October 2010 

NASA/CR–2010-216860 

Optimizing Air Transportation Service to 

Metroplex Airports 

Part 1: Analysis of Historical Data 

George Donohue, Karla Hoffman, Lance Sherry, John Ferguson, and Abdul Qadar Kara 

Center for Air Transportation Systems Research, George Mason University, Fairfax, Virginia 
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October 2010 

NASA/CR–2010-216860 

Optimizing Air Transportation Service to 

Metroplex Airports 

Part 1: Analysis of Historical Data 

George Donohue, Karla Hoffman, Lance Sherry, John Ferguson, and Abdul Qadar Kara 

Center for Air Transportation Systems Research, George Mason University, Fairfax, Virginia 
Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802 
1

EXECUTIVE SUMMARY

Context

This report summarizes work done for NASA Langley Research Center as part of the Airspace 

Systems Program (Airportal Project), under Contract number NNX07AT23A.The air transportation 

system is a significant driver of the U.S. economy, providing safe, affordable, and rapid transportation. 

During the past three decades airspace and airport capacity has not grown in step with demand for air 

transportation (+4% annual growth), resulting in unreliable service and systemic delays. Estimates of the 

impact of delays and unreliable air transportation service on the economy range from $32B/year 

(NEXTOR, 2010) to $41B/year (Schumer, 2008).

Government and industry are collaborating to address the capacity-demand imbalance via three 

approaches:

Increasing Capacity. Several initiatives are underway to increase the capacity of the airports and 

airspace to handle additional flights. The Airport Improvement Plan (2010) is designed to relieve the 

bottlenecks at U.S. airports by adding runways, taxiways, gates, terminal buildings and service facilities 

to key nodes of the air-transportation system. Plans are also underway to improve landing and takeoff 

technologies that will allow ―all weather‖ operations. 

The impact of these initiatives on the most capacitated airports is limited due to the lack of additional 

real-estate to accommodate needed infrastructure. 

Special use airspace (e.g. military use only) is also being made available to increase the number of 

flights that can be handled during periods of peak demand. 

Increasing Effective-Capacity and Productivity. Modernization of U.S. Air Traffic Control 

(ATC) infrastructure, known as NextGen, is a $37B program. NextGen will improve productivity and the 

utilization of existing airspace. This will yield increases in the effective-capacity of the airspace and 

airports. Improvements in flow management, airborne re-routing, 4-D coordination of flights, and superdense operations will increase the number of flights that can be handled during peak-periods. Estimates 

for increasing effective capacity at the bottlenecks range from a total increase of 10% to 30%. These 

increases are significantly lower than a compounded 4% annual growth rate in demand.

Increasing Seat Capacity. This approach incentivizes airlines to increase the size of aircraft to 

transport more passengers per runway/airspace slots. To create these incentives the government or port 

authority regulates the number of scheduled flights to match the number of runway slots and gates 

available. The slots are allocated to ensure competition between airlines to maintain competitive airfares 

and service, as well as to provide economies of scale and network integrity for airline networks. 

Allocation schemes range from administrative (e.g. grandfathering or political allocations) to marketbased mechanisms (e.g. congestion pricing, auctions). Care must be taken to ensure the most efficient 

economic and socio-political use of the slots, and to avoid monopolies and ensure competition. 
2

Problem

Currently there is not enough emphasis is being placed on the third approach, improved utilization 

through increased aircraft size.

The idea of improved utilization of runway/airspace capacity through increased aircraft size gained 

some traction in 2007 and 2008. A Department of Transportation initiative coordinated capacity limits at 

the three New York airports: at JFK the limit was set at 81 operations per hour (1/18/2008), EWR at 81 

per hour (5/21/2008), and at LGA was decreased from 75/hour + 6 unscheduled to 71/hour + 3 

unscheduled (1/15/2009).

The slots at each of the airports were allocated by grandfathering. The concept of auctioning the slots 

to maximize the economic efficiency in the allocation and to ensure competitive airfares met strong 

criticism and was withdrawn. 

The objections to the concept were based on concerns that the introduction of capacity limits and 

market-based allocation schemes would affect:

Geographic access to air transportation service (i.e. elimination of service at smaller markets)

Economic access to air transportation service (i.e. increased operational costs leading to increased 

airfares, leading to pricing-out segments of the population)

Airline finances in a negative manner (i.e. reduced profits due to additional costs of operation)

Objective & Method

This report describes the results of an analysis of airline strategic decision-making with regards to: (1) 

geographic access, (2) economic access, and (3) airline finances. This analysis evaluated markets-served, 

scheduled flights, aircraft size, airfares, and profit from 2005-2009. During this period, airlines 

experienced changes in costs of operation (due to fluctuations in hedged fuel prices), changes in travel 

demand (due to changes in the economy), and changes in infrastructure capacity (due to the capacity 

limits at EWR, JFK, and LGA). 

This analysis captures the impact of the implementation of capacity limits at airports, as well as the 

effect of increased costs of operation (i.e. hedged fuel prices). The increases in costs of operation serve as 

a proxy for increased costs per flight that might occur if auctions or congestion pricing are imposed.

The ―design of the experiment‖ with the percent changes in national Gross Domestic Product (GDP), 

hedged fuel prices, and capacity limits at the airports is summarized below. Since the analysis is based on 

what actually occurred historically, this analysis is a ―quasi-experiment‖ where only four of a possible 27 

treatments are examined. A companion study using a model of airline operations describes the full 

―design of experiment‖ with all combinations of fluctuations.

Airports included the three major New York airports (LaGuardia Airport (LGA), John F. 

Kennedy International Airport (JFK), and Newark Liberty International Airport (EWR)), 

Philadelphia International Airport (PHL), and San Francisco International Airport (SFO)

Fuel prices ranged from -47% to +72% annual change

GDP ranged from -2.4% to +2.7% annual change

Capacity limits were added to JFK and EWR in 2008. And Capacity limits were reduced at LGA 

in 2008. These effects can be observed in the airline behavior changes from 2008 to 2009.
3

Only domestic markets examined

The first treatment (2005-2006) show effects from an increase in fuel prices, an increase in GDP, 

and no changes to capacity limits.

The second treatment (2006-2007) show effects from no change in fuel prices, an increase in 

GDP, and no changes to capacity limits.

The third treatment (2007-2008) show effects from an increase in fuel prices, no change in GDP, 

and no changes to capacity limits.

The fourth treatment (2008-2009) show effects from a decrease in fuel prices, a decrease in GDP, 

and reduced capacity limits.

Results

The following results represent statistically significant trends with a 95% confidence interval.

Geographic Access. The number of domestic markets served directly by an airport is primarily 

affected by the demand for air transportation. The number of flights per day to a market is also primarily 

determined by passenger demand.

Capacity limits at airports did result in a small reduction in domestic markets served and flights per 

day. Airline network restructuring (e.g. Delta’s expansion at JFK), airlines financial restructuring (e.g. 

USAirways bankruptcy filing impacted PHL), and inter-airline competition also affected domestic 

markets served and flights per day. 

1. The growth/decay in demand for air transportation (as measured by the Gross Domestic Product 

(GDP)) is the primary determinant of the number of domestic markets served. A linear regression 

showed that for every +1% change in GDP, there is a +1.44% change in the number of domestic 

markets with direct service. The linear regression also showed that for every 1% change in GDP, 

there is a 1.83% change in the number of flights per day serving these markets. As the economy 

slowed, the number of markets with direct service decreased.

2. The introduction of Capacity Limits at congested airports also affected the number of domestic 

markets served. In response to the economic down-turn (2008 and 2009), airports without 

capacity limits (SFO, PHL) experienced a reduction in the number of domestic markets served by 

an average of -7% (SFO -8%, PHL -6%). During the same period, heavily congested airports that 

had capacity limits implemented (EWR) or reduced (LGA), experienced a loss of an additional 

4% of markets served (EWR -12%, LGA -10%) versus (SFO -8%, PHL -6%) and 9% reduction 

in flights per day (LGA -16%, EWR -16%, JFK – None) versus (SFO -4%, PHL -7%).

Economic Accessibility. Passenger accessibility to air transportation through airfares at these airports 

followed established patterns of passenger demand during this period. Changes in the economy 

significantly affected demand for air transportation. The economic downturn had an order of magnitude 

greater effect on airline airfares than did the change in airlines’ operating costs (as measured by changes 

in fuel costs). 

1. Cumulative elasticity at the airports ranged between -3.1 to -1.8 during this period. Specifically, a 

-3 elasticity means a 1% increase in airfare (e.g. $300 to $303) resulted in a 3% reduction in 

demand for air service at that fare. This result is consistent with prior studies that showed 

passenger demand to be elastic.
4

2. The change in airfare was driven by changes in travel demand (reflected by changes in the GDP) 

and changes in hedged fuel prices. This correlation accounted for 79.6% (i.e. R2=79.6) of the 

observed variation in airfares. At the five airports studied (LGA, JFK, EWR, PHL, and SFO), 

airfares changed on average +1.84% for every +1% change in GDP and +0.12% for every +1% 

change in hedged fuel prices. A $1 increase in hedged per-gallon fuel prices resulted in an 

average of $16 increase in airfares, which yielded an average reduction in passenger demand of 

1.5%.

Airline Profitability. Airline profitability for the routes serviced at these five airports is a complex 

phenomenon driven by demand for air transportation, passenger responses to price increases, and 

operating costs. During this period, airline profitability was primarily determined by the industry’s ability 

to raise airfares relative to the cost of operations (i.e. when hedged fuel prices were escalating 

dramatically). During the spike in fuel costs, the airlines were faced with significantly greater operating 

costs and decreased demand due to the economic downturn. During this period:

1. airline profitability decreased as airlines were unable to increase airfares as fast as hedged fuel 

prices increased.

2. airlines shed less profitable domestic markets in order to improve profitability. The decrease in 

markets was driven primarily by reduced economic demand for air transportation and the 

sensitivity of passengers in certain markets to increased airfares.

3. airlines decreased aircraft size in order to maintain profitability as demand decreased.

4. the change in airport congestion was driven by changes in domestic markets served, scheduled 

flights per day, and by aircraft size (R2=76.5%). At the five airports studied (LGA, JFK, EWR, 

PHL, and SFO), congestion changed on average 2.16% for every 1% change in markets served, 

1.64% for every 1% scheduled flights per day and -4.04% for every 1% increase in aircraft size.

5. the change in airline profitability was driven by changes in airfare and changes in aircraft size 

(R2=75.6%). At the five airports studied (LGA, JFK, EWR, PHL, and SFO), airline profitability 

decreased an average of 0.28% for every 1% increase in hedged fuel prices. 

Implications of Results

The historic data paints a complex, nuanced picture of the airline strategic decision-making in the 

presence of changes in economic demand for air travel, fluctuations in hedged fuel prices, and regulatory 

adjustments to airport capacity limits. 

There are two main implications:

1. A stable or growing economy with non-fluctuating operating costs (i.e. hedged fuel prices) 

provides the optimum balance between geographic and economic accessibility for passengers 

(sought by regulatory authorities) and airline financial stability (sought by airlines).

2. Airport Capacity Limits (at congested airports) provide a critical mechanism for providing 

balanced geographic and economic access with profitable operations during periods of economic 

growth and fluctuating operating costs (i.e. hedged fuel prices)
5

Table of Contents

EXECUTIVE SUMMARY .........................................................................................................................1

Context......................................................................................................................................................1

Problem.....................................................................................................................................................2

Results.......................................................................................................................................................3

Implications of Results .............................................................................................................................4

List of Figures...............................................................................................................................................6

List of Tables................................................................................................................................................7

1 Introduction...........................................................................................................................................9

Increasing Capacity...................................................................................................................................9

Increasing Effective-Capacity and Productivity .....................................................................................10

Increasing Seat Capacity.........................................................................................................................10

Problem Statement..................................................................................................................................10

Objective of this Research ......................................................................................................................12

Research Approach .................................................................................................................................13

Benefits of This Research .......................................................................................................................15

2 Functional Model of Airline Strategic Decision-making....................................................................16

3 Method ................................................................................................................................................19

3.1 Data Sources ...............................................................................................................................19

3.2 Statistical Trend Analysis ...........................................................................................................20

3.4 Scope...........................................................................................................................................21

3.5 Design of Experiment .................................................................................................................22

4 Results.................................................................................................................................................23

4.1 Statistical Trend Analysis Results (2005-2008) (NY, SF)..........................................................23

4.1.1 Domestic markets Served .........................................................................24

4.1.2 Flights per day.....................................................................................................................24

4.1.3 Aircraft Size ........................................................................................................................26

4.1.3 Airline Profitability.............................................................................................................27

4.2 Regression Analysis of Trends (2005-2009) (NY, SF, PHL).....................................................31

4.2.1 Geographic Access..............................................................................................................33

4.2.1.1 Domestic markets Served................................................................................................33

4.2.1.2 Scheduled Flights per Day ..............................................................................................36

4.2.1.3 Airport Level Schedule Analysis....................................................................................38

4.2.1.4 Aircraft Size ....................................................................................................................41
6

4.2.2 Economic Access................................................................................................................44

4.2.2.1 Airfares ...........................................................................................................................44

4.2.3 Airline Profits......................................................................................................................46

4.2.3.1 Profitability of airlines....................................................................................................46

4.2.3.2 Congestion ......................................................................................................................49

5 Conclusions.........................................................................................................................................52

5.1 Summary of Statistical Trends (2005-2009) (NY, SF, PHL)......................................................52

5.2 Summary of Functional Relationships (2005-2009) (NY, SF, PHL)..........................................53

5.2.1 Geographic Access.....................................................................................................................54

5.2.2 Economic Access................................................................................................................55

5.2.3 Airline Profitability....................................................................................................................55

5.3 Relationship between Airports and Metroplexes........................................................................55

5.4 Additional Observations .............................................................................................................56

5.5 Policy Maker’s View of Results .................................................................................................57

5.6 Recommendations.......................................................................................................................58

5.7 Future Work................................................................................................................................59

5.7.1 Airline Schedule Optimization Model (ASOM) to Complete the ―Design of Experiment‖......59

5.7.2 Absence of Economies-of-Scale through Up-gauging (or ―Cash for Clunkers‖). .....................59
7

List of Figures

Figure 1 Fluctuations in year-over-year annual changes in Hedged Fuel Prices from 1978 to 2009. Blackline shows the average (+8% annual growth). Green lines show one standard deviation. .........................14

Figure 2 Fluctuations in year-over-year annual changes in Gross Domestic Product from 1930 to 2008. 

Black-line shows the average from 1950 to 2008 (3.3% annual growth). Green lines show one standard 

deviation......................................................................................................................................................14

Figure 3 Airline behavior in the presence of demand, regulatory, and technological changes..................16

Figure 4 Airline decision-making: Business Planning, Scheduling, and Operations.................................17

Figure 5 Number of Destinations served with daily service from either one, two or three airports in the 

NY Metroplex .............................................................................................................................................24

Figure 6 NY Metroplex Arrivals per Day..................................................................................................25

Figure 7 SF Metroplex Arrivals per Day ...................................................................................................25

Figure 8 NY Metroplex Average Aircraft Size..........................................................................................26

Figure 9 SF Metroplex Average Aircraft Size ...........................................................................................27

Figure 10 NY & SF Metroplex Cost..........................................................................................................28

Figure 11 NY & SF Metroplex Average Airfare .......................................................................................28

Figure 12 NY & SF Metroplex Revenue from Fares................................................................................29

Figure 13 NY Average annual revenue by aircraft size.............................................................................30

Figure 14 SF Average annual revenue by aircraft size ..............................................................................30

Figure 15 Longitudinal analysis of the domestic markets served by the five congested airports..............35

Figure 16 Longitudinal analysis of the scheduled flights per day at the five congested airports. ............37

Figure 17 SFO Schedule Adjustments 2007-2009 by time of day............................................................39

Figure 18 PHL Schedule Adjustments 2005-2007 by time of day ...........................................................39

Figure 19 EWR Schedule Adjustments 2007-2009 by time of day..........................................................40

Figure 20 LGA Schedule Adjustments 2007-2009 by time of day...........................................................41

Figure 21 JFK Schedule Adjustments 2007-2009 by time of day ............................................................41

Figure 22 Aircraft fuel burn rate/seat-hr versus seat size .........................................................................43

Figure 23 Average airfares by airport (2005-2009)..................................................................................45

Figure 24 Summary of multipliers between the exogenous, economic access, geographic access and 

airline profitability factors ..........................................................................................................................54
8

List of Tables

Table 1 Stakeholders Research Questions.................................................................................................12

Table 2 Historic Design of Experiment .....................................................................................................13

Table 3 Historic partial ―Design of Experiment‖.......................................................................................22

Table 4 Historic partial ―Design of Experiment‖.......................................................................................22

Table 5 Summary of statistical trend analysis for the New York and San Francisco Metroplexes...........23

Table 6 Longitudinal Changes in Metrics...................................................................................................31

Table 7 Economic and Policy Impacts......................................................................................................32

Table 8 Analysis of the domestic markets served by the three metroplexes (NY, SF, and Philadelphia).33

Table 9 Analysis of the domestic markets served by the five congested airports.......................................34

Table 10 Airport correlation of significant factors that influence airline decisions on domestic markets 

served..........................................................................................................................................................35

Table 11 Analysis of the scheduled flights per day at the three metroplexes (NY, SF, and Philadelphia).

....................................................................................................................................................................36

Table 12 Analysis of the scheduled flights per day at the five congested airports. ..................................37

Table 13 Airport correlation of significant factors that influence airline decisions on scheduled flights 

per day.........................................................................................................................................................38

Table 14 Analysis of the average aircraft size at the three metroplexes (NY, SF, and Philadelphia).......42

Table 15 Analysis of the average aircraft size at the five congested airports . .........................................42

Table 16 Results of correlation analysis of aircraft size, hedged fuel prices, Gross Domestic Product, and 

airport capacity limits .................................................................................................................................43

Table 17 Analysis of the average airfares at the three metroplexes (NY, SF, and Philadelphia). .............44

Table 18 Analysis of the average airfare at the five congested airports . .................................................45

Table 19 Analysis of correlation between airfare, hedged fuel prices, Gross Domestic Product, and 

airport capacity limits .................................................................................................................................46

Table 20 Analysis of the airline profits at the three metroplexes (NY, SF, and Philadelphia).................47

Table 21 Analysis of the airline profit at the five congested airports . .....................................................48

Table 22 Analysis of correlation between profitability, airfare and domestic markets ............................48

Table 23 Analysis of the congestion at the three metroplexes (NY, SF, and Philadelphia). ....................49

Table 24 Analysis of the congestion at the five congested airports .........................................................50

Table 25 Analysis of correlation of changes in airport congestion with changes in airfare, markets served 

and flights per day.......................................................................................................................................51

Table 26 Economic and Policy Impacts....................................................................................................52
9

1 Introduction

The air transportation system is a significant driver of the U.S. economy, providing safe, affordable 

and rapid transportation. 

During the past three decades airspace and airport capacity has not grown in step with demand for air 

transportation (+4% annual growth), resulting in unreliable service and systemic delays (Sherry, 2008). 

Estimates of the impact of delays and unreliable air transportation service on the economy range from 

$32.3B/year (NEXTOR, 2010) to $41B/year (Schumer, 2008).

Government and industry are collaborating to address the capacity-demand imbalance via three 

initiatives: Increasing Capacity, Increasing Effective-Capacity and Productivity, and Increasing Seat 

Capacity.

Increasing Capacity

Several initiatives are underway to increase the capacity of the airports and airspace to handle 

additional flights. The Airport Improvement Plan (2010) is designed to relieve the bottlenecks at U.S. 

airports by adding runways, taxiways, gates, terminal buildings and service facilities to key nodes of the 

air-transportation system. 

The Airports Improvement Program (AIP) is administered by the FAA and funded from the Airport 

and Airway Trust Fund (A&ATF). The A&ATF is created from user fees (e.g. 7.5% ticket tax) and fuel 

taxes. 

The AIP provides about 18% of the capital funds for improvements that include enhancements of 

capacity, safety, and other aspects of airport infrastructure. AIP funds are also applied toward projects 

that ―support aircraft operations including runways, taxiways, aprons, noise abatement, land purchase, 

and safety, emergency or snow removal equipment‖ (Kirk, 2003; p. 3). To be eligible for AIP funding, 

airports must be part of the National Plan of Integrated Airport Systems (NPIAS), which imposes 

requirements on the airport for legal and financial compliance (Wells & Young, 2003; p. 329).

The NPIAS has two goals: To ensure that airports are able to accommodate the growth in travel, and 

to keep airports up to regulatory standards (FAA, 2008; p. v).

The AIP funds are distributed to passenger, cargo, and general aviation airports, in two categories 

(Kirk, 2003; pp. 6-7):

1. Formula funds: Formula funds (also known as ―apportionments‖) are apportioned according to 

formulas based on the volume of throughput (e.g. enplaned passengers) and location. The 

formulas vary depending on the type of airport.

2. Discretionary funds: Discretionary funds are approved by the FAA and are distributed based on 

factors such as project priority and congressional mandates. Although it is not the sole 

determinant factor, project selections are based on a project’s score in the National Priority 

Rating (NPR) equation, which assigns projects a rating from 0 to 100 (high or 100% aligned with 

agency goals) (FAA, 2000; p. 5). Projects with safety and security purposes receive higher ratings 

than those focused on capacity (Dillingham, 2000; p. 32).
10

Special use airspace (e.g. military use only) is also being made available to increase the number of 

flights that can be handled during periods of peak demand. 

The impact that these initiatives will have on system-wide bottlenecks at the most capacitated airports 

is limited due to the lack of additional real-estate to accommodate needed infrastructure. 

Increasing Effective-Capacity and Productivity

Modernization of U.S. Air Traffic Control (ATC), known as NextGen, is a $37B program. NextGen 

will improve productivity and the utilization of existing airspace yielding increases in the effectivecapacity of the airspace and airports. Improvements in flow management, airborne re-routing, 4-D 

coordination of flights, and super-dense operations will increase the number of flights that can be handled 

during peak-periods. 

NextGen is an umbrella term for the ongoing, wide-ranging transformation of the National Airspace 

System (NAS). At its most basic level, NextGen represents an evolution from a ground-based system of 

air traffic control to a satellite-based system of air traffic management. This evolution is vital to meeting 

future demand, and to avoiding gridlock in the sky and at our nation’s airports (FAA, 2010; p. 4).

NextGen will realize these goals through the development of aviation-specific applications for 

existing, widely-used technologies, such as the Global Positioning System (GPS) and technological 

innovation in areas such as weather forecasting, data networking, and digital communications. Hand in 

hand with state-of-the-art technology will be new procedures, including the shift of certain decisionmaking responsibility from the ground to the cockpit.

When fully implemented, NextGen will allow more aircraft to safely fly closer together on more 

direct routes, reducing delays and providing unprecedented benefits for the environment and the economy 

through reductions in carbon emissions, fuel consumption and noise. 

FAA estimates show that by 2018, NextGen will reduce total flight delays by about 21 percent while 

providing $22 billion in cumulative benefits to the traveling public, aircraft operators and the FAA. In the 

process, more than 1.4 billion gallons of fuel will be saved during this period, cutting carbon dioxide 

emissions by nearly 14 million tons. These estimates assume that flight operations will increase 19 

percent at 35 major U.S. airports between 2009 and 2018, as projected in the FAA’s 2009 traffic forecast.

Estimates for increasing effective capacity at the bottlenecks range from a total increase of 10% to 

30%. These increases are significantly lower than a compounded 4% growth rate in demand.

Increasing Seat Capacity

This approach incentivizes airlines to increase the size of aircraft to transport more passengers per 

runway/airspace slot. To create these incentives the government or port authority regulates the number of 

runway slots and gates available to match the available supply. The distribution of the available slots is 

then allocated through some combination of administrative (e.g. grandfathering) and market-based 

mechanisms (e.g. congestion pricing, auctions) in order to ensure the most efficient economic and sociopolitical use of the slots, and to avoid monopolies and ensure competition. 

Problem Statement

Currently there is not enough emphasis is being placed on improved utilization of the air 

transportation system through increased aircraft size.
11

The idea of improved utilization of runway/airspace capacity through increased aircraft seat capacity

is mired in uncertainty about the impacts on the airlines, on domestic markets served and on costs to both 

the airline industry and passengers. Access to airlines has been shown to have significant effect on local 

economies, and delays at airports have been shown to alter the economy of a region. Elected officials, 

representing their constituents, have been very sensitive to these consequences.

In 2008, the concept of market-based methods gained some traction at the congested New York 

airports. The Department of Transportation (DOT) published a rule to limit the number of arrival and 

departures at the New York airports and to allocate a small proportion of the available slots via an auction 

(Federal Registry volume 73, pages 60544-60601).

An agreement was reached with incumbent airlines to set capacity limits at the three New York 

airports. After sharp debates about how those capacity limits should be set and about how the limited 

capacity would be allocated, capacity limits at JFK were set at 81 per hour (1/18/2008) and at EWR were 

set at 81 per hour (5/21/2008). The capacity limits at LGA were decreased from 75/hour + 6 unscheduled 

to 71/hour + 3 unscheduled (1/15/2009). No equivalent capacity restrictions were placed on other 

congested airports with similar congestion during peak operations (e.g. Philadelphia, Atlanta). 

The slots at each of the airports were allocated by grandfathering only. 

The concept of auctioning the slots to maximize the economic efficiency in the allocation and to 

ensure competition that would lead to lower airfares was roundly criticized (Schumer, 2008; LaHood, 

2009; Bethune, 2008; Air Transport Association, 2008; Regional Airline Association, 2008). 

The objections to the concept were based on concerns that the introduction of capacity limits and 

market-based allocation schemes would affect:

1. Geographic access to air transportation service by elimination of service at smaller domestic 

markets

2. Economic access to air transportation service as a result of increased operational costs that would 

in turn lead to increases in airfares to the point where a segment of the population could no longer 

afford to fly

3. Negative financial impact to airlines through additional costs of operation

This DOT concept of auctioning the slots for the New York airports was put into law in the federal 

registry in October 2008 (Federal Registry volume 73, pages 60544-60601). This rule established 

procedures to address congestion in the New York City area by assigning slots at airports in a way that 

allows carriers to respond to market forces to drive efficient airline behavior. The rule also extended the 

caps on the operations at the two airports, assigns to existing operators the majority of slots at the airports, 

and develops a robust secondary market by annually auctioning off a limited number of slots in each of 

the first five years of this rule. Auction proceeds would be used to mitigate congestion and delay in the 

New York City area. The rule also contained provisions for minimum usage, capping unscheduled 

operations, and withdrawal for operational need. The rule would sunset in ten years. This rule was due to 

go into effect October 2009, but was rescinded in May 2009 (Federal Registry volume 74, page 22714) 

for JFK and EWR and in October 2009 (Federal Registry volume 74, pages 52132) for LGA.

The rule proposed the FAA would auction 10% of slots at EWR and JFK and 15% of the slots at 

LGA above the 20-slot baseline annually for the first 5 years of the rule. As a result, 96 of the total 1,219 
12

slots at the airport would be auctioned over the 10-year span of the proposal; between 91 and 179 slots 

out of 1,245 total slots at JFK would be affected.

Three categories of slots were proposed: Common Slots, Limited Slots and Unrestricted Slots. Most 

would be Common Slots, which would be leased for ten years and revert to FAA when the rule sunsets. 

Carriers would have property rights to Common Slots, allowing the slots to be collateralized or subleased 

to another carrier for consideration, but Common Slots would revert to FAA under the rule's minimum 

usage provision and could be withdrawn for operational reasons. Limited Slots would consist only of 

slots operated on a daily, year-round basis, and leases for Limited Slots would also be assigned by 

cooperative agreements between the FAA and carriers. However, during each of the first 5 years of the 

rule, a percentage of Limited Slots would be made available by auction, at which point they would be 

converted to Unrestricted Slots, which are slots leased directly from FAA under the auction process. 

Five protests were filed on August 14, 2008 by airline carriers. On the same date, a protest also was 

filed by the Air Transport Association. Two additional protests were filed by the Port Authority of New 

York and New Jersey on August 28, 2008 and another by the New York Aviation Management 

Association (NYAMA) on August 29, 2008. The NYAMA protest was dismissed as the organization is 

not considered a legitimate stakeholder. 

The protests presented legal arguments contending that the FAA lacks legal authority to conduct the 

slot auction. According to the protesters, the slots are not actual "property," and as such, cannot be subject 

to a lease. According to the protests, the auction transaction involves not a lease, but rather the sale of a 

license by the FAA to a carrier to use a designated flight departure and/or flight landing time. Arguing 

that only a license - rather than a tangible property interest - is involved, the protests maintain that the 

FAA's Property Management Authority does not permit this auction effort. The protests also contend that 

the slot auction is not authorized under the FAA's "Airspace Management Authority," which is frequently 

cited as providing the Administrator's management authority over the United States' navigable airspace 

(FAA, 2008; pg 5.).

Objective of this Research

The objective of this research is to inform the policy-makers, research and technology entities, and 

decision-makers on the concept of better utilizing the seat capacity per runway-slot. Specifically, this 

research answers the following questions in Table 1:

Stakeholder Factor

Congress, Department of Transportation, 

Department of Commerce, and Department of 

Justice as advocates for consumers and the U.S. 

economy

What happens to Geographic access to air 

transportation service by introduction of capacity 

limits at certain highly-congested airports and an

increase in airlines costs (to pay for the slots). 

Would this result in an elimination of service at 

smaller domestic markets?

Congress, Department of Transportation, 

Department of Commerce, and Department of 

Justice as advocates for consumers and the U.S. 

economy

Economic access to air transportation service as a 

result of increased operational costs. Would this in 

turn lead to increases in airfares to the point where 

a segment of the population could no longer afford 

to fly?

Airlines What is the financial impact to airlines through 

additional costs of operation

Table 1 Stakeholders Research Questions
13

Research Approach

During the period between 2005 and 2009, the economy and hedged fuel prices fluctuated. This 

provided an opportunity to answer questions about how airline operating costs, economic conditions and 

an airlines’ access to an airport impact the markets it serves, ticket prices and aircraft sizes. 

This analysis, summarized in the factorial design in Table 2, represents four of the possible 27 

treatments possible (3 GDP % change possibilities x 3 Fuel Price % change possibilities x 3 Airport 

Capacity Limit % change possibilities). 

Table 2 Historic Design of Experiment

This analysis examined airline schedule behavior in the presence of application of airport capacity 

limits and fluctuations in fuel prices and Gross Domestic Product (GDP). Figures 1 and 2 respectively 

show that that between 2005 and 2009 fuel prices and GDP changed a lot. Therefore, this is an excellent 

time period to examine airline behavior. The following metrics will be used to examine airline’s behavior 

in response to these exogenous factors: (a) Aircraft size, (b) Flights per day, (c) Daily domestic markets 

served and (d) average load factors. It also tracks airline (e) average ticket airfare on departures and 

arrivals. The impacts of these behaviors are examined by tracking (f) Airport congestion and (g) 

Profitability of the airports.

Two types of analysis were applied. In the first study, a statistical analysis of trends for the New York 

and San Francisco airports from 2005 to 2008 was conducted.

 

In the second study, a regression analysis of these trends from 2005 through 2009 was conducted, to 

evaluate airlines’ business and operational behaviors in response to economic and policy changes at the 

New York, Philadelphia and San Francisco airports. 

2005-2006 2006-2007 2007-2008 2008-2009

Regions NY PHL SF NY PHL SF NY PHL SF NY PHL SF

Fuel 

Prices

+21% No Change +72% -47%

Gross

Domestic 

Product

+2.7% +2.1% +0.4% -2.4%

Capacity 

Limits

No Change No Change No Change Effect of 2008 caps at 

EWR, JFK and LGA 

seen here
14

Figure 1 Fluctuations in year-over-year annual changes in Hedged Fuel Prices from 1978 to 2009. 

Black-line shows the average (+8% annual growth). Green lines show one standard deviation.

Figure 2 Fluctuations in year-over-year annual changes in Gross Domestic Product from 1930 to 

2008. Black-line shows the average from 1950 to 2008 (3.3% annual growth). Green lines show one 

standard deviation.

-1 5

-1 0

-5

0

5

1 0

1 5

2 0

2 5 Annual % change in GDP 1930 1932 1934 1936 1938 1940 1942 1944 1946 1948 1950 1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

% GDP change

3.33% Average (since 1950)

2.42% Standard Dev (since 1950)

(50.0)

(40.0)

(30.0)

(20.0)

(10.0)

0.0 

10.0 

20.0 

30.0 

40.0 

50.0 

60.0 % change in jet fuel price

% fuel price change

8% Average

+ 1STD Dev

- 1STD Dev

23% Standard Deviation

Source: http://www.airlines.org/Energy/FuelCost/Pages/MonthlyJetFuelCostandConsumptionReport.aspx
15

Benefits of This Research

Multiple stakeholders for the US air transportation system can benefit from modeling and 

understanding airline behavior in the presence of economic, regulatory, & technological changes. 

Government policy-makers will be provided a quantitative analysis of impact of changes to airline 

scheduling and pricing behavior from changes in economic conditions like Gross Domestic Product and 

hedged fuel prices. The government policy-makers will also be provided insight into how airline 

scheduling and pricing behavior changes with changes in airport capacity limits or with additional fees. 

This model built on 5 years analysis of historical data will provide the ability to forecast expected airline 

scheduling and pricing behavior for non-historical economic and regulatory scenarios. 

Research Managers (e.g. NASA) will be provided insights into impacts of improved technologies 

(e.g. aircraft fuel efficiency). The economic analysis identifies the aircraft and engine performance 

requirements that would be required to incentivize up--gauging to larger aircraft. 

This research complements the NextGen research, since 49 of 131 NextGen OI’s involved upgrades 

in aircraft capabilities (Sherry, 2007). Airline up-gauging increases effective-capacity to the system, just 

like the NextGen initiatives do through improvements in air traffic flow management, reduced airline 

separation and more efficient use of current TRACON airspaces. 
16

2 Functional Model of Airline Strategic Decision-making

Airlines are continuously adjusting their operations in the presence of economic, regulatory, & 

technological changes (Figure 3). Demographics, social values, and the economic benefits of rapid, 

affordable transportation afforded by airlines determine the demand for airline operations. Regulatory

changes incentivize airline behavior and can also alter or curtail airline operations. Technological changes 

increase productivity and the range and performance of the air transportation service.

Figure 3 Airline behavior in the presence of demand, regulatory, and technological changes

Airlines make the following choices:

Domestic markets Served

Frequency of Service

Flight Schedules

Aircraft Size

Airfares

The following metrics are affected by airline behavior or decisions:

Profit

Congestion/ Delays

These decisions are made in the presence of:

National and regional economic conditions

Operational costs (e.g. fuel prices, crew costs, aircraft costs)

Airport capacity limits (e.g. slot controls, gate and ground operations access)

Aircraft Performance Capabilities 

Origin and Destination domestic market demand, revenue, airfare vs demand elasticities 

Airline Behavior in 

Presence of Economic, 

Regulatory, & 

Technological Changes

Technological Changes

• Aircraft Performance

•NAS Operations

Regulatory Changes

• Airport Caps

• User Fees

Demand Changes

• Demographics

• Social Values

• Utility of Airline Transportation

1. Markets Served

2. Frequency of Service

3. Flight Schedules

4. Aircraft Size

5. Airfares

6. Congestion/Delays

7. Profit

1 . O/D market demand

• Gross Domestic Product

2 . Operating Costs

• Fuel Prices

• Labor Costs

3 . Airport Capacity Limits

4 . Aircraft Performance Capabilities

5 . Revenue

6 . Airfare Elasticities
17

Figure 4 shows a functional representation of airline business planning, scheduling, and operational 

functions and decisions. The green boxes represent the impact of the exogenous factors on these 

decisions. Other key factors which influence airline decision making includes access to airport facilities, 

regional economic conditions, aircraft costs, and anticipation of regulation. The diamonds in the figure 

represent a simplified view of airline strategic decisions. The arrows show the functions and decision 

impacted by strategic decisions. 

Figure 4 Airline decision-making: Business Planning, Scheduling, and Operations

The Airline Business Planning function sets airfares based on expected operational costs, estimated 

demand and a variety of strategic decisions related to maintaining or growing market share. An airline 

must determine if an increase in operational costs such as hedged fuel prices must be absorbed or can be 

passed on to the consumer through increased airfares. Often, because of competition and because demand 

for air travel is highly elastic, the airlines may not be able to recover all of the additional costs they might 

incur when operational costs (such as fuel costs, crew or maintenance costs, additional fees imposed 

through regulation) increase.

As the figure shows there is a two-way relationship between airfare and the airlines’ estimated market 

demand. Demand is also influenced by overall economic conditions. In our analysis, we have used 

National Gross Domestic Product to evaluate the state of the economy. When the economy is good, 

potential travelers have more disposable income to buy airline tickets. During recessions, both business 

travel and vacation travel are reduced.

After the airlines determine the potential demand within a given market at various ticket prices, they 

then evaluate the likely revenue, costs, and competition within a market in order to determine whether to 

serve the market and if so, with what frequency. 
18

Airlines then use a variety of optimization tools to determine the overall schedule of service, the 

allocation of aircraft to markets, and the allocation of crews to the aircraft. Other models determine the 

maintenance schedule and the dynamic pricing of tickets based (revenue management) on competition 

and relative demand. 

On the day of service, other models are used to determine the operational decisions to mitigate 

congestion and delay while at the same time trying to serve the maximum amount of passengers possible 

and maintain their network structure. Airlines make decisions to delay flights, cancel flights or let flights 

fly on time based upon traffic flow management constraints. These constraints might be from weather or 

congestion-driven ground delay programs or may be from capacity limits applied to airport operations. 
19

3 Method

This section describes how data for our analysis was collected, the methods used to track trends and 

the methodology used to determine if the changes observed were statistically significant. 

3.1 Data Sources

The following metrics are used in the analysis to measure airline behavior in response to economic 

and policy changes. 

Aircraft size: The average number of seats per aircraft for various O/D pairs as reported to BTS. 

(BTS, 2009)

Flights per day: The number of arrivals per day from other airports as reported to the Bureau of 

Transportation Statistics (BTS). (BTS, 2009)

Domestic markets Served: A count of the number of destinations served from a NY/NJ airport with 

at least sixty or more arrivals and departures per month, as reported to BTS. (BTS, 2009)

Load Factor: The number of passengers on a given aircraft divided by the seat capacity of that 

aircraft, as reported to BTS. (BTS, 2009)

Average Airfare: The average airfare during a given quarter, as reported in the 10% price sample 

provided to BTS. (BTS, 2009) This analysis does not completely include the passenger’s cost of travel 

since it does not reflect any baggage, fuel fees, or other incidentals (e.g. blanket, movie, food). ]

The response to these airline actions is recorded by the following metrics: 

Flight Delay: The number of delayed flights (flights with delays of 15 minutes or more), as reported 

to BTS. (BTS, 2009)

Flight Cancellations: The total number of flights cancelled for each O/D, as reported to BTS. (BTS, 

2009)

Congestion: Percentage change in the number of flights delayed more than 15 minutes plus 

percentage change in the number of flights cancelled divided by two. The measure of congestion is based 

on a weighted average of both delays and cancellations.

Airline Revenue: For each market, total revenue is determined by multiplying the average single 

segment fare for that O/D pair by the total passengers flown in that quarter. Thus, revenue does not 

include any additional fees charged by the airline. 

Airline Costs: For each market, we collect the reported operational costs (personnel, fuel, insurance, 

taxes, maintenance, and depreciation per flight hour) by aircraft type as reported to BTS. The operational 

costs reported to BTS do not include any additional costs that the airline might incur due to delays or 

cancellations. The costs reported to BTS are per flight hour. We therefore multiply the per hour cost by 

the average flight time as reported in the Aviation System Performance Metrics (ASPM), and then 

multiply that result by the number of flights of that type flown that quarter, as reported in the BTS 

database. (BTS, 2009) 
20

Profitability: Operational profitability is defined as the difference between airline revenue and airline 

cost for a given market. When examining changes between periods, we calculate the percentage change 

in airline profit. (BTS, 2009)

Delay Costs: In addition, the operational cost of delay is estimated by using a model derived from 

the EuroControl 2004 study of airline delays. (Eurocontrol, 2004) For more on these calculations see the 

paper on a domestic delay cost model by Kara, et.al. (Kara, et. al., 2010) The model is additive with four 

components to the overall cost: fuel costs, crew costs, maintenance costs, and all other costs. The costs 

are segmented into short, medium, and long delays. 

The BTS data was examined for the following timeframes:

Airline On-Time Performance Data (Jan 05 – Dec 09)

Air Carrier Financial: Schedule P-52 (1QTR05-3QTR09)

Origin and Destination Survey : DB1BMarket (1QTR05-3QTR09)

Air Carriers : T-100 Segment (Jan 05 – Dec 09)

Aviation System Performance Metrics (ASPM) (Jan 05 – Dec 09)

The following additional metrics are used in the analysis to measure significant trends in airline 

behavior in response to economic and policy changes. 

Average Flight Delay: Average flight delay is computed by dividing the total delay from delayed 

flights by the total number of flights. 

Load Factor: Load factor is the ratio of passengers flown to the average seat capacity for each O/D 

pair studied, as reported to BTS.

3.2 Statistical Trend Analysis

The metrics were analyzed to determine statistically significant trends in longitudinal historic data. 

The analysis of significant trends was necessary to understand the different thresholds of significance for 

a variety of different factors. For example, a 1% reduction in aircraft size for the San Francisco airports 

from 2005 to 2007 was found to be significant, while a 34% increase in cancellations for the New York 

airports from 2005 to 2007 was found not to be significant.

The analysis of statistically significant trends for the New York and San Francisco airports required 

the following multi-step process:

1. The data was processed into the metrics of interest at the metroplex level.

2. The monthly data was tested for normality conditions using the Kolmogorov-Smirnov (K-S) test 

in Minitab. Since the data analyzed had seasonal trends embedded, most of the data sets 

examined failed the K-S test for normality. For such instances, non-parametric tests were 

applied. The Kolmogorov-Smirnov normality test was used to test for normality. When data sets 

were found to be normally distributed the Student’s t-test was used. 

3. The Wilcoxon Signed Rank test was used whether differences in two sets of data (either two 

related samples or repeated measurements on a single sample) were statistically significant. This 

test is a non-parametric test. Significant trends among metrics for the airports were examined. 

4. Because very small trends can be found to be significant using the Wilcoxon Signed Rank test, 

trends were not considered significant unless greater than 2% in magnitude.

5. Trends that were found to be statistically significant were aggregated to determine general airline 

scheduling and pricing behavior relationships with the exogenous factors to the system.
21

3.3 Method for Regression Analysis of Trends

A stepwise regression was conducted on all of the metrics that were found to be statistically 

significant to determine the strength and sign of statistical correlations between the exogenous factors and 

airline scheduling and pricing behavior, and the impacts of these behaviors on airport congestion and 

airline profitability.

The analysis of statistically significant trends required the following multi-step process:

1. The data was processed into the metrics of interest at the airport and metroplex level.

2. A regional or metroplex level analysis was performed to identify the relationships between the 

exogenous factors and the resulting airline behavior.

3. Because of the confounding of intra-regional airport behavior, the trends were de-aggregated to 

the airport level to identify the significant relationships between the exogenous factors and the 

resulting airline scheduling and pricing behavior and the significant relationships between airline 

behavior and airline profitability and airport congestion. 

4. First a correlation analysis of factors was done to identify the individual relationships between 

factors. The Pearson Product moment correlation coefficient was used to measure the degree of 

linear relationship between two variables. The correlation coefficient assumes a value between -1 

and +1. If one variable tends to increase as the other decreases, the correlation coefficient is 

negative. Conversely, if the two variables tend to increase together the correlation coefficient is 

positive. For a two-tailed test of the correlation:

H0: r = 0 versus H1: r ≠ 0 where r is the correlation between a pair of variables.

5. Next a step-wise regression was performed to identify the factors that most impact the 

independent variable. Thus, stepwise regression adds variables sequentially, choosing the most 

significant variable first and continues until the adding of another variable degrades the relative 

R2 coefficient (i.e. the R2 adjusted for the number of independent terms in the regression 

equation. 

6. Then these separate individual regression model results were aggregated to develop a picture of 

the statistically significant relationships between the exogenous factors, the airline scheduling and 

pricing behavior, and the impacts of these behaviors on airport congestion and airline 

profitability.

3.4 Scope

This paper describes two longitudinal studies. The first study is a statistical analysis of trends for the 

New York and San Francisco airports from 2005 to 2008 to identify statistically significant trends. 

The second study is a regression analysis of trends from 2005 through 2009 that evaluates airlines’ 

business and operational behaviors in response to economic and policy changes to the New York, 

Philadelphia and San Francisco airports. This analysis established how airline behavior is affected by 

three specific impulses: tightened slot controls, changes in hedged fuel prices, and national economic 

conditions. When any of these three factors were varied historically, the airlines’ responses, in terms of 

changes to their schedules, to ticket prices, to domestic markets served and to aircraft size chosen for a 

given market, were examined.

In addition, the effect on changes to an airlines’ profitability was measured, as was the overall 

usage/congestion of the airspace as measured by amount of delay and cancellations. 
22

3.5 Design of Experiment

A partial ―Design of Experiment‖ is illustrated in Table 3. This analysis examined airline schedule 

behavior by tracking (a) aircraft size, (b) flights per day, (c) daily domestic markets served and (d) 

average load factors. It also tracks airline (e) average ticket airfare on departures and arrivals. The 

impacts of these behaviors are examined by tracking (f) airport congestion and (g) profitability of the 

airports. The trends in these metrics are measured from summer to summer to capture the impacts from 

changes in hedged fuel prices, economy (as measured by national gross domestic product) and capacity 

limits.

Hedged fuel prices

Up Constant Down

Economy 

Up 2005-2006 2006-2007 

Constant 2007-2008 

Down

2008-2009

Airport capacity 

limits introduced at 

EWR, LGA, JFK

Table 3 Historic partial “Design of Experiment”

The effects of new and adjusted capacity limits placed on the NY airports in 2008 can be seen in the 

summer 2008 to summer 2009 trends (see Table 4). These trends were calculated by taking the weighted 

average change from January to September data to the next years January to September data for each 

respective metric. This methodology eliminates seasonality from the trends and captures the greatest 

fluctuation in hedged fuel prices as they peaked in third quarter 2008. At the time of this analysis the 

fourth quarter 2009 data was unavailable.

Table 4 Historic partial “Design of Experiment”

2005-2006 2006-2007 2007-2008 2008-2009

Regions NY PHL SF NY PHL SF NY PHL SF NY PHL SF

Fuel 

Prices

+21% No Change +72% -47%

Gross

Domestic 

Product

+2.7% +2.1% +0.4% -2.4%

Capacity 

Limits

No Change No Change No Change Effect of 2008 caps at 

EWR, JFK and LGA 

seen here
23

4 Results

This section describes the results of the analyses. Section 4.1 describes the results of the statistical 

trend analysis; section 4.2 describes the relationship between parameters from the regression analysis.

4.1 Statistical Trend Analysis Results (2005-2008) (NY, SF)

Airline schedule behavior can be observed by examining the trends in geographic access, economic 

access, and airline profitability. 

The annual changes, 2005-2007 and 2007-2008, for the New York and San Francisco Metroplexes 

were tested for significance at the 95% level (Ferguson, et. al., 2009a; 2009b). Since most of the data, 

measured as monthly observations, did not pass the Kolmogorov-Smirnov test assumption of normallydistributed data, the Wilcoxon Sign Rank Test (a non-parametric test) was used to test these trends for 

significance. A summary of the results of this analysis are shown in Table 5. 

Table 5 Summary of statistical trend analysis for the New York and San Francisco Metroplexes

Fuel Prices Slot Controls Market **Economic Flight Performance

+57%

2005-2007

Yes – LGA

No – EWR, JFK

Markets reduced 3% Operating Cost 

increased 20%

*Flight Delays 

increased 39%

*Aircraft Size 

reduced 3%

Airfare increased 

8%

*Average Delay 

increased 48%

*Flights/ Day 

increased 4%

Revenue increased 

22%

Cancellations 

increased 34%

*Load Factors 

increased 3%

No – SFO, SJC, 

OAK

*Markets increased 

4%

Operating Cost 

increased 9%

*Flight Delays 

increased 30%

*Aircraft Size 

reduced 1%

Airfare increased 

10%

*Average Delay 

increased 29%

*Flights/ Day 

increased 4%

Revenue increased 

15%

*Cancellations 

increased 47%

Load Factors remained 

the same

+70%

2007-2008

Yes – LGA, 

EWR,JFK

Markets reduced 2% Operating Cost 

increased 24%

*Flight Delays 

decreased 18%

Aircraft Size 

increased 1%

Airfare increased 

7%

*Average Delay 

decreased 14%

*Flights/ Day 

decreased 6%

Revenue increased 

5%

*Cancellations 

decreased 18%

*Load Factors 

decreased 3%

No – SFO, SJC, 

OAK

Markets remained 

the same

Operating Cost 

increased 28%

Flight Delays 

decreased 7%

Aircraft Size 

remained the same

Airfare increased 

2%

Average Delay 

increased 9%

*Flights/ Day 

decreased 5%

Revenue increased 

3%

Cancellations 

increased 6%

*Load Factors 

decreased 3%

* 95% Statistically Significant w/ Wilcoxon Sign Rank Test ** Too few data points for statistical tests
24

The results indicate that slot controlled airports yielded improved throughput performance (i.e. flight 

delays and cancelled flights) through reductions in frequency and de-peaking schedules. Even in the 

presence of fluctuations in passenger demand and economic shocks, passengers saw an improvement in 

service (i.e. a decrease in delays and cancellations) without any significant loss in markets served or 

frequency to those markets. 

4.1.1 Domestic markets Served

There was little change to the number of domestic markets served by the New York and San 

Francisco Metroplex airports since January 2005. Although New York serves over 100 markets, the 

number of markets served by at least 60 flights per month (i.e. having at least one arrival and departure 

daily) is approximately 70. Similarly, the number of San Francisco markets served by at least 60 flights 

per month is approximately 50.

The number of domestic markets served by one, two or three of the metroplex airports remains 

unchanged from 2005 through 2008 for San Francisco; however this is not the case for New York as seen 

in Figure 5. The analysis shows a sharp decrease in markets served by two or more airports in the New 

York Metroplex starting in the spring of 2008, when fuel costs were high.

Figure 5 Number of Destinations served with daily service from either one, two or three airports in 

the NY Metroplex

4.1.2 Flights per day

There was a 4% increase of flights to the New York Metroplex from 2005 to 2007 and a 6% decrease 

in flights from 2007 to 2008, bringing the number of flights per day in 2008 to 3% less than the 2005 

levels. There was a 5% increase of flights to the San Francisco Metroplex from 2005 to 2007 and a 4% 

decrease in flights from 2007 to 2008, bringing the number of flights per day in 2008 back to the 2005 

levels. 

The increase in fuel costs and the downturn in the economy influenced the decision of airlines to 

reduce their schedules in 2008. This is observed in the reductions in scheduled flights for both the New 

York and San Francisco airports. 

The imposition of slot controls by Department of Transportation (DOT) in 2008 clearly contributes to 

the reductions in scheduled flights in 2008 for the New York Metroplex. Close examination of the 

changes in hourly operations show that the change to airline schedules exactly match the regulations set 

by DOT. The significant fluctuations in fuel costs did not change airline schedules as suggested by earlier 

0

5

10

15

20

25

30

35

New York Metroplex Market Analysis 

(>60 flights/ month)

Served by one Airport Served by two Airports Served by three Airports
25

arguments against market based management of airline operations. These arguments against market 

based management of airline operations included:

Small domestic markets would stop being served

Airfares would be increased

Airline profits would go down

Although the number of flights per day remained relatively constant, seasonality trends exist for the New 

York major domestic markets (see Figure 6). More flights are observed to Miami in the colder months 

and more flights to overseas locations in the summer. This seasonality was not observed for the San 

Francisco major markets, see Figure 7.

Figure 6 NY Metroplex Arrivals per Day

Figure 7 SF Metroplex Arrivals per Day

0

20

40

60

80

100

120

New York Metroplex Arrivals per Day

Miami Boston Washington ATL Chicago MCO Los Angeles CLT

CLT

MIA

ATL

WAS

BOS

CHI

0

20

40

60

80

100

120

140

160

180

200

San Francisco Metroplex Arrivals per Day

Los Angeles SAN LAS SEA PHX New York DEN Chicago

CHI

LOS

SAN
26

Two other changes in the markets served during this period were: (a) a drop of 25 arrivals per day 

from the Washington D.C. metroplex to EWR, and (b) a reduction in the number of arrivals per day at 

EWR and LGA from the Chicago Metroplex. 

4.1.3 Aircraft Size

Two important events occurred in 2008: capacity limits were imposed at the New York airports and 

hedged fuel prices increased. Either of these events might have triggered a reduction in frequency 

coupled with an up-gauging to larger, more efficient aircraft since such actions can improve an airline’s 

profitability. However, the data does not show any significant overall up-gauging in aircraft (see Figures 

8 and 9). The analysis does indicate that on average, the aircraft size used to service shuttle markets 

(NY-BOS, NY-WAS, SF-LOS, and SF-SAN) has increased, but is still significantly smaller than aircraft 

used to service other metroplexes. A slight down-sizing in seat sizes to other locales has kept the average 

aircraft size constant over time with little seasonal differences. 

Figure 8 NY Metroplex Average Aircraft Size

0

20

40

60

80

100

120

140

160

180

New York Metroplex Average Seat Sizes 

Other Markets Boston Chicago Los Angeles Miami Washington ATL CLT MCO

LOS

BOS

Other

WAS

CHI

MIA

CLT

ATL

MCO
27

Figure 9 SF Metroplex Average Aircraft Size

4.1.3 Airline Profitability

To better understand the economic impact of increased hedged fuel prices, slot controls and/or 

congestion, ticket prices over time at both the New York and the San Francisco airports were examined. 

This study examines the airline profitability for the New York and San Francisco airports as a whole, on 

shuttle service markets, on major hubs, and on long haul markets. Finally the estimated revenue 

generated by flights to a variety of locations is graphed.

4.1.3.1 Airline Costs – The airline costs per flight hour were analyzed beginning with data from the first 

quarter (1Q) of 2005, and reflect the changes to hedged fuel prices, but show no seasonality. While 

personnel, training, maintenance, and depreciation costs have remained level, the fuel costs have 

increased 166% since first quarter 2005. The air carriers have experienced over 70% of this increase fuel 

since the first quarter 2007. Thus, the only significant variability in airline costs over this period is fuel 

costs.

When applying these cost factors to operations at the New York and San Francisco Metroplexes the 

analysis shows a 49% and 39% increases in costs, respectively. The estimated operating cost for flights 

into and out of the New York and San Francisco Metroplexes since 1Q 2005 shows seasonality (see 

Figure 10). 

0

20

40

60

80

100

120

140

160

180 New York Metroplex Average Seat Sizes 

Other Markets Chicago Los Angeles New York DEN LAS PHX SAN SEA

NY

Other

SAN

CHI

LOS

DEN

San Francisco
28

Figure 10 NY & SF Metroplex Cost

4.1.3.2 Airfare – The average airfares paid by passengers since 1Q 2005 reflect the changes to hedged 

fuel prices and show no seasonality (see Figure 11). This analysis showed that the average airfare for the 

New York and San Francisco Metroplexes increased 15% and 12% respectively in this timeframe.

Figure 11 NY & SF Metroplex Average Airfare

4.1.3.3 Airline Revenue – The estimated revenue generated by flights into and out of the New York and 

San Francisco Metroplexes since 1Q 2005 shows seasonality similar to the trends seen in airline costs 

$0

$50

$100

$150

$200

$250

New York and San Francisco Metroplex Average 

Airfare (2005-2008) 

NY Metroplex avg fare

SF Metroplex avg fare

$-

$100 

$200 

$300 

$400 

$500 

$600 

$700 

$800 

$900 Cost ($M)

Operating Cost

EWR JFK LGA SFO OAK SJC

EWR

EWR

JFK

JFK

LGA

LGA

SFO

SFO

OAK

OAK

SJC

SJC
29

(Figure 12). The analysis shows that airlines have compensated for the increased operational costs. Even 

without the additional revenue generated from new fees for fuel, baggage, food, blankets, movies and 

other similar charges, the airlines have increased revenue 28% from 2005 through 2008 in New York and 

18% in San Francisco.

Figure 12 NY & SF Metroplex Revenue from Fares

When evaluating the average annual revenue realized by different-sized aircraft serving the New 

York and San Francisco Metroplexes, the airlines’ revenues are derived primarily from aircraft in the 113-

187 seat ranges (see Figures 13 and 14). However in New York 14% of the revenue is generated from 

aircraft with seats ranging from 38-62, which is not the case for San Francisco. 

$-

$200 

$400 

$600 

$800 

$1,000 

$1,200 

$1,400 Revenue ($M)

Revenue from fares

EWR JFK LGA SFO OAK SJC

EWR

EWR

JFK

JFK

LGA

LGA

SFO

SFO

OAK OAK

SJC

SJC
30

Figure 13 NY Average annual revenue by aircraft size

Figure 14 SF Average annual revenue by aircraft size

<38 38-62 63-87 88-112 112-137 138-162 163-187 >187

LGA $28 $242 $127 $156 $1,302 $1,217 $428 $-

JFK $11 $203 $50 $381 $206 $1,736 $802 $264 

EWR $42 $852 $88 $173 $1,303 $1,361 $778 $56 

$-

$500 

$1,000 

$1,500 

$2,000 

$2,500 

$3,000 

$3,500 

$4,000 

$4,500 Average Annual Revenue ($M)

New York Metroplex Average Annual Revenue 

(averaged over 15 QTRs 1QTR05-3QTR08)

<38 38-62 63-87 88-112 112-137 138-162 163-187 >187

SJC $4 $30 $59 $91 $945 $1,308 $464 $29 

OAK $0 $27 $43 $48 $1,207 $1,536 $394 $-

SFO $175 $184 $56 $107 $817 $1,341 $1,082 $604 

$-

$500 

$1,000 

$1,500 

$2,000 

$2,500 

$3,000 

$3,500 

$4,000 

$4,500 Average Annual Revenue ($M)

SFO Average Annual Revenue 

(averaged over 15 QTRs 1QTR05-3QTR08)
31

4.1.3.4 Flight Performance – From 2005 to 2007 the reduced aircraft size combined with an increase in 

arrivals per day increased load factors, delays and cancellations for the New York and San Francisco 

Metroplexes (see Table 6). However, analysis of the New York Metroplex shows a reversal in this trend 

in 2008, with airlines reducing the number of flights (-6%) and increasing aircraft size (1%). As a result 

from airline schedule changes (-6% flights per day) from 2007 to 2008 the New York Metroplex showed 

significant reductions in the number of flights delayed (-18%), the average amount of time flights were 

delayed (-14%), the number of flight cancellations (-18%), and overall passenger trip delay (-16%) from 

both cancellations and delayed flights. 

Table 6 Longitudinal Changes in Metrics

In contrast, from 2007 to 2008, the San Francisco Metroplex reduced flights (-5%) and showed 

reduced flight delays (-7%), increased average flight delay (9%), increased flight cancellations (6%), and 

increased overall passenger delays (1%). The fact that DOT imposed slot controls at all three NY 

Metroplex airports probably contributes to this contrast in system behavior. 

Overall from 2005 to 2008 there were significant increases in the number of flights delayed, average

amount of time flights were delayed, the number of flight cancellations, and in the delay incurred by the 

passenger from both cancellations and delayed flights.

4.2 Regression Analysis of Trends (2005-2009) (NY, SF, PHL)

This analysis compares the behavior of the air transportation system (e.g. markets served, airfares, 

delays, load factors, aircraft size) during the recent run-up in hedged fuel prices at capacity-limited New 

York airports and non-slot controlled San Francisco and Philadelphia airports (Ferguson, et. al., 2010).

The results indicate:

i. Airfares change in proportion to changes in hedged fuel prices and gross domestic product

ii. Flights per day and domestic markets served change in proportion to changes in gross domestic 

product

iii. Flights per day and domestic markets served change in proportion to changes in airport capacity 

limits

Metric % Change

2005 to 2007

% Change

2007 to 2008

% Change 

2005 to 2008

NY SF NY SF NY SF

Fuel Prices 57% 57% 70% 70% 166% 166%

Markets -3% 4% -2% 0% -4% 4%

Operating Cost 20% 9% 24% 28% 49% 39%

Revenue 22% 15% 5% 3% 28% 18%

Average Fare 8% 10% 7% 2% 15% 12%

Aircraft Size -3% -1% 1% 0% -2% -1%

Arrivals per Day 4% 4% -6% -5% -3% 0%

Load Factors 3% 0% -3% -3% 1% -3%

# of Flight Delays 39% 30% -18% -7% 14% 21%

Average Flight Delay 48% 29% -14% 9% 27% 41%

Flight Cancellations 34% 47% -18% 6% 10% 55%

Passenger Delay 66% 61% -16% 1% 39% 63%
32

iv. Delays and cancellations change in proportion to flights per day and domestic markets served and 

negatively proportional to aircraft size

v. Airline profitability changes negatively proportional to aircraft size, airfares and domestic 

markets served

This study expands on prior efforts to analyze the behavior of airlines at congested airports. It 

compares the performance at the New York airports to the performance at the Philadelphia and San 

Francisco airports from 2005 through 2009. During this time period, there were significant fluctuations in 

hedged fuel prices and in the overall economic condition of the country. In addition, tighter regulations on 

the scheduling of flights at the three New York airports were imposed. 

The results indicate that capacity limited airports yielded reduced congestion (i.e. fewer delayed and 

cancelled flights) through reductions in schedules and de-peaking schedules. There was no significant 

change in domestic markets served. While airfares were affected by changes in hedged fuel prices, they 

did not significantly increase at New York airports after capacity limits were applied. 

San Francisco and Philadelphia airports reduced schedules during the economic downturn. 

Congestion was reduced due to these reductions in schedule. The overall conclusions are shown in Table 

7.

Table 7 Economic and Policy Impacts

The key observations from this analysis are as follows (Table 7):

Airfares increase when hedged fuel prices increase and when the gross domestic product 

decreases.

Flights per day and domestic markets served decrease when gross domestic product decreases. 

Flights per day and domestic markets served decline when airport capacity limits are imposed. 

Delays and cancellations decrease as flights per day and domestic markets served are decreased; 

delays and cancellations increase as aircraft size is decreased. 

Airline profitability decreases as aircraft size, airfares or domestic markets served increases. 

Up-gauging of airline fleets is unlikely to occur without the introduction and purchase of a new 

class of aircraft that has better efficiency for aircraft carrying between 100-150 passengers. 

Currently, the aircraft stock in the 100-120 seat size is the oldest and least fuel-efficient portion of 

the fleet.

With the imposition of slot controls in 2008, LGA and EWR reduced schedules while JFK was 

capable of keeping the same number of flights as were flown prior to the imposition of slot 

controls. Because of a peaked schedule at JFK, there was opportunity to move flights from peak 

times to off-peak times rather than having to remove the flights. LGA and EWR were at capacity Annual Changes Observations

Caps Economy Fuel Prices Airfare Schedule Congestion Profit

05-06 Yes Up Up Up No ∆ Up Down

05-06 No Up Up Up Up Up Down

06-07 Yes Up No ∆ No ∆ No ∆ Up Down

06-07 No Up No ∆ No ∆ Up Up No ∆

07-08 Yes Constant Up Up No ∆ No ∆ Down

07-08 No Constant Up Up Down Down Down

08-09 Reduced Down Down Down 2xDown 2xDown Up

08-09 No Down Down Down Down Down Up
33

at virtually all times and so had to remove flights from the schedule in order to adhere to the new 

regulations. 

In 2008, the airlines were less profitable. Even though delays and the resultant cost of delays 

were reduced, the increase in fuel costs was considerably greater. Thus, overall, airlines’ 

operational costs increased in 2008. These costs were greater than the increase in ticket prices. 

Airports with capacity limits reduced operations and congestion. Airlines’ profitability at these 

airports (as measured by operational revenue minus operational costs) increased. 

The analysis showed a positive correlation between:

airfare and hedged fuel prices

Gross Domestic Product and airline schedule 

capacity limits and airline schedule

airline schedule and airport congestion

A negative correlation was found between:

airfare and airport profitability

airport congestion and airport profitability. 

The analysis showed no significant change in load factors, or aircraft size.

4.2.1 Geographic Access

4.2.1.1 Domestic markets Served 

The results of an analysis of the domestic markets served by the three metroplexes (NY, SF, and 

Philadelphia) are shown in Table 8. When the economy was expanding (2005-2007) the number of 

markets served by each metroplex (except Philadelphia) remained unchanged. Philadelphia saw a 

contraction in service due to the bankruptcy and reorganization of USAirways.

When the economy began its slide into recession in the 2008-2009 time period, the number of 

domestic markets serving these metroplexes decreased; hedged fuel prices did not have any impact on the 

markets they served.

Table 8 Analysis of the domestic markets served by the three metroplexes (NY, SF, and 

Philadelphia). 2005-2006 2006-2007 2007-2008 2008-2009

Regions NY PHL SF NY PHL SF NY PHL SF NY PHL SF

Fuel 

Prices

+21% No Change +72% -47%

Gross 

Domestic 

Product

+2.7% +2.1% +0.4% -2.4%

Capacity 

Limits

No Change No Change No Change Effect of 2008 caps at 

EWR, JFK and LGA 

seen here

Markets 

Served

No ∆ -10% No ∆ No ∆ No ∆ No ∆ No ∆ No ∆ No ∆ -7 % No ∆ -6 %
34

The results of an analysis of the domestic markets served by the airports at each of the Metroplexes 

(NY, SF, and Philadelphia) are shown in Table 9. These results show a more nuanced view of the 

dynamics.

Examination of the individual airports shows that domestic markets served tracked well with changes 

in the national economy and the introduction of new or reduced capacity limits. During an expanding 

economy (2005-2007), the number of markets served increased. When the economy was contracting 

(2008-2009), the number of markets served decreased. At the New York airports, the reduction in 

markets served was twice that at the San Francisco airports. The effect of the capacity limits at the New 

York Metroplex airports contributed to this trend.

The effect of airport capacity limits is observed at LGA and EWR. These airports are congested 

throughout the day. To meet the capacity limits, airlines serving these airports must reduce the number of 

domestic markets served. Markets served at JFK were not impacted by the capacity limits. The morning 

and evening peaks where rescheduled and the midday uncongested period increased.

Table 9 Analysis of the domestic markets served by the five congested airports.

The total number of domestic markets served by the individual metroplex airports is shown in Figure 

15. The reduction in markets served by the airlines at EWR and LGA are highly correlated with the 

reduction in capacity limits applied in 2008.

The markets served by airlines at SFO & JFK increases during the period 2005 – 2009.

Fuel Prices Economy Airport Markets

LGA -3%

EWR no change

JFK no change

PHL -10%

SFO 8%

LGA no change

EWR no change

JFK 8%

PHL no change

SFO no change

LGA -4%

EWR 4%

JFK no change

PHL no change

SFO no change

LGA* -10%

EWR* -12%

JFK* no change

PHL -6%

SFO -8%

Historical Changes Longitudi

-47% 2008-

2009 (*caps in 

NY)

-2.4%

72% 2007-

2008

0.4%

1% 2006-

2007

2.1%

21% 2005-

2006

2.7%
35

Figure 15 Longitudinal analysis of the domestic markets served by the five congested airports.

The results of a linear regression for domestic markets served for each of the five airports over a 

period of 4 years provides insights into the correlations between the variables and the significant factors 

that influence airline decisions on markets served (Table 10).

Table 10 Airport correlation of significant factors that influence airline decisions on domestic 

markets served

The closer the Pearson correlation coefficient is to +1 or -1 the greater the positive or negative 

correlation (or relationship) is between the factors.

A P-value of .05 or less means that there is 95% confidence in the statistical correlation.

The analysis shows positive correlation between domestic markets served, the Gross Domestic 

Product (GDP) and airport capacity limits. 

A stepwise regression analysis of the dependent variable percent change in domestic markets served 

versus percent change in hedged fuel prices, national gross domestic product and capacity limits 

identified the change in Markets to be a function of changes in national Gross Domestic Product (GDP). 

This relationship was found to be positive. The correlation accounted for 25.2% (i.e. R-squared) of the 

observed variation in markets served. The remaining effects are inter-airline competition, changes in 

airline business models, and airline restructuring when emerging from bankruptcy. The effect of capacity 

PHL

PHL

EWR

EWR

JFK

JFK

LGA

LGA

SFO

SFO

35

50

65

80

95 Jan-05 Mar-05 May-05 Jul-05 Sep-05 Nov-05 Jan-06 Mar-06 May-06 Jul-06 Sep-06 Nov-06 Jan-07 Mar-07 May-07 Jul-07 Sep-07 Nov-07 Jan-08 Mar-08 May-08 Jul-08 Sep-08 Nov-08 Jan-09 Mar-09 May-09 Jul-09 Sep-09 Nov-09 # of Daily Markets Served

# of Daily Markets Served from 2005 to 2009

JFK’s markets starting to track with EWR & SFO Fuel 

Prices GDP Caps

Markets

Pearson correlation 0.44 0.539 -0.452

P-Value 0.052 0.014 0.046
36

limits was ambiguous due to the small number of data points reflecting this change. The regression 

model found for changes in domestic markets is as follows:

Domestic markets = - 0.0266 + 1.44 GDP

4.2.1.2 Scheduled Flights per Day

The results of an analysis of the scheduled flights per day at the three metroplexes (NY, SF, and 

Philadelphia) are shown in Table 11. When the economy was expanding (2005-2007) the number of 

flights per day at the metroplexes remained relatively unchanged. One exception was Philadelphia which 

saw a contraction in service due to the bankruptcy and reorganization of USAirways. When the economy 

started shrinking (2009), the number of flights per day at the metroplexes decreased. Hedged fuel prices 

did not have any impact on flights per day at the metroplex.

Table 11 Analysis of the scheduled flights per day at the three metroplexes (NY, SF, and 

Philadelphia).

The results of an analysis of the flights per day at the individual airports for each of the metroplexes 

(NY, SF, and Philadelphia) are shown in Table 12. These results show a more nuanced view of the 

dynamics.

Examination of the individual airports shows that flights per day tracked well with changes in the 

national economy and the introduction of new or reduced capacity limits. During an expanding economy 

(2005-2007), the number of flights per day increased. When the economy was contracting (2008-2009), 

the number of flights per day decreased. At the New York airports, the reduction in flights per day was 

twice that of the San Francisco airports. The effect of the capacity limits at the New York metroplex 

airports contributed to this trend.

The effect of airport capacity limits is observed at LGA and EWR. These airports are congested 

throughout the day. To meet the capacity limits, airlines serving these airports are required to reduce the 

number of flights per day. Flights per day at JFK were not impacted by the capacity limits. The morning 

and evening peaks where rescheduled and the midday uncongested period increased.

2005-2006 2006-2007 2007-2008 2008-2009

Regions NY PHL S F NY PHL S F NY PHL S F NY PHL SF

Fuel 

Prices

+21% No Change +72% -47%

Gross 

Domestic 

Product

+2.7% +2.1% +0.4% -2.4%

Capacity 

Limits

No Change

(LGA caps, others no 

caps)

No Change No Change Effect of 2008 caps at 

EWR, JFK and LGA 

seen here

Schedule

d Flights 

per Day

No ∆ -16% +3% +3% -4 % No ∆ -5 % -4 % -3 % -11% -7 % -11%
37

Table 12 Analysis of the scheduled flights per day at the five congested airports.

The total number of departures per day by the individual metroplex airports is shown in Figure 16. 

The reduction in departures per day at EWR and LGA are highly correlated with the reduction in capacity 

limits applied in 2008 at LGA and new capacity limits applied at EWR in 2008. The departures per day at 

SFO & JFK increased during the period 2005 – 2009.

Figure 16 Longitudinal analysis of the scheduled flights per day at the five congested airports.

Fuel Prices Economy Airport Flights/ Day

LGA no change

EWR no change

JFK 4%

PHL -16%

SFO 3%

LGA -3%

EWR no change

JFK 20%

PHL -4%

SFO 4%

LGA no change

EWR -8%

JFK -5%

PHL -4%

SFO 4%

LGA* -16%

EWR* -16%

JFK* no change

PHL -7%

SFO -4%

Historical Changes Longitudinal 

-47% 2008-

2009 (*caps in 

NY)

-2.4%

72% 2007-

2008

0.4%

1% 2006-

2007

2.1%

21% 2005-

2006

2.7%

PHL

PHL

EWR

EWR

JFK

JFK

LGA

LGA

SFO

SFO

400

500

600

700

800

900 Jan-05 Mar-05 May-05 Jul-05 Sep-05 Nov-05 Jan-06 Mar-06 May-06 Jul-06 Sep-06 Nov-06 Jan-07 Mar-07 May-07 Jul-07 Sep-07 Nov-07 Jan-08 Mar-08 May-08 Jul-08 Sep-08 Nov-08 Jan-09 Mar-09 May-09 Jul-09 Sep-09 Nov-09 Departures per Day

Departures per Day (2005 to 2009)

• Schedule Reductions at PHL, EWR, & LGA

• Schedule Growth at SFO & JFK
38

The results of a linear regression for scheduled flights per day at the airports at each of the five

airports over four years provides insights into the correlations between the variables and the significant 

factors that influence airline decisions on scheduled flights per day (Table 13).

Table 13 Airport correlation of significant factors that influence airline decisions on scheduled 

flights per day

The analysis shows correlation between scheduled flights per day, the Gross Domestic Product (GDP) 

and airport capacity limits. The analysis showed no correlation between hedged fuel prices and scheduled 

flights per day.

A stepwise regression analysis of the dependent variable percent change in flights per day versus 

percent change in hedged fuel prices, national gross domestic product and capacity limits identified the 

change in scheduled flights per day to be a positive correlation to changes in national Gross Domestic 

Product (GDP). The correlation accounted for 15.6% (i.e. R-squared) of the observed variation in 

markets served. The remaining effects are inter-airline competition, changes in airline business models, 

and airline restructuring when emerging from bankruptcy. The effect of capacity limits was ambiguous 

due to the small number of data-points reflecting this change. The regression model found for changes in 

flights per day to all markets is as follows:

Flights/ Day = - 0.0368 + 1.83 GDP

4.2.1.3 Airport Level Schedule Analysis

This section describes the details of the schedule profiles at the airports, providing insights into the 

dynamics of the hourly schedules.

Figure 17 shows that even with a 2.0% drop in the economy from 2007 to 2009, airlines increased 

their operations at SFO. There were 44 additional daily flights added by the airlines at SFO. This 

included an increase of 10 operations for the 7am hour, 12 operations for the 11am hour, and 7 operations 

for the 1pm hour; a decrease of 6 operations for the 4pm and 5pm hours, and increases of 10 operations 

for the 7pm hour, and 7 operations for the 10pm hour. The results of these changes left the airlines with a 

more peaked schedule.

Figure 18 shows that even with a 4.8% increase in the economy from 2005 to 2007, airlines decreased 

their Operations at PHL. There were 94 fewer daily flights scheduled by the airlines at PHL. This 

included increases of 9 operations for the 8am, 9am and 10am hours, decreases of 11 operations for the 

11am hour, and 18 operations for the 12pm hours, an increase of 13 operations for the 1pm hour, and 

decreases of 12 operations for the 5pm hour, 7 operations for the 6pm hour, and 14 operations for the 9pm 

hour. The results of these changes left the airlines with a reduced schedule. 

Fuel 

Prices GDP Caps

Flights/

Day

Pearson correlation 0.214 0.447 -0.43

P-Value 0.364 0.048 0.058
39

Figure 17 SFO Schedule Adjustments 2007-2009 by time of day

Figure 18 PHL Schedule Adjustments 2005-2007 by time of day

0

10

20

30

40

50

60

70

80

90

100

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Average Daily Operations

Hour of Day

PHL 3QTR Schedule Adjustments 2005-2007

2005

2006

2007

0

10

20

30

40

50

60

70

80

90

100

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Average Daily Operations

Hour of Day

SFO 3QTR Schedule Adjustments 2007-2009

2007

2008

2009
40

Figures 19-21 highlight how the NY airports reacted to new capacity limits in 2008. The airlines 

operating out of LGA and EWR reduced operations (since de-peaking was not an option), while the 

airlines operating at JFK de-peaked and maintained schedule. Specifically airlines operating out of JFK 

moved flights from the 8am, 4pm and 6pm hours to the 1pm, 2pm and 10pm hours as shown in the chart 

below. 

Figure 19 EWR Schedule Adjustments 2007-2009 by time of day

0

10

20

30

40

50

60

70

80

90

100

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Average Daily Operations

Hour of Day

EWR 3QTR Schedule Adjustments 2007-2009

2007

2008

2009
41

Figure 20 LGA Schedule Adjustments 2007-2009 by time of day

Figure 21 JFK Schedule Adjustments 2007-2009 by time of day

4.2.1.4 Aircraft Size 

The results of an analysis of the average aircraft size at the Metroplex (NY, SF, and Philadelphia) are 

shown in Table 14. The average size of aircraft used did not significantly change throughout this study 

with the exception of JFK (2007), SFO (2006/2009) and PHL (2006). In 2006-2007 Delta Airlines 

started using JFK as a hub for international travel. Delta scheduled flights to feed the international flights

using smaller aircraft to match the feeder route passenger demand. The size of aircraft serving JFK 

reflected this change in strategy (decreased). 

0

10

20

30

40

50

60

70

80

90

100

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Average Daily Operations

Hour of Day

LGA 3QTR Schedule Adjustments 2007-2009

2007

2008

2009

0

10

20

30

40

50

60

70

80

90

100

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Average Daily Operations

Hour of Day

JFK 3QTR Schedule Adjustments 2007-2009

2007

2008

2009
42

Table 14 Analysis of the average aircraft size at the three metroplexes (NY, SF, and Philadelphia).

The changes in aircraft size in the San Francisco Metroplex can be attributed to the increase of service 

in 2006 due to an upswing in the economy and a corresponding decrease in service due to a drop in the 

economy in 2009 and a consolidation of Southwest Airlines (SWA) operations into SFO after the SWA 

buyout of ATA Airlines. 

The results of an analysis of the aircraft size at the airports for each of the metroplexes (NY, SF, and 

Philadelphia) are shown in Table 15. These results show a more nuanced view of the dynamics. 

Examination of the individual airports shows there is a lack of a relationship at the metroplex and 

airport level of analysis. 

Table 15 Analysis of the average aircraft size at the five congested airports .

Fuel Prices Economy Airport A/C Size

LGA no change

EWR 3%

JFK no change

PHL -4%

SFO no change

LGA no change

EWR no change

JFK -12%

PHL no change

SFO no change

LGA no change

EWR no change

JFK 3%

PHL no change

SFO no change

LGA* no change

EWR* no change

JFK* no change

PHL no change

SFO no change

Historical Changes Longitudi

-47% 2008-

2009 (*caps in 

NY)

-2.4%

72% 2007-

2008

0.4%

1% 2006-

2007

2.1%

21% 2005-

2006

2.7%

2005-2006 2006-2007 2007-2008 2008-2009

Regions NY PHL SF NY PHL SF NY PHL SF NY PHL SF

Fuel 

Prices

+21% No Change +72% -47%

Gross 

Domestic 

Product

+2.7% +2.1% +0.4% -2.4%

Capacity 

Limits

No Change No Change No Change Effect of 2008 caps at 

EWR, JFK and LGA 

seen here

Aircraft 

Size

No ∆ -4 % +3% -3 % No ∆ No ∆ No ∆ No ∆ No ∆ No ∆ No ∆ -6 %
43

The results of a linear regression for aircraft size at the airports at each of the five airports over four

years provides insights into the correlations between the variables and the significant factors that 

influence airline decisions on aircraft size (Table 16).

Table 16 Results of correlation analysis of aircraft size, hedged fuel prices, Gross Domestic 

Product, and airport capacity limits

The analysis shows no correlation between aircraft size, hedged fuel prices, Gross Domestic Product, 

and airport capacity limits.

A stepwise regression analysis of the dependent variable percent change in aircraft size versus percent 

change in hedged fuel prices, national gross domestic product and capacity limits identified no correlation 

between any of them. 

The hypothesis that when congestion increased substantially, or when slot controls were imposed, 

airlines would respond by up-gauging the airplanes used on these routes was not observed. A closer 

examination of the data shows that there are economies of scale related to crew and maintenance costs. 

However, there are no such economies of scale related to fuel costs. The newest part of the airline 

industry fleet is regional jets that are more fuel efficient than the larger aircraft in the overall fleet. As 

hedged fuel prices increase, there is more incentive for the airlines to move to smaller aircraft. Figure 22

illustrates this point where it can be seen that the smaller aircraft have better average fuel usage per seat 

hour. By using smaller aircraft the airlines can assure higher demand (and load factors) through increased 

frequency. 

Figure 22 Aircraft fuel burn rate/seat-hr versus seat size Fuel 

Prices GDP Caps

A/C

Size

Pearson correlation 0.12 -0.169 0.071

P-Value 0.615 0.476 0.765 0

2

4

6

8

10

12

14

0 100 200 300 400 500 600 Average Fuel Gallons/ seat-hr

Seats

Average of Fuel Gallons/ seat-hr (Aviation Daily)

A320 A321

737-200

767-200

777

787 A380

747

• Airlines reluctance to upgrade explained by

Fuel Gallons/ seat-hr plot below

Source: Aviation Daily (June 2009)

Not cost efficient to upgage for markets
44

4.2.2 Economic Access

4.2.2.1 Airfares

The results of an analysis of the airfares at the metroplex (NY, SF, and Philadelphia) are shown in Table 

17. Analysis of changes in the airfares for flights servicing the Metroplex airports in this study indicates 

that airfares are primarily a function of changes in hedged fuel prices (and not capacity limits).

Specifically a positive relationship was found between hedged fuel prices and airfares. Increases in 

hedged fuel prices leads to increased airfares, and decreases in hedged fuel prices leads to decreased 

airfares. Gross Domestic Product and capacity limits seem to have no effect on airfare, as shown in the 

table.

Table 17 Analysis of the average airfares at the three metroplexes (NY, SF, and Philadelphia).

The results of an analysis of the airfares at the airports for each of the metroplexes (NY, SF, and 

Philadelphia) are shown in Table 18. These results show a more nuanced view of the dynamics.

Increases in hedged fuel prices lead to increased airfares and decreases in hedged fuel prices leads to 

decreased airfares. Gross Domestic Product and capacity limits seem to have no effect on airfare, as 

shown in the table. The analysis at the airport level shows more clearly the positive relationship between 

hedged fuel prices and airline airfare. 

2005-2006 2006-2007 2007-2008 2008-2009

Regions NY PHL S F NY PHL S F NY PHL S F NY PHL SF

Fuel 

Prices

+21% No Change +72% -47%

Gross 

Domestic 

Product

+2.7% +2.1% +0.4% -2.4%

Capacity 

Limits

No Change No Change No Change Effect of 2008 caps at 

EWR, JFK and LGA 

seen here

Airfares +8% +13% +13% No ∆ No ∆ No ∆ +10% +11% +3% -12% -10% -6 %
45

Table 18 Analysis of the average airfare at the five congested airports .

 

Figure 23 shows the average airline airfare for the five congested airports over the five years 

examined. This analysis highlights the significant airline airfare growth for JFK and how the airline 

airfares at JFK are starting to track with EWR & SFO.

Figure 23 Average airfares by airport (2005-2009)

Fuel Prices Economy Airport Airfare

LGA 10%

EWR no change

JFK 12%

PHL 13%

SFO 7%

LGA no change

EWR no change

JFK 4%

PHL no change

SFO no change

LGA 11%

EWR 11%

JFK 8%

PHL 11%

SFO no change

LGA* -13%

EWR* -10%

JFK* -13%

PHL -10%

SFO -9%

Historical Changes Longitudi

-47% 2008-

2009 (*caps in 

NY)

-2.4%

72% 2007-

2008

0.4%

1% 2006-

2007

2.1%

21% 2005-

2006

2.7%

EWR EWR

JFK

JFK

LGA

LGA

PHL

PHL

SFO

SFO

$140 

$160 

$180 

$200 

$220 

$240 Average Airfare

Average AirFares 2005-2009
46

The results of a linear regression for airline airfares at the Airports at each of the 5 airports over 4 

years provides insights into the correlations between the variables and the significant factors that 

influence airline decisions on airfare (Table 19).

Table 19 Analysis of correlation between airfare, hedged fuel prices, Gross Domestic Product, and 

airport capacity limits

The analysis shows correlation between Airfare, Hedged fuel prices, Gross Domestic Product, and 

Airport Capacity Limits.

A stepwise regression analysis of the dependent variable percent change in airline airfares versus 

percent change in hedged fuel prices, national gross domestic product and capacity limits identified the 

change in airfare to be a positive correlation function of changes in national Gross Domestic Product 

(GDP) and hedged fuel prices. The correlation accounted for 79.6% (i.e. R-squared) of the observed 

variation in domestic markets served. The remaining effect is inter-airline competition, changes in airline 

business models, and airline restructuring when emerging from bankruptcy. The effect of capacity limits 

was ambiguous due to the small number of data-points reflecting this change. The regression model 

found for changes in average airfare to all markets is as follows:

∆Airfare = - 0.0114 + 1.84 ∆GDP + 0.124 ∆Hedged fuel prices

4.2.3 Airline Profits

4.2.3.1 Profitability of airlines – The results of an analysis of the airline profitability at the metroplex

(NY, SF, and Philadelphia) are shown in Table 20. The analysis shows a negative relationship between 

airline airfares and airport profitability. When airfares are increased, airport profitability is reduced. 

When airfares are reduced, airport profitability is increased. 

The analysis showed hedged fuel prices increased more than airfares. Also note that extra baggage 

fees and other new fees introduced by the airlines are not included in this analysis so the conclusions may 

be misleading since these fees now account for a considerable addition to the airline revenue.

Fuel Prices GDP Caps

Airfare

Pearson

correlation 0.826 0.73 -0.666

P-Value 0 0 0.001
47

Table 20 Analysis of the airline profits at the three metroplexes (NY, SF, and Philadelphia).

Some caveats to the analysis are as follows:

NY airports do not have strong competition between airlines.

Airlines may be strategically lagging airfare response to fuel price fluctuations since there is only 

so much airlines can raise airfares before getting a very negative response from the markets price 

elasticity curves. 

The results of an analysis of the airlines profitability at the airports for each of the metroplexes (NY, 

SF, and Philadelphia) are shown in Table 21. These results show a more nuanced view of the dynamics. 

The analysis at the airport level shows the positive relationship between hedged fuel prices and airline 

airfare. This level of analysis also highlights the negative relationship between airfare and airline 

profitability and the negative relationship between congestion and airline profitability. 

2005-2006 2006-2007 2007-2008 2008-2009

Regions NY PHL S F NY PHL S F NY PHL S F NY PHL SF

Fuel 

Prices

+21% No Change +72% -47%

Gross 

Domestic 

Product

+2.7% +2.1% +0.4% -2.4%

Capacity 

Limits

No Change No Change No Change Effect of 2008 caps at 

EWR, JFK and LGA 

seen here

Airfares +8% +13% +13% No ∆ No ∆ No ∆ +10% +11% +3% -12% -10% -6%

Airline 

Profits

-5% +5% No ∆ -5% No ∆ -4% -26% -18% -26% +10% +9% +20%
48

Table 21 Analysis of the airline profit at the five congested airports .

The results of a linear regression for airline profitability at the airports at each of the five airports over 

four years provides insights into the correlations between the variables and the significant factors that 

influence airline profitability (Table 22).

Table 22 Analysis of correlation between profitability, airfare and domestic markets

The analysis shows correlation between profitability, airfare and markets.

A stepwise regression analysis of the dependent variable percent change in airline profitability versus 

percent change in hedged fuel prices, national gross domestic product, capacity limits, markets served, 

flights per day, aircraft size, airline airfare, and airport congestion identified the change in airline 

profitability to be a function of changes in hedged fuel prices. Hedged fuel prices were found to have 

negative relationships with profitability. The correlation accounted for 52.5% (i.e. R-squared) of the 

observed variation in markets served. The remaining effect is inter-airline competition, changes in airline 

Fuel Prices Economy Airport Airfare Profit

LGA 10% -5%

EWR no change no change

JFK 12% -9%

PHL 13% 5%

SFO 7% no change

LGA no change -13%

EWR no change -12%

JFK 4% 4%

PHL no change no change

SFO no change no change

LGA 11% -17%

EWR 11% -32%

JFK 8% -22%

PHL 11% -18%

SFO no change -20%

LGA* -13% 9%

EWR* -10% 10%

JFK* -13% 11%

PHL -10% 9%

SFO -9% 25%

Historical Changes Longitudinal 

-47% 2008-

2009 (*caps in 

NY)

-2.4%

72% 2007-

2008

0.4%

1% 2006-

2007

2.1%

21% 2005-

2006

2.7%

Fuel GDP caps Airfare Markets A/C Size

Flights/ 

Day Congestion

Profitability -0.877 -0.396 0.424 -0.664 -0.454 -0.211 -0.13 -0.092

0 0.084 0.062 0.001 0.044 0.371 0.584 0.698
49

business models, and airline restructuring when emerging from bankruptcy. The regression model found 

for changes in airline profitability to all markets is as follows:

Profitability = - 0. 004712 - 0.280 Hedged Fuel Price 

4.2.3.2 Congestion 

The results of an analysis of the airport congestion at the metroplex (NY, SF, and Philadelphia) are shown 

in Table 23. Congestion is clearly a function of flights per day. 

The longitudinal analysis of the congestion at these metroplexes highlights the differences between 

these metroplexes. For example, SFO has excess capacity and can grow without increasing congestion. 

As the economy slowed down in 2008, SJU and OAK lost flights while SFO increased flights by 20%. 

Some hypothesis for this metroplex behavior: (1) airlines operating in SFO are ramping up flights in 

anticipation of capacity limits; (2) SFO local wages incentivizing switch to SFO (San Jose City Council 

Study Session, 2010) to improve options for their passengers; and (3) SWA purchase of ATA enabled 

consolidation at SWA operations to SFO (out of OAK and SJU).

This metroplex analysis is also confounded by airport growth at JFK with the introduction of 

significant Delta Airline Operations by the summer of 2007, the Southwest Airlines purchase of ATA 

lead to consolidation of services in SFO from SJC and OAK in 2008, and the PHL consolidated 

operations in 2006. Therefore these airports will not always track with the observed trends from 

economic and policy changes. 

Table 23 Analysis of the congestion at the three metroplexes (NY, SF, and Philadelphia).

The results of an analysis of the airport congestion for each of the metroplexes (NY, SF, and 

Philadelphia) are shown in Table 24. These results show a more nuanced view of the dynamics. 

As expected growth in airline schedules leads to increased congestion (delays and cancellations) and 

reductions in schedule reduce airport congestion. 

The capacity limits applied at New York airports have reduced congestion for EWR and LGA, since 

reductions in congestion are 50% higher than that observed at JFK and PHL. Additional analysis of the 

schedules shows that EWR and LGA reduced their schedules and JFK de-peaked its schedule as a result 

of the new or changed capacity limits, as shown in previously in Figures 19-21.

2005-2006 2006-2007 2007-2008 2008-2009

Regions NY PHL S F NY PHL S F NY PHL S F NY PHL SF

Fuel Prices +21% No Change +72% -47%

Gross 

Domestic 

Product

+2.7% +2.1% +0.4% -2.4%

Capacity 

Limits

No Change No Change No Change Effect of 2008 caps at EWR, 

JFK and LGA seen here

Scheduled 

Flights per 

Day

No ∆ -16% +3% +3% -4% No 

Chan

ge

-5% -4% -3% -11% -7% -11%

Congesti

o n

+6% -3 4% +27% +37% +32% +3% -11% -25% +9% -27% -20% -31%
50

This should serve as a warning to the US air transportation community, that when the economy 

recovers, expect congestions at major airports to get worse unless these airports are capped.

Table 24 Analysis of the congestion at the five congested airports .

Longitudinal analysis of congestion for the airports examined showed with more clarity the trend that 

was observed at the metroplex level of analysis. The analysis showed that congestion is a function of 

flights per day (and peak period scheduling) and that congestion can be mitigated through the application 

of caps, as seen at EWR and JFK (and lower caps at LGA).

The results of a linear regression for airport congestion at each of the five airports over four years 

provides insights into the correlations between the variables and the significant factors that influence 

airport congestion (Table 25).

This analysis illustrates the impact of airline schedules on airport congestion. As expected growth in 

airline schedules leads to increased congestion (delays and cancellations) and reductions in schedule 

reduce airport congestion. 

Fuel Prices Economy Airport Flights/ Day Congestion

LGA no change 5%

EWR no change 11%

JFK 4% -3%

PHL -16% -34%

SFO 3% 40%

LGA -3% 21%

EWR no change 23%

JFK 20% 111%

PHL -4% 32%

SFO 4% 6%

LGA no change no change

EWR -8% -13%

JFK -5% -22%

PHL -4% -25%

SFO 4% 20%

LGA* -16% -30%

EWR* -16% -31%

JFK* no change -16%

PHL -7% -20%

SFO -4% -29%

Historical Changes Longitudinal Metrics

-47% 2008-

2009 (*caps in 

NY)

-2.4%

72% 2007-

2008

0.4%

1% 2006-

2007

2.1%

21% 2005-

2006

2.7%
51

Table 25 Analysis of correlation of changes in airport congestion with changes in airfare, markets 

served and flights per day

The correlation analysis shows that changes in airport congestion are correlated with changes in 

airfare, markets served and flights per day. These relationships shown from the statistics reinforce the 

understood relationships between congestion and airline schedules. A positive relationship was found 

between congestion and markets and flights per day. A negative relationship was found between 

congestion and aircraft size.

A stepwise regression analysis of the dependent variable percent change in airport congestion versus 

percent change in hedged fuel prices, national gross domestic product, capacity limits, markets served, 

flights per day, aircraft size, airline airfare, and airline profitability identified the change in congestion at 

an airport to be a function of changes in markets, aircraft size and flights per day. Markets and flights per 

day were found to have a positive relationship with congestion. Aircraft size was found to have a 

negative relationship with congestion. The correlation accounted for 76.5% (i.e. R-squared) of the 

observed variation in markets served. The remaining effect is inter-airline competition, changes in airline 

business models, and airline restructuring when emerging from bankruptcy. The regression model found 

for changes in airport congestion is as follows:

congestion = 0.0778 + 2.16 Markets – 4.04 A/C Size + 1.64 Flights/ Day

Fuel GDP caps Airfare Markets A/C Size

Flights/ 

Day Profitability

Congestion 0.111 0.538 -0.352 0.177 0.734 -0.618 0.825 -0.092

0.642 0.014 0.128 0.456 0 0.004 0 0.698
52

5 Conclusions

5.1 Summary of Statistical Trends (2005-2009) (NY, SF, PHL)

This analysis compares the behavior of the air transportation system (e.g. domestic markets served, 

airfares, delays, load factors, aircraft size) during the recent run-up in hedged fuel prices at capacitylimited New York airports and non-slot controlled San Francisco and Philadelphia airports. 

The results indicate:

i. Airfares change in proportion to changes in hedged fuel prices and gross domestic product

ii. Flights per day and domestic markets served change in proportion to changes in gross domestic 

product

iii. Flights per day and domestic markets served change in proportion to changes in airport capacity 

limits

iv. Delays and cancellations change in proportion to flights per day and domestic markets served and 

in negative proportion to aircraft size

v. Airline profitability change in negative proportion to aircraft size, airfares and domestic markets 

served

This study expands on prior efforts to analyze the behavior of airlines at congested airports. It 

compares the performance at the New York airports to the performance at the Philadelphia and San 

Francisco airports from 2005 through 2009. During this time period, there were significant fluctuations in 

hedged fuel prices and in the overall economic condition of the country. In addition, tighter regulations on 

the scheduling of flights at the three New York airports were imposed. 

The results indicate that capacity limited airports yielded reduced congestion (i.e., fewer delayed and 

cancelled flights) through reductions in schedules and de-peaking schedules as shown in previously in 

Figures 19-21. There was no significant change in domestic markets serviced. While airfares were 

affected by changes in hedged fuel prices, they did not significantly increase at New York airports after 

capacity limits were applied. 

San Francisco and Philadelphia airports reduced schedules during the recession. These airports also 

reduced congestion through the reduction in schedules. The overall conclusions are shown in Table 26.

Table 26 Economic and Policy Impacts

Annual Changes Observations

Caps Economy Fuel Prices Airfare Schedule Congestion Profit

05-06 Yes Up Up Up No ∆ Up Down

05-06 No Up Up Up Up Up Down

06-07 Yes Up No ∆ No ∆ No ∆ Up Down

06-07 No Up No ∆ No ∆ Up Up No ∆

07-08 Yes Constant Up Up No ∆ No ∆ Down

07-08 No Constant Up Up Down Down Down

08-09 Reduced Down Down Down 2xDown 2xDown Up

08-09 No Down Down Down Down Down Up
53

The key observations from this analysis are as follows:

Airfares increase when hedged fuel prices increase and when the gross domestic product 

decreases.

Flights per day and domestic markets served decrease when gross domestic product decreases. 

Flights per day and domestic markets served decline when airport capacity limits are imposed. 

Delays and cancellations decrease as flights per day and domestic markets served are decreased 

and delays and cancellations increase as aircraft size is decreased. 

Airline profitability decreases as aircraft size, airfares or domestic markets served increases. 

Up-gauging of airline fleets is unlikely to occur without the introduction and purchase of a new 

class of aircraft that has better efficiency for aircraft handling between 100-150 passengers. 

Currently, the aircraft stock in the 100-120 seat size is the oldest and least efficient portion of the 

fleet.

With the imposition of slot controls in 2008, LGA and EWR reduced schedules while JFK was 

capable of keeping the same number of flights as were flown prior to the imposition of slot 

controls. Because of a peaked schedule at JFK, there was opportunity to move flights from peak 

times to off-peak times rather than having to remove the flights. LGA and EWR were operating 

at capacity at virtually all times and so had to remove flights from the schedule in order to adhere 

to the new regulations. 

In 2008, the airlines were less profitable. Even though delays and the resultant cost of delays 

were reduced, the increases in fuel costs were considerably greater. Thus, overall, airlines’ 

operational costs increased in 2008, and these costs were greater than the increase in ticket prices. 

Airports with capacity limits reduced operations and congestion. Airlines’ profitability at these 

airports (as measured by operational revenue minus operational costs) increased. 

The analysis showed a positive correlation between: 

airfare and hedged fuel prices, 

Gross Domestic Product and airline schedule, 

capacity limits and airline schedule, and 

airline schedule and airport congestion. 

A negative correlation was found between:

airfare and airport profitability and

airport congestion and airport profitability. 

The analysis showed no significant change in load factors, or aircraft size.

5.2 Summary of Functional Relationships (2005-2009) (NY, SF, PHL)

The summary of functional relationships found from the stepwise regression analysis and their 

multipliers between the exogenous factors, economic access, geographic access and airline profitability 

are shown in Figure 24. All results are based on statistically significant trends with a confidence interval 

of 95%. The stepwise regression results are listed below:

Domestic markets = - 0.0266 + 1.44 GDP

Flights/ Day = - 0.0368 + 1.83 GDP

∆Airfare = - 0.0114 + 1.84 ∆GDP + 0.124 ∆Hedged fuel prices

Profitability = - 0. 004712 - 0.280 Hedged Fuel Price 

congestion = 0.0778 + 2.16 Markets – 4.04 A/C Size + 1.64 Flights/ Day
54

Figure 50 identifies the relationships between factors. For example, a one percent change in hedged 

fuel prices lead to a 0.12% change in airfares. A negative relationship indicates that a positive increase in

the source, leads to a decrease in the result.

Figure 24 Summary of multipliers between the exogenous, economic access, geographic access and 

airline profitability factors

5.2.1 Geographic Access. 

The number of domestic markets served directly by an airport is primarily affected by the health of 

the economy as measured by demand for air transportation. Capacity limits at airports did result in a small 

reduction in domestic markets served, as did airline network restructuring, airline financial restructuring, 

and inter-airline competition. 

The growth/decay in demand for air transportation (as measured by the Gross Domestic Product 

(GDP)) is the primary determinant of the number of domestic markets served. The results of the stepwise 

regression show that for every 1% change in GDP, there is a 1.44% change in the number of domestic 

markets with direct service. The linear regression showed that for every 1% change in GDP, there is a 

1.83% change in the number of flights per day serving these markets. As the economy slowed, the 

number of markets with direct service decreased. 

The introduction of capacity limits at congested airports also affected the number of domestic markets 

served. In response to the economic down-turn (2008 and 2009), airports without capacity limits (SFO, 

PHL) experienced a reduction in the number of domestic markets served by an average of -7% (SFO -8%, 

PHL -6%). During the same period, heavily congested airports that had capacity limits implemented 

(EWR) or reduced (LGA), experienced a loss of an additional 4% of markets served (EWR -12%, LGA -

10%). 

Airlines’ network restructuring (e.g. Delta’s expansion at JFK), airlines’ financial restructuring (e.g. 

USAirways bankruptcy filing impacted PHL), and inter-airline competition also affected the number of 

markets served. 

% Hedged

Fuel Prices

% Airfares

% Profitability

Airport 

Capacity 

Limits

% Gross 

Domestic 

Product % Scheduled 

Flights per Day

+1.64 % Congestion

% Aircraft 

Size

Exogenous Factors

Economic Access

Airline Profitability

% Markets

+1.83

-4.04

Geographic Access
55

5.2.2 Economic Access 

Passenger accessibility to air transportation through airfares at these airports followed established 

patterns for passenger demand. Changes in the economy significantly affected demand for air 

transportation. 

Cumulative elasticity at the airports ranged between -3 to -2.7 during this period. Specifically, a 

1% increase in airfare (e.g. $300 to $330) resulted in a 3% reduction in demand for air service at 

that fare. This result is consistent with prior studies that showed airfares to be highly elastic.

The change in airfare was driven by changes in travel demand (reflected by changes in the GDP) 

and changes in hedged fuel prices (R2=79.6%). At the five airports studied (LGA, JFK, EWR, 

PHL, and SFO), airfares changed on average 1.84% for every 1% change in GDP and 0.12% for 

every 1% change in hedged fuel prices. A $1 increase in hedged fuel prices resulted in an 

average of $16 increase in airfares, which yielded an average reduction in passenger demand of 

1.5%.

5.2.3 Airline Profitability. 

Airline profitability for the routes serviced at these five airports is a complex phenomenon driven by 

demand for air transportation, passenger’s responses to price increases, and operating costs. During this 

period, airline profitability was primarily determined by the industry’s ability to raise airfares relative to 

the cost of operations (i.e. when hedged fuel prices were escalating dramatically). During the spike in 

fuel costs the airlines were faced with significantly greater operating costs and decreased demand due to 

the economic downturn. 

1. During this period, airline profitability decreased as airlines were unable to increase airfares as 

fast as hedged fuel prices increased.

2. During this period, airlines shed less profitable domestic markets in order to improve 

profitability. The decrease in markets was driven primarily by reduced economic demand for air 

transportation and the sensitivity of passengers in certain markets to increased fares. 

3. During this period, airlines decreased aircraft size in order to maintain profitability as demand 

decreased.

4. The change in airport congestion was driven by changes in domestic markets served, scheduled 

flights per day, and by aircraft size (R2=76.5%). At the five airports studied (LGA, JFK, EWR, 

PHL, and SFO), congestion changed on average 2.16% for every 1% change in markets served, 

1.64% for every 1% scheduled flights per day and -4.04% for every 1% increase in aircraft size. 

5. The change in airline profitability was driven by changes in airfare and changes in aircraft size 

(R2=75.6%). At the five airports studied (LGA, JFK, EWR, PHL, and SFO), airline profitability 

changed on average -.28% for every 1% increase in hedged fuel prices. 

5.3 Relationship between Airports and Metroplexes 

No two metroplexes are the same. 

Analysis of these metroplexes shows different aggregate airline behavior between the metroplexes, 

and between the individual airports in a metroplex.

The two metroplexes examined in this study (New York and San Francisco) were found to be 

comparable in many ways. They both have international hubs. They both have substantial shuttle 

markets. They both have airports with a primary domestic focus. And they both have cross country 

markets to connect east coast and west coast travelers to the US domestic air transportation network. 

However, the New York airports are controlled by the Port Authority of New York / New Jersey and 

the San Francisco airports are managed separately. The New York Metroplex has two airports operating 

at capacity and one airport operating near capacity. All three New York airports are OEP 35 
56

airports. The San Francisco Metroplex has one OEP 35 airport which operates at near capacity, but is still 

less capacitated than any of the three New York airports. 

Airports and metroplexes will respond differently to economic growth. 

Airline behavior will depend on how capacitated the airports are within a metroplex. For example, 

from 2005 to 2007 when the economy experienced a 4.8% growth in national gross domestic product, the 

two capacitated airports in New York (LGA and EWR) experienced no increase in scheduled 

operations. During this same time period, JFK experience a 25% increase in scheduled operations, Delta 

airlines introduced domestic operations into JFK.

Airports and metroplexes will respond differently to economic decline.

Airline behavior will depend on the airline competition within the airports and within a 

metroplex. For example, from 2007 to 2009 when the economy experienced a 4% drop in national Gross 

Domestic Product, there was a difference in the response between the San Francisco and New York 

metroplex. Airline scheduled operations were consolidated into SFO from OAK and SJC. During this 

same time period New York airports experienced reductions in scheduled operations with no intra-airport 

interaction.

Airports and metroplexes will respond differently to capacity limits.

This same phenomenon can be observed when capacity limits are introduced in a metroplex. In 2008, 

capacity limits were introduced at the New York airports. These capacity limits were set or adjusted to 

the perceived capacity limits at the respective airports. Therefore, since airlines operating at EWR and 

LGA were operating at or slightly above these capacity limits for most of the day, these airlines had no 

choice but to reduce schedules. On the other hand the airlines operating at JFK were only operating at or 

above these capacity limits for a few hours during the day, so these airlines were able to move scheduled 

operations to non-congested times rather than reduce the overall schedule or the domestic markets served.

Airports and metroplexes will respond differently to capacity limits and market based incentives. 

Metroplexes operating at or near capacity will reduce operations in response to reduced capacity 

limits and maintain operations in response to market based incentives, as seen with increased operational 

costs from fuel prices. On the other hand, metroplexes like San Francisco with airports of different sizes 

will consolidate operations to the larger more economically robust airport in response to market based 

incentives, as seen in San Francisco from 2007 to 2008.

Airlines will up-gauge when policy incentives are matched with available fuel efficient fleet options. 

Currently there is insufficient inventory of 80 to 120 seat aircraft to support domestic up-gauging to 

reduce congestion. The aircraft available at the 85-120 seat capacity are not as fuel efficient per seat as 

the smaller aircraft between 50 and 80 seats.

5.4 Additional Observations 

The historic analysis indicates that slot controlled airports yielded improved performance (i.e., flight 

delays and cancelled flights) through reductions in frequency and de-peaking schedules without degrading 

service or frequency. On the other hand, the non-slot controlled airports in the San Francisco region 

showed an increase in flight delays and cancellations during the same period. The number of domestic 

markets served and airfares did not change. These results provide some justification for slot-controls at 

airports that need to manage network congestion. Even in the presence of fluctuations in passenger 

demand and economic shocks, passengers saw an improvement in service (i.e. a lessening of delays and 

cancellation) without any significant loss in domestic markets served or frequency to those markets. The 

only significant reduction in frequency was to the Washington DC, Boston and Chicago shuttle markets 
57

where there was a significant decrease in frequency and an up-gauging of aircraft to accommodate the 

passenger demand.

Even though the number of markets served in a metroplex are robust to economic changes (GDP, 

Fuel) and regulatory changes (airport capacity limits), examination of the airports within the metroplex 

identifies major differences among the airports. At the airport level, increases in Gross Domestic Product 

resulted in corresponding increases in airport operations and markets; similarly, decreases in Gross 

Domestic Product result in corresponding decreases in airport operations and markets. Additionally, 

analysis of the New York airports from 2005 to 2009 showed that capacity limits resulted in reduced 

operations at capacitated (EWR & LGA) airports and de-peaking of operations at un-capacitated airports 

(JFK). Reduced airline operations as the result of capacity limits led to reduced airport congestion and 

increased airline profitability at these airports. 

This analysis refutes the concern that congestion pricing is likely to reduce the markets or the 

profitability of the airlines. Congestion prices are likely to be far less than the change in fuel price 

experienced by the airlines in 2008. Since markets were relatively insensitive to that spike in costs, we 

see no reason to believe that a congestion pricing fee would impact markets or airline profitability 

significantly. Similarly, capacity limits at airports did not impact the domestic markets served, airline 

profitability or network connectivity when imposed at the NY airports. 

Economic and regulatory changes have not incentivized the airlines to increase capacity through upgauging. Therefore, regulators must determine mechanisms that will incentivize the industry to consider 

up-gauging, either through the introduction of energy efficient aircraft in the 100-150 seat category and/or 

through the restriction of access to the most congested airports without up-gauging. 

The airlines are capable of adapting relatively quickly to significant economic and regulatory 

changes. They have responded to increased costs by imposing new fees (baggage handling fees, food 

charges, higher cancellation fees, etc). They are careful to maintain market share during economic 

downturns, and respond quickly to threats by low-cost carriers to their most profitable markets. Airlines 

have also responded to downturns in the economy by maintaining their frequency to small markets 

through down-sizing of the aircraft flown to these domestic markets. 

This analysis indicates that when the economy recovers, airlines will increase schedules. Congestion 

levels can be expected to increase accordingly. 

5.5 Policy Maker’s View of Results 

What was the impact of Capacity Limits on Geographic Accessibility? Capacity limits at congested 

airports that forced a reduction in scheduled flights (i.e. EWR and LGA) resulted in a slight reduction in 

the number of domestic markets served (approx -4%) and a reduction of frequency of service to domestic 

markets served (-9%). The introduction of capacity limits at JFK had the effect of causing de-peaking in 

the late afternoon/evening congested period, and an increase in flights arriving and departing during the 

previously uncongested mid-morning and early afternoon periods.

What was the impact of Capacity Limits on Economic Accessibility? The impact of capacity limits 

on airfares was not significant, but such effects may have been overshadowed by the larger impacts of 

fuel costs and the economic downturn. During the period when capacity limits were present there was 

little change in airfares. 
58

What was the impact of Capacity Limits on Airline Profitability? For those airlines that were 

grandfathered slots at the capacity-limited airports (EWR, LGA, and JFK), profitability improved at these 

airports when compared to profits at airports (PHL and SFO) that did not have capacity limits.

What was the impact of Increasing Hedged Fuel Prices on Geographic Accessibility? There was 

only indirect evidence that hedged fuel prices had any impact on markets served. Markets served are 

directly determined by the economy and the demand for passenger travel. The airlines were not able to 

raise airfares to match increased operating costs because passengers were unwilling or unable to accept 

higher airfares during a period of economic downturn. Regressions on increased hedged fuel prices on 

markets served did not provide statistically significant results (i.e. the null hypothesis ―that increased 

hedged fuel prices negatively impacted markets served‖ could not be rejected).

What was the impact of Increasing Hedged Fuel Prices on Economic Accessibility? Changes in 

airfare were driven by changes in travel demand (reflected by changes in the GDP) as well as changes in 

hedged fuel prices (R2=79.6%). At these congested airports, airfares changed on average 1.84% for 

every 1% change in GDP, and 0.12% for every 1% change in hedged fuel prices. A $1 increase in hedged 

fuel prices resulted in an average of $16 increase in airfares, which yielded an average reduction in 

passenger demand of 1.5%.

What was the impact of Increasing Hedged Fuel Prices on Airline Profitability? Airline 

profitability is determined by the dynamics of the economic demand for air transportation and the rate of 

change of operating costs (as measured by changes in fuel costs, since all other operating costs remained 

constant). During periods of economic predictability in both demand for air transportation and costs of 

operations, airlines are profitable. During periods of decreasing demand or increasing operating costs, 

airlines are no longer profitable.

During periods when hedged fuel prices were constant or when hedged fuel prices dropped (e.g. -47% 

2008-2009), the airlines achieved profitability. During the period in which hedged fuel prices rose rapidly 

(e.g. +72% 2007-2008), the airlines were unable to increase airfares fast enough to maintain profitability.

Did Capacity Limits or Increasing Hedged Fuel Prices affect Aircraft Size? No increase in 

aircraft size was observed during this period. During periods of increasing fuel costs, aircraft size 

decreased to take advantage of aircraft with lower labor and fuel costs. At airports with capacity limits, 

aircraft size remained unchanged.

5.6 Recommendations

1. DOT and FAA evaluate options to relax Pilot Union Scope Clauses that would allow incentives 

for aircraft manufacturers to produce, and airlines to acquire and deploy, the correct sized aircraft 

(88 - 112 seats) to match the passenger O/D market demand.

2. Introduce Capacity Limits at congested airports. This has the effect of significantly reducing 

congestions and delays (across the NAS), without financial penalties to airlines or loss of 

geographic and economic access.

3. Research and develop aircraft technologies (e.g. engines) to provide improved performance for 

the 88 - 112 seats class. The use of such aircraft will increase the efficiency of the airspace (i.e. 

more passenger throughput) and will also reduce emissions. 
59

5.7 Future Work

5.7.1 Airline Schedule Optimization Model (ASOM) to Complete the “Design of 

Experiment”. 

The historical analysis was dictated by the events that occurred during the period under study and 

provided an analysis of four of a possible 27 treatments (3 GDP % change possibilities x 3 Fuel Price %

change possibilities x 3 Airport Capacity Limit % change possibilities). 

To examine the effect of the remaining treatments, an optimization model was developed. The Airline 

Schedule Optimization Model (ASOM), calibrated using the historical data, is used in a follow-up study 

to evaluate the consequences of alternative combinations of economic conditions, changes in operating 

costs, runway capacity restrictions and airfare changes. The results of the study will complete the design 

of experiment and forecast what the airlines are likely to do if the economy has a significant upswing or if 

the government imposes capacity restrictions at other airports.

5.7.2 Absence of Economies-of-Scale through Up-gauging (or “Cash for Clunkers”). 

During the calibration of the ASOM, it was observed that the optimization model failed to show the 

airlines increasing aircraft size from 80 to 100 seats. The ability to up-gauge in this range is critical to 

taking advantage of the concept of using the same runway slots to ferry additional passengers. 

This behavior is a result of the absence of economies-of-scales in up-gauging in this range. The 

significantly higher costs of operation at the 100 seat and 200 seat class of aircraft prevent airlines from 

up-gauging. In the 100 seat range the only aircraft in revenue-service are the older, DC-9 class that is 

more expensive to operate. No new, efficient aircraft are available in this range. This phenomenon 

becomes more pronounced as the price of fuel increases.
60

References:

Bethune, Broadbent, May 2008. Airport Slots Up For Auction in New York, Airport International

Bureau of transportation statistics (BTS) databases and statistics. Accessed March 2010. 

Dillingham, G.L., 2000. Aviation and the Environment: FAA's Role in Major Airport Noise Programs, 

DIANE Publishing.

Eurocontrol, May, 2004. ―Evaluating the True Cost to Airlines of One Minute of Airborne or Ground 

Delay,‖ University of Westminster Final Report 

FAA, 2000. Airports Capital Improvement Plan, Available at: 

http://www.faa.gov/airports/resources/publications/orders/media/AIP_5100_39A.pdf [Accessed 

June 27, 2010].

ATA, 2008. FAA Order ODRA-08-475:, Available at: 

http://www.raa.org/Portals/0/Presentations/ny_slots_auction_protest_-_pat_mcnall.pdf [Accessed 

June 27, 2010].

FAA, 2008. National Plan of Integrated Airport Systems 2009-2013, Available at: 

http://www.faa.gov/airports/planning_capacity/npias/reports/media/2009/npias_2009_narrative.p

df. [Accessed June 27, 2010].

FAA, 2010. 2010 NextGenImplementation Plan, Available at: 

http://www.faa.gov/about/initiatives/nextgen/media/NGIP_3-2010.pdf [Accessed June 27, 2010].

Federal Register, 2008. Vol. 73, No. 19, Available at: 

http://www.access.gpo.gov/su_docs/fedreg/a081010c.html [Accessed June 27, 2010].

Ferguson, Hoffman, Sherry, Kara, and Calderon, Jan 2009. Effects of Fuel Prices and Slot Controls on 

Air Transportation Performance at New York Airports, Eighth USA/Europe Air Traffic 

Management Research and Development Seminar (ATM2009)

Ferguson, Hoffman, Sherry, Kara, and Calderon, May 2009. Effects of Fuel Prices on Air Transportation 

Performance at New York and San Francisco Airports, 2009 Integrated Communications 

Navigation and Surveillance (ICNS) Conference, Washington, D.C. (ICNS 2009)

Ferguson, Hoffman, Sherry, Kara, and Calderon, May 2010. Airline Response to Changing Economics 

and Policy, 2010 Integrated Communications Navigation and Surveillance (ICNS) Conference, 

Washington, D.C. (ICNS 2010)

La Hood , Jan 2009. ―Eliminate auctions for takeoff and landing slots at New York's three major airports‖ 

URL link: http://www.msnbc.msn.com/id/28793672/

Kara, Ferguson, Hoffman, Sherry , June 2010. ―Estimating Domestic U.S. Airline Cost of Delay based on 

European Model‖ ,Accepted at 4th International Conference on Research in Air Transportation -

ICRAT 2010, Budapest Hungary

Kirk, R.S., 2003. CRS Issue Brief for Congress: Airport Improvement Program, Congressional Research 

Service.

RAA, 2008. Comments of the Regional Airline Association, Available at: URL Link: 

http://www.raa.org/LinkClick.aspx?fileticket=5UZvtwzEE3s%3D&tabid=176&mid=646 

[Accessed June 27, 2010].

San Jose City Council Study Session, March 2010. ―Competing in the Marketplace: City Budget and 

Policy Issues Affecting Airport Competitiveness‖,

http://www.sjc.org/about/newsroom/2010_releases/SJC_comp_pres.pdf

Schumer, C.E., May 2008. ―Flight Delays Cost Passengers, Airlines and the U.S. Economy Billions‖. A 

Report by the Joint Committee Majority Staff.

Schumer, Meeks, Nadler, Sires, and Kuhl, 2008. H. R. 6299: PROHIBITION ON FEDERALLYIMPOSED AUCTIONS AND CONGESTION PRICING AT COMMERCIAL AIRPORTS, 

110th Congress 2d session. http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=110_cong_bills&docid=f:h6299ih.txt.pdf

Sherry, and Donohue, 2008. ―U.S. Airline Passenger Trip Delay Reports (2007&2008),‖ Center for Air 

Transportation Systems Research Report, George Mason University, pp. 5.

Wells, A.T. & Young, S.B., 2003. Airport planning & management, McGraw-Hill Professional.

URL link: http://www.airport-int.com/news/airport-slots-up-for-auction-in-new-york.html
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Optimizing Air Transportation Service to Metroplex Airports - Part 1: 

Analysis of Historical Data

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NNX07AT23A

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Donohue, George; Hoffman, Karla; Sherry, Lance; Ferguson, John; Kara, 

Abdul Qadar

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NASA Langley Research Center

Hampton, VA 23681-2199

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National Aeronautics and Space Administration

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14. ABSTRACT

The air transportation system is a significant driver of the U.S. economy, providing safe, affordable, and rapid transportation. During the past three decades 

airspace and airport capacity has not grown in step with demand for air transportation (+4% annual growth), resulting in unreliable service and systemic delays. 

Estimates of the impact of delays and unreliable air transportation service on the economy range from $32B to $41B per year. This report describes the results 

of an analysis of airline strategic decision-making with regards to: (1) geographic access, (2) economic access, and (3) airline finances. This analysis evaluated 

markets-served, scheduled flights, aircraft size, airfares, and profit from 2005-2009. During this period, airlines experienced changes in costs of operation (due 

to fluctuations in hedged fuel prices), changes in travel demand (due to changes in the economy), and changes in infrastructure capacity (due to the capacity 

limits at EWR, JFK, and LGA). This analysis captures the impact of the implementation of capacity limits at airports, as well as the effect of increased costs of 

operation (i.e. hedged fuel prices). The increases in costs of operation serve as a proxy for increased costs per flight that might occur if auctions or congestion 

pricing are imposed. 

15. SUBJECT TERMS

Air transportation; Airports; Airline operations; Metroplex operations; Airline schedules

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