods, passenger
traffic has increased, then accessibility has also increased. Simply examining unadjusted
passenger volume is less useful because if passenger traffic is up 25% but all trips out of an
airport have gone from non-stop to one-connection, then accessibility has not necessarily
increased. The path quality weighted passenger measure presented here is a way to remedy
this.
At the airport level, when aggregated across all of an airport's O-D markets, this measure
can be considered the number of quality-weighted passengers served by an airport. Another
way of thinking of the measure is as the equivalent number of non-stop passengers served
at an airport.
Path quality weighted passengers
=
(
Weightm * Passengers,
(4.5)
O-D markets m
An airport's quality-weighted passengers is one way to compare accessibility across airports and across time periods. Consider again, the data from Table 4.1 and imagine that
BOI only serves ABQ. If all passengers flew one-coupon (non-stop) itineraries, then the
airport's quality-weighted passengers is equal to 798, the number of passengers, since a
one-coupon flight has a weight of 1. Imagine another identical airport where all passengers
4. Data description and methodology
to ABQ traveled on a one-connection itinerary. In this case the airport's quality-weighted
passengers is 798 * 0.3 = 239.4. The equivalent non-stop passenger value of 239.4 reflects
the lower accessibility associated with a one-connection itinerary versus a non-stop.
The measure can also be used to capture changing accessibility at a single airport over
time. For example, because the weights are based on passenger preferences, an increase
in passengers may not necessarily mean an increase in accessibility. For example, while
between 2 time periods an airport may have doubled the number of passengers served,
making it appear as though accessibility improved, if during the same time period, all
itineraries flown went from non-stop to one-connection, then instead of increasing by 100%,
the equivalent non-stop passengers measure of accessibility actually decreased by 40%. In
this manner, the measure captures the reality that accessibility is more than just the number
of passengers. It also has a quality component. After all, the whole of the continental US
is connected to anyone with two working legs, but that does not capture the important
quality component, which explains how accessible it really is.
4.4.6. Destination based measure
Another way to understand accessibility changes is based on destinations served rather than
passengers. By summing the PQI for each O-D market at an airport, a measure of quality
weighted, or equivalent non-stop, destinations results:
Path quality weighted destinations =
(
PQIm
(4.6)
Markets m
Simply calculating the feasible destinations based on schedule data as many models do,
gives a picture of connectivity but not accessibility. The benefit of this measure, which
captures and weights the O-D pairs that people are actually flying, is that is provides a
picture of accessibility from a given airport. By summing the quality for each O-D pair
multiplied by the number of destinations served, the effect of passenger volume is removed.
Thus, the measure is dependent on the average quality of each O-D market and the number
4. Data description and methodology
of destinations only. This makes the measure particularly useful for analysis across airports
because two airports with the same number of destinations with the same mix of connection
types will vary based on the number of passengers served, which is typically more a function
of the population in the surrounding area than it is the actual accessibility available from
that airport.
Much the same way that an airport's quality-weighted passengers can be thought of as
equivalent non-stop passengers, when considering destinations the result can be thought of
as equivalent non-stop destinations. In this way, an airport with 100 non-stop destinations
has the same number of equivalent non-stop destinations as an airport with 333 destinations
available via one-connection. Again, this reflects the trade-offs implicit in the weighting of
various types of connecting flights where a weight of 0.2 implies that a passenger values a
flight one-fifth as much as a non-stop flight with a weight of 1.
4.5. Conclusion
The measures presented here based on the path quality of itineraries provide a picture of
changes in accessibility that captures the perspective of passengers. What makes these
measures particularly interesting is that they are based on the data of actual itineraries
flown, not feasible itineraries. These are the journeys that passengers have selected after
weighing all their feasible itineraries against factors such as time and cost.
This offers
insight into passenger accessibility and allows these measures to depict changes in actual
travel patterns over time. The subsequent chapters will use the data to present these path
quality weighted measures along with descriptive statistics for 1980, 1990, 2000, and 2010.
5. Data analysis
Supporters of deregulation argued that it would lead to improved service for passengers
and increased efficiency for airlines. The freedom from route and pricing regulation held
the promise of an opportunity for airlines to provide the services that customers wanted
in the markets they wanted to fly. The question asked here is whether the prediction of
deregulation proponents regarding the accessibility of service for passengers has come to
pass; specifically, has accessibility improved.
Accessibility can be measured in different ways. For example because the purpose of the
airlines is to move passengers, accessibility can be measured through passenger volume.
Another way is by looking at the average circuity because it can serve as a proxy for the
convenience of flying. Fares are another way of understanding accessibility changes because
as fares decrease the pool of people for whom flying is financially accessible increases. Lastly,
accessibility could be captured by the number of destinations available from an airport
because geographic accessibility increases as the number of available destinations from an
airport rises. This chapter analyzes accessibility changes based on each of these measures
as well as those developed in Chapter 4: path quality, equivalent non-stop passengers,
and equivalent non-stop destinations. The analysis shows that making a broad conclusion
about accessibility changes is not straight forward because the directionality of changes in
accessibility depends on which measure is used.
The analysis of these accessibility measures is aggregated to the FAA airport classification
levels defined in Chapter 2: large hub, medium hub, small hub, non-hub, and Commercial
Service. As noted previously, this analysis covers the period from just after deregulation
5. Data analysis
through the present by evaluating the years 1980, 1990, 2000, and 2010. The data analysis
is based on the origin airport and is confined to Primary and Commercial Service airports in
the continental US plus Anchorage and Honolulu because they are large enough to function
as typical US airports. Other airports in Alaska and Hawaii are atypical because of the
small number of markets they serve and the different demand patterns created by their
geographic location so they are excluded because they would skew the results. While the
measures here are aggregated, the subsequent chapter then selectively drills down within
the classifications to understand which airports have seen gains in accessibility and which
have seen losses i.e., "winners" and "losers".
5.1. Path quality
The path quality measure developed in Chapter 4 is helpful in understanding the way air
travel has changed from the perspective of passengers. Recall that path quality is derived by
weighting the connection type of each O-D market. A decrease in path quality is produced
by an increase in the percentage of connecting itineraries. Conversely, a higher path quality
indicates a smaller number of connecting itineraries and more non-stop flights. Figure 5.1
depicts the average path quality within each airport classification and weights each O-D
market equally. Because each O-D market is weighted equally, this means that the path
quality value in each airport classification can be interpreted as the expected path quality
of a randomly selected airport within the classification.
5. Data analysis
*1980
*2000
E1990
*2010
1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
-l-
0.00
Large Hub
Medium Hub
Small Hub
Non-hub
Commercial Service
Figure 5.1.: Average path quality by airport type
In the shift towards greater reliance on hub-and-spoke networks, large hubs saw path
quality stay roughly constant with only minor decreases in path quality. The reason for
this is that as airlines shifted towards hub-and-spoke operations, the large hub airports
were used as the hub nodes in the network meaning that most destinations from those hub
nodes were reachable in a non-stop flight. Medium and small hubs did not have the benefit
of being a node and average path quality from 1980 to 1990 dropped by 7.5% and 9.7% at
medium and small hubs respectively. While rebounding in 2000 and 2010, the path quality
has not returned to the levels seen in 1980. This means that as the network structure shifted
towards hub-and-spoke, markets that had previously been served via non-stop flights in the
point-to-point network required a connecting itinerary through a hub airport.
The smallest airport categories, non-hub and Commercial Service, did not experience
similar quality declines with non-hubs remaining relatively constant and Commercial Ser-
5. Data analysis
vice airports increasing in average path quality. The likely reasons for this are two-fold.
First, in the regulated era many of smaller airports were served by what amounted to huband-spoke service with the regional carriers feeding the passengers into larger hub airports
with service by the main line carriers. Because this was effectively hub-and-spoke service,
when the deregulated airline industry shifted towards a hub-and-spoke structure passengers at the small airports saw little change. The second reason is that at small airports the
number of destinations decreased by over 20% from 1980 to 1990 and by 38% from 1980 to
2010. Thus many of the O-D markets with lower path quality were eliminated, elevating
the average path quality of the remaining markets. While this second effect does represent
a technical increase in average path quality, from a total accessibility perspective it is the
opposite of the desired outcome because it means a loss in service at some airports.
While Figure 5.1 showed the average path quality across an airport's O-D markets, Figure
5.2 shows average path quality based on the O-D markets weighted by passenger volume.
This means that a heavily trafficked market is weighted more than a market with less traffic
in Figure 5.2 while they are weighted equally in Figure 5.1. The difference in understanding
the two is that Figure 5.1 depicts average path quality for an O-D market selected at
random while Figure 5.2 depicts average path quality for a random passenger. The reason
that both measures are included in this chapter is that in some cases they will tell different
stories and highlight the disparity in highly trafficked markets, which typically have better
accessibility, and lower trafficked markets. To only examine average accessibility measures
would give an unfairly poor impression of accessibility, while only analyzing passengerweighted accessibility measures would miss the perspective of travelers who are not flying
in the most heavily trafficked markets.
5. Data analysis
N1980
i 2000
01990
*2010
1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
Large Hub
Medium Hub
Small Hub
Non-hub
Commercial Service
Figure 5.2.: Passenger-weighted path quality
Looking at passenger-weighted path quality in Figure 5.2, the shapes are generally the
same and the patterns already described hold though there are some noteworthy differences.
At small hubs, non-hubs, and Commercial Service airports we see that passenger weighted
path qualities are a bit higher than the average path quality at equally-weighted airports
suggesting that the airlines are serving the more highly trafficked markets with more nonstop service. Also worth noting is that when passenger weights are included, non-hubs
behave like the other Primary Service airports with path quality decreasing since 1980
while Commercial Service airports still show the large increase in path quality driven by
the pull back in service.
Based on passenger-weighted path quality we can conclude that accessibility has increased
considerably at Commercial Service airports while decreasing around 6.5% at medium and
small hubs and remaining roughly constant at large hubs and non-hubs. On the whole, other
5. Data analysis
than at Commercial Service airports, fewer than half of the airports that had service in 1980
had better path quality in 2010. At large hubs 13 of 29 had better path quality, at medium
hubs 9 of 34, at small hubs 18 of 59, and at non-hubs 61 of 185.
The only exception
was Commercial Service airports which saw path quality increase at 29 of 51 airports.
Examining path quality in this way demonstrates that accessibility has not improved at
the average airport as there were fewer markets receiving non-stop service in 2010 than in
1980 and even when weighting for passenger volume a majority of the airports examined
saw average path quality fall.
In conclusion, for both the random passenger and the random airport path quality has
declined. Path quality is an important accessibility measure because it captures the percentage of flights made non-stop compared to the percentage of connecting itineraries. A
decrease in path quality indicates that more passengers are using connecting itineraries and
experiencing the extra travel time of connections which is at an absolute minimum 45 minutes extra time for passengers and often much longer and the increased chance of delay, lost
luggage, etc. On the other hand, there is a benefit to passengers from increased hubbing.
As a subsequent section shows, improved efficiency from hubbing has lowered airline's costs
and the increased competition following deregulation has forced the airlines to pass some
of that lower cost on to consumers in the form of lower fares.
5.2. Changes in passengers
Given that airlines exist to move passengers, passenger volume can be used to measure how
accessibility has changed because presumably if more passengers are flying air travel has
become more accessible. Figure 5.3 shows that passenger volume has steadily increased
since 1940 with noticeable declines in the last decade. During the birth of the industry,
airlines saw growth as aviation began to establish a strong position over rail as the travel
method of choice in long haul markets. Subsequent growth has been driven by the expansion
of the US economy from a regionally based one to a nationally and globally based economy,
5. Data analysis
overall economic growth demanding more business travel, decreasing fares, and increased
income available for spending on leisure travel.
Passengers (in 000s)
800000
700000
600000
500000
400000
300000
200000
100000
1940
1950
1960
1970
1980
1990
2000
2010
Figure 5.3.: US domestic passengers 1940-2010 [6]
Figure 5.4 shows the breakout of passenger traffic by airport categories. Of particular
note is the dominance of large hub airports with large hubs accounting for more than twice
as many passengers as medium hubs. This makes sense because hub classifications are
based on airport traffic volume. Nevertheless, the dominance of 29 airports out of the
over 300 airports with commercial air service is quite pronounced and has implications for
accessibility. The dominance of large hub airports helps to explain why the service quality
and geographic extent of the network has evolved the way that it has. When the majority
of traffic is concentrated, from an economic and efficiency perspective it makes sense for
airlines to focus service around those airports.
5. Data analysis
N1980
02000
*1990
E2010
25,000,000
20,000,000
15,000,000
10,000,000
II
5,000,000
0
Large Hub
1
Large Hub
Medium Hub
Small Hub
Non-hub
Commercial Service
,III
Medium Hub
Small Hub
Non-hub
Commercial
Service
1980
1990
2000
2010
9,765,633
4,034,688
1,944,094
1,061,070
66,950
17,150,799
7,372,199
3,430,428
1,664,709
73,904
24,588,662
11,127,420
4,390,686
1,824,251
60,725
24,850,223
10,437,095
4,497,983
1,702,375
29,756
Figure 5.4.: Passengers by airport type (10% sample)
One way to capture concentration of traffic at airports is the Gini coefficient, which is
often used in economics to measure inequality in countries. The Gini coefficient captures
the area between a 45-degree line that represents perfect equality and the Lorenz curve
that shows the cumulative distribution. Gini values are between 0 and 1 where a 0 implies
perfect equality with an equal number of enplanements occurring at each airport and a 1
implying that all enplanements occurred at a single airport. In this case we measure the
Gini coefficient as[64]:
5. Data analysis
(OYi-
Gini = 1 -
1
+ oYi) (aXi_1 - aXi)
(5.1)
airportsi
Where aY and cX are the cumulative percentages of airports and passengers. Figure 5.5
depicts the 45-degree equality line and Lorenz curve for the airports analyzed in 1980. The
figure shows that roughly 25% of the airports analyzed account for 90% of the passenger
volume.
0.2
0.3
0.5
0.7
0.8
1.0
Percent of Airports
Figure 5.5.: 1980 Lorenz Curve
In 1980 the Gini coefficient for the airports analyzed here was 0.802 and in 2010 it
was 0.801. These values imply a high concentration of activity at a small number of US
airports. This finding is consistent with Figure 5.6 from Reynolds-Feighan who calculated
Gini coefficients in the US from 1969 to 1993. His analysis breaks down Gini coefficients
by airport categories which explains why the values are somewhat lower around 1980 than
those shown here. If aggregated across all airports the Gini coefficient he calculated would
increase. His analysis also shows that since 1969 the Gini coefficient has been steadily rising
5. Data analysis
suggesting the previously mentioned hubbing by airlines that occurred before deregulation.
The fact that the Gini coefficient has not risen from 1980 to 2010 suggests that concentration
at airports has not increased since deregulation. Perhaps because the airlines were already
hubbing by 1980. The data on concentration and inequality among airports do not address
the quality of the airports. It may be that the more heavily trafficked airports receive
most of the attention from the airlines to the detriment of the smaller airports, or it may
be that while the more heavily used airports are improving while the smaller airports are
simply maintaining the status quo which would be indicative of an overall accessibility
improvement. In either case, this is not a critique of airlines since a strategy to focus on
the most heavily trafficked airports makes sense in a deregulated environment but is merely
a recognition of the existence of high traffic concentrations and the associated accessibility
implications.
5. Data analysis
Mean Gini coefficient
0.8
0.6
0.4'
0.2
_
01
1969
1973
1977
1
__
1981
1985
1989
1993
Year
Hub type
-
Large
* Small (all)
Medium (all)
* Medium (actual)
-X-Small (actual)
fut . 4 V5 Coimercial Air Traic. AverageGin elllcient at eachhubtype- "All"indcate
full sampleof hubsin 1089hubcategors. "Actual"indicates
scoreforhubsin relevantFAA hub
dassin each period.
Figure 5.6.: Gini Coefficient 1969-1993 [62]
Returning to passenger growth, Figure 5.41 shows the number of passengers by airport
size category and offers a visual representation of passenger growth. The concentration of
passengers is also visible in the figure which shows that traffic at large hubs far exceeds
other airport sizes despite accounting for the fewest number of actual airports.
While large hub airports dominated in size, as seen in Figure 5.4, most airport sizes saw
sizable increases in traffic. Annual passenger volume at large hubs grew by 154% between
1980 and 2010, 159% at medium hubs, 131% at small hubs, 60% at non-hubs, while declining
55% at Commercial Service airports. During the same time period, the US population grew
'Recall that the data set is a 10% sample, so a 0 should be added to these numbers to roughly reflect
actual values, though those values are still lower than the actual number of domestic travelers due to
the exclusion of most airports in Alaska and Hawaii and all US territories.
5. Data analysis
by only 36% which suggests that air travel had become more accessible to more people or
at least that more frequent travel had become more accessible to existing travelers.
Also of note is the slowing of passenger growth between 2000 and 2010. While the first 2
decades of the study period saw tremendous passenger growth, in the last decade passenger
volume at large hubs increased by only 1%, it decreased 6% at medium hubs, increased
by 2% at small hubs, decreased by 7% at non-hubs, and decreased by 51% at Commercial
Service airports. There are a number of factors that contribute to the stagnancy in passenger
growth including the global recession that the US was just beginning to come back from
in 2010 along with loss of market share on short haul market as passengers opted for other
modes because of improved rail and bus services and the decreased convenience of short
haul flying due to post-9/11 security regulations.
Whereas Figure 5.4 shows just passengers, Figure 5.7 shows the number of equivalent
non-stop passengers which captures the passengers in each O-D market weighted by the
path quality for the O-D market. Based on the weights chosen in Chapter 4, 1 non-stop
passenger is roughly equivalent to 3 one-connection passengers. In this way, it is possible to
combine the passenger volume and path quality measures into a single measure that shows
non-stop traffic equivalency. This measure is particularly useful for comparing the same
airport across different time periods because it captures how the airport's accessibility has
evolved in 2 dimensions.
5. Data analysis
01980
01990
*2000
E2010
25,000,000
20,000,000
15,000,000
10,000,000
I'll
5,000,000
mil"
M. U.
Small Hub
Non-hub
1980
1990
2000
2010
8,224,824
3,145,244
1,216,803
542,950
28,481
13,730,251
5,330,062
1,956,496
792,845
39,489
20,085,883
8,162,083
2,450,299
833,634
32,061
20,693,850
7,486,620
2,599,741
799,150
17,001
0
Large Hub
Medium Hub
1
Large Hub
Medium Hub
Small Hub
Non-hub
Commercial Service
Commercial
Service
Figure 5.7.: Equivalent non-stop passengers by airport type (10% sample)
Comparing the shapes of the graphs in Figures 5.4 and 5.7 it appears as if the graphs
are equivalent because they have roughly the same shape. Though the shapes are similar,
upon closer comparison of the numbers a couple of differences are apparent. One finding
that emerges is that when passengers are weighted by connection quality, the dominance of
large hubs increases from 59% of all passengers to 65% of all equivalent non-stop passengers
because the large hub airports have comparatively better path quality. The growth of
equivalent non-stop passengers since 1980 remains roughly the same 151% vs. 154% at
large hubs, while growth at smaller hubs is lower on a percentage basis due to the declining
5. Data analysis
path quality. At medium hubs it is 138% vs. 159% at small hubs 113% vs. 131% and at
non-hubs 47% vs. 60%.
The lower growth rates in equivalent non-stop passengers compared to passengers suggest
that the growth in passengers seen in the earlier discussion came at the cost of decreased
path quality and increased inconvenience to passengers which is captured in the equivalent
non-stop passengers measure. As already noted, the decreased path quality was primarily
a result of hubbing. In light of diminished path quality, the case is less compelling that
accessibility has increased. However, even controlling for connection quality, the number of
equivalent non-stop passengers has increased since 1980 at a rate greater than US population
growth for all but Commercial Service airports, likely caused by a combination of lower fares
and increased income. The growth in passengers indicates that by this measure accessibility
has increased as many more passengers flew in 2010 than in 1980.
5.3. Changes in destinations served
The number of destinations served is perhaps the simplest measure of accessibility because
it is absolute. If a destination is not served from an origin airport, then it is completely
inaccessible, while a reachable destination is technically accessible notwithstanding fares,
number of connections, travel time, etc. The benefit of using this data set is that the destinations included are those actually flown by passengers. In many of the studies critiqued in
Chapter 3, instead of using actual travel data the researchers created feasible destinations
using a computer and airline schedules. By using the actual travel data here the measure is
more realistic because it captures the fact that most any destination is feasible with enough
connections, but that at some point the number of connections is too great. By showing
destinations that passengers flew, this data set saves us from having to make an assumption
on the maximum number of connections which differs from passenger to passenger.
In calculating the averages in Figure 5.8, destinations are included from each airport
provided at least one passenger (in the 10% sample) flew to that destination in the year
5. Data analysis
analyzed.
While a higher cut-off could have been utilized to capture the more heavily
traveled destinations, to do so would have diminished the benefit of the data set which is
being able to observe changes with a measure of granularity not typically available from
aggregated statistics.
N1980
x2000
Large Hub
Medium Hub
N 1990
m2010
lii' liii
Small Hub
Non-hub
Commercial Service
1980 1990 2000 12010
Large Hub
Medium Hub
Small Hub
488
421
335
460
412
332
433
391
318
391
357
301
Non-hub
201
202
195
191
Commercial Service
131
103
93
81
Figure 5.8.: Average number of destinations by airport type
Across the board, airports on average saw decreases in the number of destinations served.
In absolute terms, large hubs experienced the largest decrease in destinations dropping on
average by 97 destinations or 20% from 1980-2010. The large numerical drop was due to
5. Data analysis
the fact that they had the most room to fall by virtue of their higher starting position.
Commercial service airports experienced the largest decrease on a percentage basis, 50
destinations or 38% from 1980-2010. Medium, small, and non-hub airports fared somewhat
better decreasing in destinations with service by 15%, 10%, and 4% respectively.
These changes in destinations served are partially the result of changes in airline operating
practices. In 1980, the industry had just begun to operate in a deregulated environment
and had not had much time to adjust operating strategies from the regulated era that
was characterized by geographically expansive service coverage.
In the years following
deregulation, airlines oriented themselves towards profit maximization in ways that had
not been done when CAB practices guaranteed profits through payments to the airlines or
cross-subsidies with profitable routes. As a result of the freedom to enter and exit markets,
the airlines pulled back service from low- or un-profitable routes. Typically, this was not a
reduction of service to large, medium, or in most cases even small hub airports, but rather
from the smallest markets that did not have the population to generate demand sufficient
to sustain scheduled service. This was the type of reduction in service that the Essential
Air Service (EAS) was designed to counter. While EAS maintained a base level of service,
there was still a pull back because EAS retained service to only one larger airport from
the small airport and in other cases airports that did not meet EAS criteria lost service
entirely.
Equivalent non-stop destinations, as discussed in Chapter 4, offer an even better picture
of accessibility and the ease of reaching destinations because the measure captures the
connection quality of trips between serviced O-D markets.
5. Data analysis
N1980
01990
02010
.2000
Large Hubs
Medium Hubs
liii
I'llI
Small Hubs
Non-hubs
IEEE
Commercial Service
Large Hub
Medium Hub
1980
403
313
1990
365
286
2000
351
279
2010
323
251
Small Hub
200
181
170
165
Non-hub
97
96
84
79
Commercial Service
55
54
41
37
Figure 5.9.: Average equivalent non-stop destinations by airport type
In percentage terms all airports except for Commercial Service saw a decrease in equivalent non-stop destinations between 18-20% while Commercial Service airports lost 33%
of their equivalent non-stop destinations. Again, the decline in destinations served is a
direct result of the free entry and exit in individual markets made possible by deregulation.
For passengers wishing to travel to and from those markets that lost service, the effect on
accessibility is a harsh decline. Overall when measuring accessibility based on the number
of destinations available, we must conclude that accessibility declined from 1980 to 2010.
The measure of the number of destinations does not address whether the loss of service
to those markets was necessarily a good or a bad thing from the perspective of society as
a whole. From a narrowly focused, short-run viewpoint it probably was a net benefit to
5. Data analysis
society. As airlines cut unprofitable routes and passengers paid less -as the airlines were
able to operate more efficiently and the deregulated market increased competition leading
to lower fares for passengers. From a long-term perspective, the effects are less obvious. The
research mentioned in Chapter 2 suggests that scheduled commercial air services helps foster
economic growth and thus the communities that lost service suffer economically which has
a negative ripple effect through the larger economy. It is beyond the scope of this thesis to
measure the net societal effects from loss of service, but the issue is raised here to point out
the possibility that the societal effects are not as clear as they may seem at first glance. The
economic benefits or dis-benefits not withstanding, the conclusion evident from the data
is that from the passenger perspective, accessibility as measured by destinations served
decreased from 1980 to 2010.
5.4. Circuity
The path quality measure earlier discussed is based on the number of connections a passenger must fly on average to reach a given destination. Path quality is a relatively high-level
measure because it does not tell anything more about the connection other than that one
did or did not exist. The reality is that not all connections are equally inconvenient. For
example, an itinerary from Boston to Richmond, VA connecting in Philadelphia is much
less onerous than the Boston to Richmond itinerary connecting in Orlando, FL that has
been offered in this author's searches for tickets to travel home. An itinerary that requires
an intermediate connection in roughly the direct path to the final destination is much less
inconvenient than one that is far out of the way thus requiring much longer flying time.
Recognizing that connections are not equal, circuity can be a proxy for the inconvenience
of taking a connecting flight. It is a measure of the average distance flown in an O-D market
divided by the non-stop distance between the origin and destination. It can serve as an
inconvenience measure because it captures the extra time spent flying, though it does not
include the time spent waiting in connecting airports which does not change with circuity
5. Data analysis
but is simply a function of having to connect or not. This is an unfortunate omission
because the connecting time in an airport is likely longer than the extra time spent flying;
nevertheless, we can use circuity as a rough proxy. The circuity shown in Figure 5.10 is
passenger-weighted circuity so the circuity in each O-D market is weighted by the number
of passengers traveling in that market.
N1980
f 2000
.1990
n2010
1.02
1.00
Large Hub
Medium Hub
1 1980
Large Hub
Medium Hub
Small Hub
Non-hub
Commercial Service
1.03
1.05
1.08
1.06
1.09
Commercial Service
Non-hub
Small Hub
11990 12000 2010
1.03
1.05
1.09
1.10
1.12
1.03
1.05
1.10
1.12
1.12
1.03
1.06
1.10
1.11
1.13
Figure 5.10.: Passenger-weighted circuity
The most consistent change seen during the last 30 years is the increase at small hubs,
non-hubs, and Commercial Service airports from 1980 to 1990. This resulted from the
5. Data analysis
increased use of hubbing that resulted from the airline's shift towards the use of huband-spoke networks to consolidate traffic. Figure 5.11 which shows the percentage of hub
flights by carrier reveals that hub flights now account for the vast majority of flights from
US carriers save Southwest. Following 1990, the changes are less consistent across airport
types with the overall percentage increase in circuity from 1980-2010 between 0-5%. We
see also that the average circuity of itineraries from an airport increases as the size of the
airport decreases. This occurs for two reasons. First, by virtue of their smaller size, the
smaller airports are more reliant on a larger hub airport for connecting flights because
the airport's catchment area does not have sufficient demand to merit non-stop service.
Second, as the upcoming section on fares shows, market distance (the non-stop distance
from origin to destination) is shorter at smaller airports and shorter trips tend to have
greater circuity.[53] This occurs because while an itinerary from Boston to Los Angeles
via Chicago and an itinerary from Boston to Norfolk, VA via New York both experience
increased travel distances due to the connection, the Boston to Norfolk itinerary will have
greater circuity because the extra distance accounts for a larger percentage of the total
flight distance because the market distance is shorter.
5. Data analysis
|||| ||||II
,
~
-
/
4*
~d
~
~, /
$
(~b
~/
~/
C?
,
u 2000 au2005 * 2010
Figure 5.11.: Hub flights by carrier [67]
Figure 5.12 shows a different picture of circuity that is not weighted by passenger traffic
with each airport receiving equal weight. The difference in interpretation is that Figure 5.10
represents the average random passenger while Figure 5.12 represents that average random
destination and helps to illustrate the differences in accessibility on heavily trafficked routes
versus less highly trafficked ones.
5. Data analysis
*1980
* 2000
N1990
32010
1.30
1.25
1.20
1.10
1.15
1.05
Large Hub
Medium Hub
Small Hub
Non-hub
Commercial Service
11980 1990 12000 [ 2010
Large Hub
Medium Hub
Small Hub
Non-hub
Commercial Service
1.12
1.14
1.18
1.19
1.20
1.14
1.16
1.20
1.22
1.23
1.16
1.17
1.21
1.22
1.21
1.16
1.20
1.23
1.23
1.25
Figure 5.12.: Average circuity
Figure 5.12 shows that by 2010 the random O-D market at all but large hub airports
required between 20% and 25% of more flight distance than a non-stop flight would have
required. The first thing to notice is that the average circuity is much higher than passengerweighted circuity. For example, at large hubs the average circuity in 2010 was 1.16 while
on a passenger-weighted basis it is 1.03. These values in comparison to those of passengerweighted circuity suggest that airlines give more non-stop service to markets with high
traffic levels. This is not a startling revelation as one would expect more non-stop flights in
more heavily trafficked markets, but it offers an important picture of accessibility because
5. Data analysis
it highlights the great differences in accessibility between different markets. This gives a
picture of what it can be like to fly to small out of the way destinations in contrast to a
more frequently trafficked market. Unlike passenger-weighted circuity, we can see that at
all airport types, average circuity has increased since deregulation meaning that passengers
are flying further distances since deregulation.
The circuity values can be used to create a more concrete picture of what this means. By
multiplying the passenger-weighted circuity by the average passenger-weighted, non-stop
market distance we can put numbers behind the circuity to understand how many excess
miles were flown by the average random passenger. Those are shown in Table 5.1.
1 1980 1990 2000 2010
Large hub
Medium hub
Small hub
Non-hub
Commercial Service
28
34
57
39
56
36
44
72
75
75
39
49
92
107
82
40
64
100
108
98
Table 5.1.: Average excess miles flown
At the speeds of modern jets, the distances for large and medium hubs are fairly insubstantial, but at the smaller airports, particularly those served by turbo-prop aircraft, the
roughly 100 excess miles adds up to about 20 minutes at 300mph turbo prop speeds, which
when summed across all passengers is a considerable amount of time.
It is worth noting that the circuity presented in this analysis is a percentage of all 0D markets whether they were served with non-stop flights or connecting itineraries.
If
circuity were calculated based only on connecting flights we would see higher values for
circuity and hence more excess miles flown while on a connecting itinerary. When Morrison
and Winston analyzed circuity from 1978-1993, they removed nonstop flights and found
passenger-weighted circuity was 1.11 in 1978 and 1.13 in 1993, much lower than the 1.03
and 1.06 seen here at large and medium hubs where the majority of passengers originate. [53]
Morrison and Winston's analysis offers several insights not captured in this analysis of
5. Data analysis
circuity. First, they established that between 1978 and 1993, the percentage of passengers
in their data set that flew a connecting itinerary had risen from 28% in 1978 to 32% in
1993. If that analysis were done on this data set that figure would be higher because the
authors excluded itineraries involving more than 2 flight segments which account for more
than a small percentage of flights at smaller airports. Second, and more interesting, is that
in 1978, 14% of all passengers (50% of all connecting passengers) also changed airlines. By
1993 only 1% of all passengers changed airlines.[53] While this is not a technical increase
in accessibility, it is an increase in service quality because on-line connections are more
convenient from the passenger's perspective, particularly when it comes to rescheduling
delayed or canceled flights.
In summary, the data indicate that in terms of circuity, accessibility has been decreasing.
In 2010, at all airport types, passengers flew further in excess of non-stop distances than they
had in 1980, from 12 more miles at large hubs to 69 more miles at non-hubs. These excess
travel distances are a reality of the hub-and-spoke network and the increased distances are
a manifestation of the decrease in path quality already discussed.
5.5. Fares
The bright side of the decreased path quality and increased hub-and-spoke use is that
airlines are able to operate more efficiently by consolidating passengers. Improved efficiency
and the increased competition in the passenger airline industry has resulted in lower fares for
passengers. There are certainly other reasons that contribute to the lower fares including the
elimination of booking commissions, efficiency savings from computerization, and increased
competition from low cost carries like Southwest and JetBlue; but the reorganization of the
network is an important piece. Through the combination of all these factors, Figure 5.13
shows that across all airport categories, fares have declined by dramatic amounts even when
adjusting for inflation. In 1980 average ticket prices were between $270 and $323 (inflation
adjusted) depending on airport size. Throughout 1990 and 2000 fares dropped steadily and
5. Data analysis
by 2010 they averaged between $151-$184, a drop of 41-45%.2
E1980
.2000
01990
02010
$350
$300
$250
$200
$150
$100
$50
$0
Large Hub
Medium Hub
Small Hub
Non-hub
Commercial Service
Figure 5.13.: Average inflation-adjusted fares by airport type
As if the drops in fares were not impressive enough, they are even lower on a fare per mile
basis because passengers are on average flying to more distant destinations than they were
in 1980, in non-stop terms excluding circuity. Figure 5.14 shows the non-stop or market
distance of passenger weighted O-D markets. At large hubs the average market distance
increased by 19%, by 35% at medium and small hubs, by 44% at non-hubs, and by 19% at
Commercial Service airports. The increased average market distance is brought about by
declines in the percentage of trips in short-haul markets. The histograms in Figures 5.155.19 show the distribution of trips by market distance and show that trips of the shortest
distance have been decreasing in percentage terms. While there is no data in this data
2
There is an important data clarification to make here which is that the data on fares includes passengers
flying on frequent flier tickets which are typically $0 fares. These tickets could have been excluded, but
they were included because they reflect the flying environment today in which frequent flier tickets are
prevalent and a driver of passenger loyalty.
5. Data analysis
set to explain passenger choices the reduction is likely the result of shifts to other modes
like rail and busses particularly as short-haul flying became less convenient following 9/11
as well as shifts in the focus of carriers to longer-haul flights which are less expensive to
operate on a per mile basis.
*1980
* 2000
31990
§2010
1,200
1,000
800
600
400
200
0 r
Large Hub
Small Hub
Medium Hub
1
Large Hub
Medium Hub
Small Hub
Non-hub
Commercial Service
Non-hub
Commercial Service
1980 11990
2000
2010
1,073
854
816
776
615
1,107
907
882
857
699
1,187
996
972
980
774
1,001
738
719
681
650
Figure 5.14.: Average passenger weighted market distance by airport type
The histograms show what was already mentioned that at all airport categories except
for Commercial Service, itineraries under 500 miles decreased on a percentage basis while
trips in categories of 500 or more miles increased. At large hubs the percentage of sub-500
mile itineraries went from 30% in 1980 to 17.4% in 2010, at medium hubs they were down
5. Data analysis
to 27.7% from 48.7%, small hubs fell from 44.7% to 23%, and non-hubs dropped to 19.5%
from 43.6%. The conclusion from this is that with trip distances increasing substantially,
the relative fare decreases are even larger than when simply looking at the average fare
paid.
.1980
S2000
0-249
250-499
500-749
.1990
02010
750-999 1000-1249 1250-1499 1500-1749 1750-1999
Market Distance
Figure 5.15.: Large hub distance histograms
2000+
5. Data analysis
III
0-249
250-499
500-749
01980
*1990
a2000
02010
'Iiill!
ilil ilil
illi
750-999 1000-1249 1250-1499 1500-1749 1750-1999
Market Distance
iiI
2000+
Figure 5.16.: Medium hub distance histogram
10%
5%
01980
*1990
02000
02010
1111
Ftdl
I
11
I
0%r
0-249
250-499
500-749
750-999 1000-1249 1250-1499 1500-1749 1750-1999
Market Distance
Figure 5.17.: Small hub distance histogram
2000+
5. Data analysis
E198
*2000
0-249
I
250-499
500-749
0 1990
N2010
III
1000-1249 1250-1499 1500-1749 1750-1999
750-999
2000+
Market Distance
Figure 5.18.: Non-hub distance histogram
0-249
250-499
500-749
31980
01990
.2000
N2010
750-999
1000-1249 1250-1499 1500-1749 1750-1999
Market Distance
Figure 5.19.: Commercial Service distance histogram
2000+
5. Data analysis
The lower fares are also evident in the scatterplots in Appendix B. The scatterplots show
market distance and fares (not adjusted for inflation) for all O-D markets. To clean up
the data and make the analysis simpler, the markets were sorted based on market distance
and then every 10 markets averaged together. Table 5.2 gives the coefficients and fit of the
linear regression on those scatter plots.
1980
Large Hubs
Medium Hubs
Small Hubs
Non-hubs
Commercial Service
intercept
216.6
196.9
209.7
221.6
208.2
slope
.185
.217
.217
.222
.090
intercept
201.5
207.0
230.4
258.4
272.4
slope
.105
.103
.097
.097
.103
2010
2000
1990
1
fit
.855
.892
.869
.845
.869
fit
.717
.725
.648
.533
.372
intercept
162.7
161.2
192.2
214.5
222.2
slope
.087
.087
.079
.073
.079
fit
.765
.691
.650
.483
.438
intercept
123.7
125.4
162.4
199.3
208.5
slope
fit
.076 .725
.079 .737
.071 .599
.056 .404
.057 1 .333
Table 5.2.: Linear regression statistics on O-D markets (inflation adjusted)
The table displays the linear regression's Y-intercept, slope, and R 2 goodness of fit measure.
The intercept can be roughly interpreted as the base ticket price and the slope
represents the increased cost in dollars for each mile traveled. For example, the intercept
for large hubs in 2010 is 123.7 and the slope is 0.076; this means that for the average 500
mile flight from a large hub airport the average fare is 123.7 + (.076 * 500) = $151.70. The
R 2 is the scatter of the fares where a higher value indicates a tighter grouping and increased
accuracy of the slope and intercept where a lower value means greater variation and thus
decreased predictive benefit from the values.
5. Data analysis
-1980
-2000
Fare($)
1990
-2010
900
800
700
600
500
400
300
200
100
0
500
1000
1500
Miles
2000
2500
3000
Figure 5.20.: Average fares by mileage (1980-2010)
The regressions are combined for each year in a passenger-weighted average in Figure
5.20. What the graph shows is that not only have fares decreased in terms of the base fare
(Y-intercept) but the average cost per mile has also decreased (lower slope). The much
steeper slopes seen in 1980 indicating that fares were more closely tied to market distance.
This was probably a relic of the CAB's Standard Industry Fare Level formula that set fares
based partially on distance. This assumption is supported by the high R 2 values which
indicate that fares are were closely tied to distance.
Excluding 1980 with its universally high fit, we see better fit values for large and medium
hubs comparing 1990 to 2010 and good values at small hubs. At non-hub airports the slope
has remained roughly the same while decreasing at Commercial Service airports. The R 2
values are also lower at non-hub and Commercial Service airports which is expected given
the variety in the types of markets served from these smaller airports.
5. Data analysis
The series of figures and graphs on fares all depict the same thing, that flying in 2010 was
significantly less expensive than flying in 1980. Across all airport categories this conclusion
holds. The freedom of airlines to enter and exit the market has brought about the changes in
service, differentiation in service, and competition in the market that economists like Alfred
Kahn predicted would occur. These changes, along with technological improvements have
made the fare decreases possible. From the perspective of passengers we can conclude that
these changes have increased accessibility for all travelers.
The passenger's short-term perspective is that lower fares translate to higher accessibility
because as fares decline more passengers who could not afford to fly because higher fares can
now afford to access the aviation network. There are, however, issues associated with this
interpretation of accessibility and fares. Specifically, whether the low fares seen recently
are good for the financial health of the industry and hence for passengers in the longrun. That said, the tremendous decrease in fares since deregulation suggests that there
is some margin available for increasing fares to correct industry loses while still retaining
considerable accessibility benefit. A subsequent chapter in this thesis looks to the future of
the airline industry and that chapter will discuss how low fares affect the future of passenger
air travel. The issue is simply raised here to add the caveat that while lower fares have
increased accessibility for passengers in the short-run, in the long-run the excessively low
fares may not be good for the industry and hence the passengers.
5.6. Conclusion
Examining the evolution of traffic at Commercial and Primary Service airports from 1980 to
2010 we found that the accessibility changes have been mixed depending on the accessibility
measure used. The chapter examined accessibility from a variety of measures designed to
evaluate the different facets of accessibility and found some patterns across airports and
some dissimilarities.
The chapter began by evaluating the path quality of flights which is a measure of the con-
100
5. Data analysis
venience of flying and the number of connections required on average to reach a destination.
Path quality was highest at large hub airports and generally decreased as passenger volume
decreased at an airport. Further, we found that path quality had remained relatively constant at large hubs and non-hubs while declining at medium hub and small hub airports
due to increased hubbing. On the basis of this accessibility measure, we conclude that
accessibility decreased across many airport types with the particularly notable exception of
Commercial Service airports which saw increased path quality. The increased path quality
at Commercial Service airports was likely due to the decreased number of destinations with
service from Commercial Service airports and thus service quality being relatively higher
for the remaining O-D markets.
The chapter next examined passenger volume 3 which relates to accessibility in 2 respects.
First, since accessibility is about the movement of people, as passenger volume increases we
can conclude that accessibility has also increased. Second, because higher passenger volume
means more revenue for the airport to improve the passenger experience traveling through
the airport. We saw that among the majority of airport classifications passenger volumes
increased substantially between 1980 and 2010, but we also saw that between 2000 and 2010,
passenger growth slowed greatly and even declined in some markets. This slowdown was
due primarily to several events that occurred between 2000 and 2010 including the economic
downturn associated with the DotCom bubble, the global recession in 2008 lasting through
2010, and the terrorist attacks on September 11 all of which decreased demand for air
travel. We examined the distribution of passenger volume and volume growth and found
that large hub airports dwarfed all others and that traffic was highly concentrated at those
airports. We did find that in terms of passenger volume growth, medium hub airports had
even greater growth than large hubs attributable mostly to the arrival of low cost carriers
to most medium hub airports.
The number of destinations available on average from each airport category was also
3
Passenger volume was not normalized for population growth or income growth not related to inflation.
101
5. Data analysis
reviewed. The results showed that across the board, there were dramatic decreases in the
number of destinations available, likely the result in the loss of service to some airports or
the increased number of connections required to reach the airport. The reduction in the
number of destinations available was likely the result of deregulation which allowed airlines
to service only the O-D markets they chose. As a result, a number of airports that were
protected by the CAB during regulation lost some destinations or in some cases service
all together in the deregulated market. These reductions in O-D market choices translated
directly into a loss in accessibility for all passengers desiring to travel in those markets.
In addition to path quality, we also examined circuity as a measure for the convenience
of flying. The review of circuity found that in terms of the random O-D market, circuity
had risen for all airport sizes and was inversely related to the size of the originating airport.
In terms of the average random passenger, we see that circuity is much lower than for the
average random airport because the airlines are economically able to provide better path
quality to more highly trafficked routes. We see that in 2010 vs 1980 circuity added between
40 and 108 miles to the average passenger trip. Circuity is a product of the increased use
of connecting flights via hub-and-spoke systems that were evident in the decreasing path
quality measures and while hubbing has increased airline efficiency, those efficiency gains
come at the cost of increased circuity and passenger inconvenience which in turn translates
into decreased accessibility.
The last measure examined here was fares.
During the debates about deregulation,
fares were repeatedly brought up as one of the benefits that passengers would see from
deregulation. This has proven to be true with airports across the board seeing large drops
in fares. From the perspective of most passengers this can be regarded as a major gain in
accessibility which came about because of increased competition and the efficiency gains
from some of the other changes depicted here that decreased accessibility.
Not all accessibility measures are equal so it is not so simple as adding up the number of
measures that showed an increase and comparing it with the number that showed a decrease
5. Data analysis
to make a pronouncement about whether accessibility has increased since deregulation. For
example, a sizable percentage of leisure travelers would be willing to take a connecting
flight in exchange for the more than 40% reduction in fares seen at many of the airports.
However, many business travelers who want as smooth a trip as possible and are relatively
price insensitive would not make this trade-off. Again, this highlights that difficulty of
making a general pronouncement about accessibility or predicting political reactions both
because of the inequality among measures and the different needs and desires of different
types of travelers.
That said, the Table 5.3 summarizes the accessibility measures discussed in this chapter
with arrows showing the directionality of accessibility changes along with the percentage
that that accessibility measure changed. The table shows that 3 of 5 measures suggest a decrease in accessibility. Amongst those showing increased accessibility, the most convincing
is the dramatic decrease in fares, while the most convincing example of decreased accessibility is the large drop in the number of destinations served. We see some variation across
airport sizes, but by and large the directionality remains the same while the magnitude of
changes differs substantially with large hubs having the best accessibility and accessibility
decreasing as airports get smaller in terms of passenger enplanements.
Changes in accessibility from 1980 to 2010
Path Quality
Passenger Volume
Circuity
Fares
Number of Destinations
Large Hub
Medium Hub
Small Hub
Non-hub
++ (-0.3%)
? (154.5%)
4 (39.8%)
T (-46.7%)
4 (-19.9%)
4 (-6.7%)
4 (-6.6%)
t (158.7%)
? (131.3%)
4 (76.7%)
? (-41.7%)
4 (-10.2%)
++ (-0.2%)
? (61.1%)
4 (175.5%)
T (-42.9%)
4 (-5.2%)
4 (88.7%)
? (-44.1%)
4 (-15.2%)
Commercial Service
? (28.2%)
4 (-55.6%)
4 (73.1%)
? (-45.2%)
4 (-37.7%)
Table 5.3.: Summary of accessibility measures
This chapter examined the airports at an aggregate level. Chapter 6 drills down a bit
to examine the "winners" and "losers" among airport types by examining which airports
deviate substantially from the mean.
Chapter 6 also offers a specific example of using
these measures to understand changes in accessibility at Boston's Logan Airport and the
103
5. Data analysis
surrounding airports in Manchester, NH and Providence, RI whose passenger catchment
areas overlap with Boston's.
104
6. A closer look: Airport specific impacts
Chapter 5 showed that while accessibility has changed over the last 30 years, it has not
changed in the same way in all areas. For example, large hubs, as a whole, did not see
some of the decreases in accessibility that many smaller airports did. This chapter seeks to
increase the understanding of the varied accessibility changes by examining the "winners"
and "losers" in terms of accessibility. We also look for geographic patterns that could be
useful in developing focused policies to improve accessibility.
In that vein, the chapter also looks at accessibility for a timely policy topic in US passenger aviation, Essential Air Service. The program which subsidizes air service to small
communities is under pressure from many who see it as a waste of taxpayer dollars, but
it has supporters who argue that it preserves a vital economic link for small communities.
This chapter does not weigh in on whether the program should exist or not, but rather
seeks to understand the outcomes of the program as they relate to the program's goal of
preserving a reasonable level of accessibility for the communities it supports.
Finally, this chapter turns its attention to Boston and shows how the accessibility measures developed in this thesis can be applied to a large airport to understand accessibility
changes in a detailed way and to understand how changes at competing airports, in this
case Manchester, NH and Providence, RI, can impact accessibility.
105
6. A closer look: Airport specific impacts
6.1. Winners and losers
This section of Chapter 6 examines the "winners" and "losers" in the changing passenger
aviation environment. Chapter 5 examined the airport categories in relation to one another
and showed that Large hubs offer the highest levels of accessibility to passengers and that
accessibility tends to trend downward as the size of the airport declines. In this section individual airports within the categories are examined. The analysis is focused on 2 elements:
movement among quartiles and a quartile based accessibility ranking.
Chapter 5 showed that airports saw declining accessibility in some categories and increasing accessibility in others; this chapter compares the movement of an airport relative
to movements of other airports in the same category to see which airports fared better
or worse. The movement between quartiles is how a "winner" or "loser" is defined. If an
airport was in the bottom quartile within its airport classification in 1980 but moved up
the middle quartiles or even the top quartile by 2010 in terms of the accessibility measure
examined, that airport is considered a "winner", and vice versa for "losers". This quartile
movement is shown for 2 accessibility measures: path quality and number of destinations.
The section also combines the path quality and number of destinations quartile ranking
into an accessibility ranking for airports in 2010.
6.1.1. Large hubs
The figures in this section examine movement of large hub airports between quartiles within
the large hub category. In Figure 6.1 the lines represent the values for each airport to
facilitate visual comparison between airports and the colors depict the quartiles with red
indicating the bottom quartile, black representing the middle 2 quartiles, and green showing
those airports in the top quartile.
106
6. A closer look: Airport specific impacts
Airport
ATL
BOS
BWI
CLT
DCA
DEN
DFW
DTW
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IAD
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TPA
1980
1990
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ii;
liii
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2010
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Figure 6.1.: Large hub path quality quartiles
Examining path quality among large hubs in Figure 6.1 shows few big winners in the
sense that an airport in the bottom quartile of path quality moved to the top quartile. More
commonly, those airports with relatively high path quality in 1980 remained relatively high
in 2010, Atlanta (ATL) being a good example. In fact, Newark (EWR) is the only large hub
in the top quartile in 2010 that was not in the top quartile in 1980. That said, Figure 6.1
does show some airports that were at the bottom of the distribution in 1980 that have moved
into the middle quartiles; specifically, Baltimore (BWI), Honolulu (HNL), Houston (IAH),
107
6. A closer look: Airport specific impacts
and Orlando (MCO). Going in the other direction, there were 4 airports that moved in the
opposite direction falling from the middle quartiles to the bottom; including Boston (BOS),
Seattle (SEA), Tampa (TPA) and Washington-National (DCA) which saw the largest path
quality point decline among large hub airports. Interestingly, these are coastal airports that
by virtue of their location are not well suited to serve as a major domestic hub (though
some have developed as international hubs). While these are both relatively unconcentrated
markets with service from a number of carriers, the fact that they are not major hubs creates
the need for more connecting flights.
Beyond relative comparisons, the figure shows that there were 13 absolute winners and 16
absolute losers among the large hubs. That is, 13 airports saw an increase in path quality
from 1980 to 2010, while 16 saw a decrease.
A similar pattern emerges among large hubs in terms of destinations. Every airport that
was in the top quartile in terms of destinations served in 2010 was in the top quartile in
1980. There was more movement among the middle and bottom quartiles. Miami (MIA)
and Tampa (TPA) lost relative ground dropping from the middle quartiles in 1980 to the
bottom quartile in 2010 while Charlotte (CLT) and Newark (EWR) moved up to the middle
quartiles.
Figure 6.2 shows that in absolute terms most airports lost destinations-only
Chicago-Midway (MDW) and Washington-Dulles (IAD) had service to more destinations
in 2010 than they did in 1980.
108
6. A closer look: Airport specific impacts
Airport
ATL
BOS
BWI
CLT
DCA
DEN
DFW
DTW
EWR
FLL
HNL
IAD
IAH
JFK
LAS
LAX
LGA
MCO
MDW
MIA
MSP
ORD
PHL
PHX
SAN
SEA
SFO
SLC
TPA
2010
2000
1990
478 Ill||iII
453|||||
Iii 540 IllIl||l
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539 |||| ||| 489 illiiI
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ii 1111111
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mill'
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Figure 6.2.: Large hub destination quartiles
Figure 6.3 combines path quality and destinations to depict accessibility at the large hubs
in 2010. In each category, path quality and number of destinations, an airport was given
a 1 if it was in the top quartile in 2010, a 0 if in the 2nd or 3rd quartile, and a -1 if in
the bottom quartile. The middle quartiles are treated equally to highlight airports at each
end of the spectrum as opposed to more closely examining those closer to the median. The
values were added together for each airport to give the values depicted on the map. A value
of 2 represents the highest accessibility and an airport in the top quartile in both path
109
6. A closer look: Airport specific impacts
quality and number of destinations, a 1 implies top quartile in one category and middle in
the other, this pattern continues down to -2 indicating the lowest accessibility.
-2(_
a
e
_es
- -
b
I.
1
1
LAXf
00
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o
Figure 6.3.: Large hub accessibility
Geographically, the map shows that the highest accessibility is in the middle of the country at Denver (DEN), Dallas-Fort Worth (DFW), and Chicago-O'Hare (ORD), locations
that make the airports well suited as network hubs which brings high accessibility. The
pattern that emerges for large hubs with lower accessibility is that they tend to be located in
coastal corners making them not well suited to function as network hubs specifically, Boston
(BOS), Miami (MIA), San Diego (SAN), and Tampa (TPA), which has the lowest accessibility of all large hubs, though, as already noted, some have developed as international
hubs.
Fares are not examined here because the wide variety in average market distance at
airports makes any comparison between airports not particularly relevant. Though, as the
110
6. A closer look: Airport specific impacts
results in Chapter 5 showed, average fares at Large hubs have dropped steadily between
1980 and 2010, an average of 47%. The largest decreases were seen at Washington-Dulles
(IAD) 63%, Denver (DEN) 58%, Phoenix (PHX) 56%, and Seattle (SEA) 55%.
6.1.2. Medium hubs
Turning to medium hubs, Figure 6.4 shows that 10 of the 35 medium hub airports experienced increased path quality between 1980 and 2010 with the remaining 25 seeing diminished path quality. Among those moving up the rankings were Austin (AUS), Columbus
(CMH), and Raleigh/Durham (RDU) which moved from the bottom to middle quartiles
and Cleveland (CLE), Milwaukee (MKE), Cincinnati (CVG), and Palm Beach, FL (PBI)
which moved to the top quartiles from the middle. Falling from the top to the middle quartiles were Ontario, CA (ONT) and Orange Country, CA (SNA) while Albuquerque (ABQ),
San Antonio (SAT), and Tuscon (TUS) moved to the bottom from middle quartiles.
111
6. A closer look: Airport specific impacts
19980
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Figure 6.4.: Medium hub path quality quartiles
Only 4 medium hub airports saw increases in destinations from 1980 to 2010, although
in all 4 cases those airports started from such a low base that in 2010 they remained
in the bottom quartile despite their increase in destinations. Anchorage (ANL) was the
clear winner, moving from the bottom quartile to the top between 1980 and 2010. No
airports moved from the top to the bottom quartile, though those airports losing the largest
112
6. A closer look: Airport specific impacts
percentage of destinations were St. Louis (STL) 29% reduction, Cleveland (CLE) 29%,
Pittsburgh (PIT) 28%, and Hartford, CT (BDL) 26%.
Airport
ABQ
ANC
AUS
BDL
BNA
BUF
BUR
CLE
CMH
CVG
DAL
HOU
IND
JAX
MCI
MEM
1990
1990
30
222
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Pal
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PDX
PIT
PVD
RDU
RNO
RSW
SAT
SDF
SjC
SMF
SNA
STL
TUS
33
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11111 432
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452
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2010
2000
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Figure 6.5.: Medium hubs destinations quartiles
Looking at accessibility in 2010 for medium hubs shows that accessibility is geographically
dispersed. None of the medium hubs fell into the category for the highest accessibility
meaning no medium hub was in the top quartile for both path quality and destinations.
113
6. A closer look: Airport specific impacts
On the other end of the spectrum Hartford, CT (BDL) and Providence, RI (PVD) in New
England fall into the lowest level category. While New England fared the worst, Rust
Belt cities and California show a pattern of relatively good accessibility compared to other
airports of similar size.
&T
--
0
HC
90-
AXL
*-2 (lwest accessbt)
0-1
0 0
0
1
Figure 6.6.: Medium hub accessibility
Again, the variety among medium hub airports and varying average market distances
make comparison of fares and fare changes not particularly insightful. However, the overall
trend indicates increased accessibility brought about by the 44% average fare decrease
seen since 1980. Since 2000 despite slowing growth and rising fuel prices, fares continued to
diminish with only 4 airports seeing average inflation adjusted fares rise, Dallas-Love (DAL)
13%, Burbank, CA (BUR) 12%, Reno, NV (RNO) 10%, and Houston-Hobby (HOU) 4%.
6.1.3. Small hubs
Looking at small hubs, between 1980 and 2010 we see that 22 airports saw path quality
increase while 42 saw declines in path quality. Because of the large number of airports
114
6. A closer look: Airport specific impacts
moving among path quality quartiles, a full listing of quartile movement is not given in the
text. Of particular note are the biggest movers; Santa Barbara (SBA), Tallahassee (TLH),
and Fresno (FAT) which fell from the top quartile to the bottom implying large declines in
accessibility. Trading places from the bottom to the top quartile were Atlantic City (ACY),
New Burgh, NY (SWF), and Manchester, NH (MHT).
Airport
1980
ABE
||1l1l 0529
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ALB
|||||||||| 0.667
AMA
in 0.366
|
Ali .73
1990
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0.631
LGB
0.595
11111111
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BTR
STV
11||||| 0.628
CAE
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|||11110.535
|1
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|||1|
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0.565
Illil
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1111111
0.651
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0.708
0.596 111111111
11111111
0.428
111111
0.454
11111
0.493
11111|
1111111
0.482
11111111
0586
1110111
0.34
0.488
11111
1i1l0.451
i 11 0.449
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0.643
DAY
1ill11i1
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0.735
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0.420
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GRB
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MHT
MLI
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11111111
0.543
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1111111
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|I111|1 0.738
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11
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IIIIII| 0.724
2000
11 0.542
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2010
11111111
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||||| 0.413
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0.502
1|||10.486
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0.919
1I1||||I||I|| 0.986 II|||||I 0.762 I I 0.781 III1I1I11
11111111
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11111
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11|10.559
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111111111
0.555
11||1
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1illi1 0.605
ii
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11||0554
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|||Illl| 0.648
0.606
1IIII1I
SAV
11ll 0.460
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0.498
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11111
0A59
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0.689
111111
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11 0.591
||11|| 0.545
1il 0A54
0.551
1110
SFB
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SRQ
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1111 0.615
111 0.410
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l1||0.374
1i 0.373
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ii 0.517 1il 0.439
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0.534
l81ll 0.689 11111911
liii|0.435
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0.780
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100|0.861
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0.562
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1980
11111111
0.562
111 0.478
0.727
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0.443
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HPN
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1||11
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0.689
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illili 0.694
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11110363
GPT
JAN
||11 0.664
BIL
B01
GEG
Airport
0.542
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0383
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Cos
CRP
2010
|01||61 0470
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iii 0.437
2000
0552 111i 0.556
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11|1111
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0397
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i 0.449
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ll0A440
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027
0-|
1ig0.550
Figure 6.7.: Small hub path quality quartiles
In terms of destinations served, Figure 6.8 shows that all but 9 airports lost accessibility
115
6. A closer look: Airport specific impacts
in the form of destinations served since 1980. The size of destination declines varied across
airports. Those small hubs seeing the largest decline in destinations served were Dayton,
OH (DAY) 117, Des Moines, IA (DSM) 113, Tulsa, OK (TUL) 110, and Sarasota, FL 107.
On the other hand, several airports saw even larger increases: Long Beach, CA (LGB) 222,
Rio Grande, TX (HRL) 196, Westchester, NY (HPN) 179, and Manchester, NH (MHT)
135.
1980
Airport
ABE
1990
2000
11111111
354 1lili11348
ACY
11
ALB
1|| 407
87
1|11||||339
2010
I1 286
Ill 165
388
1|||11 332
LBS
LEX
111 261
LGB
AMA
1|1ill
331
269
BHM
Il1101 398
371
Ill111372
Hug
331
BIL
I11||1
354
IIliIll
330
|||111 337
BOI
illli
381
iI
366
|||1111381
||10
BTR
llili
BTV
lilll
345
313
|Rill
323
11li11 354
379
l11111l 327
|lli
CHS
il
CID
ilillIl
379
Cos
DSM
1||
ELP
1|| 396%
FNT
437
GRR
GSO
GSP
HPN
%
1111 293
MSN
404
1llllIll
llull
339
|1111 302
MYR
OKC
381
1111337
ORF
|||111
233
11111
1||| 178
Hli 239
111111
243
11111111
323
283
1111111
19II 292
111 288
PHF
1||1 387
PNS
1|||
367
iii359
337
11|11324
ll
366
|11111349
PWM
igi 333
11111339
RIC
1||1 395
||||
li1l| 278
111111368
ROC
SAV
||Blu417
0100
11I
343
293
302
SBA
li01 313
376
11111343
SGF
1l1|il1 364
|1111 327
SRQ
1i1||1 356
11lli1
321
111|275
SWF
234
TLH
i1111
iI 268
1111|283
l111
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315
i11vii 384
1in11 368
11||| 356
19111 309
111
1
268
|||l
273
38
HSV
101||| 322
||111 332
mmi432
mmg397
|||gl|||
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Iii230
|||||
1||||1 328
Im||1
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||m 416
|||||1
308
TUL
110 455
1111| 380
|||||1
259
352
1|||| 286
1||11 333
234
i1||i1 286
TYS
VPS
ISP
Jl
178
l1111294
||||11 274
Jill 185
XNA
II1it1
|||11
0011272
313
304
319
1 11i 285
ggg
345
1111 328
|11
|l||
361
389
11l11322
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364
386
328
Hug
358
1111 323
1ill1|
1||11 324
ligg1330
1
304
1111316
liig1 313
5
33
1111111
315
1111 327
322
11111111
359 11111111
li111295
|||||
1||317 11111
306
||1l
1811 289
359
372
||||
SFB
ILM
268
ng||340
PSP
SYR
111
11111111
354
279
322
376 11||||| 369
421 11111111
464 1111111111
361
0
111111111
40 1 11||11
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439 111111111
1ii 281
p~l 252 ||I 239
|Il247
200 332 NHH 331 1111 327
||p|327
391
11
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1111111
294
11111111
352
111111111
374
I111
326
1ggli
HRL
ICT
Ii
1011 253
11111111
322
11111
278
11
363
111111111
360 111111111
377 111111111
345
l11|11340
388
li
MDT
11111309
282
11|11l390
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MAF
361
355
||llig
417
0111||324
1101|1 380
343
111111111
384
11111111
||1li|||
imil
1||111111
4 11
MLI
11
||i' 255
1ll1ll1
340
LIT
1111 322
1ull 276
ill||11
378
1111 280
1|| 316
1|1| 317
33
|11111347
280
GPT
GRB
111111
284
Im1it 358
tN 320
111 370
|||I11
11111
262
11111111
320
111111
340
1111111
319
Ji 166
MHT
1111l 360
1l11l0l422
296
£11111
1111111377
MFE
322
1111 341
GEG
I111 288
l111315
335
11111111
i1101344
111111326
1111 294
|ill1l
2010
2000
11l111l313
Illi1 290
327
019|378
1illi1j454
1990
1980
375
11111111
1111111
319
11
373
DAY
FAT
||al
||R382
CRP
JAN
1ll 177
III11I
365
1111278
CAE
CAK
Airport
201
||11111l 399
111111
266
|111|111 371
268
Il
222
Ill11 327
|||
279
111111
290
111111
295
||||1l
345
388 111111354
111111111
|ill||343
11111111
359 111111111
367
I
285
H1ll268
||||l
252
111 306
1111|11
302
Figure 6.8.: Small hub destination quartiles
The accessibility metric previously developed is shown in Figure 6.9 for small hubs. The
116
6. A closer look: Airport specific impacts
Northwest has the 2 airports with the highest accessibility Spokane, WA (GEG) and Boise,
ID (BOI). The Southern US fares the worst, particularly along the Gulf and up the Atlantic
coasts with Eglin, FL (VPS), Talahassee (TLH), and Wilmington, NC (ILM) having the
lowest accessibility scores and a number of other airports in the -1 category.
The other geographic pattern that emerges across the large, medium, and small hub
airports is that (ignoring the accessibility rankings) the sheer number of airports is much
higher in the Eastern half of the country than in the West until California. While, population is lower in those areas meriting fewer airports, there still appears to be an advantage
for Eastern cities in terms of average distance to the nearest airport and hence easier access
to the airline network.
BL
~
I
M~GR&
9'ROSYR
.
-----
0-
.
~W
LGBPSP'
LBB
ELP
O0
01
*
2 (n~inestaccessiomlty)
Figure 6.9.: Small hub accessibility
Across small hub airports, inflation adjusted fares fell at all airports except Rio Grande,
TX (HRL) between 1980 and 2010 falling an average of 42%, indicating considerable improvement in accessibility. The airports that saw the largest percentage decline in real fares
were Atlantic City (ACY) 74%, Colorado Springs (COS) 60%, Newport News, VA (PHF)
117
6. A closer look: Airport specific impacts
60%, and Myrtle Beach (MYR) 58%. As noted HRL, in Rio Grande, TX was the only
airport to see average fares rise over that period from $90.43 in 1980 to $136.92 in year
2010 dollars (51%).
6.1.4. Non-hubs
There are too many non-hub airports to include the full list here, though it is included
in Appendix C and D. Focusing on the path quality measure, there were many non-hub
airports that moved either from the bottom to the top or vice versa. The airports improving
from the bottom quartile to the top were: Block Island, RI (BID), Port Angeles, WA
(CLM), Kearney, NE (EAR), Eagle County, CO (EGE), Laramie, WY (LAR), Macon, GA
(MCN), Punta Gorda, FL (PGD), St. Petersburg, FL (PIE), Provincetown, MA (PVC),
Rockland, ME (RKD), and Sonoma County, CA (STS). Notably, many of these airports
are in desirable vacation-type communities. Moving in the other direction were Waterloo,
IA (ALO), Beaumont, TX (BPT), Carlsbad, CA (CLD), Dubuque, IA (DBQ), Klamath,
OR (LMT), and Mason City, IA (MCW). The number of destinations saw fewer large
movements with only New Bern, NC (EWN) and Redmond, OR (RDM) moving from the
bottom to top quartile in terms of destinations served.
118
6. A closer look: Airport specific impacts
Figure 6.10.: Non-hub accessibility
Figure 6.10 shows the accessibility metric at non-hubs in the continental US. Among those
with the lowest accessibility values were Altoona, PA (AOO), Chico, CA (CIC), Inyokern,
CA (IYK), Mason City, IA (MCW), Oxnard, CA (OXR), Plattsburg, NY (PBG), and
Tupelo, MS (TUP). On the other end with the highest accessibility among Non-hubs are
Bismark, ND (BIS), Sioux Falls, SD (FSD), Great Falls, MT (GTF), Medford, OR (MFR),
Missoula, MT (MSO), Peoria, IL (PIA), and Redmond, OR (RDM).
One pattern in the airports with the highest accessibility ranking is that they are more
concentrated across the norther Rockies and Plain States. When comparing the location
of the non-hubs with the greatest accessibility to the maps depicting large, medium, and
small hubs, it is apparent that these are some of the areas least served by larger airports.
This suggests that in many of these communities these non-hub airports are the only option
for fliers which may help to explain why these airports have higher accessibility levels.
As with other airport sizes, non-hubs saw real fares decline 42.8% on average from 1980
to 2010. Among the airports benefiting from the largest percentage decline in fares were
119
6. A closer look: Airport specific impacts
Hyannis Port, MA (87.9%), Youngstown, OH (79%), St. Petersburg, FL (77.2%), and
Bellingham, WA (76.4%).
6.1.5. Commercial Service
Less space is devoted to Commercial Service airports here because many of the Commercial
Service airports are also Essential Air Service (EAS) airports which are covered extensively
in Section 6.2. There were a handful of big winners and losers. First, in terms of path
quality there were 3 airports making big moves from the bottom to the top quartile: Athens,
GA (AHN), Eastsound, WA (ESD), and Adirondack, NY (SLK). Interestingly 2 are EAS
airports, AHN and SLK, and at least some of their improved path quality must be attributed
to that government subsidy. On the other end of the spectrum, Jamestown, ND (JMS) fell
from the top quartile to the bottom quartile in terms of path quality. The only major
changes in destinations served occurred at Devil's Lake, ND (DVL) which fell from the top
to the bottom quartile.
Figure 6.11.: Commercial Service accessibility
Figure 6.11 shows the accessibility measure across Commercial Service airports. The map
120
6. A closer look: Airport specific impacts
depicts the interesting situation in which none of the Commercial Service airports had a
score of 2 or -2 indicating the highest and lowest accessibility respectively. This means that
accessibility is less disparate across Commercial Service airports than other airport sizes.
There does appear to be a larger number of more highly ranked airports in the middle of
the country with a concentration of airports with average or below average accessibility
around the Ohio River Valley.
In terms of inflation-adjusted fares, as noted in Chapter 5, they fell 45.2% overall, the
airports with the biggest percentage declines were Eastsound, WA (ESD) 87.6%, Athens,
GA (AHN) 79.2%, and Silver City, NM 73.0%.
6.1.6. Summary
This section examined "winners" and "losers" within each airport categories. There was
little consistency across airport categories and no discernible geographic pattern relating to
accessibility emerged across airport types. For example, in the middle of the country people
living in Chicago, Dallas-Fort Worth, or Denver have the highest levels of accessibility
anywhere in the country, but many other cities in the same or nearby states have only
average-to-poor accessibility or have long distances to travel to reach the nearest airports.
On the other hand, airports on the East Coast have some of the lowest accessibility levels
in their categories (Tampa, Providence, Hartford, Tallahassee), but the Eastern half of the
US has so many airports that the lower relative accessibility may be made up for by the
sheer number of airports.
121
6. A closer look: Airport specific impacts
Figure 6.12.: US accessibility (2010)
Figure 6.12 takes the earlier geographic analysis further by looking at accessibility on a
state-by-state basis using the quartile-based accessibility ranking based on path quality and
number of destinations. There are some limitations to looking at accessibility this way. The
first is that state boundaries are arbitrary, thus someone living in northern California derives
more benefit from accessibility in Oregon than they do from accessibility in Los Angeles.
Second, because the accessibility depicted here is passenger weighted, high passenger volume
at one airport can swamp lower accessibility at another airport in the state. For example,
Massachusetts falls into the highest accessibility category because Boston-Logan accounts
for 97.9% of all traffic in the state which masks the lower accessibility at the 6 other airports
in the state.
Despite these reservations, the map is useful for understanding accessibility in the US. It
shows that the northern Plain States, the South (save Georgia with the hub in Atlanta),
and New Hampshire, Vermont, and Maine in New England all have lower levels of accessibility while the West Coast in particular has the largest concentration of higher levels of
122
6. A closer look: Airport specific impacts
accessibility along with states that are home to hubs like Colorado with Denver, Georgia
with Atlanta, and Minnesota with Minneapolis/St-Paul.
Another way of examining the data geographically (though not done here) is to analyze
airport catchment area accessibility. Looking at the airport catchment area would counteract the lumpiness created by a state-by-state review. It would also depict the accessibility
benefits that can occur when airports have overlapping catchment areas. The case study
of Boston's Logan Airport in Section 6.3 demonstrates how this approach to evaluating
accessibility can be used to explore accessibility geographically, particularly in the case of
overlapping catchment areas. In the case of Logan Airport, nearby airports in Providence,
RI and Manchester, NH provide accessibility gains to Logan by relieving strain on the
airport infrastructure and providing competition that encourages lower fares. These are
benefits that would be missed in a state-by-state analysis.
6.2. Essential Air Service
Essential Air Service (EAS) is a program created by the Congress during the deregulation
process. During the deregulation discussions, there was concern that small communities
would be unable to generate enough demand to sustain economically viable, regularly
scheduled service once the CAB ceased to mandate the service.
As a result, Congress
passed legislation creating the EAS. The structure of the program is such that the US DOT
establishes a minimum service level required at each community and the government pays,
if necessary, a subsidy to a single carrier to ensure that the minimum level of service is
achieved. According to the law establishing EAS, there are four basic components of EAS
service levels[55]:
" service to a large or medium hub airport
" no more than one connection required to reach the large or medium hub airport
123
6. A closer look: Airport specific impacts
" for communities with 12 or more daily passengers on average, the airplane servicing
the airport must have at least 15 seats
" flights must occur at reasonable times to facilitate connecting flights at the large or
medium hub airport
In response to concerns about the cost of the program, Congress has tried to tighten the
eligibility requirements. Beginning in 2000, the requirement for EAS subsidy was tightened
so that the EAS airport must be located 70 or more highway miles away from the nearest
large or medium hub airport and the subsidy could not exceed $200 per passenger unless
that distance is 210 surface miles.[55]
As Table 6.11 shows, as of May 2010, there were 107 airports receiving EAS service
outside Hawaii and Alaska. The number of EAS airports fluctuates and had declined to 74
communities in 2001. Following the terrorist attacks of 9/11, demand for travel fell which
resulted in more small communities unable to sustain service, and since 2001 the 74 EAS
airports have grown to 107.
The map in Figure 6.13 shows the 107 EAS airports in the continental US and highlights
which EAS airports are included in this analysis. The map shows that the heaviest concentration of EAS airports is in the mid-West and along the Appalachian mountains in the
East.
'Many EAS airports do not meet the FAA requirements for classification as a Primary or Commercial
Service airport due to low enplanement figures. As a result they are classified as General Aviation
airports. The analysis in this thesis is confined to Primary and Commercial Service airports and as such
General Aviation EAS airports are excluded from the EAS analysis here. Those excluded airports are
marked by an asterisk in 6.1.
124
6. A closer look: Airport specific impacts
+
+4
+~
+
+
+
+
...
.
. .....
........
r*+
+
+
~+
++
------
+
EAS(includedin analysis)
+
EAS(excludedfrom analysis)
- -------I
Figure 6.13.: EAS airports
As noted, the EAS program faces scrutiny from the public and Congress when budget
cutting measures are discussed. However, cuts are rare because of the value to politicians
of preserving service to communities in their districts despite the fact that traffic is quite
low on many of the routes and has been declining since 1980. Traffic has been particularly
poor at EAS airports where there are alternative airports reasonably close by that have
good accessibility or are served by low cost carriers, resulting in potential EAS passengers
driving further for lower fares or better accessibility. Table 6.12 shows the current EAS
airports along with the large or medium hub to which they have service and the subsidy
for that service. A list of EAS communities that no longer receive an EAS subsidy is in
Appendix G.
2
Airports marked with an asterisk are excluded from this EAS analysis because they are too small to be
classified as Commercial Service airports. See previous footnote.
125
6. A closer look: Airport specific impacts
Code
Community
Hub
Subsidy ($)
MSL
ELD
HRO
HOT
JBR
IGM
PGA
PRC
SOW
CEC
IPL
MER
VIS
ALS
CEZ
PUB
AHN
MCN
BRL
FOD
MCW
DEC
MWA
UIN
DDC
GCK
GBD
HYS
LBL
SLN
OWB
PAH
HGR
AUG
BHB
PQI
RKD
ESC
CMX
IMT
IWD
MBL
MKG
HIB
TVF
CGI
COU
TBN
JLN
IRK
GLH
LUL
MEI
GGW
Muscle Shoals, AL
El Dorado/Camden, AR*
Harrison, AR*
Hot Springs, AR*
Jonesboro, AR*
Kingman, AZ*
Page, AZ
Prescott, AZ
Show Low, AZ
Crescent City, CA
El Centro, CA
Merced, CA*
Visalia, CA*
Alamosa, CO
Cortez, CO
Pueblo, CO
Athens, GA
Macon, GA
Burlington, IA
Fort Dodge, IA
Mason City, IA
Decatur, IL*
MarionHerrin, IL
Qunicy, IL*
Dodge City, KS
Garden City, KS
Great Bend, KS*
Hays, KS
Liberal, KS/Guymon, OK
Salina, KS
Owensboro, KY*
Paducah, KY*
Hagerstown, MD*
Augusta/Waterville, ME
Bar Harbor, ME
Presque Isle/Houlton, ME
Rockland, ME
Escanaba, MI*
Hancock/Houghton, MI
Iron Mtn/Kingsford, MI
Ironwood/Ashland, MI*
Manistee, MI*
Muskegon, MI
Chisholm/Hibbing, MN
Thief River Falls, MN
Cape Girardeau, MO*
Columbia/Jefferson City, MO
Fort Leonard Wood, MO
Joplin, MO
Kirksville, MO*
Greenville, MS
Laurel/Hattiesburg, MS*
Meridian, MS
Glasgow, MT*
MEM
MEM
MEM/MCI
MEM
MEM
LAS
PHX
ONT/DEN
PHX
SFO/SMF
LAX
LAS
ONT
DEN
DEN
DEN
ATL
ATL
STL/ORD
MSP
MSP
STL/ORD
STL
STL
DEN/MCI
DEN/MCI
MCI
DEN
DEN
MCI
BNA
ORD
BWI
BOS
BOS
BOS
BOS
DTW/MSP
ORD
DTW/MSP
MKE
MKE
ORD
MSP
MSP
STL
MEM
STL
MCI
STL
MEM
MEM
ATL
BIL
1,782,928
2,096,517
1,695,929
1,419,102
836,241
1,275,771
1,995,273
1,622,719
1,407,255
1,136,896
662,551
1,541,365
1,494,319
1,853,475
1,297,562
1,299,821
1,051,386
1,386,306
2,171,241
1,112,607
1,112,607
3,082,403
2,053,783
1,946,270
1,842,749
1,884,303
1,257,617
1,954,327
1,958,570
1,489,435
1,068,773
569,923
1,203,167
2,086,251
2,086,251
2,643,588
1,522,770
1,435,118
1,404,714
1,435,118
1,492,865
1,799,395
660,720
2,938,878
1,230,322
1,573,818
2,186,590
1,292,906
997,680
806,169
1,355,693
1,191,435
686,489
928,433
]
Code
GDV
HVR
LWT
MLS
SDY
WYS
OLF
DVL
DIK
iMS
AIA
CDR
GRI
EAR
MCK
LBF
BFF
LEB
1 ALM
CNM
CVN
SVC
ELY
JHW
MSS
OGS
PBG
SLK
ART
PDT
AOO
BFD
DUJ
JST
LNS
FKL
HON
ATY
MKL
VCT
CDC
CNY
VEL
SHD
RUT
EAU
BKW
CKB
LWB
MGW
PKB
LAR
WRL
TOT
Community
Hub
Subsidy ($)
Glendive, MT*
Havre, MT*
Lewistown, MT*
Miles City, MT*
Sidney, MT*
West Yellowstone, MT
Wolf Point, MT*
Devils Lake, ND
Dickinson, ND
Jamestown, ND
Alliance, NE*
Chadron, NE*
Grand Island, NE*
Kearney, NE
McCook, NE*
North Platte, NE
Scottsbluff, NE
Lebanon, NH/White River Jnct, VT
Alamogordo/Holloman AFB, NM*
Carlsbad, NM*
Clovis, NM*
Silver City/Hurley/Deming, NM
Ely, NV*
Jamestown, NY
Massena, NY*
Ogdensburg, NY*
Plattsburgh, NY
Saranac Lake/Lake Placid, NY
Watertown, NY*
Pendleton, OR
Altoona, PA
Bradford, PA
DuBois, PA
Johnstown, PA
Lancaster, PA*
Oil City/Franklin, PA*
Huron, SD*
Watertown, SD
Jackson, TN*
Victoria, TX
Cedar City, UT
Moab, UT
Vernal, UT
Staunton, VA
Rutland, VT
Eau Claire, WI
Beckley, WV
Clarksburg, WV
Greenbrier/Lewisburg, WV
Morgantown, WV
Parkersburg/Marietta, WV
Laramie, WY
Worland, WY
107 airports
BIL
BIL
BIL
DEN
DEN
SLC
BIL
MSP
DEN
MSP
DEN
DEN
DEN
DEN
DEN
DEN
DEN
BOS/HPN
ABQ
ABQ
ABQ
ABQ
DEN
CLE
ALB
ALB
BOS
BOS
ALB
PDX
IAD
CLE
CLE
IAD
BWI
CLE
DEN
MSP
BNA
IAH
SLC
DEN
DEN
IAD
BOS
ORD
IAD
IAD
CLE
IAD
IAD
DEN
DEN
1,056,152
1,036,616
1,036,616
1,056,152
2,159,591
427,757
928,433
1,459,493
2,274,177
1,963,220
977,609
977,609
2,271,640
1,978,386
1,583,277
1,860,229
1,535,085
2,245,669
1,169,337
1,046,284
1,517,277
1,442,174
1,864,717
1,350,803
1,297,613
1,353,916
1,379,257
1,366,538
1,228,334
1,608,394
1,394,423
1,350,803
2,020,095
1,394,423
1,372,474
1,226,773
1,781,159
1,338,321
1,225,628
1,593,922
1,477,125
1,798,370
1,421,478
1,911,466
797,141
1,732,372
2,092,844
1,058,325
2,330,725
1,058,325
2,190,281
1,215,603
1,735,814
161,288,633
Table 6.1.: EAS Airports (as of May 1, 2010) [34]
126
6. A closer look: Airport specific impacts
Table 6.23 shows the distance from each EAS community to the nearest jet-serviced
airport, which in many cases is not a medium or large hub airport.
Few question the
importance of EAS service to communities in Alaska, which has the most EAS airports
of any state, and in states like Maine, North Dakota, and Montana where residents can
be hundreds of miles from the nearest major airport. However, in areas where there is
good quality service reasonably close by like in Lancaster, PA, a 66 mile journey from
Philadelphia International Airport4 [7], the service appears to be wasteful. The table shows
that 42 of the 107 airports on the list are within 90 miles of a jet-serviced airport, though
not necessarily a large or medium hub. At 90 or fewer miles, these communities are easily
within a 2 hour drive of those jet-serviced airports. Statistics like these are what lead many
to question the rules for inclusion. The table also shows that, overall, the average distance
from an EAS airport to a jet-serviced airport is 114 miles, a roughly 2 hour journey at
highway speeds.
3
The most recently available list of mileage was updated in 2003, distances with an asterisk were not on
the list and thus were calculated by the author via internet mapping.
4
This distance should make Lancaster ineligible for EAS funding, but Senator Arlen Specter, who represents Lancaster, successfully lobbied the US DOT arguing that the airport was 80 miles via the route
that most people drive, preserving the service.[71
127
6. A closer look: Airport specific impacts
Airport
Miles
Airport
Miles
Airport
Miles
MSL
ELD
HRO
HOT
60
87
77
53
RKD
ESC
CMX
IMT
65
114
228*
101
ALM
CNM
CVN
SVC
91
141
103
133
JBR
IGM
79
33
IWD
MBL
97
115
ELY
JHW
237
47
PGA
PRC
SOW
CEC
IPL
MER
VIS
ALS
CEZ
PUB
AHN
280
102
168
79
68*
55
39*
122
201
43
72
MKG
HIB
TVF
CGI
COU
TBN
JLN
IRK
GLH
LUL
MEI
77*
39
75
123
120*
86
61*
137
111*
90
85*
MSS
OGS
PBG
SLK
ART
PDT
AOO
BFD
DUJ
JST
LNS
143
123
78
126
65
47*
108
77
110*
82
71*
MCN
91*
GGW
280
FKL
62
BRL
FOD
MCW
DEC
MWA
UIN
DDC
GCK
GBD
HYS
LBL
SLN
OWB
PAH
HGR
AUG
BHB
PQI
96
129*
82
43
100
106
149
201
120
180
153
93
42
152*
80*
61
54
159
GDV
HVR
LWT
MLS
SDY
WYS
OLF
DVL
DIK
JMS
AIA
CDR
GRI
EAR
MCK
LBF
BFF
LEB
197
120
108
146
231
107
293
86*
101
93
177
121
93
127
225
277
212
75
HON
ATY
MKL
VCT
CDC
CNY
VEL
SHD
RUT
EAU
BKW
CKB
LWB
MGW
PKB
LAR
WRL
AVG
121
103
85
108
178
118
147
34
69
93*
60
112*
80
80*
89*
144
164
114
Table 6.2.: Distance to nearest jet service [33]
128
6. A closer look: Airport specific impacts
The purpose of this section is not to argue whether EAS service is necessary, but rather
to understand how EAS serviced airports, as a representation for the smallest rural communities, have seen accessibility change since 1980.
We start with the path quality measure developed in Chapter 4. Table 6.3 shows path
quality values for EAS airports along with values for Commercial Service and non-hub
airports for reference because many EAS airports fall within those classifications. The table
shows that in 1980, the average PQI values was .417, the lowest of all airport categories.
This is to be expected because these are small, typically remote airports with limited
demand thus we would expect the minimum service level defined by the FAA to be lower
than at those airports able to sustain service without subsidy.
Average passenger-weighted path quality
EAS Airports
Commercial Service
Non-hub
1980
.417
.425
.512
1990
.503
.534
.476
% Change
20.7%
25.6%
-7.0%
2000
.425
.528
.457
% Change
-15.4%
-1.1%
-4.0%
2010
.501
.571
.471
% Change
17.8%
8.1%
3.1%
Table 6.3.: Average passenger-weighted path quality
Between 1980 and 1990, path quality rose at EAS airports by about 20% though by less
than at Commercial Service airports. Subsequently, path quality fell to near 1980 levels in
2000 before rebounding in 2010. As of 2010, the EAS airports analyzed here had better
path quality than non-hubs and only slightly below Commercial Service airports. Given
that the minimum standard as set forth by the FAA is one-connection to the hub airport,
or a path quality of .3, the .501 value represents quite good path quality and the .084 point
increase since 1980 shows that EAS has done a good job of preserving accessibility for EAS
communities and, in fact, improving path quality.
Examining passengers, Table 6.4 shows that EAS passengers peaked in 1990 at just over
97,500 passengers. 5 Since then, passengers fell by 21% in 2000 and another 29% in 2010.
5
Note that not all EAS airports are included in this analysis so the number of passengers is actually higher
than the table shows.
129
6. A closer look: Airport specific impacts
This more or less mimics the path of Commercial Service airports, though the decline from
2000 to 2010 was less on a percentage basis than the halving of passenger volume seen at
Commercial Service airports.
EAS Airports
Commercial Service
Non-hubs
1980
89,709
66,950
1,061,070
Passengers (10% sample)
1990
% Change
2000
97,599
8.8%
76,735
73,904
10.4%
60,725
1,664,709
56.9%
1,824,251
% Change
-21.4%
-17.8%
9.6%
2010
54,340
29,756
1,709,859
% Change
-29.2%
-50.9%
-6.3%
Table 6.4.: Passengers
At EAS and Commercial Service airports one of the factors driving passengers down is
leakage to other airports. Leakage refers to the movement of passengers from their closest
airport to another airport further away. In the case of EAS airports this leakage occurs
because lower fares (and hence better accessibility) or better flight options are available at
airports within a reasonable driving distance. Consider that the decline in passengers from
1980 to 2010 was 54% at airports 90 or fewer miles away from jet-serviced airports and only
26% at airports over 90 miles away. Likewise, at EAS airports in markets within driving
distance of a Southwest-served airport, passengers dropped 57% while dropping 29% at
airports not close to a Southwest market.These findings indicate that passengers are willing
to drive to airports further away in search of better accessibility because most jet-serviced
airports are at least Non-hub airports, which have better service. The findings also suggest
that an increase in the 70 mile minimum distance might improve the cost effectiveness of
the remaining program.
Combining passengers and path quality to look at equivalent non-stop passengers we
see a similar story at EAS airports in Table 6.5, except for the larger equivalent nonstop passenger growth from 1980 to 1990 on a percentage basis because of the increase in
path quality. Following the growth in 1990, we see similar declines after 2000. However,
because of the path quality improvements at EAS airports the number of equivalent nonstop passengers in 2010 was 73% of the equivalent non-stop passengers in 1980 while that
130
6. A closer look: Airport specific impacts
figure is only 61% for total passengers. This indicates that while the number of passengers
has dropped, as a result of path quality increases accessibility has gone up for the remaining
passengers.
EAS Airports
Commercial Service
Non-hub
Equivalent non-stop passengers (10% sample)
1980
1990
% Change
2000
% Change
-33.5%
37,367
49,049
31.3%
32,633
38.6%
32,061
-18.8%
28,481
39,489
542,950 792,845
46.0%
833,634
5.1%
2010
27,223
17,001
804,701
% Change
-16.6%
-47.0%
-4.1%
Table 6.5.: Equivalent non-stop passengers
In terms of destinations, the EAS program has done a better job preserving available
destinations than if the EAS airports functioned as Commercial Service airports without
government intervention.
Number of destinations
EAS Airports
Commercial Service
Non-hub
1980
137
131
201
1990
115
103
202
Change
-23
-28
1
2000
108
93
195
Change
-7
-10
-7
2010
93
81
191
Change
-15
-12
-4
Table 6.6.: Number of destinations
Between 1980 and 2010, Commercial Service airports lost 50 destinations while EAS lost
44 destinations and in 2010 EAS airports had service to 12 more airports than Commercial
Service airports likely due to the good connections to a medium or large Hub airport through
the program.
The same holds true for equivalent non-stop destinations in Table 6.7, though because
EAS airports have a lower path quality their advantage in equivalent non-stop destinations
is lower.
131
6. A closer look: Airport specific impacts
Equivalent non-stop destinations
EAS Airports
Commercial Service
Non-hubs
1980
57
55
97
1990
58
54
96
Change
1
-1
-1
2000
43
41
84
Change
-13
-13
-12
2010
41
37
79
Change
-4
-4
-5
Table 6.7.: Equivalent non-stop destinations
Lastly, fares are another way of looking at accessibility and comparing airports. The
reality of doing business is that when demand is lower, as it is at EAS airports, prices will
be higher to account for the lack of volume and because the smaller aircraft have higher
unit costs. Table 6.8 shows that the EAS program has done an excellent job of keeping fares
at EAS airports competitive with other airports. This has much to do with the structure of
the program in which operators make a bid for the subsidy they require to operate service.
Once contracted to operate in an EAS market, the operator is required to operate the
service through the contract period regardless of the number of passengers. Thus it is in
the operator's interest to price fares competitively to prevent leakage. The table shows that
operators have done exactly this, pricing tickets competitively with Commercial Service and
Non-hub airports (though these fares do not include the EAS subsidy).
1980
EAS airports
Commercial Service
Non-hubs
$319.51
$322.80
$323.79
Average fares (inflation adjusted)
2000
% Change
1990
% Change
$262.74
$249.71
$256.39
-17.8%
-20.6%
-22.6%
$231.15
$217.05
$223.80
-12.0%
-12.7%
-13.1%
2010
% Change
$183.57
$176.83
$184.15
-20.6%
-17.6%
-18.5%
Table 6.8.: Average fares (inflation adjusted)
In summary, the tables show that the EAS program has done a reasonably good job
of preserving accessibility in small, rural communities. The program is costly, but in the
last 30 years EAS airports saw improvement in path quality for passengers while passenger
volume decreased at a lower percentage than at Commercial Service airports. The results
also show that the program has preserved the number of destinations available from an
132
6. A closer look: Airport specific impacts
EAS airport more than Commercial Service airports all the while maintaining competitive
fares from the passenger's perspective. This said, the comparison between airports near
Southwest serviced airports or EAS airports near jet-serviced airports show that perhaps
there are opportunities for improvement of the program to increase the cost-effectiveness
of the program.
6.3. Boston Logan
The measures developed in this thesis are useful for examining not just small airports, but
also for large airports like Boston's Logan Airport (BOS). BOS was the nation's 8th largest
airport in 2010 in terms of originating domestic passengers and is the largest airport in
the New England region. Logan Airport is an interesting airport to examine in terms of
accessibility changes because Boston is one of the largest metropolitan areas in the country
and continues to grow but the airport cannot. 6 By virtue of its location, Logan's growth
is constrained by Boston Harbor and the developed neighborhoods of East Boston. This
means that the airport is limited in the capacity it can add to accommodate more fliers as
the area's population grows. This, coupled with the close proximity to the medium hub
airport PVD in Providence, RI (62 miles) and the small hub airport MHT in Manchester,
NH (55 miles), shown in Figure 6.14, makes this trio of airports interesting to study.
Beyond the appeal of studying these three airports, this case study is included as an
example of the ways the methodology in this thesis can be used to conduct more detailed
regional analysis. The purpose of exploring accessibility at the regional level is to demonstrate how accessibility can be factored into regional planning, at a scale where details like
ground access, inter-modal strategies, air congestion relief, and other location specific issues
may be factored in. For example, as footnote 5 notes, Logan could increase capacity by
6
While Logan Airport cannot expand its capacity to handle more aircraft movements, it could handle more
passengers by increasing the size of the aircraft leaving the airport. Logan has a sizeable percentage
of private and regional jet aircraft using the airport and if they were limited the airport could handle
many more passengers on larger aircraft.
133
6. A closer look: Airport specific impacts
limiting the use of smaller aircraft both because those aircraft could be replaced by larger
aircraft carrying more passengers and because smaller aircraft decrease take-off and landing
capacity due to separation rules regarding large and small aircraft operating in proximity
to each other. However, to limit smaller aircraft could have serious accessibility impacts
because BOS serves as the connecting hub for 7 EAS airports: Rutland, VT; Ogdensburg,
NY; Plattsburgh, NY; Augusta/Waterville, ME; Bar Harbor, ME; Preque Isle/Houlton,
ME; and Rockland, ME all of which use smaller aircraft to connect to Logan. By conducting granular analysis like this, important details that are easily overlooked on a national
review become apparent.
iFi
Legend
Primaryairpors
A Secondwyairport
Otherairport
PoputMdarm
Figure 6.14.: Boston Logan area map
[11]
Examining passenger volume shows that Logan is a larger airport than MHT or PVD
with close to 10 times as many passengers as Manchester or Providence in 2010. All 3
134
6. A closer look: Airport specific impacts
airports grew between 1980 and 2000 with Manchester growing the most with an incredible
growth rate of 1198% between 1980 and 1990 as service at the airport developed. Despite
the expanding service at PVD and MHT, the contraction in passenger volume discussed
elsewhere in the thesis between 2000 and 2010 did not exclude these 3 airports with only
BOS seeing any passenger growth, at 3.7%. Nevertheless, the story that the data tell is one
of large passenger volume growth throughout the region during the overall study period.
Passengers (10% sample)
BOS
1980
477,315
1990
826,334
% Change
73.1%
2000
997,466
% Change
20.7%
2010
1,034,279
% Change
3.7%
MHT
PVD
2,894
42,120
37,569
118,255
1198.1%
180.8%
154,324
259,120
310.7%
119.1%
135,194
187,510
-12.4%
-27.7%
Table 6.9.: Passengers at BOS, MHT, and PVD
With such size discrepancies between airports, it would be natural to ask how the smaller
PVD and MHT could compete with BOS in terms of accessibility and therefore passengers.
The answer lies in what is known as the Southwest effect. Low cost carrier, Southwest, began
serving PVD in 1996 and MHT in 1998.[11] The lower fares and expanding destination
options made these airports more attractive to passengers, particularly passengers who
lived closer to these airports, and consequently drove passenger volume up.
Following
Southwest's entry into PVD other carriers followed including Northwest, Continental, Delta,
American Eagle, and Air Canada as the airport became an important secondary market in
the region.[11] Their entry further enhanced competition and options for passengers in the
region. MHT experienced a similar result with Southwest's entry drawing American, ACA,
Continental Express, and Northwest to the airport.[11]
The data in Figure 6.15 from Ben Abda's thesis shows the effect that Southwest and
the subsequent carriers' entry into these markets had on the overall passenger split in the
region. In 1995, PVD and MHT had a combined passenger share of 14%, but following
the increased service, that had risen to 29% by 2000 and continued rising to 33% in 2005.
135
6. A closer look: Airport specific impacts
Logan regained some of its market share by 2008 as it began to improve its perpetually
poor on-time performance and as low cost carrier options increased at Logan.
MBOS
*PVD
*MHT
50%
60%
1990
1995
1
1
2000
~1 I
2005
2008
10%
20%
30%
40%
70%
80%
90%
100%
Figure 6.15.: BOS, MHT, PVD passenger share [9]
Examining the path quality measure developed earlier, we see the higher path quality
at BOS indicating better accessibility and a larger percentage of non-stop flights. This is
expected at a large hub airport given the earlier results seen in Chapter 5. However, Table
6.10 shows two things that contribute the the increasing passenger share seen at MHT
and PVD. First is the increase in path quality from 1990 to 2000 at both MHT and PVD
brought about by the entrance of Southwest and the subsequent carriers. This also brought
about a reduction in the percentage of connections at MHT and double connections at both
airports. For those passengers who had been driving further to BOS because of its superior
accessibility, MHT and PVD became viable alternatives. Second, from 2000 to 2010 path
quality at MHT and PVD continued to increase while it fell slightly at BOS. As the path
136
6. A closer look: Airport specific impacts
quality began to converge at the 3 airports, given their close proximity, passengers were
able to chase the lower fares at these secondary airports, also contributing to the decreasing
passenger share at BOS.
The other factor contributing to the passenger share of MHT and PVD that is not
captured in the data is the ground access to the airports. Logan has high parking charges
and Boston is known to be a stressful place to drive. On the other hand, the roads to MHT
are typically un-congested which makes for an easier drive. The situation is similar in PVD
which also has a rail link. All else equal, ground access may contribute to the choice of
PVD or MHT over BOS, particularly for passengers on the outskirts of the BOS catchment
area.
Average passenger-weighted path quality
BOS
MHT
PVD
1980
.838
.412
.648
1990
.780
.551
.572
% Change
-6.9%
33.7%
-11.3%
2000
.800
.601
.648
% Change
2.6%
9.1%
13.3%
2010
.795
.646
.655
% Change
-1.0%
7.5%
1.1%
Connections
BOS
MHT
PVD
Non-stop
1 connection
2+ connections
77.3%
21.8%
>1%
2010
70.8%
28.6%
>1%
1980
22.1%
63.2%
14.7%
2010
50.2%
47.9%
1.9%
1980
51.0%
46.0%
3.0%
2010
51.3%
47.3%
1.4%
1980
Table 6.10.: Average passenger-weighted path quality and percentage connecting
In addition the path quality convergence seen in Table 6.10, the spread between the
number of destinations available at the airports decreased from 1980 to 2010 with the
spread at PVD decreasing from 186 fewer airports than BOS to 76 and from 361 at MHT
to 85. Again, these changes were brought about by the growing number of carriers at MHT
and PVD as they emerged as viable secondary markets as well as the 140 destination drop
137
6. A closer look: Airport specific impacts
at BOS between 1980 and 2000.
Number of destinations
BOS
MHT
PVD
1980
539
178
353
1990
489
323
368
Change
-50
145
15
2000
438
354
384
Change
-51
31
16
2010
399
313
323
Change
-39
-41
-61
Table 6.11.: Number of destinations
Lastly, Table 6.12 shows that average fares were lower at MHT and PVD. 7 The difference
was particularly large in 2000 when the spread was more than $50 between MHT/PVD and
BOS. This was also the time when according to Figure 6.15 PVD and MHT had their
largest passenger share. However, by 2010 fares dropped by a larger percentage at BOS,
putting BOS fares in rough parity with MHT and PVD. This decrease follows the decline
at other Large hubs but was also likely influenced by the increased presence of low cost
carriers JetBlue, AirTran, and Southwest at BOS as well as the need for legacy carriers to
compete with the lower fares at MHT and PVD.
Average fares (inflation adjusted)
BOS
MHT
PVD
1980
$298.31
$306.37
$296.76
1990
$256.38
$253.38
$237.74
% Change
-14.1%
-19.9%
-17.3%
2000
$236.19
$178.36
$174.41
% Change
-7.9%
-26.6%
-21.2%
2010
$159.30
$148.17
$151.19
% Change
-32.6%
-13.3%
-16.9%
Table 6.12.: Average fares (inflation adjusted)
This brief case study demonstrates how the series of measures developed in this thesis can
be used to understand accessibility and the way that it has changed both at an individual
airport and at a series of airports like BOS-MHT-PVD. The results offer several interesting
insights. First, that accessibility since 1980 has improved in the secondary markets of MHD
and PVD and has only decreased slightly at BOS while serving many more passengers at
7
Note that these fares are not stage length adjusted, which is important because secondary markets (PVD
and MHT) tend to serve shorter market distances than larger airports (BOS).
138
6. A closer look: Airport specific impacts
each of the airports. The results also show how the improved accessibility at PVD and
MHT increased the share of the region's passengers using the secondary markets. While
passengers flying out of PVD and MHT were able to take advantage of the increased
accessibility, the passengers at Logan also saw improvements in accessibility because of
the reduced strain on the infrastructure at Logan. Over the period 1998 to 2010 Logan
saw its on-time departure percentage increase from 80.3% to 83.4% moving it from 25th
out of 29 Large hub airports to 17th out of 29.[36] While some of this is attributable to
improvements at Logan like the opening of runway 14-32,8 the increased share of traffic at
MHT and PVD also allowed Logan the breathing room needed during peak periods and
bad weather to improve on-time performance.
Finally, the analysis of fares shows that financial accessibility has increased at all airports.
The emergence of low cost carriers at PVD and MHT and the airports' development as a
viable alternative to BOS created a competitive environment that led average fares at BOS
to fall by 46.5% while also falling at MHT and PVD, 51.6% and 49.1% respectively from
1980 to 2010.
6.4. Summary
Chapter 6 continued the examination of accessibility changes by using the accessibility
measures from Chapter 5 to examine individual airports in greater detail. The first section
presented data on path quality and number of destinations for all the airports examined
and then looked at which airports had seen the biggest gains in accessibility and which had
seen the biggest losses. The results showed that areas with lower population density tended
to have the lowest accessibility. This included the northern Plain States, the Northeast,
and a number of Southern states. The results also indicate that has only been a small
8Runway 14-32 had only a minimal impact on capacity during normal operating conditions. Its purpose
was to improve on-time performance during high Northwest winds, when only one runway was available.
Its use is currently restricted to only those times when Northwest winds exceed 10-knots. During the
first 4 years of operation 14-32 accounted for only 2% of jet aircraft departures.[4]
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6. A closer look: Airport specific impacts
number of airports that experienced big swings in relative accessibility between 1980 and
2010. This means that as the deregulated industry has developed, the accessibility rankings
of airports within the categories have been relatively stable.
Examining the subset of airports that are part of the EAS program, the analysis suggested
that the EAS program had produced better accessibility in terms of destinations for EASserved communities than was seen on average at Commercial Service airports and better
path quality than was observed at non-hub airports.
These outcomes demonstrate the
benefits of the federal subsidy. Fortunately for EAS passengers, the EAS program subsidy
also allowed them to reap these accessibility benefits while paying fares that are competitive
with other airports of similar size.
One recommendation for improving the program came from the data analysis which was
that the cost effectiveness of the program could be improved by increasing the minimum
distance required for a community to participate in the EAS program. This finding comes
from the observed result that EAS airports closer to jet-serviced airports saw passenger
volume decline at a rate greater than at airports further away. This suggests that passengers
are willing to drive around 90 miles to reach airports with better accessibility but that at
some threshold driving becomes too time consuming thus the continued need for EAS at
isolated airports. Taking advantage of the possibility that TSA screening could be done
quickly at less crowded TSA airports, it may be possible to serve these airports within 90
miles with high-quality bus service with ticketing and screening done at the EAS airport and
passengers driven directly to the gate area at the destination airport. Such a program would
provide better service than driving to passengers who would lose service if the minimum
distance was increased to 90 miles while still providing considerable cost savings to the EAS
program.
Lastly, this chapter demonstrated how the accessibility measures from this thesis can be
used to understand changes at larger airports and in metropolitan areas with multiple airports. As a case study, the trio of airports BOS, PVD, and MHT were examined from 1980
140
6. A closer look: Airport specific impacts
to 2010. The review of airports showed how BOS began as the region's dominant airport
but because of the entrance of Southwest and subsequently other carriers into the secondary
markets at PVD and MHT those airports developed into viable alternatives to BOS and
provided more options for passengers in the area and easier ground access. In addition to
the specific findings about the New England region, this section demonstrated how these
measures can be used to understand how changes at one airport affected accessibility at
other airports.
The next and final chapter of this thesis looks back and then it looks forward. It looks
back and reviews the major accessibility trends observed in the data. It looks forward to
think about how the changing state of the airline industry may impact accessibility in the
future.
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7. Conclusion
This research resulted in 3 distinct products: a methodology for analyzing and understanding accessibility changes, a review of the evolution of air passenger accessibility in the US
from 1980 through 2010, and a regional view of the Boston area as an example of the way
this methodology can be used regionally. The historical review presents a static picture of
past and current accessibility (2010); but the airline industry is not static, it is changing.
The industry today faces challenges that will demand that airlines and the industry change.
While the data analysis is retrospective, the methodology developed here is a tool that can
also be used to understand how future changes in government policies or airline operating
practices may impact the passenger's access to the aviation network.
This concluding chapter begins by summarizing the retrospective analysis and the conclusions reached by reviewing the questions posed in the introduction. It then discusses
some of the dynamic aspects of the industry today that will play a role in affecting future
accessibility such as rising fuel and operating costs, financial instability, declining service
to rural communities, government deficits which may affect EAS funding, the possibility
of increased government regulation, airport congestion, and climate change. The chapter
finishes with several suggestions for extension of this analysis in future work.
7.1. Summary of results and general conclusions
This thesis began by reviewing the history of the airline industry and the factors that led
to its deregulation in 1978.
It then proposed a methodology for analyzing accessibility
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7. Conclusion
changes and presented results based on the methodology for the years 1980, 1990, 2000,
and 2010. While the beginning of the data analysis coincides with the end of regulation this
thesis looked at the overall accessibility outcomes instead of trying to attribute outcomes
to specific industry changes like deregulation, the growth of hub-and-spoke networks, or
the emergence of low cost carriers. Chapter 1 posed the following questions related to
accessibility which directed the analysis:
1. What are the components of accessibility?
2. How has accessibility changed since 1980?
3. Have the benefits accrued equally across the air network or have certain regions or
types of airports benefited more than others? Have some airports lost accessibility?
4. What are the benefits and disadvantages of the changes in accessibility?
5. What are the future challenges to accessibility from the passenger's perspective?
The remainder of this section summarizes the results of the analysis by addressing each of
the questions using data on passenger travel from the Bureau of Transportation Statistics'
DB1B 10% sample of all passenger air travel.
What are the components of accessibility?
Rather than trying to define accessibility with a single measure, this thesis took a holistic
approach by presenting a number of the components of accessibility. The starting point for
thinking about accessibility was connectivity, but connectivity that incorporates the perspective of the passenger. In much of the previous research, connectivity measures examined
the existence of itinerary options but the measures did not reflect how the passenger experiences the journey. For example, a connectivity measure would take airline schedules, set a
limit on the maximum number of connections and the maximum wait time in a connecting
airport, and based on those assumptions calculate all the feasible itineraries and destinations. The result would be simplistic measure of connectivity. In contrast, an accessibility
143
7. Conclusion
measure might account for the passenger's perspective by looking at fares to determine
if passengers can afford to fly the feasible itineraries or by examining how the number of
connections required to reach the destination had changed to reflect the convenience of the
travel. This is an important distinction, because if an O-D pair was served with a non-stop
flight in the past but now requires one or two connections the connectivity between the
O-D pair remains but accessibility has gone down.
Because the DB1B consists of actual travel data, some of the measures have the passenger's perspective built-in. For example, the number of destinations flown measure already
includes the passenger's perspective because it only includes the destinations to which passengers actually flew, not all the destinations that were feasible. In other cases, the measures
in this thesis attempt to capture the passenger's perspective in what would have otherwise
been a connectivity measure. For example, measures like the number of passengers incorporated the passenger's perspective by weighting them by the path quality measure developed
in the methodology which accounts for the number of connections required and the way
passengers perceive them.
Using this approach, the analysis examined 4 measures of accessibility: path quality1 ,
number of passengers, number of destinations flown, circuity 2 , and fares. There are other
measures of accessibility that would have benefited the analysis but were not available in
the data set used such as flight frequency, aircraft size/type, delays, or time spent waiting
at the airport for a connecting flight.
How has accessibility changed since 1980?
Table 7.1 shows the basic accessibility measures weighted by the number of passengers
on each route, thus heavily traveled routes have a greater impact on the average than less
'Path quality reflects the number of connections required to reach the final destination. Therefore, when
path quality decreases, each passenger that is now connecting instead of flying non-stop faces at least
45 minutes of wait time at the intermediate airport.
2
Recall that circuity is a measure that captures the excess distance that passengers flew as a result of
connecting itineraries. It is the total distance traveled by the passenger divided by the non-stop distance
between the origin and final destination. The values presented here include all itineraries in the data
base, non-stop and connecting. If non-stop flights were filtered out the circuity would be higher.
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7. Conclusion
traveled routes. What the table shows is that for the average random passenger accessibility
has gone down in terms of path quality, number of destinations, and circuity but that in
terms of fares and passenger volumes, accessibility has improved.
Path quality
Passengers (10% sample)
Fares (inflation adjusted)
Circuity
Number of destinations
1980
.780
16,872,435
$293.53
1.043
469
1990
.736
29,682,039
$240.45
1.048
431
2000
.752
41,991,744
$202.05
1.051
409
2010
.761
41,524,916
$157.92
1.051
371
% Change 1980-2010
-2.4%
146.1%
-46.2%
-0.8%
-20.9%
Table 7.1.: Passenger-weighted accessibility averages 1980-2010
The table shows that path quality saw a modest decrease for the average passenger but
that there were many more passengers. Between 1980 and 2010 the number of passengers
grew 146%, far exceeding the 36% US population growth in the same period, indicating
that many more passengers accessed the network in 2010 than in 1980. This increase was
brought about by both a growing appetite for air travel as the economy expanded nationally
and globally and also by lower fares, which dropped by 46% on an inflation adjusted basis.
The one accessibility measure that saw a large decline was the number of destinations
flown which fell 20%. These destinations lost were unweighted by the demand for travel to
them meaning that lost service to Chicago was counted the same as lost service to Sioux
City, IA. The reality is that many of those lost destinations were small communities that
either lost service entirely or had previously required fewer connections but because of the
shift towards hub-and-spoke networks now required so many connections that passengers
chose not to fly. The changing service, particularly at smaller airports, highlights another
question this thesis examined:
Have the benefits accrued equally across the air network or have certain regions or types
of airports benefited more than others? Have some airports lost accessibility?
While Table 7.1 showed passenger-weighted averages, Chapter 5 presented results that
were not passenger-weighted. When compared with the passenger-weighted, those values
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7. Conclusion
showed that accessibility levels vary considerably across airport sizes. While many passengers experienced the accessibility levels just discussed with only modest declines in accessibility, there are some passengers who saw much lower accessibility, notably in less traveled
markets which tend to be in small communities. The non passenger-weighted values show
the disparity in accessibility on highly traveled routes versus less traveled ones.
This thesis evaluated whether accessibility levels were different, but did not evaluate
the broader effects or consider whether the differing levels of accessibility warrant different
policies.
While increased accessibility is a positive result, to provide a higher level of
accessibility would increase costs and the fact that airlines do not currently offer higher
accessibility suggests that it is not financially efficient from their perspective. Because of
this, any increase in accessibility in small communities would likely come from a government
decision to subsidize service beyond current EAS levels. It is not immediately apparent
whether increased subsidization would be socially beneficial, though future scholars may
wish to evaluate the costs and benefits in greater detail.
Regardless of the benefits or dis-benefits of variation, the answer to the question posed
by this thesis is that accessibility has developed differently. The smaller airports and less
traveled markets have generally fared worse than the larger airports in terms of changing
accessibility on a percentage basis (either a larger percentage decline or a smaller percentage
increase) in addition to starting from a lower base of accessibility. A full analysis of changes
and geographic variations is available in Chapter 5 and Chapter 6.
What are the benefits and disadvantages of the changes in accessibility?
For better or worse, many of these change in accessibility are intertwined. Lower accessibility levels in one measure may not be a negative outcome because they may allow
increased efficiency or improved accessibility in another accessibility measure. For example,
the increased efficiency of hubbing, coupled with increased competition, was one factor contributing to lower fares but is also decreases path quality. Taking each of the accessibility
measures in turn:
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7. Conclusion
Path quality: As the data showed, path quality has declined modestly since 1980.
What the change means is that a slightly larger percentage of passengers were flying on
connecting itineraries in 2010 than were in 1980. The data also showed that at smaller
airports the path quality was lower than at larger airports.
The downside of declining path quality is that flying has become more inconvenient for
passengers in several ways. First, a connecting itinerary is more inconvenient because it
means that the passenger must transfer planes and wait at an intermediate airport for the
connecting flight. Second, a connecting itinerary increases the chance for delayed airplanes,
missed connections, and lost baggage.
The advantage that has accrued to passengers over the past 3 decades is lower fares.
Lower fares result because hubbing allows carriers to consolidate passengers from one airport
bound for multiple final destinations on a single flight increasing the load factor and reducing
airline operating costs on a per passenger basis even as passenger convenience may decline.
In a monopolistic or less competitive industry, firms would capture all or most of this
improved efficiency as profits, but the highly competitive nature of the current airline
industry means that passengers benefit from the efficiency gain through lower fares because
a primary method of competition in the industry is low fares. This is a trade-off that
some argue is not worth the benefit, but the fact that in markets with both non-stop and
connecting options many passengers choose the connecting itinerary in exchange for lower
fares suggest that at least some passengers see the trade-off as a beneficial one.
Circuity: Circuity is directly tied to path quality because it relates to the use of connections. The benefit offsetting higher circuity is the same as the benefits offsetting lower path
quality discussed in the previous paragraph; passengers pay lower fares. The disadvantage
has to do with the inconvenience of connecting itineraries which is that on a connecting
itinerary the flight distance is longer than on a non-stop flight. This translates into increased flying time and waiting time (which is not captured here because temporal data
are not available in the data set used) for the passenger.
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7. Conclusion
Number of destinations served: To the average passenger, the decrease in the number
of destinations produces a benefit. The airlines choose to fly to destinations based on the
estimated demand for travel to the destination. As the airlines cut service in low profit
or un-profitable markets, their average profit from each passenger increases. As discussed
with path quality and circuity, given the high degree of competition in the market, when
an airlines are more cost efficient that typically translates into lower fares for passengers.
In addition, the removal of unprofitable routes may help to improve the financial viability
of struggling airlines.
The disadvantage of the decline in destinations is experienced by passengers living in or
desiring to travel to those smaller markets that saw service cut. For those passengers the
disadvantage is severe. However, in a deregulated environment, it is not the role of the
airlines to provide service to markets that they cannot serve profitably. That decision falls
on the shoulders of elected officials who thus far have not chosen to subsidize service beyond
what is already done at EAS airports. There are likely negative economic impacts associated with decreased service frequency, but the magnitude of those impacts will vary from
community to community depending on factors like the size of the community, availability
of travel alternatives, and the types of industry there and thus are not easily generalizable.
Fares: From the perspective of passengers, the percentage decrease in fares represents a
tremendous accessibility gain. As fares decline, more potential passengers have the economic
ability to access the aviation network and the passengers who already fly have the ability to
fly more frequently. This relationship is evident in the large increases in passenger volume
seen in Chapter 5.
Many would be surprised to hear that lower fares could have a disadvantage from the
passenger's perspective. After all, when fares are lower, more passengers can afford to afford
to fly and existing passengers can afford to fly more frequently. The problem is that the
continuing pressure on airlines to keep fares low in order to compete even when airlines
are losing money raises the concern that this may not be a stable equilibrium. If airlines
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7. Conclusion
are unable to raise fares to cover costs, the danger is that passengers many lose service as
airlines cut service or go out of business. The other challenge beyond simply covering costs
is the need to build capital reserves for the purchase of new aircraft. The US airlines have
some of the oldest fleets in the world[67] which will soon need replacement, but the financial
strain many airlines face make fleet renewal difficult. On the other hand, fares are so much
lower than they were in 1978, that if an increase in fares is required to regain profitability,
fares will still be substantially lower than before deregulation.
What are the future challenges to accessibility from the passenger's perspective?
The work thus far has noted areas where accessibility has changed between 1980 and
2010, but there are also challenges in the future that are addressed in Section 7.2.
7.2. The future
As previously noted, the majority of the data analysis and commentary in this thesis has
been retrospective. However, the methodology outlined in this thesis is a tool that can be
used to analyze important industry trends that have implications for future accessibility.
This section discusses some of those trends and their potential impact on future accessibility.
7.2.1. Low fares and rising costs
Throughout this thesis the discussion of fares has noted that low fares are beneficial to
passenger and improve accessibility in the short run, but that in the long run the effects on
accessibility are less obvious. The reason for this is that many airlines have had financial
difficulties that challenge their ability to operate profitably and ensure future operations.
There are a number of factors that create these conditions, but one is the highly competitive
nature of the airline industry today inhibits airlines from using the most obvious solution
to this problem, raising fares.
The airline industry has struggled with profitability since deregulation was implemented.
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7. Conclusion
Figure 7.1 shows that from 1947 through deregulation in 1978 US airlines had positive
profit margins for all but 4 years. Since deregulation the figure shows that the profitability
of airlines has become cyclical and the amplitude of the cyclicality has been increasing.
Profitability has been hindered by rising fuel prices, higher maintenance and labor costs at
the legacy carriers, and economic cycles that make predicting future demand difficult. If
the airlines all faced similar costs, they might all be forced to raise fares to cover their costs;
but bankruptcy 3 and new entrants to the market have created disparities in the costs faced
by airlines. In the face of operating cost disparities, airlines with higher operating costs
(typically legacy carriers) are forced to keep fares lower to compete with new entrants.
3
Airlines in bankruptcy gain a competitive advantage by renegotiating wages and work rules with unions
and reducing or eliminating payments to creditors; all of these allow the airlines to decrease costs and
offer fares below competitors.
150
7. Conclusion
15%
10%
5%
0%
1 7
1957
1967
1977
1987
1997
-5%
I
2
-10%
-15%
-20%
Figure 7.1.: US airline system-wide profit margin 1947-2009 [6]
The cost disparities exist for several reason. First, new entrants to the market like JetBlue
and Virgin American have the advantage of owning newer aircraft (just over 5 and 3 years
old on average, respectively) resulting in lower maintenance costs.[67] Many legacy carriers
have older fleets and thus higher operating costs. For example, in 2010 Delta and American
both had an average fleet age of around 15 years.[67] JetBlue also started by operating
only 1 type of aircraft which allowed them to operate more efficiently. This is a luxury
not available to legacy carriers which serve a wide network of airports requiring multiple
sizes of aircraft while JetBlue is able to select the markets that it views as profitable and
that fit with its operating strategy. Furthermore, many carriers that began operating after
deregulation have lower labor costs. Lastly, while it might seem like fuel costs would affect
151
7. Conclusion
all carriers equally, some carriers, like Southwest, have done better at hedging fuel costs
than others leading to increased operating cost disparity.
Unequal costs mean that one carrier's cost per available seat mile (ASM) could be only
71% of another's cost as was the spread between Virgin America and American Airlines in
2009 shown in Table 7.2. When cost disparities are high in an industry that competes on
price, it becomes difficult for firms with higher costs to remain competitive.
1995
12000
2005
2
American
Continental
Delta
Northwest
United
US Air
America West
Network Carrier Average
Southwest
9.44
8.28
8.61
9.01
8.59
11.12
7.19
9.00
6.93
10.32
9.70
9.18
9.83
10.00
12.39
8.50
10.01
7.69
10.64
10.26
10.25
12.31
10.10
10.41
9.16
10.55
7.91
12.45
10.74
11.61
11.86
10.55
11.06
11.45
10.26
JetBlue
AirTran
-
8.70
9.18
6.87
9.34
9.35
9.29
Frontier
8.98
9.14
9.34
8.97
Virgin America
-
-
-
8.85
Low Cost Carrier Average
6.95
7.92
7.99
9.82
Table 7.2.: System total operating expense in cents per available seat mile [3]
Unequal operating costs coupled with the high fixed and sunk costs in the airline industry
can create an environment that leads to what economists call destructive competition. [10]
Destructive competition occurs when competition becomes so fierce that no one is making
a profit and companies are operating at a loss over many periods. For example, once a
firm has already sunk capital into planes, contrary to economic rationale, it will continue
to fight for market share, often by lowering prices below costs because the relative loss on
operations is small compared to its sunk costs. The hope is that the firm will gain enough
market share to stabilize itself and raise prices, but in doing so other firms must match the
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7. Conclusion
lower prices to remain competitive and as a result they also suffer economic losses.[10]
Destructive competition was an issue often discussed by economists during the deregulation process though most agreed that the structure of the industry would produce beneficial
levels of competition without reaching destructive levels.[10] However, since deregulation
the opinion of some has changed.
For example, regulatory economist and former CAB
Chairman Alfred Kahn said: "I talked about the possibility that there might be really destructive competition, but I tended to dismiss it and that certainly has been one of the
unpleasant surprises of deregulation." [27]
The ability of low cost carriers to offer lower fares is one of the causes of the high competition, the other is overcapacity. Many airlines have had difficulty predicting future demand
which has led to overcapacity. Because airlines have to order new aircraft several years
in advance, if they over-estimate demand they are left with capacity exceeding demand.
During periods of low demand, the airlines are not able to change the size of their aircraft
so they are left with excess capacity that they try to fill through low fares. Robert Crandall
notes: "because of the huge capital costs, airlines have an incentive to sell excess seats for
virtually any price that will cover incremental costs. The problem is that these prices do not
necessarily cover the fixed costs. Because of this history, it is likely that an over-populated
airline system, with large amounts of excess capacity, will be perpetually unprofitable."[1]
This is evidenced by the fact that since 1970 the revenue per passenger mile (yield) has
been steadily declining.[54]
The continued provision of accessibility by the airlines relies on them being profitable.
The bankruptcy restructuring of many of the legacy carriers has allowed the airlines to
renegotiate labor contracts, leases, and other debts all lowering their costs. However, if
they are to regain sustained profitability they also need the ability to raise fares to cover
costs, but competition is keeping fares down and inhibiting airlines from taking this step.
While this benefits passengers in the short run, it has potentially negative implications for
the future.
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7. Conclusion
It has been argued that destructive competition is a positive outcome of deregulation.
The free market will force inefficient firms out of business in favor of more efficient firms.
This argument follows the thinking of Schumpeter, the economist who became famous for
his work on creative destruction, the idea that economic destruction can lead to creative
innovation. What makes the airline industry unique is that there is little evidence that the
successful low cost carriers like JetBlue and Southwest have made creative breakthroughs
in large-scale operation. Rather, these airlines have been successful through specialization
strategies like flying a limited number of aircraft types and selecting only those routes that
they deem profitable. Were the low cost carriers to drive the major legacy carriers out
of the market, one of two outcomes is likely. Either the legacy carriers would go out of
business and there would be no service in markets that are deemed unprofitable under the
low cost carrier's operating strategy or JetBlue and/or Southwest would enter those markets
and almost certainly have to alter the operating strategies that have made them successful
which would quite possibly move them towards the situation that the legacy carriers find
themselves in.
Either way, this is not a desirable outcome and suggests that there is a
public interest in reigning in destructive competition in the airline industry.
7.2.2. Service to small communities
Another question this thesis raises about the future is service to small communities. The
earlier discussion of smaller airports and EAS airports showed that their level of accessibility
is typically lower than at larger airports. The challenge is that small communities often lack
sufficient demand to make it financially possible for the airlines to service the communities.
As private companies in a deregulated industry without cross subsidization, the airlines are
not in a position to provide continually unprofitable service.
As Chapter 6 showed, the EAS program has been reasonably successful in preserving air
service to isolated communities. The EAS program is an example of what must happen
to preserve service at many small hubs, non-hubs, and Commercial Service airports. With
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7. Conclusion
the financial constraints currently facing the Congress, it is unlikely that there will be
an increase in subsidy for air service, a funding decrease is more likely. The alternative
is a new paradigm. It has been suggested that the fate of small communities may be a
move towards a regionalization of air service with small, but somewhat larger, airports
consolidating demand across a region to make service economical.[68]
Building on that idea, one approach that could preserve connectivity to the air network
takes pieces from the low-cost bus service in the Northeast and the rail-to-air integration
practiced in Germany. The emergence of non-stop bus services in the Northeast like Bolt
Bus and Megabus, which operate between major cities like DC, New York, and Boston,
provides a potential model for service in communities that could lose air service. Bus service
from the community to the closest airport with good access to the network is a lower cost
way of preserving air transport access. Buses can be operated at much lower costs than
planes and what would distinguish these buses from Greyhound service (beside the lack
of intermediate stops) is integration with the airlines and TSA. Following the German rail
model, passengers could check-in at the bus terminal and get their airline tickets, and
possibly check their bags to their final destinations and go through TSA security screening.
This type of high quality bus service would preserve better access to the air network
than through private auto at much lower costs than air service. The bus service could
be organized under a competition for the market scenario similar to the model that has
proven successful with the EAS program. High quality bus service could be used to both
save money and add value by expanding rural accessibility. The lower cost of operating
buses instead of planes might allow expansion of EAS to additional airports closer than the
current 70 mile minimum and it would also allow the program to increase the minimum
distance to receive air service to something around 90-120 miles thus realizing a large savings
by switching from air to bus service. There would be limited increased costs because the
security screening equipment and TSA employees are already in place and funded at the
EAS airport and significant savings by switching service to buses.
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7. Conclusion
This is just one example of the many creative ideas that will be needed to preserve service
to rural communities given the economics of rural air service. The methodology presented in
this thesis is also an important aid in this goal because it offers a way to track accessibility
changes and understand how policy or operational changes will affect rural accessibility.
7.2.3. Re-regulation
The discussion of the loss of service at some small airports highlights what has been the most
fundamental of all shifts in the airline industry since deregulation, the shift from an orientation around ensuring the provision of quality air service to much of the country towards
a profit maximization approach. That is not to say that the CAB did not work to ensure
the financial viability of airlines, it did. However, the CAB also used cross-subsidization to
fund service on unprofitable routes because that service was deemed worthwhile to society.
In the intensely competitive market today, airlines cannot afford to provide service that is
not profitable despite the societal benefits that might be associated with the service.
Beyond loss of service to small communities, as already noted, there is a great deal of
financial instability in the industry. Collectively, the airlines have lost $60 billion (2009
dollars) in domestic markets since deregulation.[13] Together these issues raise the question
of should the airline industry be re-regulated either entirely or partially.
A US Government Accountability Office (GAO) study examined the benefits that have
accrued to both passengers and airlines as a result of deregulation and found them to be
substantial. The study analyzed whether some form of re-regulation of fares and markets
could fix some of the acknowledged problems in the industry. The report concluded that reregulation would reverse the gains for passengers and not fix the problems with the industry
and airline employee pensions.[57]
Not all are convinced by the GAO and there is discussion of government intervention.
The questions for the future are will the government intervene to help temper the extreme
competition and financial loses and, if so, what form will the intervention take. Robert
156
7. Conclusion
Crandall, former CEO of American Airlines, has called for a partial re-regulation that will
help airlines to cover their costs by changing fare structures and encouraging carriers to
decrease frequency but use larger planes and increase point-to-point flying. [73] He also calls
for increased stringency in the financial tests that new airlines must pass before beginning
service and a change in the bankruptcy laws that currently allow carriers in bankruptcy
to offer fares below their costs, a practice that has contributed to the destructive spiral
down in fares noted here.
Crandall concludes that "a dollop of regulation, along with
new government policies and appropriate investment, would help the carriers get back on
the right track."[23] James Oberstar, former chairman of the House Transportation and
Infrastructure Committee, has also expressed concern about the direction of the industry
and has indicated that re-regulation is a policy measure that should be considered.[48]
The regulation need not be nationally based. As the case study of the Boston region in
Chapter 6 showed, there are local and regional factors that affect accessibility and local
officials who understand a region's needs may also be equipped to make changes.
One
approach to partial regulation would be to put authority in the hands of state and local
officials and allow them to experiment with new ideas. For example, states with a high
number of EAS airports might be the first to adopt the use of buses to maintain rural service.
Logan Airport, which has a problem with congestion at peak times, has demonstrated this
concept with a unique peak pricing program for landing fees designed to spread out traffic.
Recent MIT research has examined slot reduction programs with an eye toward the New
York region.[70] A less central regulation program has the advantage of allowing the FAA
to try a number of different approaches to solving problems without the cost and risk
of deploying nationally and an initially less uniform approach might be more politically
palatable than a nation-wide regulation.
157
7. Conclusion
7.2.4. Network structure
Just as the air transportation network evolved from the trunk-lines in the early years of the
industry to the hub-and-spoke networks that dominate today, the network and its use will
continue to evolve. While hub-and-spoke has been the dominant network structure for the
last 30 years, it is not without problems. For example, hubbing is responsible for the lower
path quality discussed here that negatively impacts accessibility. Hubbing also contributes
to congestion at major airports because more aircraft fly into a hub than are needed to
serve the local market.
In contrast to hubbing and connecting itineraries, point-to-point, non-stop flying is more
convenient for passengers but typically results in higher fares.
While fares would likely
increase, as the discussion of financial difficulties noted, this is a necessary outcome for the
airlines to achieve financial stability. With the dramatic decrease in fares that has been
discussed elsewhere, there is room for fares to increase somewhat and still remain much lower
than they were prior to deregulation. This would have the benefit of allowing the airlines
to achieve financial stability while passengers would still retain lower fares than under
deregulation and experience accessibility gains in the form of higher path quality. There
are also environmental gains that can be realized from point-to-point flying.
Annually,
aircraft emission lead to about 10,000 premature deaths globally and have an estimated
cost between $32B and $84B.[60] If airlines could fill aircraft with passengers all bound for
the same destination it would be more efficient 4 for them to provide point-to-point flights
than connecting itineraries and would also result in fewer miles flown and fewer take-offs
and landings.
The most economically feasible way for airlines to provide increased non-stop, pointto-point flights is by decreasing frequency and thereby aggregating passengers who were
previously on multiple flights onto a single flight. This strategy is not currently employed
4
The reason fares would likely go up despite increased efficiency is that the frequency of flights would go
down, meaning that each aircraft would be utilized less each day. Maximizing aircraft utilization is a
primary driver of productivity in the industry.
158
7. Conclusion
because the airlines are afraid of losing market share to airlines offering more frequent service, even if it is connecting (as evidenced by the "S Curve" concept discussed in Chapter2).
The increased population density seen throughout the US, particularly in cities, since 1978
should, in theory, make it easier for airlines to offer point-to-point flights because there is a
larger pool to draw from making it easier to fill non-stop flights. Nevertheless, this would
still likely require airlines to trim frequency, and in competitive markets this leaves the
airlines fearful of lost market share. This is the type of problem where some form of partial
government regulation might be beneficial and help solve collective action problems.
In a forward-looking speech Robert Crandall, the former head of American Airlines who
was one of the early proponents of hub-and-spoke, suggested several changes to improve the
industry including shifting towards more point-to-point flying. A major theme in Crandall's
proposal is government intervention to prevent the type of "unfettered competition" that
is hurting the industry, including a pricing policy that encourages non-stop flying over
hubbing.[231 Crandall proposes achieving this outcome with a "sum of locals" pricing rule
that would require airlines to charge fares for connecting itineraries that are no less than
the sum of the fare that would be charged for the 2 segments of the connecting itinerary if
they were non-stop local flights. Crandall argues that not only would this increase point-topoint flying, but it would also allow airlines to recoup more of their costs as well as making
more efficient use of fuel and decrease emissions. Again, this would require some regulatory
intervention from the government to overcome the collective action issues associated with
changing pricing policy.
7.3. Future work and final remarks
There have been many studies that evaluated changes in fares or passenger volumes or flights
between airports. What makes this research different is that it sought to understand how the
air passenger network had changed from the perspective of passengers by looking at what
this thesis called accessibility. Previous studies too casually brushed over the passenger
159
7. Conclusion
treating them as a given in their pursuit of optimal network efficiency, but this thesis
attempted to give due attention to the experience of the passenger and the convenience of
flying for them. Beyond simply understanding the evolution of the passenger experience,
this thesis also provides a model for measuring changes in accessibility and modeling the
accessibility effects of changes in government policies or operating practices in the airline
industry.
While this research is an important look at accessibility, it is not an exhaustive one. This
analysis did not include a number of factors related to accessibility that are needed to more
fully understand the ways accessibility has evolved and to produce accurate predictions on
the accessibility effects of industry changes. In particular, an analysis that incorporates
the time component not evaluated here would be useful.
The measure would augment
the methodology produced here by including factors such as frequency of service, time
spent waiting for connecting flights in airports 5 , and changes in the delays experienced by
passengers originating from different airport size classifications.
Beyond additions to the methodology introduced here, this work can be extended as a
tool for thinking about the future and understanding the accessibility impacts of proposals.
For example, if Crandall's ideas or something like them come to pass, it will be a departure
for the way the industry operates today. From the passenger's perspective, a change to more
non-stop flying would mean an increase in path quality, likely accompanied by increased
fares. Frequency of service would probably decrease at airports in the middle of the country
that serve as hubs while airports closer to the coasts would likely see increased frequency
and higher path quality.
This potential outcome is an example of the type of situation where this methodology can
5
The example at Boston-Logan in Section 6.3 showed that in 1980 77.3% of all passengers flew non-stop
itineraries. In 2010 that percentage fell to 70.8 indicating that about 67,000 passengers flew a connecting
itinerary in 2010 when they would have flown a non-stop under the conditions in 1980. A back-of-theenvelope calculation assuming one hour of additional travel time from connecting and the US DOT value
of time of $33.30 for inter-city personal air travel implies an economic cost associated with increased
connecting itineraries of $2,238,696 for air travel originating at BOS. If summed across all US airports,
the costs would be substantial.
160
7. Conclusion
be used as a foundation for detailed analysis. The measures of path quality, for example,
provide one way of understanding how Crandall's ideas would impact accessibility both
at the airports gaining frequency and at those losing it. The other measures also provide
a starting point for analysis of the accessibility impacts of this and other proposals for
improving the airline industry.
Lastly, the study of accessibility also has implications for economic growth.
Recent
research in transit has shown that there are productivity benefits that result from increased
accessibility and decreased travel times.[43] There are certainly benefits that result from
the accessibility provided by air service. This is an underdeveloped field of research and
being able to track accessibility changes with this methodology could contribute to better
understanding of the economic benefits of scheduled air service in future study.
This final chapter discussed a few of the trends in the industry, but there are almost
certainly more changes in store. While the results shown in this research look back, the
methodology and ideas about accessibility are just as relevant for the future and this work
provides a foundation from which to interpret future changes and judge policy proposals.
161
A. Sensitivity Analysis
The purpose of a sensitivity analysis is to understand how the results would have changed if
different modeling decisions had been made. In this case, the main decision was regarding
the weights for connection types in the path quality measure. A sensitivity analysis is
particularly important here because the weights were not derived from any econometric or
preference analysis but were instead chosen based on precedent and intuition.
The results in the Table A.1 show that under both the higher and lower connection
weights the directionality remained the same. That is to say that if using the main weights
accessibility was shown to have decreased as measured by path quality, then accessibility
still decreased using the higher and lower weights. The higher and lower weights do have
an effect on the magnitude of the change, but not the overall directionality.
162
A. Sensitivity Analysis
1980
1990
2000
2010
0.813
0.720
0.547
0.473
0.508
0.827
0.708
0.564
0.466
0.541
0.842
0.770
0.610
0.512
0.579
0.847
0.807
0.607
0.542
0.836
0.669
0.544
0.315
0.218
0.325
0.809
0.678
0.522
0.400
0.517
Main weights
Large hub
Medium hub
Small hub
Non-hub
Commercial Service
0.829
0.759
0.604
0.469
0.422
0.796
0.701
0.545
0.507
0.606
High weights
Large hub
Medium hub
Small hub
Non-hub
Commercial Service
0.864
0.809
0.672
0.569
0.490
0.829
0.725
0.629
0.527
0.543
Low weights
Large hub
Medium hub
Small hub
Non-hub
Commercial Service
0.821
0.750
0.575
0.452
0.363
0.773
0.651
0.515
0.402
0.444
Table A.1.: Path Quality sensitivity analysis
163
B. Fare scatterplots
Large hub, 1990
Large hub, 1980
500
450
400
350
300
250
200
150
100
50
0
900
y = 0.070x +81.88
R2= 0.855
e
*
Average Fare
Linear (Average Fare)
-
700
600
500
y = 0.063x + 120.8
R= 0.717
:400
*
300
200
100
0
-
0
2000
4000
6000
8000
Average Fare
Linear (Average Fare)
10000
Market Distance
Market Distance
Large hub, 2010
Large hub, 2000
1200
1200
1000
1000
y = 0.069x + 125.8
2
R = 0.765
800
600
y = 0.076x + 123.7
2
ALA
800
R = 0.725
600
400
*
Average Fare
-
Linear (Average Fare)
400
200
200
0
0
0
2000
4000
6000
8000
0
10000
2000
4000
6000
8000
Market Distance
Market Distance
Figure B.1.: Large hub, fares (not inflation adjusted) vs distance
164
10000
*
Fare
-
Linear (Fare)
B. Fare scatterplots
Medium hub, 1990
Medium hub, 1980
600
500
y =0.082x + 74.43
400
R= 0.892
*
300
*
200
100
0
0
2000
4000
y =0.062x + 124.1
R' = 0.725
IL400
Average Fare
Linear (Average Fare)
-
900
8oo
700
600
500
*
300
200
100
0
-
2000
0
6000
4000
6000
8000
Average Fare
Linear (Average Fare)
10000
Market Distance
Market Distance
Medium hub, 2010
Medium hub, 2000
1200
1200
1000
1000
y = 0.069x + 127.3
800
2
R =0.691
y = 0.079x + 125.4
R' = 0.737
800
600
600
*
400
-
Average Fare
Linear (Average Fare)
*
400
-
200
200
0
0
0
2000
4000
6000
0
8000 10000
2000
4000
6000
8000 10000
Market Distance
Market Distance
Figure B.2.: Medium hub, fares (not inflation adjusted) vs market distance
165
Average Fare
Linear (Average Fare)
B. Fare scatterplots
Small hub, 1980
Small hub, 1990
600
500
y = 0.082x + 79.27
R'= 0.869
400
300
*
200
Average Fare
Linear (Average Fare)
-
100
0
y = 0.058x + 138.1
2
R = 0.648
*
-
0
4000
2000
0
1000
900
800
700
600
500
400
300
200
100
0
2000
4000
6000
Average Fare
Linear (Average Fare)
8000 10000
Market Distance
Market Distance
Small hub, 2010
Small hub, 2010
1200
1200
y = 0.071x + 162.4
1000
1000
R'=0.599
y = 0.063x + 151.8
800
800
R2= 0.650
1600
600
*
400
-
Average Fare
*
400
-
Linear (Average Fare)
200
200
0
0
0
2000
4000
6000
8000 10000
0
Market Distance
2000
4000
6000
8000 10000
Market Distance
Figure B.3.: Small hub, fares (not inflation adjusted) vs market distance
166
Average Fare
Linear (Average Fare)
B. Fare scatterplots
Non-hub, 1990
Non-hub, 1980
1000
900
800
700
600
600
500
y = 0.084x +83.74
R= 0.845
400
y = 0.058x + 154.9
Ra= 0.533
300
*
200
400
300
200
100
Average Fare
-Linear
(Average Fare)
100
*
Average Fare
Linear (Average Fare)
-
0
2000
4000
0
6000
2000
Market Distance
4000
6000
8000 10000
Market Distance
Non-hub, 2010
Non-hub, 2000
1200
1400
1000
1200
y = 0.058x + 169.4
R' = 0.483
800
y = 0.056x + 199.3
1000
So
g
600
*
400
-
"-
Average Fare
Linear (Average Fare)
200
R' = 0.404
800
*
600
-
400
200
0
0
0
2000
4000
6000
0
8000 10000
Market Distance
2000
4000
6000 8000 10000
Market Distance
Figure B.4.: Non-hub, fares (not inflation adjusted) vs market distance
167
Average Fare
Linear (Average Fare)
B. Fare scatterplots
Commercial Service, 1990
Commercial Service, 1980
600
450
400
350
300
250
500
y=0.09x + 78.69
2
R = 0.869
400
% 300
*
200
Average Fare
Linear (Average Fare)
-
100
0
1000
2000
3000
4000
y = 0.062x + 163.3
R2= 0.372
*
150
100
5000
Average Fare
-Linear
1000
0
(Average Fare)
3000
2000
Market Distance
Market Distance
Commercial Service, 2010
Commercial Service, 2000
600
700
600
y = 0.057x + 208.5
2
R = 0.333
So
500
e
400
y = 0.063x + 175.5
R' = 0.438
*
300
Average Fare
Ow
400
300
200
-
*
A 200
Linear (Average Fare)
100
Average Fare
Linear (Average Fare)
1000-0
0
0
1000
2000
3000
4000
5000
0
1000
2000
3000
4000
5000
Market Distance
Market Distance
Figure B.5.: Commercial Service, fare (not inflation adjusted) vs market distance
168
C. Non-hub path quality quartiles
Akport
ABI
ABR
ABY
ACK
ACT
ACV
AEX
AGS
ALO
ALW
AOO
ASE
ATW
AVL
AVP
AZO
BFF
BFI
BFL
BGM
BGR
BHB
BID
BIS
BjI
BLI
BLV
BMI
BPT
BOK
BRD
BRO
BTM
BVU
BZN
2010 Airpor
2000
1990
1980
1||||1 0.424 1ill 0.33 INL
0.3881ll1ll1 0.510
111
0.3 IPT
lii
0.337
||111 0.479
Jill 0.320 11| 0.29 SN
11i0.395
|||1 0.396
111 iTH
1|l11 0.657 1111||111 0.920 11110111 0.900
11110.88
l 0.314
ii 0.3 IYK
111110.339
i 0.256
li
0.669
1111 0.548
0A7
0.35
0.31
0.28
I1||1 0.52
1000l 0.580
||11 0.380 0.27
0.559
|||
-4
0.360 |||||
Jill 0.305 Jill
111111 0.446
11
11
11||
i|
0.337
Jill 0.324
0596 11ll 0.570
iiiii
i1|||| 0.682|1111111 0.659
1110.270
0.400
0.452 ||11110.511
111111
11 0421
11111
0.464
0.549
11111111
||1 0.403
11 0.437
0.509
1111111
JAC
LAN
LAR
LAW
LBE
LBF
1i1 0.393 ||11
1u1 0.382
1||11
||m 0.335 111
|1|1| 0.397
||||
1|| 0,359 1l1
111 0.363[1111
0.4 LCH
0.47 LFT
0.43 LMT
0.32 LNK
||11 0407
0,31 LRD
0.411
11111
0.61 LSE
0.517 11ligl 0.606
1111111
1|||11111111
1.000 lii [i|il I LW 5
g101.000
1|||11 0.577 11110.503 11|11 0.475 111 0.38 LYH
Ji| 0.3 MBS
0000.548 I l1 0.364
in1 0.409
111 0.533
gig0.513 I 1|| 0.458 1111 0.5 MCN
i1 0.275
00010.746 I 1110.5031 111 0.36 MCW
2000
1990
0.462
1111
11i0.354
ii 0.497
113 0.363
A 0.433
|11l 0.335 11
||1g|3 0.559
11 0.596 I11l1 0.507
0.616
01111
im0.321
ii 0.326
11 0.454
0.417
0.426
1111|
m111
0.345
1|Im
0.410
313 0.364
1||A3 0.464
II 0.332
0.353 111 0.482
Jl 0.310 Jil 0.282
000.289
0.318
0.255 1111I1 0.499
0A97
A971 11111111
0.537 Igg0.382
|
1980
Iill 0.562
0.263
1u110.371
||1||11 0.534
0.491
111111
min 0.492
0.567
11111111
11 0.358
1110.4
14
1111 0.654
1110.415
2010
0.33
Ill 0.27
0.67
0.36
0.19
0.38
0.32
ini
191101
0.57
0.28
0.61
Ill
0.35
11||111 0.39
0.29
111lig
11110.42
i llil1 0.580
0.515
lu1 0.491
111111
IIm 0.52
10110.506
||||111 0.536 IIm1| 0.642
0.3
m10.335
i 0.364
||1i1|i 0.537
0.49
1111 0.649 I1111 0.636 mi313 0.685
0.35
IllAg 0.430
0.464 11 0.434
|11
0.3
11|| 0.441 ||i 0.371 1ig 0.364
ml 0.333
0.295
I'l 0.74
0.303
0.279
111 0.443
II 11
I 0.27
0.60
0.31
311 0.362
I MEI
Il1 0.352
11 0.155 0111
|11 ||1
1.000 1||1|1111111|giig
1.000
11|
fill] 0.387
I||m
0.57
0.419
11||1 0.56 MFR
111||0.573
|HIM 0.441 111111
0.681
|1111111 0.51 11 0.589
1111111111
111
i1l
0.36
0.366
11 0.28 MGM
m1 0.347
1il 0.357
1111111
0.510
111111
0.523
111 0.391
1111
0.32
1m0.339
0.83 MGW
11 0.441
0.322
i 0.242
111111 0.53 131 0.443 II
1111
0.46
MHK
11l1|0.541
|| 0.279 Illll 0.767
11111
0.27
0.461 111 0.48 MKG
11331
0.464
111111
0.289
0.382
||0A457 111g 0469
11111
||i|
0.38
0,41 OA3913 0.443
01 0.29 MLB
0.516
1111111
11 0.588
1111111 0.522 111111 0.428
iii
0.37
111111
0.432
0.530 l111 0.41 MLU
111 0.472
1111111
111111
0A56
l3131 0.51
11111
0.4
111111
0.453
11 0.446
1i1l 0.312 111 0.28 MOB
111111 0.527
Iu 0.315
il 0.380
111
0.22
131 0.405
0.364
1|1 0.466
-411 0 MOD i 0.258
11111
0
101 0.339
1111
0.373
Jl 0.29 MOT
||111 0.38
li 0.377 111 0.484
lilli1 0.525 lli3 0.439
13111 0.505
OAS
MRY
O1S1 0.658
1i1g0A90.
illi 0.549
11111
0.497iummi0.3 i 1.000
0.54
0409
||
S 0A5 MSO
||111 0.576
11 0.420
111111
1901111
go0.369 3111 0.363
111111 0A497
169
C. Non-hub path quality quartiles
CEC
CHA
CHO
CIC
CIU
CKB
CLD
CLL
CLM
CMI
CMX
COD
COU
CPR
CRW
CSG
CWA
CYS
DAB
DBQ
DHN
DLH
DRO
EAR
EAT
EAU
EGE
EKO
ELM
ERI
EUG
EVV
EWB
EWN
EYW
FAR
11111111 0.37
|11111| 0.597
11111
0.376
111110.385
1111110.438
|11|11110.557
111111 0.475
1111111 0.48
|| 0.276
0.446
111111
11111
0.338
111110.752
glili 0.414
|||1 0.344
0.752
.4
||||10.307
0. 11111||||11111|11
11111
|| 0.253
1|11 0.398
11 0.32 MT
111r 0.540 111111
0.668
11111 0.446
1111l 0.42
1111111 0.47 MVY11ilil 0.628 11|111111 0,843 ilingl 0,851
||iv1101 0.85
1111111
0.522
11111111
0.552
I11111 0.4 NYL 11111111
0.578
j1l 0.341
IIH 0.322
wi 0.28
0.374
III 0.25 OAJ
11111
|im| 0.28 OTH 110.232 IllglfI 0.586
1ii1 0.31
0.503
1111111
11111 0.35 OXR 111110.417
1111111
0.495
1111 0.387
i1 0.22
1111111
0.28 PAH i1| 0.288
0.499
|l|
11i H0.337
0.565
111|111|
Jll[ 0.35
1 0.240
Jll 0.32 PFN
Hill0.391
11i i0.349
Jill 0.32
0.431
1i 0.371
1111ff
0.504
1111111
| PGA
1|||1
1.000 Ilillflig 0.73
0A56 1011
||| 0.255
0.484
0.594 11111111
111|11
1111111
0.508
11111ff
110.248
0.451
0.477
|||| 0.351 11111 0.35 PGD
1111111
111111
1i1 0.397l1lill11 0.550[11111 0.415 ilIl 0.29 PGV
0.29
||| 0.238
il
111111 0.419
1111111
0.485
0.612
f111|0.35 PA
111 0.426
11111 0.380
|11l1 0.526 111l11l11
11f 0.394
111i111 0.590
0l1ii 0.61
||11 0.340
|||
0.26
0.529
0.459 11 0.459
ff1|1 0.429 1Illlf1 0.542 111111
1il 0.32 1
111111
0.530 11 0.437
1li 0.300
.
liilii0.925 |10
1
1
IflI 0.45 PIE
0.590
1111111
illill
0.535
iii
0.28
i 0.383 0iu11i1|| 0.918
0.497 11l 0.452
11 0.45 PlH
11111111
ff1l1 0.544
11111ff
Jll 0.31PiR
11||| 0.639 I1111111
0.575
111111 0.430
|||1 0.328
111111 0.44
i 0.299
Jll 0.300
||i 0.31PLN
111111
0.429
111111 0.407
IIl
0.3
0.455 ||||| 0.358
|Hug0.491
111111
||||l1 0.455
11110.383
i1 0.343
0.458 Jil 0.331 ||11|| 0.51PMD
0.528
11|l| 0.353
1111111
1111ff
1 0.232 111111
i 0.17
0.421 1111f
0.485
0.446
1111111
111111
[ill 0.34 PQI
0.659
lillill 0.522
111111111
0.662 1111 0.529
lli 0.31PSC
0.415 ill 0.339
111111 0.460
1i1ll 0.52
1111ff
|1|1|1|| 0.622
|||| 0.3 PSM
110.359 ||11 0.312
||||1O1l1|||
0.998
f11111
0.409
11||111 0.643
10i0|| 0.65
0.44 PUW
|111111g0.646li1iI 0.647
I1|11| 0.523 1i1l0.341 JIMl0.335 [il[
lil 0.292lI91l0iI 0.996 1I1111111 0.783 1ll1li12l 0.84
11111l1 0.544
l|1111111 0.638
0.511 11ll 0.44 PVC
1111111
0.50
1
.516
111 0.393
Iffff1 0.47
0.464 1f 0.406 lillil 0.66 RAP
111111
i|| 0.220
0.524
042 RDD
1il1l11 0.611
igllil
0.729
|Hg 0.498
fff 046
1111111
1lIlll1li 0.640 il11il 0.549 111
ff1 0.37 RDM
lilI 0.585 l||il 0.598
Il||l
0.557
1111
0.53
tlil
0.448 l11| 0.359 |11| 0.326
ii 0.276 |||llif 0.535 1111i0.545 I||h||| 0.58 RFD
ffff 0.463
1|f 0.433
IIllgl 0.730 Ilillg|| I
0.31
ill
11111 0.357
1|11IllI 0.740 1111 0.553 11l0l1l11 0.908 fim 0.37 RHI
I 0.552 11111f0.499
ff111 0.38 RIW
HHHf
0.543 1IlIf11 0.575
111111 0.414
11f1f111 0.57
111111 0.404
111111
0.48
0.462
11111ff
l1 0.286 J1l1 0.32 RKD
0.426
1111ff
0.389
0.276 Illll1I 0.653 f111111 0493 1l11li1 0.8
11111
1il
0.517 1111111
1111111
0.49 RKS
1||| 0.511
11111ff
0.494 111111il1
0.616
f111111 0.496 1f1l 0.34
|||||il1| 0.640
fl1||1 0.449
1111111
0.642 1111111
0.486
111111
0.453
0.548
ff11110.48
11111 0.522||||111
[ilf[ 0.42 ROA
ROW
Oililll .43
1111111 0.5
00010 .0001111111li11111
will0.460 illlill 0.777 il0l100 0.833
11 0.224
11 0.32 RST
11111 0.345
m1u
0.400
11111ff
0.517
11i0.405
I1 0.345
111111 0.41
SBN
|||||i|||
0.632
11111 0.38 1lf
f11111 0.433
0.492
11111ff
0403
111 0.400
11 0.340
1
0.49
||11ii 0.52i||||SBP0.734
11111 0.394
0.599
|11111 0.629
|11111 0.489
0.421
11|| 0.33
111111
||||i||i
170
C. Non-hub path quality quartiles
FAY
FCA
FHR
FLG
FLO
FMN
FNL
FSD
FSM
FWA
GCC
GCK
GCN
GFK
GGG
GJT
GNV
GTF
GTR
GUC
HDN
HHH
HLN
HTS
HVIN
HYA
IDA
IFP
Hl 0.33 SBY
0.43 SCK
111111
SGU
0.237 1101||111
0.896
11 0.435
Jill 0.3 SHR
||||||| 0.514
Il| 0.339
||||| 0.4 14
Jil1 0.32 SHY
0.74 SJT
1|1|10.523 11111
0.8 17
I SLE
1.00010001
1110.262|1111111111
0.56 SMX
11111
0.368 11111111
111111
0.447
0.651
111111
111111
0.439
11 0.355
|1||1 0.437
Jll 0.31 SPI
|||| 0.405
111|0.391
|0|0.484
||||1|1 0.47 SPS
I 0.289
||1I1 0.4 STC
11 0428
l 0.544
0.67 STS
i|||g 0.471
I1ll1 0.487 1000i|i 0.835
0.570 1111 110.9721 1 0.998 1||||||1||11 I SUN
1l1li11
0.5 SUX
ig 0.360
I i 0.392
1111111
lilill 0.565
iI 0.341
0.270
0.342
Jill 0.3 TEX
0A5 TOL
1|1l1|0.649
1110.464
0.527
11|1|1|
|i 0.452
0.33 TRI
I11 0.396
||111 0.513
11g0420
1111
0-405
ill' 0.57 TUP
iig0.591
III
0.375
1 0.366
i 0.391
Jill 0.3 TVC
0.4 TWF
1110.463 illi|||I0.545
1|1 0.489
0.46 TXK
1li10.414
0110.5|11 11 0.452
IIIII
0.33 TYR
iii 0.379
Jl 0.331
0.276
1110418
0.38 VGT
OA|111 1|10.415
0.652
'IIIII
I1|| 0.412
0.74 VLD
11||1||
0.513
Ill 0.402
1111
0.525
1110.457
Jill 0.32 WST
ilil 0.531
0.96 YKM
1iI 0.452 lijlmilili 0.871 IIIHIII 0.945
0.46 YNG
11111
0.476
0.370
I11 0.388
11111
I
0.831 i11l1,|ll
0.520
||11|| 0.520
1110.348
111110.374
Jill0.316
11111
0.388
|||10 .860
11111
0.391
11111
0.347
|1111110.710
1111
luiil
10
i11
111|
Hl
Jill
||ll 0.275 1 ||11111
1111111
0.536
ill 0.274
H 0.218
||111 0.568
0.706 |
1i0.411
||| 0.384
|||1l1 0.496
0.394
11111
|
11110.563
li1|||
0.505
ingl 0.556
1110.382
ig1
0.469
0.546
ill 0.269
1li 0.269
1000 ||||11
ll LO|||||
1|1 0.422
iII
li
0.349
||||||Ii|||i|l 1.000
0.648
1111111
0A79
gillill
0.640
1li10.364
ii|
111111
Jill
gB
0.392
g0.4132
0.408
ill 0.330
lii11 0.494
0.321
lil 0.402
11111
0.498
Figure C.1.: Non-hub path quality quartiles
171
111
0.256
0.385
11111
I111 0.507
igIll|| 0.782 1i1l01
0.633
|i
0.788 liigili| 0.782 ||1111 0.775
11 0.548
IIg0.390
||||| 0.491
1i1||0.473
ng0.418
0.618
DI 0.464
0.476 iillI 0.598
| 0.260
li 0.386
Jl 0.300
III 0.471 111111
0.527
|i 0.581
1111111
1lui|i| 0.621
0.495
|||I1I 0.535
1il 0.308
0.333
iF0.246
1|0
ill 0.309
Ill
11111
0.390
g 0.437
Jill]
20
||i 0.344
I
11l10.381
lill
I01.000
21|1||||111
lll 0.338
11
1I
i1ing
0.493
01||0
mm1
Jll 0.313 iiIIIlI
D. Non-hub destinations quartiles
Akport
ABI
ABR
ABY
ACK
ACT
ACV
AX
AGS
ALO
ALW
AOO
ASE
ATW
AVL 4
AVP
AZO
BFF
8F9
BFL
BGM
BGR
BHB
BID
815
BjI
BLI
BLV
BMI
BPT
BQK
BRD
BRO
BTM
BVU
BZN
190
1990
195
1111 232
||
III
1111 201
Ji 180
1111 315
111111
256
11110
Ji 161
[Jill 222
111111
258
u11111
310
111 301
mw 301
Jll1 179
2
1111 229
11111
238
i|m 305
I 64
2
1111111
315
143
119
Jill 187
J1111232
III 156
11111262
Jill 175
11
241
2000
1lul 228
[11 165
l1j1 204
111 131
1111 211
Jill 188
111111
323
1Il1 229
11 116
Jill 171
111111
240
HIM1
272
1111 303
111111
281
ilmi 320
11 126
111111
279
11111
239
1111111
314
1 63
3
1111111
281
I 159
111111
252
11111
205
111111
248
t1l1 200
JIll 178
||l
190
111111
276
2010
219
ll 178
il| 186
Jil 174
11111
213
11111
216
111111
257
1|||1| 268
11111
211
Jil 164
I11 124
11111
238
1111111
304
|111 282
11 275
1m111331
11 131
12
11111
272
111111
256
i11t 294
1112
ill
I
111111
265
1111178
Illi 271
5
111111
244
111111
246
11 156
Jil 171
Jill 187
|il
192
I
11ng 290
Airport
11111
246 INL
111 171IPT
ll 190 ISN
I11 133ITH
11111
212 IYK
111 228 JAC
li11 260 LAN
11111296 LAR
1 158
LAW
1 135 LBE
11 99 LBF
Il1 247 LCH
1111111
282 FT
1111 295 LMT
11111
273 LNK
1m11252 LRD
i III LSE
6 LWS
111111
261 LYH
11111
232 MBS
111111
272 MCN
||| 136 MCW
I MEl
111111
278 MFR
Jill 185MGM
11111
225 MGW
MHK
11111
235 MKG
Jil 161MLB
Jil 178 MILU
Jil 167 MOB
11111
235 MOD
1111164 MOT
MRY
307 MSO
1111111
172
1980
1990
2000
2010
ill
in 141
Jl 198
1 103
111 210
11 97
Jim 204
Ioomo348
11 184
111111
278
I
87
Jil 173
11111
206
111111
255
I 134
m
11111331
IiI 137
Im1275
143
11 207
Jil 185
1111203
11 84
l11 222
1i1 158
mml 248
11111111
330
I
91
11|| 276
I
136
i11 8
Hil 223
11111
227
11 156
111111
303
Jil 164
111111
265
1111167
11111
219
Jil 168
1|1 157
1
94
Jl 186
1 106
11111243
1111111
318
11 133
11111251
11 137
1 107
[ill 219
|1|| 276
11 158
11111308
Jill 193
11111256
1111196
11111
225
102
220
286
11|11|
hil 163
111111
259
11111
209
11111
262
1i1 221
11111
216
111111
371
111111
266
11 124
Jil 198
111111
256
1111111
306
Jill 176
11111
266
111111
250
111111
295
111111
274
|ittlil 340
ll 180
111111
272
11111 320
293
1111111
111111
316
1lp 211
11 149
111 186
111111
272
1111111
306
|l|
174
1 145
11111
209
111111
278
11111252
tlillil 330
111 143
11111
236
111111
313
1111111
314
11111111
325
Ji 170
|11 143
Jl
190
1111111
291
1111111
291
Ii 152
H 130
Jil 190
1111 205
11111
234
1111111
301
1
143
11111
219
1111111
294
298
1111111
111111
261
27
|||
123
111 157
1111111
312
111111
269
I 129
111 165
1 149
111111
243
111111
245
n1111m
300
Jil 166
111111
267
111111
270
1111111
308
ill
162
163
1 I17
111l 230
1 135
1111261
1i1lt 263
1 128
11111
235
!
lill
D. Non-hub destinationsquartiles
CEC
CHA
CHO
Cic
CIu
CKB
CLD
CLL
CLM
CMI
CMX
COD
COU
CPR
CRW
CSG
CWA
CYS
DAB
DBQ
DHN
DLH
DRO
EAR
EAT
EAU
EGE
EKO
ELM
ERI
EUG
EVV
EW8
EWN
EYW
FAR
19
38
87
||
1|||| 319 ||11ll 303
In| 235 I1||| 253 lill1 254
|
79
I
97
144
l
128
89
il 147
|i| 162
ill 185
127
24
6
Jl|| 176
11111
228
|||||
200 11ll 258
H 94
1i 135 Jll
184
242
11ig 309 ||||||
287 111111
158
|ll 182
Jill17 1
121
10l 181
1i 137
J||| 167
||
131
1|1|1 258
229 lili 228
ll
290
1l11|
318
lull 326 lili 293
11ll1 259
317 |||11 304
1in1 288
264
1ull 272 111
lill 238 lill 175 Jl|l 164
1il1 290
1111202
luH 318
I1|| 203 |||j|
203
|1||| 217
||H 245 h||
259 |i||
226
||111315
u||| 273 ||i|
265
202
ti| 215
226
lilmi
353
ii 127 MTJ
111 298 MVY
ill11 274 NYL
1i 146 OA
Ji| 160 OTH
1 12 OXR
167 PAH
231 PBG
||||
I PFN
212 PGA
Jill 165 PGD
1W185 PGV
il 157 PIA
||n 240 PIB
11 294 PIE
||||1
227 PIH
uin 276 PIR
IllI 180 PLN
1ili 252 PMD
162 PQ
||||
PSC
209
|||||
||111 261 PSM
|||1| 261 PUW1
u 96 PVC
||
98
I
75
163
|ll192
ill 167 RAP
138 RDD
||I 182
Jl|l 168
|| 94 ||||u 227
239 RDM
148
178
Jil 170 RFD
g||| 229
239 ||uu 247 RHII
ulli 292 1|
270
Ili 241 R1W
314 RKD
11lt|
337 111|
333 |||||||
ullil 308 ||Ii 307 ||||1 266 RKS
3 ROA
2
228
11 268 ROW
Ji|| 189
1111 263 RST
uill271
11lli 248
01|311 58N
294 1ull 291
g|||
S88
S85
1ii 202
40
105
||j||
I1li1
251
290
1u11u1
340
|||||uu 338
17
Ii 134
nil
193
11111111
32 1
iil
173
ISO
97
1
236
11|| 262
131
Jll 199
Jll 197
|||
||
11|| 251
I
111| 233
I
54
11108
35
65
I1|| 203
Ii 134
|I
227
109
206
192
Jill179
188
||||
|i|| 182
1il
1|1I
|||||
248
2
Jll 181
144
364 i|||| 306
illilll
li11277
1111111
283
11| 158
||
87
111 151
130
1g|| 214
|
61
5
|||||
209
li1|| 265
|||
149
34
J|| 165
|||
151
Jll 163
125
30
30
Jill 174
I11|203
|11 143
Jill 17 1
Jill 174
iu142
Jill 170
ml 187
|||| 169
111183
322
lu|| 279 1l11l 296 ||I1|||
6
137
1i 133
1I 152
3
I
55
I
58
296
111l
308
h0ll 299 1111
||||| 210
Jll 189
Il| 201
263 1||11
282
111 201 00i
5
127
1iu 216
Jil 165 hill 205
Jl|| 183
I1I
142
|| 103
Ii 133
I 73
I 84
I
74
116
lig 173
|
2||11 1|111
292 322
||1l1291
I 109
iI
148
l|l 194
I11ll 274 ill
262 Illill 252
344 ||||1
11111111
273
lill1ll 338
Jill 175
2
hl 190
Jill 163
|||l 187
i 77
Jill 167
111111
257
134
D. Non-hub destinationsquartiles
FAY
FCA
FHR
FLG
FLO
FMN
FNL
FSD
FSM
FWA
GCC
GCK
GCN
GFK
GGG
GJT
GNV
GTF
GTR
GUC
HDN
HHH
HLN
HTS
HVN
HYA
IDA
IFP
306 L1|I|| 308 1111 259 1111 292 SBP
1111111
||0I 274 SBY
111 206 11|| 235 11|| 265
SCK
41
I
55
I 57
1i 136
1I 122
11|| 207
wi 221 SGU
11 203
218 SHR
l
Jill 179 1111 203
i1 129 SHV
202
1
|11
225
|ll
184
1
3 SJT
1184
311 |||1||1 308 SLE
L1|I||| 346 L1|||| 313 |||1|||
11I 231 SMX
lil11 283 1H1 251 111 251
272 SPI
339 |||III1 333
|liLL
362 ||||||L|
||| 128
II 137
i
97
Jll 196 SPS
|| 109 STC
|| 104
53
|| 114
31
3 STS
I
87
80
111 272 HIII1 252 111 230 11ll 246 SUN
156 SUX
iI
II 135
||i
16 1
LI 199
306 TEX
1111162 ||I1iJ 267 |||||1
1111 291
111111
244
11ll 250 TOL
1 263
1111 299
||1| 293 1111 300 |||||||
280
11|300 TRI
1ll1 179 TUP
111 221
1l1 218
111 208
li 178 TVC
II 148
LI 166
J||| 194
228 TWF
11 160
11111
223
Jll 199
ij 196 TXK
24
Jill 173
JIIl 185
1ill 221 ||I1 213
1I1 232 1111 255 TYR
Jil 192 VGT
111111
274 111 238
11 193
Jill 176
1I 135
|| 149 VLD
Jll 171
64 WST
Il 133
J11 176
11 191
277 YKM
111 223
111111
258 I1111 240 ||||1
S 70 YNG
|
63
222 lilill 281
207
Jil 201
163
99
In 155
in 143
134
335 lill1i 313
Jill 196
230
104
2
202
Jll 177
202
206
11111
11111
226
243
Jil 17 1
illI 164 11 131
201
11111
|||
146
11|1| 254 111 220
113
100
i
|||1|||
3181LILI 311
Ill11l 294 IILLI 291
|I| 158
|| 137
290
11|1 247 2LILL
Jll 161
II
187
1|| 210 Jill 193
11111 221 11111
212
7
Jll 167
Jll 161
11 158
111
ill 173
111
111l 218
Jll
55
||
Il 136
|||
|||II||||
369 11||
lill 186
111
|
1 51
Jil 174
1
111
11111
281
||11
247 1ill
I
I
|||||||
63
114
289
111111
372
Il||||| 327
1I 152
111 271
||
155
|l| 165
Jill
163
lill1 207
1111111
281
111 210 Ill1l1 250
Jill 189 Ji|l 172
Figure D.1.: Non-hub destinations quartiles
174
111111 258
Jil 190
3
1I 151
I 136
1||||1| 300
11111
219
Jill 177
1111 218
11111 219
I
I 114
Jll 173
||1 173
85
|||1| 218
11ll1 282
1I 140
11|| 268
|111 174
179
|ll
11111 239
I
11111 202
Il 210
5
E. Commercial Service path quality
quartiles
Airport
AHN
1980
Ii 0.318
1i1 0.392
1990
ALS
APF
ll
11111|1 0.661
l1Ig; 0511
0.513
i 0.250
APN
1111
0.434
11111111
0.598
ATY
1111| 0.495
will 0.443
AUG
BED
BFD
BKW
BRL
CDC
11
li
2000
2010
0.358 1111111111 0.95
0.452
|11111
11111 0.391
||||
0.325
1l
1il
ii1 0.311
111111111 0.608 11||10443
0.A27
Airport
0.37
1 0.265ill 0.300Ill 0.319
11111
1 |11 0.525
0.35
1111111 0.501
111111
0.444
JIM1
0.62 JlW
JLN
1I1|||| 0.555
0.25 JMS
0.27 JST
11 0.25 LBL
1l 0.264
FOD
GLH
GRI
GUP
Il1l1 0.501 |1111|||| 0.66 RUT
||11 0.34 SHD
Jil 0329
1111111 0.477
||1Ill 0.564
il 0.474
1Ii! 0.420
111111 0.433
111111 0496
1111
0.366
0.259 |||ilil
0.840 |11111 0.865 01||
gini
0.954
GYY
HIB
HYS
IMT
IPL
0.448
I111
0.533
1|1|1
||Inl
|||
0.366
||IM|
0.657
Jll|
1|1|1
0.411
A||||
lil 0.569
0i|l1|| 0.707
lB0.322
0.3%
0. JI|M||
|||1|| 0.456
10l|l1l 0.634
1m 0.34 VEL
ili 0.63 WRIL
Jll 0.31 WYS
Jill 0.31
111111 0A22
0.318
111111
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ill0.25
JIM
0.395
Ji 0.269 101|||||m1
111110.79
0A58 0.868
11111
lm
illim
0.903imi
||||il
1il 0.401 ml
ill111111
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0.581
Ilo 0.661
0.87
000 |mul
|||1||
||l
ilml
0.628
0.219
fil||l
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0.825 ii00iin 0.903 ilmii 0.93
0.35
OAg0.34
Ig
|| | | 0.956
11 || 1
0539
lg0.37
Og A07
ilgli
Illjig
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0.625
2111111
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Jll
||1111
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I11l1l1l1 0.714
1|1
Figure E.1.: Commercial Service path quality quartiles
175
0.73
ill 0.273 1111111|||1 1.000
0.297
VCT
1
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|11111 I1l11
|||||
0.349
0A2
0.85
11iill||11|
1ill 0.380 0.543
llllllli 0.860
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Jill 0.329111111 0.557
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ill 0.29 S1LN
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0A72
|||
0.26 SVC
1i111 0529 Jill0.285
0.392
||l 0.336
ll 0.309
il 0.23 TBN
mmli
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illi 0.432 Il001||l 0.735 limig
TVF
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111111 11|9
0.6690.62
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ll
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|| 0.27
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mmljlj
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ii
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||111 0.5330
DDC
DUJ
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i111||
111111 0.07milli |111111
0.603 0.608
||||l||lil 0.69
1i11 0.513
111111 0.4610.61
|||||
0.403 hg| 111111
0.343 0.422 Iii0.32
IlEB
1.000
0.300 1r111u1 0.990 1|11
p 0.261
11i 0.371
1li1 0.384
11111 0.37 LWB
|||1 0.381
11l 0.411
1111111 0.497
111111 0.42 MSL
I
111 0.554
|111111 003
111111 0.459 mmiiim 0.99 MWA
11111 0A2 PDT
101110615 I l1 0.595
111 0.671
I
111111 0.526 n 00 i110.73 IPKB
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m 0.228 m1111111111 0.894111i11111111 0.995 111111111 0.62 PRC
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1111111 0.488 ||||1||1
11 0A62 1|||1||11 0.817
CEZ
CNY
1980 2000
1990 201
0
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111111
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ISO
11 0.379
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||||l
0.3
ilm 0.67
lig
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F. Commercial Service destinations
quartiles
Airport
1980
AHN
ALS
il
APF
1990
|111| 213
||i|l
150
216
||
94
Jil 177
111111
266
il
180
115 JHW
JLN
||||l
J|l|
Jl||
|ll
136
||i
153
il
iI
142
|
i||
AUG
BED
II
125
5
133
79
6
BKW
BRL
CDC
il
|I|
I
DDC
DIK
jl
158
i1 146
Jil 165
Il
il
1
I
lill
Il
112
I
59 LWB
l1
157
||1
|
45
I
61 MSL
||||
179
111
190
67
||
127
108
||
98 PDT
81 PKB
Jili 164
I
62 PRC
31
I
62 PUB
103 RUT
111|1 205
|
70
Jl
86 SHD
1120 SLK
Jill 181
1| 83
111
1
137 SLN
3 SOW
||l|
89
24
|188
|
72
||
I
128
|
I
152
78
|
55
ESC
ESD
Ill
I
170
46
|||
147
30
FOD
GLH
||
109
181
ei
|||
228
||
il1
||
I
il
182
Jill
1
64
81
52
il
103
125
I
Il
74
91
150
73
102
103
107
Ill
121
I
91
11
104
||
107
|I
|
130
75
I
79
I
69
||
85
|
54
92
158
|
||
65
I
13
142
||
106 SVC
40
|110 TBN
11 144
I|
||
|||
102
il
87 TSS
TVF
28
98
21
i
27
IH
128
98
146
147 VEL
|
67
HYS
IMT
107
96 WRL
113
|
47
li
||
I
|
104 WYS
69
|
68
1
IPL
107
GUP
||
110
GYY
HIB
Ill
6
VCT
|
1
123
II 110
143
71
Figure F.1.: Commercial Service destinations quartiles
176
63
124
63
32
126
||
I
i
Ill
1
129
98
26
135
173
Il93
4 MWA
Ii 114
2
I
lill
||
DUJ
DVL
GRI
11 101LBL
LEB
I1 101
i1
127
13
CNY
141jMS
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145
125
CEZ
3
251 SO
|I|
2010
2000
1990
1980
11104
APN
ATY
BFD
Airport
2010
2000
146
80
98
73
70
62
G. EAS communities no longer receiving
subsidy
Airport
Airport
Moultrie/Thomasville, GA
Kokomo/Logansport/Peru, IN
Hutchinson, KS
Independence/Parsons/Coffeyville, KS
Lewiston/Auburn, ME
Beloit/Janesville, WI
Winslow, AZ
Blythe, CA
Elkhart, IN
Clinton, IA
New Bedford/Fall River, MA
Benton Harbor/St. Joseph, MI
Jackson, MI
Battle Creek, MI
Columbus, NE
Sidney, NE
Rocky Mount/Wilson, NC
Winston-Salem, NC
Mansfield, OH
McAlester, OK
Salem, OR
Clarksville, TN/Ft. Campbell/Hopkinsville, KY
Brownsville, TX
Temple, TX
Montpelier/Barre, VT
Manitowoc, WI
Gadsden, AL
Stockton, CA
Galesburg, IL
Bloomington, IN
Muncie/Anderson/New Castle, IN
Terre Haute, IN
Laconia, NH
Santa Fe, NM
Paris, TX
Hot Springs, VA
Elkins, WV
Trenton, NJ
Danville, IL
Danville, VA
Worthington, MN
Anniston, AL
Sterling/Rock Falls, IL
Mt. Vernon, IL
Fairmont, MN
Goodland, KS
Lamar, CO
Mattoon/Charleston, IL
Yankton, SD
Ottumwa, IA
Seward, AK
Utica, NY
Gallup, NM
Oshkosh, WI
Topeka, KS
Norfolk, NE
Brownwood, TX
Bluefield/Princeton, WV
Enid, OK
Ponca City, OK
Ephrata/Moses Lake, WA
Table G.1.: EAS communities no longer receiving subsidy [34]
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