Table Of Content©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
©2012 American Aviation Institute. Protected document, all rights reserved.
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).
February 16, 2012
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©2012 American Aviation Institute. Protected document, all rights reserved.
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
February 16, 2012
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©2012 American Aviation Institute. Protected document, all rights reserved.
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,
February 16, 2012
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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,
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
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©2012 American Aviation Institute. Protected document, all rights reserved.
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”
February 16, 2012
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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
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Description:Is the airline practice of scheduling arriving and departing banks of aircraft to We focus in particular on strategic delay management programs into their schedules . static benchmarks for runway capacity, but these benchmarks do not GARCH. LaGuardia average arrival delay and. National Airspace.
