Comprehensive Analysis of Airline Schedules & … Analysis of Airline Schedules & Airport Delays...

©2012 American Aviation Institute. Protected document, all rights reserved.

The State of U.S. Aviation

Comprehensive Analysis of Airline Schedules & Airport Delays

Darryl Jenkins, Chairman [email protected]

Joshua Marks, Executive Director

[email protected]

Michael Miller, Vice President, Strategy [email protected]

February 16, 2012

AMERICAN AVIATION INSTITUTE

4833 Rugby Avenue, Suite 301 Bethesda, MD 20814 USA

Report and supporting materials available at:

www.aviationinstitute.org


February 16, 2012 Page 1

ABSTRACT

As the U.S. economy rebounds from cyclical recession, inevitably there is a point where the media

asks if our national aviation system is at capacity, or is about to reach capacity. Consumer advocates and

regulators claim that airline “over-scheduling” (which we define as scheduling more flights at an airport

than reasonably sustainable over the long term) is responsible for excessive flight delays and cancellations.

Claims are often made about airport and airspace capacity, where airport and airspace choke points exist,

and how to fix them through government intervention.

This report intends to illustrate the dependencies and intricacies of airline, airport and air traffic

control constraints on the U.S. air transportation system. AAI has conducted an extensive analysis of

flight-level delays, airline capacity, operating metrics and flight schedule design. We believe it the most

extensive airline schedules and delay analysis undertaken to date, using more than 200 million data records

spanning 20 years, and investigating delays and aircraft performance at 400 United States airports. We

conclude that airline scheduling decisions are consistent with the expected capacity of given airports, as

defined by FAA Operational Metrics, historical and reasonable future weather conditions, and changes

made to airline scheduling practices including aircraft gate turn times, schedule de-peaking and en-route

scheduled block time. Significant variability of ramp, taxiway and local airspace capacity at certain

airports impacts real-world airline departure flow capacity and is the primary driver of unexpected lengthy

delays. We illustrate that direct government intervention in airline scheduling through demand

management (e.g. airport slot control) would be counter-productive. If the U.S. government undertook

“demand managing” of flights at major airports to match worst-case runway capacity, it would cause $18.7

billion in annual economic harm. However, long-term investment in airspace systems set in recent passage

of the FAA Reauthorization bill will have a significant benefit to both airspace efficiency and delays, if

both airlines and general aviation operators meet minimum thresholds of new technology equipage.

Recent airport runway capacity increases and schedule rationalization by airlines have improved

on-time performance at major airports. To continue this improvement, we propose solutions in this report

to further improve communication among airlines; establish airspace capacity benchmarks; incorporate

taxiway, ramp and human factors into airport capacity benchmarks; and alleviate operational restrictions

that encourage gate delays and cancellations. This study utilizes public data sets from the FAA and DOT

and available data sets from NOAA and OAG, a commercial information vendor. Analytics in this report

were prepared using databases from DOT and FAA, as well as analytical tools from the masFlight data

platform (www.masflight.com).



CONTENTS

1. Executive Summary

2. Key Findings & Conclusions

3. Flight Delay Trends & Analysis

4. Airport Capacity Benchmarking

5. Schedule Peaks, Capacity & Delays

6. Rebutting The Overscheduling Argument

7. Conclusions and Recommendations

8. Exhibits & Appendices



SECTION ONE: EXECUTIVE SUMMARY

This paper presents an exploratory analysis of the relationship among flight delays,

airline schedules and airport capacity. It is the most extensive data-driven analysis of airline

schedules and delays even undertaken. Data cited is for scheduled airlines. Airport data also

includes non-scheduled capacity, including cargo, military and general aviation operations. Flight

delays are gate departures or arrivals 15 minutes or greater after the scheduled times. Airline

scheduling is the assignment of specific aircraft and departure times to routes served. Airline

over-scheduling is the practice of scheduling more flights in a given time window than an airport

can accommodate without chronic delays given environmental conditions that influence ramp,

taxiway and runway capacity. To date, the connection between controllable schedule planning

and flight delays has been neither quantitatively defined nor proven.

We review the connection between airline flight schedules, airport conditions and

constraints, and observed flight delays and cancellations. We assess the relationship during

varying weather and operational conditions with focus on the following three questions:

1. How have airlines adapted their flight operations and schedule planning to mitigate

the impact of flight delays and cancellations?

2. Is the airline practice of scheduling arriving and departing banks of aircraft to

minimize connecting time for passengers (defined as peaked schedules) causing

major delays at large airports?

3. Do airlines, on average, individually and in aggregate, overschedule their operations?

If so, is this overscheduling a major determinant of flight delays observed?

These are critical questions in both determining the optimal level of airport capacity

planning and delay management strategies as the industry transitions to GPS-based navigation

systems (“NextGen”) and manages associated capital investments. Various academic, regulatory

and industry commentators have linked overscheduling and airline flight delays, but no

determinative link between the issues has been made. Existing research is highly quantitative,

sometimes ignoring real-world flight and operational constraints; is based on systemwide data or

data for broad airport groupings; and does not account for historical weather conditions and

advance predictions of weather, ramp and airspace congestion made by individual airlines when

scheduling airports. We believe this is why existing research has yet to translate into concrete,

Section One: Executive Summary !

Page 2

actionable policy recommendations regarding slot creation, slot trading and systemwide delay

management, either in the current ATC environment or under future NextGen infrastructure.

We find that intentional scheduling decisions by airlines play a limited role in causing (or

addressing) flight delays observed. Weather factors, regional choke points and intersecting

departure and arrival corridors between airports remain the most important factors connected to

observed delays. The overall level or composition of airline demand has minimal impact

(positive or negative) on observed delays except where the airport is operating at a very high level

of demand relative to capacity. In these limited situations, government-organized demand-

management programs (in the form of reservations and/or slots with rigid restrictions on trading

and re-allocation) limit airlines’ ability to optimize flight schedules in order to minimize delays.

As a result, we observe abnormal flight delays at airports with these government-managed

demand controls, and a significant follow-on impact of these controls systemwide.

We find no evidence that airport-wide airline scheduling decisions create excessive

exposure to external delay factors (weather and airspace). Balanced departure flows, buffers

in en-route scheduled time, and extended airport turn times provide a meaningful buffer against

weather and airspace congestion impact. There have been changes in each of these strategies

since the delay peak of the mid-2000s. We demonstrate that since 2005 most carriers have taken

significant pro-active steps insulate flights from the follow-on impact of delays.

We focus in particular on strategic delay management programs into their schedules,

reviewing how airlines expanded en-route flight times to internalize probable delays. We

demonstrate that the increased variability of taxi-in and taxi-out times drives an average increase

in en-route planned time (“block time”) of between 2% and 4% (depending on time of day)

between the late 1990s and 2010. We posit that delay internalization is largely driven by DOT

monthly media reports that rank airlines based on gate arrival within 15 minutes, without

consideration to en-route flight time or normal operational variability. By holding airlines

accountable to a specific metric – and one of debatable value to consumers – DOT has guided

airline operational behavior towards schedule padding. This in turn creates gate and ramp

congestion at airports as flights await runway departure or gates after landing.

Internal schedule planning factors are largely dependent upon the airport and airspace

infrastructure on which those decisions are made. To assess the relationship between schedules

and airport capacity, we have analyzed 10 years of operational data. We review the FAA



Operational Benchmarks published for U.S. airport facilities, the actual capacity of the given

runway infrastructure (the ongoing sum of departure and arrival rates) and weather variability

observed during the past 10 years. We map airline schedule demand against capacity and observe

that operating in excess of the FAA Benchmarks does not necessarily result in flight delays. We

identify problems inherent in the FAA Benchmarking methodology, including exclusion of ramp

and taxiway capacity, weather variability by hour of the day, and other qualitative factors such as

controller experience that materially impact airport operation rates, as factors airlines take into

account today when setting schedules. We confirm that the FAA Benchmarks offer a useful

starting point for airline schedule planning, but require adjustment to gauge absolute capacity of a

given airport under specific weather conditions.

Consumer advocates frequently discuss the theoretical concept of airline over-scheduling

in the media. Commentators casually define over-scheduling as the practice of consciously

operating more flights than an airport can reasonably accommodate. We find past evidence of

over-scheduling in only one market – New York City – and only during two specific windows, at

LaGuardia airport after the revocation of commuter slots in 2000, and at JFK airport during the

fall of 2006. Both proved expensive lessons for both airlines and the traveling public, and

resulted in a significant overhaul of airline scheduling practices. In today’s environment, we find

no evidence connecting flight planning decisions made by a specific carrier at a specific

airport and resulting delays or cancellations that can be traced directly to that schedule.

We also find the claim that controllable factors such as airline schedules can

meaningfully reduce flight delays to be unsupported by available data. We assess the economic

cost of reducing airline schedules to worst-case runway capacity levels. Demand-management

programs with this objective would cause $18.7 billion in annual economic harm to both airlines

and the tourism industry, not counting lost productivity or business harm. Furthermore, we

demonstrate that on-time performance improvements from such demand-management programs

are marginal, and would not achieve the target on-time performance rates set by regulators.

During the past 10 years, U.S. carriers have introduced new “rolling” hub scheduling

models where operating banks are smoothed out. Rolling hub structures – versus peaked models

– are particularly cost-effective at airports that operate close to FAA Operational Benchmark

capacity. During normal weather conditions, rolling bank structures utilize gate and ramp

infrastructure efficiently. During irregular operations, rolling hub structures actually contribute to

higher follow-on flight delays as the consistent level of hub operations prevents “catch-up”

Section One: Executive Summary !

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periods present in banked models. We show that concentrated or “banked” hub models operating

below maximum capacity actually drive the lowest overall delay metrics relative to highly

utilized or rolling peers.

We conclude that delay reduction through strategic flight scheduling has become an

integral part of airline planning and decision-making. Rolling hubs, strategic delay management

through expanded block times (or “schedule padding”) and increased aircraft turn times

internalize the impact of weather and airspace variability.

We propose three policy or regulatory options that we believe could drive significant

further improvements in delays and cancellations.

First, we observe a critical need for open-market slot-trading mechanisms at demand-

managed U.S. airports including New York’s JFK and LaGuardia, and Washington Reagan

National. Political interference (driven by regional air service demands) restricts airlines’ ability

to re-allocate slots economically to match demand to capacity. In 2011 DOT approved an

elaborate slot exchange between Delta and US Airways at LaGuardia and Washington National,

but the conditions for divestment in this transaction indicate an increased ambition by

government to micro-manage flights and competition at slot-restricted airports. In addition, this

transaction took more than two years to complete, a further delay of open market competition.

Pooling commuter and mainline slots – currently there are rules that force an airline to

operate aircraft that conform to one type of slot or the other – would allow larger and more

economically viable aircraft to serve key routes and potentially dampen the number of smaller

aircraft that currently are forced by government rule to be used at these key airports. We observe

the negative impact of political interference and arbitrary slot re-allocation on flight delays.

Second, we believe that limited anti-trust immunity for airline scheduling decisions at

key U.S. airports would benefit the traveling public. Such collaboration should be limited to

advance information exchange regarding departure and arrival demand preferences, aircraft types

and destinations. Current rules forbid any discussions. This would be a natural extension of

today’s Collaborative Decision Making (CDM) framework that allocates departure and arrival

capacity during operational disruptions to carriers. By allowing airlines to exchange schedule

priorities in advance, a fuller picture of airport demand could be achieved, potentially reducing

schedule peaks, especially at high-demand East Coast U.S. airports. We do not believe anti-trust



immune discussions give airlines unfair leverage over consumers, since they are incentivized to

reduce block time and reduce taxi times. Carriers would achieve lower labor and fuel costs.

Third, operational regulations including invasive limits on taxi times could be improved,

as these have had a significant impact on gate holds and flight cancellations since April 2010.

The flight cancellation rate during bad weather events has been consistent across operational

seasons. However, bad weather cancellation rates increased from 3.6% to 5.2% excluding the

severe weather patterns observed during the winter of 2010-2011. DOT consumer protection

regulations have shifted delay time from post-gate departure to before boarding, with the impact

of congesting gate and ramp facilities. We believe that clear enforcement guidance, reasonable

fines (versus multi-million dollar penalties in force today that exacerbate cancellation behavior)

and re-framing the regulation to measure gate return decisions by the pilot in command, versus

the aircraft at-gate, would significantly improve flight completion rates nationwide and help

hundreds of thousands of consumers to reach their destination faster.

We find that airline schedules are based on reasonable estimates of weather probability

and competitive runway demand, grounded in historical weather statistics and justified by FAA-

published operational metrics. Weather and regional airspace congestion outside airline control

are the primary drivers of excessive delays, just as they remain the key drivers behind flight

cancellations and extended on-board tarmac times. We conclude that changes in aircraft turn

times and incorporation of expected delays into published en-route times mitigates the impact of

delays, providing consumers with a reasonable expectation of arrival times. Fundamental change

to airline delays will require introduction of next-generation air traffic management systems such

as the FAA’s NextGen air traffic management program to alleviate regional airspace congestion

and improve departure and arrival rates at key airports.

We use four primary data sets in our analysis. First, FAA operational data provides

aggregate-level demand, delay and congestion metrics, as well as overall arrival and departure

capacity rates. Second, DOT Part 234 Airline Service Quality Performance data provides airline-

and airport-specific demand and delay information for domestic flights. Third, airline flight

schedules as published by OAG augment the Part 234 data set with international flight schedules

and demand. Finally, the model uses hourly weather data provided by the National Climactic

Data Center at the U.S. Department of Commerce. All data in this model is obtainable by public

researchers with permission. Our data samples are based on the aggregate period from January

2000 through December 2009, plus on-time and schedule data through March 2011.

Section Two: Key Findings and Prior Work !


SECTION TWO: KEY FINDINGS AND PRIOR WORK

This paper presents an exploratory analysis of flight delays, airport capacity, airline

schedules and their corresponding relationships to departure and arrival delays observed at major

airports in the United States. Using weather, flight schedule, and delay information from aviation

(DOT and FAA), climate (NOAA) and commercial (OAG) sources, the paper explores the

relationships between schedules, capacity and flight-level operational data.

2.1 Key Findings

From our exploratory analysis, we observe the following:

1. Airline scheduling practices have adapted with an increased focus on on-time

performance. We focus on three broad categories of operational strategy changes:

en-route scheduled time (“block time” and “schedule padding”), hub-airport

connecting flight structures and banking, and scheduled/actual aircraft turn times at

key U.S. airports.

2. Over the past 10 years, airlines have progressively de-peaked their flight schedules

at major hubs. Outside of the New York metropolitan area, most hubs operate on a

rolling structure at 58% of published capacity.

3. The airports with the most significant peaks in operational scheduling are also the

airports with the lowest observed aggregate delay rates. We recognize that this

runs counter to popular opinion, but it is supported by data from a geographically

diverse pool of airports of varying sizes. Peaked schedules are generally observed

at airports with lower overall capacity utilization. Airports with higher capacity

utilization generally have a more even distribution of flights.

4. There is no significant statistical correlation between peaked schedules and flight

delays. Flight delays correlate to total utilization at the margin, not to the structure

of banks or at airports with moderate capacity utilization. In other words, the

aggregate utilization of a given airport – as driven by gate and ramp facilities, by

air-traffic control facility flexibility and airspace design – has a more direct

relationship to delays than the scheduling decisions made by carriers at that given

airport, particularly when overall airline demand for resources at a given airport is

high.


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5. For short-term weather and airspace disruptions, peaked schedules insulate flights

from rolling delays originating at the airport. The “valleys” between peaks in a

schedule provide an opportunity to recover from short-term disruptions. In

contrast, airports that operate near capacity but without peaks offer little

opportunity or “slack capacity” to recover until the end of the day – so delay and

cancellation events snowball. These airports can be demonstrated to originate

flight delays that continue throughout the system.

6. Peaked schedules – as determined by individual airline scheduling decisions – are a

red herring when considering flight delays at a given facility. Peaked schedule

decisions by individual carriers were relevant 15 years ago as delay drivers, but are

not as relevant today.

7. With the shift from peaked to rolling banked models at major hubs, airlines have

generally expanded the time allocated to turn aircraft at these facilities. Average

aircraft turn times have increased 4.2% systemwide between 2005 and 2010.

Longer turn times provide insulation against downstream impact from delayed

inbound arrivals. They also provide additional time to address carrier-caused

delays such as mechanical and crew staffing events.

8. There has been a significant shift in the variability of taxi-times at key U.S. airports

relative to changes in actual flight times. This is driven by the first-in, first-out

airspace management system in place today. Aircraft are accepted into the system

when ATC can route the flight without excessive delays. Ground delay programs

create ground congestion at originating airports as planes queue for scarce arrival

positions at the impacted destinations. Airlines have internalized the impact of

GDPs through additional block-time minutes (called “schedule padding”) between

2% and 4% system wide.

9. Given bank structures, turn times and en-route padding, airline schedules are based

on operational assumptions about the frequency and severity of weather events that

reduce airport capacity. Airlines assess available capacity relative to the expected

weather, airspace and regional constraints at a given airport. The FAA uses three

static benchmarks for runway capacity, but these benchmarks do not differentiate

between morning and evening, or between winter and summer.



10. FAA Operational Benchmarks of airport capacity do not fully incorporate gate

capacity, ramp and runway layout, immediate airspace design and other factors that

significantly impact airport capacity. Airlines take into account the probability of

given weather conditions based on observed historical conditions. Such metrics

must be seasonally dynamic and differentiated by time of day. The probability of

delay-inducing weather by season and time of day must be incorporated into

planning, along with the level of operations for non-scheduled passenger, cargo and

military flight operations.

11. We find no correlation between flight schedules and flight delays, examining both

individual airline schedules and all schedules/delays in aggregate. We filter by

time of day, day of week and month of year without significant changes in

correlations observed.

12. When aggregate flight schedules – as independently determined by competitive

carriers in the absence of information sharing – approach or exceed the FAA static

definitions of maximum capacity, delays can result. This is particularly true for the

bad-weather threshold capacity (“IFR”) at six major airports. This suggests that the

definitions used by FAA for optimal capacity do not properly incorporate

variability in weather, as for significant periods of each year airports operate above

the FAA’s maximum capacity definition without causing significant flight delays.

13. We find no evidence that airlines have overscheduled for competitive or market-

dominance reasons at any major airport in the past 10 years. In two cases,

aggressive scheduling by a combination of carriers at the same airport, through

independent competitive decision-making, has resulted in aggregate operational

levels in excess of the overcapacity threshold we define. In these situations,

resolution must either incorporate solutions at an airport level, or some level of

anti-trust immunity or approved information sharing to facilitate inter-airline

coordination of capacity.

14. We demonstrate that reducing flight schedules at major U.S. airports to the worst-

case runway capacity would have an $18.7 billion annual cost to airlines and the

U.S. economy. We also demonstrate that such a change may result in a marginal

improvement in on-time performance, but would fall well short of the target on-


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time rates set by the FAA. We point to airspace capacity improvements –

particularly implementation of NextGen infrastructure – as the key to improving

flight performance without economic harm.

15. We define potential follow-on research that could identify the specific marginal

conditions when schedules become unreliable, the recursive relationship of

weather, runway, taxiway and ramp congestion on operational performance, and

how airlines could exchange scheduling intentions to reduce the impact of

cumulative competitive decisions without violation of anti-trust regulations.

2.2 Prior Work

Our work continues analysis of delays, airport capacity and related trends by both

academic and industry practitioners. Table 1 below presents key research papers during the past

10 years that assess the relationships between flight delays and airport capacity.

Table 1: Prior Work on Airline Delays and Airport Capacity For more information, see Xu, Laskey & Sherry Method for Deriving Multi-Factor Models for Predicting Airport Delays (2007)

Response variable Predictor variables Author Methods Probability of on-time performance, delays and flight cancellations at New York LaGuardia Discussion of general flight delay and flight cancellations

Economic (e.g. revenue, load factors), Route Competition (e.g. Monopoly), Airport competition (e.g. Concentration at origination, hub destination), Logistical (e.g. slot origination, distance, hours until next flights). Weather (e.g. rain, minimum temperature, frozen)

Rupp 2005 Nested Logit Model

Airport Arrival Rate (AAR)

Scheduled arrivals, visibility, wind, visibility and operational condition, time and season

Hansen and Zhang 2005

GARCH

LaGuardia average arrival delay and National Airspace System average arrival delay (daily)

Airport and airspace-specific flight delays, with consideration to exogenous variables: derived queuing delay, adverse weather, ground-delay program holding and overall flight operations levels

Hansen and Zhang 2005

Two-stage least-squares model with GARCH model

Weather-weighted traffic count (surrogate for system delay)

Expected traffic demand, Weather Impacted Traffic Index (WITI), IMC, wind speed

Chatterji and Sridhar 2005

Recursive OLS

Arrival and Departure Delays Observed

Departure delays at origin, arrival delays at destination, loads on departure flights, stations and stop-times.

Vigneau 2003

Recursive OLS



Response variable Predictor variables Author Methods Airspace delay impact, cancellations and diversions

En route WITI, instrument weather conditions and wind speed

Callaham 2001

OLS

Taxi-out Times Runway configuration, airline, VFR/IFR, downstream restrictions, departure demand, queue size

Idirs et al. 2002

OLS

Our methodology builds on prior work in four respects.

a. Ground-Up Flight Analysis. To address differences among airports and flight scheduling

strategies, our flight information is based on the complete records of DOT On-Time

Performance Data as filed by reporting carriers under Part 234.

b. Tail number tracking. To identify changes to en-route scheduled time and aircraft turn

times in the U.S. airspace system, we reconcile tail-number information reported by U.S.

carriers with FAA Registry data in order to isolate differences among airlines and fleet

types.

c. Weather integration. We fully integrate weather and runway configuration data in order

to differentiate good-weather and bad-weather performance.

d. Exploratory objectives. We correlate factors related to airline schedules, weather

conditions, delays and cancellations in order to establish links among the factors and

identify changes in airline strategy. Our objective is to develop further discussion on

these topics and identify assumptions not supported by observable data.

Section Three: Flight Level Delay Trends !

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SECTION THREE: FLIGHT LEVEL DELAY TRENDS

The fundamental question when assessing delay causes is this: Do independent airline

scheduling decisions, made with rational internal justification in pursuit of each airline’s

commercial objectives, cause delays in the national airspace system?1

The answer has both obvious and non-obvious components. To start with the simplest

case, if airlines scheduled no flights, there would be no flight delays. As system utilization builds,

there is a general relationship between demand and delays. However, previous work has

demonstrated that the correlation between demand and delays is weak until aggregate demand

levels approach the absolute capacity limits of a given airport.2 There are few cases where

airports actually operated at or above the published capacity limits, and in the most recent case –

at New York LaGuardia after slot restrictions on regional jets were lifted in 2000 – the impact on

delays was severe. Government-administered demand management programs such as slots or

allocations are imposed before airports reach the break point. It is worth noting that delay and

congestion issues are prevalent at airports with government-administered programs, for reasons

that we discuss later.

Weather, airspace congestion, airport capacity and aircraft dispatch reliability all have

stronger relationships with delays than airline schedule demand for airport resources. In this

paper, we investigate flight delay trends, airport capacity benchmarks and changes in airline hub

scheduling to explore the relationships among these factors and correlation to aggregate flight

delays and cancellations. The question we seek to address is whether changes to airline

scheduling strategies, in the absence of significant capacity reduction, can address flight delays

and cancellations.3 We also seek to address whether airline planning and operating strategies have

adapted since the delay peak of 2007 with changes to schedule design and implementation.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!1 It is worth noting that airline scheduling decisions, unless coordination is explicitly authorized by the United States government, occurs among airlines in relative isolation compared to the open slot-exchange and demand-managed environment prevalent in Europe. A key driver is the United States Federal Aviation Administration (FAA) requirement that airport recipients of federal funds must commit to open and fair access by all airlines for all airport assets. 2See GAO, National Airspace System: Setting On-Time Performance Targets at Congested Airports Could Help Focus FAA's Actions, GAO-10-542 May 26, 2010 3 We note that several consumer advocates have called for a reduction in domestic flight capacity in order to address flight delays. We agree that very significant reductions in flight capacity would indeed result in a material improvement in flight operational quality and cancellations. However, the cuts in capacity would likely need to be 35-40% of currently scheduled flights – even during the late 1980s and early 1990s, with flight operations 20% lower than 2010, on-time arrival performance was similar. A reduction in domestic capacity of 35-40% would have a severe impact on the U.S. economy and the traveling public. We therefore discount material (systemwide) capacity reduction

Section Three: Flight Level Delay Trends


To examine optimal scheduling levels, one must construct a definition for excessive

scheduling. As noted in prior works on a systemwide basis, there is an inherent trade-off between

capacity (number of flights) and delays.4 The higher the system load, the less flexibility airlines

have to recover their operations when weather, airspace, mechanical or security factors prevent

scheduled flight operations. We define airline over-scheduling as the practice of consciously

scheduling more flights to and from a given airport than the capacity level at that airport

reasonably expected based on a probability distribution of weather, airspace, and systemic

delay factors.

This definition parallels the independent scheduling decisions that occur at airlines as

they plan future schedules, block times and gate utilization. We will utilize this definition

throughout this paper, reviewing where capacity definitions used by the FAA and other regulatory

bodies differ from our standard.

3.1 Exploratory Analysis Objectives

Our exploratory analysis is intended to establish relationships among capacity, weather

and airline schedules in order to ultimately model flight delays. Our approach is granular, using

schedule, weather and on-time data on an airport-specific basis. Our approach builds up to

systemwide estimates, rather than focusing on systemwide performance metrics that mask

important differences in specific airport designs, schedules and weather conditions. Our analysis

uses publicly available data from government aviation sources (including FAA and DOT

historical data), weather information (from NOAA and the National Climate Data Center) as well

as industry sources (OAG).

We explore five issues in depth:

a. What factors cause airline delays? What trends can be identified to connect independent

scheduling decisions made by carriers with both carrier and systemic delays observed?

b. Is the airline practice of scheduling arrival and departure banks in order to minimize

passenger connect time (peaking) causing major delays at large hub airports?

c. What changes to airline turn times and en-route scheduled block time to internalize the

impact of systemwide airspace and weather delays can be observed?

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!as a potential response to flight delays. However, there is potential for demand management solutions at key airports such as New York LGA and JFK as long as airlines are permitted to exchange slots without government interference. 4Xu, N. et al. Multi-Factor Model for Predicting Delays at U.S. Airports. Transportation Research Board, 2005.


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d. Do airlines, individually or in aggregate, over-schedule their operations? If so, to what

extent do airline schedules correlate to flight delays observed?

e. Given the core conditions described above, what improvements in physical infrastructure

or airline practices could insulate schedules from repetitive delay drivers?

These are critical to consider in determining capacity planning decisions and delay

management strategies. Under the current radar-based traffic management infrastructure, capacity

flexibility is limited by absorption rates of airport-specific airspace. The transition to NextGen

GPS-based ATM infrastructure will offer new flexibility to airlines and airports. Understanding

the delay implications and relationships will be critical to justifying infrastructure investment by

both government and industry. Various academic, regulatory and industry reports have linked

airline schedules and flight delays, but no comprehensive model has yet demonstrated a direct

(causal) link between the two.

Much of the existing research on factors relevant to airline scheduling and delays is

quantitative and theoretical, sometimes ignoring real-world operational constraints. By starting

with systemwide data (as opposed to ground-up airport analysis) the body of research understates

key differences in runway configuration, weather patterns, airline competition and schedule

drivers for each airport. The optimal framework for schedule and delay analysis mirrors the

everyday scheduling decisions made by carriers. The optimal flight schedule tempers the

revenue-maximizing schedule with a given probability expectation of weather and other

operational disruptions. No airline plans to operate at 100% on-time performance levels, and

mandating high on-time performance would only encourage thin schedules and flight

cancellations that would harm consumers. The FAA’s internal target of 88% on-time

performance is equally unrealistic given the wide variation among airports, geography, weather

and terminal capacity at U.S. airports. A more surgical approach to on-time performance is

required, and the question of what operational reliability to target is an airline-specific decision.

Weather, airport design and airspace congestion are all key variables in this equation.

This is why we believe existing delay research has yet to translate into concrete and actionable

policy recommendations that result in delay improvements, particularly for slot-controlled

airports and those susceptible to long delays. In particular, the connections between schedules,

weather and delays (under our current radar-based traffic system and under NextGen

infrastructure) are highly relevant in discussions of slot creation and trading, schedule

coordination (arms-length or with explicit antitrust immunity) and airport-specific recovery plans.



In this section, we review the general delay trends and effects observable through the past

20 years. In particular, we focus on flight-level delay and cancellation data since October 2008,

when U.S. airlines reporting flight performance data to DOT began to report more detailed flight

ground delay, diversion and cancellation data. In Section Four, we will review airport capacity

benchmarking in order to compare delay trends against changes in airport capacity, and changes

in airline schedules, focusing on hub de-peaking and increased capacity utilization at key hub

airports between 2000 and 2010. Finally, in Section Five we will review the arguments made by

consumer advocates to support claims of airline “overscheduling” and demonstrate why such

claims are based on questionable assumptions and estimates.

3.2 General Delay Causes

Our objective for this section is to review the primary causes of airline delays, and

investigate the scheduling, equipment and network patterns that drive observable differences

among key U.S. airlines. There are three primary factors that cause airline delays and disruption

to the planned flight schedule:

1. Non-systemic, airline-specific factors, including mechanical failures, labor

resources, inability to load or deplane aircraft, inability to de-ice and unavailability

of required gate and ramp assets;

2. Competitive scheduling and operational decisions made by other carriers at the

airport, which may impact the availability of ramp, taxiway and runway assets

during the airline’s planned operation; and

3. The physical capacity of the airport and surrounding airspace, including ramp

space, taxiways, runways and regional airspace. This physical capacity defines how

many aircraft to and from a given airport can be absorbed in a timely manner.

Each of these primary factors is influenced by weather conditions, making environmental

factors the most important determinant of whether airline schedules optimized on the three

criteria above will be operationally robust. Weather influences the availability of crew (which

must transit to base or to airports after layovers) and ramp personnel. Daily weather creates

disruptions that flow through airline networks and force delay or concentration of flight

operations. And most importantly, weather has a significant impact on the physical capacity of

an airport and surrounding airspace. Weather curtails departure and arrival corridors,

contaminates runways and creates conditions requiring higher levels of separation between


Page 16

aircraft. Weather not only impacts controllable airline factors, but it also impacts the core arrival

and departure capacity of an airport and its surrounding airspace.

For this reason airline scheduling requires a predictive estimate of the impact of weather

conditions and environmental factors on a given airport’s departure and arrival capacity. It

requires estimation of the frequency with which weather or environmental conditions will occur.

And weather amplifies the game theory of carrier schedule design, as independent carriers make

isolated scheduling decisions for flights at the common resources of major airports. While we

show that delays and schedule activity have low direct correlation, severe weather and flight

delays have a very high correlation.5 In fact, weather conditions are the primary driver of flight

delays, as conditions result in significant loss of effective airport and airspace capacity.6

To what extent can severe weather events be predicted and built into airline schedules?

This is a fundamental question for airline planning and operations. Every major airline has (from

observation and experience) a measure of airport capacity and performance under a variety of

weather conditions. Each airline also has historical data to plan the average impact of weather

across long periods of time. Predicting the average impact of weather across a network at the

beginning of a season is difficult, but with long-range forecasting not impossible. Predicting

which specific airports will be hit by weather on which specific days with certainty sufficient to

merit reduction in flight schedules and loss of revenue is beyond today’s technology. Given that

flight schedules are set months in advance, this means airline weather planning is relatively blunt,

based on general statistical metrics and focused more on operational recovery than on culling

flight schedules months in advance.

3.3 Planning for Airport Capacity

Airlines use a variety of information sources when assessing long-range schedule

susceptibility to weather events. The first step is often to assess an airline’s historical flight

performance at a given airport, along with historical weather information to determine average

occurrences of convective, winter weather and other disruptive environmental conditions. But

while airlines have a wealth of internal data to refer to, information about a given airport’s

capacity has been difficult to obtain.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!5 As shown in Exhibit F, the correlation between airline weather delay minutes and airline cancellations is 0.63, a relevant, strong and positive correlation. 6For more information, see Exhibit E.



To address this information gap, the FAA has published operational capacity benchmarks

for each major U.S. airport.7 The FAA's estimates provide three predicted capacity levels for

arrivals and departures: the rate during good weather, marginal weather and bad weather. Last

updated in 2004, the FAA estimates establish a baseline of data to use for analysis of airport

capacity. Because the Operational Benchmarks exclude information about a given airport

facility’s ramp and taxiway infrastructure, gate capacity, local airspace congestion and the very

relevant human factors around controller flexibility and recovery experience, the Operational

Benchmarks are a better estimate of runway capacity versus airport capacity. In Section Four,

we examine how those benchmarks have evolved in practice as new runway capacity and

schedule optimization since 2004 have impacted airport and airline operations. We also show

why the Operational Benchmarks must be reviewed in conjunction with airline schedule, airport

movement area and terminal capacity issues.

In addition to core Operational Benchmarks, the FAA also sets arrival and departure

capacity for key U.S. airports throughout the day.8 These metrics are called Airport Arrival Rates

(AAR) and Airport Departure Rates (ADR). The available airport capacity is the sum of ADR

and AAR. These metrics represent the maximum flow of arrivals and departures that a given

airport can process. They incorporate more real-world factors than the theoretical Operational

Capacity Benchmarks, including the inherent trade-off between arrivals and departures (that is,

increasing arrival capacity by a given amount may result in a disproportionate decrease in

departure capacity because of runway configurations and arrival/departure corridors in the airport

vicinity).

These published arrival and departure rates provide an insight into the relationship

between aggregate airline demand9 and specific airport capacity at a given point in time.

Published arrival and departure rates may differ significantly from the airline flight schedules for

a given facility. For secondary markets with large airports, airport capacity can be significantly

higher than airline demand. At large airport facilities in metropolitan airports, where one or more

carriers may operate a hub facility, total utilization of the airport can average over 75%. Airline

demand can routinely exceed the level of operations sustainable during severe weather conditions.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!7 FAA Airport Capacity Benchmark 2004 (Updated June 1, 2010); http://www.faa.gov/about/office_org/headquarters_offices/ato/publications/bench/ 8 Airport arrival rates (AAR) and Airport departure rates (ADR) are actually set on a quarter-hour basis through the day at 77 major U.S. airport facilities. They are available under authorization at aspm.faa.gov. 9Aggregate airline demand means the total demand by all carriers that have scheduled flight departures from a given airport at a point in time. Individual airline demand would capture flight departures for a given carrier only.


Page 18

Table 2: Airport Utilization vs. Capacity Source: FAA ASPM Full-Year 2009, 7am-10pm Time Window

% DepDly = Percent of flights delayed by 15+ minutes at departure Av Delay = average minutes of delay for delayed flights

Name of Airport Capacity Flights Utilization % DepDly Av Delay New York LaGuardia 424,913 341,380 80.3% 21.3% 34.7 Newark Liberty 469,685 366,744 78.1% 26.6% 41.0 Atlanta Hartsfield-Jackson 1,205,933 926,551 76.8% 22.4% 41.0 New York John F. Kennedy 485,967 368,778 75.9% 22.5% 40.8 Philadelphia International 579,855 404,097 69.7% 24.2% 38.0 Chicago O'Hare 1,141,379 765,937 67.1% 21.0% 45.6 Washington Reagan 394,189 256,876 65.2% 16.4% 27.6 San Francisco International 531,344 321,660 60.5% 23.1% 35.5 San Diego Lindbergh 278,107 162,753 58.5% 18.8% 20.4 Charlotte/Douglas Int'l 768,646 445,110 57.9% 18.6% 29.1

In Table 2, capacity refers to the rate of arrivals and departures that the aircraft reported it

could handle during the full year of 2009. This is based on the cumulative capacity sum of every

quarter-hour measurement reported. Flights reflect the departures and arrivals scheduled as the

annual sum of each quarter-hour, and utilization is the planned ratio of flights to capacity.

There is an observable relationship between airport utilization and delays, particularly as

the percentage utilization of given airports exceeds 70%. The correlation coefficient for major

airport facilities between utilization and departure delays in 2009 (during peak hours of 7am to

10pm) was 0.56, and the relationship strengthens as utilization approaches 100%. But there are

many highly utilized airports in the U.S. that operate during peak hours with above-average

performance and low flight delays. Examples include San Diego (60.2% utilized during peak

hours, but with delays 20% below average) and Houston Intercontinental (also 60% utilized, with

departure delays approximating the national average). There are also airports with utilization

below 50% that suffer from significant delays. Examples include Orlando International,

Washington Dulles and Denver. But when smaller airport facilities are mixed in among major

hub airports, the correlation co-efficient drops to less significant levels.

Airport capacity and utilization alone are not sufficient to explain which airports will

suffer from extended, frequent or regular delay patterns from external causes such as weather.10

Airlines cannot assess capacity at a given airport and draw conclusions about what operational

demand will be acceptable given that carrier’s parameters and targets for on-time performance. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!10 It is worth noting that at connecting hubs, delays may be caused by airlines holding aircraft to maximize connections. This is particularly true on longer-haul flights where en-route times may permit “catching up” after a late departure to ensure an on-time arrival at the destination.



Airport capacity is fungible. It is a subjective metric given the quantitative (runway,

ramp and airspace) and qualitative (human factors) involved. Some airports (including Seattle,

Baltimore, Cleveland, Los Angeles and Tampa) routinely achieve operational throughput well in

excess of their respective FAA capacity benchmarks, while others (including Memphis, Portland,

Detroit, and Boston) operate comfortably below the FAA benchmark definition. For this reason,

the FAA benchmarks are not sufficient to assess whether airline schedules are appropriate, or

whether conscious over-scheduling of airport resources is occurring.

Qualitative factors include human factors (controller experience and flexibility, for

example) and independent decisions made by airlines to push or gate hold, which can be quite

relevant when assessing an airport’s throughput. Airlines must review all these factors to align

schedules with airport capacity. Airlines have multiple objectives in planning and executing flight

schedules. First, they flight schedules to match the times of day when passengers want to fly, and

when destination arrivals are available. This simple objective offers direct insight into why

airline schedules are bunched at origin and destination airports (versus connecting hubs) around

key departure and arrival windows. Business passengers value departures and arrivals that permit

full working days. International flights are usually timed around available slots and must account

for time zone differentials. As a result, there is an inevitable crowding of departure demand at

U.S. airports with strong local market demand during morning and evening hours, with lower

volume during the middle of the day. Because of demand patterns, a carrier’s flexibility to spread

flights more consistently through the day in key origin and destination markets is limited.

In connecting hubs, airline schedules maximize connecting opportunities for passengers

who originated from key markets during peak business hours. Connecting hubs offer an

opportunity to “fill the gaps” in passenger demand peaks and valleys with flights oriented towards

connecting passengers instead. This gives airlines more flexibility to spread flights through the

day. Indeed, when the major function of an airport is to connect traffic, achieving an on-time

operation for both arrivals and departures is key to minimizing cost and maximizing connections.

This reformulation of hub operations from peaked to un-peaked (or rolling) flight schedules has

had a varying impact on delay levels at major hub airports.

3.4 Delay Patterns and Trends: Overview

Before analyzing delay patterns and trends, it is important to assess what “normal” is in

the national airspace system. Over the past 20 years, on-time performance and flight delays have


Page 20

been highly variable. To assess the causes of this variability, we start with a review of the various

factors that impact schedule execution. There are three fundamental trends that impact our

analysis of flight delays and cancellation trends:

1. Flight operations have increased over the past 20 years as airlines have introduced new

regional jet fleets and developed new operations. Excluding the downturn post-9/11,

industry capacity steadily increased to a peak in 2007.

2. On-time arrival performance (percentage of flights to arrive at the destination gate within

15 min. of scheduled arrival) has fluctuated between 65% and 90% on a seasonal basis.

3. The correlation between flight operations and on-time performance is present but weak

on an annualized basis. However, as Chart 1b illustrates, when specific months are

isolated, the correlation is clearly identifiable.

During the winter season, airlines have adapted flight planning in order to favor advance

cancellations over risky departures during snow events. Similarly, during the summer, the impact

of thunderstorms on arrival performance is amplified at airports where capacity is constrained.

Chart 1a: Flight Operations and On-Time Performance Data Source: DOT Part 234 On-Time Reporting Data (Annual -0.26)

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Chart 1b: Correlation Between On-Time Performance and Flight Operations by Month Source: DOT Part 234 On-Time Reporting Data (Annual -0.26)

There are also internal trade-offs at airlines between decisions to delay a given flight

versus cancel. As we have shown in prior papers, every flight cancellation decision in today’s

hub and spoke networks usually results in at least one follow-on flight cancellation due to an

aircraft out of position. In contrast, particularly when sufficient aircraft turn time is built into

downstream flight operations; aircraft delays can sometimes be isolated to a specific flight

segment.

Chart 2a: Systemwide Cancellations & Diversions vs. Operations, by Hour Source: DOT Part 234 On-Time Reporting Data

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Operations! Cancellations/Diversions!


Page 22

Chart 2a above shows the distribution of airline flight operations for domestic U.S. flights

by reporting carriers during 2010. The peak of flight operations during the early morning and late

afternoon can be clearly observed. In Chart 2a, the blue line tracks the total number of flight

operations scheduled by hour of the day (local time). The red line tracks the number of flight

cancellations and flight diversions, demonstrating the sum of irregular flight operations that

significantly disrupt downstream flights. There is a spike in cancellation and diversion decisions

during the late afternoon hours (from 4pm to 8pm local time). This is primarily due to the

combined impact of summer thunderstorm activity and cancellation decisions designed to contain

follow-on impact from upstream flight delays.

How do airline cancellation decisions by hour of the day compare to flight delay

decisions? Given the significant differences in numbers of flight delays, cancellations and

diversions relative to total flight operations, it is clearest to express these data on a percentage

basis. Chart 2b below demonstrates that (1) airline flight delays start at a relatively low level in

the morning and snowball through the day, and (2) cancellation decisions remain more constant

on a percentage basis.

Chart 2b: Systemwide Cancellations/Diversions versus Flight Delays As Percentage of Total Scheduled Flight Operations (Full Year 2010)

Source: DOT Part 234 On-Time Reporting Data

Chart 2b has significant implications for our discussion of flight delays. What is causing

the steady increase in flight delays through the mid-day and afternoon hours? Are these delay

causes controllable by the airlines, and what scheduling differences among carriers make certain

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carriers more susceptible to snowballing delays versus others? We examine these factors in the

next section.

3.5 General Delay Causes

Schedule-impacting factors can be broken into three categories: factors that impact flight

schedules prior to gate departure, factors that impact flights after push-back from the gate and

before takeoff, and factors that influence flights en-route and after landing. Before gate departure,

there are many events that can delay a departure, including mechanical issues, staffing, catering

and passenger boarding. Security and late inbound arrivals can also disrupt crew boarding and

aircraft availability. After gate departure, congestion on the ramp and taxiway can slow progress

towards the runway, and de-icing requirements can cause long delays as aircraft queue for

position on the de-icing pads. Airspace availability and capacity also impacts not only the pace

with which departures can occur, but also may cause ground-delay programs (or “EDCT”

programs, for Expect Departure Clearance Time) that result in long taxi-out times. Table 3 below

lists these general factors.

Table 3: Factors that Influence On-Time Performance

Before Gate Departure Awaiting Takeoff En-Route and Landing Mechanical events Crew availability Supplier delivery Passenger & cargo boarding Ramp personnel & equipment Weather conditions Late inbound arrival of aircraft Security delays (airline or TSA)

Ramp congestion Taxiway congestion Visibility Number of aircraft: • Awaiting taxi • Queued for runway • In the arrival sequence

De-icing time & sequencing Runway contamination Departure flow (runways) Airspace capacity: • En-route airspace separation • En-route airspace blocks • Acceptance rate at destination

Convective activity, turbulence and en-route winds Weather at destination and alternate airport (IFR, etc.) Departure and arrival queues at destination airport Concurrent runway operations Taxiway and ramp congestion Gate availability Visibility approach & ground

To track delay causes and effects, we utilize airline-reported flight on-time performance

detail. Starting in October 2008, major U.S. airlines (and several regional affiliates) began

reporting to DOT the causes of flight delays, when flights arrived at their destination 15 minutes

or more after their scheduled arrival time. No reporting was required for flights that arrived at

their destination within 15 minutes of the scheduled arrival time, or for diverted flights.


Page 24

Airline reports categorize delays into five categories. Carriers may elect to report all

causes of a given flight delay, if more than one cause exists, or simply to report the predominant

causal factor. The DOT specifies which delay codes airlines should use, the criteria that apply for

determining the causal factor of a given flight delay.11 Table 4 below provides general flight

departure and on-time arrival information by carrier during the full year 2010. On-time arrival

performance ranged from a low of 73.1% for Comair, a regional airline, to 92.5% for Hawaiian,

which operates primarily in the good-weather state of Hawaii. On-time arrival performance does

not fully state a given carrier’s marketed flights, as several significant regional airlines and

smaller mainline carriers do not report on-time performance data to DOT.

Table 4: On-Time Performance by Reporting Carrier, Full Year 2010 Source: Domestic flights, reporting airlines, DOT Part 234 Data (ASQP)

Airline Scheduled % Operations On-Time Arrivals On-Time % Southwest (WN) 1,124,487 17.4% 907,274 79.5% Delta (DL) 732,973 11.4% 583,548 77.4% SkyWest (OO) 599,621 9.3% 487,689 79.1% American (AA) 540,963 8.4% 441,855 79.6% Eagle (MQ) 436,976 6.8% 349,840 77.1% US Airways (US) 407,111 6.3% 344,988 83.0% ExpressJet (XE) 385,077 6.0% 308,658 77.8% United (UA) 343,081 5.3% 297,927 85.2% ASA (EV) 319,921 5.0% 261,626 79.2% Pinnacle (9E) 261,364 4.1% 213,543 78.5% AirTran (FL) 248,844 3.9% 209,440 82.8% Continental (CO) 239,271 3.7% 197,412 81.4% JetBlue (B6) 201,434 3.1% 157,066 75.7% Mesa (YV) 174,797 2.7% 149,458 83.3% Comair (OH) 147,633 2.3% 113,894 73.1% Alaska (AS) 136,950 2.1% 121,089 87.6% Frontier (F9) 81,966 1.3% 67,249 81.4% Hawaiian (HA) 67,649 1.0% 62,677 92.5% All Reporting 6,450,118 100.0% 5,275,233 79.8%

Table 4 above demonstrates a significant variance in on-time arrival performance by

carriers, clearly impacted by differences in weather, airport efficiency and airline-specific

operational factors. These differences can also be observed by plotting airline on-time

performance based on the hour of the day (local time). To demonstrate the differences among

airlines, we incorporated all available flight performance data for 2010 from reporting carriers

and measured on-time performance by departure hour.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!11 http://www.bts.gov/programs/airline_information/accounting_and_reporting_directives/technical_directive.html



Chart 3 below shows that major network carriers such as Delta, American, United and US

Airways follow a similar profile of flight delays by hour of the day. Delays start in the morning

primarily due to carrier-related factors including late-inbound flights and mechanical events.

Delays build through the day, with between 15% and 25% of departures during the 6pm-8pm

window delayed. In contrast, point-to-point carriers such as JetBlue and particularly Southwest

Airlines have a more rapid accumulation of flight delays through the day. Southwest (WN) far

exceeds other airlines with flight delays during the evening, with more than 40% of departures

delayed by the 8pm hour.

Chart 3: Delayed Departures (as Percentage of Scheduled Departures) by Airline By Hour of Departure (Full Year 2010)

To identify why delay trends build through the day, we first review how airlines track the

different causes of flight delays when data are reported to DOT on a monthly basis. Delay data is

collected from 15 U.S. carriers representing 1% or more of total system revenue. Data include

specific flight information including departure, arrival, schedule, diversion and cancellation data,

but airlines also provide specific delay minutes when a flight is delayed on arrival for 15 minutes

or more.

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The relevant delay tracking codes include:

Carrier caused. Factors involved in carrier-caused cancellations include aircraft

servicing, maintenance or damage repair, connecting passengers, gate/ramp resources, crew

legality, paperwork and fueling. In addition, gate congestion, airport curfews, passenger-related

security (including bag-match), ramp congestion where the blocking aircraft is under the airline’s

control, weight and balance, and snow removal are considered carrier-caused factors.

Weather. Only weather conditions that explicitly prevent flights from operation are

considered causal factors for delays and cancellations. Examples include below-minimum

weather, icing and de-icing requirements, extreme temperatures, hail, lightning, snow and

thunderstorms. In addition, advance delays (including gate holds and pre-cancellations in

advance of gate departure) are considered weather-driven if forecast weather was the primary

cause of the cancellation.

National Airspace System. Responsibility for NAS delays resides with the FAA, airport

operators or state and local agencies. Examples include airport conditions, airport construction,

ATC clearance delays, ATC-driven en-route restrictions (including quota flow programs), closed

runways, general ramp congestion and volume delays. ATC-outages are also charged to NAS

codes.

Security. Security-related delays are outside the control of the airline. They include

resolution of bomb threats and weapon issues, delays due to Sky Marshals and DHS personnel,

and excessive lines at security screening areas or delays resulting from security breaches.

Late Arriving Aircraft. When an inbound flight is delayed, the follow-on flight with that

specific aircraft may also depart late. In this circumstance, the late arriving aircraft delay minutes

are calculated by adding the arrival time of the inbound flight to the scheduled turn time, then

subtracting the scheduled departure time of the second delayed flight.

Table 5 below summarizes the total number of flights scheduled and the delay minutes

associated with each. It shows that carrier-related delay causes represented 30% of overall flight

delays, and that the distribution of carrier-related events through the day was relatively steady.

Weather- and Airspace- related events constituted 31% of total flight delays, but were highly

concentrated during the afternoon and evening hours. Finally, late-arriving inbound aircraft

represented the largest share of delays at 39%, with afternoon and evening impact.



Table 5: Minutes of Flight Delays Source: DOT ASQP Part 234 Reports, Calendar Year2010

Flights Carrier Weather Airspace Security Late A/C Total Min. 6am-10am 1,748,868 4,601,508 570,538 3,265,070 27,701 1,391,586 9,856,403 10am-2pm 1,678,564 4,297,114 486,972 3,912,112 18,530 5,546,464 14,261,192 2pm-6pm 1,641,416 5,307,713 862,096 5,509,225 30,251 8,836,291 20,545,576 6pm-10pm 1,199,314 4,432,816 793,201 3,282,911 28,992 8,633,832 17,171,752 10pm-6am 181,956 578,354 72,405 260,062 1,936 510,113 1,422,870 Total 6,450,118 19,217,505 2,785,212 16,229,380 107,410 24,918,286 63,257,793 % of Total 30% 5% 26% <1% 39%

Chart 4: Minutes of Flight Delays and Percentage of Total Delays Source: DOT ASQP Part 234 Reports, Calendar Year 2010

Chart 4 above provides graphical representation of the delay causes. It is notable that

weather-related delay causes are a relatively small share of the overall minutes charged. This is

misleading, however, because weather-related delays are usually manifested through airspace

congestion and slow-down in departure flows. These effects are attributed to airspace-related

delays and can be seen in late inbound delays.

We have three observations about flight delays from Table 5. First, the overall

incurrence of carrier- and security- related flight delays is largely proportional to the operations of

Security 107,410

0%

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5%

Airspace 16,229,380

26%

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30%

Late Inbound 24,918,286

39%


Page 28

that carrier. This is not a surprise. Mechanical, crew, security and ramp factors all influence

specific flights, so the more flights that operate, the more risk of failure from these causes.

Second, weather and airspace factors are linked, reflecting the concurrent impact of

weather patterns on airports and the surrounding airspace. In addition, weather delays are

strongly influenced by convective weather activity that tends to hit airports during afternoons and

evenings.

Third, late aircraft delays snowball through the day as the follow-on impact of carrier,

weather and airspace delays is felt on future flight departures using the impacted aircraft.

Plotting the average minutes of flight delays by cause confirms this. Weather-related

delays remain steady throughout the day. Airspace-related delays rise during the afternoon and

subside in the evening. Carrier-related cancellations are steady, while the impact of late-inbound

aircraft by the mid- and late-afternoon shows the follow-on impact of delay events earlier in the

day. Chart 5a shows the average minutes of flight delay per scheduled flight operation.

Chart 5a: Average Delay per Scheduled Flight, by Cause and Departure Hour Full Year 2010, U.S. Reporting Carriers on Domestic Flights

Late Inbound!

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Chart 5b: Delay Causes for Impacted Flights, by Departure Hour

Full Year 2010, U.S. Reporting Carriers on Domestic Flights

Chart 5b captures all flight delay causes that result in a late arrival (15 or more minutes

after scheduled arrival time). It groups Carrier, Weather, Airspace and Security causes into a

single category (Red) and isolates Late Arriving Aircraft (Blue). The “snowball” effect is clear.

3.6 Isolating Different Delay Factors

To investigate the causes of airline delays further, we explore factors that we hypothesize

are related to delays observed by each carrier.

1. Length of flight by airlines;

2. Exposure to major hub airports;

3. Exposure to airports with high susceptibility to weather events;

4. Exposure to airports with high utilization during peak hours;

5. Scheduled turn times for aircraft at the gate; and

6. Strategic delays (“schedule padding”) built into the operational plan

Delays Caused by Late Arriving Inbound

Aircraft!

Other Delay Causes!(Weather, Airspace,Carrier & Security)!

0!

20,000!

40,000!

60,000!

80,000!

100,000!

120,000!

!000

1-05

59!

!060

0-06

59!

!070

0-07

59!

!080

0-08

59!

!090

0-09

59!

!100

0-10

59!

!110

0-11

59!

!120

0-12

59!

!130

0-13

59!

!140

0-14

59!

!150

0-15

59!

!160

0-16

59!

!170

0-17

59!

!180

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59!

!190

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!200

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59!

!210

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!230

0-23

59!

Num

ber o

f Flig

hts

Impa

cted

(All

Flig

hts

2010

)!


Page 30

3.6.1 Length of Flight

Our first step is to identify any meaningful breaks in on-time performance results for

different types of airline routes. We isolated on-time arrival performance for 2010 by the

distance of flight, to the nearest 250 miles. As Chart 6 shows, on-time performance is stronger

for shorter-haul flights than for longer-haul. For consistency, we excluded all flights that were

delayed because of a late inbound aircraft. The resulting correlation coefficient between distance

and on-time arrival performance was -0.91, confirming a basic link between the factors.

Chart6: On-Time Domestic Performance by Flight Distance Full Year 2010, U.S. Reporting Carriers (Excludes Late Arriving Aircraft Delays)

3.6.2 Major Hub Exposure

Earlier we reviewed the systemwide relationship between flight operations and on-time

performance. We observe a general correlation coefficient of -0.26, but when isolating specific

summer and winter months, the relationship is stronger. Some posit that larger airports, with

fuller flight schedules, are more susceptible to departure and arrival delays than smaller facilities.

At these airports, how does on-time performance vary across airports in the United States?

To assess this, we compare airport data on an individual basis, versus systemwide. We

group our base analysis into five sections:

1. Delay and cancellation metrics, by specific airport;

2. Delay and cancellation metrics, by airport size;

3. Percent of flights impacted by different delay codes, by airport size; and

4. Average minutes of delay from different delay codes, by airport size.

90.9%! 90.6%!

89.3%!

89.5%! 89.1%! 88.8%! 88.3%! 88.1%!

86.7%!86.9%!

84.8%!

0! 500! 1,000! 1,500! 2,000! 2,500! 3,000!Distance of Flight (miles)!



Table 6a summarizes 2010 operating metrics for major airports in the U.S. It captures

domestic flight operations for reporting air carriers, using the DOT Part 234 ASQP data set.12

Table 6a: Key Delay and Cancellation Metrics, by Airport Ranked by Airport Departures (Domestic Flights by Reporting Carriers)

Source: DOT ASQP Part 234 Reports, Calendar Year 2010 On-Time Arrivals represents completion of flight from selected airport within 15 minutes of schedule

Delay minutes are based on impacted flights only

Rank Airport On-Time Departures

On-Time Arrivals

Cancelled Flights

Carrier Delay (Avg)

Weather Delay (Avg)

Airspace Delay (Avg)

Inbound AC Delay (Avg)

1 ATL 81.0% 79.9% 2.0% 39.5 min 45.8 min 21.5 min 46.8 min 2 ORD 80.0% 79.0% 2.5% 35.2 31.7 24.1 45.3 3 DFW 80.5% 79.6% 1.7% 33.4 34.4 20.2 33.4 4 DEN 81.3% 81.2% 1.0% 27.5 35.6 20.5 38.3 5 LAX 83.5% 82.5% 1.1% 30.1 54.6 21.1 36.7 6 IAH 83.9% 81.1% 0.8% 31.3 26.6 20.3 42.0 7 PHX 84.1% 83.5% 0.8% 28.6 42.8 20.9 33.5 8 DTW 80.2% 78.1% 2.0% 41.0 53.8 23.2 43.4 9 LAS 79.9% 81.5% 0.7% 25.6 42.5 19.7 37.7

10 SFO 77.3% 78.0% 1.9% 33.8 39.2 16.2 56.5 11 MSP 81.4% 78.7% 1.7% 36.0 46.1 23.9 42.4 12 CLT 84.7% 81.6% 1.3% 34.4 44.6 28.2 38.6 13 SLC 85.7% 82.7% 0.8% 32.3 29.9 21.0 32.8 14 MCO 82.0% 82.4% 1.0% 30.4 34.8 28.3 41.3 15 EWR 79.2% 79.8% 3.2% 32.9 32.5 27.4 56.1 16 BOS 83.8% 81.7% 2.7% 36.8 34.7 27.6 46.4 17 JFK 78.1% 78.9% 3.4% 45.4 69.1 30.7 46.6 18 BWI 77.6% 79.8% 2.2% 26.9 42.6 23.8 37.5 19 LGA 84.5% 81.7% 4.2% 35.1 41.1 29.3 51.7 20 SEA 88.5% 86.9% 0.5% 35.3 32.5 25.4 40.0

We can observe from Table 6a that on-time departures and arrivals are highly variable

across airports, even within a specific region. Similarly, flight cancellations range widely, with

higher reported numbers in the eastern United States than in the west. Weather delays tend to be

significant when they occur compared to airspace delays, but as shown in earlier analysis airspace

delays are more prevalent overall. Finally, inbound aircraft delays are more prevalent in the

congested northeast than in the west.

Table 6b groups airports by the number of flight departures reported to DOT under Part

234 ASQP. Airports are grouped by 25,000 flight increments. The largest bracket of airports

report around 125,000 annual departures, with significant concentration of facilities in lower

departure levels. Table 6b shows the on-time departure performance for those airports, plus the !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!12 DOT ASQP available at www.bts.gov. Data set used includes all 2010 reported information.


Page 32

ultimate on-time arrival rates for flights leaving those airports. The data confirm that on average,

larger hub airports indeed have higher departure delay rates than smaller facilities.

Table 6b: Key Delay and Cancellation Metrics, by Departure Level Groupings Source: DOT ASQP Part 234 Reports, Calendar Year 2010

ASQP Flights per Year

% of Total OTD% OTA% Cxl%

0-12,500 11.2% 83.3% 80.8% 2.4% 12,500-37,500 12.2% 83.5% 81.9% 1.6% 37,500-62,500 14.3% 81.6% 81.5% 1.3% 62,500-87,500 8.3% 79.9% 79.4% 2.0%

87,500-112,500 7.9% 79.5% 78.8% 2.6% 112,500-137,500 11.3% 81.1% 79.1% 1.8% 137,500-162,500 6.9% 77.6% 77.4% 1.5% 162,500-187,500 5.7% 83.2% 81.3% 0.8% 187,500-212,500 3.1% 82.4% 81.2% 1.1% 237,500-262,500 3.7% 80.2% 79.9% 1.0% 262,500-287,500 4.2% 78.8% 77.7% 1.7% 312,500-337,500 4.9% 77.5% 76.3% 2.5% 412,500-437,500 6.4% 79.0% 77.7% 2.0%

All Airports 100.0% 81.0% 79.8% 1.8%

Table 6c: Percent of Flights Impacted by Delays, by Departure Level Groupings Source: DOT ASQP Part 234 Reports, Calendar Year 2010

Size of Airport Carrier Weather Airspace Late Inbound

0-12,500 5.1% 0.9% 10.2% 9.2% 12,500-37,500 6.3% 0.7% 9.5% 9.0% 37,500-62,500 8.5% 0.7% 8.6% 9.9% 62,500-87,500 9.5% 1.1% 9.9% 9.0%

87,500-112,500 9.1% 1.0% 10.7% 8.0% 112,500-137,500 9.2% 1.1% 11.2% 8.3% 137,500-162,500 10.8% 0.8% 10.2% 11.0% 162,500-187,500 10.0% 0.9% 10.3% 7.0% 187,500-212,500 8.8% 0.2% 10.1% 9.3% 237,500-262,500 10.2% 0.8% 9.9% 10.8% 262,500-287,500 12.5% 2.8% 11.1% 8.8% 312,500-337,500 10.0% 2.2% 12.2% 10.4% 412,500-437,500 10.8% 1.2% 10.7% 8.9%

All Airports 8.8% 1.0% 10.2% 9.1%

The next step is to assess the percentage of flights departing from each airport group that

are impacted by carrier, weather, airspace and late inbound delays. Table 6c shows that smaller

airports have significantly lower incidence of carrier, weather and airspace-related delays than



larger hubs. Of note in Table 6c is the consistency of carrier-related delays incurred at larger

(hub) airports. Given that crew staffing, catering, maintenance and other infrastructure is

concentrated at hubs, it is not surprising that delays are concentrated in larger airports.

Table 6d: Of Delayed Flights, Minutes of Delay by Cause, by Departure Groupings Source: DOT ASQP Part 234 Reports, Calendar Year 2010

Size of Airport Carrier Weather Airspace Late Inbound

0-12,500 44.3 57.8 28.8 47.9 12,500-37,500 35.3 47.8 27.8 42.7 37,500-62,500 29.0 41.5 24.9 38.5 62,500-87,500 34.0 45.7 25.5 40.2

87,500-112,500 35.5 52.4 27.9 43.8 112,500-137,500 33.8 37.0 25.9 42.7 137,500-162,500 34.0 48.8 20.3 47.2 162,500-187,500 29.9 28.7 20.5 37.1 187,500-212,500 30.1 54.6 21.1 36.7 237,500-262,500 27.5 35.6 20.5 38.3 262,500-287,500 33.4 34.4 20.2 33.4 312,500-337,500 35.2 31.7 24.1 45.3 412,500-437,500 39.5 45.8 21.5 46.8

All Airports 34.0 42.4 24.8 42.3

Of note is the high degree of impact from airspace delays at smaller airports. This is

primarily the impact of ground-delay programs for flights into congested airspace. In contrast,

flights from major airports to small cities generally do not face the same airspace constraints.

3.6.3 Performance by Aircraft Type

We review key metrics by aircraft type in Table 6e. We grouped all airports together and

use airline-supplied tail numbers to match against aircraft types in the FAA ownership registries.

Larger aircraft such as Boeing 767s and Boeing 777s have better arrival and departure

performance than regional jets and turboprops. Mid-size aircraft performance varies widely

across airlines. The late-inbound aircraft metrics are skewed by Southwest Airlines’ Boeing 737

fleet, which suffers from chronic on-time performance caused by late inbound arrivals.


Page 34

Table 6e: Key Metrics by Aircraft Type Source: DOT ASQP Part 234 Reports, Calendar Year 2010, Domestic Flights Only

Aircraft Type Category

% of Report

% Deps Ontime

% Arrs Ontime

Carrier Delay %

Carrier Del Mins

Weather Delay %

Weather Del Mins

Late Inb. Delay %

Late Inb. Del Mins

Boeing 767 1.1% 81.8% 78.8% 11.8% 51.7 10.9% 28.4 5.0% 52.5 Boeing 777 0.2% 80.3% 80.5% 11.0% 48.5 11.2% 29.2 3.9% 71.2 Airbus A320 13.1% 83.9% 81.6% 8.5% 31.9 11.4% 27.4 7.9% 42.1 Boeing 717 3.6% 88.1% 86.3% 3.7% 34.7 7.2% 29.5 6.6% 56.5 Boeing 737 28.6% 79.6% 80.9% 10.3% 24.1 9.4% 22.8 10.8% 36.7 Boeing 757 6.4% 81.0% 79.6% 9.5% 39.6 12.7% 27.9 7.6% 44.4

MD-80 Series 9.4% 80.6% 79.2% 9.1% 39.1 12.4% 27.7 8.5% 42.2 Regional Jets 35.0% 81.6% 79.1% 8.1% 41.2 12.6% 27.3 9.1% 45.4 Turboprops 2.6% 80.8% 79.3% 5.9% 36.1 10.3% 26.0 12.3% 50.2

All Types 100.0% 81.4% 80.2% 8.8% 34.0 11.2% 26.4 9.2% 42.3

3.6.4 On-Time Performance vs. Departures

To conclude the discussion of general on-time performance metrics versus annual

departures, we cross-plot the on-time departure performance of major U.S. airports against the

number of annual departures handled. As Chart 7 illustrates, there is a small negative correlation

between the size of an airport (measured in annual departures) but the distribution of performance

among the bulk of U.S. airport facilities is too significant to draw a meaningful conclusion. In

fact, many small airports perform substantially worse than larger facilities on an annual basis.

Chart 7: On-Time Performance vs. Airport Size (Departures)

Top 35 OEP Airports for Calendar Year 2009

70%!

72%!

74%!

76%!

78%!

80%!

82%!

84%!

86%!

88%!

90%!

0 ! 100,000 ! 200,000 ! 300,000 ! 400,000 ! 500,000 ! 600,000 ! 700,000 ! 800,000 ! 900,000 ! 1,000,000 !

On-

Tim

e De

partu

re P

erfo

rman

ce

(% <

15 m

in d

elay

)!

Annual Departures (2009 Flights)!



3.6.5 Introducing Capacity Utilization

Airport size and observed delays do not show conclusive correlation, so the next step is to

assess the utilization of the airport facility. We define utilization as the ratio of scheduled flight

operations (arrivals and departures) versus the runway capacity available. Utilization is a

physical and quantitative measure. It is one factor in considering whether an airport is scheduled

efficiently, as ramp, gate, and human factors must be incorporated as well.

Using comprehensive FAA airport data for the period from 2000-2010, we reviewed the

average airport utilization (comparing scheduled operations with available runway capacity) on

an annualized basis during daytime and early evening hours (7am to 10pm local time). To create

a holistic view of airline operations – and not restrict our analysis to data by reporting airlines via

Part 234 filings – we utilized the FAA ASPM data set for total scheduled flight operations versus

reported arrival and departure capacity (ADR + AAR). We plotted the resulting capacity

utilization ratio against the on-time departure performance reported for each airport under Part

234. The resulting chart is shown as Chart 8.

There is a minor negative correlation between scheduled utilization and on-time

departures, but again many highly utilized airports nationally outperform facilities with lower

aggregate utilization.

Chart 8: On-Time Performance (Departures) versus Scheduled Utilization All Flights 2009 between 7am and 10pm, OEP 35 Airports (from FAA ASPM)

40%!

50%!

60%!

70%!

80%!

90%!

100%!

0%! 10%! 20%! 30%! 40%! 50%! 60%! 70%! 80%! 90%!

On-

Tim

e De

partu

res !

(Gat

e De

partu

re D

elay

s <=

15

min

)!

Scheduled Utilization of Airport (OAG divided by Runway Capacity)!


Page 36

The absence of conclusive correlation between aggregate flight schedules, capacity

utilization, and late arrivals and departures can be observed in Table 7 below, which provides a

comparison of late departure and arrival frequency along with average minutes of delay for

airports ranked by utilization of available runway capacity. Not only are there significant

differences in the actual level of delays observed, but the composition of those delays (departures

vs. arrivals) also materially differs by airport.

Table 7: Utilization, Late Departures and Arrivals, Selected U.S. Airports 2009 Operations, from FAA ASPM Data Set (Utilization = Scheduled / Capacity)

Code Name of Airport Utilization % Late Arrivals Delay Minutes Late Deps Delay Minutes LGA New York LaGuardia 80.3% 21.3% 34.7 27.3% 37.4 EWR Newark Liberty 78.1% 26.6% 41.0 32.2% 47.0 ATL Atlanta Hartsfield-Jackson 76.8% 22.4% 41.0 26.9% 45.3 JFK New York John F. Kennedy 75.9% 22.5% 40.8 27.4% 42.0 PHL Philadelphia International 69.7% 24.2% 38.0 26.7% 37.9 ORD Chicago O'Hare 67.1% 21.0% 45.6 19.4% 47.1 DCA Washington Reagan 65.2% 16.4% 27.6 16.3% 25.5 SFO San Francisco International 60.5% 23.1% 35.5 26.9% 38.7 SAN San Diego Lindbergh 58.5% 18.8% 20.4 20.5% 21.3 CLT Charlotte/Douglas Int'l 57.9% 18.6% 29.1 20.0% 28.9

Table 7 confirms that conclusive relationships between schedule utilization and late

arrivals and departures are difficult to establish. However, observable trends are present. Delay

minutes generally decrease as utilization decreases. Late arrivals and departures result in similar

delay minutes.

3.6.6 Exposure to Airports with High Weather Susceptibility

One measure of weather impact on flight operations is the frequency of periods, and the

length thereof, when airports report weather conditions that require additional radar separation.

Because radar separation requirements decrease the usable capacity of airspace corridors to and

from each airport, weather reduces the arrival and departure flow to and from an airport. This

creates the conditions for weather- and airspace-related delays reported by airlines.



Table 8: Occurrence of Inclement Weather Conditions IMC = Instrument Conditions; VMC = (Good) Visual Conditions;

Ceiling = ceiling < 1000 feet; Visibility = Visibility < 1 mile

CODE IMC VMC CEILING VISIBILITY

SEA 30.3% 69.7% 6.9% 2.1% MEM 25.6% 74.4% 5.8% 0.3% STL 24.4% 75.6% 5.1% 0.3% ATL 23.7% 76.3% 9.9% 3.0% MSP 23.6% 76.4% 3.0% 0.5% IAH 21.6% 78.4% 5.5% 1.6% DTW 20.1% 80.0% 3.9% 1.0% PDX 20.0% 80.0% 3.6% 1.6% SFO 20.0% 80.0% 3.0% 0.1% CLT 19.8% 80.2% 8.1% 1.6% IAD 18.6% 81.4% 7.7% 1.8%

Weather has a varying impact on airports in different regions of the country. As Table 8

above shows, airports such as Seattle/Tacoma are frequently impacted by negative weather

conditions, with low ceilings, low visibility and precipitation. As Exhibit E shows in detail,

airports such as Miami, Las Vegas, Phoenix and Honolulu are all impacted by inclement weather

less than 2% of the time. Simply comparing the incidence of weather conditions with on-time

departures reveals a low correlation coefficient of just -.011. As Chart 9 indicates, many airports

with high occurrence of IFR weather routinely outperform airports with lower occurrence.

Chart 9: Occurrence of Bad Weather Conditions vs. Late Departures

(Correlation = -.11)

0.0%!

5.0%!

10.0%!

15.0%!

20.0%!

25.0%!

30.0%!

0.0%! 5.0%! 10.0%! 15.0%! 20.0%! 25.0%! 30.0%! 35.0%!

Perc

ent o

f Flig

hts

Dela

yed

Depa

rutre

s!

Occurrence of Instrument Approach Conditions!


Page 38

Exploratory analysis confirms that the overall frequency of bad weather conditions is not

correlated to low on-time performance, but the variability of that weather is a critical factor.

Airports such as Seattle and Memphis are highly optimized for instrument approaches and radar

separation. Airlines can also plan schedules with confidence in the arrival and departure rates

during bad weather conditions. But when considering airports that have a high variability in

weather conditions, such as mid-Atlantic and Southern airports subject to thunderstorm activity,

we can observe two factors. First, many airports have a significant loss of capacity during bad

weather conditions. Second, some airports have infrequent and unpredictable weather conditions

that can paralyze arrivals and departures for extended periods of time.

3.7 Diversions and Cancellations

Our analysis has thus far focused on delay causes, but delays are both driven by and

causal to cancellations and aircraft diversions. In the next section, we review diversions and

flight cancellations by carrier, to compare the rates and causes of incidents against flight delays.

3.7.1 Diversions

Airlines report diversion data for domestic flight segments to the DOT. Airlines report

when flights return to their origin, for mechanical or weather, and when flights divert en-route.

Flight completion information is included to determine whether the flight ultimately landed at its

destination. Table 9 shows flight diversions for reporting U.S. airlines during 2010. The highest

diversion rates were reported by American Airlines and its affiliates, reflecting strategic changes

made for flights into Dallas/Fort Worth airport under new tarmac taxi-time restrictions. However,

regional airlines exhibited higher diversion rates than mainline aircraft, as diversions were more

prevalent into smaller airports that sometimes lack precision approach equipment for bad-weather

landings. 82% of flights diverted once ultimately landed at their final destinations. For the 1% of

diversions that that diverted twice, just 25% completed and the balance cancelled.



Table 9: Diversions by Reporting U.S. Carriers, 2010 Source: Reporting Carriers, Domestic Flights, FY2010, DOT Part 234 (ASQP)

Airline Flight Diversions

Div. Rate per 1,000

Flights w/ 1 Div.

1 Div. Flts Completed

Flights w/ 2 Divs.

2 Div. Flts Completed

Southwest (WN) 2,166 1.9 2,135 74% 30 23% Delta (DL) 1,621 2.2 1,600 87% 21 29% SkyWest (OO) 1,748 2.9 1,721 64% 27 4% American (AA) 1,957 3.6 1,940 94% 17 41% Eagle (MQ) 1,073 2.5 1,056 87% 17 35% US Airways (US) 683 1.7 675 86% 8 0% ExpressJet (XE) 1,072 2.8 1,065 93% 7 43% United (UA) 748 2.2 737 88% 11 9% ASA (EV) 617 1.9 617 83% Pinnacle (9E) 745 2.9 733 74% 12 33% AirTran (FL) 631 2.5 627 94% 4 75% Continental (CO) 620 2.6 616 95% 4 50% JetBlue (B6) 504 2.5 504 86% Mesa (YV) 373 2.1 373 80% Comair (OH) 301 2.0 301 78% Alaska (AS) 384 2.8 380 44% 4 25% Frontier (F9) 167 2.0 166 94% 1 0% Hawaiian (HA) 64 0.9 64 97% All Reporting 15,474 2.4 15,310 82% 163 25%

3.7.2 Cancellations

In prior reports we investigated cancellation causes, rates and trends during 2010 versus

prior years.13 We found that new consumer-protection regulations effective from April 29, 2010

resulted in a significant spike in cancellation rates. As a result, full-year data from 2010

incorporates both lower (pre-rule) cancellation rates and higher (post-rule) data. Overall

cancellations in 2010 were 1.76% of scheduled departures, an increase from 1.39% in 2009

without any material change in flight operations level. Table 10 below captures cancellation

rates by reporting U.S. airline along with the reported causes for each cancellation. Note that

cancellations due to the consumer protection regulations may be classified as carrier, weather or

airspace by the carrier, and DOT has not issued guidance about reporting these events nor offered

to collect such data in a definitive and consistent format from airlines.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!13 More information can be found at www.tarmaclimits.com


Page 40

Table 10: Cancellations by Reporting U.S. Carriers, 2010 Source: Reporting Carriers, Domestic Flights, FY2010, DOT Part 234 (ASQP)

Airline Sched. Flights

Total Cancels

Cancel Rate (all)

Cancels (Carrier)

Cancels (Weather)

Cancels (Airspace)

Cancels (Security)

Southwest (WN) 1,124,487 11,597 1.03% 4,940 6,223 431 3 Delta (DL) 732,973 14,857 2.03% 6,583 6,961 1,313 0 SkyWest (OO) 599,621 11,932 1.99% 3,801 5,702 2,428 1 American (AA) 540,963 9,146 1.69% 3,572 4,667 905 2 Eagle (MQ) 436,976 12,075 2.76% 2,169 6,246 3,656 4 US Airways (US) 407,111 6,290 1.55% 2,902 2,445 942 1 ExpressJet (XE) 385,077 8,114 2.11% 812 5,577 1,725 0 United (UA) 343,081 5,010 1.46% 2,178 2,479 353 0 ASA (EV) 319,921 7,517 2.35% 5,137 1,560 820 0 Pinnacle (9E) 261,364 7,653 2.93% 5,029 1,690 933 1 AirTran (FL) 248,844 2,674 1.07% 751 1,632 291 0 Continental (CO) 239,271 1,986 0.83% 211 1,677 78 20 JetBlue (B6) 201,434 4,116 2.04% 548 3,512 55 1 Mesa (YV) 174,797 3,439 1.97% 1,397 1,513 525 4 Comair (OH) 147,633 5,645 3.82% 1,686 3,882 75 2 Alaska (AS) 136,950 797 0.58% 313 461 23 0 Frontier (F9) 81,966 352 0.43% 89 263 0 0 Hawaiian (HA) 67,649 55 0.08% 38 17 0 0 All Reporting 6,450,118 113,255 1.76% 42,156

37.2% 56,507 49.9%

14,553 12.8%

39 0.1%

How do the causes of cancellations as reported during 2010 compare with the causes of

flight delays? Carrier-related cancellations represented 37% of all cancellations, while carrier-

related delays were 30% of total reported delays. Weather-related cancellations were 49.9% of

cancellations, while just 5% of delays. Airspace factors caused 12.8% of cancellations, versus

26% of delays. Security factors caused less than 1% of both delays and cancellations. And 39%

of delays were due to late arriving aircraft, with no comparable cancellation category.

Table 11 below splits these causal factors into controllable factors (carrier-coded) and

uncontrollable factors (weather- and airspace-coded), with a negligible number of security-

related cancellation data points. The table shows that airlines with significant northeastern

exposure were most impacted by uncontrollable cancellation factors. In contrast, most major

carrier cancellations were split between controllable and uncontrollable causes.



Table 11: Cancellation Types by Reporting U.S. Carriers, 2010 Source: Reporting Carriers, Domestic Flights, FY2010, DOT Part 234 (ASQP)

Controllable: Carrier-Related Causes; Uncontrollable: Weather- and Airspace-Related Causes

Airline Cancel Rate Controllable Uncontrollable Hawaiian (HA) 0.08% 69% 31% ASA (EV) 2.35% 68% 32% Pinnacle (9E) 2.93% 66% 34% US Airways (US) 1.55% 46% 54% Delta (DL) 2.03% 44% 56% United (UA) 1.46% 43% 57% Southwest (WN) 1.03% 43% 57% Mesa (YV) 1.97% 41% 59% Alaska (AS) 0.58% 39% 61% American (AA) 1.69% 39% 61% SkyWest (OO) 1.99% 32% 68% Comair (OH) 3.82% 30% 70% AirTran (FL) 1.07% 28% 72% Frontier (F9) 0.43% 25% 75% Eagle (MQ) 2.76% 18% 82% JetBlue (B6) 2.04% 13% 87% Continental (CO) 0.83% 11% 89% ExpressJet (XE) 2.11% 10% 90% All Reporting 1.76% 37% 63%

3.8 Airline-Specific Delays and Adaptations

We have demonstrated that controllable delay factors, including delays due to carrier-

specific factors (including mechanical events, ramp and gate availability delays, and crew staffing

imbalances) are largely proportional to the total volume of flight operations by the carrier. The

occurrence of carrier-caused delays changes linearly in proportion to total flight operations by the

carrier.

In an environment where on-time performance is measured by a single standard – arrival

at gate within 15 minutes – and where DOT routinely ranks carriers based on this single standard,

is it reasonable to expect that carriers can absorb variations in uncontrollable factors through

scheduling decisions in advance?

In Table 6a through 6d, we presented flight delay metrics by airport category, focusing on

the number of departures from a given airport and the causes of delays observed. In Table 12

below, we aggregate all departures across airports and break the results by airline. We observe


Page 42

immediately that system delay minutes are largely proportional to total flight operations, and but

that there are notable differences between delay patterns among airlines.

Table 12: Delay Composition by Reporting U.S. Carriers, 2010 Source: Reporting Carriers, Domestic Flights, FY2010, DOT Part 234 (ASQP)

System Statistics Delay per Flight Cause of Delay Airline Flights Delay Min All Flights Impacted Carrier Weather &

Airspace Late

Inbound Southwest (WN) 1,124,487 10,029,222 8.9 46.2 28% 17% 55% Delta (DL) 732,973 8,195,204 11.2 54.8 36% 33% 32% SkyWest (OO) 599,621 6,211,902 10.4 55.5 22% 27% 50% American (AA) 540,963 5,565,988 10.3 56.2 36% 33% 31% Eagle (MQ) 436,976 4,777,239 10.9 54.8 29% 34% 37% US Airways (US) 407,111 2,961,104 7.3 47.7 29% 40% 30% ExpressJet (XE) 385,077 4,320,595 11.2 56.5 24% 37% 39% United (UA) 343,081 2,660,172 7.8 58.9 25% 33% 43% ASA (EV) 319,921 3,880,916 12.1 66.6 34% 23% 42% Pinnacle (9E) 261,364 2,614,748 10.0 54.7 35% 29% 36% AirTran (FL) 248,844 2,250,331 9.0 57.1 18% 32% 50% Continental (CO) 239,271 2,076,570 8.7 49.6 30% 46% 24% JetBlue (B6) 201,434 2,717,652 13.5 61.3 35% 30% 35% Mesa (YV) 174,797 1,411,861 8.1 55.7 35% 26% 39% Comair (OH) 147,633 1,903,634 12.9 56.4 48% 44% 8% Alaska (AS) 136,950 741,330 5.4 46.7 32% 33% 35% Frontier (F9) 81,966 722,951 8.8 49.1 22% 29% 49% Hawaiian (HA) 67,649 216,374 3.2 43.5 73% 2% 25% All Reporting 6,450,118 63,257,793 9.8 53.8 30% 30% 39%

The next section assesses how airlines have internalized the expected delay patterns observed in

the domestic system. To what extent have airlines changed their gate turn patterns (the amount of

time between gate arrival and gate departure for aircraft on subsequent flights)?

3.8.1 Scheduled Turn Times

In May 2010, GAO commented that it believed flight delays for carriers with short

scheduled turn times would be higher towards the end of the day, versus carriers with more turn

time built into their schedules – and therefore more slack to recover from operational disruptions.

To assess this hypothesis, we analyzed turn time and delay data for domestic flights by reporting

carriers during June 2010. The volume of data and complexity of matching aircraft turns

necessitated picking a single month for analysis. Our analysis focused on scheduled flights, not

actual performance, but on-time performance and cancellation rates during June 2010 were not

abnormal.



To ensure consistency in the data sample, we used the following criteria to narrow our

analysis. We first collected all flight-level data for June 2010 from reporting U.S. carriers on

domestic flight operations. There were a total of 551,687 scheduled flights in the overall data set.

From this set, we excluded all flights that were either (i) diverted, (ii) cancelled, (iii) did not have

a tail number assigned, or (iv) occurred as the first flight of the day. This resulted in a set of

398,723 flights over one month where aircraft turns from a previous flight and to a subsequent

flight could be clearly identified on an aircraft-specific basis.

Our methodology created an authoritative (actual) data set, not one based on interpolation

from published airline schedules. However, our data set was based on domestic flights only, and

to exclude “inside turns” for international flight operation – where an aircraft would operate a

quick international round-trip before returning to the domestic system – we excluded aircraft

turns from our analysis that were greater than 240 minutes. We also excluded aircraft turns of

less than 20 minutes, as these flights were universally aircraft swaps not planned in advance. The

final data set had 302,399 flight turns for analysis across the reporting carriers.

The result was the following average turn time by carrier, systemwide during 2010.

Chart 10 below shows the distribution of planned turn times between 20 minutes and 100 minutes

(224,096 total flights). It shows a steady distribution of aircraft turns between 25 minutes and 50

minutes, with a downward slope of aircraft turns for 55 minutes or greater.

The importance of Chart 10 is that the shorter the turn, the more likely a late arrival

(defined as 15 minutes or greater after scheduled arrival time) will impact a follow-on flight

segment. If an airline can actually turn a flight in a minimum of 30 minutes, then a 45-minute

turn time is required to isolate a late arrival from inbound delays.


Page 44

Chart10: Turn Time Distribution (20-100 minutes scheduled) Source: Reporting Carriers, Domestic Flights, June 2010, DOT Part 234 (ASQP)

We now break turn times out by carrier to demonstrate the significant differences among

airlines, and set the foundation to link aircraft turn times to delay and cancellation metrics. Table

13 presents scheduled turn-time statistics by airline, by aircraft type. To isolate aircraft types, we

matched the tail numbers reported for each flight arrival and departure pair against the aircraft

type data in the FAA Registry. We grouped aircraft into three categories: regional (including all

Bombardier CRJ, Embraer 145 and 170/190 series and turboprop aircraft), narrowbody (primarily

Boeing 717 and 737, Airbus 320 series and DC-9/MD-80 aircraft) and widebody and

international (including the Boeing 757). Given the high utilization of Boeing 757 aircraft in

international operations – and the subsequent difficultly of isolating 757 turns as purely domestic

pairings without short inside turns – we grouped the type with its international peers.

Table 13 provides both mean and standard deviations of turn times. The highest

prevalence of turns is Southwest Airlines, which has not only the lowest mean turn time, but also

the smallest standard deviation. Southwest adheres firmly to its model of fast turns with few

exceptions. Other low-cost carriers (Frontier and AirTran) also have low variability in turn

design, with standard deviations less than major airlines, but buffer their operations with higher

mean levels. Table 14 breaks turn times into departure hours, showing that carriers generally

budget more turn time during the peak afternoon hours than during the mornings.

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Dom

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2010

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tal)!

Turn Time Rounded to Nearest 5 Minutes!



Table 13: Scheduled Turn-Time Statistics, by Carriers That Report Source: DOT ASQP Part 234 Reports, June 2010

Carrier All Types Regionals Narrowbody 757 & Widebody

Turns Mean St Dev. Mean St Dev Mean St Dev Mean St Dev Southwest (WN) 77,251 30.0 10.3 30.0 10.3 Hawaiian (HA) 4,797 39.6 25.2 33.5 12.1 112.4 28.1 Eagle (MQ) 26,069 43.2 30.7 43.2 30.7 ExpressJet (XE) 25,821 44.8 28.6 44.8 28.6 SkyWest (OO) 38,781 44.9 28.6 44.9 28.6 AirTran (FL) 17,039 45.2 16.4 45.2 16.4 Frontier (F9) 5,420 48.4 18.0 53.3 11.5 48.4 18.0 Mesa (YV) 10,856 49.7 25.1 49.7 25.1 ASA (EV) 21,510 49.9 31.9 49.9 31.9 JetBlue (B6) 11,280 52.1 22.2 44.4 20.4 56.6 21.9 Pinnacle (9E) 17,193 54.3 32.8 54.3 32.8 Comair (OH) 7,921 58.4 37.1 58.4 37.1 American (AA) 28,550 59.0 20.9 55.5 17.5 77.4 26.6 Delta (DL) 43,174 61.2 29.5 57.5 26.7 73.8 34.8 Alaska (AS) 8,697 61.9 30.8 61.9 30.8 United (UA) 19,317 62.8 28.9 56.5 24.7 73.8 32.3 US Airways (US) 23,170 67.4 26.1 60.6 24.2 68.1 25.9 88.9 40.6 Continental (CO) 11,877 68.5 28.2 66.8 27.7 80.8 28.2 Note: Airlines including Spirit, Virgin America and Republic do not report this and other data

Table 14: Turn Time Distribution (20-240 minutes) by Time of Day Source: Reporting Carriers, Domestic Flights, June 2010, DOT Part 234 (ASQP)

Row Labels American JetBlue Continental Delta Southwest All Carriers 0001-0559 49 49 0600-0659 76 94 61 51 30 42 0700-0759 69 68 76 61 26 42 0800-0859 62 49 65 58 28 44 0900-0959 55 49 73 59 29 48 1000-1059 56 49 63 60 30 47 1100-1159 56 47 67 58 29 48 1200-1259 57 51 62 54 30 46 1300-1359 57 52 71 57 31 48 1400-1459 57 49 64 56 31 48 1500-1559 58 53 63 60 31 48 1600-1659 60 54 64 55 31 46 1700-1759 60 53 64 59 31 48 1800-1859 58 48 71 57 31 47 1900-1959 61 50 71 70 30 52 2000-2059 63 49 72 70 29 53 2100-2159 73 60 85 78 28 66 2200-2259 77 74 85 91 78 2300-2359 80 70 124 115 95 24 Hours 59 52 69 61 30 49


Page 46

We now connect airline turn times to departure delay times observed. Table 15 groups

carriers into four categories: all carriers, representing all reporting airlines during June 2010;

regional airlines (ExpressJet, Mesa, Pinnacle, Atlantic Southeast, SkyWest and Comair); Majors

(US Airways, Delta, Northwest, United, US Airways, Frontier, AirTran, Alaska and Hawaiian);

and finally Southwest. Southwest is isolated because its turn time and delay metrics are

significantly different from its peers.

Table 15 shows that (1) scheduled turn times for all carriers generally increase during the

evening hours, but delay minutes also increase; (2) regionals are more impacted by delay minutes

than mainline; and (3) Southwest’s fast turn times are directly connected to follow-on flight

impact as the day progresses.

Table15: Turn Time Distribution (20-240 minutes) by Time of Day, Southwest vs. Others Source: Reporting Carriers, Domestic Flights, June 2010, DOT Part 234 (ASQP)

In Minutes All Carriers Regionals Only Majors (ex. WN) Southwest Departure Turn Delay Turn Delay Turn Delay Turn Delay 0001-0559 49.3 0.0 49.3 0.0 0600-0659 41.6 2.2 30.4 2.7 56.4 1.0 29.5 10.6 0700-0759 42.2 1.3 39.2 1.8 61.7 0.7 26.1 0.9 0800-0859 44.4 1.7 45.6 2.4 55.3 1.0 28.4 1.3 0900-0959 48.2 2.1 51.5 2.9 57.1 1.6 29.4 1.4 1000-1059 46.9 2.9 45.4 4.4 56.0 2.0 29.8 2.0 1100-1159 47.7 3.4 44.9 4.8 57.1 2.2 29.2 3.4 1200-1259 46.3 4.1 42.5 5.7 56.3 2.9 29.9 4.1 1300-1359 47.8 4.4 44.5 6.0 57.3 3.1 30.6 4.5 1400-1459 47.9 5.3 45.9 7.1 56.6 3.8 31.3 5.5 1500-1559 48.0 5.8 44.3 7.3 57.8 4.3 30.8 6.3 1600-1659 46.1 7.2 41.4 8.7 57.6 5.5 31.5 7.5 1700-1759 47.9 8.4 44.0 9.8 58.5 6.7 30.9 9.6 1800-1859 47.5 9.6 43.0 10.7 58.7 7.6 30.6 12.3 1900-1959 52.4 10.4 56.1 10.9 60.6 8.7 29.6 12.8 2000-2059 53.0 10.3 60.7 10.2 61.6 8.1 28.8 14.5 2100-2159 65.9 7.4 73.2 7.2 71.3 6.3 27.6 11.8 2200-2259 78.2 3.9 79.8 5.4 77.7 3.4 2300-2359 94.8 1.0 73.9 3.1 95.5 0.9 All Deps. 49.2 5.8 47.5 6.8 59.2 4.5 30.0 6.7

This “Southwest Impact” of incurring significant follow-on flight delays from closely

spaced aircraft turns can also be observed in Chart 11, which measures the minutes of delays per

flight measured by the scheduled turn time. Not surprisingly, tighter turn times incur higher

delays from late inbound arrivals. Other causes are relatively constant across turn times.



Chart 11: Delay Minutes per Impacted Flight, By Scheduled Turn Time Source: Reporting Carriers, Domestic Flights, June 2010, DOT Part 234 (ASQP)

How does Southwest’s turn time and delay pattern compare with a major airline peer?

To conduct a specific comparison, we analyzed Southwest’s scheduled turn times by time of day

against US Airways. We selected US Airways as a peer for three reasons: (1) similar exposure in

both the western United States (with shared hubs/focus cities at Phoenix and Las Vegas) as well

as prominent operations in Florida, the mid-Atlantic and the Northeast; (2) similar fleet

composition, with extensive narrowbody aircraft; and (3) US Airways’ operational performance

was among the best of its peers during 2010, making it a useful benchmark against Southwest.

Table 16a calculates the average scheduled turn time for Southwest Airlines by hour of

the day. Hour windows are in local time, and based on the departure of a given flight. The

scheduled turn time measures the amount of time at gate scheduled for that departure after the

aircraft arrives from its inbound segment, excluding overnight stays. We also calculated the

delay minutes incurred for flights during that window. Table 16b presents the same information

for US Airways operations during June 2010. Even though the airlines are primarily domestic in

capacity focus, it is immediately clear that US Airways is much more conservative in turn time

planning. While US Airways incurs higher airspace impact – primarily due to its higher exposure

in the Northeast and Charlotte hub operation – Southwest’s exposure to late inbound aircraft is

pronounced.

0.0!

5.0!

10.0!

15.0!

20.0!

25.0!

30.0!

35.0!

40.0!

45.0!

50.0!

20! 25! 30! 35! 40! 45! 50! 55! 60! 65! 70! 75! 80! 85! 90! 95! 100! 105! 110! 115! 120!

Carrier! Weather! Airspace! Late Inbound!


Page 48

Table16a: Southwest Turn Time Distribution (20-240 minutes) by Time of Day Source: Reporting Carriers, Domestic Flights, June 2010, DOT Part 234 (ASQP)

Row Labels Sch. Turn Carrier Weather Airspace Security Late Inb. 0600-0659 29.5 0.3 0.0 30.5 0.0 58.5 0700-0759 26.1 12.5 2.4 10.8 0.0 14.3 0800-0859 28.4 7.4 6.3 14.4 0.0 18.8 0900-0959 29.4 8.0 6.2 9.6 0.1 16.5 1000-1059 29.8 8.3 3.5 10.4 0.1 20.0 1100-1159 29.2 8.3 1.2 6.7 0.1 26.7 1200-1259 29.9 8.4 2.6 7.4 0.1 26.8 1300-1359 30.6 7.3 2.4 8.6 0.0 26.7 1400-1459 31.3 7.3 4.5 8.0 0.1 26.0 1500-1559 30.8 7.9 5.5 7.7 0.2 26.5 1600-1659 31.5 8.4 5.0 8.1 0.0 29.1 1700-1759 30.9 7.8 4.4 8.1 0.0 32.1 1800-1859 30.6 7.6 3.5 5.4 0.0 36.9 1900-1959 29.6 8.3 1.9 4.8 0.0 37.3 2000-2059 28.8 8.4 0.8 3.6 0.0 38.1 2100-2159 27.6 9.1 1.1 3.2 0.0 34.9 24 Hours 30.0 8.0 3.3 6.8 0.1 31.0

Table 16b: US Airways Turn Time Distribution (20-240 minutes) by Time of Day Source: Reporting Carriers, Domestic Flights, June 2010, DOT Part 234 (ASQP)

Row Labels Sch. Turn Carrier Weather Airspace Security Late Inb. 0700-0759 66.7 10.5 0.0 21.1 0.0 3.2 0800-0859 63.0 13.1 0.0 19.8 0.0 3.1 0900-0959 67.0 13.0 0.7 13.0 0.3 8.4 1000-1059 63.9 19.5 0.0 14.2 0.2 5.9 1100-1159 69.1 10.3 0.1 17.9 0.0 9.6 1200-1259 61.9 13.9 1.1 19.3 0.9 16.5 1300-1359 63.2 10.0 2.2 24.1 0.0 12.0 1400-1459 64.7 9.2 2.3 30.1 0.0 9.9 1500-1559 68.9 7.4 5.7 26.8 0.2 10.1 1600-1659 71.6 8.2 4.7 25.3 0.0 16.3 1700-1759 65.7 7.0 4.8 22.8 0.0 21.6 1800-1859 70.7 10.8 4.7 19.2 0.0 17.7 1900-1959 59.3 6.6 2.0 13.2 0.1 28.1 2000-2059 66.8 9.7 0.2 14.8 0.1 21.0 2100-2159 70.0 9.0 0.5 8.1 0.0 32.0 2200-2259 83.4 10.7 0.0 9.4 0.1 14.7 24 Hours 67.4 9.6 2.7 20.0 0.1 15.9

Finally, we investigated whether Southwest’s weighting of short turns was significantly

different from other low-cost carriers, and whether US Airways’ moderate pattern was abnormal

for other major carriers. We calculated the percentage of total flights scheduled with turn times



between 20 minutes and 90 minutes. As Chart 12 shows, JetBlue and American have similar

aircraft turn weightings, grouping most aircraft turns between 40 and 65 minutes. Southwest

skews to faster turns, with most occurring between 20 and 40 minutes, while US Airways

schedules longer turns on average.

Chart 12: Turn Time Distribution (20-240 minutes) by Flight Total Source: Reporting Carriers, Domestic Flights, June 2010, DOT Part 234 (ASQP)

3.8.2 Increases in Aircraft Turn Times, 2005-2010

Based on the trends observed, we believed that a significant improvement in on-time

performance between 2007 and 2010 was achieved through strategic delay management – that is,

by building buffers into flight schedules through longer turn times and through longer en-route

times. The first step to test this hypothesis was to assess the change in scheduled turn times, both

for key airports with a history of congestion and for specific carriers.

To collect this data, we used the same methodology as above to build a pool of identified

aircraft turns where a specific aircraft performed consecutive inbound and outbound flights at a

specific airport. To compare data across years, we isolated reported schedules for the first

Wednesday of June in each year and identified valid turns, excluding overnight stays.

Table 17 below shows that on a systemwide basis, scheduled turn times increased by

4.2% between 2005 and 2010. At this summary level, trends in on-time performance mirror

changes in aircraft turn times, with minimum turns and maximum delays observed in 2007. By

0%!

5%!

10%!

15%!

20%!

25%!

30%!

35%!

40%!

45%!

20! 25! 30! 35! 40! 45! 50! 55! 60! 65! 70! 75! 80! 85! 90!

American! JetBlue! US Airways! Southwest!


Page 50

airline, American, Continental, AirTran, US Airways and Southwest showed significant increases

in turn times. Delta’s reduction was likely the result of integrating Northwest Airlines in 2008-

2009, which had used shorter turn time strategies than Delta at its Minneapolis and Detroit hubs.

JetBlue and United showed no meaningful changes.

Table 17: Change in Average Scheduled Turn Time, By Airline, 2005-2010 Source: Reporting Carriers, Domestic Flights, First Wednesday of June, DOT Part 234 (ASQP)

2005 2006 2007 2008 2009 2010 Change American 54.1 53.3 52.0 56.6 57.6 59.1 9.3% JetBlue 56.2 55.6 51.5 54.2 51.6 54.8 -2.4% Continental 62.4 64.0 59.6 63.8 67.8 72.3 15.8% Delta (a) 64.9 65.1 64.6 64.9 62.3 61.6 -5.0% AirTran 41.2 41.8 41.7 43.2 42.7 45.5 10.4% United 63.7 61.0 59.5 62.4 63.7 63.7 -0.1% US Airways 54.1 59.8 58.8 64.7 67.6 66.7 23.2% Southwest 26.4 26.3 27.6 29.1 28.6 30.3 14.8% Group 48.2 48.0 46.7 48.7 48.3 50.2 4.2%

(a) Delta turns impacted by Northwest integration 2009-2010

We then isolated the change in aircraft turn times by airport, across all reporting airlines.

The changes ongoing at former Northwest hubs as Delta adapted former Northwest strategies to

its combined network were evident. Detroit, Memphis and Minneapolis all showed 40% or

greater increases in scheduled turn times. Phoenix and Las Vegas showed significant increases

due to changes to US Airways’ turn strategies and on-time performance focus. Increases were

evident at congested airports such as JFK (23.7%), Houston Intercontinental (21.9%), Newark

(18.3%) and Philadelphia (17.3%). LaGuardia showed a decrease of 5.9% in turn times, but this

is distorted by slot controls and trading restrictions that force specific departure and arrival times,

which result in sub-optimal aircraft turns.

Of the airports showing a decrease in turn times, several reflect a change in the overall

traffic mix towards low-cost carriers, and particularly to Southwest. Atlanta, Seattle, Fort

Lauderdale, Pittsburgh, and Denver all saw increased low-cost carrier penetration, with fewer

(longer) legacy airline turns. Denver in particular saw rapid growth by both Frontier and

Southwest. Finally Honolulu became less congested in general as Aloha Airlines went bankrupt,

leaving Hawaiian as the dominant inter-island competitor to dominate domestic operations.



Table 18: Change in Average Scheduled Turn Time, By Airport, 2005-2010 Source: Reporting Carriers, Domestic Flights, First Wednesday of June, DOT Part 234 (ASQP)

Airport 2005 2010 Change Airport 2005 2010 Change DTW 42.2 66.2 57.0% IAD 57.5 60.7 5.5% PHX 34.9 52.6 50.8% DFW 53.3 55.9 4.9% MEM 43.6 62.9 44.2% CLT 62.2 65.0 4.5% MSP 47.4 66.8 40.9% MIA 63.9 66.5 4.1% BWI 32.3 41.8 29.1% MCO 44.7 46.5 3.9% JFK 63.0 77.9 23.7% DCA 51.4 53.0 3.1% MKE 39.2 48.3 23.4% LAX 54.5 56.2 3.0% SMF 31.0 38.2 23.2% ATL 64.1 61.6 -3.9% HOU 26.1 31.9 22.0% SEA 55.3 52.6 -4.9% IAH 65.3 79.6 21.9% FLL 47.4 44.7 -5.8% BTV 35.3 42.0 19.1% BOS 54.9 51.7 -5.8% LAS 37.4 44.5 18.8% LGA 54.4 51.3 -5.9% EWR 58.8 69.6 18.3% SLC 57.4 53.2 -7.4% MDW 29.8 34.9 17.2% SFO 71.0 61.2 -13.9% PHL 50.1 58.7 17.2% PIT 51.2 43.7 -14.7% ORD 55.9 63.5 13.5% DEN 59.7 50.3 -15.7% CLE 55.6 59.6 7.1% HNL 134.4 107.9 -19.7%

From this analysis, we conclude that airlines have internalized the risk from late

inbound flights at congested U.S. airports by consciously expanding aircraft turn times.

This buffer we believe explains why flight delays have declined significantly after the peak

in 2007, when both on-time performance was at its worst and turn times at their shortest.

The outlier in turn times continues to be Southwest, which not surprisingly has recently reported

sub-average on-time performance statistics.

Scheduled turn times represent one buffer against the follow-on impact of flight delays in

an airline’s system. Adding time buffer to scheduled en-route times – or “padding” the flight

schedule, as some call the practice – is another method to ensure that ordinary variability in flight

schedule performance due to weather, carrier, and airspace factors is incorporated into customer

expectations and aircraft utilization plans.

In the next section, we review changes to airline en-route times and assess the degree to

which airlines have embraced differences in en-route time planning.


Page 52

3.9 Schedule Padding

Schedule padding reflects strategic delay management by airlines, incorporating into

planned en-route (gate to gate) time the expected delays from controllable, uncontrollable and

follow-on factors. There are three primary reasons to build some level of flight delays into en-

route times, versus incurring a delay only on the impacted flights.

First, DOT press releases and media activity focus on airline arrival performance

measured against the scheduled arrival time, without consideration to mitigating factors (such as

the average en-route time on a given route). It is understandable that DOT seeks a single standard

to apply to all domestic flight operations, but this arrivals-based standard independent of other

factors creates a strong incentive for airlines to “manage” delays by ensuring all but extraordinary

situations will be internalized into their schedule. Airlines can create whatever on-time

performance they want to achieve by adding scheduled minutes to delay-prone flights, although

there is a costly trade-off in crew pay and aircraft utilization. In turn, placing high in the DOT on-

time list creates an objective standard for an airline to advertise and compare with its peers.

Second, customer flight connections are based on scheduled arrival and departure times.

For connecting itineraries, it is essential that minor variability in the inbound arrival time at the

hub is incorporated into the minimum connect time standards applied. Building a schedule buffer

into each flight minimizes the number of missed connections.

Third, passengers complain about late arrivals, but few complain about landing early,

even if a gate wait is involved. The actual en-route time that a flight should require is opaque to

the customer. Customer satisfaction is a key driver. So why aren’t flights padded with 15 or 30

minutes of extra time? The answer is cost and utilization. Most carriers pay crews based on the

greater of actual or scheduled block time, so schedule padding means higher labor costs. Schedule

padding can reduce the available time for a aircraft, as the subsequent departure time is fixed

regardless of what time an inbound arrival occurs.

3.9.1 Schedule Padding in 2010

In considering airline schedule buffers, our hypothesis was that airlines with

comparatively short turn times at each airport would exhibit higher schedule padding than carriers

with longer turn times. We also expected to see a significant increase in schedule padding as

carriers managed towards (somewhat arbitrary) 15-minute arrival delay standards defined by



DOT. Data is from the DOT Part 234 ASQP focused on the differences between scheduled and

actual en-route times. We exclude flights delayed due to late inbound aircraft.

Chart 13 presents the average schedule pad by time of day, across the U.S. system for all

reporting carriers. The percentages denote the average of the differences between actual en-route

time and scheduled en-route time for each route. For example, during the 1300-1359 time block,

the average difference between scheduled and actual en-route time was 2.9%, with the actual time

faster. As the day progresses, and as late arriving aircraft compound irregular operations, airlines

build additional buffer into their flight schedules. Table 13 shows that airlines put a roughly 2-4%

buffer in their operation. We then grouped the difference between actual and scheduled en-route

time by the distance of planned flight. Table 19 below shows that as the flight distance decreases,

the schedule pad increases dramatically (due to the variability in taxi times, as we show later).

Chart 13: Schedule Padding (Normal Operations Scheduled vs. Actual Block Time) All Carriers, Full Year 2010, by Departure Time Block

Table 19: Schedule Padding (Normal Operations Scheduled vs. Actual Block Time) 2010

By Length of Flight Segment (Domestic Flights by Reporting Carriers)

DISTANCE % PADDING DISTANCE % PADDING DISTANCE % PADDING 100 27% 1,100 11% 2,100 8% 200 23% 1,200 10% 2,200 7% 300 20% 1,300 9% 2,300 8% 400 17% 1,400 9% 2,400 8% 500 16% 1,500 8% 2,500 8% 600 14% 1,600 9% 2,600 7% 700 13% 1,700 9% 2,700 8% 800 12% 1,800 9% 2,800 7% 900 11% 1,900 8% 2,900 7%

1,000 11% 2,000 8% 3,000 5%

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Page 54

Chart 14 below graphs the information in Table 19 with the addition of a trend line, and

without the 100-mile groupings in the table. A pronounced curve is observed with deviation

increasing at transcontinental and US-Hawaii flights.

Chart 14: Spread in En-Route Performance by Distance of Route Percentage difference between actual/scheduled block time ratios under normal and irregular

operation conditions (Full Year 2010)

The data suggest that the downward curve observed in Table 19 and Chart 14 is due to

variability in ground time. As Exhibit G shows, the variability in scheduled airtime (block time

minus ground taxi time) is significantly lower than overall gate-to-gate times.

3.9.2 Taxi-Out Time Variability

When planning schedules, airline operations teams must incorporate the natural

variability of airport taxi times. The primary bottleneck after gate departure is the rate at which

the national airspace system can accept flight departures from a given airport. This causes taxi

times for flight departures to be longer, and more variable, than taxi times for flight arrivals at a

destination, when the primary causes for long taxi times are related to gate availability and airline

ramp congestion.

0%!

5%!

10%!

15%!

20%!

25%!

30%!

0! 500! 1,000! 1,500! 2,000! 2,500! 3,000! 3,500! 4,000! 4,500! 5,000!

Spre

ad b

etw

een

Actu

al a

nd S

ched

lued

En-

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e Ti

me

(Del

ayed

vs.

Not

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ayed

)!

Distance of Flight (miles)!



Both taxi-out and taxi-in times are impacted by physical factors as well, including (1) the

absolute distance from gates to the runway; (2) runway intersections that force aircraft to hold for

arriving or departing runway operations; (3) intersecting runways that restrict departure flows;

and (4) the amount of ramp and taxiway infrastructure available for run-ups, ground delay

programs and passenger services during extended tarmac waits.

To begin our analysis of taxi-out time variability in 2010, we collected average taxi-out

times for the full year across 13 key airports subject to long taxi times and congestion. We

grouped the average taxi-out times (excluding cancellations and first taxis before gate returns)

that resulted in a successful runway departure. Table 20a provides average taxi-out times,

demonstrating a minimum average of 14.6 minutes at DFW to a maximum of 27.6 minutes at

New York JFK. Taxi times at all airports increase through mid-day and afternoon hours. There

are significant increases during the 12pm-8pm time window at all three New York airports, where

taxi times at JFK routinely exceed 30 minutes.

Table 20b captures the variability of these taxi-out times. There is significant afternoon

variability in taxi times for Atlanta, Boston, Washington Reagan and Dulles, Chicago,

Philadelphia and all three New York airports, while Dallas, Denver, Houston and Los Angeles

remain within a narrower window of taxi times. This is illustrative of two factors: the

appearance of afternoon weather activity that stalls the departure flows from these airports, as

well as normal schedule peaks that are discussed more in Section 4.


Page 56

Table 20a: Mean Taxi-Out Time by Airport (Minutes per Flight) For Key U.S. Airports, Full Year 2010, by Departure Time Block

DEPS: ATL BOS DCA DEN DFW EWR IAD IAH JFK LAX LGA ORD PHL

0001-0559 15.5 17.2 15.3 15.6 14.2 14.1 14.3 14.5 18.5 15.9 12.7 15.5 14.1 0600-0659 15.2 20.5 15.3 15.1 15.2 19.7 16.7 13.7 21.7 15.0 18.5 14.8 17.5 0700-0759 19.0 20.0 17.0 14.6 15.1 21.8 17.5 17.7 24.7 15.1 21.7 15.3 21.4 0800-0859 23.2 19.7 18.7 15.9 15.0 24.3 17.0 15.1 27.9 15.8 26.8 17.0 22.4 0900-0959 23.9 19.8 18.0 13.1 14.7 26.4 14.4 16.9 23.8 14.5 30.6 16.5 22.0 1000-1059 22.5 17.9 17.4 16.5 14.8 16.3 13.3 16.3 20.7 14.8 27.8 16.5 25.7 1100-1159 22.3 17.3 15.7 14.8 14.5 16.2 14.0 15.6 20.3 15.1 26.3 15.5 19.2 1200-1259 17.4 18.4 16.2 13.7 16.1 16.7 17.4 14.8 20.3 15.9 24.9 16.0 14.8 1300-1359 19.0 18.8 17.0 13.0 16.0 16.7 14.6 15.8 23.9 15.2 24.2 17.0 17.3 1400-1459 20.0 18.9 18.4 12.9 15.7 18.1 16.4 15.5 29.2 12.9 25.4 16.3 19.5 1500-1559 21.9 19.6 17.6 14.2 15.6 20.7 17.3 16.7 34.0 12.8 29.9 17.1 20.9 1600-1659 22.3 19.6 19.2 14.0 15.4 20.1 18.4 16.1 38.7 14.0 28.6 17.9 22.8 1700-1759 22.3 21.2 19.0 14.5 15.3 22.0 20.6 16.4 35.0 12.7 27.0 18.3 23.5 1800-1859 21.2 22.7 18.2 15.3 15.2 27.8 17.7 16.9 37.6 12.7 27.4 19.6 32.1 1900-1959 24.5 20.3 18.9 16.1 15.0 26.9 16.9 17.6 39.9 11.3 28.7 18.6 23.2 2000-2059 20.9 16.2 20.5 13.3 14.1 24.3 16.6 14.4 34.0 11.3 30.1 17.4 21.1 2100-2159 21.8 15.1 17.3 15.0 13.0 20.3 15.7 15.1 30.6 14.4 24.1 16.0 23.8 2200-2259 18.7 0.0 14.7 14.1 11.8 17.6 16.9 10.5 23.2 17.6 14.8 14.7 15.9 2300-2359 13.2 16.2 0.0 15.3 12.0 9.0 0.0 9.8 19.7 18.0 0.0 0.0 11.0 24HRS 20.3 17.9 16.5 14.6 14.7 20.0 15.6 15.2 27.6 14.5 23.7 15.8 20.4

Table 20b: Standard Deviation of Taxi-Out Time by Airport (Minutes per Flight)

For Key U.S. Airports, Full Year 2010, by Departure Time Block TIME ATL BOS DCA DEN DFW EWR IAD IAH JFK LAX LGA ORD PHL

0001-0559 7.7 8.3 5.8 8.7 6.8 5.5 6.4 6.8 6.7 5.6 5.7 8.1 7.7 0600-0659 6.1 8.8 7.8 8.7 8.7 9.0 8.2 5.2 9.5 6.1 8.8 6.9 9.7 0700-0759 8.5 10.2 8.3 8.5 7.2 10.4 9.0 7.3 10.5 6.4 11.9 7.9 12.8 0800-0859 9.6 8.7 9.5 8.5 7.0 11.4 7.7 6.3 12.4 7.0 14.0 8.5 14.1 0900-0959 10.2 8.2 10.4 6.0 6.1 12.9 6.2 6.4 12.1 7.3 15.1 8.7 11.4 1000-1059 9.1 7.7 10.5 7.4 7.7 8.9 7.6 7.1 9.7 6.7 14.0 9.1 12.4 1100-1159 9.2 8.0 8.8 6.9 8.3 7.5 6.8 7.4 9.7 6.6 12.9 8.0 9.9 1200-1259 7.6 10.2 9.3 6.5 8.5 7.4 9.9 7.0 10.2 6.9 12.8 8.2 7.7 1300-1359 9.2 9.7 11.5 5.8 8.4 8.2 10.5 7.9 12.5 6.5 12.4 8.8 9.4 1400-1459 9.9 10.5 13.1 5.8 8.0 9.2 12.4 7.7 14.5 6.1 14.4 10.0 12.3 1500-1559 10.2 11.5 12.4 7.9 8.7 9.9 12.7 9.2 17.2 5.7 15.8 10.0 12.6 1600-1659 11.7 10.1 13.0 9.8 8.6 10.9 12.9 7.7 19.1 5.7 16.4 10.6 15.7 1700-1759 12.4 11.7 12.7 9.8 8.0 11.9 13.8 7.6 19.0 5.7 16.4 10.9 16.4 1800-1859 11.4 11.5 13.8 8.9 7.2 13.4 12.5 7.7 20.5 5.4 15.7 11.8 17.4 1900-1959 10.5 10.3 12.9 10.4 6.6 13.1 11.6 8.6 21.7 5.2 17.1 11.2 17.3 2000-2059 9.4 7.6 13.4 7.8 6.4 12.3 9.4 5.8 20.0 5.9 15.2 10.8 12.6 2100-2159 9.1 5.4 12.3 8.4 6.9 11.5 8.8 5.3 16.9 6.2 12.6 9.7 12.9 2200-2259 7.4 0.0 8.5 5.4 4.5 9.2 8.1 3.6 12.1 6.5 7.1 7.5 8.0 2300-2359 4.1 7.0 0.0 7.5 10.5 0.0 0.0 2.9 9.4 6.9 0.0 0.0 0.0 24 HRS 9.1 8.7 10.2 7.8 7.6 9.6 9.2 6.7 13.9 6.2 12.5 8.8 11.6



What is the importance of Tables 20a and 20b in airline planning? When determining

how large a schedule buffer is appropriate, airlines must assess not only the average taxi time, but

the deviation of taxi-out times from the mean. For example, an airline targeting an 80% on-time

performance with sole consideration to taxi-out time would estimate a taxi-out time at JFK of 58

minutes (mean plus one standard deviation) and add this to the estimated flight time and arrival

taxi-in time in order to calculate the expected block time. As Chart 15 below shows, achieving a

95% confidence interval on flight departures from these airports requires taxi-out time estimates

significantly in excess of the mean.

Chart 15: 95% Confidence Interval Taxi-Out Time, 24 Hours and Peak Hour 95th Percentile Taxi-Out Time (95% of departures under) by Key U.S. Airport (Full Year 2010)

3.9.3 Taxi-In Variability

We now repeat the same calculations for taxi-in times after landing at U.S. airports

during 2010. As expected, taxi-in times on average are significantly lower than taxi-out times.

At Atlanta, the average taxi-out time was 20.3 minutes, while the average taxi-in time was 10.8

minutes. The difference is the time spent queuing for departure flow and ATC resources. Taxi-in

time is a more direct measurement of the time required to taxi from gate to runway, since it is

generally unimpeded (but can be impacted by crossing runway restrictions).

Table 21a provides the mean values, while Table 21b provides the standard deviation for

each airport. Chart 16 shows the 95% confidence interval for taxi-in times by airport.

38.

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ATL! BOS! DCA! DEN! DFW! EWR! IAD! IAH! JFK! LAX! LGA! ORD! PHL!

ALL DAY! 6PM-7PM!


Page 58

Table 21a: Mean Taxi-In Time by Airport (Minutes per Flight) For Key U.S. Airports, Full Year 2010, by Arrival Time Block

Arrival: ATL BOS DCA DEN DFW EWR IAD IAH JFK LAX LGA ORD PHL

0001-0559 7.1 5.3 4.6 6.8 4.9 7.7 5.8 6.3 8.3 8.1 4.8 6.0 5.7 0600-0659 8.1 6.8 3.5 5.9 5.7 8.0 6.5 6.2 8.6 7.7 6.2 7.0 6.4 0700-0759 8.9 6.5 4.9 7.9 7.3 8.0 6.7 7.2 9.1 9.0 7.2 8.1 7.1 0800-0859 12.1 8.0 5.9 7.4 9.6 9.0 6.3 7.6 9.1 8.7 9.1 11.0 6.0 0900-0959 13.5 7.1 5.0 7.9 10.1 8.1 5.8 8.4 8.5 9.2 8.5 9.7 6.1 1000-1059 13.3 6.5 5.4 11.9 9.8 7.4 5.7 7.5 8.5 9.3 8.8 8.1 7.7 1100-1159 9.5 6.5 4.8 8.7 9.9 7.3 6.5 7.3 7.4 9.8 8.5 7.8 6.5 1200-1259 9.0 6.5 5.1 7.1 10.5 7.6 6.4 6.9 7.5 9.7 7.3 8.2 6.6 1300-1359 9.4 6.4 5.2 7.3 10.2 7.9 6.0 7.4 8.3 9.2 8.2 8.5 6.7 1400-1459 10.6 6.2 5.7 7.6 9.5 8.2 6.4 7.8 9.9 7.7 7.8 8.7 7.1 1500-1559 11.6 6.4 6.2 8.1 9.2 8.9 7.4 6.9 11.8 8.1 8.1 8.5 6.5 1600-1659 11.0 6.8 5.5 7.2 9.3 8.9 7.5 7.6 11.5 8.2 7.9 9.1 7.3 1700-1759 10.7 7.4 5.9 8.0 10.0 10.0 7.9 8.7 17.1 7.7 8.3 9.9 7.1 1800-1859 11.3 7.2 5.7 9.0 10.3 10.1 7.1 9.4 15.0 8.3 8.7 11.6 9.5 1900-1959 13.6 7.5 6.1 8.5 10.5 10.8 6.5 8.2 14.2 8.2 8.7 11.8 7.2 2000-2059 13.6 6.8 5.5 8.2 9.9 10.4 6.9 8.0 12.8 8.9 9.1 12.8 7.4 2100-2159 12.6 6.2 6.0 7.7 8.3 9.2 7.2 6.3 10.5 11.2 8.6 8.3 6.5 2200-2259 11.9 5.8 5.4 6.9 5.8 8.2 6.9 5.9 10.4 12.1 9.0 6.7 6.5 2300-2359 7.4 5.8 4.6 7.1 5.2 7.4 6.8 6.7 10.6 11.6 7.5 6.3 6.8 24 HRS 10.8 6.6 5.3 7.8 8.7 8.6 6.7 7.4 10.5 9.1 8.0 8.8 6.9

Table 21b: Average of Taxi-In Time by Airport (Minutes per Flight)

For Key U.S. Airports, Full Year 2010, by Arrival Time Block

Arrival: ATL BOS DCA DEN DFW EWR IAD IAH JFK LAX LGA ORD PHL

0001-0559 7.7 8.3 5.8 8.7 6.8 5.5 6.4 6.8 6.7 5.6 5.7 8.1 7.7 0600-0659 6.1 8.8 7.8 8.7 8.7 9.0 8.2 5.2 9.5 6.1 8.8 6.9 9.7 0700-0759 8.5 10.2 8.3 8.5 7.2 10.4 9.0 7.3 10.5 6.4 11.9 7.9 12.8 0800-0859 9.6 8.7 9.5 8.5 7.0 11.4 7.7 6.3 12.4 7.0 14.0 8.5 14.1 0900-0959 10.2 8.2 10.4 6.0 6.1 12.9 6.2 6.4 12.1 7.3 15.1 8.7 11.4 1000-1059 9.1 7.7 10.5 7.4 7.7 8.9 7.6 7.1 9.7 6.7 14.0 9.1 12.4 1100-1159 9.2 8.0 8.8 6.9 8.3 7.5 6.8 7.4 9.7 6.6 12.9 8.0 9.9 1200-1259 7.6 10.2 9.3 6.5 8.5 7.4 9.9 7.0 10.2 6.9 12.8 8.2 7.7 1300-1359 9.2 9.7 11.5 5.8 8.4 8.2 10.5 7.9 12.5 6.5 12.4 8.8 9.4 1400-1459 9.9 10.5 13.1 5.8 8.0 9.2 12.4 7.7 14.5 6.1 14.4 10.0 12.3 1500-1559 10.2 11.5 12.4 7.9 8.7 9.9 12.7 9.2 17.2 5.7 15.8 10.0 12.6 1600-1659 11.7 10.1 13.0 9.8 8.6 10.9 12.9 7.7 19.1 5.7 16.4 10.6 15.7 1700-1759 12.4 11.7 12.7 9.8 8.0 11.9 13.8 7.6 19.0 5.7 16.4 10.9 16.4 1800-1859 11.4 11.5 13.8 8.9 7.2 13.4 12.5 7.7 20.5 5.4 15.7 11.8 17.4 1900-1959 10.5 10.3 12.9 10.4 6.6 13.1 11.6 8.6 21.7 5.2 17.1 11.2 17.3 2000-2059 9.4 7.6 13.4 7.8 6.4 12.3 9.4 5.8 20.0 5.9 15.2 10.8 12.6 2100-2159 9.1 5.4 12.3 8.4 6.9 11.5 8.8 5.3 16.9 6.2 12.6 9.7 12.9 2200-2259 7.4 0.0 8.5 5.4 4.5 9.2 8.1 3.6 12.1 6.5 7.1 7.5 8.0 2300-2359 4.1 7.0 0.0 7.5 10.5 2.9 9.4 6.9 24 HRS 9.1 8.7 10.2 7.8 7.6 9.6 9.2 6.7 13.9 6.2 12.5 8.8 11.6



Chart 16: 95% Confidence Interval Taxi-In Time, 24 Hours and Peak Hour 95th Percentile Taxi-In Time (95% of arrivals under) by Key U.S. Airport (Full Year 2010)

In conclusion, we observe that there is significant variation in taxi-out and taxi-in

times across airports, and that taxi times can vary widely by time of day. At some airports,

including JFK, LGA, EWR and PHL, taxi times during the afternoon are much higher than during

the morning and late evening. At others, including DEN, DFW and IAH, taxi times are more

evenly distributed. Planners must take these differences into account when incorporating

expected taxi times into their scheduled en-route times. But how are these taxi times changing

over time?

3.9.4 Changes in Taxi Times, 1995-2010

In assessing block time padding over time, the mean and standard deviation taxi times

change by time of day, are variable across airports and have widely different patterns as discussed

in Section 3.9.3. It is also critical to consider how these patterns have changed over time.

Using on-time data since 1995, we compared taxi-out and taxi-in times on average across

a set of 35 airports for domestic flights. At each airport, we reviewed taxi times by month and by

airline. Plotting the annual averages in Chart 17, one can observe a steady increase in taxi-out

times. In contrast, taxi-in times increase but less significantly. Table 22 provides the averages

for five-year periods from 1995-2009, plus 2010. The significant jump between 1995 and 2010 is

observable.

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ATL! BOS! DCA! DEN! DFW! EWR! IAD! IAH! JFK! LAX! LGA! ORD! PHL!

ALL DAY! 6PM-7PM!


Page 60

Chart 17: Change in Taxi Times (In and Out) Excluding Cancellations and Diversions Airport Set: ATL, BNA, BOS, BUF, BWI, CLE, CLT, DAL, DCA, DEN, DFW, DTW, EWR, FLL, HNL, HOU, IAD,

IAH, JFK, LAS, LAX, LGA, MCO, MDW, MEM, MSP, OAK, ORD, PHL, SAN, SEA, SFO, SLC, STL, TPA

Table 22: Average Taxi-In Time by Airport (Minutes per Flight) Full Year 2010, by Arrival Time Block

Airport Set: ATL, BNA, BOS, BUF, BWI, CLE, CLT, DAL, DCA, DEN, DFW, DTW, EWR, FLL, HNL, HOU, IAD, IAH, JFK, LAS, LAX, LGA, MCO, MDW, MEM, MSP, OAK, ORD, PHL, SAN, SEA, SFO, SLC, STL, TPA

Averages (Minutes) 1995-1999 2000-2004 2005-2009 2010 Taxi In 5.7 6.3 6.8 7.0 Taxi Out 15.2 16.2 17.0 16.2

3.9.5 Comparing Changes in Taxi Time and Flight Time, 1996-2010

In Section 3.9 we have established that:

• The practice of schedule padding exists and is designed to maximize on-time

performance, minimize missed flight connections by passengers, maximize

customer satisfaction from on-time arrivals and ensure operational integrity.

Schedule padding adds cost by reducing usable aircraft time and increasing

labor and maintenance cost.

• Typical schedule padding varies between 2% and 4% across all airlines and all

stage lengths. Some flights are more aggressively padded than others.

0!2!4!6!8!

10!12!14!16!18!20!

1995

!

1996

!

1997

!

1998

!

1999

!

2000

!

2001

!

2002

!

2003

!

2004

!

2005

!

2006

!

2007

!

2008

!

2009

!

2010

!

Min

utes

of T

axi T

ime!

Taxi In! Taxi Out!



• The variability of taxi times on both arrival and departure, both in means

observed and in the standard deviations therein, are primary drivers of schedule

pads. Taxi times largely incorporate the weather- and airspace-related delay

factors reported by airlines. To achieve 80-95% confidence intervals in block

times, taxi times greater than 30 minutes must be incorporated for key

congested airports.

• Taxi times (both in and out) have increased since 1995. This is partially due to

changes and general utilization of runway and en-route assets, but it is also due

to new runway construction and other physical factors.

To complete our analysis of changes in block times since 1996, we compiled scatter

charts for all routes in operation consistently between 1996 and 2010, by any reporting

airline. For each route, we collected the mean taxi-time (out + in) for all operators between

1996-2000 and 2007-2010, and then compared the two averages.

As Chart 18a shows, not all routes had longer taxi-times between 2007-2010 versus

1996-2000, but more than half did. The horizontal axis in Chart 18 represents the change in mean

values: 100% means that the averages in 1996-2000 and 2007-2010 are identical. More than

100% means that the mean 2007-2010 exceeds the mean value 1996-2000.

One can observe that the shorter the flight distance, the greater the (positive) change in

mean taxi times. We attribute this to two factors. First, shorter-haul flights shifted from mainline

carriers (plus turboprops, mainly from non-reporting small airlines) to regional flights by

reporting express carriers such as Mesa, ExpressJet, ASA, SkyWest and Comair. Second, this

new regional jet flying was accompanied by a significant increase in ground delay programs into

major airports since 2002.

We then run the same analysis for airborne (takeoff to landing) time, using the same data

set and inter-year averages. There are several reasons why flight time may change on a given

route, given that weather conditions average out over long periods of time. First, airlines have

recently employed fuel management strategies, although this impacts only the tail end of 2007-

2010 data. Second, substitution of regional jets for mainline flights results in a marginal flight

time increase due to slower cruising speed. Chart 18b captures the overall change in flight time.


Page 62

15-Year Snapshot Chart 18a: Average Taxi-Time by Route Distance

Mean Taxi-Time by Distance, Change 1996-2000 vs. 2007-2010 (>100% = 2007-2010 Longer)

Chart18b: 15 Year Change - Average Flight Time by Route Distance Mean Actual Flight Time by Distance, Change 1996-2000 vs. 2007-2010

(>100% = 2007-2010 Longer)

0!

1,000!

2,000!

3,000!

4,000!

5,000!

6,000!

0%! 100%! 200%!

Rout

e Di

stan

ce (M

iles)!

Change in Taxi Time (Average 2007-2010 vs. Average 1996-2000)!

The shorter the flight, the more variable the taxi time

has become

0!

500!

1,000!

1,500!

2,000!

2,500!

3,000!

3,500!

4,000!

4,500!

0%! 100%! 200%!

Flig

ht D

istn

ace

(Mile

s)!

Change in Flight Time (Average 2008-2010 vs. Average 1995-1997)!

There is little variability in the change in actual flight

time by route, compared to taxi time variance



It can be difficult, even with side-by-side comparisons of the changes in flight and taxi

averages, to observe precisely where the increase in block time originates. Cross-plotting the data,

however, shows that taxi-time changes are clearly driving the overall increase in en-route times,

and forcing airlines to increase block times. Chart 18c overlays the data.

Chart 18c: Change in Average Flight Time by Route Distance Mean Actual Flight Time by Distance, Change 1996-2000 vs. 2007-2010

(>100% = 2007-2010 Longer)

3.9.6 Conclusions

“The inflation in block time is really because of more variability.”

Bill Owen, Director of Schedule Planning, Southwest Airlines (WSJ Feb 2010)

Some consumer advocates and media have accused airlines of arbitrarily increasing

scheduled en-route times purely to game the DOT On-Time Arrivals reporting system. There is

no doubt that airlines must carefully watch their on-time performance, particularly since DOT

invests heavily in communicating on-time arrival metrics as a key indicator of the national

airspace system. However the data suggest that increases in block time are driven by

0%#

50%#

100%#

150%#

200%#

250%#

300%#

0# 500# 1,000# 1,500# 2,000# 2,500# 3,000#

Chan

ge,(2008,2010(vs.(1995,1997(

DIstance(of(Route(

Taxi#

Flight#


Page 64

fundamental changes in airport operations and have been required in order to sustain historical

levels of on-time performance.

In this section, we have investigated the factors that cause airline delays, isolating which

causal factors impact specific airlines and airports. We began with a snapshot of airport

utilization and on-time performance, establishing a baseline of delay and cancellation data. We

have reviewed scheduled turn time strategies and connected flight delays due to late arriving

equipment to those turn strategies. We have established that airlines internalize variability in taxi

time and en-route flight time into their block time calculations.

From our discussion, we conclude that:

1. Airlines do assess and internalize various factors that cause ordinary flight delays

into their scheduled operations. The specific strategies that each airline uses to

maximize schedule reliability or aircraft utilization through the day has differential

impact on delay impact, both on the primary flight and on following flight operations.

We identified the trade-off at Southwest Airlines between fast turns (maximizing

utilization) and the unique impact of late-arriving aircraft delays during the afternoon

hours.

2. Schedule padding is a fact, and driven by real-world factors that are not arbitrary

or immeasurable. Taxi times have increased over the past 15 years, as have en-route

times, due to airport utilization, substitution of regional jets for mainline equipment, and

fuel savings strategies. The resulting “pads” of between 2% and 4% systemwide are

necessary to maintain on-time performance in the historical range expected by consumers.

3. Simply measuring the number of arrivals and departures at an airport, or relying on the

individual scheduling decisions made by a specific carrier, does not explain why some

airports generate significant delays while others do not. We have reviewed several

generic factors that impact delays – including regional airspace congestion caused by

competing facilities in a specific corridor or metropolitan region – but explaining

“overscheduling” through individual airline decisions is not supported by the data.

We therefore turn our attention to the airport, with a review of both FAA Operational

Metrics that define the target operational maximum level, and specific schedule designs that

either alleviate or aggravate irregular operations recovery.

Section Four: Airport Capacity


SECTION FOUR: AIRPORT CAPACITY

In prior sections, we have established delay, cancellation and diversion patterns for

airlines on a flight-specific basis. We will now incorporate our analysis of airport factors,

including general capacity definitions, tendency for capacity reduction through weather events,

airline scheduling patterns in aggregate and capacity changes over time. Specifically we will:

• Review the definition of airport capacity;

• Discuss the FAA Operational Benchmark metrics;

• Identify the factors that cause a reduction in available capacity;

• Identify new runway capacity and other factors that address this reduction;

• Review the traffic mix at key airports;

• Review schedule design by airlines at airports, and introduce a Peak Index

definition to measure schedule design; and

• Compare Peak Indices and delay factors across the nation’s largest airports.

4.1 Defining Airport Capacity

What is airport capacity? The FAA defines capacity as “the maximum number of flights

an airport can routinely handle in an hour, for the most commonly used runway configuration in

each specific weather condition.”14 A slightly different definition is also used, sometimes

concurrently, calling capacity “the number of departures and arrivals per hour that an airport can

handle safely and routinely.”15 The FAA’s definition of capacity does not represent an absolute

maximum measure of departures and arrivals; rather, it represents a standard measure of full

occupancy that “could be exceeded occasionally under favorable conditions.”16

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!14FAA Airport Capacity Benchmark Report 2004, US Department of Transportation, FAA/MITRE CAASD. September 2004. 15 Letter, Marion Blakey, Administrator, October 2004. Page 1. 16FAA Capacity Report.The first benchmarks were developed by the FAA in 2001 and were changed in 2004 to reflect differences in methodology. According to the FAA, these benchmarks are not to be considered absolute in terms of policy. The FAA cites Atlanta as an example: “This issue can be demonstrated by examining busy airports such as Hartsfield-Jackson Atlanta International Airport or Chicago O’Hare. At Atlanta, scheduled operations may exceed the benchmarks in optimum weather, and frequently do so in bad weather. A simple comparison of schedule to benchmarks might suggest that some action is needed to curtail the schedule. However, air traffic controllers, airlines, and the airport operator have indicated in discussions that they are relatively comfortable with the traffic schedule, and believe that it makes efficient use of the airport. Their judgment is based on long experience and a broad understanding of air transportation. Some of the considerations behind this judgment are applicable to transfer hub airports in general (the concentration of traffic into schedule peaks to allow passengers to make convenient transfers between flights; the ability to catch up with traffic between peaks in the schedule; and the ability of hubbing carriers to cancel and

Section Four: Airport Capacity !

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To correct for the variance in weather conditions and specific runway conditions at given

airports, the FAA publishes a range of capacity values under a trio of weather conditions:

- Optimum weather conditions, representing good weather with visual separation by pilots

between aircraft in the local airspace;

- Marginal weather conditions, representing weather better than standard instrument

conditions but requiring radar separation of aircraft in the local airspace. Generally

marginal conditions refer to ceilings between 1,000 and 3,000 feet above ground level

and visibility between 3 and 5 miles; and

- Instrument flight rules, representing bad weather conditions where radar is required to

keep aircraft separated in the airport area. Instrument (“IFR”) conditions refer to ceilings

below 1,000 feet above ground level and visibility less than 3 miles.17

On a quarter-hour basis, the FAA records weather conditions and airport metrics for 77

major and mid-size airports (both commercial and general aviation) located across the United

States. From these 77 airports, the FAA developed capacity benchmarks for 35 major hub

facilities with a history of high utilization and airport delays. The objective of the capacity

projections is to support policy discussions and long-term investment strategies at the airport,

assessing when additional physical capacity (for example, runway construction) or airspace

capacity (new radar and departure/arrival corridors) are required.

4.2 FAA Airport Capacity Benchmarks

The FAA’s Airport Capacity Benchmarks (“ACB”) introduce a baseline measurement of

airport infrastructure capability into discussions of airline schedules, delay expectations and

weather impact. For that reason, it is important to review what the FAA has published, how their

methodology incorporates certain aspects of airport design and operational constraints, and how

additional runway capacity since the ACB were published in 2004 have improved capacity at

certain facilities in the United States.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!consolidate some flights during poor weather conditions). Other considerations are applicable to all busy airports, namely the premise that some amount of congestion and delay is not inconsistent with efficient and affordable air transportation.” 16 This means that what most use as airport capacity figures are runway capacity numbers. This is not a trivial distinction. 17 For more information, see http://aviationweather.gov/adds/metars/description_ifr.php.



Capacity definitions are not absolute. At major hub airports such as Chicago O’Hare and

Atlanta Hartsfield-Jackson, scheduled operations routinely exceed good-weather capacity without

any material change in delay or cancellation rates. When weather conditions restrict airport

capacity, a larger number of airports will have scheduled flight demand in excess of the projected

airport capacity.18

Consumer groups have represented that bad-weather capacity reductions are infrequent

and have a minor impact on operations at a given airport. By this line of reasoning, excessive

flight delays are caused by airlines that schedule excessive arrivals and departures at a given point

in time. Key advocates of this position include Kate Hanni, Founder of FlyersRights.org,19 and

Kevin Mitchell, Head of the Business Travel Coalition.20 To determine whether this position is

reasonable, one must first assess whether their core assumption is correct – that severe weather

impact on airports is minor, predictable and can be incorporated into forward schedule plans.

Given that we have already reviewed flight-specific delay trends in Section Three, we now focus

on airport-level trends and benchmarks.

The first step is to review the Operational Benchmarks published by the FAA in October

2004, which incorporated a range of runway capacities based on the three environmental

constraints described above: optimal, marginal and instrument (IFR) weather. The full

Benchmark list is below in Table 23. Optimal, Marginal and IFR represent the range of airport

capacity available during that weather condition, based on runway configuration and other factors.

The column “% of Time” represents the frequency of that weather condition at the given airport

during the period from January 2000 through July 2002, excluding the airspace closure period

after September 11, 2001.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!18FAA Operational 2004.“The benchmarks are the sum of takeoffs and landings per hour that are possible under the given conditions, if the demand is present. The benchmark capacity represents balanced operations, with equal number of arrivals and departures. However, if air traffic control (ATC) at the airport frequently reports an unbalanced rate, the benchmark value will reflect this. For example, the airport might be able to handle 40 arrivals per hour but as many as 60 departures per hour. Clearly, the airport cannot operate more departures than arrivals for an extended period: such rates describe the capability of the airport to accommodate operations, not necessarily actual hourly traffic. These benchmarks are based on routine operations, and therefore they might be exceeded under favorable conditions. Conversely, lower rates would be expected under adverse conditions, such as lower capacity runway configuration or very low ceiling and visibility, of if demand is significantly less than capacity.” 19 Kate Hanni, FlyersRights.org, http://crankyflier.com/2010/03/12/kate-hanni-and-i-talk-about-delays-we-disagree-part-two/. 20 Kevin Mitchell, BTC Editorial, http://meetingsnet.com/corporatemeetingsincentives/news/0315-tarmac-delay-rule/.


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Table 23: FAA Capacity Benchmark Metrics, 2004, by Weather Condition Prevalence (a) Indicates estimated weather conditions

Airport Optimal % of Time Marginal % of Time IFR % of Time ATL 180 to 188 76% 172 to 174 14% 158 to 162 10% BOS 123 to 131 82% 112 to 117 7% 90 to 93 11% BWI 106 to 120 85% 80 to 93 6% 60 to 71 9% CLE 80 to 80 78% 72 to 77 12% 64 to 64 10% CLT 130 to 131 82% 125 to 131 9% 102 to 110 9% CVG 120 to 125 55% 120 to 124 35% 102 to 120 10% DCA 72 to 87 86% 60 to 84 8% 48 to 70 6% DEN 210 to 219 92% 186 to 202 2% 159 to 162 6% DFW 270 to 279 81% 231 to 252 13% 186 to 193 6% DTW 184 to 189 74% 168 to 173 16% 136 to 145 10% EWR 84 to 92 82% 80 to 81 9% 61 to 66 9% FLL 60 to 62 82% 60 to 61 16% 52 to 56 2% HNL (a) 110 to 120 99% 60 to 85 1% 58 to 60 0% IAD 135 to 135 80% 114 to 120 11% 105 to 113 9% IAH 120 to 143 71% 120 to 141 22% 108 to 112 7% JFK 75 to 87 86% 75 to 87 5% 64 to 67 9% LAS 102 to 113 98% 77 to 82 2% 70 to 70 0% LAX (a) 137 to 148 76% 126 to 132 16% 117 to 124 6% LGA 78 to 85 81% 74 to 84 10% 69 to 74 9% MCO 144 to 164 91% 132 to 144 4% 104 to 117 5% MDW 64 to 65 84% 64 to 65 7% 61 to 44 9% MEM 148 to 181 76% 140 to 167 17% 120 to 132 7% MIA 116 to 121 95% 104 to 118 3% 92 to 96 2% MSP 114 to 120 64% 112 to 115 28% 112 to 114 8% ORD 190 to 200 84% 190 to 200 7% 136 to 144 9% PDX 116 to 120 75% 79 to 80 21% 77 to 80 4% PHL 104 to 116 86% 96 to 102 6% 96 to 96 8% PHX 128 to 150 99% 108 to 118 1% 108 to 118 0% PIT 152 to 160 86% 143 to 150 5% 119 to 150 9% SAN 56 to 58 64% 56 to 58 32% 48 to 50 5% SEA 80 to 84 64% 74 to 76 29% 57 to 60 7% SFO 105 to 110 74% 81 to 93 20% 68 to 72 6% SLC 130 to 131 85% 110 to 120 9% 110 to 113 7% STL 104 to 113 76% 91 to 96 17% 64 to 70 7% TPA 102 to 105 93% 90 to 95 3% 74 to 75 4%

Four observations from Table 23 are relevant for discussions of advance planning.

First, the frequency of high-impact weather events (IFR) ranges between less than 1% of

observed time periods at airports such as Las Vegas, Phoenix, and Honolulu to more than 10% of

the observed time periods at Atlanta, Boston, Cincinnati, and Detroit. As importantly, the

occurrence of marginal weather conditions – that also require radar separation during initial

approach phases and during departures – varies widely from under 5% in many Western airports

to more than 25% at several facilities.



Second, the loss of capacity at a given airport during inclement weather conditions is

particularly relevant for advance schedule planning decisions. Table 24 below represents the

overall capacity drop between the maximum throughput during optimal weather conditions and

the minimum throughput during IFR conditions.

Table 24: Capacity Loss During Inclement (IFR) Weather Conditions Relative to Optimal Weather Conditions (Min vs. Max)

Source: FAA Operational Benchmarks (2004)

Airport Code

Capacity Loss During IFR Conditions

Airport Code

Capacity Loss During IFR Conditions

HNL 51.7% JFK 26.4% BWI 50.0% PIT 25.6% DCA 44.8% IAH 24.5% STL 43.4% MIA 24.0% SFO 38.2% IAD 22.2% LAS 38.1% CLT 22.1% MCO 36.6% LAX 20.9% PDX 35.8% CLE 20.0% MEM 33.7% LGA 18.8% EWR 33.7% CVG 18.4% DFW 33.3% PHL 17.2% SEA 32.1% SAN 17.2% ORD 32.0% FLL 16.1% BOS 31.3% SLC 16.0% TPA 29.5% ATL 16.0% DTW 28.0% MSP 6.7% PHX 28.0% MDW 6.2% DEN 27.4%

As Table 24 shows, the capacity loss during bad weather varies widely across U.S.

airports, even within geographically similar areas. Airports with intersecting runways (or

runways too close to permit simultaneous instrument arrival patterns) are particularly susceptible

to capacity loss during bad weather. Eleven of the top 35 airport facilities lose a third or more of

available capacity during inclement weather.

Not only is the total occurrence of inclement weather highly variable across the U.S.

airspace system, but also the frequency with which inclement weather occurs also varies. This is

particularly true during the summer season in the Eastern half of the U.S., where thunderstorm

activity can erupt without significant advance warning.


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Third, when the incidence of bad weather is plotted alongside the maximum loss in

departure and arrival capacity, the operational advantages of certain airports becomes clear.

Chart 19: Airport Capacity Loss vs. Occurrence of Inclement Weather Conditions Source: FAA Operational Benchmark Report, 2004; Weather Impact 2009

In Chart 19 above, the red columns represent the frequency with which IFR weather

conditions (defined here as ceilings less than 1,000 feet and/or visibility less than 3 miles).

Capacity loss represents the maximum decrease in the airport departure and arrival rates per hour

between Optimal and IFR conditions.

- Airports including Seattle, Memphis, St. Louis, Washington Dulles, Detroit, Portland,

San Francisco, and Charlotte have a 20% or greater incidence of inclement weather and a

20% loss in airport capacity during these conditions.

- Airports including Denver, Orlando, Tampa, Miami, Las Vegas, Phoenix and Honolulu

have inclement weather less than 10% of the time, but lose between 24% and 52% of

available capacity during such weather conditions.

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% CAP LOSS % WEATHER



- The majority of airports incur inclement weather between 10% and 20% of the time. The

average capacity loss is 27%.

Fourth, capacity improvements continue at key U.S. airports, particularly the construction

of new parallel runways that often allow additional concurrent approaches during inclement

weather conditions. New parallel runways result in improvements in arrival capacity, and

therefore reduce the downside risk during weather events. Each of the six airports below in Table

25 received a new parallel runway between 2004 and 2009. Five of the six showed a significant

improvement in capacity; the new runway at Miami did not permit an increase in arrival rates due

to its close proximity to other runways.

Table 25: Improvements in Capacity Losses during Inclement Weather Resulting from New Runway Construction, 2004-2009

Airport Code

Full Name of Airport

2009 Capacity Loss

2004 Capacity Loss

Improvement 2004-2009

DEN Denver International 14.7% 27.4% 46.5% STL St. Louis Lambert Field 24.5% 43.4% 43.5% MCO Orlando International 24.4% 36.6% 33.2% SEA Seattle-Tacoma International 29.4% 32.1% 8.5% ATL Atlanta Hartsfield-Jackson 14.8% 16.0% 7.5% MIA Miami International 23.5% 24.0% 2.0%

To summarize:

1. The FAA Benchmarks provide only a general index of departure and arrival capacity

under three basic weather categories, but do not combine frequency of weather events or

the severity of the events into an aggregate measurement of capacity that would align

with long-term airline schedules that span all operating conditions. In other words,

comparing flight schedules at a given airport – where airlines set demand months in

advance, and plan a consistent schedule across all weather conditions – against the FAA

benchmark rates has limited utility.

2. Consumer advocates who point to airlines planning flights in excess of bad weather

capacity are not incorporating critical weather probability and impact severity into their

analysis. Our data disproves statements made often by FlyersRights.org and Business

Travel Coalition. Schedule planning must, by definition, incorporate the unique weather

factors at each facility an airline operates to and from.


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3. The FAA Benchmarks under a single weather condition can vary widely based on

different runway configurations. Wind conditions on an hourly basis create significant

variability in airport capacity. At Washington Reagan, for example, wind direction and

speed even under “optimal” weather conditions can reduce available capacity by 17%.

The inherent variability of these weather factors mean statistical averages of overall

weather patterns is required.

4. New runway construction can alleviate arrival bottlenecks during inclement weather, but

they are not a panacea in curing departure delays.

To translate these baseline benchmarks into metrics that compare, apples-to-apples,

against planned schedules, we must incorporate other factors, including overall capacity planned

at the given airport and human factors. These are discussed in the next section.

4.3 Augmenting and Enhancing Capacity Metrics

The previous discussion showed that the FAA’s metrics are based on simple groupings of

airport capacity during three weather categories. Capacity benchmarks facilitate inter-airport

comparisons and the benefits of new runway construction, but they do not incorporate constraints

such as aircraft movement (taxiway and ramp) capacity, local airspace congestion, and human

factors. In essence, the FAA Benchmarks provide the first step in assessing how operating

airlines schedule a given facility. To assess whether airlines intentionally overschedule airports

– particularly relative to the benchmarks and standards defined by the FAA as reviewed above – a

number of additional factors must be incorporated.

We focus our analysis on six factors that provide additional information about a given

airport’s sustainable capacity and demand profile.

1. Gate, ramp and airport movement area available, including taxiway space available and

the airport layout.

2. Terminal airspace capacity, particularly availability of departure and arrival corridors

during inclement weather conditions.

3. Aircraft type mix at the airport, impacting en-route miles in trail restrictions and

congestion.



4. Human factors, including the ability for air traffic control and airport management to

respond to and clear bottlenecks.

5. The mix of general aviation, military and emergency flight operations at a given airport.

6. The local-market strength of the airport (revenue base), importance of connecting traffic

and the degree to which airlines are inflexible about specific arrival and departure

windows.

These six factors enhance runway capacity benchmarks to provide meaningful

information about how many flights are likely to be scheduled and whether the airport will be

able to absorb delays. Each factor is discussed below.

4.3.1 Gate, Ramp and Airport Surface Movement Area

The physical, non-runway resources of a given airport are relevant in assessing whether

airlines can schedule arrivals and departures at or near the given FAA Benchmark rate. Available

gate and terminal facilities drive the number of concurrent departures and volumes of passengers

that can be handled. But non-terminal factors, including the volume of ramp space available to

push-back aircraft, or hold them immediately prior to gate arrival or after gate departure, is also a

relevant factor in projecting congestion at the airport, and the amount of schedule buffer required.

4.3.2 Regional Airspace Capacity

Even with ample runway and ramp capacity available, if the local airspace around an

airport is unable to process the volume of arrivals and departures demanded, then congestion,

delays and potentially cancellations will result. This is particularly important in regional

airspace where multiple, high-demand airports are co-located, such as the metropolitan areas of

Chicago, Los Angeles, San Francisco, Washington and New York. In these regions, runway

configurations and weather conditions can concentrate arrival and departure corridors from

multiple airports. Airlines not only must estimate expected demand from competitors at their

origin, but also consider potentially conflicting demand from other regional airports.

4.3.3 Aircraft Type Mix

The mix of aircraft departing a given airport can impact the pace at which aircraft depart.

The FAA Capacity Benchmarks are based on standardized aircraft types, but in reality a mix of


Page 74

slow turboprop and piston aircraft, heavy widebody aircraft, and mid-size narrowbody aircraft

can create wide variance in the spacing required between arrivals and departures. This is

particularly relevant at airports with high numbers of international, wide-body flight departures

during evening hours. Spacing requirements for these flights slow the pace of departures, causing

airports to operate below their published departure rates.

4.3.4 Human Factors

While every airport in the United States operates to the same regulatory standards for

traffic separation, the process of executing air traffic management is still a human process – that

is, a human controller is ultimately responsible for setting the pace of arrivals and departures at a

facility. The degree to which controller procedures differ across airlines therefore has a

measurable impact on that airport’s arrival and departure performance. As the FAA states in its

Benchmark report, “Human factors play a critical role in the benchmark rates reported by the air

traffic facility. Benchmarks are strongly affected by how busy the airport is and how aggressively

the management team sets target rates.”

For example, controllers may not be willing to operate multiple arrival and departure

runways concurrently. Or controllers may be well trained in dealing with short-term disruptions

such as brief thunderstorms and are comfortable with ad-hoc re-routing and re-sequencing.

4.3.5 Traffic Mix

Aircraft type mix, as noted above, is an important determinant of departure and arrival

flow sequencing and separation. However, the very mix of traffic at an airport – scheduled

passenger, unscheduled charter, military, emergency, and general aviation in nature – determines

the resources available to airline schedulers. As Table 26 below illustrates, in 2010 the daytime

mix of commercial and non-commercial traffic at major U.S. airports varied significantly, from a

high of 99% at Atlanta Hartsfield-Jackson to a low of 73.8% at Memphis.

Why does this concern airline planners? While general levels of non-scheduled and non-

commercial traffic can be estimated in advance, the specific demand by non-scheduled entities for

runway, ramp and airport departure/arrival capacity by hour and month cannot be estimated in

non-slot-controlled airports.



Table 26: Commercial Traffic Mix at Major U.S. Airports

Between 7am and 10pm during 2010

Airports % Commercial Airports % Commercial ATL 99.0% LGA 98.3% ORD 98.5% SFO 95.8% DFW 97.7% IAD 87.2% DEN 98.1% MIA 91.4% IAH 95.5% SLC 88.5% CLT 95.4% SEA 96.5% LAX 95.6% MCO 94.3% DTW 97.9% BWI 90.8% MSP 95.0% DCA 98.5% PHL 95.2% MEM 73.8% PHX 89.9% MDW 84.5% BOS 93.8% FLL 85.7% EWR 96.3% PDX 75.9% JFK 97.2% CLE 93.8% LAS 89.2% SAN 90.2%

4.3.6 Local Market Dynamics

The dominant use of an airport facility, for passengers originating or terminating their

journeys at that airport, or connecting at the facility to and from other cities, impacts how airlines

schedule their flights – and therefore to what extent any given airline can expect competitors to

concentrate arrivals and departures within specific time windows. Airports with a dominant hub

carrier may have a baseline of operations spread throughout the day, while airports with

fragmented competition or slot-controlled runway use may necessarily have departures spread

throughout the day. Airlines schedule according to methodologies that do incorporate basic

runway design and adverse weather patterns. But airlines do require information about overall

infrastructure, surrounding airspace, demand profiles and non-scheduled operations to result in

useful available capacity to that given carrier’s flight demand.

Airline scheduling decisions are highly focused investment decisions in a given airport

facility. Airlines invest capacity in a given airport with an associated risk estimate that the airport

capacity will permit operations at the demanded level.


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4.4 Introducing the Peak Index Metric

We recognize that measuring and comparing airline schedules across different airports,

relative to each other and relative to overall capacity, is challenging. A single theoretical

construct must be developed to compare schedule levels across airports, since airport-specific

factors other than raw runway capacity are not included in the FAA’s ACB construct. For inter-

airport comparisons, FAA capacity metrics are useful and appropriate. For measuring airline

schedules at a given field, however, they are not. A more granular approach is needed.

One of the main methodological issues to be dealt with in this analysis is the matter of

scale. Data can be used in many different metrics including annual, monthly, daily, one hour and

fifteen minute increments. To create a consistent metric for assessing the variation of schedules

and airport capacity through a given time period – daily, monthly or annually – we will utilize the

coefficient of variation as our measurement. It is called The Peak Index.

Our definition of Peak Index is simply the ratio of the standard deviation to the mean. We

chose the coefficient of variation because it is well developed in statistical literature and a

straightforward metric to calculate on a highly automated basis across large-scale data sets.

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In this case, the Coefficient of Variation measures the mean level of scheduled operation

levels during a given time period and the standard deviation of those scheduled operations levels

around the mean. A highly peaked schedule would have a high standard deviation – and

therefore a high coefficient of variation. A flat schedule would have a low coefficient of

variation. The coefficient of variation will always be a positive numeric value between 0 and 1.

As a normalized measure of dispersion, the coefficient of variation is a unitized metric

and can be used to compare dispersion over different samples. Using standard deviation alone is

insufficient, as it is a unique measurement for each sample.

Scaling of data – and the relative size of the sample set taken to assess flight schedules –

is a critical factor. To determine the best sample methodology for airport schedules, we

considered groupings by year, month, day, hour, and quarter-hour. We also examined airport-

wide data sets and specific airline data for schedules and capacity.



Simply put, when aggregating data over long periods of time, the critical variances

around the mean are lost. These are important for testing the hypotheses of peaking and the

change over time of scheduling patterns. Conducting a granular analysis based on averages in a

given month samples the boundary conditions that are important in capacity discussions.

Our analysis:

1. Collects arrival and departure data based on published OAG schedules, hour by hour

at key U.S. airports, and on total flight operations including general aviation, cargo,

charter and military flights. For the OAG schedules, we include all scheduled flights,

including international operations and flights by non-reporting scheduled carriers.

We average scheduled demand for specific days of the week by month and year to

create an aggregate data pool that still provides boundary conditions and breaks in

patterns.

2. Measures the mean level of scheduled operations during the sample period, based on

operations per hour, and measures the standard deviation of schedule peaks and

valleys versus the mean.

3. Compares the mean and variance data against the FAA Operational Benchmarks and

actual arrival and departure capacity (ADR + AAR) reported by the airport facility

for that hour.

4.5 Peak Index: Application to Hub Airports

We now apply our definition of Peak Index – using the Coefficient of Variation of flight

schedule operation demand by hour versus the mean demand – to assess the change in hub

scheduling over the past six years. To start the discussion, we must transition from the DOT Part

234 ASQP data sets we used in Section 3 to the full set of international, reporting domestic and

non-reporting domestic flights captured in the OAG and FAA ASPM data sets.

Table 27 below shows the total count of domestic and international flight operations by

year between 2000 and 2010. Flight operations peaked in 2007, driven by increases of both

domestic and international scheduled flights.


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Table 27: Domestic and International Flight Operations, 2000-2010 Source: DOT T-100 Flight Segment Information

Year Domestic International Total 2000 7,905,617 1,036,757 8,942,374 2001 7,626,312 1,038,867 8,665,179 2002 8,089,140 1,024,056 9,113,196 2003 9,458,818 1,129,990 10,588,808 2004 9,968,049 1,220,677 11,188,726 2005 10,038,373 1,267,681 11,306,054 2006 9,712,750 1,299,209 11,011,959 2007 9,839,578 1,335,593 11,175,171 2008 9,376,251 1,336,938 10,713,189 2009 8,753,567 1,259,829 10,013,396 2010 8,701,135 1,297,623 9,998,758

Table 27 demonstrates that flight operations have declined since the operational peak in

2007. This has resulted from capacity decreases driven by both economic slowdowns, by airline

mergers, by airline bankruptcies without corresponding startup ventures, and by ruthless capacity

control by U.S. carriers, particularly in the domestic market.

Given this backdrop of flight operations peaking in 2007 and then declining through 2010,

we now apply our Peak Index construct to key nationwide airports. Table 28 shows that key

airports nationwide have been effectively depeaked, particularly since the traffic decline started in

2007. In other words, capacity reductions have occurred by thinning down the peaks of hub

operations while sometimes boosting off-peak departures.

Table 28 shows two extremes. The airport with the lowest number of peaks in 2010 is

LaGuardia, where artificial demand-management programs – in the form of slot controls and

limitations on where aircraft can fly – restrict the ability for airlines to control their schedules.

On the other extreme, Memphis is the most peaked hub. Memphis remains a second-tier hub in

the combined Delta/Northwest network, focusing on connecting traffic where traffic is aggregated

in specific time windows through the day. The contrast between the two airports is notable.

Memphis operates a traditional hub-and-spoke design with arriving and departing banks.

LaGuardia has a constant rate of operations dictated by slot controls. LaGuardia’s total operations

throughout the day are close to the maximum capacity, whereas those at Memphis are a fraction

of even the constrained bad-weather capacity at the airport.

Table 28 also demonstrates that depeaking strategies began in 2005 and were consistently

applied through the following six-year period. In some airports, peaking increased, but this can



be tied in most cases to the introduction of new international banks, where peaked departures are

required based on European slot controls and overnight flight schedules.

Table 28: Peak Index Values By Year, Showing Most Peaked Airports Includes All Traffic Types (Scheduled + Charter + Military + Cargo + General Aviation)

Airport 2005 2006 2007 2008 2009 2010 Change MDW 15.80% 21.00% 18.90% 16.10% 27.20% 22.20% 40% IAD 34.10% 40.30% 41.10% 43.80% 45.70% 46.60% 37% PHL 10.90% 13.80% 13.30% 14.80% 14.80% 13.60% 25% MCO 21.60% 27.50% 26.00% 24.20% 26.80% 26.30% 22% SAN 12.30% 13.50% 14.20% 14.70% 16.50% 14.80% 20% SLC 26.90% 31.20% 29.30% 30.30% 30.50% 32.10% 19% BWI 17.60% 16.70% 15.20% 14.30% 16.60% 20.50% 17% TPA 23.40% 24.80% 25.00% 23.50% 32.30% 27.30% 17% ATL 21.70% 25.80% 25.60% 23.70% 23.40% 24.90% 15% BOS 18.60% 21.60% 20.50% 18.90% 20.90% 21.20% 14% LGA 10.80% 9.60% 9.10% 9.10% 11.20% 11.80% 9% MEM 68.10% 71.20% 71.50% 74.10% 73.70% 73.70% 8% SEA 12.80% 12.80% 15.40% 16.20% 11.40% 13.80% 7% LAX 14.30% 12.60% 13.00% 12.20% 13.90% 14.80% 4% CLE 36.40% 36.30% 34.60% 36.90% 37.10% 36.50% 0% PDX 16.70% 18.40% 17.10% 17.00% 19.40% 16.30% -2% DFW 23.20% 21.60% 20.70% 22.60% 23.10% 22.80% -2% CLT 17.50% 18.80% 17.30% 17.80% 17.70% 16.80% -4% MSP 27.50% 25.50% 25.70% 24.60% 25.90% 26.40% -4% IAH 28.60% 25.00% 23.00% 22.40% 27.70% 26.90% -6% PIT 30.00% 27.10% 26.80% 26.70% 29.70% 27.60% -8% DEN 31.60% 30.20% 29.50% 27.70% 29.60% 28.00% -11% JFK 26.80% 27.80% 25.50% 19.10% 20.60% 23.50% -12% STL 30.60% 24.20% 23.20% 21.40% 20.90% 26.50% -14% FLL 28.60% 31.50% 27.50% 26.90% 27.10% 24.80% -14% HNL 25.30% 18.60% 19.80% 19.30% 19.10% 21.20% -16% MIA 27.00% 26.70% 26.50% 24.50% 24.50% 22.60% -16% LAS 22.00% 20.00% 18.60% 17.60% 21.80% 18.60% -16% ORD 18.60% 16.70% 16.40% 16.50% 16.80% 15.40% -17% SFO 22.70% 19.00% 18.40% 19.60% 21.80% 18.70% -17% PHX 27.10% 23.10% 23.80% 19.90% 24.90% 21.80% -19% DTW 29.90% 26.40% 24.60% 25.40% 21.40% 21.40% -29% EWR 22.10% 20.10% 18.50% 15.90% 13.50% 13.90% -37%


Page 80

4.6 Peak Indices and Capacity

We compare the Peak Index for each airport against the total operational capacity of the

field. We find that the more pronounced the peaks in schedule, the less likely the airport is seen

as operating to maximum capacity. Conversely, the less peaked the total airport operations are,

the more likely it is to be close to maximum capacity. The correlation coefficient between

Utilization and Peak Index is -0.59, suggesting a consistent trend.

Table 29: Peak Index and Utilization by Airport (June 2009) Measured by OAG Scheduled Commercial Traffic and by All Traffic Types

Source: FAA ASPM and ETMS, June 2009 Snapshot

OAG Scheduled Traffic Only All Traffic Types Airport Utilization Peak Index Utilization Peak Index New York LGA 84% 11% 84% 12% Newark Liberty EWR 81% 14% 81% 14% New York JFK 80% 15% 80% 24% Atlanta Hartsfield-Jackson ATL 79% 22% 79% 25% Philadelphia PHL 75% 17% 75% 14% Chicago O’Hare ORD 72% 14% 72% 15% Boston Logan BOS 69% 13% 64% 21% San Francisco SFO 64% 14% 63% 19% Denver DEN 63% 18% 59% 28% Charlotte CLT 59% 22% 58% 17% Houston Bush IAH 58% 24% 55% 27% Los Angeles LAX 55% 28% 55% 15% Dallas/Ft. Worth DFW 55% 13% 53% 23% Baltimore-Washington BWI 53% 19% 51% 21% Detroit Metro DTW 51% 16% 50% 21% Minneapolis/St. Paul MSP 50% 32% 50% 26% Fort Lauderdale FLL 50% 30% 49% 25% Miami MIA 49% 23% 36% 23% Washington Dulles IAD 36% 20% 35% 47%

Another comparison of airport utilization measures schedule levels against both the FAA

Operational Benchmark and against the actual Arrival and Departure Rates (ADR+AAR)

reported by the airports during specific quarter-hour time periods. Table 30 presents an

operational snapshot of actual demand versus ADR/AAR and the ACB metrics for June 2009,

incorporating both domestic and international flight operations. It shows that for many key hubs,

airline schedules are aligned with the actual reported capacity metrics, and not the FAA’s ACB.



Table 30: Actual Schedule Demand versus Actual Capacity (ADR+AAR) and published FAA Operational Benchmark (ACB)

Source: FAA ASPM, OAG, ACB 2004-2009

JUNE 2009 ACTUAL ADR+AAR FAA BENCHMARK ATL 79% 89% BOS 64% 40% BWI 51% 34% CLT 58% 59% DEN 59% 50% DFW 53% 39% DTW 50% 39% EWR 81% 75% FLL 49% 52% IAD 35% 35% IAH 55% 71% JFK 80% 80% LAX 55% 57% LGA 84% 83% MIA 36% 40% MSP 50% 59% ORD 72% 73% PHL 75% 67% SFO 63% 53%

4.7 Peak Indices and Delays

In this section, we examine the correlation between schedules and delays. First, we

compare the correlation between the Peak Index values for each year and the on-time

performance reported through Part 234 ASQP data. In particular, we focused our attention on 10

airports with chronic delay histories: SFO, EWR, LGA, JFK, BOS, MIA, PHL, DTW, ORD, and

ATL. We compared the correlation coefficients between Peak Index values and On-Time

Performance for these airports. In two of the years (2008 and 2009) there were negative signs on

the resulting coefficients. This implies a counterintuitive result: as the Peak Index goes up,

observed delays go down.

These results show that in general, the Peak Index of a given flight schedule is poorly

correlated with the resulting flight delays. The assertion that airline hub peaks cause flight delays

(or that de-peaking hubs alleviates delays) is not supported by operational data. In addition,

because we observe a wide variance in correlation coefficients across our six-year period of

analysis, delay conditions are neither sustained nor systemic over time.


Page 82

There has been significant de-peaking over the last six years, as we showed in Table 28.

The first airline to significantly de-peak was American Airlines during the late 1990s at both

Chicago O’Hare and Dallas/Fort Worth airports. Depeaking does not address flight delays alone.

Depeaking is about efficient capacity utilization and resource management – all of which can

reduce fares for passengers and improve competition.

Depeaking has a profound benefit for irregular operations, as we suggest in Section Three.

Having a uniform distribution of schedules (or, put differently, a very low Peak Index) is a

causative factor in flight delays. This is because if delays begin in the morning at an airport with

high capacity utilization and low Peak Index, they back up through the remaining hours of the

day because there is no trough to clear delayed aircraft while still accommodating that hour’s

arrivals and departures. LaGuardia is the classic case study for this; when delays impact the

airport in the morning, flight cancellations are the only proven method for restoring the schedule

without delays rolling through the day, both at LaGuardia and throughout the national system.



4.8 Airport-Specific Analysis

In the following section, we present specific airport detail, using a schedule and

performance snapshot for June 2009. We selected June 2009 because it represents a peak

summer month after the significant schedule retractions of 2007-2008. For each airport, we

identify the primary operators, define the Peak Index and capacity utilization, show on-time

departure and arrival performance (measured from the gate through Part 234 ASQP data), and

comment on the airport’s performance relative to its peers. The graphs at left show scheduled

and actual demand versus the published airport capacity for each airport. For the Peak Index, the

higher the number, the more peaked the airport has become.

ATLANTA HARTSFIELD-JACKSON

JUNE 2009 CAPACITY & SCHEDULE

• Primary hub for Delta, AirTran

• Peaked schedule model

• Scheduled Peak Index 22% (σ/λ)

• Morning peak crosses maximum capacity during 8am departure hour

• Operated at 79% daily capacity

• On-time performance 76% departures, 76% arrivals

• Dominant carrier Delta Air Lines (34% of capacity mainline + about 41% of capacity regionals)

• Above-average schedule performance versus peer group

BOSTON LOGAN INTERNATIONAL


• No dominant carrier (US 17%, B6 17%, AA 11%, DL 11%)

• Flat peak structure

• Scheduled Peak Index 13.9% (σ/λ)

• Afternoon schedule peak plus international departures


• On-time performance 72.8% departures, 61.1% arrivals

• Significant weather impact during month with below-average airport performance relative to peers, particularly for regional carriers

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BALTIMORE WASHINGTON INT’L


• Moderate schedule model;

• Scheduled Peak Index15.7% (σ/λ)

• Flat schedule dominated by low-fare Southwest (52% of arrivals) and AirTran (18% of arrivals)



• Low-fare airlines operate rolling structure through the day, few peaks or valleys

CHARLOTTE DOUGLAS INT’L


• Primary hub for US Airways


• Flat schedule dominated by US Airways (63% mainline, 17% regional = 80% of total capacity)

• Operated at 58.3% daily capacity


• Concentration by a single carrier allows coordination of departure and arrival slots to optimize performance. Available runway capacity.

WASHINGTON REAGAN NATIONAL


• Hub for US Airways and focus city for other carriers.


• Dominant carriers US (27%), AA/MQ (26%), and other carriers.



• Slot-controlled, where departures and arrivals metered by FAA, DOT

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DENVER INTERNATIONAL


• Primary hub for United and Frontier; focus city for Southwest

• AM and PM banks


• Dominant carriers UA (26% mainline plus 22% regional), WN (17%) and F9 (18%).



• Ample runway and ramp capacity

DALLAS/FT. WORTH INTERNATIONAL


• Primary hub for American and Eagle

• Unpeaked relative to other major hubs; AA rolling hub.


• Dominant carrier AA/MQ (88%)

• Operates at 53% daily capacity


• Ample runway and ramp capacity

DETROIT METROPOLITAN


• Primary hub for Delta/Northwest

• Very strong peak structure


• Dominant carriers DL/NW and regionals (79%)



• Six connecting banks at two hour intervals at hub focused on transit passengers

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NEWARK LIBERTY INT’L (EWR)


• Primary hub for Continental

• Consistent arrival and departure flow through day with minimal peaks.


• Dominant carriers CO/XE (74%)



• Highly susceptible to airspace congestion in New York, resulting in frequent ground delay programs into the airport

FT. LAUDERDALE INTERNATIONAL (FLL)


• Low-cost carrier focus city

• Flights timed for leisure passengers


• Dominant carriers Southwest (29%), JetBlue (20%), US Airways (11%)



• Airport is susceptible to delays and ground stop programs in the NYC, PHL and WAS areas

WASHINGTON DULLES (IAD)


• Primary international hub for United

• Four-bank model that feeds international flight departures.


• Dominant carrier United and affiliates (51% of total departures)



• New runway opened 2008

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HOUSTON INTERCONTINENTAL (IAH)


• Primary hub for Continental

• Five-bank structure to feed international schedules, domestic connections


• Dominant carrier CO/XE (90%)



• Single carrier dominance, operations at 55% capacity and good weather.

NEW YORK KENNEDY (JFK)


• Competitive airport with demand-managed capacity program by FAA


• Competition strong between JetBlue (39% of domestic departures), Delta (39%) and American (14%)



• Congested NYC airspace results in frequent delay programs in and out of JFK airport and region

LOS ANGELES INTERNATIONAL (LAX)


• International gateway hub with evening departures to Asia, Europe


• Primary carriers WN (21%), AA (20%), UA (20%)



• Runway improvements, parallel approaches result in high departure and arrival flow capacity

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NEW YORK LAGUARDIA (LGA)


• Fragmented competition with heavily regulated slot structure that spreads demand through the day


• Highest utilization in U.S.



• Congested terminal, ramp and taxiway infrastructure

• Frequent ground delay programs into airport, surrounding airspace

MIAMI INTERNATIONAL (MIA)


• Primary hub for American


• Dominant carrier AA/MQ (75%)



• Terminal capacity constrained relative to runway capacity

• Susceptible to delay programs in and out of Northeast and Mid-Atlantic

MINNEAPOLIS/ST. PAUL (MSP)


• Primary hub for Delta/Northwest

• Four/five bank schedule. Scheduled Peak Index 30.2% (σ/λ)

• Dominant carriers DL/NW (75%)



• Connecting hub (domestic and international) with banked structure designed to maximize feed

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CHICAGO O’HARE (ORD)


• Primary hub for United and American




• New runway capacity resulted in significant capacity increase

• Terminal facilities are constrained, limiting additional operations

• Historical demand management programs in place by FAA/DOT

PHILADELPHIA INTERNATIONAL (PHL)


• Primary hub for US Airways, presence by AirTran and Southwest


• Dominant carrier US (approx. 50%) plus Southwest (23%)



• Highly utilized, land-constrained airport with mix of local and connecting traffic

SAN FRANCISCO INT’L (SFO)


• Primary hub for United with strong presence by other carriers

• Earlier bank structure than more eastern airports to feed Asia flights. ScheduledPeak Index 18.1% (σ/λ)

• Dominant carrier UA (approx. 60%)



• June capacity impacted by weather conditions (morning fog) with capacity increase during afternoon hours.

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Page 90

4.9 Viability of Capacity Enforcement: Economic Harm Throughout Section Four, we have considered the balance between scheduled airport

operations and the defined FAA Airport Benchmark Capacity and actual departure and arrival

flow capacity for each airport. We have identified why the ACB offers a useful starting point for

inter-airport comparison, but needs to be augmented with capacity data regarding ramp and

taxiway space, local airspace capacity and human factors to be a useful metric against airline

scheduling methods. We recognize that at key U.S. airports, scheduled arrival and departure

capacity can exceed the available runway capacity.

Consumer advocates have stated that if airlines reduce their flight operations levels such

that total planned flight operations never exceed the available capacity of the airport, long and

recurring flight delays would be cured. As observed in Section 3.8, airports that operate well

under their defined benchmark capacity still operate under similar on-time performance metrics

as their more congested peers. To add depth to this dialogue between advocates and airlines, we

explored the following:

- If every airport operated at its minimum departure and arrival rates for a given calendar

month (that is, if airlines planned operating schedules such that total planned operations

never exceeded the available runway departure and arrival capacity) how many flights

would be lost at the 77 airports included in the FAA ASPM data set?

- For the airports that are operating under this standard (the airports already scheduled such

that maximum departures and arrivals never exceed capacity) how does on-time

performance for departures and arrivals compare with airports operating over this

standard (where some reduction in scheduled operations would occur)?

- What is the economic cost of reducing operations to never exceed 100% of capacity, and

how does that cost vary at between 70% of capacity and 130% of capacity?

To define the scope of this analysis, we reviewed 72 scheduled-service airports in the

FAA ASPM data set and considered the daytime hours of 7am through 9pm. We utilized flight

schedule information based on OAG to include both domestic and international flight operations.

We defined our time period as April 1, 2010 through September 30, 2010.



First, for each hour in the time window for these days, we tallied (1) the total scheduled

arrivals and departures and (2) the available runway capacity measured as the sum of Airport

Arrival and Departure Rates. We also summed total facility operations and operations by type.

Second, we calculated the minimum airport capacity observed for each hour during each month,

excluding the rare cases where reported capacity was zero due to full airport closure. Because

our sample set included only summer data, we did not expect (nor did we observe) any multi-hour

airport closures.

In our third step, we compared the actual scheduled operations (arrivals and departures)

against the minimum capacity observed at the airport for that hour during that specific month.

We believe this is a reasonable proxy for how airline planners assess airport capacity based on

historical weather performance, which can vary significantly by month. We counted any excess

of scheduled operations over the minimum observed capacity as “displaced” operations for our

model.

Fourth, we explored the resulting displacement of operations if the target capacity level

were adjusted in a range between 70% and 130% of the worst-case arrival/departures rate. That

is, if an airport had a worst-case rate of 100 per hour, we explored the schedule displacement

resulting from a capacity of 70 through 130 operations (arrivals + departures) per hour. We then

calculated the economic harm from each of those levels to the U.S. economy including only

direct tourism expenses and not lost productivity or time.

4.9.1 Exploring Capacity Loss and On-Time Performance

After completing an hour-by-hour analysis of scheduled operations (between 7am and

9pm from April 1, 2010 through September 30, 2010) and the worst-case ADR+AAR capacity

reported for the airport during each month for the respective hour of day, we tallied the total

number of flights “displaced” by reducing planned flight operations to the worst-case capacity

scenario. This method takes into account real-world factors such as the occurrence of weather

events, human factors associated with the tower and ramp management, and taxiway congestion.

We found that of 72 airports with reported arrivals and departures, 35 had a positive number of

displaced flights, while 37 did not require any schedule adjustment to meet this standard. Table

31 below differentiates between the airports with displacement and those without.


Page 92

Table 31: Airport Displacement from 100% ADR+AAR Capacity Restriction Based on April 2010 through September 2010 (OAG Scheduled vs. Minimum Capacity/Month)

Airports With Displacement Airports Without Displacement ORD IAH SLC LAX AUS RSW LGA MKE MIA LAS SJC PBI PHL MCO BWI MEM MSY TUS JFK MDW SEA TPA PIT SJU BOS IAD DCA CLE IND BHM CLT HOU SDF STL ABQ OGG SFO SAN FLL BNA JAX PVD ATL DAL PSP HNL BUR DAY EWR PDX ANC MCI OMA LGB DFW MSP SAT CVG BUF HPN DEN DTW MHT RDU ONT ISP SNA PHX OAK BDL SWF

SMF

For the airports with displacement, found that the average schedule displacement was

4.0% of capacity between 7am and 9pm during the target months. This is a considerable

number; it translates to about 198,000 flights, of which two-thirds are in Chicago O’Hare, New

York LaGuardia, Philadelphia, New York JFK, Boston and Charlotte. Chart 20 below illustrates

the concentration of schedule displacement in a handful of airports; the full list of both sets of

airports can be found in Exhibits H and I.

Chart 20: Displaced Scheduled Flights if Worst-Case ADR+AAR Assumed Based on April 2010 through September 2010 (OAG Scheduled vs. Minimum Capacity/Month)

14%$

13%$

11%$

8%$

8%$

4%$

4%$

4%$

3%$

3%$

3%$

2%$

2%$

2%$

2%$

2%$

1%$

1%$

1%$

1%$

1%$

1%$

1%$

ORD

$LG

A$PH

L$JFK$

BOS$

CLT$

SFO$

ATL$

EWR$

DFW$

DEN$

SNA$

IAH$

MKE$

MCO

$MDW

$IAD$

HOU$

SAN$

DAL$

PDX$

MSP$

DTW$



This does not automatically mean these airports are overscheduled – it simply means that

the capacity reduction from assuming the worst-case weather scenario for a given month and time

of day, without consideration to the frequency or duration of such a disruption, would generate

the most reduction in schedule at these airports.

But what would be accomplished by reducing flight schedules at these airports? To

answer this, we compared on-time arrival and departure performance across the two groups of

airports – those with displacement and those without. We found that the airports with schedule

displacement operated at an 80.5% on-time departure rate during April-September 2010 and an

80.6% on-time arrival rate. In comparison, airports without displacement operated at 83.9%

departures and 82.0% arrivals. Table 32 below illustrates the two categories.

Table 32: Performance by Displacement Category (April through September 2010) Based on April 2010 through September 2010 (OAG Scheduled vs. Minimum Capacity/Month)

On-Time Performance Data from DOT Part 234 ASQP for Reporting Carriers

Airports Scheduled Capacity Utilization OTD% OTA% w/ Displacement 35 4,915,554 7,108,627 69.1% 80.5% 80.6% w/o Displacement 37 1,733,141 6,172,742 28.1% 83.9% 82.0%

This is a notable difference between the two airport categories. It confirms both that

airports with higher overall utilization have somewhat lower arrival and departure performance

than less-utilized peers, and that airports with significant differences between good-weather and

bad-weather runway capacity are more susceptible to flight delays.

But Table 32 confirms an even more important point – even airports that operate

comfortably under their actual runway capacity are still susceptible to flight delays and

cancellations. They are susceptible at a moderately lower rate than their peers, but simply

reducing operations is no guarantee that any material performance improvement will occur. For

example, four of the ten top-performing on-time departure airports (and 8 of the top 20) had

schedule displacement from bad weather. Conversely, 16 of the 20 worst performing on-time

departure airports were in the displacement category. Having schedule displacement from

operating at 100% of worst-case runway capacity does not guarantee bad on-time performance

relative to peers, but it does put airports at a relative disadvantage.


Page 94

4.9.2 Flight and Economic Loss from Varying ADR+AAR Capacity Limits

From the previous section, we concluded from our exploratory analysis airports with

scheduled flights in excess of the worst-case runway capacity had marginally worse on-time

performance relative to their peers. We then explored the schedule displacement resulting from

varying levels of runway capacity, measured as a percentage of worst-case arrivals (AAR) and

departures (ADR). Using the worst-case capacity (ADR+AAR) for each hour, measured monthly,

we calculated the rate based on a range of 70% to 130% of the worst-case rate.

Our objective was to address a simple question: if airport operations were reduced to a

suitable margin underneath the worst-case airport capacity rate, how many flights would be

displaced, and what would be the consumer harm?

We used the actual scheduled capacity for each airport, by hour and day, to calculate the

loss in scheduled flights from operating at a strict capacity limit of the target percentage and rate.

We focused on 24 major airports where we could measure both loss in capacity and passenger

traffic, in order to ultimately consider the economic harm from varying levels of ADR+AAR

compliance. This group represented a total of 4.4 million scheduled flights during the period

from April 2010 through September 2010.

Table 33 (on the next page) illustrates the various levels of capacity (measured as a

percentage of worst-case ADR+AAR) and the resulting schedule displacement. We found that at

many major airports, even operating at 130% of the worst-case ADR+AAR still resulted in some

loss of flights. This is due to the wide difference between the worst-case and best-case runway

capacity rates. We calculated that reducing airport operations to 80% of the worst-case

ADR+AAR rate would result in the loss of more than 11% of capacity.

Finally, we translated the loss of capacity above into a measurement of economic harm.

We used the number of flights from Table 33 to estimate the loss in scheduled capacity from

compliance with an absolute percentage limit on airport runway capacity. To estimate the

number of passengers impacted by this schedule reduction, we used average seat factor for each

airport above for the period from April through September 2010, based on DOT T-100 data.

Each airport therefore had a unique seat factor (passengers divided by seats) that was applied to

the number of displaced departures and arrivals at each airport.



Table 33: Schedule Displacement from Various Percentages of Minimum ADR+AAR Rates Based on April 2010 through September 2010 (OAG Scheduled vs. Minimum Capacity/Month)

Airport OAG Flts 70% 80% 90% 100% 110% 120% 130% ORD 400,526 141,451 108,365 79,431 56,799 39,791 27,419 19,205 LGA 165,938 59,264 45,853 33,501 22,262 12,907 5,767 1,973 PHL 184,863 58,183 42,645 29,567 19,701 12,598 7,638 4,099 ATL 427,043 95,485 61,829 35,613 18,098 8,989 5,464 4,086 JFK 160,055 46,506 32,802 21,668 13,442 7,948 4,755 2,885 BOS 149,459 43,850 30,789 19,931 11,818 6,399 3,078 1,309 CLT 210,625 30,910 20,195 13,648 9,380 6,485 4,497 3,049 DFW 292,135 35,415 18,531 11,486 7,919 5,735 4,412 3,545 DEN 286,806 39,097 24,407 14,400 7,748 3,973 1,974 995 SFO 148,343 31,997 19,942 11,489 6,299 3,182 1,496 674 EWR 165,057 40,917 25,714 13,616 5,451 1,628 355 65 IAH 234,751 34,233 19,019 9,694 5,193 3,033 1,795 1,017 MCO 126,309 8,022 5,827 3,839 2,208 1,191 567 252 MDW 84,015 10,376 5,134 2,466 1,408 1,022 842 759 MSP 185,356 11,021 4,672 2,162 1,188 613 209 21 IAD 121,794 8,692 4,646 2,322 1,127 548 250 109 DTW 205,798 13,452 6,001 2,433 1,102 526 247 105 PHX 177,103 8,520 3,446 1,435 793 558 363 191 SAN 71,922 10,791 5,286 2,097 619 179 38 8 BWI 108,532 12,263 4,647 1,325 325 75 5 0 SEA 129,225 5,133 1,543 540 271 122 33 9 DCA 122,113 17,812 7,461 1,858 229 17 0 0 LAX 214,177 10,265 2,482 229 1 0 0 0 STL 71,536 448 110 15 1 0 0 0 Group 4,443,481 774,103 501,346 314,765 193,382 117,519 71,204 44,356

With Table 34, if airlines matched flight schedules to 100% of the worst-case airport

capacity rate during each month last April through September, the displacement would be 16.8

million passengers. Extrapolated to a full-year (with April-September traffic representing 52.5%

of the 2010 total) this means that fixing airport schedules to 100% of worst-case capacity would

displace 32.1 million passenger enplanements. Fixing capacity to 80% of the worst-case rate – to

leave a “buffer” for severe weather – would displace nearly 85 million passenger enplanements.


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Table 34: Passengers Displaced from Various Capacity Restrictions (in thousands) (Percent of Worst-Case ADR+AAR by Month, April 2010 – September 2010)

100% = Schedule Limit Equals Actual Worst-Case Airport Capacity Rate Observed

120% = Schedule is 20% higher than Actual Worst-Case Rate Observed

Airport Pass. (000s) 70% 80% 90% 100% 110% 120% 130%

ORD 32,042 11,316 8,669 6,354 4,544 3,183 2,194 1,536 ATL 41,423 9,262 5,997 3,454 1,756 872 530 396 JFK 20,647 5,999 4,231 2,795 1,734 1,025 613 372 LGA 11,782 4,208 3,256 2,379 1,581 916 409 140 PHL 14,234 4,480 3,284 2,277 1,517 970 588 316 BOS 12,555 3,683 2,586 1,674 993 538 259 110 CLT 17,061 2,504 1,636 1,105 760 525 364 247 DFW 26,876 3,258 1,705 1,057 729 528 406 326 SFO 17,059 3,680 2,293 1,321 724 366 172 78 DEN 25,526 3,480 2,172 1,282 690 354 176 89 EWR 15,515 3,846 2,417 1,280 512 153 33 6 IAH 18,780 2,739 1,522 776 415 243 144 81

MCO 15,536 987 717 472 272 146 70 31 MDW 8,738 1,079 534 256 146 106 88 79 MSP 15,199 904 383 177 97 50 17 2 IAD 10,352 739 395 197 96 47 21 9 PHX 18,419 886 358 149 82 58 38 20 DTW 15,229 995 444 180 82 39 18 8 SAN 7,696 1,155 566 224 66 19 4 1 BWI 10,745 1,214 460 131 32 7 0 0 SEA 13,956 554 167 58 29 13 4 1 DCA 8,548 1,247 522 130 16 1 0 0 LAX 25,273 1,211 293 27 0 0 0 0 STL 6,009 38 9 1 0 0 0 0

Apr-Sep 409,200 69,463 44,616 27,758 16,873 10,160 6,147 3,847 Full Year 778,299 132,119 84,860 52,797 32,092 19,324 11,693 7,318

These are big numbers, and they confirm why airlines schedule peak airports at rates

above the worst-case capacity observed. Displacing 32 million passengers per year would have

significant economic impact on both airlines and the general economy. To measure this impact,

we began with the full-year displaced passenger totals from above, measured in thousands.

- According to BTS data, during the period from April through September 2010, 79% of

passenger enplanements were domestic and 21% international. We applied these

percentages to the full-year total above into domestic and international enplanements.

- We divided each count of passenger enplanements in two to reflect unique passengers;

we assumed the average number of segments per unique passenger was two, mixing one-

way flights, multi-stop flights and round-trip itineraries.



- We assumed that 43% of international passengers originated offshore, so the loss of this

traffic would have negative economic impact for the U.S. economy.21

- We assumed that offshore visitors to the United States spent an average of $4,000 per

unique visitor, based on a study by the U.S. Travel Association. We assume no negative

economic impact from displacement of U.S. citizens visiting abroad, since the negative

impact would be on offshore economies.

- We assumed that each unique passenger spent an average of $644.15 on hotels, car

rentals and other non-airline expenses for each domestic trip.

- We assumed an average lost revenue of $188 per domestic passenger (based on Q1-Q3

average revenue per enplanement, U.S. carriers from DOT Form 41) and $344 per

international passenger enplanement.

- Finally, we assumed a benefit to carriers from not operating as many flights. We

assumed that all fleet, crew and maintenance costs were fixed, but that fuel savings were

appropriate. We used an average fuel burn per flight segment of 2,000 gallons and an

average fuel price of $3.00 per gallon.

Table 35 contains the results of these calculations. Fixing flight schedules at no more

than 100% of worst-case airport runway capacity available would reduce total flights by just over

193,000 per year. This would displace a total of 32 million passengers, of which 25 million

would be domestic and the balance international. The loss of airline revenue would be $7 billion

per year, offset by $2.2 billion in fuel savings for a net loss of $4.9 billion. The indirect

economic harm to the hotel, car rental and other tourism-related industries would be $13.8 billion

per year.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!21 See http://poweroftravel.org/statistics/media_fotw.htm


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Table 35: Economic Harm from Schedule Reductions to Match Worst-Case Capacity (Percent of Worst-Case ADR+AAR by Month, April 2010 – September 2010)

100% = Schedule Limit Equals Actual Worst-Case Airport Capacity Rate Observed

120% = Schedule is 20% higher than Actual Worst-Case Rate Observed

Base Case (Current)

Schedule Limit @ Percent of Worst ADR+AAR 80% 90% 100% 110% 120%

Full Year Impact (000s) 778,299 84,860 52,797 32,092 19,325 11,693 DomesticPax (000s) 614,856 67,039 41,710 25,353 15,266 9,238 Unique DomesticPax (000s) 307,428 33,520 20,855 12,677 7,633 4,619 Direct Value/Trip (mil) (a) $194,954 $21,256 $13,225 $8,039 $4,841 $2,929 InternationalPax (000s) 163,443 17,821 11,087 6,739 4,058 2,456 US-Originating (000s) 93,162 10,158 6,320 3,841 2,313 1,400 Int'l-Originating (000s) 70,280 7,663 4,768 2,898 1,745 1,056 Unique Inbound Pax (000s) 35,140 3,831 2,384 1,449 873 528 Total Spend (mil) (b) $140,561 $15,326 $9,535 $5,796 $3,490 $2,112 Lost Airline Revenue (mm) Domestic (mm) 115,593 $12,603 $7,841 $4,766 $2,870 $1,737 International (mm) 56,224 $6,130 $3,814 $2,318 $1,396 $845 Saved Flights 952,557 598,054 367,426 223,286 135,288 Saved Fuel (Millions) -$5,715 -$3,588 -$2,205 -$1,340 -$812

Negative Impact (Millions) $49,600 $30,827 $18,715 $11,257 $6,810

To summarize Table 35, limiting airport schedules to the worst-case runway capacity

is likely to result in $18.7 billion dollars per year in economic harm. This is likely to result in

higher fares and higher prices at hotels, car rental agencies and other tourism-related industries.

The result is clear: curtailing scheduled operations in order to match the worst-case

runway capacity in a given month is a tremendous loss of revenue for airlines, and an even

greater loss for tourism industries. The more significantly capacity is cut, the more the loss to

airlines and the economy accelerates.

We conclude that:

1. Reducing flight schedules to match the worst-case runway capacity rates observed is

neither practical nor economic as a solution to reducing flight delays.

2. Of the 72 airports studied, 37 airports already operate under this threshold level. While

they have a marginally better on-time performance record relative to their peers, they still

incur significant flight delay and on-time performance below the FAA’s 88% target rate.



3. Reducing capacity is expensive, both to airlines and to the economy. Matching schedules

to the worst-case runway capacity rates will drive $18.7 billion per year in economic

harm, borne both by airlines ($4.9 billion) and by the tourism industry ($13.8 billion).

4. Reducing capacity is expensive to the American consumer – total enplanements would

decline by more than 32 million per year relative to 2010.

5. Increasing airspace capacity to reduce flight delays is a more practical approach, as it

preserves the economic benefits of flights while potentially offering a more effective path

to reducing flight delays.

4.10 Conclusions

In Section Three, we reviewed flight-specific delay and cancellation information. We

identified key factors relevant to airline planning that mitigated the follow-on impact of flight

delays. We illustrated that individual airline decisions, made in isolation, did not result in the

fabled follow-on flight delays that frame the “overscheduling” debate. In this section, we

reviewed airport-specific factors to present the other side of the overscheduling equation – if

independent flight schedules do not cause overscheduling, do sudden losses of capacity at the

airport level create delays? And how do the scheduling decisions of all carriers, aggregated into a

single demand level, compare with available capacity?

An airport-level definition of over-scheduling, isolated from probabilistic estimates of

weather impact, non-scheduled and type mixes, and relative capacity losses is indeterminate from

benchmark capacity studies. In other words, the concept of optimum capacity as defined by FAA

Benchmark Studies has no operational relevance to airline-specific flight scheduling and block

time decisions. Using Benchmark studies to claim a given airport is “overscheduled” is therefore

a leap of logic that ignores core constraints of operational relevance to airlines.

We conclude that meaningful estimates of maximum airport capacity can only be

estimated through real-world operational patterns and competing demand profiles for airport

resources (among scheduled and non-scheduled airlines), airspace (among competing airports in a

given region) and the composition of demand (among different aircraft types with varying

separation requirements).


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The answer to our exploratory analysis appears simple: no individual scheduling factor or

airport configuration issue can solely explain variations in flight delays and on-time performance

trends discussed. The villain appears to be neither airline nor airport – that is, the inability

of our national airspace system to accept flights in a timely manner due to antiquated

infrastructure is a primary driver of the trends illustrated at both the airline and airport

level. Indeed, the very definitions of capacity used by the FAA wholly excuse airspace

inefficiency and ATC shortcomings by excluding those factors from capacity estimates. An

airline flight schedule may be perfectly aligned for a given airport’s capacity, under a variety of

weather conditions – but excessive delays result from human factors, from outdated ATC

infrastructure and from conflicting airspace requirements.

Section Five: Rebutting Overscheduling Arguments


SECTION FIVE: REBUTTING OVERSCHEDULING ARGUMENTS

In this section, we address specific arguments made to advocate the existence of

“overscheduling” as a controllable industry decision made by carriers as part of normal

competition. We address three common misconceptions, advocated by notable regulatory leaders

and consumer advocates. These misconceptions are representative of other statements that follow

three common themes:

1. That airlines have not de-peaked schedules, and that de-peaking will solve departure

delays across the country;

2. That flight schedules are unreliable; and

3. That over-scheduling causes tarmac delays and other long flight delays.

For each theme, we present a representative statement along with our analysis based on

the research and exploratory evidence collected in Sections Three and Four of this paper.

5.1 Airlines Must De-Peak Schedules

“When the airport can handle 120 in an hour, and you try to have 80 go in the first 20 minutes, it’s just not going to work. We will have to work better together and the FAA won’t just sit back and be the scapegoat in the future. We truly need additional transparency to the facts behind many of our delays. Too often I hear a Captain announce; “Well, we’re gonna be delayed another 20 minutes until we get a departure slot from the FAA,” when in reality, they’re waiting because their carrier scheduled 26 departures during a five minute window. De-peaking is the answer here. Check the departure boards at our busiest airports, and you’ll see what I’m talking about. This is a cooperative effort, and all the parties involved need to take responsibility and find a sensible path forward.” Randy Babbitt, FAA Administrator, April 20, 2010.

Recognizing that the Administrator’s statement contained a measured dose of hyperbole

regarding the concentration of flight departures into a single 20-minute window, the

Administrator’s statement reflects a common belief by the public and by regulators – that airlines

have not embraced de-peaking. However, this line of reasoning is faulty and misguided.

First, airlines have embraced de-peaking of key hubs, as we have demonstrated in Section

Three and Four. Spreading departures and arrivals over longer time periods allows more efficient

utilization of gate and terminal assets. Starting with American’s depeaking efforts in the late

Section Five: Rebutting Overscheduling Arguments !

Page 102

1990s, all major U.S. carriers have embraced the depeaked hub over the past decade. But de-

peaking hubs, as we showed, is not connected alone to improving flight delays.

De-peaking can improve asset utilization and capacity, reducing fares for passengers and

improving competition. It also comes with a price – the loss of between-bank flexibility to

recover flight operations during operational disruptions. Long-term schedule disruptions such as

extended weather events will impact both peaked and un-peaked schedules.

What the FAA Administrator is likely driving at is the issue of competitive demand by

multiple airlines for constrained departure and arrival resources. It is conceivable that multiple

airlines, acting independently, may schedule flight departures within a narrow window that

conflict with one another. This is a valid concern, but it misses a fundamental reality for airline

scheduling, a reality that should be clear to an aviation regulator: anti-trust laws explicitly

prevent airlines from collaborating on forward schedules, forcing each scheduling decision to be

made in isolation.

Forcing demand management programs on airlines through slot controls and binding

allocation of departure and arrival resources has not addressed delays and cancellations. The

FAA’s management of airport slots at LaGuardia, Newark and Kennedy has resulted in the worst

on-time performance in the country for many years. DOT and DOJ intervene when airlines

propose slot transfers that would more efficiently allocate capacity at the airports and concentrate

scheduling decisions in a smaller set of carriers. There are strongly conflicting objectives from

different agencies regulating airlines – FAA, DOT and DOJ.

If the FAA prioritizes schedule efficiency and resource allocation by replicating the core

schedule coordination efforts – with appropriate anti-trust immunity explicitly granted – used to

reallocate and manage Chicago O’Hare capacity in 2007, then airlines may be able to exchange

priorities and demand objectives to efficiently allocate runway resources among carriers.

5.2 Airline Schedules are Unreliable

“Airlines need to take a step back on scheduling practices that are at times out of line with reality... Passengers are growing weary of schedules that aren't worth the electrons they're printed on." Marion Blakey, FAA Administrator, September 12, 2007

Airline scheduling practices have adapted, particularly since the operational peak of

2007. The summer of 2007 was the worst period of flight delays observed during the past 20

Section Five: Rebutting Overscheduling Arguments


years, and consumers were infuriated with the combination of peak capacity, ATC inefficiency

and bad weather that impacted the nation. During 2007 as a whole, on-time arrival performance

hit 71.3%, a drop of more than 5% year over year. During the summer of 2007, performance

averaged under 70%. As Blakey noted, airline schedules had crossed the threshold of

unreliability and customers were upset.

But since that time, airlines have responded by:

- Increasing scheduled turn times at key hubs nationwide, and across the system, in order

to insulate follow-on flights from the impact of late arriving aircraft; and

- Added additional en-route time to flights with highly variable taxi-out times, internalizing

the impact of departure queues.

Combined with a 13% decrease in domestic capacity, results from these initiatives were

quickly evident, as on-time performance reached 79% in 2009 and has remained above 75%

since. During 2009 and 2010, the lowest on-time performance recorded was 72% during the

blizzard-filled month of December 2010.

In Section Three, we demonstrate that these factors have allowed airlines to sustain high

on-time arrival performance even as the variability of airport operations and airspace capacity has

increased. Flight schedules today are just as “reliable” as they were in the mid-1990s, while

overall system utilization has increased 20% in the 15 years following.

5.3 Overscheduling is the Problem

“The primary reason for tarmac delays is not weather, as the airlines would have us believe. It stems from the fact that the nation's most congested airports are routinely overscheduled during peak travel times. Rather than use the weather as a shield to hide behind as justification for delays and cancellations, the airlines should work on moving capacity to underutilized time slots and secondary airports.” Kate Hanni, FlyersRights.org, Editorial in USA Today, March 3, 2010

As a consumer advocate, Kate Hanni responds to complaints voiced to her organization

after long flight delays and other operational disruptions. Yet her consistent advocacy that

overscheduling causes tarmac delays is flat wrong. Even DOT has agreed that weather is the

primary cause of long tarmac delays, not airline schedules or other airline-specific factors. Hanni

defines overscheduling as the practice of intentionally scheduling more flights at a given airport

Section Five: Rebutting Overscheduling Arguments !

Page 104

than it can handle during bad weather situations. The problem, of course, is that during severe

weather conditions, even a 50% reduction in flight schedules on impacted days may not be

sufficient to prevent long delays and flight cancellations. Weather conditions are random, at least

until our weather technology permits accurate forecasts that predict weather conditions by the

hour on specific days, months in advance.

Weather is the direct and causal factor in flight delays. This fact is indisputable.

Weather delays are manifested through a variety of reported causes, from carrier-related delays

(when weather prevents personnel from accessing the airport or the ramp) to airspace-related

congestion. Weather is highly variable. And as discussed, weather conditions at de-peaked hubs

can cause rolling flight delays through the day.

The question never answered by Hanni is what capacity reduction would be required to

eliminate delays. The answer, of course, is impossible, since an absolute elimination of lengthy

delays would require an infinite reduction in flight schedules. Moving capacity to secondary

airports and off-peak times would be disastrous for both consumers and airlines – while a few

consumers may prefer a 2am departure from suburban New York’s Newburgh airport to Los

Angeles, the resulting revenue will not be sufficient to cover operating costs.

Hanni detracts from the valid ongoing discussion of airline delays and scheduling. She

presents no quantitative analysis to back her claims. We believe that airlines should directly

respond to consumer accusations about overscheduling with factual evidence. It is true that

NextGen air traffic control infrastructure, when fully deployed and enabled on today’s aircraft,

can make a material difference in delay performance. Until that time, however, airline

overscheduling is a fiction, as airline schedule plans are based in concrete airport operational

benchmarks and reasonable estimates of weather impact.

Section Six: Conclusions and Recommendations


SECTION SIX: CONCLUSIONS AND RECOMMENDATIONS

This paper addresses consumer and regulatory claims that airlines engage in aggressive

scheduling behavior that routinely exceeds the available capacity at key U.S. airport facilities.

We find no evidence that such practices exist today, and demonstrate that U.S. carriers have taken

important steps to address flight delays since 2007, including:

- Material increases in scheduled turn times, adding valuable gate time to absorb delays

from late inbound aircraft;

- Additional en-route planned times (from gate to gate) allowing increasing variation in

taxi-in and taxi-out times to be absorbed while meeting the planned schedule;

- Moderating schedule peaks through aggressive de-peaking campaigns, both systemwide

and at major hubs.

Through a comprehensive comparison of airline schedules against published and actual

benchmarks for airport capacity, we conclude that both individually and in aggregate, airlines are

not engaging in scheduling decisions that under reasonable variation in weather conditions exceed

the airport’s ability to operate. However, significant variability in airspace capacity continues to

drive delays and cancellations at major U.S. airports. The FAA has begun implementation of

NextGen infrastructure improvements that should address this variability by opening more

flexible departure and arrival corridors into airports. Until NextGen is operational, though, airline

accommodation of taxi-time and flight-time variability will be essential to preserve the integrity

of flight schedules as published.

Our work differs from prior analysis in two key respects. First, we conduct a ground-up

flight-level analysis to address specific differences among airlines, airports and flight operations

strategies. Using this ground-up strategy, we isolate aircraft turn times, departure and arrival

patterns, and other key features of airline-specific operating models. Second, we integrate full

weather data to isolate conditions that reduce capacity at key airports, and assess the frequency

with which operational capacity drops below scheduled demand by airlines.

Throughout our analysis, we debunk common operational assumptions about airline hub

schedules, delays and cancellations. First, we show that independent airline scheduling decisions

do not trigger capacity-driven flight delays. Even the cumulative impact of multiple, independent

Section Six: Conclusions and Recommendations !

Page 106

scheduling decisions by competing airlines (without information exchange under current anti-

trust laws) do not currently exceed the available capacity thresholds of airport and runway

infrastructure. However, we demonstrate that the FAA’s measurements of airport capacity are

really measurements of runway capacity, as they exclude both ramp/taxiway and airspace

constraints critical to a continuous departure flow.

Second, we show that the former practice of peaked schedule operations is largely

obsolete. Counter-intuitively, we show that airports with significant peaks in their flight

schedules are actually the least prone to prolonged operational disruptions. The “valleys”

between schedule “peaks” permit operational recovery and ensure flexibility to contain

disruptions. Unpredicted, severe weather events are the primary drivers of extended delays, as

they impact both local airport and regional airspace capacity. Airlines respond to this risk

through increased turn times, longer en-route times and proactive flight cancellations.

Third, we show that the composition of an airline’s flight schedule has little impact on

overall en-route and delay variability. Substitution of regional jets and turboprops for mainline

aircraft may result in marginally longer flight times. However, the flexibility to depart turboprops

or some regional jets off secondary runways may actually cut overall block time by reducing taxi-

out times.

Fourth, we show that FAA Operational Benchmarks only represent a useful starting point

for assessing the appropriate level of flight operations. The FAA’s benchmarks are a crude

measurement intended to facilitate comparison across airports. They are not useful for measuring

the alignment of airline schedules against capacity at a specific facility, where local ramp,

taxiway and airspace infrastructure must be incorporated to judge the viability of departure and

arrival rates relative to physical infrastructure.

Fifth, we recognize the limitations of proactive schedule management across airlines in

the absence of explicit anti-trust immunity for communicating preferences and objectives. Anti-

trust immunity should also be complemented by government abstention from interference in slot

transactions among carriers to more optimally distribute arrival and departure capacity.

Based on these factors, we conclude that four core conditions make airports susceptible to

flight delays. The first condition is consistently high utilization throughout the day. The pattern

of utilization is more relevant than the aggregate total of utilization. Smooth demand patterns



through the day allow airlines to match available capacity better, by metering demand for gate

and ramp resources and maximizing connecting opportunities. However, constant gate utilization

through the day also reduces opportunities to make up time through the day. Second, finite limits

on gate and ramp space at airport can frustrate departure flows and arrival rates. The less space

that is available to wait out gate availability and takeoff delays, the more congestion results on the

ramp and en-route as airlines and ATC reroute aircraft away from congested airports.

Third, significant loss of runway departure and arrival capacity during bad weather can

place actual airport capacity significantly below the aggregate demand by airlines. Finally,

highly variable rates and intervals of severe weather can make advance predictions about average

severe weather incurrence impossible. This is a particular concern at airports such as Los

Angeles, San Diego, Las Vegas and Phoenix, where severe weather is very rare but can have a

paralyzing impact when it occurs.

Recommendations

Based on our analysis, we make six core recommendations for airlines and regulators to

improve flight delays and schedule efficiency.

1. Airport capacity improvements, with focus on taxiway and runway capacity

2. Update Airport and Airspace Capacity Benchmarks

3. NextGen acceleration and equipage subsidies

4. Collaborative scheduling decisions at peak airports

5. Free-market exchanges of slots at controlled airports

6. Reduction of government intervention in departure queue management by airlines

1. Airport Capacity Improvements

The construction of geographically independent runways and associated ramp

infrastructure, with sufficient spacing to permit concurrent operations on multiple runways, can

make an immediate difference in airport departure and arrival capacity. Runways should not

intersect, or if they do, sufficient distance must remain after the intersection to permit intersection

departures and/or Land and Hold Short Operations (LAHSO). No new runways are planned for

the three delay-prone major New York area airports. That issue must be addressed locally.

Section Six: Conclusions and Recommendations !

Page 108

2. Airport and Airspace Capacity Benchmarks

We strongly advocate an update to the FAA Operational Benchmarks for airports,

incorporating not only physical improvements to airport infrastructure (including new runways

and taxiways already constructed) but also introducing new qualifiers for capacity during specific

times of the day, seasons and weather conditions. Ramp and taxiway capacity should be

explicitly incorporated into capacity metrics for airports.

We also recommend that the FAA incorporate airspace capacity into airport-level

analysis. Given the overlap of airspace capacity across independent airports (for example,

combined airspace over IAD/DCA/BWI, SFO/OAK/SJC and PHL/EWR/LGA/JFK) we

recommend that airspace capacity metrics be published on a regional basis as well. Regional

indices for airspace capacity will focus capacity demands at locally competitive airports on the

physical infrastructure available to absorb flights after departure.

3. NextGen Urgency and Equipage

NextGen airspace infrastructure should offer new flexibility for airport controllers to

manage blocked arrival and departure corridors during irregular operations. In the absence of

fundamental changes in airline capacity, NextGen offers the best hope for further improvements

in airline on-time performance – and achievement of the FAA’s target 88% on-time arrival rate

throughout the year. Implementation of NextGen requires a combination of Congressional

support and funding, airline agreement on standards and equipage of aircraft in a timeline manner.

4. Collaborative Scheduling with Antitrust Immunity

Prior and current FAA Administrators have called on airlines to work together to address

peaked schedules during key departure windows. While we demonstrated that airline de-peaking

has reduced total demand for airport resources, further rationalization of runway and ramp

resources requires communication among carriers when schedules are set months in advance.

This requires explicit anti-trust immunity. The FAA has an established template for facilitating

discussion among carriers, but the FAA should consider implementing a systemwide program to

exchange schedule preferences in advance of publication. To preserve consumer benefits,

discussions should be limited to time slots and equipment types only, not destinations or fares.

Meetings could be FAA supervised.



5. Free-Market Slot Exchanges

Currently the FAA manages demand at three key facilities (DCA, LGA and JFK) with

informal supervision being phased out at Chicago O’Hare. At slot-controlled facilities (DCA and

LGA) DOT has intervened in free-market exchanges of slots among carriers. If DOT’s primary

objective is reducing flight delays and maximizing efficiency of schedules, slot exchanges are an

important enabler by allowing airlines to trade sub-optimal departure windows for other slots that

better fit their departure and arrival flows. Allowing airlines to function like other industries –

including the ability to buy and sell a tiny percent of total assets without extensive government

oversight – is critical to long-term industry health and overall efficiency of asset use.

6. Reduction of Government Intervention in Departure Flow Management

In April 2010, DOT implemented new policies that limited taxi-out and taxi-in times to

three hours. While intended to eliminate egregious taxi delays, this intervention resulted in a

spike in airline cancellation rates. We fully agree that egregious tarmac delays should be

eliminated – a four- or five-hour time limit on taxi times would achieve this objective while

allowing airlines to respond to more frequent two- and three-hour delays caused by constrained

airspace, convective weather patterns and de-icing queues.

!

EXHIBITS AND APPENDICES



EXHIBITS AND APPENDICES !

Page 112

Exhibit A: Commentary on Literature Review

We briefly review two different literatures in this section. First we review GAO reports to

Congress on delays, and then relevant academic literature. GAO studies rely more on historical

interpretation and discussions of the particular issues under study, while academic studies are

grounded in prior work and research.

We reviewed GAO works dating from 1979. The first major report on flight delays,

entitled Aircraft Delays at Major U.S. Airports Can Be Reduced, concluded that aircraft delays

are caused from excessive air traffic and bad weather, and recommended that Congress and FAA

take action to shift traffic from peak to off-peak periods and to other less congested airports. A

follow-on report (GAO/RCED-90-154) from 1990 updated this methodology with airlines’ on-

time performance. Specifically, GAO examined if airlines had increased scheduled flight time in

response to DOT’s new on-time performance tracking, and sought to verify that flights omitted

from on-time performance because of mechanical problems were correctly excluded from

reported data. This report concluded that poor weather was the principal reason for late flights

and that adjusted block times and operating schedules did not meaningfully impact on-time

performance metrics. During the 1990s, there were a significant number of GAO reports and

House and Senate Hearings related to the issue of delays of the FAA modernization efforts. The

two most recent reports are GAO, National Airspace System: DOT and FAA Actions Will Likely

Have a Limited Effect on Reducing Delays during Summer 2008 Travel Season, GAO-08-934T

(Washington, D.C.: July 15, 2008) and GAO, National Airspace System: Summary of Flight

Delay Trends for 34 Airports in the Continental United States, an E-supplement to GAO-10-542,

GAO-10-543SP, (Washington, D.C.: May 2010).

The comprehensive academic review of delay literature is Ning Xu’s dissertation (2007),

Method for Deriving Multi-Factor Models for Predicting Airport Delays, with co-Directors

Laskey and Sherry. Xu researched five factors relating to delays: weather related factors, traffic

related factors, and traffic flow management related factors and other factors. Xu’s dissertation

contains a complete overview of literary sources and is recommended for any scholars of the

topic.



Exhibit B: Airport-Specific Turns

For June 2010, from DOT ASQP Data Set (Domestic flights by reporting carriers)

Turn time based on aircraft tail-number turns scheduled and completed without diversion, cancellation or overnight layover

Turn Time Delay Minutes per Impacted Flight Scheduled Actual Carrier Weather Airspace Inbound

ATL 61.8 67.0 13.1 2.6 13.9 18.9 ORD 58.2 61.7 11.5 2.7 17.8 31.2 DFW 55.4 63.7 14.6 4.7 11.8 18.1 DEN 49.3 56.9 11.2 1.6 10.0 24.5 LAX 51.2 57.0 9.7 0.3 12.3 22.1 PHX 53.4 59.7 13.1 0.2 10.9 19.6 IAH 60.8 64.2 13.4 2.2 12.8 16.5 DTW 63.0 68.9 18.3 3.2 12.1 15.3 LAS 43.7 51.8 11.6 0.7 8.0 26.5 SLC 61.8 65.9 10.3 0.6 11.6 18.0 SFO 54.3 57.5 8.1 0.2 7.5 43.6 MSP 69.0 73.2 15.3 3.2 12.7 16.5 CLT 62.1 68.1 12.1 2.2 20.0 16.4 MCO 46.0 55.7 8.7 4.7 16.6 22.4 BWI 39.0 48.5 11.3 2.4 13.4 29.8 SEA 61.6 68.0 10.3 0.1 16.0 17.9 MEM 74.5 79.0 19.3 1.3 13.7 17.8 BOS 48.5 57.7 8.6 2.6 21.9 24.8 LGA 51.9 59.2 8.3 1.4 23.3 28.4 MDW 35.6 47.4 11.6 6.4 10.1 32.6 EWR 60.4 67.3 14.8 1.8 18.3 21.5 SAN 38.9 43.1 4.8 0.5 12.2 27.2 JFK 68.8 81.4 21.1 3.2 22.9 12.7 PHL 57.0 65.7 11.1 3.4 19.7 18.5 IAD 57.1 66.4 12.6 4.8 21.6 26.7 DCA 52.4 58.2 9.1 1.7 27.3 21.8 TPA 42.0 50.9 7.0 6.5 17.1 23.4 STL 36.6 44.8 8.3 3.1 11.5 30.3 BNA 32.2 39.9 8.2 2.9 11.9 30.7 HNL 59.1 58.3 16.2 0.2 6.1 20.9 HOU 31.1 40.2 11.3 1.3 6.3 25.0 FLL 45.9 56.8 8.0 3.9 18.8 24.4 All U.S. Airports 49.2 55.2 10.8 2.5 15.1 25.2


Page 114

Exhibit C: Change in Scheduled Turn Times

Based on scheduled aircraft turns by U.S. domestic flights by reporting carriers under DOT Part 234 ASQP. Flight schedules based on first Wednesday of June in 2005 and 2010.

Airport 2005 2010 Change Airport 2005 2010 Change

DTW 42.2 66.2 57.0% IAD 57.5 60.7 5.5%

PHX 34.9 52.6 50.8% DFW 53.3 55.9 4.9%

MEM 43.6 62.9 44.2% CLT 62.2 65.0 4.5%

MSP 47.4 66.8 40.9% MIA 63.9 66.5 4.1%

BWI 32.3 41.8 29.1% MCO 44.7 46.5 3.9%

JFK 63.0 77.9 23.7% DCA 51.4 53.0 3.1%

MKE 39.2 48.3 23.4% LAX 54.5 56.2 3.0%

SMF 31.0 38.2 23.2% ATL 64.1 61.6 -3.9%

HOU 26.1 31.9 22.0% SEA 55.3 52.6 -4.9%

IAH 65.3 79.6 21.9% FLL 47.4 44.7 -5.8%

BTV 35.3 42.0 19.1% BOS 54.9 51.7 -5.8%

LAS 37.4 44.5 18.8% LGA 54.4 51.3 -5.9%

EWR 58.8 69.6 18.3% SLC 57.4 53.2 -7.4%

MDW 29.8 34.9 17.2% SFO 71.0 61.2 -13.9%

PHL 50.1 58.7 17.2% PIT 51.2 43.7 -14.7%

ORD 55.9 63.5 13.5% DEN 59.7 50.3 -15.7%

CLE 55.6 59.6 7.1% HNL 134.4 107.9 -19.7%



Exhibit D: Capacity Loss vs. Weather Incidence, Full Year 2009

Bad weather indicates frequency of IFR or near-IFR conditions requiring radar separation and instrument approaches. Loss of capacity indicates maximum reduction in FAA Benchmark between VFR and IFR conditions.

!32%% !34%%

!43%%

!16%%

!7%%

!27%% !28%%

!36%%!38%%

!22%%!20%% !19%%

!18%%

!33%%!31%%

!34%%

!21%% !20%%

!45%%

!50%%

!17%%

!26%%

!32%%

!6%%

!17%%

!26%%

!16%% !16%%

!27%%

!37%%

!30%%

!24%%

!38%%

!28%%

!52%%

30%%

26%% 24%% 24%% 24%%22%% 20%% 20%% 20%% 20%% 19%% 18%% 18%% 18%% 18%% 18%% 17%% 16%% 16%% 15%% 14%% 13%% 13%% 13%% 13%%

11%% 10%%8%% 7%%

5%% 4%%2%% 1%% 0%% 0%%

SEA%

MEM

%

STL%

ATL%

MSP%

IAD%

DTW%

PDX%

SFO%

CLT%

IAH%

LGA%

CVG%

DFW%

BOS%

EWR%

LAX%

CLE%

DCA%

BWI%

PHL%

JFK%

ORD

%

MDW

%

SAN%

PIT%

SLC%

FLL%

DEN%

MCO

%

TPA%

MIA%

LAS%

PHX%

HNL%

Loss$of$Capacity$in$Bad$Weather$vs.$Occurence$of$Bad$Weather$(2009)$

%%CAP%LOSS% %%WEATHER%


Page 116

Exhibit E: Key Operational Statistics, Major U.S. Airports

KEY STATISTICS: 7AM TO 10PM

Code Name of Airport Capacity Flights Util % Flights Percent Minutes Flights Percent Minutes GDP # GDP %LGA New York LaGuardia 424,913 341,380 80.3% 35,790 21.3% 34.7 47,328 27.3% 37.4 37,899 22.7%EWR Newark Liberty 469,685 366,744 78.1% 48,703 26.6% 41.0 59,259 32.2% 47.0 51,876 29.4%ATL Atlanta Hartsfield-Jackson 1,205,933 926,551 76.8% 105,093 22.4% 41.0 123,078 26.9% 45.3 43,076 9.5%JFK New York John F. Kennedy 485,967 368,778 75.9% 42,227 22.5% 40.8 49,652 27.4% 42.0 23,238 13.1%PHL Philadelphia International 579,855 404,097 69.7% 49,465 24.2% 38.0 53,326 26.7% 37.9 28,492 14.3%ORD Chicago O'Hare 1,141,379 765,937 67.1% 80,612 21.0% 45.6 74,059 19.4% 47.1 26,616 7.1%DCA Washington Reagan 394,189 256,876 65.2% 20,625 16.4% 27.6 21,299 16.3% 25.5 1,971 1.5%SFO San Francisco International 531,344 321,660 60.5% 36,340 23.1% 35.5 44,208 26.9% 38.7 27,148 16.1%SAN San Diego Lindbergh 278,107 162,753 58.5% 14,676 18.8% 20.4 17,280 20.5% 21.3 73 0.1%CLT Charlotte/Douglas Int'l 768,646 445,110 57.9% 42,381 18.6% 29.1 43,395 20.0% 28.9 3,664 1.7%BOS Boston Logan 543,091 300,685 55.4% 27,794 18.7% 31.6 34,519 22.7% 34.6 13,835 9.4%IAH Houston Intercontinental 896,673 496,168 55.3% 40,364 16.0% 29.1 46,373 19.0% 34.2 3,099 1.3%LAS Las Vegas McCarran 603,533 330,977 54.8% 36,455 22.7% 32.8 33,875 19.9% 31.4 2,149 1.2%SEA Seattle-Tacoma International 526,978 282,233 53.6% 19,036 13.6% 24.7 23,149 16.2% 25.8 273 0.2%DFW Dallas/Fort Worth Int'l 1,128,034 598,855 53.1% 74,585 25.0% 43.0 61,403 20.5% 44.6 5,458 1.8%LAX Los Angeles International 915,828 465,215 50.8% 38,079 16.9% 37.3 43,941 18.3% 39.7 53 0.0%MSP Minneapolis/St. Paul Int'l 782,496 381,805 48.8% 33,259 17.3% 27.1 37,423 19.8% 32.4 9,748 5.0%MDW Chicago Midway 367,277 178,033 48.5% 25,001 28.3% 24.0 18,237 20.3% 23.0 974 1.0%PHX Phoenix Sky Harbor 788,278 381,236 48.4% 34,650 18.3% 28.7 33,347 17.4% 28.7 473 0.2%BWI Baltimore-Washington 449,693 212,996 47.4% 24,577 23.3% 26.3 20,416 19.0% 24.4 1,584 1.4%DTW Detroit Metro 859,385 402,033 46.8% 34,900 17.3% 27.7 34,637 17.3% 31.7 1,412 0.7%DEN Denver International 1,293,382 592,415 45.8% 60,857 20.5% 36.0 58,671 19.8% 36.8 4,659 1.6%FLL Fort Lauderdale Int'l 449,946 191,370 42.5% 22,985 24.0% 28.8 20,605 21.5% 26.5 128 0.1%MIA Miami International 751,946 272,827 36.3% 39,160 27.8% 42.9 34,110 25.9% 40.2 192 0.2%IAD Washington Dulles 763,835 274,597 36.0% 34,254 24.5% 33.5 29,966 22.3% 31.0 2,116 1.5%SLC Salt Lake City International 790,149 271,572 34.4% 19,544 14.4% 19.5 22,497 16.6% 22.0 976 0.7%CLE Cleveland Hopkins 526,553 175,491 33.3% 12,921 14.8% 19.6 13,503 15.4% 20.0 146 0.2%PDX Portland International 471,103 150,464 31.9% 11,113 15.0% 15.9 13,397 17.6% 17.5 147 0.2%MCO Orlando International 925,743 261,144 28.2% 27,056 20.5% 30.6 26,264 20.3% 29.6 289 0.2%STL St. Louis Lambert Field 695,034 180,977 26.0% 17,090 19.4% 24.1 17,015 18.3% 23.8 25 0.0%HNL Honolulu Int'l 674,642 167,473 24.8% 8,974 10.8% 14.2 12,648 15.0% 19.3 0 0.0%MEM Memphis International 769,722 185,885 24.2% 19,386 21.0% 19.4 21,755 23.2% 22.1 390 0.3%TPA Tampa International 667,242 149,994 22.5% 14,909 19.8% 21.0 15,248 20.4% 21.1 116 0.2%CVG Cincinnati/N. Kentucky Int'l 1,014,640 198,226 19.5% 16,352 16.5% 16.5 14,789 14.9% 18.6 374 0.4%PIT Pittsburgh International 910,608 102,787 11.3% 11,153 22.2% 19.9 11,683 22.2% 19.3 52 0.1%

Utilization by Airlines Departure Delays from Airport Arrival Delays at Airport



Exhibit F: Correlation Matrix

On-

Tim

e

Dep

artu

re %

On-

Tim

e A

rriv

al

Tota

l Flig

hts

On-

Tim

e

Flig

hts

Can

cella

tions

Pre

-Can

cella

tions

Car

rier

Can

cella

tions

Wea

ther

C

ance

llatio

ns

Airs

pace

C

ance

llatio

ns

Car

rier

Del

ay M

inut

es

Wea

ther

D

elay

Min

utes

Airs

pace

D

elay

Min

utes

Late

Inbo

und

D

elay

Min

s

One

-Hou

r Ta

xi T

imes

Hig

h Te

mpe

ratu

res

Low

Tem

pera

ture

s

Ave

rage

Te

mpe

ratu

res

Sno

w o

r W

inte

r Pre

cip

On-Time Departure % 0.92 -0.20 0.11 -0.55 -0.55 -0.27 -0.48 -0.43 -0.53 -0.49 -0.56 -0.65 -0.40 0.12 0.07 0.09 -0.20 On-Time Arrival % 0.92 -0.17 0.11 -0.52 -0.52 -0.25 -0.45 -0.42 -0.51 -0.48 -0.69 -0.59 -0.55 0.17 0.13 0.14 -0.27 Total Flights -0.20 -0.17 0.93 0.26 0.25 0.35 0.15 0.23 0.75 0.26 0.52 0.50 0.19 0.03 0.00 0.02 -0.03 On-Time Departures 0.11 0.11 0.93 0.04 0.03 0.22 -0.03 0.03 0.52 0.04 0.29 0.19 0.05 0.07 0.02 0.04 -0.09 Cancellations -0.55 -0.52 0.26 0.04 1.00 0.59 0.93 0.55 0.38 0.63 0.48 0.46 0.35 -0.16 -0.15 -0.16 0.21 Pre-cancellations -0.55 -0.52 0.25 0.03 1.00 0.59 0.93 0.55 0.37 0.62 0.46 0.45 0.34 -0.16 -0.15 -0.16 0.21 Cancellations Post Dep. -0.32 -0.34 0.27 0.14 0.49 0.46 0.33 0.41 0.36 0.35 0.41 0.49 0.33 0.36 -0.08 -0.09 -0.08 0.12 Carrier Cancels -0.27 -0.25 0.35 0.22 0.59 0.59 0.41 0.19 0.35 0.32 0.32 0.31 0.17 -0.07 -0.07 -0.06 0.08 Weather Cancels -0.48 -0.45 0.15 -0.03 0.93 0.93 0.41 0.28 0.23 0.57 0.35 0.27 0.30 -0.18 -0.17 -0.17 0.24 Airspace Cancels -0.43 -0.42 0.23 0.03 0.55 0.55 0.19 0.28 0.41 0.42 0.47 0.59 0.30 -0.05 -0.03 -0.04 0.03 Carrier Delay Minutes -0.53 -0.51 0.75 0.52 0.38 0.37 0.35 0.23 0.41 0.41 0.67 0.72 0.36 0.00 0.00 0.01 0.05 Carrier Delay/Flight 0.00 0.00 -0.06 -0.07 0.10 0.10 0.02 0.09 0.08 0.14 0.10 0.02 -0.01 0.04 0.01 0.00 0.01 0.01 Weather Delay Minutes -0.49 -0.48 0.26 0.04 0.63 0.62 0.32 0.57 0.42 0.41 0.53 0.54 0.43 -0.01 -0.01 0.00 0.10 Weather Delay/Flight -0.23 -0.24 0.08 0.02 0.13 0.13 0.08 0.11 0.11 0.13 0.23 0.19 0.11 0.15 -0.04 -0.04 -0.04 0.05 Airspace Delay Minutes -0.56 -0.69 0.52 0.29 0.48 0.46 0.32 0.35 0.47 0.67 0.53 0.66 0.76 -0.06 -0.05 -0.05 0.14 Airspace Delay/Flight -0.18 -0.24 -0.13 -0.17 0.15 0.14 0.04 0.13 0.13 -0.03 0.13 0.36 0.02 0.31 0.02 0.01 0.01 0.04 Late Arriving Aircraft -0.65 -0.59 0.50 0.19 0.46 0.45 0.31 0.27 0.59 0.72 0.54 0.66 0.37 0.00 0.02 0.01 0.01 LAA/Flight -0.29 -0.27 -0.08 -0.19 0.24 0.24 0.08 0.17 0.30 0.10 0.25 0.17 0.31 0.18 0.05 0.06 0.05 -0.02 1 hour taxi times -0.40 -0.55 0.19 0.05 0.35 0.34 0.17 0.30 0.30 0.36 0.43 0.76 0.37 -0.04 -0.03 -0.03 0.14 Maximum temperatures 0.12 0.17 0.03 0.07 -0.16 -0.16 -0.07 -0.18 -0.05 0.00 -0.01 -0.06 0.00 -0.04 0.94 0.95 -0.63 Minimum temperatures 0.07 0.13 0.00 0.02 -0.15 -0.15 -0.07 -0.17 -0.03 0.00 -0.01 -0.05 0.02 -0.03 0.94 0.94 -0.62 Average temperatures 0.09 0.14 0.02 0.04 -0.16 -0.16 -0.06 -0.17 -0.04 0.01 0.00 -0.05 0.01 -0.03 0.95 0.94 -0.62 Snow present -0.20 -0.27 -0.03 -0.09 0.21 0.21 0.08 0.24 0.03 0.05 0.10 0.14 0.01 0.14 -0.63 -0.62 -0.62


Page 118

Exhibit G: Schedule Padding by Average Air Time

!12.0%'

!10.0%'

!8.0%'

!6.0%'

!4.0%'

!2.0%'

0.0%'0' 100' 200' 300' 400' 500' 600'

Schedule(Padding:((By(Average(Air(Time((Not(Including(Taxi(Time)(



Exhibit H: Key On-Time Statistics for Airports Operating Above Worst-Case Capacity (ADR+AAR) Rates, April-September 2010

Scheduled Departures

On-Time Departures

OTD % Scheduled Arrivals

On-Time Arrivals

OTA % OAG Scheduled

Capacity Displaced % Displaced

ORD 160,698 124,048 77.2% 160,646 124,581 77.6% 400,526 344,715 56,799 14.2% LGA 52,507 43,163 82.2% 52,506 39,987 76.2% 165,938 145,857 22,262 13.4% PHL 45,072 37,423 83.0% 45,076 36,635 81.3% 184,863 182,812 19,701 10.7% JFK 56,181 41,991 74.7% 56,179 43,049 76.6% 160,055 151,000 13,442 8.4% BOS 57,824 48,252 83.4% 57,807 46,022 79.6% 149,459 158,032 11,818 7.9% CLT 63,147 53,046 84.0% 63,157 53,060 84.0% 210,625 299,085 9,380 4.5% SFO 71,662 55,483 77.4% 71,682 52,604 73.4% 148,343 169,309 6,299 4.2% ATL 210,671 166,923 79.2% 210,644 166,840 79.2% 427,043 433,209 18,098 4.2% EWR 57,316 45,146 78.8% 57,321 43,558 76.0% 165,057 174,890 5,451 3.3% DFW 135,329 105,064 77.6% 135,336 109,930 81.2% 292,135 376,638 7,919 2.7% DEN 122,047 97,992 80.3% 122,090 102,339 83.8% 286,806 377,494 7,748 2.7% SNA 21,069 18,057 85.7% 21,072 17,892 84.9% 37,006 102,276 854 2.3% IAH 92,738 76,608 82.6% 92,726 75,200 81.1% 234,751 317,174 5,193 2.2% MKE 22,674 18,554 81.8% 22,677 18,216 80.3% 68,940 124,962 1,330 1.9% MCO 61,664 50,801 82.4% 61,668 51,079 82.8% 126,309 297,550 2,208 1.7% MDW 43,687 31,988 73.2% 43,687 35,963 82.3% 84,015 142,020 1,408 1.7% IAD 39,950 33,288 83.3% 39,987 33,063 82.7% 121,794 248,689 1,127 0.9% HOU 26,579 19,023 71.6% 26,578 21,011 79.1% 47,551 106,785 416 0.9% SAN 40,814 34,858 85.4% 40,813 34,017 83.3% 71,922 101,550 619 0.9% DAL 23,428 17,745 75.7% 23,429 19,089 81.5% 42,775 123,493 310 0.7% PDX 27,860 24,843 89.2% 27,858 23,645 84.9% 71,682 131,752 472 0.7% MSP 65,641 52,479 79.9% 65,638 51,609 78.6% 185,356 299,499 1,188 0.6% DTW 82,714 64,256 77.7% 82,748 64,427 77.9% 205,798 302,572 1,102 0.5% PHX 90,690 76,838 84.7% 90,693 78,796 86.9% 177,103 304,272 793 0.4% SLC 63,923 55,100 86.2% 63,919 53,999 84.5% 127,910 261,515 490 0.4% MIA 34,445 25,653 74.5% 34,436 26,605 77.3% 133,316 239,647 403 0.3% BWI 55,555 43,040 77.5% 55,558 45,234 81.4% 108,532 157,188 325 0.3% SEA 52,709 46,840 88.9% 52,699 45,463 86.3% 129,225 210,424 271 0.2% DCA 38,988 32,668 83.8% 38,982 31,123 79.8% 122,113 150,240 229 0.2% SDF 9,497 7,916 83.4% 9,494 7,489 78.9% 26,527 127,755 39 0.1%

Others 72,414 61,230 84.6% 72,402 58,514 80.8% 202,079 546,223 216 0.0% Airport Set 1,999,493 1,610,316 80.5% 1,999,508 1,611,039 80.6% 4,915,554 7,108,627 197,910 4.0%


Page 120

Exhibit I: Key On-Time Statistics for Airports Operating Below Worst-Case Capacity (ADR+AAR) Rates, April-September 2010

Scheduled Departures

On-Time Departures OTD % Scheduled

Arrivals On-Time Arrivals OTA % OAG

Scheduled Capacity Displaced % Displaced

LAX 102,507 85,662 83.6% 102,517 84,848 82.8% 214,177 324,674 1 0.0% LAS 74,071 59,755 80.7% 74,065 61,919 83.6% 146,429 240,338 52 0.0% MEM 40,828 34,401 84.3% 40,821 33,672 82.5% 93,829 317,441 19 0.0% TPA 34,123 28,640 83.9% 34,119 28,145 82.5% 67,896 185,446 0 0.0% CLE 29,162 25,302 86.8% 29,168 24,110 82.7% 81,957 174,605 0 0.0% STL 28,836 22,739 78.9% 28,837 23,075 80.0% 71,536 229,671 1 0.0% BNA 28,150 22,301 79.2% 28,151 22,493 79.9% 59,002 224,165 1 0.0% HNL 27,521 25,498 92.6% 27,523 24,372 88.6% 67,560 231,179 0 0.0% MCI 25,618 21,142 82.5% 25,618 20,451 79.8% 58,385 132,864 0 0.0% CVG 24,276 19,615 80.8% 24,278 19,705 81.2% 69,431 333,486 0 0.0% RDU 24,238 20,211 83.4% 24,234 19,302 79.6% 54,068 154,650 0 0.0% OAK 24,094 19,984 82.9% 24,097 19,981 82.9% 43,315 176,370 0 0.0% SMF 22,958 19,428 84.6% 22,775 18,536 81.4% 39,870 190,670 0 0.0% AUS 21,728 18,590 85.6% 21,729 17,662 81.3% 39,842 129,699 0 0.0% SJC 21,042 17,741 84.3% 21,045 17,442 82.9% 39,912 119,126 0 0.0% MSY 19,418 16,270 83.8% 19,416 15,643 80.6% 37,926 149,331 0 0.0% PIT 18,904 16,167 85.5% 18,907 15,420 81.6% 48,867 324,630 0 0.0% IND 18,336 15,157 82.7% 18,330 14,533 79.3% 45,363 142,251 17 0.0% ABQ 16,963 14,299 84.3% 16,962 13,853 81.7% 34,288 130,950 0 0.0% JAX 15,105 12,815 84.8% 15,105 12,050 79.8% 28,158 150,818 0 0.0% BUR 13,489 11,530 85.5% 13,488 11,320 83.9% 25,959 134,762 0 0.0% OMA 12,930 10,916 84.4% 12,931 10,100 78.1% 25,028 110,660 2 0.0% BUF 12,905 10,920 84.6% 12,901 10,152 78.7% 30,494 178,400 0 0.0% ONT 12,619 10,940 86.7% 12,619 10,533 83.5% 22,475 130,252 0 0.0% BDL 12,164 10,357 85.1% 12,159 9,627 79.2% 28,493 137,036 0 0.0% RSW 12,032 10,436 86.7% 12,024 10,051 83.6% 26,287 126,976 0 0.0% PBI 11,663 9,838 84.4% 11,654 9,307 79.9% 21,632 162,822 0 0.0% TUS 11,139 9,870 88.6% 11,138 9,147 82.1% 18,840 94,956 4 0.0% SJU 11,131 9,299 83.5% 11,122 8,942 80.4% 55,789 185,560 0 0.0%

Others 56,952 48,961 86.0% 57,135 46,994 82.3% 136,333 848,954 12 0.0% Airport Set 784,902 658,784 83.9% 784,868 643,385 82.0% 1,733,141 6,172,742 109 0.0%

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