stateofusaviation comprehensive analysis of airline schedules & airport delay

7/28/2019 Stateofusaviation Comprehensive Analysis of Airline Schedules & Airport Delay

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

The State of U.S. Aviation

Comprehensive Analysis ofAirline Schedules & Airport Delays

Darryl Jenkins, [email protected]

Joshua Marks, Executive [email protected]

Michael Miller, Vice President, [email protected]

February 16, 2012


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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, asdefined 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


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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


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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


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Section One: Executive Summary

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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,

i i h i li d d t fli ht ti t i t li b bl d l W


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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 usefulstarting 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 ofover-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 todays 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


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Section One: Executive Summary

!

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

furtherimprovements in delays and cancellations.

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

managed U.S. airports including New Yorks 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


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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 turntimes 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


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Section Two: Key Findings and Prior Work

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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


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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

i t t i l t d Ai ft t d i t th t


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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 & SherryMethod for Deriving Multi-Factor Models for Predicting Airport Delays (2007)

Response variable Predictor variables Author Methods

Probability of on-timeperformance, delaysand flight cancellations

at New York LaGuardia

Discussion of generalflight delay and flightcancellations

Economic (e.g. revenue, loadfactors), Route Competition (e.g.Monopoly), Airport competition

(e.g. Concentration at origination,hub destination), Logistical (e.g. slotorigination, distance, hours untilnext flights). Weather (e.g. rain,minimum temperature frozen)

Rupp 2005 Nested Logit Model


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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 airlines

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

d l d ll ti Th ti k t dd i h th h t i li


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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

hi t i l d t ) th i f ti (f NOAA d th N ti l Cli t D t C t ) ll


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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


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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


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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

facilitys 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 ofrunway capacity versus airportcapacity. 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).


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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% 45.6Washington 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


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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 airports 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 carriers flexibility to spread

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


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Chart 1b: Correlation Between On-Time Performance and Flight Operations by MonthSource: 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 todays

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

0.08

-0.43

-0.27-0.22

-0.35

-0.48

-0.58

-0.43

-0.21

-0.37

-0.21

-0.46

1 2 3 4 5 6 7 8 9 10 11 12

18,000!20,000!

900,000!1,000,000!

Total

BLUE)


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!

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

20%!

25%!

Cancellations/Diversions Delay Rate


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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, cateringand 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 eventsCrew availability

Supplier deliveryPassenger & cargo boardingRamp personnel & equipmentWeather conditions

Ramp congestionTaxiway congestion

Visibility

Number of aircraft: Awaiting taxi Queued for runway

Convective activity, turbulenceand en-route winds

Weather at destination andalternate airport (IFR, etc.)

Departure and arrival queues at


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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)

15%!20%!25%!30%!35%!40%!45%!50%!

arturesbyH

ourofScheduledDeparture

Time

WN!B6!DL!AA!UA!


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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%


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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

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!

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!0600-0659

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!1900-1959

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!NumberofFlights

Impacted(AllFlights2010)


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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

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)!


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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 2010On-Time Arrivals represents completion of flight from selected airport within 15 minutes of schedule

Delay minutes are based on impacted flights only

Rank AirportOn-Time

DeparturesOn-TimeArrivals

CancelledFlights

CarrierDelay (Avg)

WeatherDelay (Avg)

AirspaceDelay (Avg)

Inbound ACDelay (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 37.519 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


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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 perYear

% 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


Size of Airport Carrier Weather AirspaceLate

Inbound

0-12,500 5.1% 0.9% 10.2% 9.2%

12 500 37 500 6 3% 0 7% 9 5% 9 0%


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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


Size of Airport Carrier Weather AirspaceLate

Inbound

0-12,500 44.3 57.8 28.8 47.9

12,500-37,500 35.3 47.8 27.8 42.737,500-62,500 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 38.3262,500-287,500 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.


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Table 6e: Key Metrics by Aircraft Type

Source: DOT ASQP Part 234 Reports, Calendar Year 2010, Domestic Flights Only

Aircraft TypeCategory

% ofReport

% DepsOntime

% ArrsOntime

CarrierDelay %

CarrierDel Mins

WeatherDelay %

WeatherDel 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


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Table 8: Occurrence of Inclement Weather ConditionsIMC = 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)


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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 itsdestination. 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


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Table 9: Diversions by Reporting U.S. Carriers, 2010

Source: Reporting Carriers, Domestic Flights, FY2010, DOT Part 234 (ASQP)

Airline FlightDiversions

Div. Rateper 1,000

Flights w/1 Div.

1 Div. FltsCompleted

Flights w/2 Divs.

2 Div. FltsCompleted

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

13


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Table 10: Cancellations by Reporting U.S. Carriers, 2010


Airline Sched.Flights

TotalCancels

CancelRate (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 0Frontier (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% 42,15637.2%

56,50749.9%

14,55312.8%

390.1%

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

ll i hil j f d l i f d f ll i


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Table 11: Cancellation Types by Reporting U.S. Carriers, 2010Source: 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-


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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


System Statistics Delay per Flight Cause of Delay

Airline Flights Delay Min All Flights Impacted Carrier Weather &Airspace

LateInbound

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


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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

(d fi d 15 i t t ft h d l d i l ti ) ill i t f ll fli ht


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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

5,

260

!

23,

858

! 33,328

!

22,

810

!25,

207

!

23,

881

!23,

604

!

15,

447

!

11,684

!

8,

054!

5,

706

!4,

506

!3,

064

!2,

313

!1,

808

!1,

349

!993

!

20! 25! 30! 35! 40! 45! 50! 55! 60! 65! 70! 75! 80! 85! 90! 95! 100!

DomesticTu

rnsJune2010

(224,0

96total)

Turn Time Rounded to Nearest 5 Minutes!


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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 32.3US 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


Row Labels American JetBlue Continental Delta Southwest All Carriers

0001-0559 49 490600-0659 76 94 61 51 30 42

0700-0759 69 68 76 61 26 42

0800 0859 62 49 65 58 28 44


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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) Southwests 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


In Minutes All Carriers Regionals Only Majors (ex. WN) SouthwestDeparture 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 4.11300-1359 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


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Chart 11: Delay Minutes per Impacted Flight, By Scheduled Turn Time


How does Southwests turn time and delay pattern compare with a major airline peer?

To conduct a specific comparison, we analyzed Southwests 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

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!


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Table16a: Southwest Turn Time Distribution (20-240 minutes) by Time of Day


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 26.71400-1459 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


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 3.10900-0959 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


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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


3.8.2

Increases in Aircraft Turn Times, 2005-2010

Based on the trends observed, we believed that a significant improvement in on-time

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!


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!

airline, American, Continental, AirTran, US Airways and Southwest showed significant increases

in turn times. Deltas 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% orgreater 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


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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 ChangeDTW 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


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!

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,


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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

-1.9%

!

-2.5%

!

-3.0%

!-2.9

%!

-2.8%

!-2.8%

!-2.9

%!

-3.1%

!-2.9

%!

-2.8%

!-2.8

%!

-3.2%

!-3.3%

!

-3.8%

!-3.7%

!

!

0700-0759

! !

0800-0859

! !

0900-0959

! !

1000-1059

! !

1100-1159

! !

1200-1259

! !

1300-1359

! !

1400-1459

! !

1500-1559

! !

1600-1659

! !

1700-1759

! !

1800-1859

! !

1900-1959

! !

2000-2059

! !

2100-2159

!


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!

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 irregularoperation conditions (Full Year 2010)

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!SpreadbetweenActual

andSchedluedEn-route

Time(Delayedvs.NotDelayed)

Distance of Flight (miles)!


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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.


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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)

38.

5

35.

3

37.

0

3

0.

2

2

9.

8 3

9.

2

33.

9

28.

7

55.

3

26

.9

48.

7

33.

3 4

3.

6

44.

0

45.

6

45.

8

33.

0

2

9.

6

54.

5

42.

8

32.

2

78.

7

23.5

58.

8

43.

1

66.

9

ALL DAY! 6PM-7PM!


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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?

29.

0

24.

0

25.

7

23.

5

23.

9

27.

8

25.

0

20.

8

38.2

21.

6

33.

1

26.

4

30.

1

34.

1

30.

1

33.

3

26.

8

24.

6

36.

8

32.

2

24.

7

56.

0

19.

1

40.

1

35.

1

44.

3

ATL! BOS! DCA! DEN! DFW! EWR! IAD! IAH! JFK! LAX! LGA! ORD! PHL!

ALL DAY! 6PM-7PM!


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!

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 BlockAirport 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 7.0Taxi Out 15.2 16.2 17.0 16.2

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

MinutesofTaxiTime

Taxi In! Taxi Out!

S i Th Fli h L l D l T d


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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

S ti Th Fli ht L l D l T d


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!

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%!

RouteDistance

(Miles)

Change in Taxi Time (Average 2007-2010 vs. Average 1996-2000)!

The shorter the flight, the

more variable the taxi time

has become

S ti Th Fli ht L l D l T d


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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)

0%#

50%#

100%#

150%#

200%#

250%#

300%#

0# 500# 1,000# 1,500# 2,000# 2,500# 3,000#

Change,

20082010(v

s.

19951997(

DIstance(of(Route(

Taxi#

Flight#

Section Three: Flight Le el Dela Trends


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stateofusaviation comprehensive analysis of airline schedules & airport delay

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