October 2010 

NASA/CR–2010-216861 

Optimizing Air Transportation Service to 

Metroplex Airports 

Part II: Analysis Using the Airline Schedule Optimization 

Model (ASOM) 

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–2010-216861 

Optimizing Air Transportation Service to 

Metroplex Airports 

Part II: Analysis Using the Airline Schedule Optimization 

Model (ASOM) 

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

 Begin your report below this instruction. Press F1 or Help for more. 

 

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 which is projected to grow at average annual growth of +4% (BTS, 2010). The failure to 

increase capacity at the same rate as the growth in demand will result in unreliable service and systemic 

delays (BTS, 2010). 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:

1. Increasing 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 impact of these initiatives on the most capacitated airports is limited due to the lack of additional 

real-estate to accommodate needed infrastructure (e.g. additional runways).

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. 

Plans are also underway to improve landing and takeoff technologies that will allow ―all weather‖ 

operations. 

2. Modernization of U.S. Air Traffic Control (ATC) infrastructure. A $37B modernization 

program, known as 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 super-dense 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.

3. Increase Passenger Capacity per Flight. 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, voluntary agreements 

between airlines and the FAA, or political allocations) to market-based 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 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: JFK - 81 per hour (1/18/2008), EWR - 81 per hour (5/21/2008), LGA -

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 and service 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:

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

markets)

2. Economic access to air transportation service (i.e. increased operational costs could lead to 

increased airfares, that might be too costly for certain segments of the population.

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

operation)

4. Air Transportation Efficiency as measured by the seats per runway/airspace slot (also 

known as aircraft size or aircraft gauge), by the total arrival and departure seats, and by the 

total available seat miles scheduled in and out of the target airport. 

Objective & Method

This report describes the results of an analysis of airline strategic decision-making that affects: (1) 

geographic access, (2) economic access, and (3) airline finances. This report extends the analysis of these 

factors using historic data (provided in Part 1 of the report).

The Airline Schedule Optimization Model (ASOM) was used to evaluate how exogenous factors 

(passenger demand, airline operating costs, and airport capacity limits) affect geographic access (marketsserved, scheduled flights, aircraft size), economic access (airfares), airline finances (profit), and air 

transportation efficiency (aircraft size).

This analysis captures the impact of the implementation of capacity limits at the 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 model also incorporates demand elasticity curves based on historical data that provide 

information about how passenger demand is affected by airfare changes.

Results

Two analyses were conducted. The first experiment examined airline strategic decision-making in 

response to the introduction of airport capacity limits for three fixed passenger demand and operating 

costs scenarios (i.e. Gross Domestic Product and Hedged Fuel Prices). The design of the experiment 

included 45 possible treatments (five airports times three capacity levels times three demand and 

operating cost changes).

The second experiment examined airline strategic decision-making in response to the introduction of 

airport capacity limits for varying operating cost conditions (i.e. hedged fuel prices) for fixed passenger 
3

demand (based on a given economic situation as described by Gross Domestic Product). The design of 

the experiment, summarized in the table below, included 18 possible treatments (one airport times three

capacity levels times three hedged fuel prices times two values for Gross Domestic Product).

Statistically significant trends with a confidence interval of 95% were as follows:

Note: The ASOM model is based on the assumption of a benevolent monopolist. Thus, this is the 

best that one can expect in terms of up-gauging. With competition among airlines, it is likely that the 

demand will be shared among airlines and up-gauging will be somewhat reduced. 

Geographic Access. The number of markets with direct Metroplex airport service is determined by 

passenger demand, operating costs, and airport capacity limits (R2=83%). The number of flights per day 

to a market is also determined by passenger demand, operating costs, and airport capacity limits 

(R2=88%). 

1. The growth/decay in demand for air transportation is often attributed to economic conditions. 

A proxy for overall National economic health, changes in Gross Domestic Product (GDP)), is 

used to examine changes in impact to the number of markets served and scheduled flights per 

day. A linear regression on the results of the ASOM showed that for every incremental 

increase in the GDP index, there is an increase of 1.8 in the number of markets with direct 

service. Similarly, a linear regression showed that for every incremental increase in the GDP 

index, there is an increase of 17.3 in the number of scheduled flights per day across all 

markets. 

2. The fluctuations in hedged fuel prices (which impacts airline operational costs) also impacts 

markets served and flights per day. A linear regression showed that for every $1 increase in 

hedged fuel prices, there is a decrease of 1.9 in the number of markets with direct service and 

a decrease of 17.8 in the total number of scheduled flights per day across all markets 

3. The introduction of Capacity Limits (as measured by limits on number of operations per 

hour) is a determinant of the number of markets served and scheduled flights per day. A 

linear regression showed that for every additional operation per hour allowed, there is an 

increase of 0.1 in the number of markets with direct service and an increase of 2.4 in the 

number of scheduled flights per day across all markets.

Economic Accessibility. Passenger accessibility to air transportation is determined by airfares. 

Changes in the economy affect demand for air transportation. Changes in fuel prices reflected in changes 

in airfares also affect the demand for transportation. In general, the model results indicate that an 

economic downturn has an order of magnitude greater effect on airline airfares than does the change in 

airlines’ operating costs. 

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

Specifically, 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 show 

passenger demand to be relatively elastic.

2. The change in airfare was driven by changes in hedged fuel prices (which impacts airline 

operational costs) (R2=83.1%). At the five airports studied (LGA, JFK, EWR, PHL, and 

SFO), every $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%. This 

result is valid within the hedged fuel price range of $1.50 and $4 per gallon.
4

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 airfare, and airline 

operating costs. 

Changes in airline profits are driven by changes in economic conditions (as measured in this study by 

GDP), operational costs (as measured in this study by hedged fuel prices), aircraft size, and flights per day 

(R2=94.9%). For example for passenger demand and operations at EWR, daily airline profits were 

increased $456K for every $1 increase in hedged fuel prices, increased $423K for every incremental 

increase in the GDP index, reduced $8K for every seat increase in aircraft gauge, and increased $6K for 

every additional flight per day. This result is valid within the hedged fuel price range of $1.50 and $4 per 

gallon.

Note that airline profits are affected by the airline’s ability to: (1) increase airfares as fast as hedged 

fuel prices increase, (2) shed less profitable markets in order to improve profitability, and (3) right-size 

aircraft to maintain profitability as demand changes. 

Air Transportation Efficiency: Air transportation efficiency is measured by the throughput of 

passengers through the network based on aircraft size (i.e. number of seats) per runway/airspace. A linear 

regression showed that for every incremental increase in the GDP index, there is a 12.5 seat increase in 

the average aircraft size flown. Also, for every $1 increase in hedged fuel prices, there is a 6.4 seat 

increase in the average aircraft size flown. 

Note: These results are not consistent with the observed historical data. The historical data did not 

show the up-gauging results from the ASOM model. There are several explanations for this, including: 

airline competition, fleet inflexibility, and airline pilot union scope clauses.

Implications of Results

The results of the analysis using the ASOM have the following implications:

1. The Air Transportation System is robust: Geographic access, economic access, airline 

profitability, and air transportation efficiency exhibit proportional and stable relationships: 

For a fixed passenger demand and hedged fuel price, as capacity limits are imposed (e.g. -

10%), markets are reduced (-5%) and scheduled flights decrease (-8%), profit decreases (-

4%). In this scenario average aircraft size increases (1%). 

For a fixed passenger demand and fixed capacity limits at the airports, as hedged fuel prices 

increase (e.g. +43%), markets are reduced (-3%), scheduled flights per day decrease (-6%), 

profit increases (+4%). In this scenario average aircraft size increases (+10%). This result is 

valid within the hedged fuel price range of $1.50 and $4 per gallon.

2. Airport Capacity limits have no negative effects: when regulatory authorities choose to impose 

capacity limits on runway access in order to reduce congestion, little impact is seen on geographic access, 

economic access, and airline profits. Aircraft size does not change, but congestion and delays are 

significantly improved. Note: even in a model that does not take into consideration an airline’s likelihood 

to continue access to markets during economic downturns for strategic (competitive) reasons, little to no 

change in markets served is observed when capacity restrictions are imposed.
5

3. Hedged fuel prices and economic health drive air transportation performance: Regulatory 

authority to manipulate the market through the introduction of airport capacity (and airport capacity 

limits) is only one of three factors affecting geographic access, market access, and airline financial 

stability. When airline operating costs increase significantly, or when the economic health of the nation 

changes dramatically, significant effects on airline behavior are observed.

For example, for a fixed passenger demand and fixed capacity limits at the airports, as hedged fuel 

prices increase (e.g. +43%), markets are reduced (-3%), scheduled flights per day decrease (-6%), profit 

increases (+4%). In this scenario average aircraft size increases (+10%). This result is valid within the 

hedged fuel price range of $1.50 and $4 per gallon.

3. In the presence of increased passenger demand (and in the absence of cut-throat airline 

competition) airlines will increase aircraft size. However, the ability to up-gauge in the real-world is 

restricted by additional factors not modeled: (1) lack of available aircraft at the 90-120 seat size (2) the 

airline’s preference toward frequency (in order to maintain market share and provide passengers with 

more time-specific options), and (3) labor cost structure for pilots, which is significantly higher for larger 

aircraft than for regional jets. 
6

Table of Contents

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

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

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

Objective & Method .................................................................................................................................2

Results.......................................................................................................................................................2

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

List of Figures...............................................................................................................................................9

List of Tables..............................................................................................................................................10

1 Introduction.........................................................................................................................................10

Increasing Infrastructure Capacity..........................................................................................................12

Increasing Effective-Capacity and Productivity .....................................................................................13

Increasing Runway/Airspace Efficiency by Increasing Seat Capacity per slot ......................................13

Problem Statement..................................................................................................................................14

Objective of this Research ......................................................................................................................16

Research Approach .................................................................................................................................16

Benefits of This Research .......................................................................................................................18

2 Functional Model of Airline Strategic Decision-making....................................................................20

3 Method ................................................................................................................................................23

3.1 Airport Schedule Optimization Model (ASOM).........................................................................23

3.1.1 ASOM Overview ................................................................................................................23

3.1.2 ASOM Scope and Assumptions..........................................................................................24

3.2 Data Sources ...............................................................................................................................26

3.2.1 ASOM Preprocessing..........................................................................................................28

3.2.2 ASOM Control Adjustments in the preprocessing..............................................................32

3.2.2.1 Airline Profits......................................................................................................................33

3.2.2.2 ASOM Fuel Price Adjustments...........................................................................................34

3.3 ASOM Optimization...................................................................................................................33

3.3.1 ASOM Master Problem ......................................................................................................34

3.3.2 ASOM Sub Problems..........................................................................................................36

3.3.3 ASOM Post-Processing.......................................................................................................36

3.3.4 ASOM Limitations and Consistency Check .......................................................................38
7

3.5 Scope and Design of Experiment................................................................................................39

3.5.1 Experiment #1.....................................................................................................................40

3.5.2 Experiment #2.....................................................................................................................40

Limitation of Design of Experiments......................................................................................................41

4 Results.................................................................................................................................................42

4.1 Experiment #1 Results: ASOM Capacity Variation Results 3QTR (2007-2009) (LGA, EWR, 

JFK, SFO, PHL)......................................................................................................................................42

4.1.1 Geographic Access..............................................................................................................42

4.1.1.1 Profitable Markets...................................................................................................................42

4.1.1.2 Scheduled Flights per Day ..................................................................................................44

4.1.2 Airline Profits......................................................................................................................46

4.1.3 Air Transportation Efficiency. ...................................................................................................47

4.1.3.1 Aircraft Size ........................................................................................................................47

4.2 Experiment #2: ASOM Capacity and Fuel Price Variation Results ...........................................50

4.2.1 Geographic Access..............................................................................................................50

4.2.1.1 Profitable Markets...................................................................................................................51

4.2.1.2 Scheduled Flights per Day ..................................................................................................52

4.2.2 Airline Profits......................................................................................................................53

4.2.3 Air Transportation Efficiency. ...................................................................................................54

4.2.3.1 Aircraft Size ........................................................................................................................54

4.2.3.2 Total arrival and departure seats...........................................................................................56

4.2.3.3 Total available seat miles scheduled.....................................................................................58

5 Conclusions.........................................................................................................................................59

5.1 Recommendations.......................................................................................................................62

5.2 ASOM Experiment 1 & 2 Summary...........................................................................................62

5.2.1 Geographic Access.....................................................................................................................63

5.2.2 Economic Access.......................................................................................................................64

5.2.3 Airline Profitability....................................................................................................................64

5.2.4 Air Transportation Efficiency. ...................................................................................................65

5.3 Recommendations.......................................................................................................................66

5.4 Future Work................................................................................................................................66

5.4.1 Airline Schedule Optimization Model (ASOM) to Complete the ―Design of Experiment‖......66

5.4.2 Absence of Economies-of-Scale through Up-gauging (or ―Cash for Clunkers‖). .....................66
8

Appendix A.................................................................................................................................................71

Validation of ASOM Model .......................................................................................................................71

A.1 ASOM Consistency with Historic Results for Geographic Access............................................71

A.1.1 ASOM Consistency with Historic Results for Markets Served ..........................................71

A.1.2 ASOM Consistency with Historic Results for Scheduled Flights per day..........................72

A.1.3 ASOM Consistency with Historic Results for Aircraft Gauge ...........................................74

A.1.4 ASOM Comparison of Opposite Markets...........................................................................75

A.1.5 Historic versus ASOM Functional Relationships...............................................................75
9

List of Figures

Figure 1 Airline behavior in the presence of demand, regulatory, and technological changes..................20

Figure 2 Airline decision-making: Business Planning, Scheduling, and Operations.................................21

Figure 3 Airport Schedule Optimization Model (ASOM).........................................................................24

Figure 4 ASOM inputs are preprocessed from 5 primary data sources.....................................................27

Figure 5 ASOM Inputs are calculated through SQL and Matlab scripts (in Yellow). Several ASOM 

controls are adjusted in the preprocessing of the inputs (in Green)............................................................28

Figure 6 The ASOM model is run through on several software packages in order to preprocess, optimize 

and evaluate results.....................................................................................................................................28

Figure 7 ASOM Airline Profit Model........................................................................................................32

Figure 8 Airfare versus hedged Fuel Price Relationship (2005-2009).......................................................33

Figure 9 ASOM Master Problem...............................................................................................................34

Figure 10 ASOM sub-problem ..................................................................................................................35

Figure 11 ASOM Log File.........................................................................................................................37

Figure 12 ASOM schedule file ..................................................................................................................37

Figure 13 ASOM Flight Schedules slightly reduce as Airport Capacity Limits are reduced ...................45

Figure 14 ASOM average Aircraft Gauge insensitive to Capacity Limits ...............................................49

Figure 15 Poor aircraft performance ($/seat-hr) in 100- and 200-seat aircraft classes...............................50

Figure 16 ASOM increases 75 and 275 seat aircraft in EWR’s schedule as fuel prices increase.............55

Figure 17 Poor Aircraft Performance ($/Seat-Hr) in 100 & 200 Seat Classes ..........................................56

Figure 18 Summary of multipliers between the exogenous, economic access, geographic access, and 

airline profitability factors ..........................................................................................................................60

Figure 19 ASOM Distribution of daily flights matches historical distribution.........................................72

Figure 20 Graphical illustration of ASOM and historic analysis relationships..........................................75
10

List of Tables

Table 1 Research Questions for each of the Stakeholders.........................................................................16

Table 2 ―Design of Experiment‖ for ASOM experiment #1. This experiment represents 45 of 45 possible 

treatments....................................................................................................................................................17

Table 3 ―Design of Experiment‖ for Hedged Fuel Price and Capacity Limit experiment. Experiment #2 

represents 18 of 18 possible treatments. .....................................................................................................18

Table 4 Summary of seat-capacity grouping of aircraft historically used for domestic operations...........25

Table 5 The ASOM preprocessing fills data holes from the lack of fidelity in available data sources (in 

Red).............................................................................................................................................................29

Table 6 ―Design of Experiment‖ for ASOM experiment #1. This experiment represents 45 of 45 possible 

treatments....................................................................................................................................................40

Table 7 ―Design of Experiment‖ for hedged fuel price and capacity limit experiment. Experiment #2 

represents 18 of 18 possible treatments. .....................................................................................................41

Table 8 Sensitivity of Direct service Markets to Capacity Limits. ............................................................43

Table 9 Correlation of significant factors that influence airline decisions on markets served. .................43

Table 10 Sensitivity of Scheduled flights per day to Capacity Limits.......................................................44

Table 11 Table shows statistically significant correlation (95% confidence) between scheduled flights per 

day and hedged fuel prices..........................................................................................................................45

Table 12 Sensitivity of Airline Profits to Capacity Limits. .......................................................................46

Table 13 Table shows statistically significant correlation (95% confidence) between airline profits and 

scheduled flights per day and aircraft size. .................................................................................................47

Table 14 Sensitivity of average Aircraft Gauge to Capacity Limits..........................................................48

Table 15 Table shows no statistically significant correlation (95% confidence) between average Aircraft 

Gauge and exogenous factors. ....................................................................................................................49

Table 16 Sensitivity of EWR Direct service Markets to Hedged Fuel Price and Capacity Limits............51

Table 17 Table shows statistically significant correlation (95% confidence) between direct service 

markets and hedged fuel prices and Gross Domestic Product. ...................................................................51

Table 18 Sensitivity of EWR scheduled flights per day to Hedged Fuel Price and Capacity Limits. .......52

Table 19 Table shows statistically significant correlation (95% confidence) between scheduled flights per 

day and Hedged Fuel Prices and airport capacity limits. ............................................................................53

Table 20 Sensitivity of EWR Airline Profit to Hedged Fuel Price and Capacity Limits...........................53

Table 21 Table shows statistically significant correlation (95% confidence) between airline profit and 

hedged fuel prices, GDP, and aircraft gauge. .............................................................................................54

Table 22 Sensitivity of EWR Aircraft Gauge to Hedged Fuel Price and Capacity Limits. .......................54

Table 23 Table shows statistically significant correlation (95% confidence) between aircraft gauge and 

hedged fuel prices and GDP. ......................................................................................................................55

Table 24 Sensitivity of EWR total arrival and departure seats to Hedged Fuel Price and Capacity Limits.

....................................................................................................................................................................57

Table 25 Table shows statistically significant correlation (95% confidence) between total arrival and 

departure seats to gross domestic product, number of direct service markets, and average aircraft gauge.57

Table 26 Sensitivity of EWR available seat miles to Hedged Fuel Price and Capacity Limits.................58
11

Table 27 Table shows statistically significant correlation (95% confidence) between available seat miles 

to GDP, number of direct service markets, scheduled flights per day, average aircraft gauge, and total 

arrival and departure seats. .........................................................................................................................58

Table 28 Impacts of Airport Capacity Limits, Hedged Fuel Prices, and Gross Domestic Product on 

Geographic Access, Economic Access, Airline Finances, and Air Transportation Efficiency...................61

Table 29 Geographic Access Functional Models Summary ......................................................................62

Table 30 Air Transportation Efficiency Functional Models Summary......................................................63

Table 31 Airline Profitability Functional Models Summary .....................................................................63

Table 32 ASOM results for consistency check - geographic access..........................................................69

Table 33 ASOM results for consistency check – markets served..............................................................70

Table 34 ASOM annual trends for profitable markets...............................................................................70

Table 35 87% of ASOM profitable markets served within 15% of historical data ...................................71

Table 36 50% of ASOM trends for Scheduled Flights per day are within 10% of Historic trends...........71

Table 37 ASOM consistency with historic results for aircraft gauge ........................................................72

Table 38 ASOM annual trends for aircraft gauge......................................................................................73

Table 39 ASOM Comparison of opposite markets flights per day............................................................73

Table 40 The ASOM functional relationship with historical analysis.......................................................74
12

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. 

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

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

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

initiatives: 

(1) Increasing Infrastructure Capacity, 

(2) Increasing Effective-Capacity and Productivity, and 

(3) Increasing Runway/Airspace Efficiency by Increasing Seat Capacity per slot.

Increasing Infrastructure 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) (Federal Aviation 

Administration, 2000; p. 5). Projects with safety and security purposes receive higher ratings than those 

focused on capacity (Dillingham, 2000; p. 32).
13

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 the nation’s airports (Federal Aviation 

Administration, 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 Runway/Airspace Efficiency by Increasing Seat Capacity per slot

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: (i) regulates the 

number of runway slots and gates available to match the available supply, (ii) allocates the available slots 

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

congestion pricing, auctions). The allocation of slots must be accomplished in a way that ensures the most 

efficient economic and socio-political use of the slots, and avoids monopolies by guaranteeing 

competition. 
14

Problem Statement

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

transportation system through increased aircraft size.

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

in uncertainty about the impacts on the stakeholders and unintended consequences.

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

airports. The Departments of Transportation (DOT) proposed a rule to limit the number of arrivals and 

departures at the New York airports and to allocate some of the slots via an auction (Federal Registry 

volume 73, pages 60544-60601).

The rule was designed to establish 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. Specifically the rule:

extended the capacity limit on the operations at the three airports

assigned the majority of slots at the airports to existing operators,

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 remain within the aviation industry and be used to mitigate aviation 

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 had a ten year period at 

which time it would sunset. 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 introduced the notion of market-based allocation of slots by proposing that the FAA 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 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 official 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. 
15

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.).

Behind the official protests was an uncertainty on the impact of capacity limits and market-based 

allocation schemes would have on the economies of the regions and the finances of the associated 

enterprises. There were 4 main objections. 

1. Geographic access to air transportation service would be eliminated at some (i.e. smaller) 

markets

2. Economic access to air transportation service would be reduced to segments of the 

population. Increased operational costs would lead to increased airfares, to the point where 

segments of the population could no longer afford to fly

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

4. Failure to improve congestion and reliability for direct service as well as the impact on 

overall National Airspace System (NAS) operations.

In the end, the incumbent airlines reluctantly agreed to setting 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 

proposal to auction slots was withdrawn.
16

Objective of this Research

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

on the concept of better utilization of seat capacity per runway-slot. Specifically, this research answers the 

following questions for each stakeholder in Table 1.

Stakeholder Question

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? With or without additional operations 

costs (runway access costs), would these 

changes result in an elimination of service at 

smaller 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 when 

airlines incur additional operational costs of 

operation because of additional fees, costs of 

airport usage, or fuel prices?

Congress, Department of Transportation, 

Department of Commerce, Department of 

Justice as advocates for consumers and the 

U.S. economy, Airlines

What is the impact on congestion and 

reliability of air service 

Table 1 Research Questions for each of the Stakeholders

Research Approach

The Airline Schedule Optimization Model (ASOM) was developed to answer questions about how 

airline operating costs, economic conditions and an airlines’ access to an airport impact geographic 

access, economic access, airline finances and congestion and reliability of service.

Two experiments were conducted with the ASOM to determine the impact of airport capacity limits 

and the impact of changes in fuel prices. The first experiment examined airline strategic decision-making 

in response to the introduction of airport capacity limits for three fixed passenger demand and operating 

costs scenarios (i.e. a given period of time that was described economically by a Gross Domestic Product 

measure and a Hedged Fuel Price cost). The design of the experiment, summarized in the table below, 

included 45 possible treatments (5 airports X 3 capacity levels X 3 demand and operating cost changes). 

The second experiment examined the effect of fluctuations in fuel prices, passenger demand, and capacity 

limits. 

The first experiment is summarized in the factorial design in Table 2. The design of the experiment 

included 45 possible treatments (5 airports X 3 capacity levels X 3 demand and operating cost conditions. 
17

In this experiment airline behavior is evaluated for three airport capacity levels (high, normal, and low) 

for five congested airports (LGA, SFO, EWR, JFK, PHL) for three different economic scenarios:

Third quarter 2007 (3QTR07) with $2 fuel prices and 105 GDP index;

Third quarter 2008 (3QTR08) with $3.50 fuel prices and 105 GDP index;

Third quarter 2009 (3QTR09) with $2 fuel prices and 103 GDP index). 

The results provide insights on airline behavior in response to capacity changes for different 

economic scenarios.

Table 2 “Design of Experiment” for ASOM experiment #1. This experiment represents 45 of 45 

possible treatments.

The second experiment is summarized in the factorial design in Table 3. The design of the experiment 

included 24 possible treatments (1 airports X 3 capacity levels X 4 hedged fuel prices X 2 Gross 

Domestic Product). This experiment examines airline behavior for three airport capacity levels (high, 

normal, and low) and four fuel price levels ($2, $3.5, $5, $8) for one congested airport (EWR) for two 

different economic scenarios:

3QTR07 with $2 fuel prices and 105 GDP index

3QTR09 with $2 fuel prices and 103 GDP index 

3QTR 

2007

3QTR 

2008

3QTR 2009

Airports L

G

A

S

F

O

E

W

R

J

F

K

P

H

L

L

G

A

S

F

O

E

W

R

J

F

K

P

H

L

L

G

A

S

F

O

E

W

R

J

F

K

P

H

L

Hedged 

Fuel Prices

($/Gallon)

$2.08 $3.53 $1.92

Gross

Domestic 

Product 

(GDP Quantity 

Index,

2005=100)

104.9 105.3 102.8

Capacity 

Limits

(Operations/ 

hour)

Low 64 64 72 72 72 64 64 72 72 72 64 64 72 72 72

Normal 72 72 80 80 80 72 72 80 80 80 72 72 80 80 80

High 80 80 96 96 96 80 80 96 96 96 80 80 96 96 96
18

The results provide insights on airline behavior in response to capacity changes and fuel price 

changes for different economic scenarios.

Table 3 “Design of Experiment” for Hedged Fuel Price and Capacity Limit experiment. 

Experiment #2 represents 18 of 18 possible treatments.

It should be noted that, although these experiments include analysis of hedged fuel prices of $5/gallon 

and $8/gallon, historically fuel prices have not exceeded $3.70/gallon (07/2008). The analysis showed 

that the airline decision-making response remained linear throughout the full range of fuel prices allowing 

the use of the data for derivation of the linear regression equations. These results are reported, but it 

should be recognized that above $4/gallon the economy and passenger demand would undergo significant 

changes that have not been experienced (or modeled). See Appendix B for a full discussion.

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 

fuel prices. The government policy-maker 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). And understand technology’s role in increasing NAS capacity through 

3QTR 

2007

3QTR 

2009

Airports EWR EWR

Hedged Fuel 

Prices

($/Gallon)

$2 $3.5 $5 $2 $3.5 $5

Gross

Domestic 

Product (GDP 

Quantity Index,

2005=100)

104.9 102.8

Capacity 

Limits

(Operations/ 

hour)

Low 72 72 72 72 72 72

Normal 80 80 80 80 80 80

High 96 96 96 96 96 96
19

providing airlines the economic incentives to up-gauge. This research will complement the NextGen 

research, since 49 of 131 NextGen OI’s involved upgrade 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. 
20

2 Functional Model of Airline Strategic Decision-making

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

technological changes. Figure 1 provides an abstracted summary of the system under investigation. 

Demographics, social-values, and the economic benefits of rapid, affordable transportation afforded by 

airlines determine the demand for airline operations. Regulatory changes incentivize and curtail 

operations. Technological changes increase productivity and the range and performance of the air 

transportation service.

 

Figure 1 Airline behavior in the presence of demand, regulatory, and technological changes

Airlines make the following choices:

Markets Served

Frequency of Service

Flight Schedules

Aircraft Size

Airfares

Congestion and Delays (indirectly)

Profit

These decisions are made in the presence of:

National Gross Domestic Product and fuel prices

Airport capacity limits

Aircraft Performance Capabilities and Operating Costs (Fuel, Labor, Maint, etc) 

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

Figure 2 shows a functional representation of airline business planning, scheduling, and operational 

functions and decisions. The diamonds in the figure represent strategic decisions. The arrows show the 

functions and decision impacted by strategic decisions. 

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 . Aircraft Performance Capabilities

4 . Revenue

5 . Airfare Elasticities

6 . Congestion/Delays

7 . Profit
21

Figure 2 Airline decision-making: Business Planning, Scheduling, and Operations

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

estimated demand. Increases in fuel prices affect airlines in their operational costs, thus the airlines 

absorb additional operational costs by trying to increase revenue through increased airfares. 

Since demand is related to airfare based upon market price elasticity curves by passenger type, the 

airlines typically cannot recover all additional costs through their fares. 

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

demand. Demand is also influenced by the national Gross Domestic Product. When the economy is good, 

potential travelers have more disposable income to buy airline tickets. 

After the airlines determine the price elasticity and potential demand for the markets, the potential 

revenue and costs can be examined to determine the profitable markets that can be served. 

With profitable markets identified, passenger-demand forecasts for these markets coupled with the 

associated operational costs will determine the frequency of service to the market as well as the aircraft 

gauge. The best aircraft available from inventory is selected to meet passenger demand based upon 

individual aircraft performance and fuel prices. 

The number of flights per day is determined by the estimated passenger demand and type of 

passenger demand. Business travelers require more frequent service and are willing to pay for that 

frequency, while leisure passengers will not pay for the more frequent service. 

Airline Business Planning 

Fuel

Prices

Airline 

Operational 

Costs

Air 

Fares

Est Pax

Demand

Airline 

Revenue

Profitable 

Markets

Aircraft 

Size

Flights 

per Day

Airline Scheduling

Airline Operations (not modeled)

Cancelled 

Flights

Delayed 

Flights

Traffic Flow

Management

Cancelled 

Flights

# Delayed 

Flights

Avg Flight 

Delay

Capacity

Limits

Gross 

Domestic 

Product

Current/ Potential

Markets

Aircraft

Performance

Markets 

Served Time of Day Arr/ Dep

Daily Ops

O n-time

Flights

Capacity

Limits
22

Next in this scheduling process, the times for these flights need to be scheduled based upon historic 

patterns in passenger demand, available operating slots (15 min period) at the airport and airport capacity 

limits. Once these decisions on aircraft type and number of flights per day by time of day are resolved, 

the schedule will reflect all of the markets that will be served. This schedule and its associated prices are 

announced three to four months prior to service and prices are then altered during the period to account 

for changes in demand and competition. 
23

3 Method

This section describes the Airport Schedule Optimization Model (ASOM) and the analytical methods 

used in the analysis of the model results.

3.1 Airport Schedule Optimization Model (ASOM)

The ASOM is a multi-commodity flow model that optimizes the schedule of aircraft serving an 

airport while satisfying market demand. The ASOM, based on an earlier model (Le & Hoffman, 2007) 

selects an optimal schedule for an airport by selecting profitable markets that can be serviced by the 

airport, and then allowing the profitable markets to compete for scheduled flights within the fixed 

capacity of the airport.

The ASOM generates a schedule for a single airline that provides service to all of the eligible markets 

to maximize the profit generated by scheduled operations while meeting the demand. The parameters of 

the model associated with profit are set such that the ―benevolent‖ single airline: (1) posts prices that are 

consistent with current competitive prices (i.e. it does not seek monopolistic rents) and (2) attempts to 

serve as many markets as it can, while remaining profitable. 

3.1.1 ASOM Overview

The ASOM is summarized in Figure 3. The inputs to the model are:

(1) Airport capacity limits for domestic operations. The number of scheduled international flights 

and cargo flight are subtracted from the target airport capacity to obtain the airport capacity 

for domestic operations.

(2) Feasible flight segments. The list of airports that have historically been served by the target 

airport along with scheduled flight times and aircraft types

(3) Flights per Day. Daily flights by market represented by sum of quarterly arrivals and 

departures by market. 

(4) International Passenger demand for each time of day. The total passengers traveling on 

domestic segments originate or terminate their domestic travel at one of the airports examined 

in order to connect to or from an international flight segment.

(5) Market Load Factors

(6) Aircraft costs. The aircraft is grouped into aircraft fleet classes to determine average segment 

flight times, average fuel burn rates and average costs per flight hour by aircraft class. 

(7) Market demand vs Revenue curves. Demand versus revenue positions or options for each 15 

min time of the day and for the morning (12am-12pm), afternoon (12pm-5pm) and evening 

(5pm-12am) time periods.

The output of the model is a profitable, feasible schedule defined by the following:

(1) Number of markets served

(2) Schedule for service to each market defined by Frequency and Time of Day

(3) Aircraft Size on each scheduled flight

(4) Airline profits for markets served

The determination of the profitable schedule within the capacity limits of the airport is a two part 

problem. The Sub-problem, determines, for each market, the most profitable schedule that meets market 

demand by selecting the frequency of service and aircraft size based on the value of adding/deleting 

flights in each time period. These schedules are submitted as inputs to the master problem, this process is 

called Column Generation.
24

The Master-problem then determines an optimal airport schedule by selecting market schedules that 

maximize profit for the benevolent airline within the operational capacity of the airport. The Dual Prices 

from this solution are submitted to the sub problems, i.e. they provide the information about the relative 

value of having flights added/removed from that time period. This provides the information back to the 

sub-problem that will determine if it pays to keep the flights at their current times or move them because 

there is ―cheaper‖ capacity at an alternative time. 

This process continues until the profit objective function does not improve or there are no new 

schedules generated.

Figure 3 Airport Schedule Optimization Model (ASOM)

3.1.2 ASOM Scope and Assumptions

The ASOM generates profitable schedules for non-stop daily domestic markets. The schedules allow 

only one flight per 15 min to or from each market. The domestic markets are not static but compete for 

the airport’s capacity.

Aircraft that have historically been used for domestic flights are grouped into fleet classes at 

increments of 25 seats. For example, aircraft between 88 seats and 112 seats would be in the 100 seat 

fleet class as shown in Table 4. As this table shows 92.14% of the passengers flown and 81.53% of the 

departures were performed on seven fleet classes for aircraft between 13 and 187 seats. Since the ASOM 

selects only aircraft for each market’s schedule based on aircraft historically flown to each market, the 

model will be for the most part choosing between these seven fleet classes to determine the most 

profitable aircraft class to meet the demand.

Market Demand

Vs. Revenue Curves

Flights/ Day

Markets 

Served

Aircraft Size Master

Problem

Sub

Problem Sub

ProblemSub

Problem Sub

Problem

Schedules

Reduced Costs

Maximize Profit

Revenue - Cost

Each Market All Markets

Airline Scheduling Behavior

Aircraft Costs

Demand

Load Factors

Feasible Flight Segments

Market Data

Frequency/ 

Time of Day

Airport Profit

Airport Capacity 

minus Intl/ Cargo flights Profitable Schedule
25

Fleet Class # of Aircraft types seat range % Departures % Passengers

0 42 <13 5.27% 0.24%

25 17 13 - 37 16.86% 3.15%

50 6 38 - 62 41.65% 15.80%

75 11 63 - 87 50.24% 22.35%

100 4 88 - 112 51.89% 24.07%

125 9 113 - 137 76.48% 56.88%

150 6 138 - 162 92.62% 83.12%

175 4 163 - 187 98.40% 95.30%

200 188 - 212 98.40% 95.30%

225 1 213 - 237 98.79% 96.36%

250 1 238 - 262 99.53% 98.50%

275 10 263 - 287 99.96% 99.87%

300 2 288 - 312 99.97% 99.91%

325 313 - 337 99.97% 99.91%

350 1 338 - 362 99.97% 99.91%

375 1 363 - 387 100.00% 100.00%

400 1 388 - 412 100.00% 100.00%

425 413 - 437 100.00% 100.00%

450 1 438 - 462 100.00% 100.00%

Table 4 Summary of seat-capacity grouping of aircraft historically used for domestic operations

Flight demand is not captured at the 15 min level of fidelity, market demand by time of day is 

assumed to be proportionally equal to supply (seats) by time of day. The aircraft selected in the schedule 

is assumed to have a load factor of 80% or better. The airline will need to obtain sufficient revenue to 

have the flight profitable at an 80% load factor, or the optimization will choose a smaller aircraft size or 

move the flight to an alternative time period. The model allows demand to spill into different time slots, 

but restricts demand from moving between morning, afternoon, or evening time periods. This is done by 

nesting demand into 3 periods (12am-12pm, 12pm-5pm and 5pm-12am) to ensure the sum of the 15 

minutes demand does not exceed the demand from the period. 

The ASOM assumes that the price/demand data provided in the BTS DB1B database is representative 

and is a good model of the price sensitivity that exists in that market. 

When such an airline is ―benevolent‖ it posts prices that are consistent with current competitive prices 

(i.e. it does not seek monopolistic rents) and attempts to serve as many markets as it can, while remaining 

profitable. The quarterly passenger demand versus airfare relationship is assumed consistent for all days 

and times of day.

The ASOM builds the network of potential flights based on arrivals from the cluster airport to the 

direct non-stop market airport. The ASOM then assumes a 45 minute turnaround time for all fleets before 

a departure is allowed back to the cluster airport.

Since the databases used do not include all airlines, the ASOM assumes that the data from reporting 

carriers is representative of behavior from all carriers.
26

3.2 Data Sources

This sub-section summarizes the databases that were used as sources for input data for the ASOM.

The Airline Origin and Destination Survey (DB1B) is a 10% sample of airline tickets from 

reporting carriers collected by the Office of Airline Information of the Bureau of Transportation Statistics. 

Data includes origin, destination and other itinerary details of passengers transported. This database is 

used to determine air traffic patterns, air carrier market shares and passenger flows. The Survey is 

collected primarily on the basis of a stratified, scientific sample of 10 percent of tickets in all domestic 

and in all international city-pair markets. The Survey data are taken from the selected flight coupons of 

the tickets sampled: single-coupon or double-coupon round trips where the ticket serial number ends in 

zero (0).

The T-100 Domestic Segment database contains domestic non-stop segment data reported by U.S. 

air carriers, including carrier, origin, destination, aircraft type and service class for transported passengers, 

freight and mail, available capacity, scheduled departures, departures performed, aircraft hours, and load 

factor when both origin and destination airports are located within the boundaries of the United States and 

its territories. 

The schedule P-52 database contains detailed quarterly aircraft operating expenses for large 

certificated U.S. air carriers. It includes information such as flying expenses (including payroll expenses 

and fuel costs), direct expenses for maintenance of flight equipment, equipment depreciation costs, and 

total operating expenses.

The Aviation System Performance Metrics (ASPM) is an integrated database of air traffic 

operations, airline schedules, operations and delays, weather information, runway information, and related 

statistics. The ASPM data comes from ARINC’s Out-Off-On-In (OOOI), Enhanced Traffic Management 

System (ETMS), US Department of Transportation’s Aviation Airline Service Quality Performance 

(ASQP) system, weather data, airport arrival and departure rates (15-interval), airport runway 

configurations, and flight cancellations.

The Aviation System Performance Metrics (ASPM) online access system provides detailed data on 

Instrument Flight Rules (IFR) flights to and from the ASPM airports (currently there are 77 ASPM 

airports); and all flights by the ASPM carriers (currently 22 carriers), including flights by those carriers to 

international and domestic non-ASPM airports. ASPM also includes airport weather, runway 

configuration, and arrival and departure rates. This combination of data provides a robust picture of air 

traffic activity for these airports and air carriers. Preliminary next-day ASPM data is used by the FAA for 

close monitoring of airport efficiency and other aspects of system performance, and finalized ASPM data 

is invaluable for retrospective trend analysis and targeted studies.

The ASPM database is compiled piece by piece beginning with basic flight plan and other message 

data for flights captured by the Enhanced Traffic Management System (ETMS), enhanced with next-day 

OOOI data for a key set of airlines, updated with published schedule data, and further updated and 

enhanced with BTS Aviation System Quality and Performance (ASQP) records which include OOOI 

data, final schedule data, and carrier-reported delay causes for the largest U.S. carriers.

ASPM flight records fall into two groupings: Efficiency counts and Metrics counts. ASPM Efficiency 

counts include the full set of ASPM records, including those that are missing one or more pieces of key 

data. In contrast, ASPM Metrics counts only include complete records and records for which accurate 

estimates are possible for the few pieces of missing data. Metrics counts exclude most General Aviation 

and Military flights, as well as records for international flights that only include data associated with the 
27

arrival or departure to/from the U.S. airport. Flight cancellations and diversions are excluded from both 

Efficiency and Metrics Counts. The purpose of these two groupings is to allow for a more complete traffic 

count (Efficiency Counts) while ensuring that only records with fully specified flight information are used 

for calculating delay and other metrics.

The Center for Air Transportation Systems Research (CATSR) Databases contains airport time 

zone data needed to develop feasible flight segments and aircraft seat configuration data required to 

assign aircraft to different aircraft classes.

The ASOM input data is preprocessed from several databases as shown in Figure 4. The inputs for 

the model are preprocessed (1 in figure) from the following databases; the ASPM Individual Daily Flight, 

the T100 monthly flight summaries, the DB1B quarterly passenger itineraries, the P52 quarterly airline 

costs and the CATSR airport and aircraft data databases.

Once preprocessed, the inputs are placed in an access database for the model to read, and then the 

ASOM is run (2).

The outputs are then post-processed (3) to examine trends in markets served, flights per day, average 

aircraft gauge, and airline profit expected with this schedule.

Figure 4 ASOM inputs are preprocessed from 5 primary data sources 

The BTS and ASPM data was preprocessed for New York, San Francisco and Philadelphia airports 

for the following timeframes:

Air Carrier Financial (Schedule P-52): 1QTR07-3QTR09

Origin and Destination Survey (DB1BMarket): 1QTR07-3QTR09

Air Carriers (T-100 Segment): Jan 07 – Dec 09

Aviation System Performance Metrics (ASPM): Jan 07 – Dec 09

3.2.1 ASOM Preprocessing

The inputs for the ASOM are; (1) International and Domestic Market Demand, (2) Market flights per 

day, (3) Market load factors, (4) Airport Capacity minus International and Cargo flights, (5) Feasible 

flight segments, (6) Market Demand versus Revenue Curves, and (7) segment costs by aircraft class. 

(1) Preprocessing

(2) Schedule Optimization

(3) Post processing

ASPM

T100

DB1B

P52

CATSR

Individual Daily Flights

Monthly Flight Summary

Quarterly Passenger Itins

Quarterly Airline Costs

Airport Data

Aircraft Data

Airport Schedule

• Markets

• 15 min Schedule

• Gauge

• Profit
28

Figure 5 shows how all of these inputs are preprocessed from the DB1B, T100, ASPM, CATSR and 

P52 databases. The ASOM control for adjusting airfares for fuel price increases and for airline additional 

fees is performed during preprocessing. The ASOM control for adjusting segment costs by aircraft class 

for fuel price changes and for landing fee adjustments is performed during preprocessing. These controls 

are highlighted in green on Figure 5.

Figure 5 ASOM Inputs are calculated through SQL and Matlab scripts (in Yellow). Several 

ASOM controls are adjusted in the preprocessing of the inputs (in Green).

Figure 6 The ASOM model is run through on several software packages in order to preprocess, 

optimize and evaluate results. 

SQL

ASPM

T100

DB1B

P52

Indiv Daily Flights

Monthly Demand/ Seats

Qtr Passenger Itins

QTR Airline Costs

Calculate Feasible flight 

segments with Piecewise 

Segments of Market Revenue

Versus Demand Curves 

Calculate Segment Costs

by Aircraft Class

Matlab

Matlab

10% sample of Market Demand versus Airfare

$/flight hour by Aircraft Class

Fuel Burn Rate by Aircraft Class

Fuel $/ gallon $/ landing by 

Aircraft Class

Market Flights/ Day

Market Demand

Market Load Factors

Airport Capacity 

minus Intl/ Cargo flights

15 min Seats/ supply

Market Intl Demand

Average Market Flight 

hours by Aircraft Class

CATSR

Airport / Aircraft Data

Airline 

Profit Adj Airport Year/ Quarter International/ Cargo Flights

DB1B

ASPM

Airport

Airport 

Coord

T100

ASPM AC 

Capacity

P52 MS Access

Preprocess in Access DB

Matlab

Creates PW Rev-Demand

Creates Costs for Flight Segments

Creates Feasible Flight Segments

MPL

Creates cplex files

CPLEX

Subproblems run on Server

JAVA

Master Problem runs on Server

MS excel

Log and Schedule file post-processed

(1) Pre-processing

(2) Schedule 

Optimization

(3) Post-processing
29

The ASOM model requires several systems and software to pre-process the data, to run the schedule 

optimization and finally to post-process the output from the model, as shown in Figure 6.

One of the complexities in combining the data into a format usable for the optimization model is the 

fact that the lack of fidelity in most of these data sources require assumptions to made in order to fill these 

data holes. The red blocks in Table 5 highlight the data holes that need to be filled.

Table 5 The ASOM preprocessing fills data holes from the lack of fidelity in available data sources 

(in Red).

Airport Capacity minus International and Cargo flights is preprocessed from the ASPM database 

by summing the quarterly international and cargo arrivals and departures for every 15 minutes of the day. 

This data is then normalized to represent daily international and cargo arrivals and departures for every 15 

minutes of the day. The master problem uses this data to adjust daily 15 min capacity available for 

domestic flights.

Many Cargo flights at an airport are typically flown at night and early morning hours and do not 

compete for the same flight hours as the passenger flights. For those cargo flights that do compete for 

runway capacity with passenger flights, the runway capacity is adjusted to allow all such cargo flights to 

remain as scheduled. Therefore, the profitable domestic markets compete for the available capacity that 

remains after international and cargo flights are removed.

The model described is adjusted to account for the effects of international flights and domestic 

passengers connecting to and from international flights on domestic schedules. The ASOM models only 

domestic markets because international markets are controlled by treaty, are very profitable, and their 

departure and arrival times cannot be changed. Thus, all flights to international markets are assumed to 

remain. To assure that there is sufficient runway capacity for these flights, the capacity for each time 

period is reduced by the number of international and cargo flights that will be departing and/or landing in 

that time period. QTR Month Daily 15 min Aircraft Market Source

Seats Avg Seats * # Flights = Seat Supply X CATSR

Flights X X X X X X ASPM

Demand X X Demand ~ Seats X X T100

Demand X Extrapolated to T100 demand X DB1B

Revenue X PW Revenue vs Demand ~ Seats X DB1B

Cost X Avg $/hr *Block hrs = 

segment $/market/aircraft

X P52

Block Hrs X X X X X X ASPM

Load Factors X X Avg LFs used X X T100

Intl Flights X X X X X X ASPM

Intl Demand X For Intl Flights X DB1B

= Data Hole
30

International Market Demand is preprocessed from the restricted DB1B database by summing the 

total passengers traveling on domestic segments originating or terminating their domestic travel at one of 

the airports examined in order to connect to or from an international flight segment. This quarterly 

demand is then normalized to a daily international demand for all of the domestic markets connecting 

passengers to international markets. The international arrival and departure banks are determined in 

ASPM to assure that passengers arrive at the airport in sufficient time to connect. Thus, for example, for 

international flights departing at 5pm, domestic passengers have to arrive at the departing airport by 4pm. 

Domestic Market Demand is preprocessed from the T100 database by summing the quarterly 

demand by market. This is also an input to Matlab to determine the market demand versus revenue 

curves.

Market flights per day are preprocessed from the ASPM database by summing the quarterly arrivals 

and departures by market. These quarterly flights are then normalized to represent daily flights by 

market. 

Market load factors are preprocessed from the T100 database by summing the quarterly demand and 

seats by market. This quarterly demand is then divided by the quarterly amount of seats flown to provide 

the ASOM load factors for markets flown.

The feasible flight segments are calculated in Matlab by providing airport markets from the T100 

database, airport time zone differentials and aircraft seat sizes from the CATSR database and average 

flight times by aircraft fleet class from the ASPM database. The aircraft are grouped into aircraft fleet 

classes to determine average segment flight times and feasible aircraft for different markets as an input to 

Matlab’s calculations. The aircraft is assumed to have a 45 min turn around. So based on this information 

all feasible market and reverse market flight segments are provided to the ASOM.

Matlab creates all possible departure and arrival pairs to the markets from the airport being modeled. 

This includes factoring the turn-around time for these aircraft before they can fly back to the original 

airport. The average block hours for the different markets for all different aircraft which have flown these 

markets are derived from the ASPM database. Given these average block hours by aircraft class for all 

the markets, Matlab can identify all potential departures and arrivals that can operate at the airport 

modeled between 6am and 10pm. 

These feasible flight segments are determined by using Microsoft Access for the non-stop segments 

of the airport or metroplex being analyzed. This enables the scheduling model to determine optimal 

schedules from feasible roundtrip flights, to ensure the balance of flow of the different aircraft types and 

produce a typical daily schedule.

Market Demand versus Revenue Curves are calculated in Matlab by providing airport quarterly 

market demand from the T100 database, Market demand by segment fare from the DB1B database, and 

seats flown by time of day at 15 minute intervals from the ASPM database. Matlab provides the ASOM 

piecewise segments from market demand versus revenue curves by time of day at 15 minute intervals and 

for morning (12am-12pm), afternoon (12pm-5pm) and evening (5pm-12am). This enables the ASOM to 

nest demand into these three periods (12am-12pm, 12pm-5pm and 5pm-12am) to ensure the sum of the 

15 minutes demand does not exceed the demand for the entire period. 

Before this data is provided to Matlab DB1B airfares are adjusted to eliminate discount fares and to 

reflect extra airline revenue from bags and change fees, to reflect itinerary taxes and charges which don’t 

go to the airlines, and to provide revenue offsets for fuel price changes.
31

In order to develop these market demand versus revenue curves per flight segment the quarterly 

demand from the DB1B and the monthly demand from the T100 have to be allocated for an average daily 

schedule for each 15 min time of the day. In order to do this passenger demand is assumed to be 

distributed by time of day proportional to seats flown. The 10% sample of quarterly demand from the 

DB1B quarterly demand (3 months worth of data) is extrapolated to the T100 level of demand, then the 

demand is divided by the number of days in the quarter to get the average daily demand and is multiplied 

by the percentage of quarterly seats flown in each 15 min period to get the average passenger demand for 

each 15 min period of the day for each market.

From the DB1B data cumulative demand versus airfare curves are approximated for each market. 

Finally piecewise linear segments are created to represent different demand versus revenue positions or 

options for the optimization model to choose from for each 15 min time of the day and for the morning 

(12am-12pm), afternoon (12pm-5pm) and evening (5pm-12am) time periods.

The segment costs by aircraft class are calculated in Matlab by providing airport quarterly cost data 

from the P52 database, aircraft seat sizes from the CATSR database and segment flight times by aircraft 

type from the ASPM database. The aircraft is grouped into aircraft fleet classes to determine average 

segment flight times, average fuel burn rates and average costs per flight hour by aircraft class. 

In order to create the feasible flight arc with associated airline costs to fly these arcs, cost factors are 

developed for aircraft by 25 seat classes (thus aggregating over one hundred different aircraft types into 

less than 15 general classes of aircraft). All of the flight legs previously determined are costed out for any 

aircraft class which has serviced the market in the past 5 years. This is done by multiplying the block 

hours by the cost per hour for direct (minus fuel) costs from the P52 database to operate the specific class 

of aircraft. Fuel costs are determined by multiplying the selected fuel price times the aircraft classes fuel 

burn per hour and then multiplying by the block hours for each market aircraft combination.

3.2.2 ASOM Control Adjustments in the preprocessing

Changes in the historical quarter or the airport being examined require all preprocessing to be redone. 

All of the sub problem optimization software files will need to be updated to reflect these changes; these 

files then need to be moved to the server so the ASOM can be rerun to give new results based on the 

changed parameters.

Changes in airfare and revenue from fuel price changes, extra baggage or cancellation fees and from 

taxes and charges the airlines pay through the airfares are made in the DB1B data before being processed 

in Matlab. These kinds of changes require the market demand versus revenue curves to be recalculated in 

Matlab. All of the sub problem files will need to be updated to reflect these changes; these files then need 

to be moved to the server so the ASOM can be rerun to give new results based on the changed 

parameters.

Changes in aircraft operational costs from fuel price changes or landing fees are made in the Matlab 

code. This requires the Matlab network costing function to be rerun to create a new flight segment costs. 

All of the sub problem files will need to be updated to reflect these changes; these files then need to be 

moved to the server so the ASOM can be rerun to give new results based on the changed parameters.

Changes in international and cargo capacity used at the airport are a direct input into the ASOM file. 

The file would then need to be updated on the server and the ASOM can be rerun to give new results 

based on the changed parameters.
32

Lastly changes in airport capacity are done in the master problem’s ―settings‖ file on the server; the 

ASOM can then be rerun to give new results based on the changed parameters.

So changes in some parameters, like airfare, require almost as much work as developing a whole new 

scenario for the model.

3.2.2.1 Airline Profits - The model for airline profits includes the additional fees that have been 

introduced for domestic air travel and accounts for the landing fees the airlines have to pay. Before this 

data is provided to Matlab, DB1B airfares are adjusted to eliminate discount fares and to reflect extra 

airline revenue obtained from baggage and change fees. The total revenue is reduced by removing the 

itinerary taxes and charges which are not part of the airline’s revenue. The ticket prices are increased 

based on a historical analysis so that as fuel prices increase, revenues will increase in a relative way (see 

Figure 7).

The model includes a per-passenger average increase in revenue based on the current fees charged 

for baggage, re-scheduling, and in-flight services. The model includes a per-passenger average increase 

for revenue received from belly cargo (freight and mail). Finally, the ticket/ segment tax and the 

passenger facility charges (PFCs) were removed from the revenue to more accurately reflect the true 

revenue realized by the airlines. 

Similarly the operational costs are determined based on the airline costs associated with aircraft 

operations. These costs include maintenance and fuel-burn costs by aircraft type and distance flown, 

crew costs (also segregated by aircraft type). Landing fees are calculated by aircraft class and added to the 

cost of operations.

Figure 7 ASOM Airline Profit Model. 

Revenue per Passenger

• Airfare

– Ticket (-7.5%)1

– Segment Tax (-$3.60) 1

– PFC (-$3.63) 2

– 911 (-$2.50) 1

• Freight/ Mail (+2.4%) 3

• Fees (+$10.17) 4

Direct Cost (per segment)

• Fuel

• Labor

• Maintenance

• Other

• Landing Fees (BTS)

5

–Per Landing (+$306.69)

–Per Klbs(+$2.85)

2008** 2009

Ancillary Fees* $ 7.50 $ 10.17

Bags $ 2.09 $ 3.54

Cancel $ 2.20 $ 3.08

* Bags, Cancel/Change, Pets, Freq Flyer

** Based on 3rd & 4th Quarter

New Airfare = .949(Airfare) + $.44

BTS Reports 4

1- http://www.airlines.org/Economics/Taxes/Pages/GovTaxesandFeesonAirlineTravel.aspx

2- http://www.faa.gov/airports/pfc/monthly_reports/

3-Aviation Daily Airline Revenue (4QTR09)

4-BTS Airline Revenue Reports (2008-2009)

5-BTS Airline Cost Reports (2007) and BTS T100 (2007)

4QTR09 Revenue

Passenger $18,513 70.0%

Regional Affiliates $5,337 20.2%

Cargo $633 2.4%

Other Revenues $1,964 7.4%

3
33

3.2.2.2 ASOM Fuel Price Adjustments - To reflect how airfares change in response to fuel price 

changes, the airfare versus fuel price relationship was examined for the 20 quarters from the first quarter 

of calendar year 2005 (1QTR 2005) to 4 QTR 2009. A functional relationship was established and used 

in the ASOM model to reflect the airline’s response to changes in fuel price. This adjustment in airfares 

ensures the changes in fuel prices were accounted for in airline revenue as well as airline costs, since 

airlines change airfares to account for fluctuations in operational costs.

As shown in Figure 8, the relationship between hedged fuel prices and airfares exhibits two segments. 

The breakpoint between the segments is estimated to occur at $2.50 per gallon. Two linear relationships 

were calculated for changes in fuel price. The first relationship was calculated for changes in fuel price 

between $1 and $3.50 per gallon, which adjusts airfare $16.42 for every $1 change in fuel price. A 

second relationship was calculated for changes in fuel price above $3.50 per gallon, which adjusts airfare 

$8.82 for every $1 change in fuel price.

Figure 8 Airfare versus hedged Fuel Price Relationship (2005-2009)

3.3 ASOM Optimization

The Airline Optimization Scheduling Model is divided into two parts, a master problem and a 

collection of sub problems for each market pair (as shown previously in Figure 6). The master problem is 

a set packing problem that receives as input multiple alternative schedules for each market pair and 

chooses the overall profit maximizing schedule for the airline as a whole. The sub problems, one per 

market pair, determine an optimized schedule for that market given the dual prices that are provided to it 

from the master problem. In essence, the master problem indicates the value of adding/deleting one flight 

from a given time period. The sub problems use this information to determine if it there is an alternative 

schedule for flights to that market that would improve the overall profitability of the airline. The sub 

problems are multi-commodity flow problems that determine both when to fly and on what size aircraft. 

Each sub problem (one per market) generates the most profitable schedule given dual prices that are 

fed to it from the master problem. An output from a run of the sub problem optimization is either a new 

schedule that is guaranteed to increase the objective function of the master problem, or an indicator that 

no such schedule exists. 

y = 16.421x + 141.62

R2 = 0.8312

y = 8.8162x + 164.28

R2 = 0.9954

$-

$50.00 

$100.00 

$150.00 

$200.00 

$250.00 

$- $0.50 $1.00 $1.50 $2.00 $2.50 $3.00 $3.50 $4.00 Airfare

Fuel Price

Airfare versus Fuel Prices (2005-2009)

avg fare

avg fare2

Linear (avg fare)

Linear (avg fare2)

$1 in Fuel Price = 

$8.82 in Airfare

$1 in Fuel Price = 

$16.42 in Airfare
34

Given a set of new schedules obtained from the sub problems, the master problem takes these new 

schedules along with all other schedules previously generated and determines a new overall schedule that 

considers all markets simultaneously and optimizes the profitability of the benevolent operator. The 

process is iterative: the solution to the master problem provides new dual prices that are then fed to the 

sub problems and the sub problems provide alternative individual market schedules to the master 

problem. The process continues until either the objective function of the master problem is not improved 

or none of the sub problems produce new schedules. At this point, the algorithm has solved the linear 

programming relaxation of the master problem. However, if the solution obtained is not integer, then one 

must begin a branch-and-bound search tree in order to obtain an integer solution. It also outputs new dual 

prices based on that schedule, and once again return to the sub problems procedure with these new dual 

prices. 

This procedure continues until either the master problem doesn’t generate improved schedule from 

the previous one or there is no new schedule generated from any of the sub problem. When either of 

these conditions is met, the model then begins a branch-and-bound search tree approach to assure that the 

solution obtained to the Master Problem is integer. Thus, on each node of the branching tree, steps 1 and 

2 are repeated. This process continues until the entire branching tree is fathomed.

3.3.1 ASOM Master Problem

The master problem is presented in the Figure 9. The objective function maximizes total profit for the 

airport’s schedule. Notation is as follows:

Zj = Profit from schedule j

yj = Decision variable (0,1) on whether schedule j is selected

aij = Decision variable (0,1) on arrival for time i and schedule j

dij = Decision variable (0,1) on departure for time i and schedule j

Ij = average number of international or cargo arrivals (a) or departures (d) for time i

= Set of 15 minute time windows in the day

= Set of schedules submitted to master problem from sub problems

(m) = Set of schedules for market m

= Set of possible markets for schedule

Constraints 1 and 2 ensure that there are no more flights in a single 15-minute bin than the arrival and 

departure capacity available to handle these flights, respectively. Capacity is defined to be airport capacity 

minus the portion of that capacity used by other flights. Other flights refer to the capacity reserved for the 

international and freight flights, since the model optimizes only domestic air travel. Constraint 3 

guarantees that at most only one schedule per market pair is chosen.

Figure 9 ASOM Master Problem
35

3.3.2 ASOM Sub-Problem

The sub problem is presented in the Figure 10. The objective function maximizes total profit for the 

markets schedule from the airport. Notation is as follows:

Riq = Linear segment revenue for time i and segment q

iq = Decision variable (0,1) for time i and segment q

C

k

ij = Direct operating cost for one flight of fleet type k for flight arc (i,j)

x

k

ij = Decision variable (0,1) for one flight of fleet type k for flight arc (i,j)

l = average load factor

S

k

= Seats for aircraft of fleet type k

Aiq = Linear segment passenger demand for time i and segment q

Apr = Linear segment passenger demand for period r and segment p

Rpr = Linear segment revenue for period r and segment p

pr = Decision variable (0,1) for period r and segment p

= Set of 15 minute time windows in the day

= Set of periods in the day

= Set of aircraft fleet classes

Figure 10 ASOM sub-problem

The sub problem consists of an objective function and 13 constraints. 
36

Constraint 4 creates flow balance constraints that assure that, for each fleet type, there is an equal 

number of incoming and outgoing aircraft of that type. It also assures that an aircraft must arrive before it 

can depart and it must remain of the same type. 

Constraint 5 assures that there is sufficient supply for the demand, that the aircraft size can 

accommodate the demand, and that the aircraft does not fly less than 80% full. 

Constraint 6 requires that the demand per period be satisfied. 

Constraint 7 assures that the airline does not fly any flights that are unprofitable. This does the same 

for revenue. This is to ensure that even though there is no flight at some time window despite there being 

demand for it, the demand is still satisfied in the consecutive time window and passengers are not 

removed from that time period. 

Constraint set 8 requires the number of flights into a market is approximately equal to the number of 

flights out of a market (can differ by no more than one). 

Constraint 9 ensures that international passenger demand that is connecting from domestic markets is 

satisfied. Therefore, we will not eliminate a profitable market which connects domestic passengers to 

international flights.

Constraints 10 and 11 ensure that there is only one flight between the market pair in the same time 

window. 

Constraint 12 and 13 ensures that only one segment of the piecewise linear approximation for the 

revenue curve is chosen for each time window and period respectively. The piecewise linear 

approximation works here because the optimization model is maximizing profit and the revenue versus 

demand curve approximations are convex.

3.3.3 ASOM Post-Processing

There are two text files created by the model for each run. A sample log file, shown in Figure 11, 

illustrates the number of markets or sub problems initiated for the model. This file also identifies the 

number of these initial markets that are profitable. This file shows the number of iterations back and forth 

between Main and Sub-problems. Lastly, the expected profit from the final airport’s schedule is shown.
37

Figure 11 ASOM Log File

The second output file is the schedule file, Figure 12. This file shows all of the individual flights on the 

airport’s final schedule. For each flight or row of data the market served, the size of aircraft, the 

departure time, the arrival time and the frequency is shown.

Figure 12 ASOM schedule file

 

init_problems():91 markets. (initial markets)

add ABE_0_1 ,z = 14580.140000000003 cost = 13142.0, frequency = 2.0(2), throughput= 300.0, gap=0.0, reduced cost 

=14580.139000000003

..........

add TYS_0_64 ,z = 1186.4142857142815 cost = 14124.0, frequency = 2.0(2), throughput= 150.0, gap=0.0, reduced cost 

=1186.4132857142815

Generate columns – 64 Profitable Markets

add ABE_1_65 ,z = 14580.139999999994 cost = 13142.0, frequency = 2.0(2), throughput= 300.0, gap=0.0, reduced cost 

=14530.138999999994

..........

add TYS_1_128 ,z = 1186.4142857142838 cost = 14124.0, frequency = 2.0(2), throughput= 150.0, gap=0.0, reduced cost 

=1136.4132857142838

generate_columns() ended with 128columns in master_vars

generate_columns() ended with 64 columns generated at the current node.

Generate columns

add ABE_2_129 ,z = 14580.139999999996 cost = 13142.0, frequency = 2.0(2), throughput= 300.0, gap=0.0, reduced cost 

=2910.1389999999956

........

add STL_6_311 ,z = 8221.784615384611 cost = 99270.0, frequency = 10.0(10), throughput= 750.0, gap=0.0, reduced cost 

=428.78361538461104

add TPA_6_312 ,z = 312182.0929837098 cost = -5.3657078780133816E-12, frequency = 10.0(10), throughput= 2750.0, gap=0.0, reduced 

cost =312132.09198370983

generate_columns() ended with 312columns in master_vars

Total profit: 6743454.0

Market Size Dep Time Arr Time Freq

ABE 6 76 178 1.0

ABE 6 176 86 1.0

ACK 2 39 143 1.0

ACK 2 123 35 1.0

......

TYS 3 73 180 1.0

TYS 3 152 67 1.0

Market Fleet size i j freq local time seats year qtr airport cap f p dist ASM

ABE 6 76 178 1 76 150 2009 3 EWR 9 8 67 10050

ABE 6 176 86 1 86 150 2009 3 EWR 9 8 67 10050

ACK 2 39 143 1 39 50 2009 3 EWR 9 8 218 10900

ACK 2 123 35 1 35 50 2009 3 EWR 9 8 218 10900

ALB 3 26 130 1 26 75 2009 3 EWR 9 8 143 10725

Comment profit & fleet #2

airport EWR

year 2009

cap 12

fp 8

Values

Row Labels Sum of ASM Sum of seats Sum of freq Average Size

ATL 6518750 8750 42

Grand Total 99574150 94350 628 150.24
38

The aircraft sizes are grouped into classes in 25 seat intervals, so to determine the class you multiply 

the size by 25 seats, for example the first row identifies a size 6*25 = 150 seat aircraft. 

The departure and arrival times are shown in 15 min intervals starting with 1 or 12:15am. The arrival 

or departure time which is less than 96 (there are 96 15-minute intervals in a 24-hour day) determines 

whether this is an arrival or departure from our airport modeled. To determine the arrival or departure 

time at the other airport subtract 96 from its number. For example the first row shows a departure from 

our airport to ABE at 76 (1900 hrs or 7:00pm) and this flight arrives at ABE at 178-96 = 82 (2030hrs or 

8:30pm ABE local time). All times reported in the schedule are local times.

This schedule data from ASOM can be copied into a spreadsheet program to generate charts and 

tables and compare different scenarios based on different input parameters.

3.3.4 ASOM Limitations and Consistency Check

The ASOM models exhibits the following limitations:

1. The ASOM model considers airline scheduling decision strictly based on operational 

profitability rather than any decisions that are made for strategic positioning. It does not 

model airline competition, except as it uses pricing curves that are based on competitive 

behavior. 

2. The ASOM models chooses only profitable markets to serve and does not consider staying in 

unprofitable markets during down economic times in order to retain market share. Thus, the 

model is likely to move out of markets more quickly than might actually occur during 

recessionary periods.

3. The ASOM models a single airline serving these profitable markets, which finds the optimal 

schedule minus airline competition. For the analysis of EWR and SFO (hubs for large 

carriers), this assumption may be closer to actual behavior than at airports such as LGA 

where there is significant competition at the airport. 

4. The ASOM models balanced arrivals and departures and does not model the advantages of 

banking (i.e. having many incoming flights during one period that would allow passengers to 

connect to other flights during the next few periods). 

5. The model also tries to satisfy the demand based on historic data. Thus, it does not allow 

demand from the morning to spill into the afternoon.

6. Currently the ASOM models airline adjustments from increases in hedged fuel prices by 

uniformly increasing all airfares. This methodology does not account for possible changes in 

passenger demand due to economic outcomes from fuel price changes. Additionally this 

methodology does not account for any price passenger elasticity changes due to economic 

outcomes from fuel price changes.

In the presence of these limitations a tiered level of consistency checks on the ASOM can be 

performed to obtain a level of confidence in the results of the model.

Tier 1: The first consistency check compares ASOM results to the historic behavior of the airlines 

serving these five congested airports (LGA, EWR, JFK, SFO, PHL) for three different economic 

scenarios: 3QTR07 with $2 fuel prices and 105 GDP index; 3QTR08 with $3.5 fuel prices and 105 GDP 

index; and 3QTR09 with $2 fuel prices and 103 GDP index. This analysis determines the percent of 

observations within 15% of the historic data, since the ASOM schedule is expected to be more efficient.

Tier 2: The second consistency check compares annual changes in ASOM results to the annual 

changes in historic behavior of the airlines serving these five congested airports (LGA, EWR, JFK, SFO, 

PHL) for two annual changes (3QTR07 to 3QTR08, and 3QTR08 to 3QTR09). This analysis determines 

the percent of annual trends within 10% of the historic trends.
39

Tier 3: A third consistency check compares the opposite market schedules generated from the ASOM. 

For example the ASOM schedule for LGA will contain the LGA-PHL market and the ASOM schedule 

for PHL will contain the PHL-LGA market. These opposite markets will be compared for consistency.

Tier 4: Lastly the statistical relationships found in the historic data and ASOM analysis between the 

exogenous factors and the economic access, geographic access and airline profitability factors will be 

compared. Basically it’s important to see if the ASOM models similar trends and behaviors as seen in the 

historical data. But even comparing historical trends is difficult since the confounding factors from the 

historical trends cannot be removed.

The results of the 4 tier analysis are provided in Appendix A.

3.4 Method for Statistical Analysis of ASOM outputs

The ASOM outputs analyzed include: (1) the number of profitable markets served, (2) the daily 

domestic flights by market, (3) aircraft class and time of day, and (4) the airline profits for the airport 

examined. The controls or exogenous factors for the model are fuel prices, airport capacity limits and 

historical gross domestic product.

The analysis of statistically significant trends between the exogenous factors and the ASOM outputs 

required the following multi-step process:

(1) The ASOM output data was processed into the metrics of interest at the airport level.

(2) A correlation analysis of factors was done to identify the individual relationships between 

factors. The Pearson product moment correlation coefficient is 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.

(3) 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. 

(4) 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.

3.5 Scope and Design of Experiment

Two experiments were conducted with the ASOM to determine the impact of airport capacity limits 

and the impact of change in fuel prices. The ASOM first examined what happens when airport capacity 

limits are changed with fixed passenger demand (representing gross domestic product) and fuel prices. 

Next the ASOM examines what happens when fuel prices are changed with fixed passenger demand 

(representing gross domestic product) and capacity limits. 
40

3.5.1 Experiment #1

Experiment #1, summarized in the factorial design in Table 6, examined airline schedule behavior by 

tracking (a) aircraft size, (b) flights per day, and (c) daily markets served. It also tracks (d) profitability of 

the airlines servicing the airports.

Experiment #1 examines airline behavior for three airport capacity levels (high, normal, and low) for 

five congested airports (LGA, SFO, EWR, JFK, PHL) for three different economic scenarios: (1) 

3QTR07 with $2 fuel prices; (2) 3QTR08 with $3.50 fuel prices and 105 GDP index; and (3) 3QTR09 

with $2 fuel prices and 103 GDP index. The results provide insights on airline behavior in response to 

capacity changes for different economic scenarios.

Table 6 “Design of Experiment” for ASOM experiment #1. This experiment represents 45 of 45 

possible treatments.

3.5.2 Experiment #2

The second ASOM experiment, summarized in the factorial design in Table 7, examined airline 

schedule behavior by tracking (a) aircraft size, (b) flights per day, and (c) daily markets served. It also 

tracks (d) profitability of the airlines servicing the airports.

Experiment #2 examines airline behavior for three airport capacity levels (high, normal, and low) and 

four fuel price levels ($2, $3.5, $5, $8) for one congested airport (EWR) for two different economic 

scenarios: (1) 3QTR07 with $2 fuel prices and 105 GDP index, and (2) 3QTR09 with $2 fuel prices and 

103 GDP index. The results provide insights on airline behavior in response to capacity changes and fuel 

price changes for different economic scenarios.

3QTR 

2007

3QTR 

2008

3QTR 2009

Airports L

G

A

S

F

O

E

W

R

J

F

K

P

H

L

L

G

A

S

F

O

E

W

R

J

F

K

P

H

L

L

G

A

S

F

O

E

W

R

J

F

K

P

H

L

Hedged 

Fuel Prices

($/Gallon)

$2.08 $3.53 $1.92

Gross

Domestic 

Product 

(GDP Quantity 

Index,

2005=100)

104.9 105.3 102.8

Capacity 

Limits

(Operations/ 

hour)

Low 64 64 72 72 72 64 64 72 72 72 64 64 72 72 72

Normal 72 72 80 80 80 72 72 80 80 80 72 72 80 80 80

High 80 80 96 96 96 80 80 96 96 96 80 80 96 96 96
41

Table 7 “Design of Experiment” for hedged fuel price and capacity limit experiment. Experiment 

#2 represents 18 of 18 possible treatments.

Limitation of Design of Experiments

Note: These experiments include analysis of hedged fuel prices of $5/gallon and $8/gallon. 

Historically, fuel prices have not exceeded $3.70/gallon (this highest price happened in July of 2008). The 

analysis showed that the airline decision-making response remained linear throughout the full range of 

fuel prices allowing the use of the data for derivation of the linear regression equations. These results are 

reported, but it should be recognized that above $4/gallon the economy and passenger demand would 

undergo significant changes that have not been experienced (or modeled).

3QTR 

2007

3QTR 

2009

Airports EWR EWR

Hedged Fuel 

Prices

($/Gallon)

$2 $3.5 $5 $2 $3.5 $5

Gross

Domestic 

Product (GDP 

Quantity Index,

2005=100)

104.9 102.8

Capacity 

Limits

(Operations/ 

hour)

Low 72 72 72 72 72 72

Normal 80 80 80 80 80 80

High 96 96 96 96 96 96
42

4 Results

This section describes the results of the analyses of the Experiments conducted using the ASOM. 

Section 4.1 examines the results of Experiment #1: the impact of Airport Capacity Limits. Section 4.2 

examines the results of Experiment #2: the impact of Fuel Prices Changes and Capacity Limits. Section 

4.3 describes the relationship between parameters from a regression analysis. The results of the validation 

tests on the ASOM model are included in Appendix A. 

The results of the ASOM experiments indicate the following effects:

Geographic Access:

i. Markets served per day reduce slightly as capacity limits are reduced, gross domestic product is 

reduced, or hedged fuel prices are increased

ii. Flights per day are reduced as capacity limits are reduced, gross domestic product is reduced, or 

fuel prices are increased

Air Transportation Efficiency:

iii. Aircraft size increases slightly as hedged fuel prices are increased or as gross domestic product 

increases

Airline Profitability:

iv. Airline Profits increase as hedged fuel prices, gross domestic product, aircraft size, or flights per 

day increase

4.1 Experiment #1 Results: ASOM Capacity Variation Results 3QTR (2007-2009) 

(LGA, EWR, JFK, SFO, PHL)

In this experiment the ASOM examines airline behavior for different airport capacity levels (high, 

normal, and low) for five different congested airports (LGA, EWR, JFK, SFO, and LGA) for three 

different economic scenarios: (1) 3QTR07 with $2 fuel prices and 105 GDP index; (2) 3QTR08 with $3.5 

fuel prices and 105 GDP index; and (3) 3QTR09 with $2 fuel prices and 103 GDP index. The results 

provide insights on airline behavior in response to capacity changes for different economic scenarios.

4.1.1 Geographic Access

This section describes the results for profitable markets, scheduled flights per day and aircraft size

4.1.1.1 Profitable Markets - The results of ASOM analysis of the profitable markets (NY, SF, and 

Philadelphia) is shown in Table 8. This table shows the Baseline Direct service Markets for ―Normal‖ 

Capacity Limits (in Black). The remaining cells in the table show the change in Direct service Markets 

from this baseline. The results show that adjusting airport capacity limits up and down has little effect on 

profitable markets served. The analysis shows that most profitable markets can still be served for the five 

congested airports examined, even when airport capacity is reduced to 10% below current operations. 

When capacity at these airports is increased, as anticipated with NextGen, ASOM fits a few more current 

profitable markets into the schedule. 
43

Table 8 Sensitivity of Direct service Markets to Capacity Limits. 

When Airport Capacity Limits are reduced through fixed passenger demand (represented by gross 

domestic product) and fuel prices, the number of profitable markets served does not change. 

The results of a linear regression for markets served by the five airports, over 3 years, and for 3 

airport capacity limits provides insights into the correlations between the variables and the significant 

factors that influence airline decisions on markets served (Table 9).

Table 9 Correlation of significant factors that influence airline decisions on 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 correlation between markets Served and airport capacity limits. 

The stepwise regression analysis identified the profitable Markets to be a function of airport capacity 

limits. This relationship was found to be positive. The correlation accounted for 8.8% (i.e. R-squared) of 

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

changes in airline business models, and airline restructuring when emerging from bankruptcy. The 

regression equation for profitable markets served by the airlines is as follows:

Markets = 26.2 + 0.441 Caps

Caps LGA SFO EWR JFK PHL

3QTR

2007

($2.08

fuel 

price)

L - -1 -2 -4 -4

N 59 52 79 49 80

H +2 +1 - - +2

3QTR

2008

($3.53

fuel 

price)

L -1 - -1 -3 -2

N 59 39 64 49 74

H - - +3 +2 -

3QTR

2009

($1.92

fuel 

price)

L - -5 -1 -2 -2

N 60 44 73 54 83

H - +2 +2 - +2

Fuel Prices GDP Caps

Markets

Pearson

correlation -0.199 -0.101 0.329

P-Value 0.189 0.51 0.027
44

4.1.1.2 Scheduled Flights per Day

The results of ASOM analysis of the scheduled flights per day at the Metroplex (NY, SF, and 

Philadelphia) is shown in Table 10. The table shows the Baseline Scheduled flights per day for ―Normal‖ 

Capacity Limits (in Black). The remaining cells in the table show the change in Scheduled flights per day 

from this baseline. These results show that adjusting airport capacity limits up and down has little effect 

on scheduled flights per day. The analysis shows that most of the flights per day for normal capacity 

limits are still scheduled, even when airport capacity is reduced to 10% below current levels. When 

capacity is increased, as anticipated with NextGen, ASOM fits a few more scheduled flights per day into 

the schedule. 

Table 10 Sensitivity of Scheduled flights per day to Capacity Limits. 

When airport capacity limits are reduced with passenger demand (representing gross domestic 

product) and fuel prices fixed airlines will reduce the total number of flights. This is expected since the 

number of operations allowed per hour is reduced. These trends are consistent for all five airports for a 

booming economy in the third quarter 2007, a slowed economy in the third quarter 2008, and during the 

recession in the third quarter 2009. And profitable schedules were found for all three quarters for all five 

airports examined, even in the third quarter 2009 when most airlines were reporting significant losses. 

Figure 13 shows how the average daily flights for 3QTR (2007-2008) for all five airports (LGA, 

EWR, JFK, SFO, PHL) change as airport capacity limits are increased or reduced in comparison to the 

historic schedule. This analysis shows the proportion of historic non-profitable flights that were not 

scheduled by the ASOM. This figure also shows how the ASOM scheduled flights per day were reduced 

as the airport capacity limits were reduced. 

Caps LGA SFO EWR JFK PHL

3QTR

2007

($2.08

fuel 

price)

L -50 -28 -50 -64 -38

N 834 634 750 630 876

H +28 +12 +44 +26 +68

3QTR

2008

($3.53

fuel 

price)

L -18 -12 -38 -28 -32

N 718 596 644 558 798

H +8 - +24 +34 +44

3QTR

2009

($1.92

fuel 

price)

L -46 -68 -34 -58 -50

N 810 710 704 670 888

H +22 +32 +16 +32 +72
45

Figure 13 ASOM Flight Schedules slightly reduce as Airport Capacity Limits are reduced 

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

airports, over three years and for three airport capacity limits provides insights into the correlations 

between the variables and the significant factors that influence airline decisions on scheduled flights per 

day (Table 11).

Table 11 Table shows statistically significant correlation (95% confidence) between scheduled 

flights per day and hedged fuel prices.

The analysis shows correlation between scheduled flights per day and fuel price. 

The stepwise regression analysis identified the scheduled flights per day to be a function of airport 

capacity limits and fuel price. This relationship was found to be positive for airport capacity limits and 

negative for fuel price. The correlation accounted for 17.4% (i.e. R-squared) of the observed variation in 

scheduled flights per day. The remaining effects are inter-airline competition, changes in airline business Fuel Prices GDP Caps

Flights 

per Day

Pearson

correlation -0.375 -0.266 0.266

P-Value 0.011 0.077 0.078 0

500

1000

1500

2000

2500

3000

3500

4000

4500

EWR JFK LGA PHL SFO # of Scheduled Daily Flights

Airports

NY, SFO, and PHL Airports ASOM schedules 3QTR (07-09)

Hist (Profit & Non-Profit)

High Caps

Current Caps

Low Caps
46

models, and airline restructuring when emerging from bankruptcy. The regression equation for scheduled 

flights per day to all markets served by the airlines is as follows:

Flights per Day = 639 + 2.78 Caps - 55.3 fuel price

4.1.2 Airline Profits

The results of ASOM analysis of the airline profits (NY, SF, and Philadelphia) are shown in Table 12. 

This table shows the Baseline Airline Profits per day for ―Normal‖ Capacity Limits (in Black). The 

remaining cells in the table show the change in Airline Profits per day from this baseline. The results 

show that adjusting airport capacity limits up and down has little to no effect on airline profits. When 

more capacity is available, as anticipated with NextGen, ASOM shows airlines would add marginally 

profitable flights to their schedule. 

Table 12 Sensitivity of Airline Profits to Capacity Limits. 

When airport capacity limits are reduced with fixed passenger demand (representing gross domestic 

product) and fuel prices, airlines will adjust operations to maintain profits (dropping the least profitable 

flights). These trends are consistent for 87% (13/15) of cases examined across five airports for a booming 

economy in the third quarter 2007, a slowed economy in the third quarter 2008, and during the recession 

in the third quarter 2009. And profitable schedules were found for all three quarters for all five airports 

examined, even in the third quarter 2009 when most airlines were reporting significant losses. When 

examining JFK for third quarter 2007, a 25% reduction in capacity limits reduces the airlines profit by 

21%. One explanation is that JFK’s schedule for third quarter 2007 was significantly peaked. As the 

peaks were removed the profit was also removed.

The results of a linear regression for airline profits at each of the five airports, over three years and for 

three airport capacity limits provides insights into the correlations between the variables and the 

significant factors that influence airline profit (Table 13).

Caps LGA SFO EWR JFK PHL

3QTR

2007

($2.08

fuel 

price)

L -$.106 -$.108 -$.110 -$.923 -$.115

N $4.087 $5.624 $5.884 $4.748 $3.477

H +$.032 +$.056 +$.107 +$.122 +$.133

3QTR

2008

($3.53

fuel 

price)

L -$.027 -$.037 -$.224 -$.059 -$.057

N $3.205 $3.746 $5.347 $4.089 $2.679

H +$.014 +$.018 +$.063 +$.048 +$.086

3QTR

2009

($1.92

fuel 

price)

L -$.078 -$.019 -$.269 -$.514 -$.113

N $3.938 $3.358 $4.783 $4.667 $3.406

H +$.030 +$.012 +$.059 +$.099 +$.156
47

Table 13 Table shows statistically significant correlation (95% confidence) between airline profits 

and scheduled flights per day and aircraft size.

The analysis shows correlation between airline profit to scheduled flights per day and aircraft size. 

However, the stepwise regression found airline profit correlated with fuel price, gross domestic 

product, markets served and aircraft size. This relationship was found to be positive for gross domestic 

product, markets served and aircraft size and negative for fuel price. The correlation accounted for 62.0% 

(i.e. R-squared) of the observed variation in airline profit. The remaining effects are inter-airline 

competition, changes in airline business models, and airline restructuring when emerging from 

bankruptcy. The regression equation for airline daily profits for service in and out of the airport is as 

follows:

Daily Airline Profit = - 21.8 - 0.658 fuel price + 0.229 GDP + 0.0156 Markets + 0.0271 Guage

4.1.3 Air Transportation Efficiency. 

4.1.3.1 Aircraft Size - The results of ASOM analysis of the average aircraft gauge (NY, SF, and 

Philadelphia) is shown in Table 14. This table shows the Baseline average Aircraft Gauge for ―Normal‖ 

Capacity Limits (in Black). The remaining cells in the table show the change in average Aircraft Gauge 

from this baseline. The results show that adjusting airport capacity limits up and down has little effect on 

aircraft gauge. When more capacity is available, as anticipated with Nextgen, ASOM shows airlines 

would down-gauge and thus not realize the full benefits from Nextgen. 

Fuel Prices GDP Caps Markets Flights 

per Day

Aircraft 

Size

Profit

Pearson

correlation -0.254 0.068 0.184 0.012 -0.339 0.654

P-Value 0.093 0.658 0.225 0.939 0.023 0
48

Table 14 Sensitivity of average Aircraft Gauge to Capacity Limits. 

When airport capacity limits are reduced with passenger demand (representing gross domestic 

product) and fuel prices fixed, we find that airlines will slightly up-gauge. This is expected since the 

number of operations allowed per hour is reduced and the same demand must be serviced. These trends 

are consistent for 93% (14/15) of cases examined across five airports for a booming economy in the third 

quarter 2007, a slowed economy in the third quarter 2008, and during the recession in the third quarter 

2009. Also, profitable schedules were found for all three quarters for all five airports examined, even in 

the third quarter 2009 when most airlines were reporting significant losses. For JFK third quarter 2009, 

the optimal schedule down-gauges as capacity is reduced, but under alternate demand circumstance (.e.g. 

3 Qtr 2007 and 3 QTR 2008) the optimal schedule up up-gauged. This could reflect the fact that 

historically when JFK’s capacity limits were reduced the airlines de-peaked the schedule and maintained 

the number of flights.

Figure 14 shows the how the average daily flights for 3QTR (2007-2008) for all five airports (LGA, 

EWR, JFK, SFO, PHL) change as airport capacity limits are increased or reduced. This figure also shows 

how the ASOM does not significantly change the size of the aircraft as a function of airport capacity 

limits. 

Caps LGA SFO EWR JFK PHL 

3QTR

2007

($2.08

fuel 

price)

L +1 +4 +5 +3 -1 

N 78 113 139 127 79 

H -1 -1 -5 -3 -2 

3QTR

2009

($3.53

fuel 

price)

L - -1 +4 +2 -

N 69 132 142 125 78 

H -1 -1 -2 -2 -2

3QTR

2009

($1.92

fuel 

price)

L +1 +4 - -5 +2

N 81 115 110 102 71 

H -2 - -4 - -1
49

Figure 14 ASOM average Aircraft Gauge insensitive to Capacity Limits 

The results of a linear regression for aircraft size for the airports at each of the five airports, over three 

years and for three airport capacity limits provides insights into the correlations between the variables and 

the significant factors that influence airline decisions on aircraft size (Table 15).

Table 15 Table shows no statistically significant correlation (95% confidence) between average 

Aircraft Gauge and exogenous factors.

The analysis shows no correlation between aircraft gauge and fuel price, gross national product, or 

capacity limits. 

The stepwise regression analysis identified no functional relationship between aircraft gauge and fuel 

price, gross national product, or capacity limits. 

These results from the ASOM model were not surprising because of the lack of economies of scale 

related to up-gauging. A closer examination of the data shows that 100 and 200 seat class aircraft have 

historically poor performance (cost per seat-hr). The newest part of the airline industry fleet is regional 

0

500

1000

1500

2000

2500

3000

3500

4000

4500

25 50 75 100 125 150 175 200 225 275 # of Scheduled Daily Flights

Seat Class

NY, SF, and PHL Airport ASOM schedules by Seat Class

Low

Current

High Fuel Prices GDP Caps

Aircraft 

Size

Pearson

correlation 0.159 0.236 0.051

P-Value 0.298 0.119 0.741
50

jets that are more fuel efficient that the larger aircraft in the overall fleet. As fuel prices increase, there is 

more incentive for the ASOM to move to smaller aircraft. Figure 15 illustrates this point, showing how 

the smaller aircraft have better average cost per seat hour. The B787 and A380 are more efficient aircraft 

but unlikely to be used for many of the markets currently served by these airports since they are relatively 

large aircraft.

By using smaller aircraft the airlines can assure high load factors, and greater flight frequency. This 

result has significant implications for future airspace use.

Figure 15 Poor aircraft performance ($/seat-hr) in 100- and 200-seat aircraft classes

4.2 Experiment #2: ASOM Capacity and Fuel Price Variation Results 

In this experiment the ASOM examines airline behavior for different airport capacity levels (high, 

normal, and low) and for different fuel price levels ($2, $3.5, $5) for one congested airport (EWR) for two 

different economic scenarios (3QTR07 with $2 fuel prices and 105 GDP index, and 3QTR09 with $2 fuel 

prices and 103 GDP index). The results provide insights on airline behavior in response to capacity 

changes and fuel price changes for different economic scenarios.

4.2.1 Geographic Access

This section describes the results for profitable markets, scheduled flights per day and aircraft size

$-

$20 

$40 

$60 

$80 

$100 

$120 

$140 

25 50 75 100 125 150 175 200 225 250 275 375 425 Cost/ pax/ flight hour

Aircraft Size (seats)

2008 Aircraft Fuel Costs / Pax/ Flight Hour

$2 

$4 

$5 

$7 

$8 
51

4.2.1.1 Profitable Markets - The results of ASOM analysis of the profitable markets for EWR are shown 

in Table 16. This table shows the Baseline (underlined) Direct service Markets for ―Normal‖ Capacity 

Limits (i.e. 80 operations/hour), and passenger demand at $2.08/gallon and $1.92/gallon. The remaining 

cells in the table show the change in Direct service Markets from this baseline. When hedged fuel prices 

approach $5 the target airport starts to lose Direct service Markets (yellow). The results show that 

adjusting airport capacity limits up and down has little effect on profitable markets served. However, 

significant increases in fuel prices reduced the number of markets served. In the table the underlined 

quantity represents the baseline ASOM results for markets served for normal capacity limits and fuel 

prices in line with historic prices for the quarter examined. When more capacity is available, as 

anticipated with NextGen, ASOM fits a few more current profitable markets into the schedule. 

Table 16 Sensitivity of EWR Direct service Markets to Hedged Fuel Price and Capacity Limits. 

The results of a linear regression for markets served by EWR over two years, for three airport 

capacity limits and for four different fuel prices provides insights into the correlations between the 

variables and the significant factors that influence airline decisions on markets served (Table 17).

Table 17 Table shows statistically significant correlation (95% confidence) between direct service 

markets and hedged fuel prices and Gross Domestic Product.

The analysis shows correlation between markets Served to fuel prices and Gross Domestic Product. 

The stepwise regression analysis identified the profitable markets to be a function of capacity limits, 

fuel prices and GDP. This relationship was found to be positive for capacity limits and GDP, and 

negative for fuel prices. The correlation accounted for 83.4% (i.e. R-squared) of the observed variation in 

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

Fuel Prices GDP Caps

Markets

Pearson

correlation -0.505 0.515 0.376

P-Value 0.033 0.029 0.124

EWR 

Markets Caps $2 $3.5 $5

3QTR

2007

($2.08 fuel 

price)

L -2 -1 -8

N 79 -1 -9

H - - -5

3QTR

2009

($1.92 fuel 

price)

L +1 -1 -5

N 74 +1 -1

H - +1 -
52

models, and airline restructuring when emerging from bankruptcy. The regression equation for profitable 

markets served by the airlines is as follows:

Markets = - 98.4 + 0.122 Caps + 1.61 GDP - 1.36 Fuel Price

4.2.1.2 Scheduled Flights per Day - The results of ASOM analysis of the scheduled flights per day for 

EWR are shown in Table 18. This table shows the Baseline (underlined) scheduled flights per day for 

―Normal‖ Capacity Limits (i.e. 80 operations/hour), and passenger demand at $2.08/gallon and 

$1.92/gallon. The remaining cells in the table show the change in scheduled flights per day from this 

baseline. When hedged fuel prices approach $5 the scheduled flights per day significantly reduce 

(yellow). The results show that adjusting airport capacity limits up and down has little effect on 

scheduled flights per day, but significant increases in fuel prices reduce the number of scheduled flights 

per day. In the table the underlined quantity represents the baseline ASOM results for scheduled flights 

per day for normal capacity limits and fuel prices in line with historic prices for the quarter examined. 

When more capacity is available, as anticipated with Nextgen, ASOM fits a few more flights into the 

schedule. 

Table 18 Sensitivity of EWR scheduled flights per day to Hedged Fuel Price and Capacity Limits. 

These fuel price trends are consistent for all three capacities for a booming economy in the third 

quarter 2007 and during the recession in the third quarter 2009. Profitable schedules were found for both 

quarters examined, even in the third quarter 2009 when most airlines were reporting significant losses.

The results of a linear regression for scheduled flights for EWR over two years, for three airport 

capacity limits and for four different fuel prices provides insights into the correlations between the 

variables and the significant factors that influence airline decisions on markets served (Table 19).

Fuel Prices GDP Caps

Flights 

per Day

Pearson

correlation -0.607 0.435 0.558

P-Value 0.008 0.071 0.016

EWR 

Flights Caps $2 $3.5 $5

3QTR

2007

($2.08 fuel 

price)

L -50 -52 -106

N 750 -12 -86

H +44 +6 -62

3QTR

2009

($1.92 fuel 

price)

L -34 -56 -92

N 672 -16 -54

H +16 +14 -30
53

Table 19 Table shows statistically significant correlation (95% confidence) between scheduled 

flights per day and Hedged Fuel Prices and airport capacity limits.

The analysis shows correlation between markets served to fuel prices and capacity limits. 

The stepwise regression analysis identified the scheduled flights per day to be a function of capacity 

limits, fuel prices and GDP. This relationship was found to be positive for capacity limits and GDP, and 

negative for fuel prices. The correlation accounted for 88.2% (i.e. R-squared) of the observed variation in 

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

models, and airline restructuring when emerging from bankruptcy. The regression equation for daily 

scheduled flights is as follows:

Flights = - 1392 + 2.48 Caps + 18.9 GDP - 22.3 Fuel Price

4.2.2 Airline Profits

The results of ASOM analysis of airline profit for EWR are shown in Table 20. This table shows the 

Baseline (underlined) Airline Profit for ―Normal‖ Capacity Limits (i.e. 80 operations/hour), and 

passenger demand at $2.08/gallon and $1.92/gallon. The remaining cells in the table show the change in 

Airline Profit from this baseline. When hedged fuel prices exceed $3.50 the airline profit significantly 

increases (yellow). The results show that adjusting airport capacity limits up and down has little effect on 

airline profit, but significant increases in fuel prices increase airline profit. In the table the underlined 

quantity represents the baseline ASOM results for airline profit for normal capacity limits and fuel prices 

in line with historic prices for the quarter examined.

Table 20 Sensitivity of EWR Airline Profit to Hedged Fuel Price and Capacity Limits. 

These fuel price trends are consistent for all three capacities for a booming economy in the third 

quarter 2007 and during the recession in the third quarter 2009. Profitable schedules were found for both

quarters examined, even in the third quarter 2009 when most airlines were reporting significant losses.

The results of a linear regression for airline profit for EWR over two years, for three airport capacity 

limits and for four different fuel prices provides insights into the correlations between the variables and 

the significant factors that influence airline decisions on markets served (Table 21).

EWR 

Profit Caps $2 $3.5 $5

3QTR

2007

($2.08 fuel 

price)

L -.110 +.934 +1.257

N $5.884 +1.030 +1.317

H +.107 +1.110 +1.379

3QTR

2009

($1.92 fuel 

price)

L -0.269 0.537 0.569

N 6.511 +0.864 +1.071

H +0.059 +0.908 +1.096
54

Table 21 Table shows statistically significant correlation (95% confidence) between airline profit 

and hedged fuel prices, GDP, and aircraft gauge.

The analysis shows correlation between airline profit to fuel prices, GDP, and aircraft gauge. 

The stepwise regression analysis identified airline profit to be a function of capacity limits and 

aircraft gauge. This relationship was found to be positive for all factors. The correlation accounted for 

91.6% (i.e. R-squared) of the observed variation in airline profit. The remaining effects are inter-airline 

competition, changes in airline business models, and airline restructuring when emerging from 

bankruptcy. The regression equation for daily airline profit is as follows:

Profit = - 2.09 + 0.0167 Caps + 0.0489 Gauge

4.2.3 Air Transportation Efficiency. 

4.2.3.1 Aircraft Size - The results of ASOM analysis of the average aircraft size scheduled for EWR are 

shown in Table 22. This table shows the Baseline (underlined) Aircraft Gauge for ―Normal‖ Capacity 

Limits, and passenger demand. The remaining cells in the table show the change in aircraft gauge from 

this baseline. The results show that adjusting airport capacity limits up and down has little effect on 

aircraft gauge, but significant increases in fuel prices increased the aircraft gauge. In the table the 

underlined quantity represents the baseline ASOM results for aircraft gauge for normal capacity limits 

and fuel prices in line with historic prices for the quarter examined. 

Table 22 Sensitivity of EWR Aircraft Gauge to Hedged Fuel Price and Capacity Limits. 

These fuel price trends are consistent for all three capacities for a growing economy in the third 

quarter 2007 and during the recession in the third quarter 2009. Profitable schedules were found for both 

quarters examined, even in the third quarter 2009 when most airlines were reporting significant losses.

EWR 

Gauge Caps $2 $3.5 $5

3QTR

2007

($2.08 fuel 

price)

L +5 +14 +21

N 139 +13 +20

H -5 +9 +20

3QTR

2009

($1.92 fuel 

price)

L - +15 +26

N 110 +17 +30

H -4 +15 +30

Fuel Prices GDP Caps Markets Flights 

per Day

Aircraft 

Size

Profit

Pearson

correlation 0.568 0.789 0.128 0.219 0.113 0.942

P-Value 0.014 0 0.613 0.382 0.654 0
55

Figure 16 shows the how the ASOM schedule of daily flights by aircraft class for EWR 3QTR 2007 

changes as fuel prices are increased. This figure shows that the ASOM increases 75 and 275 seat aircraft 

in EWR’s schedule as fuel prices increase. 

Figure 16 ASOM increases 75 and 275 seat aircraft in EWR’s schedule as fuel prices increase

The results of a linear regression for aircraft gauge at EWR over two years, for three airport capacity 

limits and for four different fuel prices provides insights into the correlations between the variables and 

the significant factors that influence airline decisions on aircraft gauge (Table 23).

Table 23 Table shows statistically significant correlation (95% confidence) between aircraft gauge 

and hedged fuel prices and GDP.

The analysis shows correlation between aircraft gauge to fuel prices and GDP. 

The stepwise regression analysis identified aircraft gauge to be a function of fuel prices, gross 

domestic product, and airport capacity limits. This relationship was found to be positive for gross 

domestic product and fuel prices, and negative for airport capacity limits. The correlation accounted for 

96.9% (i.e. R-squared) of the observed variation in aircraft gauge. The remaining effects are inter-airline 

Fuel Prices GDP Caps

Aircraft 

Size

Pearson

correlation 0.637 0.764 -0.07

P-Value 0.004 0 0.781

0

100

200

300

400

500

600

700

800

2 5 5 0 7 5 100 125 150 275 # of Scheduled Daily Flights

Seat Class

3QTR09 EWR ASOM schedules Seat Class vsFuel Price

$2

$3.5

$5
56

competition, changes in airline business models, and airline restructuring when emerging from 

bankruptcy. The regression equation for average aircraft gauge is as follows:

Gauge = - 1112 - 0.118 Caps + 11.8 GDP + 8.43 Fuel Price

These results from the ASOM model were not surprising because of the lack of economies of scale 

related to up-gauging. A closer examination of the data shows that 100 and 200 seat classes show poor 

historic performance (cost per seat-hr). The newest part of the airline industry fleet is regional jets that 

are more fuel efficient than the larger aircraft in the overall fleet. Thus, as fuel prices increase, there is 

more incentive for the ASOM to move to smaller aircraft. Figure 17 illustrates this point, where the 

smaller aircraft have better average cost per seat hour. The B787 and A380 are more efficient aircraft but 

unlikely to be used for many of the markets currently served by these airports since they are relatively 

large aircraft.

By using smaller aircraft the airlines can assure high load factors, greater frequency. This result has 

significant implications for future airspace use.

Figure 17 Poor Aircraft Performance ($/Seat-Hr) in 100 & 200 Seat Classes

4.2.3.2 Total arrival and departure seats - The results of ASOM analysis of the total arrival and 

departure seats for EWR are shown in Table 24. This table shows the Baseline (underlined) total arrival 

and departure seats for ―Normal‖ Capacity Limits (i.e. 80 operations/hour), and passenger demand at 

$2.08/gallon and $1.92/gallon. The remaining cells in the table show the change in arrival and departure 

seats from the baseline. The results show that adjusting airport capacity limits up and down has little 

effect on total arrival and departure seats, but increases in fuel prices increases the total arrival and 

departure seats. In the table the underlined quantity represents the baseline ASOM results for total arrival 

and departure seats for normal capacity limits and fuel prices in line with historic prices for the quarter 

examined. 

$-

$20 

$40 

$60 

$80 

$100 

$120 

$140 

25 50 75 100 125 150 175 200 225 250 275 375 425 Cost/ pax/ flight hour

Aircraft Size (seats)

2008 Aircraft Fuel Costs / Pax/ Flight Hour

$2 

$4 

$5 

$7 

$8 
57

 

Table 24 Sensitivity of EWR total arrival and departure seats to Hedged Fuel Price and Capacity 

Limits. 

The results of a linear regression for total arrival and departure seats at EWR over two years, for three 

airport capacity limits and for four different fuel prices provides insights into the correlations between the 

variables and the significant factors that influence total arrival and departure seats (Table 25).

Table 25 Table shows statistically significant correlation (95% confidence) between total arrival 

and departure seats to gross domestic product, number of direct service markets, and average 

aircraft gauge.

The analysis shows correlation between total arrival and departure seats to gross domestic product, 

markets served, and aircraft size. 

The stepwise regression analysis identified total arrival and departure seats to be a function of 

scheduled flights per day, number of direct service markets and average aircraft size. This relationship 

was found to be positive for scheduled flights per day, number of direct service markets and average 

aircraft size. The correlation accounted for 99.8% (i.e. R-squared) of the observed variation in total arrival 

and departure seats. The regression equation for total daily arrival and departure seats is as follows:

Seats = - 99427 + 128 Flights + 160 Markets + 684 Gauge

Fuel Prices GDP Caps Markets Flights 

per Day

Aircraft 

Size

Seats per 

Day

Pearson

correlation 0.28 0.903 0.194 0.542 0.424 0.874

P-Value 0.261 0 0.44 0.02 0.079 0

EWR 

Seats Caps $2 $3.5 $5

3QTR

2007

($2.08 fuel 

price)

L -3350 3100 -1050

N 104000 8300 1400

H 2350 7600 5300

3QTR

2009

($1.92 fuel 

price)

L -3900 3650 5750

N 77500 9650 13350

H -1100 12350 16550
58

4.2.3.3 Total available seat miles scheduled - The results of ASOM analysis of the total available seat 

miles scheduled in and out of EWR are shown in Table 26. This table shows the Baseline (underlined) 

available seat miles for ―Normal‖ Capacity Limits (i.e. 80 operations/hour), and passenger demand at 

$2.08/gallon and $1.92/gallon. The remaining cells in the table show the change in available seat miles 

from this baseline. The results show that the total available seat miles are sensitive to changes in airport 

capacity and hedged fuel prices. In the table the underlined quantity represents the baseline ASOM 

results for total available seat miles for normal capacity limits and fuel prices in line with historic prices 

for the quarter examined. 

 

Table 26 Sensitivity of EWR available seat miles to Hedged Fuel Price and Capacity Limits. 

The results of a linear regression for total available seat miles at EWR over two years, for three 

airport capacity limits and for four different fuel prices provides insights into the correlations between the 

variables and the significant factors that influence total available seat miles (Table 27).

Table 27 Table shows statistically significant correlation (95% confidence) between available seat 

miles to GDP, number of direct service markets, scheduled flights per day, average aircraft gauge, 

and total arrival and departure seats.

The analysis shows correlation between total available seat miles to GDP, markets served, scheduled 

flights per day, aircraft size, and total arrival and departure seats. 

The stepwise regression analysis identified total available seat miles to be a function of hedged fuel 

price and total arrival and departure seats. This relationship was found to be positive for total arrival and 

departure seats and negative for hedged fuel price. The correlation accounted for 99.1% (i.e. R-squared) 

of the observed variation in total available seat miles. The regression equation for ASMs is:

ASM = - 10803115 - 1818714 Fuel Price + 1273 Seats

Fuel Prices GDP Caps Markets Flights 

per Day

Aircraft 

Size Seats

ASMs

Pearson

correlation 0.145 0.925 0.219 0.627 0.526 0.807 0.987

P-Value 0.565 0 0.382 0.005 0.025 0 0

EWR 

ASM

(millions)

Caps $2 $3.5 $5

3QTR

2007

($2.08 fuel 

price)

L -4.9 -1.2 -7.3

N 119.1 5.6 -5.3

H 1.2 3.7 2.7

3QTR

2009

($1.92 fuel 

price)

L -7.7 -1.9 -0.3

N 85.4 10.0 11.7

H -0.9 11.7 13.7
59

5 Conclusions

The results of the analysis using the ASOM are as follows:

1. The Air Transportation System is robust: Within the range of historic data, geographic access, 

economic access, airline profitability, and air transportation efficiency exhibit proportional 

relationships defined by the linear regressions below:

Markets = - 98.4 + 0.122 Caps + 1.61 GDP - 1.36 Fuel Price

R-Sq(adj) = 60.4%

Flights = - 1392 + 2.48 Caps + 18.9 GDP - 22.3 Fuel Price

R-Sq(adj) = 85.5%

Gauge = - 1112 - 0.118 Caps + 11.8 GDP + 8.43 Fuel Price

R-Sq(adj) = 96.9%

Seats = - 99427 + 128 Flights + 160 Markets + 684 Gauge

R-Sq(adj) = 99.8%

ASM = - 10803115 - 1818714 Fuel Price + 1273 Seats

R-Sq(adj) = 99.1%

Profit = - 2.09 + 0.0167 Caps + 0.0489 Gauge

R-Sq(adj) = 91.6% 

Figure 18 identifies the relationships between factors. The multipliers establish the magnitude of the 

relationships between parameters. For example, a $1 increase in hedged fuel prices lead to a 22.3 

reduction in scheduled flights per day to all markets. A negative relationship indicates that a positive 

increase in the source, leads to a decrease in the result. The bold lines show the direct and indirect 

contributions for airline profit.
60

Figure 18 Summary of multipliers between the exogenous, economic access, geographic access, 

and airline profitability factors

2. The Air Transportation System is Stable: Within the range of historic data, geographic access, 

economic access, airline profitability, and air transportation efficiency do not exhibit any 

significant changes in relationships. 

For a fixed passenger demand and hedged fuel price, as capacity limits are imposed (e.g. -4 

operations per hour), markets are reduced (-.5) and scheduled flights per day to all markets 

decrease (-10), daily profit decreases (-$67K). In this scenario average aircraft size 

increases (.44 seats per operation). 

For a fixed passenger demand and fixed capacity limits at the airports, as hedged fuel prices 

increase (e.g. +$1 per gallon), markets are reduced (-1.4), scheduled flights per day to all 

markets decrease (-22.3), daily profit increases (+$411K). In this scenario average aircraft 

size increases (+8.4 seats per operation). This result is valid within the hedged fuel price 

range of $1.50 and $4 per gallon.

3. Airport Capacity Limits Do Not Have Large Scale Negative Effects: Regulatory authority to 

manipulate the market through the introduction of airport capacity (and airport capacity limits) has no 

large scale effects. A modest reduction on the order of 4 slots per hour results in a loss of .5 market 

served and 10 flights per day. By shaving off the least profitable markets and flights per day, the 

airlines experience a small loss in profitability. Aircraft size does not increase.

Hedged

Fuel Prices

($/gallon)

Profit

(million)

Airport 

Capacity 

Limits

(ops/hr)

Gross 

Domestic 

Product

(2005 index)

Scheduled Flights 

per Day

(all markets)

# Direct 

Service Markets

Exogenous Factors

Airline Profitability

Aircraft Gauge

(avg seats/ operation)

Geographic Access

Air Transportation 

Efficiency 

Seats

ASM

(million)

-1.4

-22.3

+18.9

+11.8

+8.4

-1.8

.0167
61

These results are summarized in the Table 28.

Objections

Observations

Reduction in 4 

ops/ hr (Capacity 

Limits) leads to ...

$1 increase in 

Hedged Fuel 

Prices per Gallon 

leads to ...

1% drop in Gross 

Domestic Product 

(ref. 2005 GDP) 

leads to ...

Geographic Access -

elimination of service ( 

e.g. smaller markets)

-0.5 market &

-10 Flights/ day 

-1.4 markets & -

22 Flights/ day

-1.6 markets & -

19 Flights/ day

Economic Access -

increased airfares N/A $16 increase in airfare N/A

Airline Finances -

reduced profits

Loss of $67K per 

day

Increase of $411K 

per day through 

Aircraft Gauge 

Changes 

Decrease of 

$577K per day 

through Aircraft 

Gauge Changes 

Air Transportation 

Efficiency - increased 

congestion

Reduced 

Congestion 

Reduced 

Congestion &

+6.4 seats/ 

scheduled aircraft 

Reduced 

Congestion &

+12.5 seats/ 

scheduled aircraft 

Table 28 Impacts of Airport Capacity Limits, Hedged Fuel Prices, and Gross Domestic Product on 

Geographic Access, Economic Access, Airline Finances, and Air Transportation Efficiency.

4. Hedged fuel prices and economic health drive air transportation performance: Regulatory authority to 

manipulate the market through the introduction of airport capacity (and airport capacity limits) is only 

one of three factors affecting geographic access, market access, and airline financial stability. 

Passenger demand for air transportation (measured by GDP) and airline operating costs (determined 

by fuel prices) have significant impacts.

For example, for a fixed passenger demand and hedged fuel price, as capacity limits are imposed 

(e.g. -4 operations per hour), markets are reduced (-.5) and scheduled flights per day to all markets 

decrease (-10), daily profit decreases (-$67K). In this scenario average aircraft size increases (.44 

seats per operation).

Table 47 above demonstrates how an increase in hedged fuel price or a change in GDP results in 

loss of markets and flights, and small loss in profit. These factors both have the effect of increasing

the average number of seats per aircraft.

5. Combination of increased passenger demand and increased operating costs directly causes increase in 

aircraft size. It is financially viable for airlines to up-gauge in order to service the passenger demand. 

The airlines’ ability to up-gauge does not occur in the real-world due to: (1) distortions in the labor 

cost structure for pilots as a result of the pilot union scope clause labor agreements, and (2) (due to 

#1) aircraft manufacturers have failed to design and produce aircraft appropriate to match the 

passenger market demand.
62

5.1 Recommendations

1. Examine the possibility of relaxing Pilot Union Scope Clauses to incentivize 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. Introduce service standards at all of the 35 largest airports so 

that there is a metric in place that will warn airspace management personnel that a congestion 

problem is likely to occur unless capacity limits are imposed.

3. Research and develop aircraft technologies (e.g. engines) to provide improved performance for 

the 88 - 112 seat class of aircraft. This improves airlines profits, and has the added benefit of 

reducing emissions.

5.2 ASOM Experiments 1 & 2 Summary

Table 29 summarizes the historical and ASOM statistical analysis of geographic access. Clearly the 

ASOM experiment #2 provided a more balanced experiment to understand the impacts from the 

exogenous factors and also provided the best function model fits or adjusted R2.

Table 29 Geographic Access Functional Models Summary

Table 30 summarizes the historical and ASOM statistical analysis of air transportation efficiency. 

Relationships Historical Exp #1 Exp #2

Markets

Hedged Fuel Prices -1.4

GDP +1.44 +1.6

Caps +.44 +.12

Adj R2 25.2% 8.8% 60.4%

Flights per 

Day

Hedged Fuel Prices -55 -22.3

GDP +1.83 +18.9

Caps +2.8 +2.5

Adj R2 15.6% 17.4% 85.5%
63

Table 30 Air Transportation Efficiency Functional Models Summary

Table 31 summarizes the Historical and ASOM statistical analysis of airline profitability access. 

Clearly the ASOM experiment #2 provided a more balance experiment to understand the impacts from 

the exogenous factors and also provided the best function model fits or adjusted R2.

Table 31 Airline Profitability Functional Models Summary

5.2.1 Geographic Access. 

The ASOM generates optimally profitable airline schedules; therefore the outputs can be directly 

examined for geographic access. The number of 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 markets served and flights per day. Airline 

network restructuring, airline financial restructuring, and inter-airline competition also affected markets 

served and flights per day. 

Relationships Historical Exp #1 Exp #2

Profitability

/ Profit

Hedged Fuel Prices -.280 -.658

GDP +.229

Caps +.0167

Markets +.016

Flights per Day

Gauge +.027 +.0489

Adj R2 79.6% 62.0% 91.6%

Relationships Historical Exp #1 Exp #2

Gauge

Hedged Fuel Prices +8.4

GDP +11.8

Caps -.11

Adj R2 96.9%

Seats

Markets +160

Flights per day +128

Gauge +684

Adj R2 99.8%

ASMs

(millions)

Hedged Fuel Prices -1.8

Seats +.001

Adj R2 99.1%
64

1. The fluctuations in hedged fuel prices (which impacts airline operational costs) is the primary 

determinant of the number of markets served. A linear regression showed that: for every $1 

increase in hedged fuel prices, there is a 1.9 decrease in the number of markets with direct 

service; for every $1 increase in hedged fuel prices, there is a decrease of 17.8 scheduled 

flights per day; and for every $1 increase in hedged fuel prices, there is a 6.4 seat increase in 

the average aircraft size flown.

2. The growth/decay in demand for air transportation (as measured by the GDP) is a 

determinant of the number of markets served. A linear regression showed that: for every 

incremental increase in the GDP index, there is a 1.8 increase in the number of markets with 

direct service; for every incremental increase in the GDP index, there is an increase of 17.3 

scheduled flights per day; and for every incremental increase in the GDP index, there is a 

12.5 seat increase in the average aircraft size flown. As the economy slowed, the number of 

markets with direct service decreased.

3. The introduction of capacity limits (as measured by limits on number of operations per hour) 

is a determinant of the number of markets served. A linear regression showed that for every 

additional operation per hour allowed, there is a .1 increase in the number of markets with 

direct service; for every additional operation per hour allowed, there is an increase of 2.4 

scheduled flights per day.

4. 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 impact 

the number of markets served. 

5.2.2 Economic Access 

The ASOM generates optimally profitable airline schedules; therefore the outputs of markets served 

and airfares are a key input parameter for the model. Regression analysis of airfare versus hedged fuel 

prices provides the basis for results for 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 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 

passenger demand to be elastic.

2. The change in airfare was driven by changes in hedged fuel prices (which impacts airline 

operational costs) (R2=83.1%). At the five airports studied (LGA, JFK, EWR, PHL, and 

SFO), every $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%.

5.2.3 Airline Profitability. 

The ASOM generates optimally profitable airline schedules; therefore the outputs can be directly 

examined for 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 
65

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. Inputs for the ASOM are preprocessed from historical data reflecting airline behaviors and 

responses to the exogenous factors. Therefore the ASOM input data reflects:

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

fuel prices increased.

b. airlines shed less profitable markets in order to improve profitability. Similarly the 

ASOM algorithm will eliminate the least profitable markets and flights first, when 

adjusting the airline schedule within airport capacity.

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

Similarly the ASOM algorithm will use the most cost efficient aircraft to meet market 

passenger demand.

2. Changes in airline profits are driven by changes in air transportation efficiency (as measured 

by gauge in this study) and airport capacity limits (R2=91.6%). For example for passenger 

demand and operations at EWR, daily airline profits were increased $48.9K for every 

additional seat per flight operation, and increased $16.7 for every additional flight operation 

allowed per hour (increase to capacity limits). 

5.2.4 Air Transportation Efficiency. 

Air transportation efficiency is measured by the throughput of passengers through the network based 

on aircraft size (i.e. number of seats) per runway/airspace. Air transportation efficiency is also measured 

by the total arrival and departure seats and by the total available seat miles scheduled in and out of the 

airport. 

1. A linear regression showed that: for every incremental increase in the GDP index, there is an 

11.8 seat increase in the average aircraft size flown; for every $1 increase in hedged fuel

prices, there is an 8.4 seat increase in the average aircraft size flown; for each additional 

flight operation allowed per hour (increase to capacity limits), there is a decrease of .11 seats 

for the aircraft size flown. This statistical relationship explained for 96.9% of the variations 

in aircraft gauge (R2=96.9%).

2. Economic conditions (as measured by GDP in this study) and operational costs (as measured 

by hedged fuel prices in this study) effect changes in airline profit by driving changes in 

aircraft gauge. For example for passenger demand and operations at EWR, average aircraft 

size was increased by 8.4 seats, which causes daily airline profits to increase $411K for every 

$1 increase in hedged fuel prices; also, average aircraft size was increased by 11.8 seats,

which causes increased $577K for every incremental increase in the GDP index. This result 

is valid within the hedged fuel price range of $1.50 and $4 per gallon.

3. The change in the total arrival and departure seats was driven by changes in direct service 

markets, the scheduled flights per day to all markets, and the average aircraft size (seats per 

operation) (R2=99.8%). Every additional direct market served added 160 seats to the total 

arrival and departure seats. Each additional scheduled flight per day added 128 seats to the 

total arrival and departure, which represents the average aircraft size. Increasing the average 

aircraft size by one seat added 684 seats to the total arrival and departure, which represents 

the average scheduled flights per day.

4. The change in total available seat miles (ASM) scheduled in and out of the airport was driven 

by changes in hedged fuel prices (which impacts airline operational costs) and by the total 

arrival and departure seats (R2=99.1%). Every $1 increase in hedged per-gallon fuel prices 
66

resulted in a reduction of 1.8 million ASMs. An incremental increase in the total arrival and 

departure seats resulted in an increase of 1273 ASMs.

Note: These results are not consistent with the observed historical data. The historical data did not 

show the up-gauging experienced by the ASOM model. There are several explanations including: airline 

competition, fleet inflexibility, and airline pilot union scope clauses.

5.3 Recommendations

1. DOT and FAA should evaluate options to relax Pilot Union Scope Clauses that would allow 

incentives for aircraft manufacturers to produce, and airlines to acquire and deploy, aircraft in the 88 

– 112-seat range. Currently, the oldest and least efficient part of the total airline industry fleet is 

within this category. To economically up-gauge from a regional jet, the airlines need to move from a 

60-80 seat plane directly to a 120+ seat plane. Such a jump is often not economically viable.

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-seat class. The use of such aircraft will increase the efficiency of the airspace (i.e. more 

passenger throughput) and will also reduce emissions. 

5.4 Future Work

5.4.1 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.

5.4.2 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.
67

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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
68

Kirk, R.S., 2003. CRS Issue Brief for Congress: Airport Improvement Program, Congressional 

Research Service.
69

Appendix A: Validation of ASOM Model

A.1 ASOM Consistency with Historic Results for Geographic Access

Table 32 shows the summary results of a consistency check of the baseline ASOM results for markets 

served, scheduled flights per day and aircraft gauge when compared to the historic behavior of the airlines 

serving these five congested airports (LGA, EWR, JFK, SFO, PHL) for three different economic 

scenarios: (1) 3QTR07 with $2 fuel prices and 105 GDP index; (2) 3QTR08 with $3.5 fuel prices and 105 

GDP index; and (3) 3QTR09 with $2 fuel prices and 103 GDP index. The ASOM scheduled on average 

2% fewer markets and 12% fewer flights for aircraft 5% smaller.

Consistency in trends and relationships is the best the ASOM will allow, since the ASOM models 

airline scheduling behavior from an operational versus strategic position and the ASOM does not model 

airline competition. The ASOM models only profitable markets, thus not strategically taking a short term 

loss to retain market demand. The ASOM models balanced arrivals and departures, therefore no banking 

is allowed. 

The following sections will show consistency analysis results from comparing baseline ASOM results 

to historical data, comparing ASOM annual trends versus historic trends, and comparing opposite 

markets.

Table 32 ASOM results for consistency check - geographic access

A.1.1 ASOM Consistency with Historic Results for Markets Served

The ASOM baseline results for markets served was compared to historic results for all five congested 

airports (LGA, SFO, EWR, JFK, PHL) for three different economic scenarios: (1) 3QTR07 with $2 fuel 

prices and 105 GDP index; (2) 3QTR08 with $3.5 fuel prices and 105 GDP index; and (3) 3QTR09 with 

$2 fuel prices and 103 GDP index. The results provide insights on ASOM consistency in predicting 

airline behavior.

Table 33 shows 93% of ASOM profitable markets served within 4% of historical data. The ASOM 

results showed on average 2% fewer profitable markets served; results for EWR 3QTR 2008, with peak 

fuel prices, showed 6% fewer profitable markets than the historical data.

Markets flight/day gauge

Mean -2% -12% -5%

Standard Deviation 2% 5% 19%

Range 6% 19% 64%

Minimum -6% -21% -28%

Maximum 0% -2% 36%

Count 15 15 15
70

Table 33 ASOM results for consistency check – markets served

Table 34 shows 100% of ASOM profitable market annual trends within 3% of historical annual 

trends. Additionally, 100% of these trends were in the same direction.

Table 34 ASOM annual trends for profitable markets 

A.1.2 ASOM Consistency with Historic Results for Scheduled Flights per day

The ASOM baseline results for scheduled flights per day were compared to historic results for all five 

congested airports (LGA, SFO, EWR, JFK, PHL) for three different economic scenarios: (1) 3QTR07 

with $2 fuel prices and 105 GDP index; (2) 3QTR08 with $3.5 fuel prices and 105 GDP index; and (3) 

3QTR09 with $2 fuel prices and 103 GDP index. The results provide insights on ASOM consistency in 

predicting airline behavior.

Table 35 shows 87% of ASOM scheduled flights per day within 15% of historical data. The ASOM 

results showed on average 12% fewer scheduled flights per day. The ASOM results for LGA and SFO 

3QTR 2008, with peak fuel prices, showed 18% and 21% less scheduled flights per day respectively than 

the historical data.

Cap LGA SFO EWR JFK PHL

3QTR

07-08

Hist* -6% -25% -12% 2% -10%

Model -3% -26% -15% 4% -10%

3QTR

08-09

Hist* 2% 18% 7% 6% 16%

Model 2% 18% 9% 6% 15%

Cap LGA SFO EWR JFK PHL

3QTR

2007

($2.08 f p)

% 

from 

Hist

-3% 0% -2% -2% -2%

3QTR

2008

($3.53 f p)

% 

from 

Hist

0% -3% -6% 0% -3%

3QTR

2009

($1.92 f p)

% 

from 

Hist

0% -2% -4% 0% -3%
71

 

Table 35 87% of ASOM profitable markets served within 15% of historical data

Table 36 shows 50% of ASOM annual trends for scheduled flights per day are within 10% of historic 

trends. Additionally, 70% of these trends were in the same direction.

Table 36 50% of ASOM trends for Scheduled Flights per day are within 10% of Historic trends

Figure 19 shows PHL third quarter 2009 historical operations per hour by time of day versus the 

ASOM results for capacity limits set at 72, 80 and 96 operations per hour. Modeled operations rarely 

meet the capacity limits since the model produces a domestic schedule therefore the historic international 

operations per hour are subtracted from the available capacity (72, 80 or 96) for domestic operations. It is 

interesting to see where the model peaks its schedule when capacity limits are relaxed to 96 operations 

per hour versus where the peaks were historically. But as clearly can be shown with this chart, the model 

is trying to create an optimal schedule that meets demand by time of day, by reducing redundant service 

and up gauging when economically beneficial.

Cap LGA SFO EWR JFK PHL

3QTR

07-08

Hist* -6% 3% -10% 4% -10%

Model -17% -8% -16% -10% -11%

3QTR

08-09

Hist* -1% 9% 2% 4% 12%

Model 13% 24% 5% 13% 14%

Cap LGA SFO EWR JFK PHL

3QTR

2007

($2.08 f p)

% 

from 

Hist

-7% -13% -8% -2% -14%

3QTR

2008

($3.53 f p)

% 

from 

Hist

-18% -21% -14% -15% -14%

3QTR

2009

($1.92 f p)

% 

from 

Hist

-7% -10% -11% -7% -13%
72

Figure 19 ASOM Distribution of daily flights matches historical distribution

A.1.3 ASOM Consistency with Historic Results for Aircraft Gauge

The ASOM baseline results for aircraft gauge were compared to historic results for all five congested 

airports (LGA, SFO, EWR, JFK, PHL) for three different economic scenarios: (1) 3QTR07 with $2 fuel 

prices and 105 GDP index; (2) 3QTR08 with $3.5 fuel prices and 105 GDP index; and (3) 3QTR09 with 

$2 fuel prices and 103 GDP index. The results provide insights on ASOM consistency in predicting 

airline behavior.

Table 37 shows 67% of ASOM results showed lower aircraft gauge than historical data. The ASOM 

results showed on average 5% smaller aircraft gauges than historically flown. The ASOM results for 

EWR 3QTR 2007 and 2008 showed greater than 30% up-gauging when compared to the historical data.

 

Table 37 ASOM consistency with historic results for aircraft gauge 

Table 38 shows 50% of ASOM annual trends for aircraft gauge are within 10% of historic trends. 

Additionally, 30% of these trends were in the same direction.

Cap LGA SFO EWR JFK PHL

3QTR

2007

($2.08 f p)

% 

from 

Hist

-17% -7% 30% 7% -20%

3QTR

2008

($3.53 f p)

% 

from 

Hist

-26% 6% 36% 4% -20%

3QTR

2009

($1.92 f p)

% 

from 

Hist

-12% -9% -1% -21% -28%

0

10

20

30

40

50

60

70

80

90

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 # Operations

Hour of Day

PHL 3QTR09 Historic & Modeled Operations versus Time of Day

historical

model -72

model - 80

model - 96
73

 

Table 38 ASOM annual trends for aircraft gauge 

A.1.4 ASOM Comparison of Opposite Markets

The ASOM results for opposite markets were compared for six market/ opposite market pairs for 

3QTR09 ASOM results. The results provide insights on for predicting airline behavior.

Table 39 shows 50% of ASOM opposite markets match with flights per day. The ASOM results 

showed that LGA-PHL, EWR-SFO, and JFK-PHL market schedules matched well with their opposite 

markets. However, the ASOM results showed that EWR-PHL, JFK-SFO, and PHL-SFO market 

schedules did not match their opposite markets. The ASOM found reciprocal service from PHL to EWR 

and SFO not to be as profitable as other markets. Also, the ASOM scheduled more than twice the number

of seats from SFO to JFK as compared to the opposite market.

 

Table 39 ASOM Comparison of opposite markets flights per day

A.1.5 Historic versus ASOM Functional Relationships

Table 40 summarizes the historical and ASOM functional relationships found in the analysis. This 

ASOM analysis of airline behavior has revealed eight new functional relationships, not previously seen in 

the historic analysis (highlighted in green). This analysis has also reinforced three and contradicted one 

functional relationship previously seen in the historic analysis (highlighted in yellow). Five functional 

relationships found in the historic analysis were not evaluated by the ASOM (highlighted in orange). 

Thus the ASOM has reinforced and expanded upon known functional relationships previously found in 

historical analysis.

Opposite Market Matches

Airport Market Flights Seats

LGA PHL 14 350

PHL LGA 14 350

EWR SFO 18 2950

SFO EWR 14 2850

JFK PHL 2 100

PHL JFK 4 100

Opposite Market no Match

Airport Market Flights Seats

EWR PHL 4 700

PHL EWR 0 0

JFK SFO 38 2150

SFO JFK 46 5550

PHL SFO 12 1100

SFO PHL 0 0

Cap LGA SFO EWR JFK PHL

3QTR

07-08

Hist* 0% 2% 0% 2% -1%

Model -10% 17% 4% -1% -1%

3QTR

08-09

Hist* -1% 2% 8% 9% 2%

Model 17% -12% -21% -17% -8%
74

 

Table 40 The ASOM functional relationship with historical analysis

Figure 20 graphically illustrates the finding shown in table 60. Specifically the ASOM was consistent 

in finding positive relationships between gross domestic product and airline schedules, and the ASOM 

was consistent in finding a positive relationship between capacity limits and airline schedules. Only one 

contradiction was found with the relationship between hedged fuel prices and profit.

Relationships Historical ASOM

Hedged

Fuel Prices

Airfare + N/A

Aircraft Size +

Flights/ Day -

Markets -

Profit - +

Gross 

Domestic 

Product

Airfare + N/A

Aircraft Size +

Flights/ Day + +

Markets + +

Profit +

Capacity 

Limits

Flights/ Day +? +

Markets +

Aircraft 

Size

Profit +

Congestion - N/A

Flights/ 

Day

Profit +

Congestion + N/A

Markets Congestion + N/A

5 Historic Relationships

Not Examined in ASOM

4 Relationship founds 

Historic & ASOM

8 New Relationships

found in ASOM
75

Figure 20 Graphical illustration of ASOM and historic analysis relationships

Hedged

Fuel Prices

Airfares

Profit/ 

Profitability

Airport 

Capacity 

Limits

Gross 

Domestic 

Product Scheduled 

Flights per Day

Congestion

Aircraft 

Size

Exogenous Factors Economic Access Airline Profitability

Markets

Geographic Access

+

+ -/ +

+/ +

-

+

+

+

+

-

+/ +

-

+

+

+

+

Not Examined in ASOM

Relationship found Historic & ASOM
REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

2. REPORT TYPE 

Contractor Report

 4. TITLE AND SUBTITLE

Optimizing Air Transportation Service to Metroplex Airports - Part II: 

Analysis Using the Airline Schedule Optimization Model (ASOM)

5a. CONTRACT NUMBER

NNX07AT23A

 6. AUTHOR(S)

Donohue, George; Hoffman, Karla; Sherry, Lance; Ferguson, John; Kara, 

Abdul Qadar

 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

NASA Langley Research Center

Hampton, VA 23681-2199

 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration

Washington, DC 20546-0001

 8. PERFORMING ORGANIZATION

 REPORT NUMBER

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NASA

13. SUPPLEMENTARY NOTES

Langley Technical Monitor: Rosa M. Oseguera-Lohr

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Unclassified - Unlimited

Subject Category 03

Availability: NASA CASI (443) 757-5802

19a. NAME OF RESPONSIBLE PERSON

STI Help Desk (email: help@sti.nasa.gov)

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; the failure to increase capacity at the same rate as the growth in demand 

results in unreliable service and systemic delay. This report describes the results of an analysis of airline strategic decision-making that affects geographic 

access, economic access, and airline finances, extending the analysis of these factors using historic data (from Part 1 of the report). The Airline Schedule 

Optimization Model (ASOM) was used to evaluate how exogenous factors (passenger demand, airline operating costs, and airport capacity limits) affect 

geographic access (markets-served, scheduled flights, aircraft size), economic access (airfares), airline finances (profit), and air transportation efficiency 

(aircraft size). This analysis captures the impact of the implementation of airport capacity limits, as well as the effect of increased hedged fuel prices, which 

serve as a proxy for increased costs per flight that might occur if auctions or congestion pricing are imposed; also incorporated are demand elasticity curves 

based on historical data that provide information about how passenger demand is affected by airfare changes.

15. SUBJECT TERMS

Air transportation; Airports; Airline operations; Metroplex operations; Airline scheduling; Airline schedule optimization

18. NUMBER

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80

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(443) 757-5802

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