Produced by:
EUROCONTROL on behalf of the European Union
FAA Air Traffic Organization System Operations Services
Comparison of Air Trafc Management-Related
Operational Performance: U.S./Europe
2015
August 2016
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© Air Traffic Organization System Operations Services (FAA)
© European Commission
© European Organisation for the Safety of Air Navigation (EUROCONTROL)
This document is a joint publication of the Air Traffic Organization System Operations Services of
the FAA and EUROCONTROL on behalf of the European Union in the interest of the exchange of
information.
It is prepared in application of Annex 2 of the Memorandum of Cooperation NAT-I-9406 signed
between the United States of America and the European Union on 12 February 2013 and
managed by a joint European Commission-FAA Performance Analysis Review Committee (PARC).
The report builds on the body of work developed since 2009 between the FAA and
EUROCONTROL.
The objective is to make a factual high-level comparison of Air Traffic Management performance
between the US and Europe. It is based on a set of comparable performance indicators,
developed jointly and reviewed year after year, creating a sound basis for factual comparisons
between countries and world regions. The specific key performance indicators (KPIs) are based
on best practices from both the Air Traffic Organization System Operations Services and the
performance scheme of the Single European Sky initiative.
Maurizio Castelletti
PARC Co-Chair, European Commission
David Chin
PARC Co-Chair, US, FAA
2015 Comparison of ATM-related performance:
U.S. – Europe
August 2016
ABSTRACT
This report is the 5th in a series of joint ATM operational performance comparisons between the US and
Europe. It represents the 2
nd
edition under the Memorandum of Cooperation between the United States
and the European Union. Building on established operational key performance indicators, the goal of
the joint study conducted by the Federal Aviation Administration (FAA) and EUROCONTROL on behalf of
the European Union is to understand differences between the two ATM systems in order to further
optimise ATM performance and to identify best practices for the benefit of the overall air transport
system. The analysis is based on a comparable set of data and harmonised assessment techniques for
developing reference conditions for assessing ATM performance.
Produced by EUROCONTROL on behalf of the European Union and the Federal
Aviation Administration Air Traffic Organization System Operations Services
Federal Aviation Administration
Air Traffic Organization
System Operations Services
Performance Analysis Office
800 Independence Ave., S.W.
Washington, DC 20591
Tel: 202-267-2768
E-mail: [email protected]
European Commission
EUROCONTROL
Directorate General for Mobility and Transport
Directorate E - Aviation and international transport affairs
Unit E2 - Single European Sky
Tel. +32 2 299 1915
E-mail: [email protected]
Performance Review Unit
96 Rue de la Fusée,
B-1130 Brussels, Belgium
Tel: +32 2 729 3956
E-mail: [email protected]
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TABLE OF CONTENTS
TABLE OF CONTENTS .............................................................................................................. 3
LIST OF FIGURES .................................................................................................................... 4
LIST OF TABLES ...................................................................................................................... 5
EXECUTIVE SUMMARY ........................................................................................................... 7
1. INTRODUCTION ............................................................................................................ 10
1.1 Background and objectives ............................................................................................ 10
1.2 Report Scope .................................................................................................................. 10
1.3 Data Sources ................................................................................................................... 11
1.4 European and FAA Performance Reporting ................................................................... 13
1.5 Organisation of this report ............................................................................................. 15
2. COMPARISON OF AIR TRAFFIC MANAGEMENT (ATM) IN THE US AND EUROPE .............. 16
2.1 Organisation of ATM ...................................................................................................... 16
2.2 Airspace management (ASM) and design ...................................................................... 16
2.3 Air traffic flow management (ATFM) and air traffic control (ATC) ................................ 18
2.3.1 ATFM and ATC Facility organization ........................................................................................ 18
2.3.2 Demand Capacity balancing (DCB) .......................................................................................... 20
3. EXTERNAL FACTORS AFFECTING KEY PERFORMANCE INDICATORS ................................. 21
3.1 Traffic characteristics in the US and in Europe .............................................................. 21
3.1.1 Air traffic growth ..................................................................................................................... 22
3.1.2 Air traffic density ..................................................................................................................... 23
3.1.3 Average flight length ............................................................................................................... 24
3.1.4 Seasonality .............................................................................................................................. 25
3.1.5 Traffic mix ................................................................................................................................ 26
3.2 Airport operations and changes in airport capacity ...................................................... 27
3.2.1 Airport layout and operations at the main 34 airports ........................................................... 28
3.2.2 Declared capacity and peak throughput ................................................................................. 31
3.2.3 Capacity variation at US airports ............................................................................................ 33
3.3 Impact of Weather Conditions on airport operations ................................................... 35
3.3.1 Measuring weather conditions ................................................................................................ 35
3.3.2 Weather-related airport ATFM delays at the main 34 airports ............................................... 38
4. COMPARISON OF AIRLINE-RELATED OPERATIONAL SERVICE QUALITY ........................... 40
4.1 On-time performance .................................................................................................... 40
4.2 Airline scheduling ........................................................................................................... 43
4.3 Drivers of air transport performance – as reported by airlines ..................................... 44
4.4 Variability by phase of flight........................................................................................... 47
5. COMPARISON OF ATM-RELATED OPERATIONAL SERVICE QUALITY ................................ 50
5.1 Approach to comparing ATM-related service quality .................................................... 50
5.2 ATM-related efficiency by phase of flight ...................................................................... 52
5.2.1 ATM-Related Departure restrictions (ground holding) ............................................................ 52
5.2.2 ATM-related Taxi-out efficiency .............................................................................................. 55
5.2.3 En-route flight efficiency ......................................................................................................... 58
5.2.4 Flight efficiency within the last 100 NM .................................................................................. 64
5.2.5 Taxi-in efficiency ...................................................................................................................... 68
5.3 Summary of main results & Estimated benefit pool actionable by ATM ....................... 69
5.3.1 Estimated benefit pool actionable by ATM ............................................................................. 70
6. SUPPORTING STUDIES .................................................................................................. 73
6.1 Analysis of Air Traffic Flow and Capacity Management in the U.S. and in Europe ........ 73
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6.1.1 Introduction ............................................................................................................................. 73
6.1.2 Grouping of TMIs into Levels ................................................................................................... 74
6.1.3 Analysis by TMI Level in the US ............................................................................................... 75
6.1.4 Rerouting and Level Capping TMIs in Europe .......................................................................... 75
6.1.5 US/Europe Comparison of TMI-L2 (delay TMIs only) ............................................................... 77
6.1.6 Further work ............................................................................................................................ 78
6.2 Analysis of vertical flight efficiency in the U.S. and in Europe ....................................... 78
6.2.1 Introduction ............................................................................................................................. 78
6.2.2 Approach ................................................................................................................................. 79
6.2.3 Initial Comparison – Level Distance ......................................................................................... 80
6.2.4 Initial Comparison – Benefit Pool ............................................................................................ 81
6.2.5 Further work ............................................................................................................................ 82
7. CONCLUSIONS .............................................................................................................. 83
ANNEX I - LIST OF AIRPORTS INCLUDED IN THIS STUDY ......................................................... 89
ANNEX II - DEMAND CAPACITY BALANCING .......................................................................... 91
ANNEX III - GLOSSARY ......................................................................................................... 106
ANNEX IV - REFERENCES ...................................................................................................... 111
LIST OF FIGURES
Figure 1-1: Geographical scope of the comparison in the report .................................................. 10
Figure 2-1: Comparison of Special Use Airspace (SUA) .................................................................. 17
Figure 2-2: Comparison of en-route area control centres (2015) .................................................. 19
Figure 2-3: Generic ATFM process (ICAO Doc 9971) ...................................................................... 20
Figure 3-1: Evolution of IFR traffic in the US and in Europe........................................................... 22
Figure 3-2: Evolution of IFR traffic in the US and in Europe (2015 vs. 2010) ................................. 23
Figure 3-3: Traffic density in the US and in Europe (2015) ............................................................ 23
Figure 3-4: Seasonal traffic variability in the US and Europe (system level) ................................. 25
Figure 3-5: Seasonal traffic variability in the US and in Europe (2015) ......................................... 25
Figure 3-6: Comparison by physical aircraft class (2015) ............................................................... 26
Figure 3-7: Average seats per scheduled flight (2005-2015) ......................................................... 26
Figure 3-8: Operations at the main 34 airports (2015) .................................................................. 29
Figure 3-9: Change in operations at the main 34 airports (2015 vs. 2013) ................................... 30
Figure 3-10: Actual airport throughput vs. declared capacity (2015) ............................................ 31
Figure 3-11: Average hourly arrival rates at 34 main US airports (2013-2015) ............................. 33
Figure 3-12: Capacity variation and impact on operations at US airports ..................................... 34
Figure 3-13: Impact of visibility conditions on runway throughput ............................................... 35
Figure 3-14: Overview of weather conditions in the US and Europe ............................................. 36
Figure 3-15: Percent of time by meteorological condition at the main 34 airports (2015) ........... 37
Figure 3-16: Percent change in time during IMC at the main 34 airports (2013-2015) ................. 37
Figure 3-17: Causes of weather-related airport ATFM delays (2008-2015) .................................. 38
Figure 3-18: Airport charged weather-related ATFM delays by destination airport (2015) .......... 39
Figure 4-1: On-time performance (2005-2015) ............................................................................. 40
Figure 4-2: Arrival punctuality at the main 34 airports (2015) ...................................................... 41
Figure 4-3: Change in arrival punctuality at the main 34 airports (2015 vs. 2013) ....................... 42
Figure 4-4: Arrival punctuality by month (2010-2015) .................................................................. 42
Figure 4-5: Scheduling of air transport operations (2005-2015) ................................................... 44
Figure 4-6: Drivers of on-time performance in Europe and the US (2015) .................................... 45
Figure 4-7: Trends in the duration of flight phases (2005-2015) ................................................... 46
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Figure 4-8: Variability of flight phases (2005-2015) ....................................................................... 47
Figure 4-9: Monthly variability of flight phases (2010-2015) ......................................................... 48
Figure 5-1: Conceptual framework to measuring ATM-related service quality ............................. 50
Figure 5-2: Evolution of total ATFM delay per flight (2008-2015) ................................................. 53
Figure 5-3: Percent change in ATFM delay by cause (2015 vs. 2013) ............................................ 53
Figure 5-4: Breakdown of en-route ATFM delay by cause (2015) ................................................. 54
Figure 5-5: Breakdown of airport arrival ATFM delay by cause (2015) ......................................... 54
Figure 5-6: Airport charged ATFM delay by destination airport (2015) ........................................ 54
Figure 5-7: Additional times in the taxi-out phase (system level) ................................................. 56
Figure 5-8: Additional time in the taxi-out phase by airport (2015) .............................................. 57
Figure 5-9: Difference in additional time in the taxi-out phase by airport (2015 vs. 2013) .......... 57
Figure 5-10 Evolution of average additional minutes in the taxi out phase (2008-2015) ............. 58
Figure 5-11: Impact of Special Use Airspace in Europe (2015) ...................................................... 60
Figure 5-12: Evolution of horizontal flight efficiency (actual and flight plan) (2008-2015) ........... 61
Figure 5-13: Direct en-route extension by destination airport ...................................................... 62
Figure 5-14: San Diego/Los Angeles to Seattle flights affecting horizontal flight efficiency ......... 63
Figure 5-15: Free route development (2015) ................................................................................. 64
Figure 5-16: Evolution of average additional time within the last 100 NM (2008-2015) .............. 65
Figure 5-17: Estimated average additional time within the last 100 NM (2015) ........................... 66
Figure 5-18: Difference in average additional time within the last 100 NM (2015 vs. 2013) ........ 66
Figure 5-19: Additional times in the taxi-in phase (system level) (2005-2015) ............................. 68
Figure 5-20: Evolution of operational performance in US/Europe between 2008 and 2015 ........ 71
Figure 6-1: Overview of ATFCM study areas .................................................................................. 73
Figure 6-2 Reportable delay in the US (minutes and percentage) ................................................. 75
Figure 6-3 ATFM regulations in Europe ......................................................................................... 76
Figure 6-4 US/Europe comparison of TMI-L2 (delay TMIs only) .................................................... 77
Figure 6-5 Vertical Flight Profile – Level Segments ........................................................................ 79
Figure 6-6 US/Europe Comparison – Vertical Flight Efficiency – Average Level Distance ............. 80
Figure 6-7 US/Europe Comparison – Vertical Flight Efficiency – Potential Fuel Savings ............... 81
Figure II-1: Generic ATFM process (ICAO Doc 9971) ...................................................................... 91
LIST OF TABLES
Table 1-1: US/Europe Harmonized Key Performance Indicators ................................................... 14
Table 1-2: US/Europe - related indicators ...................................................................................... 14
Table 2-1: Organisation of ATFM (Overview) ................................................................................. 18
Table 3-1: US/Europe ATM key system figures at a glance (2015) ................................................ 21
Table 3-2: Breakdown of IFR traffic ................................................................................................ 24
Table 3-3: Comparison of operations at the 34 main airports in the US and Europe .................... 29
Table 3-4: Ceiling and visibility criteria .......................................................................................... 36
Table 5-1: ATFM departure delays (flights to or from main 34 airports within region) ................ 53
Table 5-2: Impact of ATM-related inefficiencies on airspace users’ operations............................ 70
Table 5-3: Estimated benefit pool actionable by ATM (2015 vs. 2010) ......................................... 72
Table I-1: Top 34 European airports included in the study (2015) ................................................ 89
Table I-2: US main 34 airports included in the study (2015) .......................................................... 90
Table II-3: Planning layer ................................................................................................................ 93
Table II-4: Strategic scheduling and ATFM solutions ..................................................................... 95
Table II-5: Pre-tactical planning ..................................................................................................... 99
Table II-6: Tactical ATFM .............................................................................................................. 100
Table II-7: Post-Ops ...................................................................................................................... 105
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EXECUTIVE SUMMARY
This report is the 5th in a series of joint ATM operational performance comparisons between the
US and Europe. It represents the 2nd edition under the Memorandum of Cooperation between
the United States and the European Union. The report provides a comparative operational
performance assessment between Europe and the US using Key Performance Indicators (KPIs)
that have been harmonized by both groups. The report provides demonstrated examples of the
KPIs listed in the 2016 ICAO Global Air Navigation Plan (GANP) which can be used to assess the
benefits of the global implementation of Aviation System Block Upgrades (ASBUs).
The indicators used are those proven to meet key ANSP objectives of identifying system
constraints through delay/capacity measures and improving flight efficiency by measuring actual
trajectories against an ideal. The report also includes punctuality and block time indicators that
relate performance more directly to the airline/passenger perspective. Complementary to the
well-established indicators already used in previous versions of the comparison reports, this
edition also features two supporting studies on 1) Air Traffic Flow and Capacity Management
(ATFCM) and 2) Vertical Flight Efficiency in the arrival phase.
The first part of this report examines commonalities and differences in terms of air traffic
management and performance influencing factors, such as air traffic demand characteristics and
weather, which can have a large influence on the observed performance.
Overall, air navigation service provision is more fragmented in Europe with more ANSPs and
physical facilities than in the US. The European area comprises 37 Air Navigation Service
Providers (ANSPs) with 62 en-route centres and 16 stand-alone Approach Control (APP) units
(total: 78 facilities). The US CONUS has 20 en-route centres supplemented by 26 stand-alone
Terminal Radar Approach Control (TRACON) units (total: 46 facilities), operated by one ANSP.
Although the US CONUS airspace is 10% smaller than the European airspace, the US controlled
approximately 57% more flights operating under Instrument Flight Rules (IFR) with 24% fewer
full time Air Traffic Controllers (ATCOs) than in Europe in 2015. US airspace density is, on
average, higher and airports tend to be notably larger than in Europe.
In terms of traffic evolution, there was a notable decoupling between the US and Europe in 2004
when the traffic in Europe continued to grow while US traffic started to decline. The effect of the
economic crisis starting in 2008 impacted traffic growth on both sides of the Atlantic. While
traffic in Europe decreased by 3.3%, air traffic in the US decreased by 9.9% between 2008 and
2015.
The second part of this report analyses operational performance in both systems from an airline
and from an ANSP point of view. The airline perspective evaluates efficiency and predictability
compared to published schedules whereas the ANSP perspective provides a more in-depth
analysis of ATM-related performance by phase of flight compared to an ideal benchmark
distance or time. For the majority of indicators, trends are provided from 2008 to 2015 with a
focus on the change in performance from 2013 to 2015.
Punctuality is generally considered to be the industry standard indicator for air transport service
quality. The trend in punctuality was similar in the US and Europe between 2005 and 2009 when
both systems reached a comparable level of around 82% of arrivals delayed by 15 minutes or
less in 2009. Whereas in the US performance remained stable in 2010, punctuality in Europe
degraded to the worst level on record mainly due to weather-related delays (snow, freezing
conditions) and strikes. From 2010 to 2012, punctuality in Europe improved again and continued
to improve in the US. However in 2013 and 2014, whereas punctuality in Europe remained
largely unchanged, punctuality in the US saw a sharp decline. In 2015 both systems reached
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again a similar performance level due to notable improvements in the US and performance
degradation in Europe.
While the evaluation of air transport performance compared to airline schedules provides
valuable first insights, the involvement of many different stakeholders and the inclusion of time
buffers in airline schedules limit the analysis from an air traffic management point of view.
Hence, the evaluation of ATM-related performance in this comparison aims to better understand
and quantify constraints imposed on airspace users through the application of air traffic flow
measures and therefore focuses more on the efficiency of operations by phase of flight
compared to an unconstrained benchmark distance or time.
After the bad performance due to weather and strikes in 2010, average ATM-related departure
delay in Europe decreased again until 2013. Between 2013 and 2015, total ATM-related ground
delays increased in Europe by 43.4% whereas traffic grew by 4.1% during the same time. The US
has also shown an improvement since 2008, some of which can be attributed to improving
weather and declining traffic levels. Between 2013 and 2015, total ATM-related ground delay in
the US decreased by 12.7% (mainly due to less weather-related delays) with system-wide CONUS
traffic levels increasing by 1.6% during the same time. In Europe, the notable performance
deterioration between 2013 and 2015 was due to a significant increase in capacity/volume
related delays and to a lesser extent due to weather.
ATM-related ground delay per flight in Europe (en-route and airport) was lower than in the US in
2015 (1.3 vs. 1.6 minutes per flight) however a larger percentage of flights is affected in Europe
(4.3% vs 3.3%). The underlying reasons and the application of ATM-related departure restrictions
among facilities differ notably between the two systems. Europe ascribes a greater percentage
of delay to en-route facilities (43% of total delay in 2015) while in the US the large majority is
ascribed to constraints at the airport (82.1% of total delay in 2015).
The share of flights affected by ATM-related departure restrictions at origin airports differs
considerably between the US and Europe. Despite a reduction from 5.0% of all flights in 2008 to
2.0% in 2015, flights in Europe are still over twice more likely to be held at the gate or on the
ground for en-route constraints than in the US where the share of flights affected by ATM-
related departure restrictions was 0.8% in 2015.
For airport-related ground delays, the percentage of delayed flights at the gate or on the surface
is slightly lower in Europe than in the US (2.3% vs. 2.5% in 2015). However, with 51 minutes, the
delay per delayed flight in the US is notably higher than in Europe in 2015 (33 mins). In the US,
the airports which make up a large percentage of those delays are airports like New York (LGA),
Chicago (ORD), Newark (EWR), San Francisco (SFO), New York (JFK), and Philadelphia (PHL) which
report a large number of hours with demand near or over capacity and have lower predictability
of capacity.
Taxi-out efficiency improved continuously between 2007 and 2012 in the US but deteriorated
again by 0.5 minutes per departure between 2012 and 2015. During the same period, with the
exception of 2010 where taxi-out efficiency decreased due to the strong winter, performance in
Europe improved continuously at a moderate rate but also showed a slight deterioration in
2015.
After a notable closure of the gap between the US and Europe until 2012, the performance gap
is widening again and in 2015 average additional taxi-out time in the US is, on average, some 1.5
minutes higher per departure than in Europe. This is largely driven by different flow control
policies and the absence of scheduling caps at most US airports. Whereas in Europe the
inefficiency levels in the taxi-out phase are more evenly spread among airports, the observed
taxi-out performance in the US is predominantly driven by the New York airports, Philadelphia
(PHL), and Chicago (ORD).
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Horizontal en-route flight efficiency (between a 40NM radius around the departure airport and a
100NM radius around the arrival airport) in filed flight plans and in actual trajectories is still
better in the US than in Europe in 2015. Overall, horizontal en-route efficiency on flights to or
from the main 34 airports in the US is approximately 0.1% better than in Europe in 2015.
Similar to en-route flight efficiency, the US also continued to show a higher level of efficiency in
the last 100NM before landing. Overall, the average additional time within the last 100 NM
(Arrival Sequencing and Maneuvering Area (ASMA)) was similar in the two regions in 2008 but
decreased in the US between 2008 and 2010 after which it has remained almost constant at 2.5
minutes across the main airports. At the same time, flight efficiency within the last 100 NM
deteriorated in Europe. Although at different levels, performance in the US and in Europe
remained relatively stable between 2013 and 2015.
At system level, average additional ASMA time was 2.5 minutes per arrival in the US in 2015
which was 0.4 minutes lower than in Europe. The result in Europe was significantly affected by
London Heathrow (LHR) which had an additional time of 9.5 minutes per arrival - almost twice
the level of London Gatwick (LGW) with 4.9 minutes per arrival in 2015. In the US, efficiency
levels in the terminal area are more homogenous.
As there are many trade-offs between flight phases, the aggregation of the observed results
enables a high-level comparison of the “benefit pool” actionable by ATM in both systems.
For the interpretation of the observed results, it is important to stress that the determined
“benefit pool” is based on a theoretical optimum (averages compared to unimpeded
times), which is, due to inherent necessary (safety) or desired (capacity) limitations,
clearly not achievable at system level.
Although in a context of declining traffic, system-wide ATM performance improved notably in
the US and in Europe between 2010 and 2015. The resulting savings in terms of time and fuel in
both ATM systems had a positive effect for airspace users and the environment.
The improvement in Europe over the past five years was mainly driven by a notable reduction of
ATM-related departure delay, improvements in taxi-out efficiency, and better en-route flight
efficiency. In this context it is however important to point out that 2010 was a year with
comparatively high delays in Europe due to adverse weather and ATC strikes. The performance
improvement in the US was mainly due to a substantial improvement of taxi-out efficiency,
although average additional time in the taxi-out phase in the US increased again slightly in 2015.
Overall, the relative distribution of the ATM-related inefficiencies associated with the different
phases of flight is consistent with the differences in flow management strategies described
throughout the report. In Europe ATM-related departure delays are much more frequently used
for balancing demand with en-route and airport capacity than in the US, which leads to a notably
higher share of traffic affected but with a lower average delay per delayed flight. Moreover the
share of en-route related Traffic Management Initiatives (TMIs) in Europe is close to 50% while in
the US more than 80% of TMIs are airport-related during 2015.
Consequently, in Europe flights are over twice more likely to be held at the gate or on the
ground for en-route constraints than in the US. For TMIs related to arrival airport constraints the
situation is different. The percentage of delayed flights at the departure gate or on the surface is
slightly higher in the US than in Europe and the delay per delayed flight in the US is almost twice
as high as in Europe. Most of this delay in the US is generally linked to weather-related
constraints at a number of high density airports including, New York (LGA), Chicago (ORD),
Newark (EWR), San Francisco (SFO), New York (JFK), and Philadelphia (PHL).
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1. INTRODUCTION
1.1 Background and objectives
The US-Europe Comparison Report is jointly developed under Annex 2 of the Memorandum of
Cooperation between the United States of America and the European Union signed in 2013 and
managed by a joint European Commission-FAA Performance Analysis Review Committee (PARC).
The EUROCONTROL Performance Review Unit (PRU) and the US Air Traffic Organization
1
(FAA-
ATO) have produced a series of joint performance studies using commonly agreed metrics and
definitions to compare, understand, and improve air traffic management (ATM) performance.
The initial benchmark report comparing operational performance through 2008 was completed
in 2009 [Ref.
1
]. Subsequent benchmark reports comparing ATM performance in the US and
Europe have since been published in 2010, 2012, and 2014 [Ref.
2
]. This report is the 5th in the
series of joint ATM operational performance comparisons between the US and Europe.
1.2 Report Scope
Figure 1-1 shows the geographical scope of this report with the US CONUS subdivided into 20 Air
Route Traffic Control Centers (ARTCCs) and the European area subdivided into 62 en-route
centres
2
.
Figure 1-1: Geographical scope of the comparison in the report
Unless stated otherwise, for the purpose of this report, “Europe” is defined as the geographical
area where the Air Navigation Services (ANS) are provided by the European Union Member
1
The US Air Traffic Organization (ATO) was created as the operations arm of the Federal Aviation Administration
(FAA) in December 2000, to apply business-like practices to the delivery of air traffic services.
2
The map shows European airspace at Flight Level 300. Therefore not all the en-route facilities are visible as some
control lower airspace only.
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20 US CONUS Centers vs. 62 European Area Control Centres (ACCs)
34 Airports tracked for each region
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States plus those States outside the EU that are members of EUROCONTROL
3
, excluding Oceanic
areas, Georgia and the Canary Islands.
Unless otherwise indicated, “US” refers to ANS provided by the United States of America in the
48 contiguous States located on the North American continent south of the border with Canada
plus the District of Columbia, but excluding Alaska, Hawaii and Oceanic areas (US CONUS).
In order to ensure the comparability of operational ATM performance, the analysis scope of this
report was influenced by the need to identify a common set of data sources with a sufficient
level of detail and coverage. Therefore - unless stated otherwise - the detailed analyses of ATM-
related operational performance by phase of flight in Chapter 5 are limited to flights to or from
the main 34 airports for IFR traffic in both the US and in Europe. A detailed list of the airports
included in this report can be found in Annex I.
Although they are within the top 34 airports in terms of traffic in Europe in 2015, Istanbul
Ataturk (IST), Istanbul (SAW), Antalya (AYT), and Warsaw (WAW) airports were not included in
the analysis due to data availability issues.
The 34 main airports used for more detailed performance tracking are also shown in Figure 1-1.
Although these airports remain consistent for the most part, there have been minor changes
since the last comparison report in 2013. In the US, Dallas Love (DAL) and Nashville (BNA) have
replaced Cleveland (CLE) and Raleigh-Durham (RDU).
For the US, many of these high volume airports are located on the coasts or edges of the study
region creating a greater percentage of longer haul flights in the US, especially when only flights
within the study region are considered. The airborne trajectory on these transcontinental flights
may be more affected by the influences of wind and convective weather.
TEMPORAL SCOPE
The operational analyses in this report were carried out for the calendar year 2015 and, where
applicable, comparisons to previous years were made to track changes over time. In particular,
this report contrasts the performance of 2015 versus the performance observed (and reported)
in the 2013 edition of this report.
1.3 Data Sources
Various data sources have been used for the analysis of operational ATM performance. These
data sources include, inter alia, trajectory position data, ATFM imposed delay, key event times
and scheduled data from airlines, and METAR information for weather.
DATA FROM AIR TRAFFIC MANAGEMENT SYSTEMS
Both the US and Europe obtain key data from their respective air traffic flow management
(ATFM) systems. There are two principal sources within ATM. These include trajectory/flight plan
databases used for flight efficiency indicators, and delay databases that record ATFM delay and
often include causal reasons for the delay.
For the US, flight data come from the Traffic Flow Management System (TFMS). In Europe, data
3
The list of EUROCONTROL States can be found in the Glossary.
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are derived from the Enhanced Tactical Flow Management System (ETFMS) of the European
Network Manager. These data sources provide the total IFR traffic picture and are used to
determine the “main” airports in terms of IFR traffic and the flight hour counts used to
determine traffic density.
Both ATFM systems have data repositories with detailed data on individual flight plans and
surveillance track sample points from actual flight trajectories. They also have built-in
capabilities for tracking ATM-related ground delays
by airport and en-route reference location.
The data sets also provide flight trajectories which are used for the calculation of flight efficiency
in terms of planned routes and actual flown routing. The data sets which include data in the en-
route transitional phase and in the terminal areas allow for performance comparison throughout
various phases of flight. This report features an initial assessment of vertical flight efficiency for a
subset of airports based on the aforementioned trajectory data.
DATA FROM AIRLINES
The US and Europe receive operational and delay data from airlines for scheduled flights. This
represents a more detailed subset of the traffic flow data described above and is used for
punctuality or phase of flight indicators where more precise times are required.
These data include what is referred to as OOOI (Gate Out, Wheels Off, Wheels On, and Gate In)
times which are recorded to 1 minute resolution. OOOI data along with airline schedules allow
for the calculation of gate delay, taxi times, en-route times, and gate arrival time delay on a flight
by flight basis. The data also contains cause codes for delays on a flight-by-flight basis.
In the US, most performance indicators are derived from the Aviation System Performance
Metrics (ASPM) database which fuses detailed airline data with data from the traffic flow
management system (TFMS). Air carriers are required to report performance data if they have at
least 1% of total domestic scheduled-service passenger revenues. In addition there are other
carriers that report voluntarily. ASPM coverage in 2015 was approximately 94% of the IFR traffic
at the main 34 airports with 87% of the total IFR traffic reported as scheduled operations.
Airline-reported performance data for traffic at the main 34 airports represent 71% of all IFR
flights at these airports. This percentage as well as the specific carriers that report does not stay
constant from reporting period to reporting period and this has some effect on the performance
indicators based on OOOI data. For the US, this effect was most pronounced for airports with
high use by American Airline (AAL) in which OOOI coverage increased (CLT, ORD). There was also
some effect for airports such as Detroit (DTW) with high use of certain regional carriers such as
Endeavor Air (FLG) which moved from reporting to non-reporting from 2013-2015.
In Europe, the Central Office for Delay Analysis (CODA) collects data from airlines each month.
The data collection started in 2002 and the reporting was voluntary until the end of 2010. As of
January 2011, airlines which operate more than 35 000 flights per year
4
within the European
Union (EU) airspace are required to submit the data on a monthly basis according to EU
Regulations [Ref.
3
]. In 2015, the CODA coverage was approximately 62% of total IFR flights and
approximately 74% of flights at the 34 main airports.
A significant difference between the two airline data collections is that the delay causes in the
US relate to arrivals, whereas in Europe they relate to the delays experienced at departure.
4
Calculated as the average over the previous three years.
P a g e | 13
ANS PERFORMANCE DATA
This comparison study builds on the data describing the ANS operations within the
aforementioned scope of the US and European region. Within the field of air transport statistics
a variety of sources report on air traffic. Care has to be taken when comparing the data from
different sources, as data collection and reporting requirements entail different conventions
concerning the breakdown of the data in terms of flight operations, type of flights, etc.
Within the US, the Bureau of Transportation Statistics (BTS) establishes air traffic related data
and statistics for the purpose of analysing the US air transportation market. The underlying
statistical data collection process accounts for flights of US carriers with an annual revenue of
20M USD or more and flights of foreign carriers with more than 10 000 passengers per month
(to/from the US).
Across Europe, different sources report on air traffic statistics also for the purpose of market
analysis. For example, Eurostat reports on air traffic observed at EU-28 level, while different
States (typically the national civil aviation authorities or associated statistics agencies) report
traffic at the national level with varying granularity levels or breakdowns.
The data sets used in this study are derived from the aforementioned systems and ensure
comparability of the data with respect to the provision of air navigation services and operational
ANS performance.
ADDITIONAL DATA ON CONDITIONS
Post-operational analysis should identify the causes of delay and a better understanding of real
constraints. In identifying causal factors, additional data is needed for airport capacities, runway
configurations, sector capacities, winds, visibility, and convective weather. For this report,
airport capacities and meteorological data have been used (see Chapter 3).
1.4 European and FAA Performance Reporting
Both FAA and European ANSPs have their own reporting requirements. Some Key Performance
Indicators (KPIs) such as ATM attributable delay are common to both groups using calculations
and underlying databases that are very similar. There are other indicators that are common but
have different priorities in terms of reporting status and/or regulation. For example, European
indicators use horizontal trajectory efficiency and ATFM delay for official target setting whereas
FAA management focuses on Capacity and Capacity Efficiency for official targets. FAA, under
RTCA and the NextGen Advisory Committee (NAC) also report Block Time, Track Distance,
Throughput, Taxi-out Time and Gate Departure Delay [Ref.
4
]. These metrics, using definitions
that have been harmonized for joint EU/US benchmarking are part of later chapters of this
report.
The report examines several operational key performance indicators derived from comparable
databases for both EUROCONTROL and the Federal Aviation Administration (FAA).
KEY PERFORMANCE AREAS (KPAS) AND KEY PERFORMANCE INDICATORS (KPIS)
Comparisons and benchmarking require common definitions and understanding. Hence the work
in this report draws from commonly accepted elements of previous work from ICAO, the FAA,
EUROCONTROL and CANSO. An outcome of these performance evaluations is the development
of harmonized key performance indicators (KPIs) that can be used for international
benchmarking. The KPIs used in this report are associated with ICAO’s Key Performance Areas
(KPAs) and are developed using the best available data from both the FAA-ATO and the
EUROCONTROL Performance Review Unit (PRU).
P a g e | 14
In its Manual on Global Performance of the Air Navigation System [Ref.
5
], ICAO identified eleven
Key Performance Areas (KPAs) of interest in understanding overall ATM system performance:
Access and Equity, Capacity, Cost Effectiveness, Efficiency, Environmental Sustainability,
Flexibility, Global Interoperability, Predictability, Participation, Safety, and Security.
At the time of writing this report, ICAO is in the process of updating the Global Air Navigation
Plan (GANP, ICAO Doc 9750 [Ref.
6
]). As part of this update, the 2016 ICAO assembly will endorse
the recognition of ATM performance monitoring. The 2016 update to the GANP includes
documentation for 16 potential KPIs that are recommended for tracking performance
improvements and identifying performance shortfalls. The US/Europe comparison reports
provide demonstrated application for many of these indicators. The reports also show how
common indicators can be used to benchmark performance across facilities and across ICAO
regions.
This report addresses the Key Performance Areas that relate to the operational efficiency of the
ATM system. These are the KPAs of Capacity, Efficiency, Predictability, and Environmental
Sustainability as it is linked to Efficiency when evaluating additional fuel burn.
Table 1-1 provides an overview of the harmonized KPIs used in this report that are associated
with the ICAO KPAs. Many of these indicators are linked. All flight efficiency indicators have a
degree of variability which may be reported as a KPI for Predictability.
Table 1-1: US/Europe Harmonized Key Performance Indicators
Key Performance Area
Key Performance Indicator
Capacity
Declared Airport Capacity
Maximum Airport Throughput
Efficiency
Airline-Reported Delay Against Schedule
Airline-Reported Attributable Delay
En-route and Airport ATM-Reported Attributable Delay
Taxi-Out Additional Time
Horizontal En-Route Flight Efficiency (flight plan and actual)
Additional Time in Terminal Airspace
Taxi-In Additional Time
Predictability
Airline-Reported Arrival and Departure Punctuality
Capacity Variability
Phase of Flight Time Variability
In addition to the KPIs listed in Table 1-1, this report also provides a series of related indicators
that help to explain why a KPI improved or became worse over time. These related indicators do
not fit the standard ICAO KPA framework. However they are typical indicators that would be
monitored by an ANSP to help explain how external factors may influence the core KPIs. These
Related Indicators principally address operator demand and weather. Table 1-2 below shows the
main related indicators reported.
Table 1-2: US/Europe - related indicators
Related Area
Related Indicator
Traffic/Schedules
System IFR Flight Counts
System IFR Flight Distance
Facility IFR Flight Counts
Traffic Density
Traffic Variability
Schedule Block Time
Seat capacity on scheduled flights
Weather
Operations by Met Condition
Delay by Met Condition
System Characteristics
System size & structure
P a g e | 15
1.5 Organisation of this report
The report is organised into seven chapters:
Chapter 1 contains the introduction and provides some background on report objectives,
scope and data sources used for the analyses for ATM performance in this report. It also
lists the Key Performance Indicators and related indicators that are studied in this report.
Chapter 2 provides background information on the two ATM systems that may also be
used to explain differences in the core KPIs. These include differences in air traffic flow
management techniques as well as external factors such as weather and capacity
restrictions which can be shown to have a large influence on performance.
Chapter 3 provides a quantitative overview of the indicators that may externally influence
the KPIs related to ATM performance. These are principally related to changes in traffic
levels, traffic peaks, capacity at the aerodrome, and meteorological conditions.
Chapter 4 provides a comparison of airline-related KPIs. These indicators assess delay and
operational service quality as it relates to the airline schedule. It includes the causal
reasons for delay as provided by the airlines.
Chapter 5 provides a detailed comparison of the ATM-related KPIs focusing on ATFM delay
and the efficiency of actual operations by phase of flight. It includes causal reasons for
delay as provided by the ANSP.
Chapter 6 introduces two supporting studies aimed at further expanding the scope and
the level of analysis of the U.S. / Europe comparison of operational performance. The first
study analyses air traffic flow and capacity management in more detail and the second
study addresses vertical flight efficiency in the arrival phase.
Chapter 7 concludes with a summary of findings.
P a g e | 16
2. COMPARISON OF AIR TRAFFIC MANAGEMENT (ATM) IN THE US AND EUROPE
This section provides background information on both the US and European ATM systems that
may be used to explain similarities and differences in the KPIs used throughout this report. This
section starts with a comparison in terms of physical geographic airspace and organisation of
ATM.
2.1 Organisation of ATM
While the US and the European system are operated with similar technology and operational
concepts, there is a key difference. The US system is operated by one single service provider
using the same tools and equipment, communication processes and a common set of rules and
procedures. Although ATFM and ASM in Europe are provided/coordinated centrally by the
Network Manager, at the ATC level the European system is much more fragmented and the
provision of air navigation services is still largely organised by State boundaries.
In total, there are 37 different en-route ANSPs of various geographical areas. Historically, they
have been operating different systems under slightly different sets of rules and procedures.
Since 2004, the Single European Sky (SES) initiative of the European Union aims at reducing this
fragmentation. It provides the framework for the creation of additional capacity and for
improved efficiency and interoperability of the ATM system in Europe.
2.2 Airspace management (ASM) and design
In the US the Federal Aviation Administration (FAA) is responsible for airspace management and
route design, whereas in the amalgamated European ATM system, airspace management was
traditionally the prerogative of the individual States.
In the current system, the design of airspace and related procedures is no longer carried out or
implemented in isolation in Europe. Inefficiencies in the design and use of the air route network
are considered to be a contributing factor towards flight inefficiencies in Europe, therefore the
development of an integrated European Route Network Design is one of the tasks given to the
Network Manager
5
. This is done through a CDM process involving all stakeholders.
A further challenge is the integration of military objectives and requirements which need to be
fully coordinated within the respective ATM system. To meet their national security and training
requirements while ensuring the safety of other airspace users, it is occasionally necessary to
restrict or segregate airspace for exclusive use which may conflict with civilian objectives to
improve flight efficiency as flights must then detour around these areas. To accommodate the
increasing needs of both sets of stakeholders, in terms of volume and time, close civil/military
cooperation and coordination across all ATM-related activities is a key requirement.
In terms of the organisation of the civil/military cooperation, the US and Europe both apply a
similar model:
5
EU Regulation 677/2011 defines the tasks of the Network Manager. The main ones are: the provision of ATFCM
services, the development of an integrated European Route Network Design, providing the central function of
radio frequency allocation, coordinating improvements to SSR code allocation, and providing support for network
crisis management.
P a g e | 17
In the US, the DoD Policy Board on Federal Aviation (PBFA) is the single voice of the
military services in communicating the DoD position on airspace policy and air traffic
management as both a global air navigation service provider and user; at the operational
level the FAA headquarters is the final approval authority
6
for all permanent and
temporary Special Use Airspace (SUA)
7
, and operations are organised according to a
common set of rules.
In Europe, the European Defence Agency (EDA) represents the interests of military
aviation in the development of the Single European Sky; at the operational level, through
the implementation of the Flexible Use of Airspace (FUA) concept – which is included in
EU legislation since 2005 [Ref.
7
] – the Network Manager coordinates civil and military
requirements through a dynamic CDM process which culminates in the publication of the
daily European Airspace Use Plan (AUP) on D-1 and Updated Airspace Use Plans (UUP) on
the day of operations. The AUP and UUP activate Conditional Routes and allocate
Temporary Segregated Areas and Cross-Border Areas for specific periods of time.
Looking at the map, the comparison of SUA between the US and Europe (in Europe generally
referred to as segregated airspace) in Figure 2-1 illustrates a significant difference in the number
and location of the special use airspace within the respective ATM systems
8
. It is to be
emphasised that these airspace volumes are not all active at the same time, because they are
managed flexibly.
Figure 2-1: Comparison of Special Use Airspace (SUA)
Europe clearly shows a larger number of SUA than the US with quite a number being located
directly in the core area of Europe and potentially affecting the flow of civil air traffic. In the US,
SUA tends to be more located along the coastlines allowing for less constrained transcontinental
connections.
6
FAA Order JO 7400.2J – Part 5 Chapter 21
7
Airspace of defined dimensions identified by an area on the surface of the earth wherein activities must be
confined because of their nature and/or wherein limitations may be imposed upon aircraft operations that are not
a part of those activities. Often these operations are of a military nature.
8
Based on Aeronautical Information Publication (AIP) data available from the European AIS Database (EAD).
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Density and Special Use Airspace
(flight Hr per Sq.Km)
< 1
< 2
< 3
< 4
< 5
>= 5
Special Use Airspace
P a g e | 18
2.3 Air traffic flow management (ATFM) and air traffic control (ATC)
ATFM is a function of air traffic management (ATM) established with the objective of
contributing to a safe, orderly, and expeditious flow of traffic while minimizing delays. The
purpose of ATFM is to avoid safety risks associated with overloaded ATC sectors by regulating
traffic demand according to available capacity. When ATFM also includes a capacity
management function, it is called ATFCM. At the tactical level, ATC also plays a role in flow
management.
This section compares the similarities and differences between the US and Europe in terms of
facility organization and the strategies for balancing demand and capacity.
2.3.1 ATFM AND ATC FACILITY ORGANIZATION
Both the US and Europe have established system-wide, centralised traffic management facilities
to ensure that traffic flows do not exceed what can be safely handled by ATC units, while trying
to optimise the use of available capacity. Table 2-1 provides an overview of the key players
involved and the most common ATFM techniques applied [Ref.
8
].
Table 2-1: Organisation of ATFM (Overview)
The key difference is that the European ATM system is an amalgamation of a large number of
individual ANSPs whereas the US system is operated by a single ANSP.
There are 20 Air Route Traffic Control Centres (ARTCC) in the US CONUS compared to 62 ACCs in
Europe
9
. Figure 2-2 depicts the size of the 20 US ARTCCs compared to the 20 largest ACCs in
Europe, in terms of average daily IFR flights.
9
For Europe, a 63
rd
en-route center is located in the Canaries, outside of the geographical scope of the study. In the
US, 3 additional en-route centers are operated by the FAA, outside of the US CONUS.
Tactical
DEP. RESTRICTIONS
(GROUND HOLDING)
ROUTING,
SEQUENCING, SPEED
CONTROL, HOLDING
AIRBORNE HOLDING
(CIRCULAR, LINEAR),
VECTORING
EN ROUTE
ORIGIN
AIRPORT
DESTINATION
AIRPORT
AIRPORT SCHEDULING
(DEPARTURE SLOT)
STRATEGIC
AIRPORT SCHEDULING
(ARRIVAL SLOT)
STRATEGIC
TAKE-OFF
APPROACH
Tower
control
En route
Area control
Terminal
control
LOCAL ATC
UNITS
LANDING
TAXI-IN
TAXI-OUT
Ground
control
ATFM
MEASURES
FLIGHT
PHASE
Tower
Ground
US EUROPE
Air Route Traffic
Control Center
(ARTCC): 20
US CONUS
Area
Control Centre
(ACC): 62
Terminal Radar
Approach Control
(TRACONs):
Stand-alone: 26
(US CONUS)
Collocated: 134
Approach Control
units
(APPs):
Stand-alone: 16
Collocated: 262
Airports with
ATC services:
517
Airports with
ATC services:
415
NETWORK (ATFM)
US EUROPE
Air traffic
Control
System
Command
Center
(ATCSCC)
located in
Warrenton,
Virginia.
Eurocontrol
Network
Operations
Centre
(NMOC),
located in
Brussels,
Belgium
(formerly -
CFMU).
P a g e | 19
Figure 2-2: Comparison of en-route area control centres (2015)
A further key difference between the two systems is the role of the network ATFM function. The
fact that the ATM system in the US is operated by a single provider puts the Air Traffic Control
System Command Center (ATCSCC) in a much stronger position with more active involvement of
tactically managing traffic on the day of operations than is the case in Europe.
As far as traffic management issues are concerned, there is a clear hierarchy in the US. Terminal
Radar Approach Control (TRACON) units work through the overlying ARTCC which coordinate
directly with the ATCSCC in Virginia. The ATCSCC has final approval authority for all national
traffic management initiatives in the US and is also responsible for resolving inter-facility issues.
In Europe, the Network Manager Operations Centre (NMOC) in Brussels monitors the traffic
situation and proposes flow measures which are coordinated through a CDM process with the
local authority. Usually the local Flow Management Positions (FMP), embedded in ACCs to
coordinate the air traffic flow management in the area of its responsibility, requests the NMOC
to implement flow measures.
In 2009, the role of the network function in Europe was strengthened by the second package of
Single European Sky (SES) legislation
10
. This evolution foresees a more proactive role in Air Traffic
Flow Management, ATC capacity enhancement, airspace structure development and the support
to the deployment of technological improvements across the ATM network for the European
Network Manager.
10
The SES I legislation adopted in 2004 was revised and extended by the SES II package in 2009 aimed at increasing
the overall performance of the air traffic management system in Europe, shifting the focus from capacity to
performance in general. The SES II package also introduced the comprehensive performance scheme with target-
setting at EU-level.
0 2000 4000 6000 8000
London AC
Karlsruhe
Maastricht
Langen
Paris
Munchen
Marseille
Rome
Brest
Reims
Scottish
Madrid
Bordeaux
Istanbul
Ankara
Barcelona
Zurich
Wien
Padova
Praha
Average daily IFR flights in the top 20 en route area control centres (2015)
0 2000 4000 6000 8000
WASHINGTON (ZDC)
ATLANTA (ZTL)
NEW YORK (ZNY)
JACKSONVILLE (ZJX)
CHICAGO (ZAU)
CLEVELAND (ZOB)
LOS ANGELES (ZLA)
FORT WORTH (ZFW)
INDIANAPOLIS (ZID)
BOSTON (ZBW)
MIAMI (ZMA)
MEMPHIS (ZME)
HOUSTON (ZHU)
MINNEAPOLIS (ZMP)
KANSAS CITY (ZKC)
DENVER (ZDV)
ALBUQUERQUE (ZAB)
OAKLAND (ZOA)
SALT LAKE CITY (ZLC)
SEATTLE (ZSE)
Source: PRU; FAA-ATO
P a g e | 20
2.3.2 DEMAND CAPACITY BALANCING (DCB)
In order to minimize the effects of ATM system constraints, the US and Europe use a comparable
methodology to balance demand and capacity
11
. This is accomplished through the application of
an “ATFM planning and management” process, which is a collaborative, interactive capacity and
airspace planning process, where airport operators, ANSPs, Airspace Users (AUs), military
authorities, and other stakeholders work together to improve the performance of the ATM
system (see Figure 2-3).
This CDM process allows AUs to optimize their participation in the ATM system while mitigating
the impact of constraints on airspace and airport capacity. It also allows for the full realization of
the benefits of improved integration of airspace design, ASM and ATFM. The process contains a
number of equally important phases:
ATM planning
ATFM execution
o Strategic ATFM
o Pre-tactical ATFM
o Tactical ATFM
o Fine-tuning of traffic flows by ATC (shown in Figure 2-3 as Optimized operations)
Traffic Management Initiatives (TMIs) that have an impact on traffic prior to take-off
TMIs acting on airborne traffic
Post-operations analysis.
A detailed description and comparison of the different phases – including an overview of the
various TMIs used on both sides of the Atlantic can be found in Annex II.
Figure 2-3: Generic ATFM process (ICAO Doc 9971)
11
In line with the guidance in ICAO Doc 9971 (Manual on Collaborative Air Traffic Flow Management).
P a g e | 21
3. EXTERNAL FACTORS AFFECTING KEY PERFORMANCE INDICATORS
This chapter describes and quantifies the effects of some of the key external factors that impact
the primary Key Performance Indicators. These related indicators focus on changing traffic
levels, airport capacity, and weather in the US and Europe. In addition to external factors, the
way the ATM system is managed with the US having a single provider compared to the European
system of multiple ANSPs can also influence the resulting KPIs. These differences in the ATM
systems are addressed in more detail in Chapter 2.
3.1 Traffic characteristics in the US and in Europe
This section provides some key air traffic characteristics of the ATM system in the US and in
Europe to provide some background information and to ensure comparability of traffic samples.
Table 3-1: US/Europe ATM key system figures at a glance (2015)
Calendar Year 2015
Europe
12
USA
13
US vs. Europe
Geographic Area (million km
2
)
11.5
10.4
≈ -10%
Nr. of civil en-route Air Navigation Service Providers
37
1
Number of Air Traffic Controllers (ATCOs in Ops.)
17 370
13 138
14
≈ -24%
Number of OJT/developmental ATCOs
960
1 959
≈ +104%
Total ATCOs in OPS plus OJT/developmental
18 330
15 097
≈ -18%
Total staff
56 300
31 501
≈ -44%
Controlled flights (IFR) (million)
9.8
15.3
≈ +57%
Flight hours controlled (million)
14.8
23.1
≈ +56%
Relative density (flight hours per km
2
)
1.3
2.2
≈ x1.7
Share of flights to or from top 34 airports
64%
62%
Share of General Aviation
3.7%
22%
Average length of flight (within respective airspace)
575 NM
524 NM
≈ -9%
Number of en-route facilities
62
23
15
-39
Number of stand-alone APP/TRACON units
16
27
16
+11
Number of APP units collocated with en-route or TWR fac.
262
134
-128
Number of airports with ATC services
415
517
17
+102
Of which are slot controlled
> 100
18
4
19
Number of FMPs (Europe) / TMUs (US)
20
51
≈65
≈ +14
Source
EUROCONTROL
FAA/ATO
12
EUROCONTROL States, excluding Oceanic areas, Georgia and Canary Islands. European staff numbers and facility
count refer to 2014 which is the latest year available.
13
Area and flight hours refer to CONUS only. Centre count refers to the NAS.
14
This value reflects the CANSO reporting definition of a fully trained ATCO in OPS and includes supervisors. It is
different than the total controller count from the FAA controller workforce plan which does not include
supervisors. The number of ATCOs in OPS does not include 1 292 controllers reported for contract towers.
15
20 en-route centers (ARTCCs) are in the US CONUS, 3 are outside.
16
26 stand-alone TRACONs are in the US CONUS, 1 is outside (Alaska).
17
Total of 517 facilities of which 264 are FAA staffed and 253 Federal contract towers.
18
IATA Level 2: ±70. IATA Level 3: ±100.
19
IATA Level 2: ORD, LAX, MCO, SFO. IATA Level 3: JFK, EWR (EWR will become Level 2 as of winter 2016). In addition
restrictions exist at DCA and LGA based on Federal and local rules.
20
FMPs and TMUs are the local ATFCM partners for the collaborative process with the NMOC and ATCSCC
respectively.
P a g e | 22
As shown in Table 3-1, the total surface of continental airspace analysed in the report is similar
for Europe and the US. However, the US controls approximately 57% more flights operating
under Instrument Flight Rules (IFR)
21
with less Air Traffic Controllers (ATCOs)
22
and fewer en-
route and terminal facilities.
Using the definition employed by the ACE and CANSO benchmarking reports which excludes
those designated as “on-the-job training” in Europe or as a “developmental” at the FAA, the US
operated with some 24% less full time ATCOs than Europe in 2014/2015.
For the ATM system, Europe is more fragmented and operates with more physical facilities than
the US. Currently the European study region comprises 37 ANSPs (and a similar number of
different regulators), 62 Area Control Centres (ACC) and 16 stand-alone Approach Control (APP)
units (total: 78 facilities). The US has one ANSP and the US CONUS is served by 20 Air Route
Traffic Control Centres (ARTCC) supplemented by 26 stand-alone TRACONs providing services to
multiple airports (total: 46 facilities). In addition the US has 134 Approach Control Facilities
combined with Tower services; Europe has 262 collocated APP units.
A notable difference illustrated in Table 3-1 is the low number of airports with schedule or slot
limitations in the US compared to Europe, where most of the airports are slot-coordinated.
Notwithstanding the large number of airports in the US and Europe, only a relatively small
number of airports account for the main share of traffic. The main 34 airports account for
approximately 64% of the controlled flights in Europe and the US.
3.1.1 AIR TRAFFIC GROWTH
Figure 3-1 depicts the evolution
of IFR traffic in the US and in
Europe between 2000 and
2015.
There was a notable decoupling
in 2004 when the traffic in
Europe continued to grow
while US traffic started to
decline. Whereas traffic in
Europe grew by 15.5% between
2000 and 2015, the traffic in
the US declined by -13.8%
during the same period.
Figure 3-1: Evolution of IFR traffic in the US and in Europe
Although traffic in the US CONUS grew by 1.6% from 2013-2015, the traffic at the main 34
airports was unchanged over this time period (Figure 3-9 below). The effect of the economic
crisis starting in 2008 is clearly visible on both sides of the Atlantic.
The system level averages mask contrasted growth rates within the US and Europe as illustrated
in the map in Figure 3-2. Traffic growth in Europe shows a contrasted picture between the more
21
Although not included in this study, the US also handles significantly more Visual Flight Rules (VFR) traffic.
22
ATCO’s refer to civil ATCOs – military ATCOs with a civil license were not considered in the report.
75
80
85
90
95
100
105
110
115
120
125
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Traffic index (2000=100)
Evolution of IFR traffic in the US and in Europe
US Europe
Source: EUROCONTROL/ FAA
+15.5%
vs. 2000
-13.8%
vs. 2000
P a g e | 23
mature markets in Western Europe and the emerging markets in Central & Eastern Europe which
shows a substantial growth. Also the notable shift of traffic following the tragic loss of MH17 in
Ukrainian airspace in July 2014 and the resulting airspace closure contributed to some of the
observed high growth rates in States affected by changed traffic flows.
Figure 3-2: Evolution of IFR traffic in the US and in Europe (2015 vs. 2010)
The US is a more homogenous and mature market which shows a different behaviour. Compared
to 2010, traffic levels stayed relatively constant, aside from the Florida centers, which
experienced a stronger growth. The traffic growth at the main airports in the US and Europe is
shown in Figure 3-8 and Figure 3-9 on page 29 respectively.
3.1.2 AIR TRAFFIC DENSITY
Figure 3-3 shows the traffic density in US and European en-route centres measured in annual
flight hours per square kilometre for all altitudes in 2015. For Europe, the map is shown at the
State level because the display by en-route centre would hide the centres in lower airspace.
Figure 3-3: Traffic density in the US and in Europe (2015)
In Europe, the “core area” comprising of the Benelux States, Northeast France, Germany, and
Switzerland is the densest and most complex airspace.
Traffic 2010 vs 2015
< -10%
< -5%
<= 0%
> 0%
> 5%
> 10%
> 15%
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Density
(flight Hr per Sq.Km)
< 1
< 2
< 3
< 4
< 5
>= 5
P a g e | 24
Similarly in the US, the centrally located centres of Cleveland (ZOB), Chicago (ZAU), Indianapolis
(ZID), and Atlanta (ZTL) have flight hour densities of more than twice the CONUS-wide average.
The New York Centre (ZNY) appears less dense due to the inclusion of a portion of
coastal/oceanic airspace. If this portion was excluded, ZNY would be the centre with the highest
density in the US.
3.1.3 AVERAGE FLIGHT LENGTH
Table 3-2 provides a more detailed breakdown of IFR traffic for the US and Europe in 2015. The
average great circle distances shown in Table 3-2 refer only to the distances flown within the
respective airspace and not the length of the entire flight.
Table 3-2: Breakdown of IFR traffic
The table is broken into two parts which both show similar trends. The top portion shows all
flights while the lower focuses on traffic to or from the main 34 airports. The population of
flights in the lower part of the table (traffic to or from the main 34 airports) is the basis for many
of the metrics in this report.
By far the largest share of total IFR traffic in both systems is due to traffic within the respective
region. In the US this share is 83.9% compared to 78.4% in Europe. When all IFR flights including
overflights are taken into account, the average flight length in Europe is 575 NM compared to
524 NM in the US.
However, this changes when only “domestic” flights within the respective regions are
considered. For example, en-route efficiency indicators shown later in Section 5.2.3 use “within
region” traffic to or from the main 34 airports (lower part of Table 3-2). For this population, the
average flight length in the US is 625 NM compared to 581 NM in Europe. This is due mainly to
the large amount of transcontinental traffic in the US system.
For the US, a significant amount of “Outside Region” traffic have a coastal airport as a final
destination or traverse a significant distance through Canada before entering US airspace. For
Europe, the “Outside Region” traffic is less concentrated at coastal entry airports but more
scattered with direct long haul flights to worldwide destinations from almost every capital city
airport. For instance, a flight from London Heathrow (LHR) to the Middle East would traverse
almost the entire European airspace before exiting the airspace. As a consequence, the average
distance of those flights is considerably higher in Europe than in the US.
ALL IFR TRAFFIC
N % of tota l
Avg. dis t.
(NM)
N % of tota l
Avg. dis t.
(NM)
Wi thin region 7.8 M 78.4% 506 NM 12.8 M 83.9% 524 NM
To/from outs ide region 1.9 M 19.5% 801 NM 2.1 M 14.0% 530 NM
Overfli ghts 0.2 M 2.2% 809 NM 0.3 M 2.1% 489 NM
Total IFR traffic 9.9 M 100% 575 NM 15.3 M 100.0% 524 NM
N % of tota l
Avg. dis t.
(NM)
N % of tota l
Avg. dis t.
(NM)
Wi thin region 5.1 M 80.5% 504 NM 8.2 M 82.9% 639 NM
To/from outs ide region 1.2 M 19.5% 878 NM 1.7 M 17.1% 558 NM
Total 6.3 M 100% 581 NM 9.9 M 100.0% 625 NM
EUROPE (2015)
US CONUS (2015)
Traffic to/from main 34 airports
EUROPE (2015)
US CONUS (2015)
P a g e | 25
3.1.4 SEASONALITY
Seasonality and variability of air traffic demand can be a factor affecting ATM performance. If
traffic is highly variable, resources may be underutilised during off-peak times but scarce at peak
times. Figure 3-4 compares the seasonal variability (relative difference in traffic levels with
respect to the yearly averages) and the “within week” variability.
Figure 3-4: Seasonal traffic variability in the US and Europe (system level)
Whereas weekly traffic profiles in Europe and the US are similar (lowest level of traffic during
weekends), the seasonal variation is higher in Europe. European traffic shows a clear peak during
the summer months. Compared to average, traffic in Europe is in summer about 15% higher
whereas in the US the seasonal variation is more moderate.
Figure 3-5 shows the seasonal traffic variability in the US and in Europe for 2015. In Europe, a
very high level of seasonal variation is observed for the holiday destinations in South Eastern
Europe where a comparatively low number of flights in winter contrast sharply with high
demand in summer.
Figure 3-5: Seasonal traffic variability in the US and in Europe (2015)
In the US, the overall seasonality is skewed by the high summer traffic in northern en-route
centres (Boston, Chicago, and Minneapolis) offsetting the high winter/spring traffic of southern
centres (Miami and Jacksonville) (see Figure 3-5).
0.8
0.9
1.0
1.1
1.2
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
flights relative to average
Europe US (CONUS)
0.8
0.9
1.0
1.1
1.2
Sun
Mon
Tue
Wed
Thu
Fri
Sat
flights relative to average
Within week Within year
Source: EUROCONTROL/ FAA
Traffic variability in Europe and the US (2015)
(Within respective region)
Traffic variability
(Peak week vs avg week)
<= 1.15
> 1.15
> 1.25
> 1.35
> 1.45
P a g e | 26
3.1.5 TRAFFIC MIX
A notable difference between the US and Europe is the share of general aviation which accounts
for 22% and 3.7% of total traffic in 2015, respectively (see Table 3-1 on page 21). This is
confirmed by the distribution of physical aircraft classes in Figure 3-6 which shows a large share
of smaller aircraft in the US for all IFR traffic (left side of Figure 3-6).
Figure 3-6: Comparison by physical aircraft class (2015)
In order to improve comparability of data sets, the more detailed analyses in Chapters 4 and 5
are limited to controlled IFR flights either originating from or arriving to the main 34 US and
European airports (see Annex I). The samples are more comparable when only flights to and
from the 34 main airports are analysed as this removes a large share of the smaller piston and
turboprop aircraft (general aviation traffic), particularly in the US. Traffic to or from the main 34
airports in 2015 represents some 64% of all IFR flights in Europe and in the US.
Figure 3-7 shows the evolution of the number of average seats per scheduled flight in the US and
in Europe, based on data for passenger aircraft.
Figure 3-7: Average seats per scheduled flight (2005-2015)
6% 11%
7%
14%
3%
2%
34%
56%
49%
60%
50%
66%
31%
15%
34%
17%
37%
20%
12%
13%
6%
9%
7%
11%
11.7%
1.9%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
US
EUR
US
EUR
US
EUR
Other
Piston
Turboprop
Jet Light (<7t)
Jet Medium (7t<>50t)
Jet Large (50t<>136t)
+757
Jet Heavy (>136t)
All IFR flights
Comparison by physical aircraft class (2015)
Traffic to or from
34 main airports
Traffic to or from 34
main airports (US
domestic, Intra EU)
90
95
100
105
110
115
120
125
130
135
140
145
150
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
avg. seats per IFR flight
Average seats per scheduled flight (2005-2015)
Scheduled Services (Main 34)
Scheduled Services (All)
90
95
100
105
110
115
120
125
130
135
140
145
150
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Scheduled Services (Main 34)
Scheduled Services (All)
Intra-European
Domestic US (conus)
Source: FAA/ PRU analysis
P a g e | 27
For 2015, the average number of seats per scheduled flight is 28% higher in Europe for traffic to
or from the main 34 airports. This is consistent with the observation in Figure 3-6 showing a
higher share of larger aircraft in Europe.
Whereas in Europe the average number of seats per flight increased continuously between 2005
and 2015, the number of seats per aircraft declined in the US between 2008 and 2010.
However, recent US trends since 2010 point to an increase in aircraft gauge. Figure 3-7 indicates
the potential for growing the number of US passengers with relatively flat or modest growth in
operations.
The notable difference observed in aircraft gauge in the two regions is tied to the different
practices of airlines, which are linked to demand, market competition, and other factors [Ref.
9
].
An increasing number of European low cost carriers are utilising a high density one-class seat
layout compared to a standard two-class configuration preferred by US carriers. Additionally,
since only a few US airports are slot restricted, this enables airlines to increase the frequency of
service (with smaller aircraft) to win market share and to attract high yield business travellers.
The notable increase in the US since 2013 is assumed to be the result of consolidation that
resulted, on average, in fewer frequencies but with larger aircraft.
3.2 Airport operations and changes in airport capacity
The system wide and facility level performance indicators shown in Chapters 4 and 5 are driven
by airport operations (demand), airport capacity and the imbalance that can occur between
demand and capacity. Facilities with a) high levels of operations; b) demand that is near
capacity, or; c) having capacity that is highly variable, i.e. unpredictable, will tend to form the
dominant contributors to system performance. Understanding changes in these factors can also
help in understanding year over year changes. This section, along with Section 3.3 on weather,
provides a quantification of these related factors influencing the reported KPIs.
Airport operations depend upon a number of factors as well as on interactions between them
which all affect runway capacity to some degree. In addition to physical constraints, such as
airport layout, there are “strategic” factors such as airport scheduling and “tactical” factors
which include, inter alia, the sequencing of aircraft and the sustainability of throughput during
specific weather conditions.
Safe operation of aircraft on the runway and in surrounding airspace is the dominant constraint
of runway throughput. Airport layout and runway configuration, traffic mix, runway occupancy
time of aircraft during take-off and landing, separation minima, wake vortex, ATC procedures,
weather conditions and environmental restrictions - all affect the throughput at an airport.
The runway throughput is directly related to the time needed to accommodate each flight safely.
The separation requirements in segregated mode
23
depend on the most constraining of any one
of the three parameters: (1) wake vortex separation, (2) radar separation, or (3) runway
occupancy time. The challenge is to optimise final approach spacing in line with wake vortex and
radar separation requirements so that the spacing is close to runway occupancy time. For mixed
mode runway operations
24
, throughput is driven by inter arrival spacings into which departures
are interleaved.
23
Applies to dual runway systems where runways are used exclusively for landing or departing traffic.
24
Landing and departing aircraft are mixed on the same runway.
P a g e | 28
ENVIRONMENTAL CONSTRAINTS
One of the major challenges of airport communities is the need to balance airport capacity
requirements with the need to manage aircraft noise and negative effects on the population in
the airport vicinity. Quite a number of airports in Europe operate under some environmental
constraints which invariably affect runway throughput, the level of complexity and therefore,
ATM performance.
The main affecting factors are (1) Noise Preferential Routes and Standard Instrument Departure,
(2) Restrictions on runway mode of operations and configurations, and (3) night noise
regulations. In the early morning, night noise curfews might even result in considerable arrival
holding with a negative impact on fuel burn and thence CO
2
emissions.
More work is required to better understand the differences in the impact of environmental
constraints on ATM performance in Europe and the US (i.e. how noise and emissions are handled
in the two systems and the potential impact on performance).
3.2.1 AIRPORT LAYOUT AND OPERATIONS AT THE MAIN 34 AIRPORTS
The number of operations which can be safely accommodated at an airport not only depends on
the number of runways but also to a large extent on runway layout and available configurations
(many runways may not be operated independently). The choice of the configuration depends
on a number of factors including weather conditions and wind direction, type of operation
(arrival/ departure peak) and environmental considerations such as noise constraints. The
configuration, combined with environmental restrictions, as well as apron and terminal airspace
limitations affect the overall capacity of the airport.
Some of the key factors determining runway throughput are the distance between runways
(dependent or independent
25
), the mode of operation (mixed
26
or segregated
27
), and
geographical layout (intersecting runways, crossing taxiways).
Although some airports technically have a
large number of runways, operational data
shows that the applied configurations
restrict the type of operations and runways
to be used at any one time.
For this reason, the number of runways
used for the comparison of operations at
the 34 main airports in the US and in Europe
in Table 3-3 was based on statistical analysis
(see grey box) rather than the physical
runway count. The passenger numbers are
based on Airport Council International (ACI)
data and refer to all operations.
Use of runways at the airports
In previous versions (2008 and 2010) of the report the
number of existing physical runways was used for the
computation of the indicators in Table 3-3.
Acknowledging that not all physical runways are available
for use at any one time, a different methodology was used
to determine the number of runways in use at each of the
airports.
In a first step, the number of simultaneously active runways
was determined for each 15 minute interval. A runway (e.g.
09R/27L) was considered as being active if used in any of
the directions.
In a second step, the upper 10th percentile of the
distribution was used as the number of simultaneously
active runways at the respective airport. The number of
physical runways might be higher.
25
Independent operations ensure flexibility and usually allow a higher throughput whereas dependent operations
may mean that only one runway can be used at a time. In order to operate independently, ICAO safety rules
require the runways to be far enough apart and/or configured so that aircraft operation on one runway does not
affect the other.
26
Landing and departing traffic are mixed on the same runway.
27
Applies to dual runway systems where runways are used for either landing or departing traffic only.
P a g e | 29
Table 3-3: Comparison of operations at the 34 main airports in the US and Europe
Europe
US
US vs.
Europe
(2015)
Main 34 airports
2015
vs.
2013
2015
vs.
2013
Avg. number of annual IFR movements per airport (‘000)
236
3.6%
380
-0.1%
61%
Avg. number of annual passengers per airport (million)
28.0
10.1%
36.3
9.6%
30%
Passengers per IFR movement
118
6.3%
96
9.8%
-19%
Average number of active runways per airport
2.0
-1.5%
3.4
0.9%
73%
Annual IFR movements per runway (‘000)
120
5.2%
112
-1.0%
-7%
Annual passengers per runway (million)
14.2
11.8%
10.7
8.7%
-25%
There were several airport development projects in the US since 2008, including new runways at
Chicago O’Hare (ORD), Charlotte (CLT), Seattle (SEA), and Dulles (IAD). A runway extension was
also completed for Philadelphia (PHL) that resulted in improved capacity for the airport. In
Europe, a fourth runway went into operation at Frankfurt (FRA) airport in October 2011.
Table 3-3 shows that the average number of IFR movements (+61%) and the number of annual
passengers per airport (+30%) are significantly higher in the US than in Europe. Consistent with
Figure 3-6 and Figure 3-7, the number of passengers per movement is much lower (-19%) in the
US due to the US on average utilizing a larger share of smaller aircraft and offering fewer seats
per scheduled flight.
Figure 3-8 shows the average number of daily IFR departures at the 34 main European and US
airports.
Figure 3-8: Operations at the main 34 airports (2015)
241
250
252
255
264
281
288
296
326
330
365
388
399
401
428
498
514
516
519
522
552
559
562
566
568
580
595
601
685
737
749
878
931
1187
1197
0 500 1000 1500
Nashville (BNA)
St. Louis (STL)
Tampa (TPA)
Houston (HOU)
San Diego (SAN)
Portland (PDX)
Dallas Love (DAL)
Memphis (MEM)
Baltimore (BWI)
Chicago (MDW)
Ft. Lauderdale (FLL)
Salt Lake City (SLC)
Washington (DCA)
Washington (IAD)
Orlando (MCO)
New York (LGA)
Boston (BOS)
Seattle (SEA)
Detroit (DTW)
Average
Minneapolis (MSP)
Newark (EWR)
Philadelphia (PHL)
Miami (MIA)
Las Vegas (LAS)
San Francisco (SFO)
Phoenix (PHX)
New York (JFK)
Houston (IAH)
Charlotte (CLT)
Denver (DEN)
Los Angeles (LAX)
Dallas (DFW)
Chicago (ORD)
Atlanta (ATL)
Average daily IFR departures at the main 34 airports (2015)
149
160
163
169
172
186
206
220
227
230
232
232
237
244
249
250
269
287
310
320
321
323
331
332
349
353
367
396
432
502
517
633
641
649
652
0 500 1000 1500
Lyon (LYS)
Milan (LIN)
Stuttgart (STR)
Prague (PRG)
Cologne (CGN)
Nice (NCE)
Hamburg (HAM)
Milan (MXP)
Lisbon (LIS)
London (STN)
Athens (ATH)
Helsinki (HEL)
Manchester (MAN)
Palma (PMI)
Geneva (GVA)
Berlin (TXL)
Dublin (DUB)
Dusseldorf (DUS)
Stockholm (ARN)
Brussels (BRU)
Paris (ORY)
Average
Oslo (OSL)
Vienna (VIE)
Copenhagen (CPH)
Zurich (ZRH)
London (LGW)
Barcelona (BCN)
Rome (FCO)
Madrid (MAD)
Munich (MUC)
Amsterdam (AMS)
Frankfurt (FRA)
London (LHR)
Paris (CDG)
P a g e | 30
The IFR flights are the basis for the majority of the trends and analysis presented in this report.
The average number of IFR departures per airport (522) is considerably higher (62%) in the US,
compared to 323 average daily departures at the 34 main airports in Europe in 2015
28
.
Figure 3-9 shows the change in IFR departures by airport compared to 2013. In the US, the
airports with the highest decrease in departures between 2013 and 2015 are Detroit (-64),
Denver (-55), and Washington (-50), and the airports showing a growth in departures compared
to 2013 include Seattle (+87), Dallas Love (DAL) (+55) and New York JFK (+44). Although overall
traffic in the US increased by 1.6%, the average traffic level for the main 34 population was
virtually unchanged.
Figure 3-9: Change in operations at the main 34 airports (2015 vs. 2013)
In Europe, the airports with the highest decrease in terms of departures were Lyon (-10), Vienna
(VIE) (-7), and Frankfurt (-6). The airports showing an increase in departures compared to 2013
include Athens (+46), Madrid (+46), and Dublin (+37).
28
The analysis relates only to IFR flights. Some airports - especially in the US - have a significant share of additional
VFR traffic which has not been considered in the analysis.
-90 -70 -50 -30 -10 10 30 50 70 90
Detroit (DTW)
Denver (DEN)
Washington (IAD)
Atlanta (ATL)
Minneapolis (MSP)
Philadelphia (PHL)
Charlotte (CLT)
Memphis (MEM)
Baltimore (BWI)
Chicago (ORD)
New York (LGA)
Houston (IAH)
St. Louis (STL)
Newark (EWR)
Salt Lake City (SLC)
Houston (HOU)
Chicago (MDW)
Phoenix (PHX)
Average
Washington (DCA)
Portland (PDX)
Dallas (DFW)
San Francisco (SFO)
Tampa (TPA)
San Diego (SAN)
Nashville (BNA)
Las Vegas (LAS)
Boston (BOS)
Orlando (MCO)
Miami (MIA)
Ft. Lauderdale (FLL)
Los Angeles (LAX)
New York (JFK)
Dallas Love (DAL)
Seattle (SEA)
Change in average daily IFR departures (2015 vs. 2013)
-90 -70 -50 -30 -10 10 30 50 70 90
Lyon (LYS)
Vienna (VIE)
Frankfurt (FRA)
Milan (MXP)
Nice (NCE)
Paris (CDG)
Munich (MUC)
Prague (PRG)
Dusseldorf (DUS)
Oslo (OSL)
Paris (ORY)
Helsinki (HEL)
London (LHR)
Zurich (ZRH)
Geneva (GVA)
Manchester (MAN)
Stuttgart (STR)
Milan (LIN)
Stockholm (ARN)
Average
Cologne (CGN)
Palma (PMI)
Berlin (TXL)
Copenhagen (CPH)
Barcelona (BCN)
Rome (FCO)
Hamburg (HAM)
London (LGW)
Lisbon (LIS)
Brussels (BRU)
London (STN)
Amsterdam (AMS)
Dublin (DUB)
Madrid (MAD)
Athens (ATH)
P a g e | 31
3.2.2 DECLARED CAPACITY AND PEAK THROUGHPUT
In Europe, the declared airport capacity is a limit
typically set as early as six months before the day
of operations through a coordination process
involving the airport managing body, the airlines,
and local ATC.
In the US, the FAA called arrival rates reflect
tactical, real time values based on the number of
operations scheduled, available runway
configuration, and weather, among other
considerations.
95th percentile airport peak arrival throughput
The peak arrival throughput is an approximation of
the operational airport capacity in ideal conditions. It
is the 95th percentile of the number of aircraft in the
“rolling” hours sorted from the least busy to the
busiest hour.
The indicator has, however, limitations when the
peak throughput is lower than the peak declared
capacity, in which case it is necessary to determine
whether a variation in peak arrival throughput is
driven by a change in demand or by a change in
operational airport capacity.
Figure 3-10 provides a comparison of the two types of capacities and throughput described
above. Although they are developed and used for different purposes, the values may provide
some insights into the role of capacity on operational performance.
Figure 3-10: Actual airport throughput vs. declared capacity (2015)
The figure depicts the peak arrival capacity (peak called arrival rates for US airports and peak
declared arrival capacities for European airports) together with the airports’ 95
th
percentile peak
arrival throughput (see grey box). The airports are furthermore categorised by the number of
active runways (see Section 3.2.1 for the computation of the number of active runways).
This grouping allows for a first order comparison among different airports. It is however
recognised that this simplified analysis should be viewed with a note of caution as there are
78
59
47
30
0
20
40
60
80
100
120
140
DFW
DEN
ORD
ATL
IAH
MEM
IAD
CLT
MCO
DTW
LAX
MIA
LAS
PHL
SFO
MSP
PHX
BNA
SLC
STL
BOS
JFK
EWR
SEA
BWI
HOU
TPA
PDX
FLL
LGA
DAL
MDW
DCA
SAN
Average
7 6 5 4 3 2 1 US
Arrivals per hour
Airports by Number of Active Runways
US airports
Peak Arrival Capacity (called rates in US, declared in EUR)
Peak Arrival Throughput (95th percentile)
Average Peak Arrival Capacity
74
44
40
29
0
20
40
60
80
100
120
140
AMS
CDG
FRA
FCO
ARN
ZRH
MUC
CPH
BRU
HEL
MAD
VIE
LHR
CGN
MXP
BCN
ORY
DUS
MAN
PMI
OSL
TXL
NCE
HAM
ATH
LYS
STN
PRG
STR
LGW
DUB
LIS
GVA
LIN
Average
7 6 5 4 3 2 1 EU
European airports
Actual airport throughput vs. declared capacity (2015)
P a g e | 32
significant differences in runway layout among airports in the same class that can explain the
variation.
In the US and Europe, airports with one and even two active runways are more comparable in
terms of peak arrival capacity for the two regions. For the US, the two active runway case
average value (47) is influenced by the ability to operate in mixed mode with independent
runways for Tampa (TPA) and Portland (PDX). Otherwise the grouping is more comparable.
For airports with three or more active runways, the peak arrival capacity at US airports is on
average notably higher than at European airports. The majority of US airports have three or
more active runways whereas in Europe, most of the airports have one or two active runways.
Despite normalising the comparison by grouping airports by number of active runways, airport
capacities within the same active runway grouping can be starkly different due to differing
runway layouts, runway dependencies and aircraft fleet mix. In general, the US airports with
high value arrival capacity rates in the same class indicate the use of runways in mixed mode
where arrivals are possible among all active runways. As such, Munich (MUC), Minneapolis
(MSP), Tampa (TPA), and Portland (PDX) have a considerably higher peak arrival capacity than
the other airports in their runway group.
Peak arrival throughput levels also vary in the two regions. Whereas in Europe peak arrival
throughput is usually close to the peak declared capacity, in the US peak arrival throughput
tends to be substantially lower than the peak capacity arrival rates, with the exception of a few
high impact airports (i.e, New York airports, Philadelphia) where demand and, therefore,
throughput is closer to the peak capacity level. As schedule limitations dictate a close adherence
of scheduled operations to pre-allocated airport slots (a surrogate for capacity), the slot-
controlled airports in the US and Europe tend to show a peak throughput closer to peak
capacity.
There are a number of key challenges in providing a true like-with-like comparison of airport
capacities and throughput for the two regions. One difficulty in this exercise is that airports
within each active runway group may not be directly comparable due to differences in runway
layout. Munich (MUC), having two parallel independent runways and the highest throughput in
its two-runway class, is not directly comparable to LaGuardia (LGA), which also has two active
runways, but in a dependent crossed configuration. The throughput values for the two airports
are, therefore, very different.
More analysis is needed to better group and compare European and US airports based on
runway layout, runway dependency, and mixed and single mode operations. Another difficulty is
that throughput is highly sensitive to demand. High demand drives high throughput and vice-
versa. It is difficult to properly assess throughput as demand levels are lower on both sides of
the Atlantic with some airports having larger demand drops than others. Lastly, measuring
throughput is dependent on the time interval used for the assessment. In this analysis, peak
throughput was measured every five minute rolling hour. Results using a different approach may
reveal a difference not seen at the five minute rolling hour level.
P a g e | 33
3.2.3 CAPACITY VARIATION AT US AIRPORTS
Many of the differences in performance appear to be attributable to the effects of capacity
variation between most favourable and least favourable conditions. Also, many of the
improvements at the system level observed over time track with an overall decrease in demand.
The analysis in this section seeks to quantify capacity changes using the performance sources
described in Section 1.3. Changes in capacity can in part be tied to changes in demand, weather,
and airport infrastructure. In Figure 3-11, the average hourly arrival ATC acceptance rates for the
34 main US airports between 6AM-10PM local time are shown with the percent change in arrival
capacity compared to 2013 (top of Figure 3-11).
Figure 3-11: Average hourly arrival rates at 34 main US airports (2013-2015)
Ft. Lauderdale (FLL) had the largest percent change from 2013 to 2015. Its increase was due to a
runway coming back into service in November of 2014. Salt Lake City (SLC) saw a decrease due to
a change in strategy for calling a balanced rate.
Capacity at airports can be tied to demand at the facility and also be impacted by external
factors, such as weather conditions. It is also the case that not all capacity variation and
performance changes can be explained by meteorological conditions as facilities may operate at
low capacity rates during good weather due to other events such as temporary runway
maintenance or dependencies with traffic flow of nearby airports.
For this reason, it is more straightforward to assess capacity variation using a percentile method
that does not depend on a link to all the causal reasons described above.
Figure 3-12 combines the various elements (volume, capacity reduction, and frequency) which
drive performance at US airports using percentiles. In the previous section, peak capacity and
throughput values were presented. In the following section, the focus is on how much capacity
varies from low to high values and how often this variation becomes a strain on airports due to
demand levels close to or exceeding capacity. Note that capacity and demand do not have to be
at a peak level for an airport to be impacted or strained. In general, it only takes a mismatch of
the two entities and not necessarily high levels of each.
0
20
40
60
80
100
120
140
Atlanta (ATL)
Nashville (BNA)
Boston (BOS)
Baltimore (BWI)
Charlotte (CLT)
Dallas (DAL)
Washington (DCA)
Denver (DEN)
Dallas (DFW)
Detroit (DTW)
Newark (EWR)
Ft. Lauderdale (FLL)
Houston (HOU)
Washington (IAD)
Houston (IAH)
New York (JFK)
Las Vegas (LAS)
Los Angeles (LAX)
New York (LGA)
Orlando (MCO)
Chicago (MDW)
Memphis (MEM)
Miami (MIA)
Minneapolis (MSP)
Chicago (ORD)
Portland (PDX)
Philadelphia (PHL)
Phoenix (PHX)
San Diego (SAN)
Seattle (SEA)
San Francisco (SFO)
Salt Lake City (SLC)
St. Louis (STL)
Tampa (TPA)
Average Hourly Arrival Rate
Average hourly arrival rates at 34 main US airports (2013-2015)
2015 2013
-20%
0%
20%
40%
60%
Percent Change in AAR
Arrival rate vs. 2013 (%)
P a g e | 34
Figure 3-12 combines various elements of capacity and demand, calculated using filed times
from day of operation, as one means of measuring the congestion at airports as well as the
predictability of capacity. The top chart in Figure 3-12 shows airport capacity and demand for
both 2013 and 2015 by reporting the average number of hours the demand is greater than 80%
of the called rate capacity for the airport. For example, LGA experienced a demand greater than
the 80
th
percentile capacity for 12.4 hours per day on average during 2015. This means for the
majority of the operating day, LGA’s demand exceeded the 80
th
percentile capacity. In relation
to Figure 3-9, the operations at Seattle have not only increased but by this indicator, are
becoming more comparable to the busier US airports. While Fort Lauderdale traffic has grown,
its congestion by this measure is less due to one of its runways coming back into service.
To capture this effect, a percent capacity reduction metric can be used by calculating the (85
th
-
15
th
)/85
th
. This metric in the lower part of Figure 3-12, shows the percent capacity variability by
calculating the percent decrease in capacity from the 85
th
to 15
th
percentile. This metric
produces similar results for LGA (low variability, low capacity) and CLT (high variability, high
capacity). Philadelphia (PHL), Boston (BOS), Detroit (DTW), and Nashville (BNA) report the largest
percent capacity reductions of the Main 34 airports.
Figure 3-12: Capacity variation and impact on operations at US airports
Although a percentile method was used to characterise airport capacity variation, it is still
important for performance analysis groups to link these changes to causal factors. At this time, it
is difficult to apply a practical automated process that can explain capacity variation across all
facilities. For example, it is known that for San Francisco (SFO), variation can be tied to
precipitation, haze, fog and other METAR cloud cover conditions which are not captured by
ceiling/visibility alone. For Philadelphia (PHL), the capacity variation can be linked to wind effects
[Ref.
10
]. Additional performance data development and automated procedures are needed to
assess these effects across airports.
A key challenge for ATM is to ensure safe operations while sustaining a high runway throughput
in the various weather conditions. Even small improvements at high density airports will yield a
considerable benefit for airspace users and the entire network. This will encompass the use of
new and enhanced technology as foreseen in NextGen and SESAR.
0
2
4
6
8
10
12
14
LGA
EWR
JFK
LAX
DCA
SFO
PHL
ORD
BOS
SEA
CLT
MDW
SAN
ATL
DFW
BWI
LAS
HOU
IAH
DTW
DAL
DEN
MSP
PHX
MIA
IAD
FLL
BNA
SLC
PDX
MEM
MCO
TPA
STL
Average Hours/Day Demand > 80th
Percentile Capacity
2015 2013
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
New York (LGA)
Newark (EWR)
New York (JFK)
Los Angeles (LAX)
Washington (DCA)
San Francisco (SFO)
Philadelphia (PHL)
Chicago (ORD)
Boston (BOS)
Seattle (SEA)
Charlotte (CLT)
Chicago (MDW)
San Diego (SAN)
Atlanta (ATL)
Dallas (DFW)
Baltimore (BWI)
Las Vegas (LAS)
Houston (HOU)
Houston (IAH)
Detroit (DTW)
Dallas (DAL)
Denver (DEN)
Minneapolis (MSP)
Phoenix (PHX)
Miami (MIA)
Washington (IAD)
Ft. Lauderdale (FLL)
Nashville (BNA)
Salt Lake City (SLC)
Portland (PDX)
Memphis (MEM)
Orlando (MCO)
Tampa (TPA)
St. Louis (STL)
Percent Capacity Reduction (85-15)/85
P a g e | 35
3.3 Impact of Weather Conditions on airport operations
Runway throughput at airports is usually impacted by meteorological conditions. As weather
conditions deteriorate, separation requirements generally increase and runway throughput is
reduced. The impact of weather (visibility, wind, convective weather, etc.) on operations at an
airport and hence on ATM performance can vary significantly by airport and depends on a
number of factors such as, inter alia, ATM and airport equipment (instrument approach system,
radar, etc.), runway configurations (wind conditions), and approved rules and procedures.
As illustrated in Figure 3-13,
movement rates depend on
visibility conditions. Runway
throughput can drop significantly
when Low Visibility Procedures
(LVP)
29
need to be applied.
LVPs require increased spacing
between aircraft to maintain the
signal integrity of the Instrument
Landing System (ILS) which in turn
reduces throughput.
Figure 3-13: Impact of visibility conditions on runway throughput
Wind conditions also impact runway throughput. With the separations based on distance, wind
with a high headwind component lowers the ground speed of aircraft and consequently reduces
the rate at which aircraft make their final approach.
The analysis of performance by meteorological condition provides an indication of how weather
affects system performance and which airports are most impacted by changes in weather
condition.
Section 3.3.1 provides an assessment of weather in the two regions using general criteria for
ceiling and visibility. Section 3.3.2 compares ATFM delay attributed to weather causes at US and
European arrival airports.
3.3.1 MEASURING WEATHER CONDITIONS
Both US and European performance groups use detailed weather observation reports known as
METAR
30
and both groups have developed procedures for assessing weather’s impact on
aviation performance [Ref.
11
and
12
]. A typical METAR contains data on temperature, dew
point, wind speed and direction, precipitation, cloud cover and heights, visibility, and barometric
pressure.
Historically, many of the performance analysis indicators and modelling processes at the FAA
segregate time periods into visual or instrument meteorological conditions (VMC/IMC). This
provides a simple first-order examination of the effects of weather on performance using ceiling
29
Low visibility procedures have been devised to allow aircraft to operate safely from and into aerodromes when
the weather conditions do not permit normal operations.
30
METAR is also known as Meteorological Terminal Aviation Routine Weather Report or Meteorological Aerodrome
Report.
P a g e | 36
and visibility as the primary criteria for defining weather. Performance by VMC/IMC was also
examined in the previous benchmark reports as a practical way of comparing weather changes
over time and weather differences between facilities.
Precise definitions differ between the US and Europe but for the analysis in the next section, a
cloud ceiling of less than 1 000 feet or visibility of less than 3 miles (5 km) was used for the
demarcation of IMC. Conditions better than IMC are termed visual meteorological conditions
(VMC). In addition, there are airport specific thresholds where visual approaches (and typically
visual separations) may be used. Conditions below such thresholds, but still better than IMC, are
referred to as Marginal VMC. For simplicity, the following thresholds were used for all airports to
provide a basic assessment of the frequency of various weather conditions.
Table 3-4: Ceiling and visibility criteria
Visibility (miles)
< 3
[3, 5)
> 5
Ceiling (feet)
> 3,000
Instrument
Marginal
Visual
[1000, 3000)
Instrument
Marginal
Marginal
< 1,000
Instrument
Instrument
Instrument
It is important to note that VMC does not necessarily equate to favourable or perfect weather
although it is often the case. METAR data contains records with weather events, such as rain
showers, thunderstorms and strong winds occurring during periods with high visibility and clear
skies. These weather events are currently not assessed as part of these related indicators and
more work is needed in the future to develop a more comprehensive definition for weather.
Figure 3-14 shows the
percent of time spent in
visual, marginal, and
instrument conditions in
Europe and the US at system
level in 2013 and in 2015
between 6AM-10PM local
time.
In general, weather in
Europe at system level is
less favourable than the US.
Figure 3-14: Overview of weather conditions in the US and Europe
In 2015, 84.5% of the year at the main 34 US airports was spent in VMC with 9.5% occurring in
marginal and 6% in instrument conditions. Overall, the weather in the US appears to be similar
as in 2013 with a slightly higher frequency of IMC in 2015 (+0.2%). The main 34 European
airports spend on average 77.8% of the time in VMC, 14.2% in marginal, and 8% in instrument.
At system level, weather conditions in Europe improved in 2015 compared to 2013 with a -2.0%
reduction in IMC and a -1.8% reduction in marginal conditions.
At the airport level, the share of time spent in VMC, MMC, and IMC vary based on differing
susceptibility to weather events which is largely based on geographic location (Figure 3-15). The
European airports located in the subtropical Mediterranean region including Nice (NCE), Palma
(PMI), Madrid (MAD), Rome (FCO), Athens (ATH), and Barcelona (BCN) are the airports with the
highest percentage of the VMC.
73.9%
77.8%
16.0%
14.2%
10.0%
8.0%
60%
65%
70%
75%
80%
85%
90%
95%
100%
2013 2015
Percent of time
Overview of weather conditions in the US and Europe
Visual Marginal Instrument
Europe
84.5%
84.5%
9.7%
9.5%
5.8%
6.0%
60%
65%
70%
75%
80%
85%
90%
95%
100%
2013 2015
US
P a g e | 37
Figure 3-15: Percent of time by meteorological condition at the main 34 airports (2015)
In the US, Las Vegas (LAS) and Phoenix (PHX) rarely experience anything other than VMC with
their dry desert climate. Similarly, the Florida airports (FLL, MCO, TPA, and MIA) also spend a
high percentage of time in VMC.
Figure 3-16 shows how the change in instrument conditions is broken down by airport in Europe
(-2%) and the US (+0.2%) in 2015. In terms of performance, the observed capacity gap, traffic
volume, and frequency of IMC drive overall system performance.
Figure 3-16: Percent change in time during IMC at the main 34 airports (2013-2015)
95.8%
95.4%
94.4%
92.9%
92.8%
90.9%
85.0%
83.0%
82.3%
81.1%
81.1%
79.4%
79.2%
79.0%
78.1%
78.0%
77.8%
76.9%
76.0%
76.0%
76.0%
73.8%
73.6%
73.5%
72.7%
72.5%
71.4%
70.3%
69.9%
69.7%
69.2%
67.1%
67.0%
64.7%
62.6%
40%
50%
60%
70%
80%
90%
100%
Nice (NCE)
Palma (PMI)
Madrid (MAD)
Rome (FCO)
Athens (ATH)
Barcelona (BCN)
Lisbon (LIS)
Geneva (GVA)
Stuttgart (STR)
Lyon (LYS)
Vienna (VIE)
Cologne (CGN)
Frankfurt (FRA)
Milan (MXP)
Prague (PRG)
Average
Dusseldorf (DUS)
Amsterdam (AMS)
Berlin (TXL)
Dublin (DUB)
Munich (MUC)
Zurich (ZRH)
Paris (ORY)
London (LHR)
Manchester (MAN)
Brussels (BRU)
Paris (CDG)
Copenhagen (CPH)
Milan (LIN)
Stockholm (ARN)
Oslo (OSL)
London (LGW)
Hamburg (HAM)
Helsinki (HEL)
London (STN)
Percentage of time
Weather conditions at the main 34 airports (2015)
Instrument Marginal Visual
99.7%
99.4%
95.6%
93.9%
92.6%
92.0%
91.0%
87.0%
85.2%
85.1%
84.7%
84.5%
84.4%
83.9%
83.8%
83.8%
83.5%
83.2%
83.2%
83.0%
82.7%
82.7%
82.1%
80.9%
80.2%
80.0%
79.7%
79.5%
79.5%
79.2%
79.2%
79.1%
79.0%
77.9%
76.3%
40%
50%
60%
70%
80%
90%
100%
Las Vegas (LAS)
Phoenix (PHX)
Salt Lake City (SLC)
Ft. Lauderdale (FLL)
Miami (MIA)
Tampa (TPA)
Orlando (MCO)
Denver (DEN)
Los Angeles (LAX)
Washington (DCA)
Philadelphia (PHL)
Average
Baltimore (BWI)
New York (LGA)
New York (JFK)
Newark (EWR)
Portland (PDX)
St. Louis (STL)
Washington (IAD)
San Francisco (SFO)
Boston (BOS)
Memphis (MEM)
San Diego (SAN)
Nashville (BNA)
Dallas (DFW)
Dallas (DAL)
Detroit (DTW)
Houston (HOU)
Charlotte (CLT)
Minneapolis (MSP)
Chicago (MDW)
Atlanta (ATL)
Chicago (ORD)
Houston (IAH)
Seattle (SEA)
US
Europe
-9%
-6%
-3%
0%
3%
6%
9%
Prague (PRG)
Lyon (LYS)
Vienna (VIE)
Berlin (TXL)
Zurich (ZRH)
Milan (LIN)
Paris (CDG)
London (LGW)
Stuttgart (STR)
Munich (MUC)
Cologne (CGN)
Amsterdam (AMS)
Milan (MXP)
London (STN)
Brussels (BRU)
Dusseldorf (DUS)
Hamburg (HAM)
Average
Dublin (DUB)
London (LHR)
Lisbon (LIS)
Geneva (GVA)
Madrid (MAD)
Paris (ORY)
Rome (FCO)
Helsinki (HEL)
Nice (NCE)
Manchester (MAN)
Frankfurt (FRA)
Stockholm (ARN)
Palma (PMI)
Athens (ATH)
Copenhagen (CPH)
Barcelona (BCN)
Oslo (OSL)
Change percentage points
Percent change in time during IMC at the main 34 airports (2013-2015)
-9%
-6%
-3%
0%
3%
6%
9%
Seattle (SEA)
Salt Lake City (SLC)
Portland (PDX)
Los Angeles (LAX)
Minneapolis (MSP)
San Francisco (SFO)
San Diego (SAN)
Boston (BOS)
Ft. Lauderdale (FLL)
Miami (MIA)
Las Vegas (LAS)
Phoenix (PHX)
Average
New York (JFK)
Chicago (MDW)
Tampa (TPA)
Chicago (ORD)
Nashville (BNA)
Detroit (DTW)
Memphis (MEM)
New York (LGA)
Orlando (MCO)
St. Louis (STL)
Denver (DEN)
Newark (EWR)
Philadelphia (PHL)
Baltimore (BWI)
Houston (HOU)
Washington (DCA)
Atlanta (ATL)
Washington (IAD)
Dallas (DAL)
Charlotte (CLT)
Dallas (DFW)
Houston (IAH)
US
Europe
P a g e | 38
The airports with considerably more time spent in marginal and instrument conditions and less
time in VMC may call lower called rates more often, but performance at these airports will only
be impacted if demand levels rise above the available capacity. As mentioned previously in this
section, ceiling and visibility provide only a preliminary step towards measuring weather
conditions. More work is needed to relate the impact of weather conditions on airport and air
traffic performance.
3.3.2 WEATHER-RELATED AIRPORT ATFM DELAYS AT THE MAIN 34 AIRPORTS
As weather is a major factor influencing runway throughput and airport capacity, airports
typically issue ATFM restrictions to address capacity to demand imbalances when adverse
weather occurs. Using comparable data sources in the US and Europe, this section provides a
preliminary analysis of the specific types of weather-related causes for ATFM delays at the
arrival airport. A more detailed analysis of ATFM delay for all causal factors is provided in Section
5.2.1.
Figure 3-17 shows the average airport arrival ATFM delay
31
by causal factor at system level for
the main 34 airports between 2008 and 2015.
Figure 3-17: Causes of weather-related airport ATFM delays (2008-2015)
Overall, relatively higher ATFM delays per arrival are observed in the US compared to Europe
when weather-related restrictions are present. This may be due to European capacities being set
more conservatively to allow for unforeseeable events whereas the US operates by calling a
higher capacity by presuming ideal operating conditions. Major contributors to the US values
include airports with high demand and highly variable capacity.
In Europe, ATFM airport regulations due to visibility are the main driver of delay, followed by
wind, winter operations and thunderstorms. A notable exception is observed for 2010 where
31
Please note that for Europe all ATFM delays are included whereas for the US only delays equal or greater than 15
minutes are included.
0.0
0.5
1.0
1.5
2.0
2.5
2008 2009 2010 2011 2012 2013 2014 2015
Avg. weather related ATFM delay (min. per arrival)
Winter OPS/ Other Precipitation
Thunderstorm (TS) /Cumulonimbus cloud (CB) Wind
Visibility
Causes of weather-related airport charged ATFM delays
at the main 34 airports
Source: PRU/FAA analysis
0.0
0.5
1.0
1.5
2.0
2.5
2008 2009 2010 2011 2012 2013 2014 2015
US
Europe
P a g e | 39
winter operations were the main cause for weather related airport ATFM regulations. As can be
seen in Figure 3-17, average weather related ATFM arrival delays increased in Europe between
2013 and 2015.
Similarly in the US, the primary driver for ATFM delays is visibility, however, the impact of
thunderstorms and severe weather are also very prominent. Different than in Europe, weather
related airport ATFM delays continuously decreased in the US between 2013 and 2015.
Figure 3-18 provides a breakdown of weather-related ATFM delay by arrival airport and by cause
in 2015. A high average weather-related airport arrival delay is usually the result of a notable
capacity reduction in bad weather combined with a high level of demand (i.e. peak throughput
close to or higher than the declared capacity).
Figure 3-18: Airport charged weather-related ATFM delays by destination airport (2015)
As can be seen from the figure, a few notable US airports experience delay levels that are
magnitudes higher than other airports in the country or in Europe. The New York area airports
(EWR, LGA, and JFK) experience very high average ATFM weather-related delays. For this reason,
the New York area has implemented a severe weather avoidance plan (SWAP) to handle aircraft
reroutes and departure clearances during thunderstorm events. On the west coast, fog and low
visibility are the most impactful weather cause for ATFM delays at San Francisco (SFO).
In Europe, London Heathrow (LHR) shows the highest impact of weather on operations in 2015,
followed by Amsterdam (AMS), Zurich (ZRH), and Geneva (GVA). The average weather-related
airport arrival ATFM delays at London (LHR) were mainly related to wind and visibility.
0
1
2
3
4
5
6
7
8
Amsterdam (AMS)
Stockholm (ARN)
Athens (ATH)
Barcelona (BCN)
Brussels (BRU)
Paris (CDG)
Cologne (CGN)
Copenhagen (CPH)
Dublin (DUB)
Dusseldorf (DUS)
Rome (FCO)
Frankfurt (FRA)
Geneva (GVA)
Hamburg (HAM)
Helsinki (HEL)
London (LGW)
London (LHR)
Milan (LIN)
Lisbon (LIS)
Lyon (LYS)
Madrid (MAD)
Manchester (MAN)
Munich (MUC)
Milan (MXP)
Nice (NCE)
Paris (ORY)
Oslo (OSL)
Palma (PMI)
Prague (PRG)
London (STN)
Stuttgart (STR)
Berlin (TXL)
Vienna (VIE)
Zurich (ZRH)
Avg. weather airport charged ATFM delay (min. per arrival)
Airport charged weather-related ATFM delays by destination airport (2015)
Precipitation Thunderstorms (TS) / Cumulonimbus cloud (CB) Visibility WIND Winter Operations/ Other
Source: PRU/FAA Analysis
0
1
2
3
4
5
6
7
8
Atlanta (ATL)
Nashville (BNA)
Boston (BOS)
Baltimore (BWI)
Charlotte (CLT)
Dallas (DAL)
Washington (DCA)
Denver (DEN)
Dallas (DFW)
Detroit (DTW)
Newark (EWR)
Ft. Lauderdale (FLL)
Houston (HOU)
Washington (IAD)
Houston (IAH)
New York (JFK)
Las Vegas (LAS)
Los Angeles (LAX)
New York (LGA)
Orlando (MCO)
Chicago (MDW)
Memphis (MEM)
Miami (MIA)
Minneapolis (MSP)
Chicago (ORD)
Portland (PDX)
Philadelphia (PHL)
Phoenix (PHX)
San Diego (SAN)
Seattle (SEA)
San Francisco (SFO)
Salt Lake City (SLC)
St. Louis (STL)
Tampa (TPA)
US
only delay equal to or greater than 15 minutes is included
Europe
includes all airport arrival ATFM delay
P a g e | 40
4. COMPARISON OF AIRLINE-RELATED OPERATIONAL SERVICE QUALITY
This chapter compares US and European performance using data provided by airlines. Specific
KPIs provided in this section include airline-reported punctuality, airline-reported delay against
the schedule, airline-reported attributable delay, and phase of flight time variability.
The section starts with a high level evaluation of the share of delayed flights compared to airline
schedules, which is often used as a proxy for “service quality”. There are many factors
contributing to the “service quality” of air transport. In fact, it can be seen as the “end product”
of complex interactions between airlines, ground handlers, airport operators, and ANSPs, from
the planning and scheduling phases up to the day of operation.
The KPI is reported by the US Department of Transportation [Ref.
13
] and in Europe by the
Central Office for Delay Analysis (CODA) [Ref.
14
]. The chapter furthermore assesses trends in
the evolution of scheduled block times as changes in this scheduled time can have a first order
effect on punctuality KPIs. The main delay drivers are also identified by analysing the information
reported by airlines in order to get a first estimate of the ATM-related
32
contribution towards
overall air transport performance.
4.1 On-time performance
Figure 4-1 compares the industry-standard indicators for on-time performance, i.e. arrivals or
departures delayed by less than or equal to 15 minutes versus schedule. The results need to be
seen together with the time buffers included in airline schedules in order to achieve a certain
level of on-time performance. A more detailed discussion on how increasing block time can lead
to an apparent improvement in performance is included in the next section (see Section 4.2).
Figure 4-1: On-time performance (2005-2015)
A notable difference was the gap between departure and arrival punctuality that occurred prior
to 2010 in the US, and which was not observed for Europe. The reasons for this gap are not fully
understood but may involve policy, differences in flow management techniques as well as other
incentives to have high on-time departures. While in the US, flow management strategies focus
32
In this report, “ATM-related“ means that ATM has a significant influence on the operations.
74%
76%
78%
80%
82%
84%
86%
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Departures (<=15min.) Arrivals (<=15min.)
74%
76%
78%
80%
82%
84%
86%
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
On-time performance compared to schedule
(flights to or from the 34 main airports)
Europe
US (CONUS)
Source: CODA; PRC analysis
Source: ASPM data
P a g e | 41
more on the gate-to-gate phase, in Europe flights are usually held at the gates with only
comparatively few constraints once an aircraft has left the gate. However from 2010 this gap
has largely disappeared with a trend similar to Europe.
Historically, between 2005 and 2007, on-time performance degraded in the US and in Europe
and improved notably between 2007 and 2009. It is interesting to note that, at system level,
traffic in Europe increased by 2.3% while traffic in the US declined by 11.8% between 2005 and
2009 (compare Figure 3-1).
Whereas in the US performance remained stable in 2010, punctuality in Europe degraded to the
worst level on record mainly due to weather-related delays (snow, freezing conditions) and
strikes
33
. From 2010 to 2012, punctuality in Europe improved again and continued to improve in
the US. However in 2013 and 2014, whereas punctuality in Europe remained largely unchanged
and then degraded from 2014-2015, punctuality in the US saw a sharp decline from 2012-2014
followed by a rebound from 2014-2015. Figure 4-2 and Figure 4-3 show the facilities that most
influence system wide on-time performance as well as contributed to the change from 2013-
2015 (US 80.2% vs 81.3%).
The system-wide on-time performance is the result of contrasted situations among airports.
Figure 4-2 shows the arrival punctuality at the 34 main European and US airports in 2015. The
changes in arrival punctuality compared to 2013 are shown in Figure 4-3
In the US, the New York airports (LGA, EWR, JFK) had the lowest on-time performance (arrivals),
followed by San Francisco (SFO) and Los Angeles (LAX). Compared to 2013, only a few airports
showed degradation in arrival punctuality (see Figure 4-3).
Figure 4-2: Arrival punctuality at the main 34 airports (2015)
33
The volcanic ash cloud in April and May 2010 had only a limited impact on punctuality, as the majority of the
flights were cancelled and are, thus, excluded from the calculation of on-time performance indicators.
75,0%
77,9%
78,2%
78,4%
78,8%
79,4%
79,5%
79,7%
79,8%
80,1%
80,6%
80,8%
80,9%
81,1%
81,4%
81,5%
81,6%
81.6%
81,3%
82,2%
82,4%
82,5%
82,5%
82,6%
82,7%
82,8%
82,8%
83,7%
83,9%
83,9%
84,0%
84,3%
84,5%
85,5%
87,9%
80,48%
50%
60%
70%
80%
90%
100%
New York (LGA)
Newark (EWR)
New York (JFK)
San Francisco (SFO)
Los Angeles (LAX)
Boston (BOS)
Philadelphia (PHL)
Ft. Lauderdale (FLL)
Chicago (ORD)
Miami (MIA)
Orlando (MCO)
Memphis (MEM)
Houston (IAH)
Washington (IAD)
Denver (DEN)
Washington (DCA)
Tampa (TPA)
Dallas (DFW)
Average
Houston (HOU)
San Diego (SAN)
St. Louis (STL)
Las Vegas (LAS)
Dallas Love (DAL)
Baltimore (BWI)
Charlotte (CLT)
Nashville (BNA)
Chicago (MDW)
Phoenix (PHX)
Detroit (DTW)
Minneapolis (MSP)
Seattle (SEA)
Portland (PDX)
Atlanta (ATL)
Salt Lake City (SLC)
Non-M34
Source: EUROCONTROL PRU; FAA-ATO
71,8%
75,5%
76,6%
78,6%
78,8%
79,1%
79,6%
79,9%
80,5%
80,5%
81,5%
81,6%
81,8%
82,0%
83,0%
83,4%
83,5%
83,6%
84,0%
84,3%
84,3%
84,3%
84,4%
84,6%
85,0%
85,1%
85,2%
86,2%
86,7%
86,8%
87,2%
87,5%
87,6%
87,6%
88,2%
80,5%
50%
60%
70%
80%
90%
100%
London (LGW)
London (LHR)
Dublin (DUB)
Lisbon (LIS)
Brussels (BRU)
Rome (FCO)
Barcelona (BCN)
Geneva (GVA)
Manchester…
Zurich (ZRH)
Dusseldorf (DUS)
Milan (MXP)
Madrid (MAD)
Average
Cologne (CGN)
Hamburg (HAM)
Palma (PMI)
Berlin (TXL)
Nice (NCE)
Stuttgart (STR)
Oslo (OSL)
Paris (CDG)
London (STN)
Athens (ATH)
Stockholm (ARN)
Prague (PRG)
Amsterdam (AMS)
Milan (LIN)
Frankfurt (FRA)
Paris (ORY)
Copenhagen (CPH)
Lyon (LYS)
Vienna (VIE)
Helsinki (HEL)
Munich (MUC)
Non-M34
Arrival punctuality at the main 34 airports (2015)
2015 2013
Europe
US
P a g e | 42
In Europe, the two London airports (LHR, LGW) and Dublin (DUB) had the lowest level of arrival
punctuality in 2015 (top chart in Figure 4-2). Compared to 2013, Paris (ORY) (+4.9% pt.) showed
the highest improvement and a notable deterioration can be observed for London (LGW), Rome
(FCO) and Dublin (DUB).
Figure 4-3: Change in arrival punctuality at the main 34 airports (2015 vs. 2013)
Figure 4-4 shows monthly arrival punctuality levels (red line) together with traffic levels (brown
line) for flights to or from the top 34 airports in the US and Europe between 2010 and 2015.
Figure 4-4: Arrival punctuality by month (2010-2015)
In Europe and the US, a clear pattern of summer and winter peaks is visible. Whereas the winter
peaks are more the result of weather-related delays at airports, the summer peaks are driven by
the higher level of demand and resulting congestion but also by convective weather in the en-
route airspace in the US and a lack of en-route capacity in Europe. The strong increase in Europe
in December 2010 is due to exceptional weather conditions (ice & snow).
-10%
-8%
-6%
-4%
-2%
0%
2%
4%
6%
New York (LGA)
Newark (EWR)
New York (JFK)
San Francisco (SFO)
Los Angeles (LAX)
Boston (BOS)
Philadelphia (PHL)
Ft. Lauderdale (FLL)
Chicago (ORD)
Miami (MIA)
Orlando (MCO)
Memphis (MEM)
Houston (IAH)
Washington (IAD)
Denver (DEN)
Washington (DCA)
Tampa (TPA)
Dallas (DFW)
Average
Houston (HOU)
San Diego (SAN)
St. Louis (STL)
Las Vegas (LAS)
Dallas Love (DAL)
Baltimore (BWI)
Charlotte (CLT)
Nashville (BNA)
Chicago (MDW)
Phoenix (PHX)
Detroit (DTW)
Minneapolis (MSP)
Seattle (SEA)
Portland (PDX)
Atlanta (ATL)
Salt Lake City (SLC)
Non-M34
Change in on-time percentage points
-10%
-8%
-6%
-4%
-2%
0%
2%
4%
6%
London (LGW)
London (LHR)
Dublin (DUB)
Lisbon (LIS)
Brussels (BRU)
Rome (FCO)
Barcelona (BCN)
Geneva (GVA)
Manchester (MAN)
Zurich (ZRH)
Dusseldorf (DUS)
Milan (MXP)
Madrid (MAD)
Average
Cologne (CGN)
Hamburg (HAM)
Palma (PMI)
Berlin (TXL)
Nice (NCE)
Stuttgart (STR)
Oslo (OSL)
Paris (CDG)
London (STN)
Athens (ATH)
Stockholm (ARN)
Prague (PRG)
Amsterdam (AMS)
Milan (LIN)
Frankfurt (FRA)
Paris (ORY)
Copenhagen (CPH)
Lyon (LYS)
Vienna (VIE)
Helsinki (HEL)
Munich (MUC)
Non-M34
Change in arrival punctuality at the main 34 airports (2015 vs. 2013)
Source: EUROCONTROL PRU; FAA-ATO
US
Europe
0
100
200
300
400
500
600
700
800
900
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Jan-10
Apr-10
Jul-10
Oct-10
Jan-11
Apr-11
Jul-11
Oct-11
Jan-12
Apr-12
Jul-12
Oct-12
Jan-13
Apr-13
Jul-13
Oct-13
Jan-14
Apr-14
Jul-14
Oct-14
Jan-15
Apr-15
Jul-15
Oct-15
IFR flights ('000)
% Flights Delay > 15 Minutes (schedule)
Arrival punctuality and seasonality (EUR)
(all flights to or from main 34 airports)
0
100
200
300
400
500
600
700
800
900
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Jan-10
Apr-10
Jul-10
Oct-10
Jan-11
Apr-11
Jul-11
Oct-11
Jan-12
Apr-12
Jul-12
Oct-12
Jan-13
Apr-13
Jul-13
Oct-13
Jan-14
Apr-14
Jul-14
Oct-14
Jan-15
Apr-15
Jul-15
Oct-15
IFR flights ('000)
% Flights Delay > 15 Minutes (schedule)
Arrival punctuality and seasonality (US)
(all flights to or from main 34 airports)
P a g e | 43
As already mentioned at the beginning of this chapter, it is important to understand that on-time
performance is the ‘end product’ of complex interactions involving many stakeholders, including
ATM. Arrival punctuality is influenced by departure punctuality at the origin airport and often by
delays which already occurred on previous flight legs (see also Section 4.3). Depending on the
type of operation at airports (hub & spoke versus point to point) and airline route itinerary, local
performance can have an impact on the entire network through ripple effects but also on the
airport’s own operation.
Hence, there are interdependencies between ATM performance and the performance of other
stakeholders and/or events outside the control of ATM which require a high level of cooperation
and coordination between all parties involved. This may include competing goals within airlines,
weather, or changes to airport infrastructure that affect capacity.
4.2 Airline scheduling
On-time performance can be linked to a number of different factors including traffic levels,
weather, airport capacity, and airline scheduling preferences, such as schedule peaks and
scheduled block times. Frequently, airlines may pad their schedules to achieve a higher level of
on-time punctuality. The inclusion of “time buffers” in airline schedules to account for a certain
level of anticipated travel time variation on the day of operations and to provide a sufficient
level of on-time performance may therefore mask changes in actual performance (see grey box).
Generally speaking, the wider the distribution of historic
block-to-block times (and hence the higher the level of
variation), the more difficult it is for airlines to build
reliable schedules resulting in higher utilisation of
resources (e.g. aircraft, crews) and higher overall costs.
Additionally, a number of airlines operate hub and spoke
systems that interconnect flights to and from spoke
airports to the carriers’ hubs. Therefore disturbances at
one hub airport can quickly propagate through the entire
airline schedule. Operating an aircraft servicing several
airports can further amplify and increase the delay
propagation.
Airline scheduling
Airlines build their schedules for the next
season on airport slot allocation (mainly
Europe), crew activity limits, airport
connecting times, and by applying a
quality of service target to the distribution
of previously observed block-to-block
times (usually by applying a percentile
target to the distribution of previously
flown block times).
The level of “schedule padding” is subject
to airline strategy and depends on the
targeted level of on-time performance.
Nevertheless, it should be pointed out that efficiency improvements in actual flight time
distributions do not automatically result in improved on-time performance, as the airline
schedules for the new season are likely to be reduced by applying the punctuality target to the
set of improved flight times (block times are cut to improve utilisation of aircraft and crews).
Figure 4-5 shows the evolution of airline scheduling times in Europe and the US. The analysis
compares the scheduled block times for each flight of a given city pair with the long-term
average for that city pair over the full period (DLTA metric
34
). Generally speaking, the scheduled
34
The Difference from Long-Term Average (DLTA) metric is designed to measure changes in time-based (e.g. flight
time) performance normalised by selected criteria (origin, destination, aircraft type, etc.) for which sufficient data
are available. The analysis evaluates a relative change in performance over time but does not provide an
indication of the underlying performance drivers.
P a g e | 44
block times follow the pattern of the actual block times of the previous season.
At system level, scheduled block times remained largely stable in Europe with only a slight
increase between 2008 and 2010 and again as of 2012. In the US, average block times increased
continuously between 2005 and 2010 but decreased again between 2010 and 2015. In 2015
average block times increased again notably in the US which could be due to the degraded
punctuality observed in 2014 (see Figure 4-1). These observed increases in schedule padding in
the US may result from adding block time to improve on-time performance or could be tied to a
tightening of turnaround times. More work is needed on a city pair level to accurately and more
specifically identify the numerous factors influencing the changes in on-time performance.
Figure 4-5: Scheduling of air transport operations (2005-2015)
Seasonal effects are visible in Figure 4-5 with scheduled block times being on average longer in
winter than in summer. US studies have shown that the majority of the increase is explained by
stronger winds on average during the winter period [Ref.
15
].
4.3 Drivers of air transport performance – as reported by airlines
This section aims at identifying underlying delay drivers as reported by airlines in the US and in
Europe. The reported delays relate to the schedules published by the airlines.
A significant difference between the two airline data collections is that the delay causes in the
US relate to the scheduled arrival times whereas in Europe they relate to the delays experienced
at departure. Hence, for the US the reported data also includes variability from further delays or
improvements in the en-route and taxi phase, which is not the case in Europe.
Broadly, the delays in the US and in Europe can be grouped into the following main categories:
Airline + Local turnaround, Extreme Weather, Late arriving aircraft (or reactionary delay),
Security, and ATM system (ATFM/NAS delays):
Airline + Local turnaround: Delay due to circumstances within local control including
airlines or other parties, such as ground handlers involved in the turnaround process
(e.g. maintenance or crew problems, aircraft cleaning, baggage loading, fuelling, etc.). As
the focus of the paper is on ATM contribution, a more detailed breakdown of air carrier
+ local turnaround delays is beyond the scope of the paper.
-3
-2
-1
0
1
2
3
4
Jan-05
Jan-06
Jan-07
Jan-08
Jan-09
Jan-10
Jan-11
Jan-12
Jan-13
Jan-14
Jan-15
minutes
-3
-2
-1
0
1
2
3
4
Jan-05
Jan-06
Jan-07
Jan-08
Jan-09
Jan-10
Jan-11
Jan-12
Jan-13
Jan-14
Jan-15
Europe US (CONUS)
Source: FAA/PRU
Evolution of Scheduled Block Times (2005-2015)
(flights to or from 34 main airports)
P a g e | 45
Extreme Weather: Significant meteorological conditions (actual or forecast) that in the
judgment of the carrier, delays or prevents the operation of a flight such as icing,
tornado, blizzard, or hurricane. In the US, this category is used by airlines for very rare
events like hurricanes and is not useful for understanding the day to day impacts of
weather. Delays due to non-extreme weather conditions are attributed to the ATM
system in the US.
Late-arriving aircraft/reactionary delay: Delays on earlier legs of the aircraft that cannot
be recuperated during the turnaround phases at the airport. Due to the interconnected
nature of the air transport system, long primary delays can propagate throughout the
network until the end of the same operational day.
Security: Delays caused by evacuation of a terminal or concourse, re-boarding of aircraft
because of security breach, inoperative screening equipment, and/or other security
related causes.
ATM System: Delays attributable to ATM refer to a broad set of conditions, such as non-
extreme weather conditions, airport operations, heavy traffic volume, ATC.
Figure 4-6 provides a breakdown of primary delay drivers in the US and Europe. Only delays
larger than 15 minutes compared to schedule are included in the analysis. Clearly, US airlines
attribute a larger fraction of causal delay to US ATM than what is seen in Europe.
Figure 4-6: Drivers of on-time performance in Europe and the US (2015)
In the US, ATM system delay is largely due to weather which is attributed to the ATM system and
equipment problems. In Europe, according to airline reporting, much of the primary delay at
departure is not attributable to ATM but more to local turnaround delays caused by airlines,
airports, and ground handlers.
As already mentioned, the US distribution relates to the scheduled arrival times and the higher
share of ATM-related delay at arrival is partly due to the fact that this figure is impacted by ATM
delays accrued after departure (i.e taxi-out, en-route, terminal).
It should be noted that the ATM system related delays in Figure 4-6 result from not only en-
route and airport capacity shortfalls but also include weather effects which negatively influence
ATM and aircraft operations (IMC approaches, convective weather). According to FAA analysis,
by far the largest share of ATM system related delay is driven by weather in the US [Ref.
16
].
81.4%
6.3%
0.6%
2.4%
9.0%
On Time Air Carrier + Local turnaround
Extreme Weather ATM System (NAS)/ ATFM
Security Late-arriving aircraft/ reactionary delay
81.4%
5.2%
0.6%
6.2%
6.6%
EUROPE
(departures)
Drivers of on-time performance reported by airlines (2015)
(Flights to or from the main 34 airports)
Source: Coda/ US ASQP
US
(arrivals)
P a g e | 46
Figure 4-7 provides an analysis of how the duration of the individual flight phases (gate
departure delay
35
, taxi-out, airborne, taxi-in, total) have evolved over the years in Europe and
the US. It is based on the DLTA Metric (see footnote 34) and compares actual times for each city
pair with the long-term average for that city pair over the full period (2005-2015). For example,
in the US at the peak of the curve at the end of 2008, total average actual flight time among city
pairs had increased over 5 minutes since 2005 and was 4 minutes above the long-term average.
Figure 4-7: Trends in the duration of flight phases (2005-2015)
In Europe, performance is clearly driven by gate departure delays with only very small changes in
the gate-to-gate phase (i.e. there is only a very small gap between departure time and total). The
drop in gate departure delay in 2009 when traffic levels fell as a result of the economic crisis is
significant. In 2010, despite a traffic level still below 2008, gate departure delays increased again
significantly mainly due to exceptional events (industrial actions, extreme weather, technical
upgrades). Since 2010, performance in almost all phases of flight improved again substantially.
In the US, the trailing 12-month average began to decline at the beginning of 2008. Similar to
Europe, departure delay was the largest component associated with the change in average flight
time. Between 2008 and 2010, most flight components went back to their long-term average
and improved even further between 2010 and 2012 before they decreased again in 2014-2015.
A substantial improvement is also visible for taxi-out times as a result of the initiatives to
improve performance in this area.
35
Gate departure delay is defined as the difference between the actual gate out time and the schedule departure
time published by the operators.
-5
-4
-3
-2
-1
0
1
2
3
4
5
Jan-05
Jan-06
Jan-07
Jan-08
Jan-09
Jan-10
Jan-11
Jan-12
Jan-13
Jan-14
Jan-15
Jan-16
minutes
GATE DEPARTURE DELAY
TX-OUT TIMES
AIRBORNE TIMES
TX-IN TIMES
TOTAL
Data Source: CODA/ FAA
-5
-4
-3
-2
-1
0
1
2
3
4
5
Jan-05
Jan-06
Jan-07
Jan-08
Jan-09
Jan-10
Jan-11
Jan-12
Jan-13
Jan-14
Jan-15
Jan-16
EUROPE
US (CONUS)
Trends in the duration of flight phases
(flights to or from main 34 airports)
P a g e | 47
4.4 Variability by phase of flight
This section looks at the Key Performance Area of
Predictability or variability by phase of flight using
airline-provided data for gate “out,” wheels “off,”
wheels “on,” and gate “in” data. This out, off, on, in
data is often referred to as OOOI data and is almost
entirely collected automatically using a basic airline
data-link system (see Section 1.3 for more information
on data sources).
Due to the multitude of variables involved, a certain
level of variability is natural. However, variations of
high magnitude and frequency can become a serious
issue for airline scheduling departments as they have to
balance the utilisation of their resources and the
targeted service quality.
Variability
The “variability” of operations determines the
level of predictability for airspace users and
hence has an impact on airline scheduling. It
focuses on the variance (distribution widths)
associated with the individual phases of flight
as experienced by airspace users.
The higher the variability, the wider the
distribution of actual travel times and the more
costly time buffer is required in airline
schedules to maintain a satisfactory level of
punctuality. Reducing the variability of actual
block times can potentially reduce the amount
of excess fuel that needs to be carried for each
flight in order to allow for uncertainties.
Predictability evaluates the level of variability in each phase of flight as experienced by the
airspace users
36
. In order to limit the impact from outliers, variability is measured as the
difference between the 85th and the 15th percentile for each flight phase. This captures 70% of
flights and would be representative of one standard deviation if in fact travel times were
normally distributed and not skewed due to delay. In targeting high levels of punctuality, airlines
may in fact require “certainty” around a broader population of flights than 70% and therefore
view the system as more “variable” and less predictable than what is shown below. However,
the focus on this report is to compare the US and Europe using a common methodology.
Figure 4-8 shows that in both Europe and the US, arrival predictability is mainly driven by gate
departure predictability. Variability in all flight phases is higher in the US than Europe.
Figure 4-8: Variability of flight phases (2005-2015)
36
Intra flight variability (i.e. monthly variability of flight XYZ123 from A to B). Flights scheduled less than 20 times per
month are excluded.
0
2
4
6
8
10
12
14
16
18
20
22
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Departure time Taxi-out + holding Flight time (cruising +
terminal)
Taxi-in + waiting for the
gate
Arrrival time
minutes
US - (85th-15th)/2 EU - (85th-15th)/2
Gate-to-gate phase
Source: FAA/PRC
Variability of flight phases
(flights to or from 34 main airports)
P a g e | 48
Between 2005 and 2007, gate departure time variability continuously increased on both sides of
the Atlantic. Contrary to Europe, variability increased also in the taxi-out phase in the US, which
appears to be driven by the different approaches in both scheduling operations and absorbing
necessary delay.
Historically, the differences between the US and Europe have been largest on the ground both at
the gate and in taxi-out. Despite the lower level of variability, improvement in the gate-to-gate
phase – especially in the taxi-out and terminal airborne phase – can warrant substantial savings
in direct operational and indirect strategic costs for the airlines.
Figure 4-9 shows a clear link between the various seasons and the level of variability in the US
and in Europe. The higher variability in the winter is mainly due to weather effects. The higher
airborne flight time variability in the winter in the US and in Europe is caused by wind effects and
also partly captured in airline scheduling (see Figure 4-5).
Figure 4-9: Monthly variability of flight phases (2010-2015)
In the departure phase, ATM can contribute to the variability through ATM-related departure
holdings and subsequent reactionary delays on the next flight legs. The ATM-related departure
delays are analysed in more detail in Section 5.2.1. Due to the interconnected nature of the
aviation system, variability originating at constrained airports can propagate throughout the
entire network.
The gate-to-gate phase is affected by a multitude of variables including congestion (queuing at
take-off and in TMA), wind, and flow management measures applied by ATM.
For the airborne phase of flight, it is important to note that wind can have a large impact on day-
to-day predictability compared to a planned flight time for scheduling purposes. Understanding
the ATM, airline, and weather influences on predictability is a key element of baselining system
performance. The strong jet stream winds in the winter and convective weather in the summer
impact overall predictability statistics.
At US airports, winter delays are believed to be driven to some extent by the higher frequency of
instrument meteorological conditions (IMC) combined with scheduling closer to visual
0
5
10
15
20
25
30
Jan-10
Jan-11
Jan-12
Jan-13
Jan-14
Jan-15
minutes
Departure time Taxi-out + holding
Flight time (cruising + terminal) Taxi-in + waiting at gate
Arrival time
0
5
10
15
20
25
30
Jan-10
Jan-11
Jan-12
Jan-13
Jan-14
Jan-15
US (Conus)
Monthly variability of flight phases [(85th-15th)/2]
(flights to or from 34 main airports)
EUROPE
P a g e | 49
meteorological conditions (VMC). Summer delays result from convective weather blocking en-
route airspace. The high level of variability may be related to scheduling and seasonal
differences in weather.
In Europe where the declared airport capacity is assumed to be closer to IMC capacity, the
overall effects of weather on operational variability are expected to be generally less severe.
After a high level analysis of operational performance from the airline point of view, the next
chapter provides an assessment of performance evaluated from the ATM perspective. The
following analysis of ATM-related service quality is indicative of what can be influenced by
improvements or actions taken by the ANSP.
P a g e | 50
5. COMPARISON OF ATM-RELATED OPERATIONAL SERVICE QUALITY
Although the analysis of performance compared to airline schedules (on-time performance) in
Section 4.1 is valid from a passenger point of view and provides valuable first insights, the
involvement of many different stakeholders and the inclusion of time buffers in airline schedules
require a more detailed analysis for the assessment of ATM performance.
This section compares US and European performance using Key Performance Indicators
calculated using data available to the ANSP. Specific KPIs include ATM-reported attributable
delay, flight plan additional distance, and additional time in the various phases of flight including
taxi-out, en-route, descent and arrival, and taxi-in.
The evaluation of ATM-related operational service quality will focus on the Key Performance
Areas of efficiency of actual operations by phase of flight in order to better understand the ATM
contribution and differences in traffic management techniques between the US and Europe. The
KPA of environmental sustainability is addressed as it relates to efficiency when evaluating
additional fuel burn.
The FAA-ATO and EUROCONTROL have been sharing approaches to performance measurement
over the past years. Both have developed similar sets of operational key performance areas and
indicators. The specific key performance indicators (KPIs) used in this report were developed
using common procedures on comparable data from both the FAA-ATO and EUROCONTROL (see
Section 1.3).
5.1 Approach to comparing ATM-related service quality
Figure 5-1 shows the conceptual framework for the analysis of ATM-related service quality by
phase of flight applied in the next sections of this report. The high level passenger perspective
(on-time performance) is shown at the top together with the airline scheduling. The various
elements of ANS performance analysed in more detail in the following sections are highlighted in
blue in Figure 5-1.
Figure 5-1: Conceptual framework to measuring ATM-related service quality
Departure
delays
Airport Capacity
• airport scheduling
• achieved throughput
• sustainability of ops.
• etc.
En-route
inefficiency
Origin airport
En-route
ATFM delays
Airport
ATFM delays
Terminal
inefficiency
Reactionary
delays
Management
of arrival flows
Other (airline,
airport, etc.)
Departure
punctuality
Pre-
departure
delays
(at gate)
Air Traffic
Management
Weather
Taxi-out
inefficiency
Scheduled block time (airlines)
Buffer
Efficiency and variability of operations (ANS contribution)
Ground
Airborne
En-route network Approach Arrival airport
Airline
scheduling
ANS
performance
Arrival
punctuality
Passenger
perspective
Taxi-in
inefficiency
P a g e | 51
The evaluation of ATM-related service quality in
the remainder of this report focuses on the
Efficiency (time, fuel) of actual operations by
phase of flight (see information box).
ATM may not always be the root cause for an
imbalance between capacity and demand (which
may also be caused by other stakeholders,
weather, military training and operations, noise
and environmental constraints, etc.).
Efficiency
‘Efficiency’ in this report measures the difference
between actual time/distance and an unimpeded
reference time/distance. “Inefficiencies” can be
expressed in terms of time and fuel and also have an
environmental impact.
Due to inherent necessary (safety) or desired (noise,
capacity, cost) limitations the reference values are not
necessarily achievable at system level and therefore
ATM-related ‘inefficiencies” cannot be reduced to
zero.
However, depending on the way traffic is
managed and distributed along the various
phases of flight (airborne vs. ground), ATM has a
different impact on airspace users (time, fuel
burn, costs), the utilisation of capacity (en-route
and airport), and the environment (emissions).
The overarching goal is to minimise overall direct
(fuel, etc.) and strategic (schedule buffer in the
form of added block time, etc.) costs whilst
maximising the utilisation of available en-route
and airport capacity.
While maximising the use of scarce capacity,
there are trade-offs
37
to be considered when
managing the departure flow at airports (holding
at gate vs. queuing at the runway with engines
running).
Similarly, the management of arrival flows needs to find a balance between the application of
ground holding, terminal airborne holdings and en-route sequencing and speed control [Ref.
17
].
37
It should be noted that there may be trade-offs and interdependencies between and within Key Performance
Areas (i.e. Capacity vs. Cost-efficiency) which need to be considered in an overall assessment.
Scheduling of
block times
Variable time to
complete operation
Additional time
& fuel burn
Additional
emissions
Predictability
Efficiency of Operations
Environment
Punctuality
Variability of Operations
Travel
Time
Nr. of observations
Closer to
Optimum
Reduce
Variability
P a g e | 52
5.2 ATM-related efficiency by phase of flight
Efficiency generally relates to fuel efficiency or reductions in flight times of a given flight. The
analyses in this chapter consequently focus on the difference between the actual travel times
and an optimum time of the various phases of flight illustrated in Figure 5-1. For the airborne
phase of flight, this “optimum” may be a user-preferred trajectory which would include both the
vertical and horizontal profile.
5.2.1 ATM-RELATED DEPARTURE RESTRICTIONS (GROUND HOLDING)
Both the US and Europe report delay imposed
on flights
38
by the ANSP in order to achieve
required levels of safety as well as to most
effectively balance demand and capacity.
ATFM departure delays can have various
ATM-related (ATC capacity, staffing, etc.) and
non-ATM related (weather, accident, etc.)
reasons.
The categories of delay cause codes differ in
the US and Europe; however, five general
categories were developed to encompass the
varying causal factors (see grey box). Both
systems track the constraining facility which
allows delay to be reported as either due to
terminal/airport or en-route constraints.
Figure 5-2 shows average total ATFM ground
delay (en-route and terminal) per flight
between 2008 and 2015. More detailed
analyses of causal reasons for changes
between 2013 and 2015 are provided in later
figures for both US and Europe.
For comparability reasons, only flights with
ATFM ground delays equal or greater 15
minutes were included in the analyses.
Mapping of ATFM delay causes
The table shows how the differing delay codes for EU and
US were mapped to produce the analysis in this section.
The delays are calculated with reference to the estimated take-off time in the last submitted
flight plan (not the published departure times in airline schedules).
38
In the US, ATM delay by Causal Factor is recorded in the FAA OPSNET database. FAA requires facilities to report all
delay equal or greater than 15 minutes.
En-route
inefficiency
Origin
airport
Ground hold
(en route)
Ground hold
(airport)
Terminal
inefficiency
Management
of arrival flows
Air Traffic
Management
Taxi-out
inefficiency
En-route network Approach
Arrival
airport
Taxi-in
inefficiency
EUR Code ATFM reason code (CFMU) Example US CODE
C C-ATC Capacity Demand exceeds capaci ty VOLUME
S S-ATC Staffing Illness; Traffic delays on highway VOLUME
G G-Aerodrome Capacity Demand exceeds the decl ared apt. capaci ty VOLUME
V V-Environmental Issues Noise restri ctions RUNWAY
I I-Industrial Action (ATC) Controll ers' strike OTHER
R R-ATC Routeing Phas ing in new procedures OTHER
T T-Equipment (ATC) Radar fail ure; RTF failure EQUIPMENT
W W-Weather Low Visi bility; crosswinds WX
D D-De-icing De-icing WX
A A-Accident/Incident RWY23 closed due to acci dent RUNWAY
E E-Equipment (non-ATC) Runway or taxiway lighting fail ure EQUIPMENT
M M-Military activity Brilli ant Invader; ODAX OTHER
N N-Industrial Action (non-ATC) Firemen's stri ke OTHER
O O-Other Securi ty alert OTHER
P P-Speci al Event European Cup Football OTHER
P a g e | 53
Figure 5-2: Evolution of total ATFM delay per flight (2008-2015)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
2008 2009 2010 2011 2012 2013 2014 2015
Average minutes of ATFM delay per
flight
Evolution of total ATFM delay per flight
(flights to or from the main 34 airports within region)
Europe US
Only delays equal or greater than 15 minutes were included in the analyses
Figure 5-3: Percent change in ATFM delay by cause (2015 vs. 2013)
29.9%
2.2%
1.8%
11.3%
-14.4%
-1.3%
2.8%
-2.3%
-20%
-10%
0%
10%
20%
30%
40%
50%
Europe US
Other
Equipment
Weather
Runway/Taxi
Volume
Change in ATFM delay by cause 2015 vs. 2013 (%)
Only delays equal or greater than 15 minutes were included in the analyses
In Europe, average ATFM delay
continuously decreased until
2013, following the historically
bad performance due to
weather and strikes in 2010.
Between 2013 and 2015, total
ATFM ground delays equal or
greater 15 minutes increased in
Europe by 43.4% whereas traffic
only increased by 4.1% during
the same time.
The US has also shown a decline
since 2008. Some of this
improvement can be attributed to improving local weather at SFO and declining traffic levels at
key facilities such as ORD and PHL as shown in Chapter 3. Between 2013 and 2015, total ATFM
delay decreased by 12.7% with
overall traffic levels at the main
34 held constant.
Figure 5-3 shows this change
from 2013 to 2015 by causal
factor. In the US, the decrease
between 2013 and 2015 was
largely due to weather including
en-route convective weather not
quantified in Chapter 3.
In Europe, the notable
performance deterioration was
due to a significant increase in
capacity related delays and to a
lesser extent due to weather.
Table 5-1 compares ATM-related departure restrictions imposed in the two ATM systems due to
en-route and airport constraints. As can be expected, the share of flights affected by departure
ground restrictions at origin airports differs considerably between the US and Europe. Despite a
reduction from 5.0% in 2008 to 2.0% in 2015, flights in Europe are still over twice more likely to
be held at the gate or on the ground for en-route constraints than in the US where the share of
flights was 0.8% in 2015.
Table 5-1: ATFM departure delays (flights to or from main 34 airports within region)
2008 2013 2015 2008 2013 2015
IFR flights (M) 5,5 4,8 4,8 9,3 8,4 8,2
% of flights delayed >=15 min.
5,0% 1,3% 2,0% 1,1% 0,8% 0,8%
delay per flight (min.)
1,4 0,4 0,6 0,4 0,3 0,3
delay per delayed flight (min.)
28 31 28 38 36 35
% of flights delayed >=15 min.
2,8% 1,6% 2,3% 4,1% 2,6% 2,5%
delay per flight (min.)
0,9 0,5 0,7 2,3 1,5 1,3
delay per delayed flight (min.)
32 33 33 56 57 51
Airport related
delays >=15min.
(EDCT/ATFM)
En route related
delays >=15min.
(EDCT/ATFM)
Only delays > = 15 min. are included.
EUROPE
US (CONUS)
P a g e | 54
Figure 5-4: Breakdown of en-route ATFM delay by cause (2015)
Figure 5-5: Breakdown of airport arrival ATFM delay by cause (2015)
10.4%
1.5%
22.4%
8.7%
EUROPE
43.0%
of total
0.3%
0.1%
2.9%
14.5%
Other
Runway/Taxi
Equipment
Volume
Weather
US
17.9%
of total
Only delays equal or greater
than 15 minutes were
included in the analyses
3.9%
1.4%
0.6%
19.2%
31.9%
EUROPE
57.0%
of total
1.6%
6.4%
0.2%
8.9%
64.9%
Other
Runway/Taxi
Equipment
Volume
Weather
US
82.1%
of total
Only delays equal or greater
than 15 minutes were
included in the analyses
For airport related delays, the percentage of delayed flights at the gate or on the surface is
slightly higher in the US than in Europe. However, the delay per delayed flight in the US is 55%
higher (51 vs. 33).
Whereas in the US, en-route
delays are mostly driven by
convective weather, in
Europe they are mainly the
result of capacity and staffing
constraints (including ATC
industrial actions) driven by
variations in peak demand
(see large differences
between summer and winter
in Europe in Figure 3-4 and
Figure 3-5).
At system level, the causes
for airport-related ATFM
delays are similar in both the
US and Europe. Weather is
the predominant driver of
ATFM delays in both Europe
and the US (Figure 5-5).
Figure 5-6 compares the
average minutes of airport-related ATFM departure delays attributed to the constraining
destination airport. The airports are sorted in descending order by number of ATFM delay
minutes; however, airports with a high number of flights will show lower average ATFM delays.
Figure 5-6: Airport charged ATFM delay by destination airport (2015)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
1
2
3
4
5
6
7
8
9
10
Amsterdam (AMS)
London (LHR)
Zurich (ZRH)
Rome (FCO)
Geneva (GVA)
Palma (PMI)
London (LGW)
Frankfurt (FRA)
Brussels (BRU)
Vienna (VIE)
Paris (ORY)
Barcelona (BCN)
Oslo (OSL)
Paris (CDG)
Lisbon (LIS)
Madrid (MAD)
Munich (MUC)
Hamburg (HAM)
Helsinki (HEL)
Dusseldorf (DUS)
London (STN)
Manchester (MAN)
Berlin (TXL)
Dublin (DUB)
Nice (NCE)
Stockholm (ARN)
Stuttgart (STR)
Athens (ATH)
Milan (LIN)
Copenhagen (CPH)
Prague (PRG)
Milan (MXP)
Lyon (LYS)
Cologne (CGN)
ATFM/TMI delays > = 15 minutes by arrival airport
Airport charged ATFM delay by destination airport (2015)
Delay minutes (2015) Delay minutes (2013) Cumulative %
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
1
2
3
4
5
6
7
8
9
10
New York (LGA)
Chicago (ORD)
Newark (EWR)
San Francisco (SFO)
New York (JFK)
Philadelphia (PHL)
Los Angeles (LAX)
Boston (BOS)
Atlanta (ATL)
Houston (IAH)
Denver (DEN)
Minneapolis (MSP)
Dallas (DFW)
Washington (DCA)
Las Vegas (LAS)
Seattle (SEA)
Charlotte (CLT)
Phoenix (PHX)
Detroit (DTW)
Chicago (MDW)
Baltimore (BWI)
Washington (IAD)
San Diego (SAN)
Houston (HOU)
Memphis (MEM)
Dallas (DAL)
Orlando (MCO)
Ft. Lauderdale (FLL)
Tampa (TPA)
Miami (MIA)
St. Louis (STL)
Salt Lake City (SLC)
Nashville (BNA)
Portland (PDX)
Source: EUROCONTROL PRU/ FAA-ATO
P a g e | 55
In Europe, delays are more evenly spread across airports with Amsterdam (AMS), London (LHR),
and Zurich (ZRH) generating the highest amounts of airport ATFM delay in 2015 in absolute
terms.
For the US, approximately 70% of the total delay minutes are concentrated at six airports in the
US: New York (LGA), Chicago (ORD), Newark (EWR), San Francisco (SFO), New York (JFK), and
Philadelphia (PHL). From Figure 5-6, it can be seen that flights to New York-LaGuardia (LGA) have
an average ATFM delay which is four times higher than London Heathrow (LHR). Figure 5-6 also
shows the facilities the drove the overall 2013-2015 reduction with ORD and EWR contributing
the most followed by SFO and PHL. During this time, SFO gained an improved ability to run
reduced separation under converging operations and lower minimums. Los Angeles (LAX), which
experienced increased traffic, contributed the most to increasing system wide ATFM delay.
The difference in ATFM strategy between the US and Europe is clearly visible. In the absence of
en-route sequencing in Europe, reducing ATFM delays (by releasing too many aircraft) at the
origin airport when the destination airport’s capacity is constrained potentially increases
airborne delay (i.e. holding or extended final approaches). On the other hand, applying excessive
ATFM delays risks underutilisation of capacity and thus, increases overall delay.
More analysis is needed to see how higher delays per delayed flight are related to moderating
demand with “airport slots” in Europe.
5.2.2 ATM-RELATED TAXI-OUT EFFICIENCY
This section aims at evaluating the level
of inefficiencies in the taxi-out phase.
The analysis of taxi-out efficiency refers
to the period between the time when the
aircraft leaves the stand (actual off-block
time) and the take-off time. The
additional time is measured as the
average additional time beyond an
unimpeded reference time.
In the US, the additional time observed in the taxi-out phase also includes some delays due to
local en-route departure and MIT restrictions. In Europe, the additional time might also include a
small share of ATFM delay which is not taken at the departure gate, or some delays imposed by
local restriction, such as Minimum Departure Interval (MDI).
The taxi-out phase and hence the performance indicator is influenced by a number of factors
such as take-off queue size (waiting time at the runway), distance to runway (runway
configuration, stand location), downstream departure flow restrictions, aircraft type, and remote
de-icing, to name a few. Of these aforementioned causal factors, the take-off queue size
39
is
considered to be the most important one for taxi-out efficiency [Ref.
18
].
39
The queue size that an aircraft experienced was measured as the number of take-offs that took place between its
pushback and take-off time.
En-route
inefficiency
Origin
airport
Ground hold
(en route)
Ground hold
(airport)
Terminal
inefficiency
Management
of arrival flows
Air Traffic
Management
Taxi-out
inefficiency
En-route network Approach
Arrival
airport
Taxi-in
inefficiency
P a g e | 56
Although the impact of ANSPs on total additional time is limited when runway capacities are
constraining departures, in Europe, Airport Collaborative Decision Making (A-CDM) initiatives try
to optimise the departure queue by managing the pushback times.
The aim is to keep aircraft at the stand to reduce additional time and fuel burn in the taxi-out
phase to a minimum by providing only minimal queues and improved sequencing at the
threshold to maximise runway throughput. These departure delays at the gate are reflected in
the departure punctuality indicators. However, the ATM part due to congestion in the taxiway
system is presently difficult to isolate with the available data set.
Two different methodologies
were applied for the analysis
of inefficiencies in the taxi-
out time.
While the first method used
for Figure 5-7 is simpler, it
allows for the application of
a consistent methodology.
The method uses the 20th
percentile of each service
(same operator, airport, etc.)
as a reference for the
“unimpeded” time and
compares it to the actual
times. This can be easily
computed with US and
European data.
Figure 5-7: Additional times in the taxi-out phase (system level)
On average, additional times in the taxi-out phase appear to be higher in the US with a
maximum difference of approximately 2 minutes more per departure in 2007. Between 2008
and 2012, US performance improved continuously while European performance only improved
gradually which narrowed the gap between the US and Europe.
Although the gap notably reduced since 2008, the observed differences in inefficiencies between
the US and Europe are largely driven by different flow control policies and the absence of
scheduling caps at most US airports. Additionally, the US Department of Transportation collects
and publishes data for on-time departures which could add to the focus of getting off-gate on
time.
In 2015, both European and US performance deteriorated. The increase in additional taxi-out
times in the US may be linked to worsening weather conditions for specific areas of the country
or as a result of ATFM delay taken on the ground.
Seasonal patterns emerge, but with different cycles in the US and in Europe. Whereas in Europe
the additional times peak during the winter months (most likely due to weather conditions), in
the US the peak is in the summer which is most likely linked to congestion.
The analysis by airport in Figure 5-8 and Figure 5-9 as well as the overview in Figure 5-10 is based
on the more sophisticated methodologies by each of the performance groups in the US and
Europe
40
.
40
A description of the respective methodologies can be found in the Annex of the 2010 comparison report.
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
Jan-05
Jul-05
Jan-06
Jul-06
Jan-07
Jul-07
Jan-08
Jul-08
Jan-09
Jul-09
Jan-10
Jul-10
Jan-11
Jul-11
Jan-12
Jul-12
Jan-13
Jul-13
Jan-14
Jul-14
Jan-15
Jul-15
minutes per departure
Europe (Top 34) US (Top 34)
Annual average EUR Annual average US
Additional time in the taxi-out phase compared to 20th perc. of each service
(service = same operator, same airport, monthly)
Source: FAA/PRC analysis
P a g e | 57
Figure 5-8 shows a more detailed comparison of additional time in the taxi-out phase at the
major airports in Europe and the US which illustrates the contrasted situations among airports.
Figure 5-8: Additional time in the taxi-out phase by airport (2015)
In Europe, the two London airports (LHR, LGW), and Rome (FCO) showed the highest average
additional taxi out time in 2015. On average, London Gatwick (LGW) showed an increase in
additional taxi-out time of almost 2 minutes between 2013 and 2015.
Figure 5-9: Difference in additional time in the taxi-out phase by airport (2015 vs. 2013)
0
2
4
6
8
10
12
14
London (LHR)
London (LGW)
Rome (FCO)
Frankfurt (FRA)
Dublin (DUB)
Barcelona (BCN)
Amsterdam (AMS)
Paris (CDG)
Madrid (MAD)
Average
London (STN)
Zurich (ZRH)
Lisbon (LIS)
Munich (MUC)
Geneva (GVA)
Manchester (MAN)
Dusseldorf (DUS)
Milan (LIN)
Oslo (OSL)
Stuttgart (STR)
Vienna (VIE)
Brussels (BRU)
Paris (ORY)
Milan (MXP)
Copenhagen (CPH)
Palma (PMI)
Berlin (TXL)
Prague (PRG)
Hamburg (HAM)
Helsinki (HEL)
Cologne (CGN)
Stockholm (ARN)
Athens (ATH)
Lyon (LYS)
Nice (NCE)
Average additional taxi-out time (min. per departure)
Additional time in the taxi out phase by airport (2015)
2015 2013
0
2
4
6
8
10
12
14
New York (LGA)
New York (JFK)
Philadelphia (PHL)
Chicago (ORD)
Newark (EWR)
Washington (DCA)
Charlotte (CLT)
Boston (BOS)
Houston (IAH)
Dallas (DFW)
Washington (IAD)
Miami (MIA)
San Francisco (SFO)
Average
Phoenix (PHX)
Los Angeles (LAX)
Seattle (SEA)
Denver (DEN)
Baltimore (BWI)
Minneapolis (MSP)
Chicago (MDW)
Atlanta (ATL)
Las Vegas (LAS)
San Diego (SAN)
Orlando (MCO)
Salt Lake City (SLC)
Detroit (DTW)
Dallas (DAL)
Houston (HOU)
Nashville (BNA)
St. Louis (STL)
Tampa (TPA)
Memphis (MEM)
Portland (PDX)
Ft. Lauderdale (FLL)
US
Main 34 average (min. per dep.)
2015: 5.6
Europe
Main 34 average (min. per dep.)
2015: 4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
London (LHR)
London (LGW)
Rome (FCO)
Frankfurt (FRA)
Dublin (DUB)
Barcelona (BCN)
Amsterdam (AMS)
Paris (CDG)
Madrid (MAD)
Average
London (STN)
Zurich (ZRH)
Lisbon (LIS)
Munich (MUC)
Geneva (GVA)
Manchester (MAN)
Dusseldorf (DUS)
Milan (LIN)
Oslo (OSL)
Stuttgart (STR)
Vienna (VIE)
Brussels (BRU)
Paris (ORY)
Milan (MXP)
Copenhagen (CPH)
Palma (PMI)
Berlin (TXL)
Prague (PRG)
Hamburg (HAM)
Helsinki (HEL)
Cologne (CGN)
Stockholm (ARN)
Athens (ATH)
Lyon (LYS)
Nice (NCE)
2013-2015 Change in average additional taxi
-out time
Change in average additional time in the taxi out phase (2015 vs. 2013)
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
New York (LGA)
New York (JFK)
Philadelphia (PHL)
Chicago (ORD)
Newark (EWR)
Washington (DCA)
Charlotte (CLT)
Boston (BOS)
Houston (IAH)
Dallas (DFW)
Washington (IAD)
Miami (MIA)
San Francisco (SFO)
Average
Phoenix (PHX)
Los Angeles (LAX)
Seattle (SEA)
Denver (DEN)
Baltimore (BWI)
Minneapolis (MSP)
Chicago (MDW)
Atlanta (ATL)
Las Vegas (LAS)
San Diego (SAN)
Orlando (MCO)
Salt Lake City (SLC)
Detroit (DTW)
Dallas (DAL)
Houston (HOU)
Nashville (BNA)
St. Louis (STL)
Tampa (TPA)
Memphis (MEM)
Portland (PDX)
Ft. Lauderdale (FLL)
US
Europe
P a g e | 58
In the US, the New York airports, Philadelphia (PHL), and Chicago (ORD) showed the highest
average additional time in 2015. In contrast to flights destined for ORD and San Francisco (SFO)
(Figure 5-6), flights departing these airports experienced an increase of delay on departure in the
taxi-out phase. In addition, the Texas airports of Dallas (DFW) and Houston (IAH) contributed to
the increase in system wide taxi-out delay however these airports also had decreases in airline
reported data (OOOI) flights. Atlanta (ATL) contributed the most to improvement on the system-
wide measure. Unlike ORD and SFO, Newark (EWR) showed significant improvement for both
taxi-out delay and for flights destined to EWR (Figure 5-6). Although DTW showed the largest
decrease in traffic from 2013-2015, the improvement shown should be caveated due to
significant changes in the reporting carriers for OOOI data. The most notable performance
improvement for an airport was observed for Fort Lauderdale (FLL). This is attributed to the
increase in declared capacity that occurred with the completed expansion of a runway.
Although some care should be
taken when comparing the two
indicators due to slightly differing
methodologies, Figure 5-10 tends
to confirm the trends seen in Figure
5-7.
Overall, additional times in the taxi-
out phase appear to be higher in
the US but the gap closed between
2008 and 2011. As of 2012, the US
performance started to deteriorate
again whereas the performance in
Europe remained largely stable
during the same period.
Figure 5-10 Evolution of average additional minutes in the taxi out
phase (2008-2015)
5.2.3 EN-ROUTE FLIGHT EFFICIENCY
This section evaluates en-route flight efficiency
in the US and Europe. En-route flight efficiency
indicators assess actual flight trajectories or
filed flight plans against an ideal or benchmark
condition.
From an operator’s perspective, this ideal
trajectory would be a User-Preferred Trajectory
that would have a horizontal (distance) and a
vertical (altitude) component.
Ideal altitudes are highly affected by external factors such as aircraft specific weight and
performance as well as turbulence and other weather factors. For this reason, much more
detailed data from airlines and tactical responses to weather would be needed to establish an
efficiency criterion for altitude. Furthermore, the horizontal component is, in general, of higher
economic and environmental importance than the vertical component across Europe as a whole
[Ref.
19
]. Nevertheless there is scope for further improvement, and Section 6.2 in this report
provides an initial comparison of vertical flight efficiency in the arrival phase between the US and
Europe which will help to provide a more complete picture in the future.
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
2008 2009 2010 2011 2012 2013 2014 2015
Average additional time in the taxi out
phase (minutes)
US Europe
Evolution of additional taxi-out time
(departures - main 34 airports)
En-route
inefficiency
Origin
airport
Ground hold
(en route)
Ground hold
(airport)
Terminal
inefficiency
Management
of arrival flows
Air Traffic
Management
Taxi-out
inefficiency
En-route network Approach
Arrival
airport
Taxi-in
inefficiency
P a g e | 59
The focus of this section is on the horizontal
component of the en-route phase. Two KPI’s
are reported. The first one compares the
lengths of the en-route section of the last
filed flight plan to a benchmark “achieved
distance” (apportionment of great circle
distance). The second KPI compares actual
trajectories against “achieved distance.”
For a flight, the “inefficiency” is the difference
between the length of the analysed trajectory
(filed flight plan or actual flown) and an
“achieved” reference distance (see also grey
box). Where a flight departs or arrives outside
the reference airspace, only that part inside
the airspace is considered.
“En-route” is defined as the portion between
a 40NM radius around the departure airport
and a 100NM radius around the arrival
airport. The indicator is calculated as the ratio
of the sum, over all flights considered.
Horizontal en-route flight efficiency
Horizontal en-route flight efficiency compares the length
of flight plan or actual trajectories (A) to the “achieved”
distance (H).
The achieved distance apportions the Great Circle
Distance between two airports. If the origin/ destination
airport is located outside of the reference airspace, the
entry/exit point into the airspace is used for the
calculation.
The refined methodology enables to better differentiate
between local inefficiency (deviations from GCD between
local entry and exit points and the contribution to the
network.
More information on horizontal en-route flight efficiency
in Europe is available at www.ansperformance.eu.
The methodology used for the computation of horizontal en-route fight efficiency in this report
is consistent with the flight efficiency indicators used in the Single European Sky performance
scheme.
The flight efficiency in the last 100NM before landing which also includes airborne holdings is
addressed in the next section of this report (5.2.4).
It is acknowledged that this distance-based approach does not necessarily correspond to the
“optimum” trajectory when meteorological conditions or economic preferences of airspace
users are considered for specific flights. However when used at the strategic level, the KPI will
clearly point to areas where track distance is increasing or decreasing over time.
OPPORTUNITIES AND LIMITATIONS TO IMPROVING HORIZONTAL FLIGHT-EFFICIENCY
While there are economic and environmental benefits in improving flight efficiency, there are
also inherent limitations. Trade-offs and interdependencies with other performance areas such
as safety, capacity, and environmental sustainability as well as airspace user preferences in route
selection due to weather (wind optimum routes), route availability, or other reasons (differences
in route charges
41
, avoidance of congested areas) affect en-route flight efficiency.
En-route flight inefficiencies are predominantly driven by (1) route network design (2) route
availability, (3) route utilisation (route selection by airspace users) and (4) ATC measures such as
MIT in the US (but also more direct routings).
Although a certain level of inefficiency is inevitable, there are a number of opportunities for
improvement. The following limiting factors should be borne in mind for the interpretation of
the results:
41
In Europe, the route charges differ from State to State.
(A)
ADEP
ADES
P a g e | 60
Basic rules of sectorisation and route design. For safety reasons, a minimum separation
has to be applied between aircraft;
Systematisation of traffic flows to reduce complexity and to generate more capacity;
Strategic constraints on route/ airspace utilisation.
Impact of Special Use Airspace (SUA) on flight
efficiency.
Figure 5-11 illustrates the impact of special
use airspace on horizontal en-route flight
efficiency in Europe in 2015. The filed routes
of the 15 most penalising city pairs
connecting the top 34 airports are plotted in
blue and the actually flown trajectories are
plotted in red. It is clearly visible how flights
have to circumnavigate around SUA (brown
areas). However, it also shows that directs
are being provided by ATC on a tactical basis
which improve flight efficiency in actual
trajectories but which on the other hand
introduce variability in the system.
Figure 5-11: Impact of Special Use Airspace in
Europe (2015)
Interactions with major airports. Major terminal areas tend to be more and more
structured. As traffic grows, departure traffic and arrival traffic are segregated and
managed by different sectors. This TMA organisation affects en-route structures as over-
flying traffic has to be kept far away, or needs to be aligned with the TMA arrival and
departure structures.
Route availability and route planning. Once routes are made available for flight planning,
their utilisation is in the hand of flight dispatchers and flow managers. Many airlines
prepare flight plans based on fixed route catalogues and do not have the tools/resources
to benefit from shorter routes when available. Aircraft operators often rely on tactical
ATC routings.
In Europe, en-route flight efficiency is also affected by the fragmentation of airspace
(airspace design remains under the auspices of the States).
For the US, the indicator includes the effect of en-route holding and vectoring.
Lastly, planned cruise speeds or altitudes are not known by ATC systems and may require
detailed performance modelling or information on airline intent.
While technologies, concepts, and procedures have helped to further optimise safety, add
capacity, and increase efficiency (e.g. Reduced Vertical Separation Minima, RNAV) over the past
years, it will remain challenging to maintain the same level of efficiency while absorbing forecast
demand increases over the next 20 years.
P a g e | 61
Figure 5-12 shows the
evolution of horizontal en-
route flight efficiency
(actual and flight plan)
compared to achieved
distance between 2008
and 2015.
An “inefficiency” of 5%
means for instance that
the extra distance over 1
000NM was 50NM.
Due to data availability, the
KPIs for Europe are only
shown as of 2011.
Figure 5-12: Evolution of horizontal flight efficiency (actual and flight plan)
(2008-2015)
Although much smaller in the US, there is a notable gap between flight plan and actual flight
inefficiency in the US and in Europe.
The difference between planned and actual operations reveals that in general flights fly more
direct than their flight plan in both systems. This is most likely due to more direct tracks
provided by ATC on a tactical basis when traffic and airspace availability permits.
In general the US reports less “inefficiency” in this area. Although performance improved in
Europe for both indicators over the past years, European airlines file on average some 4.6%
greater than their achieved distance compared to 3.4% in the US in 2015. For the US, many of
the heaviest travelled city pairs such as SFO to LAX or Chicago to the New York area both file
direct flight and achieve direct flight for the majority of flights.
However when actuals are compared, the gap is much more narrow and much less in terms of
an efficiency score. Between 2011 and 2014 there was a continuous improvement in Europe in
terms of flight efficiency for flights to and from the top 34 airports, which narrowed the gap.
However, flight efficiency deteriorated in Europe in 2015.
ACTUAL TRAJECTORY VS. ACHIEVED DISTANCE
The level of total horizontal en-route flight inefficiency [(A-H)/H] for flights to or from the main
34 airports in Europe in 2015 was 2.92% compared to 2.83% in the US. Overall, horizontal en-
route flight inefficiency on flights to or from the main 34 airports in Europe is approximately
0.1% higher than in the US in 2015. In assessing the US trends, much of the increase from 2013-
2014 can be traced to large additional distance incurred due to the effects of the fire at Chicago
Air Route Traffic Control Center (ZAU) that predominantly affected flights from September 26 –
October 12 of 2014. The overall increase that can be observed from 2013-2015 is directly linked
to airports (and city pairs) that experienced increases in traffic levels (SEA, LAX and DAL).
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
5.0%
5.5%
2008 2009 2010 2011 2012 2013 2014 2015
flight inefficiency (%)
Europe (actual) Europe (flight plan)
US (actual) US (flight plan)
Source: PRU; FAA-ATO
Evolution of horizontal en-route flight efficiency
(flights to/from the main 34 airports within the respective region)
P a g e | 62
Figure 5-13 shows the direct en-route extension on flights arriving at the main US and European
airports.
Figure 5-13: Direct en-route extension by destination airport
US airports show some clustering and patterns when values are summed by destination airport,
particularly for New York Area and Florida airports. In assessing specific city pairs for these
facilities, three causal reasons emerge. These include 1) Traffic into New York Area especially
from Texas and Florida, 2) Effects of Special Activity Airspace on East Coast and around San
Francisco and 3) Transcontinental Flights.
Almost all direct flights between the New York area and Florida airports would require flight
through special use airspace. Many of the flights to East Coast and West Coast airport
destinations involve long transcontinental flight where large values do not translate into high
percentages. Furthermore, these transcontinental flights require much more scrutiny as the
ideal flight would consider winds and not be limited to direct flight.
Lastly, existing route design into the New York area does not allow for direct flights for some key
city pairs (DFW and IAH to New York Area). This may be due to congestion caused by high traffic
and the presence of major airports located close together. Alternatively, it may be possible to fly
more direct to the New York area as the FAA makes continued improvements to airspace design
and more advanced traffic flow management is implemented.
In absolute terms, the average additional mileage in the US is higher due to the longer flights but
in relative terms the level of flight inefficiency is lower (i.e. inefficiency per flown distance).
Figure 5-13 also provides insight into the facilities that contributed the most to the changes from
2013-2015. For the US, the routes that had the largest impact directly related to the airports that
show the largest increase in traffic over this time period including Los Angeles (LAX), Seattle
(SEA) and Dallas Love Field (DAL).
0%
1%
2%
3%
4%
5%
0
5
10
15
20
25
30
Palma (PMI)
Lisbon (LIS)
Manchester (MAN)
Barcelona (BCN)
Nice (NCE)
London (LGW)
Madrid (MAD)
Cologne (CGN)
Brussels (BRU)
Dusseldorf (DUS)
Rome (FCO)
Helsinki (HEL)
Dublin (DUB)
Milan (MXP)
Zurich (ZRH)
London (STN)
AVG
Geneva (GVA)
Paris (CDG)
Milan (LIN)
Athens (ATH)
Stuttgart (STR)
Frankfurt (FRA)
Hamburg (HAM)
Amsterdam (AMS)
London (LHR)
Lyon (LYS)
Prague (PRG)
Berlin (TXL)
Copenhagen (CPH)
Stockholm (ARN)
Paris (ORY)
Vienna (VIE)
Munich (MUC)
Oslo (OSL)
Direct en route inefficiency (A-D)/H
Direct en route extension in NM by arrival airport (A
-D)
2015 2013 % of total extra miles
0%
1%
2%
3%
4%
5%
0
5
10
15
20
25
30
New York (JFK)
Miami (MIA)
Newark (EWR)
Ft. Lauderdale (FLL)
New York (LGA)
San Francisco (SFO)
Houston (IAH)
Los Angeles (LAX)
Orlando (MCO)
Tampa (TPA)
Dallas (DFW)
Seattle (SEA)
AVG
Chicago (ORD)
Boston (BOS)
Phoenix (PHX)
San Diego (SAN)
Philadelphia (PHL)
Las Vegas (LAS)
Denver (DEN)
Washington (IAD)
Washington (DCA)
Minneapolis (MSP)
Memphis (MEM)
Baltimore (BWI)
Detroit (DTW)
Houston (HOU)
Charlotte (CLT)
Atlanta (ATL)
Chicago (MDW)
Portland (PDX)
Salt Lake City (SLC)
Dallas (DAL)
St. Louis (STL)
Nashville (BNA)
US
Source: FAA/ PRU analysis
Europe
P a g e | 63
As traffic and the underlying network
changed, the increase is a product of both
increasing distance and the distribution of
flights among the network.
For Dallas Love Field (DAL), this was
significant with the expiration of the Wright
Amendment in 2014 which allowed for many
more city-pair services to DAL. Key city pairs
contributing to the increase for Seattle (SEA)
include west coast traffic (SAN, LAX and SFO
into SEA).
Figure 5-14 shows flights tracks for the most
popular filed flight plan (shown in blue) for
SAN/LAX into SEA.
Improvements to en-route design are, by
definition, a network issue which requires a
holistic, centrally coordinated approach.
Uncoordinated, local initiatives may not
deliver the desired objective, especially if
the airspace is comparatively small.
Figure 5-14: San Diego/Los Angeles to Seattle flights
affecting horizontal flight efficiency
In view of the fragmented European ATM system, a harmonised and well-coordinated
implementation of initiatives aimed at improving the route network at system level is more
difficult to achieve in Europe than in the US where only one entity is responsible for the
optimisation of the route network.
As technology for both aircraft and ATC has
advanced, the need for such a rigid en-route
structure has diminished, to the extent that
free-route airspace (FRA) with a positive
effect on flight efficiency would now be
possible throughout Europe (see grey box).
The airspace is undergoing significant
change which requires all stakeholders to
adapt.
The implementation of free route airspace
mandated by EU legislation aims at
enhancing en-route flight efficiency with
subsequent benefits for airspace users in
terms of time and fuel as well as a reduction
of CO
2
emissions for the environment.
Free Route Airspace (FRA) Concept
Free route airspace (FRA) is a key development with a view
to the implementation of shorter routes and more efficient
use of the European airspace.
FRA refers to a specific portion of airspace within which
airspace users may freely plan their routes between an
entry point and an exit point without reference to the fixed
Air Traffic Services (ATS) route network. Within this
airspace, flights remain at all times subject to air traffic
control and to any overriding airspace restrictions.
Deployment is ongoing, and EU Implementing Regulation
716/2014 (the Pilot Common Project regulation) stipulates
that the Network Manager, air navigation service providers
and airspace users shall operate direct routing (DCT) as
from 1 January 2018 and FRA as from 1 January 2022 in the
airspace for which the EU Member States are responsible at
and above flight level 310 in the ICAO EUR region.
By the end of 2015, the Network Manager coordinated, through the European Route Network
Improvement Plan (ERNIP) [Ref.
20
], the development and/or implementation of more than 20
airspace improvement packages relating to various FRA projects (including Night Routes and
direct routes (DCTs)).
2013
2015
P a g e | 64
Figure 5-15 shows Europe-wide free route implementation by the end of 2015. As can be seen
Ireland, Portugal, Hungary and parts of Scandinavia are most advanced in Europe and already
operate 24 hour FRA (Free Route Airspace).
Figure 5-15: Free route development (2015)
The deployment of Flexible Airspace Management and Free Route functionality needs to be
coordinated due to the potential network performance impact of delayed implementation in a
wide geographical scope involving a number of stakeholders. From a technical perspective the
deployment of targeted system and procedural changes is synchronised to ensure that the
performance objectives are met. This synchronisation of investments involves multiple
civil/military air navigation service providers, airspace users and the Network Manager.
Furthermore, synchronisation during the related industrialisation phase needs to take place, in
particular among the supply industry.
5.2.4 FLIGHT EFFICIENCY WITHIN THE LAST 100 NM
This section aims at estimating the level of
inefficiencies that occur during the
arrival/descent phase of flight. These
inefficiencies are seen through larger
downwinds or final, “S-turns” or in the worst
case airborne holding patterns within the last
100 NM of flight.
For this exercise, the locally defined terminal
En-route
inefficiency
Origin
airport
Ground hold
(en route)
Ground hold
(airport)
Terminal
inefficiency
Management
of arrival flows
Air Traffic
Management
Taxi-out
inefficiency
En-route network Approach
Arrival
airport
Taxi-in
inefficiency
P a g e | 65
manoeuvring area (TMA) is not suitable for
comparisons due to variations in shape and size
of TMAs and the ATM strategies and
procedures applied within the different TMAs.
Hence, in order to capture tactical arrival
control measures (sequencing, flow integration,
speed control, spacing, stretching, etc.)
irrespective of local ATM strategies, a standard
Arrival Sequencing and Metering Area (ASMA)
was defined (see grey box for explanation). For
the analyses, the 100NM ring was used.
Arrival Sequencing and Metering Area (ASMA)
ASMA (Arrival Sequencing and Metering Area) is defined
as two consecutive rings with a radius of 40 NM and 100
NM around each airport.
This incremental approach is sufficiently wide to capture
effects related to approach operations. It also enables a
distinction to be made between delays in the outer ring
(40-100 NM) and the inner ring (40 NM-landing) which
have a different impact on fuel burn and hence on
environmental performance.
More information and data on additional ASMA time in
Europe is available at www.ansperformance.eu.
The actual transit times within the 100 NM ASMA ring are affected by a number of ATM and
non-ATM-related parameters including, inter alia, flow management measures (holdings, etc.),
airspace design, airports configuration, aircraft type environmental restrictions, and in Europe,
to some extent the objectives agreed by the airport scheduling committee when declaring the
airport capacity.
The “additional” time is used as a proxy for the level of inefficiency within the last 100 NM. It is
defined as the average additional time beyond the unimpeded transit time. The unimpeded
times
42
are developed for each arrival fix, runway configuration and aircraft type combination.
Figure 5-16 shows the
evolution of average additional
time within the last 100 NM for
the US and Europe from 2008
to 2015.
At system level, the additional
time within the last 100 NM
was similar in the two regions
in 2008 but declined in the US
between 2008 and 2010. At the
same time, additional time
within the last 100 NM
increased in Europe.
Figure 5-16: Evolution of average additional time within the last 100
NM (2008-2015)
Although at different levels, performance in the US and in Europe remained relatively stable
since 2013.
However, the picture is contrasted across airports. Figure 5-17 shows the average additional
time within the last 100 NM by airport in 2015. The difference in average additional time within
the last 100 NM by airport is reported in Figure 5-18.
42
Although the methodologies are expected to produce rather similar results, due to data issues, the calculation of
the unimpeded times in Europe and the US is based on the respective “standard” methodologies and the results
should be interpreted with a note of caution.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
2008 2009 2010 2011 2012 2013 2014 2015
Average additional ASMA time - 100NM
to landing
Europe US
Evolution of average additional time within the last 100 NM
(arrivals - main 34 airports)
P a g e | 66
Figure 5-17: Estimated average additional time within the last 100 NM (2015)
Figure 5-18: Difference in average additional time within the last 100 NM (2015 vs. 2013)
0
1
2
3
4
5
6
7
8
9
10
London (LHR)
London (LGW)
Zurich (ZRH)
Madrid (MAD)
Athens (ATH)
Frankfurt (FRA)
Dublin (DUB)
Rome (FCO)
Average
Geneva (GVA)
Barcelona (BCN)
Vienna (VIE)
Nice (NCE)
Paris (CDG)
Paris (ORY)
Munich (MUC)
Oslo (OSL)
Berlin (TXL)
Manchester (MAN)
Dusseldorf (DUS)
Palma (PMI)
Milan (LIN)
Lisbon (LIS)
Amsterdam (AMS)
London (STN)
Hamburg (HAM)
Brussels (BRU)
Milan (MXP)
Copenhagen (CPH)
Prague (PRG)
Helsinki (HEL)
Stockholm (ARN)
Lyon (LYS)
Cologne (CGN)
Stuttgart (STR)
Average additional ASMA time - 100NM to landing (min. per arrival)
Estimated average additional time within the last 100 NM (2015)
2015 2013
0
1
2
3
4
5
6
7
8
9
10
Philadelphia (PHL)
New York (JFK)
Newark (EWR)
Detroit (DTW)
Charlotte (CLT)
Memphis (MEM)
Washington (DCA)
Boston (BOS)
New York (LGA)
Chicago (ORD)
Houston (IAH)
Baltimore (BWI)
Washington (IAD)
Average
Atlanta (ATL)
Houston (HOU)
Seattle (SEA)
Denver (DEN)
Miami (MIA)
Minneapolis (MSP)
Dallas (DFW)
Dallas Love (DAL)
Portland (PDX)
Tampa (TPA)
Orlando (MCO)
Nashville (BNA)
St. Louis (STL)
Chicago (MDW)
Salt Lake City (SLC)
Ft. Lauderdale (FLL)
Las Vegas (LAS)
Phoenix (PHX)
San Diego (SAN)
Los Angeles (LAX)
San Francisco (SFO)
Main 34 average (min. per arrival)
2013: 2.47
2015: 2.47
Europe
US
Main 34 average (min. per arrival)
2013: 2.84
2015: 2.85
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
London (LHR)
London (LGW)
Zurich (ZRH)
Madrid (MAD)
Athens (ATH)
Frankfurt (FRA)
Dublin (DUB)
Rome (FCO)
Average
Geneva (GVA)
Barcelona (BCN)
Vienna (VIE)
Nice (NCE)
Paris (CDG)
Paris (ORY)
Munich (MUC)
Oslo (OSL)
Berlin (TXL)
Manchester (MAN)
Dusseldorf (DUS)
Palma (PMI)
Milan (LIN)
Lisbon (LIS)
Amsterdam (AMS)
London (STN)
Hamburg (HAM)
Brussels (BRU)
Milan (MXP)
Copenhagen (CPH)
Prague (PRG)
Helsinki (HEL)
Stockholm (ARN)
Lyon (LYS)
Cologne (CGN)
Stuttgart (STR)
Change in average additional time within the last 100 NM
Change in average additional time within the last 100NM (2015 vs. 2013)
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
Philadelphia (PHL)
New York (JFK)
Newark (EWR)
Detroit (DTW)
Charlotte (CLT)
Memphis (MEM)
Washington (DCA)
Boston (BOS)
New York (LGA)
Chicago (ORD)
Houston (IAH)
Baltimore (BWI)
Washington (IAD)
Average
Atlanta (ATL)
Houston (HOU)
Seattle (SEA)
Denver (DEN)
Miami (MIA)
Minneapolis (MSP)
Dallas (DFW)
Dallas Love (DAL)
Portland (PDX)
Tampa (TPA)
Orlando (MCO)
Nashville (BNA)
St. Louis (STL)
Chicago (MDW)
Salt Lake City (SLC)
Ft. Lauderdale (FLL)
Las Vegas (LAS)
Phoenix (PHX)
San Diego (SAN)
Los Angeles (LAX)
San Francisco (SFO)
Europe
US
P a g e | 67
Europe shows a slight overall increase in 2015. At airport level, London Heathrow (LHR) is a clear
outlier
43
, having by far the highest level of additional time within the last 100 NM, followed by
London Gatwick (LGW), Zurich (ZRH), and Madrid (MAD) which show less than half the level
observed at London Heathrow. As seen in Figure 5-18, London Gatwick (LGW) and Dublin (DUB)
were the two European airports with the highest increases in average additional time in the last
100 NM in 2015. A notable decrease in additional time was reported at Frankfurt (FRA) as a
result of the new runway.
The US levels for average additional time held steady at 2.47 min from 2013 to 2015 with less
contrast in additional time reported among airports. Similar to taxi-out performance, there is still
a notable difference for the airports in the greater New York area, which show the highest level
of additional time within the last 100 NM. A notable increase in additional time within the last
100 NM in 2015 was observed for Boston (BOS), Dallas Love (DAL), with Seattle (SEA) airport
having the largest impact on the system wide trend. LAX and ORD also contributed to increases
given the large number of operations at the airport. Similar to en-route efficiency, the increases
are largely seen at airports with an increase in operations (SEA, DAL, LAX). These increases were
balanced at the system level with improvements for Ft. Lauderdale (FLL), Detroit (DTW), San
Francisco (SFO), Newark (EWR) and Chicago (MDW) with Atlanta (ATL) contributing the most to a
system decrease with its large number of operations.
Due to the large number of variables involved, the direct ATM contribution towards the
additional time within the last 100 NM is difficult to determine. One of the main differences of
the US air traffic management system is the ability to maximise airport capacity by taking action
in the en-route phase of flight, such as in trail spacing. Larger ATFM delay in the US also may
indicate that much of this additional time is pushed back to the departure airport and taken on
the ground.
In Europe, the support of the en-route function is limited and rarely extends beyond the national
boundaries. Hence, most of the sequencing and holding is done at lower altitudes around the
airport. Additional delays beyond what can be absorbed around the airport are taken on the
ground at the departure airports.
On both sides of the Atlantic, the operations at high density traffic airports are vulnerable to
adverse weather conditions and cause high levels of delay to airspace users.
There is a potential trade-off between additional time in terminal airspace (additional ASMA
time) and airport capacity utilisation. This can be observed for London Heathrow (LHR) and the
congested US airports. Although not quantified in this report, quantifying capacity utilisation
and assessing this trade-off would be a worthwhile subject for further study. However,
benchmarking the two systems would require a common understanding of how capacity and
throughput is measured for comparable airports.
Complementary to the analysis of additional ASMA time in this section, section 6.2 of this report
provides an initial comparison of vertical flight efficiency in the arrival phase between the US and
Europe which will help to provide a more complete picture in the future.
43
It should be noted that performance at London Heathrow airport (LHR) is consistent with decisions taken during
the airport scheduling process regarding average holding in stack. The performance is in line with the 10 minute
P a g e | 68
5.2.5 TAXI-IN EFFICIENCY
The analysis of taxi-in efficiency in this section
refers to the period between the time when
the aircraft landed and the time it arrived at
the stand (actual in-block time). The additional
time is measured as the average additional
time beyond an unimpeded reference time.
The analysis in Figure 5-19 mirrors the
methodology applied for taxi-out efficiency in
Figure 5-7.
The method uses the 20th percentile of each service (same operator, airport, etc.) as a reference
for the “unimpeded” time and compares it to the actual times. This can be easily computed with
US and European data.
Figure 5-19: Additional times in the taxi-in phase (system level) (2005-2015)
As can be observed in Figure 5-19, at system level, additional time in the taxi-in phase is higher
in the US than in Europe and remained relatively stable over time in both systems until 2015. For
2015, a notable increase can be observed in the US. Some seasonal patterns are visible
(particularly in the US) where an increase can be noted during summer.
The taxi-in phase and hence the performance indicator is influenced by a number of factors,
most of which cannot be directly influenced by ATM (i.e. airport/airline staffing, gate availability,
apron limitations etc.).
The taxi-in phase was included in the comparison for completeness reasons but, due to the
number of factors outside the direct control of ATM, it was not included in the estimated benefit
pool actionable by ATM in Chapter 5.3.
average delay criterion agreed.
En-route
inefficiency
Origin
airport
Ground hold
(en route)
Ground hold
(airport)
Terminal
inefficiency
Management
of arrival flows
Air Traffic
Management
Taxi-out
inefficiency
En-route network Approach
Arrival
airport
Taxi-in
inefficiency
1.0
1.5
2.0
2.5
3.0
3.5
Jan-05
Jul-05
Jan-06
Jul-06
Jan-07
Jul-07
Jan-08
Jul-08
Jan-09
Jul-09
Jan-10
Jul-10
Jan-11
Jul-11
Jan-12
Jul-12
Jan-13
Jul-13
Jan-14
Jul-14
Jan-15
Jul-15
minutes per arrival
Europe (Top 34) US (Top 34)
Annual average EUR Annual average US
Additional time in the taxi in phase compared to 20th percentile of each service
(service = same operator, same airport, monthly)
Source: FAA/PRU analysis
P a g e | 69
5.3 Summary of main results & Estimated benefit pool actionable by ATM
There is value in developing a systematic
approach to aggregating ATM-related
inefficiencies. Since there are opportunities
for many trade-offs between flight phases,
an overall indicator allows for high-level
comparability across systems.
This section provides a summary of the
estimated benefit pool for a typical flight,
based on the analysis of traffic from and to
the 34 main airports in Europe and the US.
Although included in this report for completeness reasons, due to the number of factors outside
the direct control of ATM, the taxi-in phase was not included in the estimated benefit pool
actionable by ATM. For the interpretation of the estimated benefit pool actionable by ATM in
this section, the following points should be borne in mind:
Not all delay is to be seen as negative. A certain level of delay is necessary and sometimes
even desirable if a system is to be run efficiently without underutilisation of available
resources.
Due to the stochastic nature of air transport (winds, weather) and the way both systems
are operated today (airport slots, traffic flow management), different levels of delay may
be required to maximise the use of scarce capacity. There are lessons however to be
learned from both sides.
A clear-cut allocation between ATM and non-ATM related causes are often difficult. While
ATM is often not the root cause of the problem (weather, etc.) the way the situation is
handled can have a significant influence on the distribution of delay between air and
ground and thus on costs to airspace users (see also Table 5-2 on page 70).
The approach measures performance from a single airspace user perspective without
considering inevitable operational trade-offs, and may include dependencies due to
environmental or political restrictions, or other performance affecting factors such as
weather conditions.
ANSP performance is inevitably affected by airline operational trade-offs on each flight.
The indicators in this report do not attempt to capture airline goals on an individual flight
basis. Airspace user preferences to optimise their operations based on time and costs can
vary depending on their needs and requirements (fuel price, business model, etc.).
Some indicators measure the difference between the actual situation and an ideal (un-
congested or unachievable) situation where each aircraft would be alone in the system
and not subject to any constraints. This is the case for horizontal flight efficiency which
compares actual flown distance to the great circle distance. Other indicators, such as
ASMA flight efficiency, compare actual performance to an ideal scenario that is based on
the best performance of flights observed in the system today. More analysis is needed to
better understand what is and will be achievable in the future.
However, when used at a strategic level, the indicators do provide clear indications of regions,
city-pair markets and facilities where additional time and distance are increasing or
decreasing. In this way, ANSPs have a clear and stable procedure for identifying the constraints
in their system, as well as a means of benchmarking performance on a global level.
En-route
inefficiency
Origin
airport
Ground hold
(en route)
Ground hold
(airport)
Terminal
inefficiency
Management
of arrival flows
Air Traffic
Management
Taxi-out
inefficiency
En-route network Approach
Arrival
airport
Taxi-in
inefficiency
P a g e | 70
5.3.1 ESTIMATED BENEFIT POOL ACTIONABLE BY ATM
By combining the analyses for individual phases of flight in Section 5.2, an estimate of the
“improvement pool” actionable by ATM can be derived. It is important to stress that this
“benefit pool” is based on a theoretical optimum (averages compared to unimpeded times),
which is not achievable at system level due to inherent necessary (safety) or desired (capacity)
limitations
44
. Moreover, the inefficiencies in the various flight phases (airborne versus ground)
have a very different impact on airspace users in terms of predictability (strategic versus tactical
– percent of flights affected) and fuel burn (engines on versus engines off).
Table 5-2 provides an overview of the ATM-related impact on airspace users’ operations in terms
of time, fuel burn and associated costs.
Table 5-2: Impact of ATM-related inefficiencies on airspace users’ operations
ATM-related impact on airspace users’
operations
Impact on
punctuality
Engine
status
Impact on fuel
burn/ CO
2
emissions
Impact on
airspace users’
costs
ATM-related
inefficiencies
At stand
Airport ATFM/TMI
High
OFF
Quasi nil
Time
En-route ATFM/TMI
Gate-to-gate
Taxi-out phase
Low/
moderate
ON
High
Time + fuel
En-route phase
Terminal area
For ATM-related delays at the gate (ATFM/TMI departure restrictions) the fuel burn is quasi nil
but the level of predictability in the scheduling phase for airspace users is low as the delays are
not evenly spread among flights. Hence, the impact of those delays on on-time performance and
associated costs to airspace users is significant but the impact on fuel burn and the environment
is negligible. It is however acknowledged that – due to the first come, first served principle
45
applied at the arrival airports - in some cases aircraft operators try to make up for ground delay
encountered at the origin airport through increased speed which in turn may have a negative
impact on total fuel burn for the entire flight.
ATM-related inefficiencies in the gate-to-gate phase (taxi, en-route, terminal holdings) are
generally more predictable than ATM-related departure restrictions at the gate as they are more
related to inefficiencies embedded in the route network or congestion levels which are similar
every day or season to season. From an airspace user point of view, the impact on on-time
performance is usually low as those inefficiencies are usually already embedded in the scheduled
block times by airlines. However, the impact in terms of additional time, fuel, associated costs,
and the environment is significant.
The environmental impact of ATM on climate is closely related to operational performance
which is largely driven by inefficiencies in the 4-D trajectory and associated fuel burn. There is a
close link between user requirements to minimise fuel burn and reducing greenhouse gas
emissions
46
.
44
The CANSO report on “ATM Global Environmental Efficiency Goals for 2050” also discusses interdependencies in
the ATM system that limit the recovery of calculated “inefficiencies.” These interdependencies include capacity,
safety, weather, noise, military operations, and institutional practices requiring political will to change.
45
“First come, first served” is generally applied to manage air traffic flows, as provided for in Annex 11 — Air Traffic
Services and in the Procedures for Air Navigation Services — Air Traffic Management (PANS–ATM, Doc 4444)
regarding the relative prioritisation of different flights.
46
The emissions of CO
2
are directly proportional to fuel consumption (3.15 kg CO
2
/kg fuel).
P a g e | 71
Clearly, keeping an aircraft at the gate saves fuel but if it is held and capacity goes unused, the
cost to the airline of the extra delay may exceed the savings in fuel cost by far. Since weather
uncertainty will continue to impact ATM capacities in the foreseeable future, ATM and airlines
need a better understanding of the interrelations between variability, efficiency, and capacity
utilisation.
Previous research [Ref.
21
] shows that at system level, the total estimated benefit pool
actionable by ATM and associated fuel burn are of the same order of magnitude in the US and
Europe (approx. 6-8% of the total fuel burn).
Figure 5-20 shows a summary of the operational performance on flights to or from the top 34
airports in the US and in Europe for four of the key indicators addressed in more detail in the
previous sections of the report.
Figure 5-20: Evolution of operational performance in US/Europe between 2008 and 2015
Building on the results shown in Figure 5-20, Table 5-3 summarises the current best estimate of
the ATM-related impact on operating time. Actual fuel burn depends on the respective aircraft
mix (including mix of engines on the same type of aircraft, operating procedures) and therefore
varies for different traffic samples.
For comparability reasons, the estimated benefit pool actionable by ATM in Table 5-3 is based on
the assumption that the same aircraft type performs a flight of 450NM in the en-route phase in
the US and the European ATM system (see also grey box for more information).
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
2008 2009 2010 2011 2012 2013 2014 2015
Average minutes of ATFM delay per
flight
Evolution of total ATFM delay per flight
(flights to or from the main 34 airports within region)
Europe US
Only delays equal or greater than 15 minutes were included in the analyses
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
2008 2009 2010 2011 2012 2013 2014 2015
Average additional time in the taxi out
phase (minutes)
US Europe
Evolution of additional taxi-out time
(departures - main 34 airports)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
2008 2009 2010 2011 2012 2013 2014 2015
Average additional ASMA time - 100NM
to landing
Europe US
Evolution of average additional time within the last 100 NM
(arrivals - main 34 airports)
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
2008 2009 2010 2011 2012 2013 2014 2015
flight inefficiency (%)
Europe (actual) US (actual)
Evolution of horizontal en-route flight efficiency
(flights to/from the main 34 airports within the respective region)
P a g e | 72
Although in a context of declining traffic, system-
wide ATM performance improved considerably in
the US and in Europe over the past five years.
The resulting savings in terms of time and fuel in
both ATM systems had a positive effect for
airspace users and the environment.
The distribution of the estimated benefit pool
along the phase of flight is consistent with the
differences in flow management strategies
described throughout the report.
The improvement in Europe over the past five
years was mainly driven by a reduction of en-
route ATFM delay at the departure gates and
improvements in the level of horizontal flight
efficiency.
Estimated benefit pool actionable by ATM
As outlined in Section 3.1, the two ATM systems differ
in terms of average flight lengths and aircraft mix (see
Figure 3-6 on page 26).
Those differences would lead to different results, as
the “inefficiencies” depend on travelled distance and
aircraft type.
For comparability reasons, the calculations in Table
5-3 are based on averages representing a “standard”
aircraft in the system.
The calculations assume that a standard aircraft
travels an average distance of 450NM in each ATM
system.
The typical average fuel burn, was equally applied to
the US and Europe (Taxi ≈ 15kg/min., Cruise.≈
46kg/min., TMA holding 41kg/min.).
Table 5-3: Estimated benefit pool actionable by ATM (2015 vs. 2010)
Estimated benefit pool actionable
by ATM for a typical flight
(flights to or from the main 34 airports)
Estimated average
additional time (min.)
Fuel
burn
Estimated excess
fuel burn (kg)
47
EUR
US
engines
EUR
US
2010
2015
2010
2015
2010
2015
2010
2015
Holding at gate per
departure (only
delays >15min.
included)
En-route-related
(% of flights)
1.9
(5.7%)
0.6
(2.0%)
0.2
(0.7%)
0.3
(0.8%)
OFF
≈0
≈0
≈0
≈0
airport-related
(% of flights)
1.1
(3.1%)
0.7
(2.3%)
1.3
(2.5%)
1.3
(2.5%)
OFF
≈0
≈0
≈0
≈0
Taxi-out phase (min. per departure)
4.7
4.0
5.8
5.6
ON
70
60
87
84
Horizontal en-route flight efficiency
2.0
48
1.8
1.8
1.8
ON
94
84
82
82
Terminal areas (min. per arrival)
2.8
2.8
2.5
2.5
ON
116
117
101
101
Total estimated benefit pool
12.5
10.0
11.6
11.4
279
260
270
267
It is an open research question on whether current performance databases capture the full
benefit pool as there may be additional efficiencies gained from using ideal cruise speeds or
from making operations more predictable. Estimating these inefficiencies would require more
information on aircraft performance and airline intent than is currently available to both groups.
Inefficiencies in the vertical flight profile for en-route and in the TMA departure phase (40NM
ring around the departure airport) were not considered in the benefit pool. Vertical flight
efficiency was addressed in a specific focus study (see Section 6.2) with a view to include it in
future benefit pool estimations in order to get an even more complete picture.
However, just as there are facets of the benefit pool not covered, there are system constraints
and interdependencies that would prevent the full recovery of the theoretical optimum
identified in this section. Performance groups will need to work with all stakeholders to quantify
these contrasting effects on the fuel benefits actionable by ATM.
47
Fuel burn calculations are based on averages representing a “standard” aircraft in the system.
48
The EUR 2010 figure is based on an estimate as the radar data was not yet available at system level in 2010.
P a g e | 73
6. SUPPORTING STUDIES
This chapter introduces two supporting studies aimed at expanding the scope and level of
analysis of this US/Europe comparison report. Initial results are presented on the
Analysis of air traffic flow and capacity management; and
Vertical flight efficiency in the arrival phase.
Both studies demonstrated the general feasibility of the researched approach and offer
opportunities to augment future editions of the benchmarking report.
6.1 Analysis of Air Traffic Flow and Capacity Management in the U.S. and in Europe
6.1.1 INTRODUCTION
In 2015, FAA and Europe initiated a joint study to evaluate the more complex performance
issues associated with ATFCM. This section reports progress and results with work expected to
continue. The general objective of this study is to deepen the understanding of Capacity
Management (CM) and Demand Capacity Balancing (DCB) methods used in the US and Europe,
and to better understand the differences and similarities between both regions. The various
aspects of ATFCM that can be considered in such a study are shown in Figure 6-1 below. Past
analysis has mainly focused on quantifying the performance outcome in terms of delays and
flight efficiency. The ATFCM study will broaden this scope by looking at additional aspects shown
on the figure. As a first priority the study has been looking into the application of Traffic
Management Initiatives (TMIs) on both sides of the Atlantic.
Figure 6-1: Overview of ATFCM study areas
Phase 1 of the study consisted of:
Conceptual analysis of the various TMI types and their application.
Identification of suitable (comparable) data in US and European data archives to support TMI
analysis. It was decided to base the study on data covering the full calendar year 2015, and
the same geographical scope as used in the US/Europe Comparison Report.
Variable
Demand
Variable
Capacity
Demand
Capacity
Balance
Overload
(Unsafe)
Unused
Capacity
Network
Management
Actions
Residual
Airspace User
Penalisation
Residual
ANSP
Inefficiency
Planned &
Unplanned
Traffic Levels
Planned &
Unplanned
Imbalance
Planned &
Unplanned
Capacity Levels
Traffic
Management
Initiatives (TMIs)
Capacity
Management
Initiatives
Cancellations
Diversions
Delays (Gnd/Air)
Flight inefficiency
(Horizontal/Vertical)
Residual unused capacity
Setting up
the day of operations
Triggers & conditions
ATFCM
Responses
Performance outcome
Reasons:
High traffic
Low staffing
Weather
Etc.
Reasons:
Low traffic
High staffing
Etc.
P a g e | 74
Extraction and interpretation of the data.
Preparation of the data for benchmarking (mapping US and European data sets to common
terminology).
The prepared data set contains the following attributes:
Metrics:
o Number of TMIs
o Duration of TMIs, with breakdown into actual duration and cancelled duration
o Number of flights impacted, with breakdown into number of flights with TMI
attributable delay ≥ 15 minutes, number of flights with 1 to 14 minutes delay,
and number of flights without TMI attributable delay
o Generated delay, with breakdown into delay for flights with TMI attributable
delay ≥ 15 minutes, and delay for flights with 1 to 14 minutes delay
Dimensions:
o TMI type
o TMI date
o TMI cancellation category (Cancelled Before Start, Cancelled After Start, Not
Cancelled)
o Facility which is protected and to which the delay is charged
o Reason for the TMI (delay causal factor)
Phase 1 has been completed and the study is ready to move to Phase 2: data analysis and the
formulation of conclusions. Initial results are presented below.
6.1.2 GROUPING OF TMIS INTO LEVELS
For the purpose of the study, TMIs are grouped into four levels:
TMI-L1 comprises “latent” TMIs which have been created during the strategic and pre-
tactical ATFCM phases. They affect scheduling and/or flight planning. Examples: airport slot
reservation programs, route programs and restrictions, permanent altitude segregation.
TMI-L2 comprises ATFM TMIs applied on the day of operations, which may result in the
allocation of a take-off slot (ATFM slot) and/or a rerouting, after flight plan filing but in
principle prior to pushback. Examples: Ground Stops (GS), Ground Delay programs (GDP),
Departure Stops (DS), Airspace Flow Programs (AFP), Collaborative Trajectory Options
Programs (CTOP), Severe Weather Avoidance Programs (SWAP), voluntary and required
rerouting.
TMI-L3 TMIs are sequencing and metering measures that are used by ATC to fine-tune the
traffic flow and that may have a delay impact on traffic prior to take-off. Examples: Miles In
Trail (MIT), Minutes In Trail (MINIT), Minimum Departure Interval (MDI), Metering (Time
Based Metering, TBM), Departure/En-route/Arrival Spacing (DSP, ESP, ASP).
TMI-L4 TMIs are longitudinal (sequencing and metering, including airborne holding), lateral
(load balancing) and vertical (level off) tactical measures that are used by ATC after take-off
with the objective to fine-tune the traffic flow.
TMI-L1 has not been quantitatively analysed in the study.
TMI-L2 is well covered by data available in both the US and Europe. Most of the benchmarking
focuses on this level. A limitation of the US data is that the measured TMI impact only includes
delay from flights delayed by 15 minutes or more (reportable delay). The European data contains
the same, but in addition also the number of flights and the associated delay of flights delayed
P a g e | 75
1 to 14 minutes, and all other flights ‘captured’ by the TMI but without any delay attributable to
the TMI.
TMI-L3 and TMI-L4 are covered by the US data set, but for Europe such data was not present in
the data used for the study. US TMI-L4 data covers airborne holding. In addition US data is
available on the departure delay of flights not otherwise involved in a TMI. Such departure
delays are attributed to conditions at the departure airport, and are associated with longer than
normal taxi times or holding at the gate.
In the subsequent sections the terms ‘delayed flight’ and ‘delay’ refer to (flights with) reportable
delay (≥ 15 minutes) unless otherwise specified. Likewise, unless otherwise specified all numbers
refer to annual values for 2015, within the geographical scope of the study
49
.
6.1.3 ANALYSIS BY TMI LEVEL IN THE US
Figure 6-2 Reportable delay in the US (minutes and percentage)
In the US, 75% of the delay is generated by ATFM (TMI-L2), mostly by GDPs; 8% by TMI-L3 (with
MIT taking 38% of that share); 6% by TMI-L4 (airborne holding, generating nearly twice as much
delay as MIT), and 11% coming from departure delays. On average the delay per delayed flight is
61 minutes for TMI-L2, and less than half of that for TMI-L3, TMI-L4 and departure delay (26, 24
and 24 min respectively).
6.1.4 REROUTING AND LEVEL CAPPING TMIS IN EUROPE
In addition to grouping into levels, TMI types are categorised according to their primary purpose:
Delay
Rerouting and level capping.
49
The ATFCM study uses the same geographical same scope as the US/Europe comparison report, but is different in
terms of flights considered: whereas the comparison report only considers delay of flights between the top 34
airports, the ATFCM study considers all TMIs, all delay and all flights in each region. For this reason the ATFCM
study results are not identical to those shown in chapter 5.
10,815,942
75%
1,151,370
8%
828,519
6%
1,595,704
11%
Reportable delay in the US (minutes and percentage)
Total reportable delay
from TMI-L2
Total reportable delay
from TMI-L3
Total reportable delay
from TMI-L4
Total reportable delay
from Departure Delay
P a g e | 76
4639
14.6%
2031
6.4%
106
0.3%
24957
78.6%
2015 ATFM regulations in Europe
Number of Level Capping (FL) TMIs
Number of Required Reroute (RR) TMIs
Number of Alternative Routeing (AR) TMIs
Number of delay TMIs
Although the focus of the study was on Delay TMIs, it was possible to look at rerouting and level
capping because ATFM regulations in Europe are used for both purposes.
Rerouting and level capping are used when a section of airspace has significantly decreased
capacity or is predicted to have excessive occupancy.
In the US, reroutes are issued as an Advisory from the ATCSCC. The analysis of archived
Advisories was not yet part of the current study scope. Hence no results on rerouting in the US
are available at this stage.
In Europe, for each area expected to have a critical demand/capacity imbalance, a number of
flows may be identified for which other routings may be suggested, that follow the general
scheme, but avoid the critical area. These measures are known as scenarios. There are four
types:
Level capping scenarios (FL): carried out by means of zero-rate ATFM regulations with level
restrictions, or through dynamic routing restrictions (e.g. RAD restrictions, EURO
restrictions).
Rerouting scenarios (RR): diversion of flows to off-load traffic from certain areas;
implemented by means of zero-rate ATFM regulations or through dynamic routing
restrictions.
Alternative routing scenarios (AR):
alternative routes which are
exceptionally made available to off-load
traffic from certain areas, implemented
by ATFM regulations with a low rate.
The other option is the application of
dynamic routing restrictions.
EU Restrictions: restrictions that affect
the flight planning phase based on route
or airspace closures.
The rerouting (RR), level capping (FL) and
Alternative Routing (AR) scenarios which are
implemented through the ETFMS show up in
the data as ATFM regulations with an RR, FL
or AR suffix in their name. In 2015, 6670
ATFM regulations (21% of all European
regulations) and 19.6% of all actual TMI time
were for rerouting and level capping
purposes in 2015. As the RR and FL
regulations force traffic to fly around the
protected area, they do not generate delay
and the data does not show any delayed
or captured traffic.
The average actual duration of rerouting
and level capping TMIs is slightly longer than for delay TMIs: 2.7 hrs vs. 2.6 hrs. However when
TMIs are initially created, the delay TMIs are on average longer, with more delay TMI time
cancelled (0.6 hrs/TMI) than rerouting TMI time (0.1 hrs/TMI).
Figure 6-3 ATFM regulations in Europe
P a g e | 77
When broken down by TMI type, 68% of the rerouting and level capping TMIs are for level
capping (FL), 30% for rerouting (RR) and only 2% are for alternative routing (AR). The average FL
duration (2.3 hrs/TMI) is significantly shorter than the average RR duration (3.8 hrs/TMI) and the
average AR duration (4.7 hrs/TMI).
6.1.5 US/EUROPE COMPARISON OF TMI-L2 (DELAY TMIS ONLY)
Figure 6-4 visualises the application of ATFM TMIs (TMI-L2) in the US and Europe. For
comparison purposes, all values have been normalised to index 100 for Europe, meaning that
the US values show the relative magnitude compared to Europe. The remainder of the section
explains the differences in terms of absolute values.
Figure 6-4 US/Europe comparison of TMI-L2 (delay TMIs only)
TMI-L2 generates 10.8 million delay minutes in the US, and 9.3 million delay minutes in Europe:
12% more delay for 56% more total traffic (15.3 vs 9.8 million flights), which corresponds to 0.71
minutes reportable delay per flight in the US, vs 0.95 in Europe. When interpreting these
numbers please bear in mind that this only covers ATFM delay (TMI-L2) of flights delayed by 15
minutes or more, and does not encompass certain other delay such as TMI-L3 in Europe for
which no data was available.
A remarkable observation is that in the US all of this TMI-L2 delay is imposed on 178 000 Flights
(1.16% of all flights, 61 minutes delay per delayed flight) and in Europe on 304 000 flights (3.10%
of all flights, 31 minutes delay per delayed flight). To generate this delay, the US uses
approximately 3 470 TMIs annually, whereas Europe uses more than seven times more (24 957
TMIs). The average actual duration of these TMIs is 3.0 hrs/TMI in the US, vs 2.6 hrs/TMI in
Europe (comparable, the US TMIs being only 15% longer). Relating TMI duration to the number
of delayed flights, we observe that 17.3 flights per TMI-hour are delayed in the US, vs 4.7 in
Europe (3.7 times more). On average TMIs in the US generate 1 054 minutes delay per TMI-hour,
vs 145 in Europe (more than 7 times more).
0 100 200 300 400 500 600 700 800
Average reportable delay per TMI hr
Average no. of flights with rep. del. per TMI hr
Average actual TMI duration
Number of TMIs (delay TMIs only)
Average delay/flight with reportable delay
Flights with reportable delay vs. total no. of flights
Total number of flights with reportable delay
Average reportable delay/flight over all flights
Total reportable delay in the region
Total number of flights in the region
2015 US/Europe comparison of TMI-L2 (delay TMIs only)
US index Europe = index 100
P a g e | 78
In summary, when not looking at tactical TMI-L3 and TMI-L4 flow restrictions, at annual level
ATFM generates more or less the same amount of delay in the US as compared to Europe.
However the operating practices are quite different: in the US the same delay outcome is
generated with only a fraction of the European number of TMIs, and penalises roughly half of
the flights as compared to Europe, and in relation to total annual traffic nearly three times less.
In other words: ATFM TMIs are used less frequently in the US and affect fewer flights, but when
they are used they penalise far more flights per TMI-hour and the delay per delayed flight is
much higher. Apparently in Europe the delay penalisation is distributed much more evenly and
over a wider population of flights. Continued work on this project will take a closer look at the
reasons for these differences.
6.1.6 FURTHER WORK
The results presented above paint a very high level picture. Analysis work is ongoing to develop a
more detailed understanding of US and European Traffic Management Initiatives, i.e. to reveal
differences between:
TMI types
TMI cancellation categories
Facility types (airports, terminal airspace, en-route airspace)
TMI reasons
Timing (month, weekday, day…), etc.
Further analysis will look at the network effects of Traffic Management Initiatives. The practices
to predict and limit network effects when developing a TMI will be investigated and compared.
The future work will also focus on the capacity management practices to understand differences
and commonalities in the en-route and airport capacity declaration practices, the sector
definition and configuration practices and the sector capacity optimization practices.
6.2 Analysis of vertical flight efficiency in the U.S. and in Europe
6.2.1 INTRODUCTION
Flight efficiency KPIs measure the degree to which airspace users are offered the most efficient
trajectory on the day of operation. So far the focus of assessing trajectory-based flight efficiency
has been on horizontal measures in order to identify opportunities of ATM improvements in the
US and European system. Throughout the recent years the focus has shifted to addresses the
identification and measurement of ATM related constraints on vertical flight profiles. In
particular the analysis of fuel-efficient continuous descent operations has gained a higher
momentum.
With the recent developments and priorities on the ICAO level continuous descent operations
are identified - inter alia – as one of the key improvement steps to enable various aspects of the
“efficiency spectrum”. In particular:
Fuel-efficiency – costs: airspace users have a strong interest in operating aircraft in a fuel-
efficient manner by avoiding fuel-burn due to ATM/ATC related constraints and hence
directly influencing the operational costs.
Environment – emissions: emissions are directly related to fuel-burn. Lower fuel-burn will
accordingly result in lower emissions. In that respect continuous descent operations are also
linked with the CO
2
footprint of aviation and will support the ambitious goals set out for the
contribution of aviation to the world-wide emissions.
P a g e | 79
Environment – noise: Vertically efficient operations also positively affect the noise contour
at and around airports. With an increasing sensitivity of the non-travelling public to aviation
operations, the positive reduction of descent-related noise contributions can ensure higher
acceptance in terms of traffic growth.
To address this spectrum the analysis of the vertical flight efficiency is a vital contribution as it
supports the appraisal of the level of implementation of continuous descent operations and
equally, the measurement of constraints imposed by ATC/ATM on such operations. Such
constraints range from airspace and procedure design through tactical interventions by air traffic
controllers, including arrangements between adjacent air traffic units.
The vertical flight efficiency study aimed at the identification, development, and
parameterization of a common vertical flight efficiency algorithm, the demonstration of the
feasibility of the analysis of vertical flight profiles on the basis of trajectory data, and the
identification of an initial set of common key performance indicators, including the extension of
the “benefit pool” estimates to account for inefficiencies in the vertical profile.
6.2.2 APPROACH
The underlying conceptual model of vertical flight operations is an abstraction of the flight
profile in distinct portions (i.e. segments). This profile is based on measured trajectory data (4D
position) of aircraft operations. A trajectory is therefore represented by the time-ordered set of
4D measurements associated to one flight, typically describing the flight path from the airport of
departure to the airport of destination (c.f. Figure 6-5). Based on the jointly agreed criteria for
describing level flight, the trajectory is mapped to level segments for further analysis. The
analysis focused on the arrival phase of a flight in terms of the top-of-descent within a 200NM
radius around the arrival airport.
Figure 6-5 Vertical Flight Profile – Level Segments
For this initial comparison, the analysis of vertical flight efficiency was performed for the top-ten
of US and European airports in terms of movements in the calendar year 2015. For this subset of
airports the following metrics have been identified:
Total level distance (in NM) or associated total level time (in minutes);
Average level distance (in NM) or associated average level time (in minute) per arrival;
Cumulative distribution function of the level distance or time;
Monthly variation of the average level distance or time; and
Variation of the level distance per altitude band.
While the distance- and time-based metrics report on the same phenomenon, it must be noted
that distance-based metrics are of higher relevance for the ANS community (e.g. procedural
Level Segments
during climb
Small Level Segments
considered as non-
relevant for VFE
analysis
Level Segments
during cruise
Level Segments
during descent
P a g e | 80
airspace characterized by geographical positions that support the evaluation of ground
distances). Airspace users gain more insight from the time-based measures as these translate
directly into aircraft performance and fuel-burn.
6.2.3 INITIAL COMPARISON – LEVEL DISTANCE
For both regions, the analysis of the total distance and time in level flight shows the same
pattern. Airports with a higher share of traffic accrue more total level distance and time. The
direct relationship between level distance and level time in terms of ground speed for the
respective fleet mix confirms this general conclusion. As can be derived from Figure 6-6 (top
row), the total level distance in the US is significantly higher than in Europe with the average of
level distance observed in 2015 for the top ten airports totalling to 8 636 688 NM in the US and
3 027 365 NM in Europe.
Considering the average level distance for arrivals at the top-ten airports (Figure 6-6 bottom
row) a more accentuated pattern emerges. Interestingly the ranking of the individual airports
changes showing the impact of the number of movements on the determined average level
distance and time.
Figure 6-6 US/Europe Comparison – Vertical Flight Efficiency – Average Level Distance
This change in ranking is more pronounced in the European case, where also the calculated
numerical average of the local averages changes its ranking. The exact causes for this behaviour
P a g e | 81
need to be studied in more detail. For example, in the case of Amsterdam, the higher number of
turbo-props has an impact on the observed airspeed and thus time at level. However, this
observation does not hold for other airports showing a distinct off-set of the average level
distance and average time at level.
Both in the US and Europe a significant share of the level flight is accrued below FL140. This is
directly linked with the procedural airspace for final approach at the different airports. Nuances
apply for the level bands between FL70 and FL140 that may be linked with specific cut-off
altitudes for operations or hand-overs between adjacent control sectors. Level segments below
FL70 can typically be mapped to procedure altitudes for the local traffic patterns at airports and
the associated vectoring to ensure synchronisation and separation of arriving traffic.
It follows that for this heavily procedurally characterised portion of the flight, a significant high
level of inefficiencies in the vertical profile applies. This also describes the major challenge and
opportunity for mitigating the inefficiency during this phase of the arrival. Improving the
observed performance in terms of reducing the number of level segments requires advanced
synchronisation and separation of the air traffic. Such benefits can be expected from the
implementation of extended arrival management operations (XMAN) that comprises the
establishment of the arrival sequence much earlier, leading to speed adjustments 150-250 NM
away from the arrival airport.
6.2.4 INITIAL COMPARISON – BENEFIT POOL
One defining characteristic of the US/Europe comparison report is the combination of the
analyses of individual phases of flight and the estimation of the “benefit pool”, i.e. the potential
improvements actionable by ANS. Next to a qualitative judgement of the impact on punctuality,
the major focus is on the additional fuel burn that drives airspace users’ costs.
To provide an initial appreciation of such an impact analysis, the initial comparison for the
chosen subset of airports summarises the potential total fuel savings per arrival (kg fuel, c.f.
Figure 6-7). With the results presented above it follows that improvements in reducing the share
of level segments for approaching aircraft can contribute to a lower fuel-burn by airspace users.
Further research is required to address the relationship between the level of traffic and demand
at the top ten airports, its associated requirements in terms of synchronisation and separation of
aircraft, and the level of implementation of continuous descent operations at these airports.
Figure 6-7 US/Europe Comparison – Vertical Flight Efficiency – Potential Fuel Savings
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6.2.5 FURTHER WORK
The results reported are derived for the calendar year 2015 for the top ten of airports in the U.S.
and Europe. Based on the limited subset of airports, the initial results need to be validated for a
wider set of airports (i.e. the 34 airports of this comparison report) and multiple years. The latter
will allow for reporting on trends in the metrics presented. This will be essential to address the
question of the level of implementation of continuous descent operations and to appraise the
respective level of implementation or associated constraints by ANS on airspace user operations.
The focus of this initial study was on the vertical flight efficiency of the arrival phase.
Conceptually, the approach and metrics presented can be extended to other phases of flight, e.g.
vertical efficiency during the en-route phase or climb-out phase. Accordingly, the findings of this
report can inform a richer set of analyses of the vertical flight efficiency and an associated
extension of the “benefit pool” in terms of gate-to-gate trajectory analysis.
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7. CONCLUSIONS
This report is the 5th in a series of joint ATM operational performance comparisons between the
US and Europe. It represents the 2nd edition under the Memorandum of Cooperation between
the United States and European Union. The harmonized Key Performance Indicators used in this
report provide demonstrated examples of the KPIs listed in the 2016 ICAO Global Air Navigation
Plan (GANP). The ability to work with harmonized KPIs fosters a unique opportunity for both
groups to learn each other’s strengths and identify opportunities for improvement across all
phases of flight.
Complementary to the well-established indicators already used in previous versions of the
comparison reports, this edition also features two supporting studies on 1) Air Traffic Flow and
Capacity Management (ATFCM) and 2) Vertical Flight Efficiency in the arrival phase, aimed at
expanding the scope and level of analysis of future reports.
The first part of the report examines commonalities and differences in terms of air traffic
management and performance influencing factors, such as air traffic demand characteristics and
weather, which can have a large influence on the observed performance.
Overall, air navigation service provision is more fragmented in Europe with more ANSPs and
physical facilities than in the US. Europe is made up of individual sovereign states. As a
consequence the European study area comprises 37 Air Navigation Service Providers (ANSPs).
Together they operate 62 en-route centres
50
and 16 stand-alone Approach Control (APP) units
(total: 78 facilities). The US study area (CONUS) has 20 en-route centres supplemented by 26
stand-alone Terminal Radar Approach Control (TRACON) units (total: 46 facilities), operated by
one ANSP.
Although the US CONUS airspace is 10% smaller than the European airspace, the US controlled
approximately 57% more flights operating under Instrument Flight Rules (IFR) with 24% fewer
full time Air Traffic Controllers (ATCOs) than in Europe in 2015. US airspace density is, on
average, higher and airports tend to be notably larger than in Europe.
In terms of traffic evolution, there was a notable decoupling between the US and Europe in 2004
when the traffic in Europe continued to grow while US traffic started to decline. The effect of the
economic crisis starting in 2008 impacted traffic growth on both sides of the Atlantic. While
traffic in Europe decreased by 3.3%, air traffic in the US decreased by 9.9% between 2008 and
2015 reaching a low of traffic in 2013. For 2013-2015, the US CONUS experienced traffic growth
of 1.6%.
While weekly traffic profiles in Europe and the US are similar (lowest level of traffic during
weekends), the seasonal variation is higher in Europe. European traffic shows a clear peak during
the summer months. Compared to average, traffic in Europe is in summer about 15% higher
whereas in the US the seasonal variation is more moderate.
At system level, the US has a notably higher share of general aviation than Europe which
accounted for 22% and 3.7% of total traffic in 2015, respectively. In order to improve
50
A 63
rd
en-route center is located in the Canaries, outside of the geographical scope of the study.
P a g e | 84
comparability of datasets, the more detailed analyses were limited to controlled flights either
originating from or arriving at the main 34 US and European airports. The samples are more
comparable as this removes a large share of the smaller piston and turboprop aircraft (general
aviation traffic), particularly in the US. Air traffic to or from the main 34 airports in Europe and in
the US in 2015 represented some 64% of all flights.
There are a number of differences between the two systems. In the US, the Air Traffic Control
System Command Center - which is the equivalent of Network Manager Operations Centre in
Europe, is in a stronger position than its European counterpart with more active involvement of
tactically managing traffic on the day of operations.
The US also operates with fewer airports applying schedule limitations which may lead to a
better utilization of available airport capacity in ideal weather conditions. The analysis of
meteorological reports suggests that weather conditions at the main 34 airports in Europe are,
on average, less favourable than in the US. In 2015, 84.5% of the year was spent in visual
meteorological conditions at the main 34 US airports compared to 77.8% in Europe. Europe
shows more airports operating closer to their declared capacity with more IFR flights per active
runway. The US operates many airports with complex runways with highly variable capacity and
several are operating at close to peak capacity. For airports with more than 3 runways, US
declared rates are in general higher than Europe. For Europe, London Heathrow, Frankfurt, and
Paris Charles de Gaulle clearly have demand/capacity characteristics comparable to the slot
coordinated airports in the US.
Each system has areas that are highly impacted by Special Use Airspace (SUA), often due to
operations of a military nature. For Europe, SUA permeates all regions and adds complexity in
some of the most densely traveled areas of Europe. For the US, those areas are more
concentrated, particularly in coastal regions. The impact of SUA on flight efficiency indicators can
be clearly seen but its unique impact is not quantified in this report.
Building on established operational key performance indicators, the second part of the
comparison report evaluates operational performance in both systems from an airline and from
an ANSP point of view. The airline perspective evaluates efficiency and predictability compared
to published schedules whereas the ANSP perspective provides a more in depth analysis of ATM-
related performance by phase of flight compared to an ideal benchmark distance or time. For
the majority of indicators, trends are provided from 2008 to 2015 with a focus on the change in
performance from 2013 to 2015.
Punctuality is generally considered to be the industry standard indicator for air transport service
quality. The trend in punctuality was similar in the US and Europe between 2005 and 2009 when
both systems reached a comparable level of around 82% of arrivals delayed by 15 minutes or
less in 2009. Whereas in the US performance remained stable in 2010, punctuality in Europe
degraded to the worst level on record mainly due to weather-related delays (snow, freezing
conditions) and strikes. From 2010 to 2012, punctuality in Europe improved again and continued
to improve in the US. However in 2013 and 2014, whereas punctuality in Europe remained
largely unchanged, punctuality in the US saw a sharp decline. In 2015 both systems reached
again a similar performance level due to notable improvements in the US and performance
degradation in Europe.
In Europe and the US, a clear pattern of summer and winter peaks is visible. Whereas the winter
peaks are more the result of weather-related delays at airports, the summer peaks are driven by
the higher level of demand and resulting congestion but also by convective weather in the en-
route airspace in the US and by a lack of en-route capacity in Europe.
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While the evaluation of air transport performance compared to airline schedules provides
valuable first insights, the involvement of many different stakeholders and the inclusion of time
buffers in airline schedules limit the analysis from an air traffic management point of view.
Hence, the evaluation of ATM-related performance in this comparison aims to better understand
and quantify constraints imposed on airspace users through the application of air traffic flow
measures and therefore focuses more on the efficiency of operations by phase of flight
compared to an unconstrained benchmark distance or time.
In order to minimize the effects of ATM system constraints, the US and Europe use a comparable
methodology to balance demand and capacity. This is accomplished through the application of
an “ATFM planning and management” process, which is a collaborative, interactive capacity and
airspace planning process, where airport operators, ANSPs, Airspace Users (AUs), military
authorities, and other stakeholders work together to improve the performance of the ATM
system.
ATM-RELATED DEPARTURE RESTRICTIONS (GROUND HOLDING)
Ground delays imposed by ATM-related departure restrictions were analysed by constraining
environment (en-route or airport/terminal) and by causal factor (weather, capacity, etc.).
After the poor performance due to weather and strikes in 2010, average ATM-related departure
delay in Europe decreased again until 2013. Between 2013 and 2015, total ATM-related ground
delays increased in Europe by 43.4% whereas traffic grew by 4.1% during the same time. The US
has also shown an improvement since 2008 some of which can be attributed to improving
weather and declining traffic levels. Between 2013 and 2015, total ATM-related ground delay in
the US decreased by 12.7% (mainly due to fewer weather-related delays) with traffic levels
increasing by 1.6% during the same time. In Europe, the notable performance deterioration
between 2013 and 2015 was due to a significant increase in capacity/volume related delays and
to a lesser extent due to weather.
ATM-related ground delay per flight in Europe (en-route and airport) was lower than in the US in
2015 (1.3 vs. 1.6 minutes per flight) but the underlying reasons and the application of ATM-
related departure restrictions among facilities differ notably between the two systems. Europe
ascribes a greater percentage of delay to en-route facilities (43% of total delay in 2015) while in
the US the large majority is ascribed to constraints at the airport (82.1% of total delay in 2015).
The share of flights affected by ATM-related departure restrictions at origin airports differs
considerably between the US and Europe. Despite a reduction from 5.0% of all flights in 2008 to
2.0% in 2015, flights in Europe are still over twice more likely to be held at the gate or on the
ground for en-route constraints than in the US where the share of flights affected by ATM-
related departure restrictions was 0.8% in 2015.
For airport-related ground delays, the percentage of delayed flights at the gate or on the surface
is slightly lower in Europe than in the US (2.3% vs. 2.5% in 2015). However, with 51 minutes, the
delay per delayed flight in the US is notably higher than in Europe in 2015 (33 mins). In the US,
the airports which make up a large percentage of those delays are airports like New York (LGA),
Chicago (ORD), Newark (EWR), San Francisco (SFO), New York (JFK), and Philadelphia (PHL) which
report a large number of hours with demand near or over capacity and have lower predictability
of capacity.
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Whereas in the US, en-route-related ground delays are mostly driven by convective weather, in
Europe they are mainly the result of capacity and staffing constraints (including ATC industrial
actions) driven by significant variations in demand in some European States during summer. At
system level, the causes for airport-related ground delays are more similar in the US and in
Europe. Weather is by far the predominant driver of ATM-related departure restrictions but
Europe has also a notable share of capacity-related delays.
ATM-RELATED OPERATIONAL EFFICIENCY (GATE-TO-GATE)
ATM-related flight gate-to-gate efficiency is measured by phase of flight (taxi-out, en-route,
arrival/descent and taxi-in) with reference to a benchmark time or distance.
Taxi-out efficiency improved continuously between 2007 and 2012 in the US but deteriorated
again by 0.5 minutes per departure between 2012 and 2015. During the same period, with the
exception of 2010 where taxi-out efficiency decreased due to the strong winter, performance in
Europe improved continuously at a moderate rate but also showed a slight deterioration in
2015.
After a notable closure of the gap between the US and Europe until 2012, the performance gap
is widening again and in 2015 average additional taxi-out time in the US is, on average, some 1.5
minutes higher per departure than in Europe. This is largely driven by different flow control
policies and the absence of scheduling caps at most US airports. Whereas in Europe inefficiency
in the taxi-out phase is more evenly spread among airports, the observed taxi-out performance
in the US was predominantly driven by the New York airports, Philadelphia (PHL), and Chicago
(ORD).
Horizontal en-route flight efficiency (between a 40NM radius around the departure airport and a
100NM radius around the arrival airport) in filed flight plans and in actual trajectories is still
better in the US than in Europe in 2015. Overall, horizontal en-route efficiency on flights to or
from the main 34 airports in the US is approximately 0.1% better than in Europe in 2015.
Although the level of inefficiency in Europe increased again slightly in 2015, there has been a
continuous improvement over the past few years in Europe which resulted in a continuous
narrowing of the gap between Europe and the US. In view of the mandatory deployment of free
route airspace in EU Member States by 2022, the en-route efficiency improvements in Europe
are expected to continue over the next years. US flight inefficiency as measured by this KPI, also
increased slightly in the 2013-2015 time frame largely driven by airports with increasing traffic.
Flight efficiency in both systems is affected by a number of factors including, inter alia, route
network design, route availability, flight planning, route charges in Europe, and the number and
location of special use airspace. The level of inefficiency in flight plan and in actual trajectory in
the US and in Europe reveal that both in the US and Europe, airlines fly a shorter distance than
they file. Particularly the large gap between planned and actual trajectories observed in Europe
suggests that more direct tracks are provided by ATC on a tactical basis not considered in filed
flight plans which improves efficiency but at the same time lowers the level of overall
predictability in the network.
Similar to en-route flight efficiency, the US also continued to show a higher level of efficiency in
the last 100NM before landing. Overall, average additional time within the last 100 NM (Arrival
Sequencing and Maneuvering Area (ASMA)) was similar in the two regions in 2008 but decreased
in the US between 2008 and 2010. At the same time, flight efficiency within the last 100 NM
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deteriorated in Europe. Although at different levels, performance in the US and in Europe
remained relatively stable between 2013 and 2015.
At system level, average additional ASMA time was 2.5 minutes per arrival in the US in 2015
which was 0.4 minutes lower than in Europe. The result in Europe was significantly affected by
London Heathrow (LHR) which had an additional time of 9.5 minutes per arrival - almost twice
the level of London Gatwick (LGW) with 4.9 minutes per arrival in 2015. In the US, efficiency
levels in the terminal area are more homogenous.
Due to the large number of variables involved, the direct ATM contribution towards the
additional time within the last 100 NM is difficult to determine. One of the main differences of
the US air traffic management system is the ability to maximise airport capacity by taking action
in the en-route phase of flight, such as in trail spacing. In Europe strategies can differ from
airport to airport and the impact of the respective air traffic management systems on airport
capacity utilisation in the US and in Europe was not quantified in this report, but would be a
worthwhile subject for further study.
Although the direct ATM-related influence is limited, additional time in the taxi-in phase was
included for completeness reasons. The level of efficiency is slightly higher in Europe and
remained relatively stable over time in both systems although there has been an increase in
average additional time between 2013 and 2015.
ESTIMATED BENEFIT POOL ACTIONABLE BY ATM
As there are many trade-offs between flight phases, the aggregation of the observed results
enables a high level comparison of the “benefit pool” actionable by ATM in both systems. For
each flight phase, the benefit pool is computed in terms of additional time and fuel burn as the
inefficiencies in the various flight phases (airborne versus ground) have a different impact on
airspace users. For comparability reasons, the computation was based on the assumption that
the same aircraft type performs a flight of 450NM in the en-route phase in the US and the
European ATM system.
For the interpretation of the observed results, it is important to stress that the determined
“benefit pool” is based on a theoretical optimum (averages compared to unimpeded times),
which is, due to inherent necessary (safety) or desired (capacity) limitations, clearly not
achievable at system level.
Although in a context of declining traffic, system-wide ATM performance improved notably in
the US and in Europe between 2010 and 2015. The resulting savings in terms of time and fuel in
both ATM systems had a positive effect for airspace users and the environment.
The improvement in Europe over the past five years was mainly driven by a notable reduction of
ATM-related departure delay, improvements in taxi-out efficiency, and better en-route flight
efficiency. In this context it is however important to point out that 2010 was a year with
comparatively high delays in Europe due to adverse weather and ATC strikes. The performance
improvement in the US was mainly due to a substantial improvement of taxi-out efficiency,
although average additional time in the taxi-out phase in the US increased again slightly in 2015.
Overall, the relative distribution of the ATM-related inefficiencies associated with the different
phases of flight is consistent with the differences in flow management strategies described
throughout the report and confirmed by a the more detailed supplementary section addressing
differences in Air Traffic Flow and Capacity Management (ATFCM) between Europe and the US.
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In Europe ATM-related departure delays are much more frequently used for balancing demand
with en-route and airport capacity than in the US, which leads to a notably higher share of traffic
affected but with a lower average delay per delayed flight. Moreover the share of en-route-
related TMIs in Europe is close to 50% while in the US more than 80% of TMIs are airport-related
during 2015.
Consequently, in Europe flights are over twice more likely to be held at the gate or on the
ground for en-route constraints than in the US. The comparatively small amount of en-route-
related TMIs in the US are mostly driven by convective weather whereas in Europe en-route-
related TMIs are mainly the result of capacity and staffing constraints with only a smaller share
of weather-related constraints.
For TMIs related to arrival airport constraints the situation is different. The percentage of
delayed flights at the departure gate or on the surface is slightly higher in the US than in Europe
and the delay per delayed flights in the US is almost twice as high as in Europe. Most of this
delay in the US is generally linked to weather-related constraints at a number of high density
airports including, New York (LGA), Chicago (ORD), Newark (EWR), San Francisco (SFO), New York
(JFK), and Philadelphia (PHL).
Overall it can be concluded that the two systems differ notably in the way TMIs are applied. In
the US, TMIs are used less frequently, are mostly airport- and weather-related, and affect fewer
flights, but when they are used the delay per delayed flight is much higher than in Europe.
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ANNEX I - LIST OF AIRPORTS INCLUDED IN THIS STUDY
Table I-1: Top 34 European airports included in the study (2015)
EUROPE ICAO IATA COUNTRY
Avg. daily IFR
departures
in 2015
2015 vs.
2013
2015 vs.
2010
Paris (CDG) LFPG CDG FRANCE 652 -0.5% -4.9%
London (LHR) EGLL LHR UNITED KINGDOM 649 0.5% 4.1%
Frankfurt (FRA) EDDF FRA GERMANY 641 -1.0% 0.8%
Amsterdam (AMS) EHAM AMS NETHERLANDS 633 5.9% 16.4%
Munich (MUC) EDDM MUC GERMANY 517 -0.5% -2.5%
Madrid (MAD) LEMD MAD SPAIN 502 10.1% -15.5%
Rome (FCO) LIRF FCO ITALY 432 4.4% -4.3%
Barcelona (BCN) LEBL BCN SPAIN 396 4.5% 4.0%
London (LGW) EGKK LGW UNITED KINGDOM 367 6.9% 11.1%
Zurich (ZRH) LSZH ZRH SWITZERLAND 353 1.1% 0.5%
Copenhagen (CPH) EKCH CPH DENMARK 349 4.0% 3.6%
Vienna (VIE) LOWW VIE AUSTRIA 332 -2.1% -8.2%
Oslo (OSL) ENGM OSL NORWAY 331 0.2% 11.1%
Paris (ORY) LFPO ORY FRANCE 321 0.3% 6.7%
Brussels (BRU) EBBR BRU BELGIUM 320 10.5% 6.6%
Stockholm (ARN) ESSA ARN SWEDEN 310 2.9% 18.4%
Dusseldorf (DUS) EDDL DUS GERMANY 287 -0.4% -2.5%
Dublin (DUB) EIDW DUB IRELAND 269 16.2% 23.6%
Berlin (TXL) EDDT TXL GERMANY 250 5.5% 16.9%
Geneva (GVA) LSGG GVA SWITZERLAND 249 2.2% 10.4%
Palma (PMI) LEPA PMI SPAIN 244 5.2% 2.1%
Manchester (MAN) EGCC MAN UNITED KINGDOM 237 2.4% 9.4%
Helsinki (HEL) EFHK HEL FINLAND 232 0.7% -0.8%
Athens (ATH) LGAV ATH GREECE 232 24.9% -9.9%
London (STN) EGSS STN UNITED KINGDOM 230 17.1% 8.9%
Lisbon (LIS) LPPT LIS PORTUGAL 227 13.7% 16.6%
Milan (MXP) LIMC MXP ITALY 220 -2.7% -17.4%
Hamburg (HAM) EDDH HAM GERMANY 206 10.0% 1.1%
Nice (NCE) LFMN NCE FRANCE 186 -3.1% 4.6%
Cologne (CGN) EDDK CGN GERMANY 172 7.2% -4.2%
Prague (PRG) LKPR PRG CZECH REPUBLIC 169 -0.9% -18.7%
Stuttgart (STR) EDDS STR GERMANY 163 4.4% -3.4%
Milan (LIN) LIML LIN ITALY 160 4.7% -1.3%
Lyon (LYS) LFLL LYS FRANCE 149 -6.6% -9.4%
Average 323 3.6% 1.5%
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Table I-2: US main 34 airports included in the study (2015)
USA ICAO IATA COUNTRY
Avg. daily IFR
departures in
2015
2015 vs.
2013
2015 vs.
2010
Atlanta (ATL) KATL ATL United States 1197 -3.3% -7.6%
Chicago (ORD) KORD ORD United States 1187 -1.3% -1.3%
Dallas (DFW) KDFW DFW United States 931 0.4% 4.5%
Los Angeles (LAX) KLAX LAX United States 878 5.1% 13.8%
Denver (DEN) KDEN DEN United States 749 -6.9% -14.0%
Charlotte (CLT) KCLT CLT United States 737 -2.7% 3.0%
Houston (IAH) KIAH IAH United States 685 -0.8% -7.0%
New York (JFK) KJFK JFK United States 601 8.0% 9.4%
Phoenix (PHX) KPHX PHX United States 595 -0.1% -3.1%
San Francisco (SFO) KSFO SFO United States 580 1.3% 11.6%
Las Vegas (LAS) KLAS LAS United States 568 2.4% 2.3%
Miami (MIA) KMIA MIA United States 566 4.8% 9.7%
Philadelphia (PHL) KPHL PHL United States 562 -5.1% -8.5%
Newark (EWR) KEWR EWR United States 559 -0.9% 1.0%
Minneapolis (MSP) KMSP MSP United States 552 -6.6% -7.5%
Detroit (DTW) KDTW DTW United States 519 -10.9% -16.6%
Seattle (SEA) KSEA SEA United States 516 20.1% 21.0%
Boston (BOS) KBOS BOS United States 514 3.9% 2.8%
New York (LGA) KLGA LGA United States 498 -2.1% -0.5%
Orlando (MCO) KMCO MCO United States 428 5.4% -0.2%
Washington (IAD) KIAD IAD United States 401 -11.1% -19.4%
Washington (DCA) KDCA DCA United States 399 0.2% 7.4%
Salt Lake City (SLC) KSLC SLC United States 388 -1.1% -13.2%
Ft. Lauderdale (FLL) KFLL FLL United States 365 9.9% 3.6%
Chicago (MDW) KMDW MDW United States 330 -0.7% 3.6%
Baltimore (BWI) KBWI BWI United States 326 -5.2% -11.2%
Memphis (MEM) KMEM MEM United States 296 -6.5% -36.0%
Dallas Love (DAL) KDAL DAL United States 288 23.5% 28.6%
Portland (PDX) KPDX PDX United States 281 0.8% -7.6%
San Diego (SAN) KSAN SAN United States 264 3.3% 2.5%
Houston (HOU) IHOU HOU United States 255 -1.3% 7.9%
Tampa (TPA) KTPA TPA United States 252 3.1% -1.4%
St. Louis (STL) KSTL STL United States 250 -2.2% -0.8%
Nashville (BNA) KBNA BNA United States 241 4.0% 4.9%
Average 522 -0.1% -1.6%
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ANNEX II - DEMAND CAPACITY BALANCING
In order to minimize the effects of ATM system constraints, the US and Europe use a comparable
methodology to balance demand and capacity
51
. This is accomplished through the application of
an “ATFM planning and management” process, which is a collaborative, interactive capacity and
airspace planning process, where airport operators, ANSPs, Airspace Users (AUs), military
authorities, and other stakeholders work together to improve the performance of the ATM
system.
Figure II-1: Generic ATFM process (ICAO Doc 9971)
This CDM process allows AUs to optimize their participation in the ATM system while mitigating
the impact of constraints on airspace and airport capacity. It also allows for the full realization of
the benefits of improved integration of airspace design, ASM and ATFM.
51
In line with the guidance in ICAO Doc 9971 (Manual on Collaborative Air Traffic Flow Management).
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The process contains a number of equally important phases:
ATM planning
ATFM execution
o Strategic ATFM
o Pre-tactical ATFM
o Tactical ATFM
o Fine-tuning of traffic flows by ATC (shown in Figure as Optimized operations)
TMIs that have an impact on traffic prior to take-off
TMIs acting on airborne traffic
Post-operations analysis.
ATFM PLANNING
In order to optimize ATM system performance in the ATM planning phase, available capacity is
established and then compared to the forecasted demand and to the established performance
targets. Measures taken in this step include:
reviewing airspace design (route structure and ATS sectors) and airspace utilization
policies to look for potential capacity improvements;
reviewing the technical infrastructure to assess the possibility of improving capacity. This
is typically accomplished by upgrading various ATM support tools or enabling navigation,
communications or surveillance infrastructure;
reviewing and updating ATM procedures induced by changes to airspace design and
technical infrastructure;
reviewing staffing practices to evaluate the potential for matching staffing resources with
workload and the eventual need for adjustments in staffing levels; and
reviewing the training that has been developed and delivered to ATFM stakeholders.
Such an analysis quantifies the magnitude of any possible imbalance between demand and
capacity. Mitigating actions may then be identified to correct that imbalance. However, before
they are implemented, it is very important to:
establish an accurate picture of the expected traffic demand through the collection,
collation, and analysis of air traffic data, bearing in mind that it is useful to:
o monitor airports and airspaces in order to quantify excessive demand and
significant changes in forecast demand and ATM system performance targets;
o obtain demand data from different sources such as:
comparison of recent traffic history (e.g. comparing the same day of the
previous week or comparing seasonal high-demand periods);
traffic trends provided by national authorities, user organizations (e.g.
International Air Transport Association (IATA)); and
other related information (e.g. air shows, major sports events, large-scale
military manoeuvres); and
take into account the complexity and cost of these measures in order to ensure optimum
performance, not only from a capacity point of view but also from an economic (and cost-
effectiveness) perspective.
The next phase is built on declared ATC capacity. It aims at facilitating the delivery of optimal
ATM services.
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Table II-1: Planning layer
US
Europe
The FAA publishes a variety of plans that take a multi-
year view on the evolution of the NAS. This includes
for example:
Aerospace forecasts
Terminal area forecast
Airport capacity profiles
Air Traffic Controller Workforce Plan
National Airspace System Capital Investment Plan
The European ATM Network Operations Plan
represents a view, at any moment in time, of the
expected demand on the ATM Network at a particular
time in the future and the resources available across
the network, together with a set of agreed actions to
accommodate this demand, to mitigate known
constraints and to optimise ATM Network
performance.
The time-frame of the Network Operations Plan is
medium to short-term, moving into pre-tactical
planning. However, this document is strategically
focussed, listing the medium to short-term activities
that contribute to the safe provision of additional
capacity and improved flight efficiency at European
ATM network level.
The Plan is developed through the formal Cooperative
Decision Making (CDM) Process established between
the Network Manager and its operational
stakeholders and is a consolidation of all network and
local capacity plans to provide an outlook of the
expected network performance for the next five year
period by comparing the expected benefit from
planned capacity enhancement initiatives with the
requirements at network and local level, as
determined by the Single European Sky Performance
Framework.
The objectives of the NOP are:
to ensure coordinated planning, execution,
assessment and reporting of all measures agreed
at operational level;
to be used as a tool in the execution of the
network management functions, under the
governance of the Network Management Board
and the Network Directors of Operations;
to assist Network Manager stakeholders, mainly
ANSPs, in carrying out agreed activities towards
enhancing and/or optimising performance;
to provide references for the monitoring and
reporting as a part of Network Management
activities; and,
to ensure formal commitment of all operational
stakeholders towards the implementation of the
agreed measures.
The document identifies potential bottlenecks and
gives early indications to the European Commission,
Network Manager, States, ANSPs, Airports and Aircraft
Operators for the need to plan better use of existing
resources or, if required, to plan for additional
resources, on network interactions and on the need to
implement improvements coordinated at Network
level.
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STRATEGIC ATFM
The ATFM strategic phase encompasses measures taken more than one day prior to the day of
operation. Much of this work is accomplished two months or more in advance.
This phase applies the outcomes of the ATM planning activities and takes advantage of the
increased dialogue between AUs and capacity providers, such as ANSPs and airports, in order to
analyse airspace, airport and ATS restrictions, seasonal meteorological condition changes and
significant meteorological phenomena. It also seeks to identify, as soon as possible, any
discrepancies between demand and capacity in order to jointly define possible solutions which
would have the least impact on traffic flows. These solutions may be adjusted according to the
demand foreseen in this phase.
The strategic phase includes:
a continuous data collection and interpretation process that involves a systematic and
regular review of procedures and measures;
a process to review available capacity; and,
a series of steps to be taken if imbalances are identified. They should aim at maximizing
and optimizing the available capacity in order to cope with projected demand and,
consequently, at achieving performance targets.
The main output of this phase is the
creation of a plan, composed of a list of
hypotheses and resulting capacity
forecasts and contingency measures.
Some elements of the plan will be
disseminated in aeronautical information
publications. Planners will use them to
resolve anticipated congestion in
problematic areas. This will, in turn,
enhance ATFM as a whole as solutions to
potential issues are disseminated well in
advance.
Scheduling at airports is not really part of
ATFM, but it is a strategic demand
capacity balancing activity with a time
horizon of several months, and is
therefore included in the table below.
Airport coordination levels
IATA has defined three levels:
A non-coordinated airport (Level 1) is one where the
capacities of all the systems at the airport are adequate to
meet the demands of users.
A schedules facilitated airport (Level 2) is one where there is
potential for congestion at some periods of the day, week
or scheduling period, which is amenable to resolution by
voluntary cooperation between airlines and where a
schedules facilitator has been appointed to facilitate the
operations of airlines conducting services or intending to
conduct services at that airport.
A coordinated airport (Level 3) is one where the expansion
of capacity, in the short term, is highly improbable and
congestion is at such a high level that:
o the demand for airport infrastructure exceeds the
coordination parameters during the relevant period;
o attempts to resolve problems through voluntary
schedule changes have failed;
o airlines must have been allocated slots before they can
operate at that airport.
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Table II-2: Strategic scheduling and ATFM solutions
US
Europe
Scheduling at airports
With regard to airline scheduling, only two airports
are slot coordinated (IATA level 3) in the US: JFK and
EWR. However EWR will become Level 2 as of winter
2016. Four airports are schedules facilitated (IATA
level 2): ORD, LAX, MCO, SFO.
For DCA and LGA, schedule restrictions are in effect
based on Federal and local regulations.
STMPs (Special Traffic Management Programs) may be
put in place. These are reservation programs
implemented to regulate arrivals and/or departures at
airports that are in areas hosting special events such
as the Masters Golf Tournament, Indianapolis 500,
Denver Ski Country. STMP reservations provide a long-
range planning capability for such events.
Scheduling at airports
In Europe, approximately 100 airports are slot
coordinated (IATA level 3).
Approximately 70 are schedules facilitated (IATA level
2).
North American Route Program (NRP)
The North American Route Program (NRP) specifies
provisions for flight planning at flight level 290 (FL290)
and above, within the conterminous U.S. and Canada.
It enables flexible route planning for aircraft operating
at FL290 and above, without reference to the ATS
route network, from a point 200 nautical miles (NM)
from their point of departure to a point 200 NM from
their destination. Additional flexibility is available by
utilizing specified Departure Procedures (DP) and
Standard Terminal Arrival Routes (STAR) that have
been identified within 200 NM of the airport(s).
Beyond 200 NM from point of departure or
destination, operators must ensure that the route of
flight contains no less than one waypoint or NAVAID,
per each ARTCC that a direct route segment traverses
and these waypoints or NAVAIDs must be located
within 200 NM of the preceding ARTCC's boundary.
Additional route description fixes for each turning
point in the route must be defined.
Operators must ensure that the route of flight avoids
active restricted areas and prohibited areas by at least
3 NM unless permission has been obtained from the
using agency to operate in that airspace and the
appropriate air traffic control facility is advised.
The ARTCCs must avoid issuing route and/or altitude
changes for aircraft which display the remarks “NRP"
except when due to strategic, meteorological or other
dynamic conditions. They must coordinate with
ATCSCC before implementing any reroute to NRP
flights beyond 200 NM from point of departure or
destination. The ATCSCC has the authority to suspend
and/or modify NRP operations for specific
Free Route Airspace (FRA)
In Europe FRA is a specified airspace within which
users may freely plan a route between a defined entry
point and a defined exit point. Subject to airspace
availability, the route can be planned directly from
one to the other or via intermediate (published or
unpublished) way points, without reference to the ATS
route network. Within this airspace, flights remain
subject to air traffic control.
Free route operations can be:
Time limited (e.g. at night) – this is usually a
transitional step that facilitates early
implementation and allows field evaluation of
the FRA while minimising the safety risks.
Structurally or geographically limited (e.g.
restricting entry or exit points for certain traffic
flows, applicable within CTAs or upper airspace
only) – this is done in complex airspaces where
full implementation could have a negative
impact on capacity.
Implemented in a Functional Airspace Block
(FAB) environment – a further stage in the
implementation of FRA. The operators should
treat the FAB as one large FIR.
Within SES airspace – this is the ultimate goal
of FRA deployment in Europe.
Route Availability Document (RAD)
The RAD is a common reference document containing
the policies, procedures and description for route and
traffic orientation. It also includes route network and
free route airspace (FRA) utilisation rules and
availability.
The RAD is also an Air Traffic Flow and Capacity
P a g e | 96
geographical areas or airports. Suspensions may be
implemented for severe weather reroutes, special
events, or as traffic/equipment conditions warrant.
Pre-defined routes
Pre-planned rerouting options are contained in the
National Playbook. It is a collection of Severe Weather
Avoidance Plan (SWAP) routes that have been pre-
validated and coordinated with impacted ARTCCs.
They have been designed to mitigate the potential
adverse impact to the FAA and users during periods of
severe weather or other events that affect
coordination of routes. These events include, but are
not limited to, convective weather, military
operations, communications, and other situations.
Other examples of predefined routes include:
Coded Departure Routes (CDR). These are a
combination of coded air traffic routings and
refined coordination procedures.
Preferred routes: routes that have been
published by ATC to inform users of the
“normal” traffic flows between airports. They
were developed to increase system efficiency
and capacity by having balanced traffic flows
among high-density airports, as well as de-
conflicting traffic flows where possible.
Altitude segregation
Altitude segregation measures are predefined in US
facilities through capping and tunnelling plans:
Capping: indicates that aircraft will be cleared
to an altitude lower than their requested
altitude until they are clear of a particular
airspace. Capping may apply to the initial
segment of the flight or for the entire flight.
Tunnelling: descending traffic prior to the
normal descent point at an arrival airport to
keep aircraft clear of an airspace situation on
the route of flight. It is used to avoid conflicting
flows of traffic and holding patterns.
Severe Weather Avoidance Plan (SWAP)
A SWAP is a formalized program that is developed for
areas susceptible to disruption in air traffic flows
caused by thunderstorms.
This is mainly used for the Northeast, to balance
throughput of arrivals and departures at the New York
City-area airports for those days that convective
weather is forecast.
There a three-tier system is used, based upon the
severity of the weather as well as the location of the
convective activity:
SWAP Level 1: Weather is expected to be 100
Management (ATFCM) tool that is designed as a sole-
source flight-planning document, which integrates
both structural and ATFCM requirements,
geographically and vertically.
The content of the RAD is agreed between the
Network Manager and the Operational Stakeholders
through an appropriate cooperative decision making
(CDM) process.
Each State ensures that the RAD is compatible with
their AIP with regard to the airspace organisation
inside the relevant FIR/UIR.
EUROCONTROL is responsible for preparing of a
common RAD reference document, collating,
coordinating, validating and publishing it, following
the CDM process as described above.
Scenarios
Scenarios are the European means by which the best
possible airspace organisation combined with the best
ATFCM measures can be implemented to meet
airspace demand and to take into account traffic
flows, airport and ATC capabilities.
A scenario is a coherent set of measures combining
airspace organisation, route flow restrictions, sector
configuration plan, capacity plan, rerouting plan
and/or regulation plan. Each scenario is accompanied
by its particular modus operandi for use of the
network in relation with the ATC sector configuration,
the route and airspace availability, special events, etc.
Scenarios are characterised by:
the traffic origin
the traffic destination
the scenario type(s)
the On-load Areas
the Off-load Areas
suggested alternative routes
There are four types of scenario:
Level capping scenarios (FL): carried out by
means of level restrictions or through dynamic
routeing restrictions (RAD restrictions, EURO
restrictions).
Rerouteing scenarios (RR): diversion of flows to
off-load traffic from certain areas.
Alternative routeing scenarios (AR): alternative
routes which are exceptionally made available
to off-load traffic from certain areas,
implemented by regulations with a low rate.
EU Restrictions: airspace restrictions that
affect the flight planning phase based on route
or airspace closures.
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miles or more from N90 (NY TRACON) airspace
and/or there is minor impact expected to ZNY
(NY Center) arrival/departure gates, and to
over flight routes. This level of SWAP provides
for developing some basic structure, route
expectations, and planning capability. The
objective is to manage expectations and
complexity early. Customers should begin filing
appropriate route solutions and managing
their flights in response to the actions taken or
planned.
SWAP Level 2: Weather is expected to be
between 50-100 miles from N90 airspace
and/or there is moderate impact expected to
ZNY arrival/departure gates, and possibly to
over flight routes. This level of SWAP provides
for increasing structure and reducing holding,
diversions, and other serious complexity
issues. The objective is to prioritize airspace
availability, reduce airborne inventory, and
manage surface congestion issues. All
initiatives in SWAP Level 1 are included.
SWAP Level 3: Weather is expected to be
within 50 miles from N90 airspace and/or
there is moderate or greater impact expected
to ZNY arrival/departure gates, and possibly to
over flight routes. This level of SWAP provides
real-time constraint, route and volume
management. The focus of this stage is to
prioritize traffic that requires more expeditious
handling, and that requires a much higher
priority than other traffic sharing the same
airspace. The objective is to reduce diversions,
holding, surface delays and taxi-back
situations. All initiatives in SWAP Level 1 and 2
are included.
Event management
Event management is used to resolve potential
capacity/demand imbalances caused by seasonal or
significant events, by applying ATFCM solutions. These
solutions are a set of ATFCM measures, including
routeing scenarios, to deliver optimum network
performance; they take the constraints of both AOs
and ANSPs into consideration. ATFCM events are:
Seasonal events happen every year at the
same time and impact on the ATFCM network
in a relatively predictable way. Examples of
seasonal events include: the South-West Axis
flows, the North-East Axis flows, the ski season
traffic flows etc.
Significant events are those that generate a
strong traffic demand in a relatively small area,
generating local congestion. Examples of
significant events are: the Olympic Games, the
Football World Cup Finals, or Summits of
Heads of States.
Military events refer primarily to military
exercises. They are coordinated with the
national AMC (Airspace Management Cells)
and addressed through specific scenarios.
The general process consists of preparing scenarios
under the Network Manager Operations Centre's
(NMOC) supervision, in coordination with FMPs from
the ACCs concerned, and the operations staff from the
airlines involved.
Axis management
The above mentioned seasonal events are dealt with
through the axis management process.
This is a CDM process which starts in advance and has
as an output ATFCM Measures (e.g., re-routings, FL
capping or alternative routings) that would be further
consolidated and applied on the day of operations.
This output is discussed and agreed through dedicated
CDM conferences (either via a meeting or an e-
conference) and there is a monitoring process to fine-
tune the event management as well.
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PRE-TACTICAL ATFM
The ATFM pre-tactical phase encompasses measures taken one day prior to operations.
During this phase, the traffic demand for the day is analysed and compared to the predicted
available capacity. The plan, developed during the strategic phase, is adapted and adjusted
accordingly.
The main objective of the pre-tactical phase is to optimize capacity through an effective
organization of resources (e.g. sector configuration management, use of alternate flight
procedures).
The work methodology is based on a CDM process established between the stakeholders (e.g.
FMU, airspace managers, AUs).
The tasks to be performed during this phase may include the following:
determine the capacity available in the various areas, based on the particular situation
that day;
determine or estimate the demand;
study the airspace or the flows expected to be affected and the airports expected to be
saturated, calculating the acceptance rates to be applied according to system capacity;
conduct a comparative demand/capacity analysis;
prepare a summary of ATFM measures to be proposed and submit them to the ATFM
community for collaborative analysis and discussion; and,
at an agreed-upon number of hours before operations, conduct a last review consultation
involving the affected ATS units and the relevant stakeholders, in order to fine-tune and
determine which ATFM measures should be published through the corresponding ATFM
messaging system.
The final result of this phase is the ATFM Daily Plan (ADP), which describes the necessary
capacity resources and, if needed, the measures to manage the traffic. This activity is based on
hypotheses developed in the strategic phase and refined to the expected situation. It should be
noted that the time limits of the pre-tactical phase may vary, as they depend on forecast
precision, the nature of operations within the airspace and the capabilities of the various
stakeholders.
The ADP is developed collaboratively and aims at optimizing the efficiency of the ATM system
and balancing demand and capacity. The objective is to develop strategic and tactical outlooks
for a given airspace volume or airport that can be used by stakeholders as a planning forecast.
The ADP covers, as a minimum, a 24-hour period. The plan may however cover a shorter period,
provided mechanisms are in place to update the plan regularly.
The operational intentions of AUs should be consistent with the ADP (developed during the
strategic phase and adjusted during the pre-tactical phase).
Once the process has been completed, the agreed measures, including the ATFM measures, are
disseminated using an ATFM message, which may be distributed using the various aeronautical
communications networks or any other suitable means of communication, such as internet and
email.
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Table II-3: Pre-tactical planning
US
Europe
Operations Plan (OP)
The FAA ATCSCC Operations Plan represents a view of
the NAS performance, constraints, and risks that are
accurate at the time it is published.
The time-frame of the Operations Plan is pre-
tactical/tactical. The Operations Plan is developed
through a collaborative process and the ATCSCC host a
Planning Webinar (PW) with FAA facilities (ARTCCs,
Large TRACONs, and large ATCTs), with flight
operators, and other stakeholders as needed. Unless
otherwise announced, the first Operations Plan is
published by FAA ATCSCC Advisory no later than 6:00
a.m. Eastern Time. Unless otherwise announced, the
first PW is conducted at 7:15 am Eastern Time and
every 2 hours thereafter until 9:15 pm Eastern Time.
The ATCSCC has a designated Planner position that is
staffed by a supervisor - National Traffic Management
Officer (NTMO) at the ATCSCC. The Planner is
responsible for developing, collaborating, conducting
the PW and for publishing the Operations Plan by
Advisory immediately following the PW. An operations
agenda web-page is available to all stakeholders for
submitting proposed constraints and mitigations
between the PWs. The Planner is responsible for
managing that web-page.
The Operations Plan has the following sections:
Terminal (airport) constraints
En-route constraints
Plain language description of the Operations Plan
Actual and anticipated traffic management
initiatives (TMIs), such as Ground Delay Programs
(GDPs), Airspace Flow Programs (AFPs), Ground
Stops (GS)
Actual and Planned Routes (sometimes referred to
as reroutes) are published. Actual TMIs and routes
include a valid time while anticipated TMIs and
routes include both a projected valid time and a
qualifying description of the confidence that it may
be needed. The qualifiers are:
o Possible – indicates that an initiative may be
needed if the constraint develops as forecast;
timing is broad and confidence is low;
o Probable – indicates that a TMI is very likely and
confidence is high; timing is less certain; and,
o Expected – indicates there is high confidence the
TMI will be implemented when the projected
time is reached.
Valid time
Three PWs contain specialized information:
o at 9:15 am Eastern Time, an extended discussion
of potential structured routes is conducted;
o at 7:15 pm Eastern Time Overnight or “Cargo”
operations are discussed;
o at 9:15 pm Eastern Time a Next Day Outlook is
discussed.
ATFCM Daily Plan (ADP)
The ADP is a proposed set of tactical ATFCM measures
(TMIs) prepared pre-tactically and agreed between all
partners concerned to optimise the European
Network. It covers a 24-hour period (the day prior to
the day of operation) for each day.
Normally the ADP starts as a draft on D-2 and it is
finalised and promulgated on D-1 by means of the
ATFCM Notification Message (ANM) and the ATFCM
Information Message (AIM) Network News. During
tactical operations the ADP is further modified
according to the developments of the day.
Airspace Use Plan (AUP)
Agencies responsible for airspace activities submit
their requests for the allocation of airspace or routes –
Temporary Segregated Areas (TSAs) or Conditional
Routes (CDRs) – to the appropriate national AMC
(Airspace Management Cell).
After the AMC has received, evaluated and
de-conflicted the airspace requests, the notification of
the airspace allocation is published in advance in a
daily AUP.
The Airspace Use Plan activates Conditional
Routes and allocates Temporary Segregated
Areas and Cross-Border Areas for specific
periods of time.
If necessary, changes to the pre-tactical
airspace allocation can be made by AMCs
through the publication of an Updated
Airspace Use Plan. This UUP notifies the
changes to the airspace allocation on the
actual day of operations. The process of
update of airspace use requests is very
dynamic.
The AUP and the UUP are published nationally
and internationally in a harmonised format.
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TACTICAL ATFM
During the ATFM tactical phase, measures are adopted on the day of the operation. Traffic flows
and capacities are managed in real time. The ADP is amended taking due account of any event
likely to affect it.
The tactical phase aims at ensuring that:
the measures taken during the strategic and pre-tactical phases actually address the
demand/capacity imbalances;
the measures applied are absolutely necessary and that unnecessary measures be
avoided;
capacity is maximized without jeopardizing safety; and
the measures are applied taking due account of equity and overall system optimization.
During this phase, any opportunity to mitigate disturbances will be used. The need to adjust the
original ADP may result from staffing problems, significant meteorological phenomena, crises
and special events, unexpected opportunities or limitations related to ground or air
infrastructure, more precise flight plan data, the revision of capacity values, etc.
The provision of accurate information is of paramount importance in this phase, since the aim is
to mitigate the impact of any event using short-term forecasts. Various solutions will be applied,
depending on whether the aircraft are already airborne or about to depart.
Proactive planning and tactical management require the use of all information available. It is of
vital importance to continuously assess the impact of ATFM measures and to adjust them, in a
collaborative manner, using the information received from the various stakeholders.
Table II-4: Tactical ATFM
US
Europe
Managing airport constraints
Airport TMIs in the US are designed to manage
inbound traffic flows (arrivals):
Ground Delay Program (GDP): GDPs will normally be
implemented at airports where capacity has been
reduced because of weather—such as low ceilings,
thunderstorms or wind—or when demand exceeds
capacity for a sustained period.
GDPs are implemented to ensure the arrival demand
at an airport is kept at a manageable level to preclude
extensive holding and to prevent aircraft from having
to divert to other airports. They are also used in
support of Severe Weather Avoidance Plan (SWAP).
A ground stop (GS) is a procedure requiring aircraft
that meet specific criteria to remain on the ground.
Ground Stops are implemented for a number of
reasons. The most common reasons are:
To control air traffic volume to airports when the
projected traffic demand is expected to exceed the
airport´s acceptance rate for a short period of
time.
To temporarily stop traffic allowing for the
Managing airport constraints
Europe uses ATFM regulations to manage airport
traffic flows. Airport ATFM regulations can apply:
To a single aerodrome (AD) or to a set of
aerodromes (AZ). This is called the Reference
Location (RL).
For the AD or AZ: to all or just to a subset of the
traffic; i.e. to arrivals only, departures only, or
both (called ‘global’). This is called the traffic
volume (TV). In most cases only arrival regulations
are used.
Airport ATFM regulations with a non-zero rate are the
equivalent of a GDP.
Airport ATFM regulations with a zero rate are the
equivalent of a GS.
In some cases, an airport ATFM regulation starts off
with a zero rate, which is later increased to accept a
limited amount of traffic. This is the equivalent of a
combined GS+GDP.
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implementation of a longer-term solution, such as
a Ground Delay Program.
The affected airport´s acceptance rate has been
reduced to zero.
A facility may initiate a local GS when the facilities
impacted are wholly contained within the facility's
area of responsibility and conditions are not expected
to last more than 30 minutes. Local GSs must not be
extended without prior approval of the ATCSCC.
The ATCSCC may implement a national GS upon
receipt of information that an immediate constraint is
needed to manage a condition, after less restrictive
TMIs have been evaluated.
Not all inbound traffic is affected by a GDP or GS. The
scope (departure scope) indicates which traffic is
included in the TMI. Traffic departing from airports
under the jurisdiction of the listed facilities will be
subjected to the TMI. The scope can be distance based
or tier based, eg the local ARTCC, the First Tier ARTCCs
(neighbours), or the Second Tier ARTCCs (neighbours
of neighbours).
Managing airspace constraints
A Departure Stop is similar to a GS. It assigns a
departure stop for a specific NAS element other than a
destination airport, such as an airway, fix, departure
gate, or sector.
An Airspace Flow Program (AFP) is a delay TMI with
parameters similar to that of a GDP. The major
difference between the two types of initiatives is that
AFPs control the flow of aircraft into or through a
volume of airspace versus controlling the flow of
aircraft to a particular airport. The volume of airspace
used is often one-dimensional (i.e. a border). All of
these volumes are referred to as Flow Constrained
Areas (FCA).
Flow Evaluation Areas (FEA) are developed on an ad
hoc basis. Just like FCAs, they are three-dimensional
volumes of airspace, along with flight filters and a time
interval, used to identify flights. They may be drawn
graphically, around weather, or they may be based on
a NAS element. They are used to evaluate demand on
a resource. FEAs and FCAs are different because an
Evaluation Area is just under study while a
Constrained Area requires action to address a
particular situation.
FEA/FCAs provide reroutes using the Create Reroute
capability and are published through a reroute
advisory with an optional flight list attached.
Stakeholders can monitor FEA/FCAs through reroute
monitor in traffic situation display (TSD), web situation
display (WSD) or collaborative constraint situation
display (CCSD).
Managing airspace constraints
Europe uses ATFM regulations to manage en-route
traffic flows. En-route ATFM regulations can apply:
To an airspace volume (AS) or to a special point
(SP). This is called the Reference Location (RL).
To all or just to a subset of the traffic crossing the
RL. This is called a traffic volume (TV).
En-route ATFM regulations can either take the form of
A delay TMI. Those are comparable to AFPs.
A TMI for rerouting purposes, not generating delay
(normally part of a scenario see above):
o Level capping (FL): implemented by a zero-rate
regulation with vertical restriction
o Required rerouting (RR): implemented by a zero-
rate regulation
o Alternative routeing (AR): implemented by a
regulation with a low rate through airspace
normally not accessible to the traffic flow.
In Europe the Network Manager has – in collaboration
with aircraft operators – put in place a process called
the Flight Efficiency Initiative (FEI). It is based on
voluntary participation by aircraft operators and aims
at offering them the most efficient routes on the day
of operation. It entails scrutinising their flight plans
and seeing if there is not a quicker or more cost-
effective way for their aircraft to fly.
The FEI operates on the basis of a dynamic route
generator and an automatically maintained catalogue
of routes flown in the past. The routes are evaluated
on the basis of subjective cost criteria provided by the
airline operators, such as:
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The Required Reroutes (RR) TMI is often applied in
conjunction with delay programs to move flows
around en-route constraints. The impact of the
reroute is dependent on how it is implemented and
what type of delay program it is interacting with.
Required reroutes are issued by Departure (ETD),
Arrival (ETA) or FCA entry time.
CTOP (Collaborative Trajectory Options Program) is a
new type of TMI, which automatically assigns delay
and/or reroutes around one or more FCA-based
airspace constraints in order to balance demand with
available capacity. The unique feature of CTOP is that
it allows for user preferences in route selection. Under
a CTOP initiative, operators submit alternative routes
of their choice around or away from a constraint, thus
providing additional options for air traffic controllers
to expedite flights away from congested airspace.
Flights that have submitted a trajectory option set
(TOS) could be exempt from ground delays or in-flight
reroutes associated with such constraints.
ICR (Integrated Collaborative Rerouting) is a process
that builds on the FCA technology. The ICR process
requires that a constraint be identified early. ICR
allows airspace users to take action with their
trajectory preferences in response to an identified
system constraint. They have an opportunity to
consider the area of concern and provide EI (Early
Intent) messages that communicate their decisions in
response to the constraint. At the expiration of the EI
window, traffic managers can analyze the customer
responses and decide if the actions taken have
resolved the issue or decide if recommended routes,
required routes, airspace flow programs, or other
traffic management initiatives (TMI) will be necessary
to further reduce demand.
flying time costs,
fuel costs and
the cost of air traffic flow and capacity
management (ATFCM) delays.
The FEI is based on a re-routing process that can take
place on the day of operations up to two hours before
the flight. It takes place in two phases:
First phase: AOs and computerized flight plan
service providers (CFSPs) can use an NM tool to
compare their flight plans with the best filed flight
plan accepted by the NM for a given city pair
Second phase: Re-routing proposals from the NM
to AOs
The FEI also contributes to a strategic and continuous
improvement of the airlines’ route catalogues.
Slot substitution (subbing)
The substitution process provides a way for airspace
users, henceforth referred to as users, to manage
their flights during a GDP, GS or AFP. Users can, for
example, swap slots between a high priority flight and
a less important flight, reducing the delay on one at
the cost of increasing the delay for another. Users
may only sub for their own flights; there is no trading
or bartering for slots.
Slot swapping
In Europe the ETFMS slot swapping functionality is
used to swap flights requested by AOs or FMPs.
Additionally it may be used to improve another flight
if an aircraft operator requests a slot extension (i.e.
instead of forcing the flight).
AOs shall only request swaps concerning flights for
which they are the responsible operator or where
there is a formal agreement between both AOs to
swap flights. For regulated flights departing from an A-
CDM, AOs shall request the swap via the FMP / TWR.
FMPs may request swaps for two flights of the same
AO or, during critical events at airports, also between
any different AOs.
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In the tactical phase Europe also uses STAM, Short Term ATFCM Measures, such as minor ground
delays, flight level capping and minor re-routings applied to a limited number of flights, both
airborne and pre-departure. STAM application allows reducing the complexity and/or demand of
anticipated/identified traffic peaks and to prevent or limit the penalization that would result
from the implementation of standard ATFCM measures.
Europe is also moving its first steps in Target Time Operations, by including the Target Time Over
in the ATFM Slot Allocation Messages. At now this is provided to create operational awareness
of the planned time at the congestion point. Further developments are planned to use Target
Time over to optimise ATFM delivery.
FINE-TUNING OF TRAFFIC FLOWS BY ATC
After ATFM measures are taken, traffic flows are further fine-tuned by ATC.
A distinction can be made between TMIs that have an impact on traffic prior to take-off, and
those acting on airborne traffic.
TMIs that have an impact on traffic prior to take-off
These are sequencing and metering measures that are used by ATC to fine-tune the traffic flow
and that may have a delay impact on traffic prior to take-off.
The resulting cleared-for-take-off time (Call For Release Time – CFR) may be different from the
slot time (EDCT/CTOT) produced by ATFM. Normally this adjustment falls within the ATFM
tolerance window:
In the US this called the EDCT Window: -5/+5min;
In Europe it is the STW (Slot Tolerance Window): -5/+10min during normal conditions and
during adverse conditions up to 15/+30min.
In specific cases sequencing and metering may create additional delay beyond the ATFM
tolerance window.
In the US the CFR Window (Call For Release Window) for ATOT is -2/+1min around the assigned
CFR time.
In Europe, for flights without an ATFM slot there is a DTW (Departure Tolerance Window) for
ATOT of -15/+15min around the ETOT during normal conditions, during adverse conditions
possibly extended to -15/+30min.
The TMIs in this category include:
CFR (Call for Release)
52
(US)
DSP (Departure Spacing) (US)
ESP (En-route Spacing)
ASP (Arrival Spacing)
Metering (en-route metering)
MDI (Minimum Departure Interval) (Europe)
MIT (Miles In Trail)
MINIT (Minutes In Trail)
52
Also known as Approval Request (APREQ).
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TMIs acting on airborne traffic
This TMI category comprises longitudinal (sequencing and metering), lateral (load balancing) and
vertical (level off) tactical measures that are used by ATC after take-off with the objective to
fine-tune the traffic flow.
TBM (Time Based Metering) not propagating to the departure airport (US)
o TBFM Speed Advisories (US) / XMAN (Cross-border Arrival Management) speed
advisories (Europe)
AH (Airborne Holding)
o Planned Holding
o Unplanned Holding
Vectoring
Tactical level offs
Point Merge (Europe)
Fix Balancing
POST-OPERATIONS ANALYSIS
The final step in the ATFM planning and management process is the post-operations analysis
phase.
During this phase, an analytical process is carried out to measure, investigate and report on
operational processes and activities. This process is the cornerstone of the development of best
practices and/or lessons learned that will further improve the operational processes and
activities. It covers all ATFM domains and all the external units relevant to an ATFM service.
While most of the post-operations analysis process may be carried out within the ATFM unit,
close coordination and collaboration with ATFM stakeholders will yield better and more reliable
results.
Post-operations analysis is accomplished by evaluating the ADP and its results. Reported issues
and operational statistics are evaluated and analysed in order to learn from experience and to
make appropriate adjustments and improvements in the future.
Post-operations analysis includes analysis of items such as anticipated and unanticipated events,
ATFM measures and delays, the use of predefined scenarios, flight planning and airspace data
issues. They compare the anticipated outcome (where assessed) with the actual measured
outcome, generally in terms of delay and route extension, while taking into account
performance targets.
All stakeholders within the ATFM service can provide feedback, preferably in a standardized
electronic format, enabling the information to be used in the post-operations analysis in an
automated manner.
Post-operations analysis is used to:
identify operational trends or opportunities for improvement;
further investigate the cause and effect relationship of ATFM measures to assist in the
selection and development of future actions and strategies;
gather additional information with the goal of optimizing ATM system efficiency in general
or for on-going events;
perform analysis of specific areas of interest, such as irregular operations, special events,
or the use of re-route proposals; and
make recommendations on how to optimize ATM system performance and to minimize
the negative impact of ATFM measures on operations.
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It is important to ensure that the relevant ATFM stakeholders are made aware of the results. The
following processes support this:
collection and assessment of data including comparison with targets;
broad review and further information gathering at a daily briefing;
weekly operations management meeting to assess results and recommend procedural,
training and system changes where necessary to improve performance; and
periodic operations review meetings with stakeholders.
Table II-5: Post-Ops
US
Europe
There are different levels of post operations analysis:
At 8:30 am Eastern time the ATCSCC conducts a
post-ops review for ATCSCC management and
staff.
At 10:00 am Eastern Time there is a National
System review (NSR) post-ops telcon that includes
flight operators and FAA Deputy Director System
Operations and ATCSCC QC.
At 10:30 am Eastern Time, the Deputy Chief
Operating Officer at FAA HQ conducts a post-ops
review that includes safety, security, system
operations (ATFM), and other significant events
from the prior day’s operation.
A NAS-AERO product that is an interactive web
product is used in the briefings and is published widely
within FAA. NAS performance, delay, airborne
holding, diversions, TMIs, and other NAS performance
data is available. There are many national, regional,
and facility level products that are created for post-
ops review, including video replays.
Traffic Management Reviews (TMRs) may be
conducted on significantly positive NAS performance
results as well as on poor results. The TMR is a very
detailed review of a particular event or constraint and
may take several days to perform.
The Network Manager provides traffic and delay
forecasts and analysis to support the global
performance of the European aviation network. The
Network Manager:
continuously assesses the performance of the
network functions and has established pan-
network processes of monitoring, analysing and
reporting on all network operational performance
aspects;
recommends measures and/or take the actions
needed to ensure the network performance;
compares these performance against the
objectives established in the network Strategy Plan
(NSP), Network Operations Plan (NOP) &
Performance Plans identifying gaps and proposing
remedial actions.
This way NM provides a consolidated and coordinated
approach to all planning & operational activities of the
network.
Playbook
The playbook is a tool that combines historical data (5
years and the last 4 weeks) to indicate the risk of delay
occurring in a particular area of the Network.
A daily delay target is allocated globally for en-route
and airports and individually for ACCs and airports
based on the relevant en-route and airport annual
targets.
An advanced playbook is produced at D-6 to facilitate
planning; this forms the template for production of
the D+1 playbook which contains actual delay data
from the day of operation for comparison and further
post operations analysis.
The Post Operations team is responsible for the
production of the en-route ATC Capacity and Staffing
and Airport playbooks.
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ANNEX III - GLOSSARY
A-CDM
Airport Collaborative Decision Making
AAR
Airport Arrival Acceptance Rate
ACC
Area Control Centre. That part of ATC that is concerned with en-route traffic coming
from or going to adjacent centres or APP. It is a unit established to provide air traffic
control service to controlled flights in control areas under its jurisdiction.
Achieved distance
The portion of the Great Circle distance between two airports that corresponds to a
given portion of a flight trajectory. This can be computed for the actual trajectory as
well as for the flight-plan trajectory. Regardless of the shape of the trajectory (and the
actual or flight-planned distance), the achieved distance of the entire flight is equal to
the Great Circle distance between the two airports.
ACI
Airports Council International (http://www.aci-europe.org/)
AD
Aerodrome
ADP
ATFM Daily Plan
ADR
Airport Departure Rate
AFP
Airspace Flow Program (US)
AIG
Accident and Incident Investigation (ICAO)
AIM
ATFCM Information Message (Europe)
AIP
Aeronautical Information Publication, sets out procedures used by pilots and air traffic
controllers
AIS
Aeronautical Information Service
AMC
Airspace Management Cell (Europe)
ANM
ATFCM Notification Message (Europe)
ANS
Air Navigation Service. A generic term describing the totality of services provided in
order to ensure the safety, regularity and efficiency of air navigation and the
appropriate functioning of the air navigation system.
ANSP
Air Navigation Services Provider
AO
Aircraft Operator
APP
Approach Control Unit
AR
Alternative routeing scenario (Europe)
ARTCC
Air Route Traffic Control Center, the equivalent of an ACC in Europe.
ASBU
Aviation System Block Upgrade (ICAO)
ASM
Airspace Management
ASMA
Arrival Sequencing and Metering Area
ASP
Arrival Spacing (US)
ASPM
FAA Aviation System Performance Metrics
ATC
Air Traffic Control. A service operated by the appropriate authority to promote the safe,
orderly and expeditious flow of air traffic.
ATCO
Air Traffic Control Officer
ATCSCC
US Air Traffic Control System Command Centre
ATCT
Air Traffic Control Tower (US)
ATFCM
Air Traffic Flow and Capacity Management
ATFM
Air Traffic Flow Management. ATFM is established to support ATC in ensuring an
optimum flow of traffic to, from, through or within defined areas during times when
demand exceeds, or is expected to exceed, the available capacity of the ATC system,
including relevant aerodromes.
ATFM delay
(CFMU)
The duration between the last take-off time requested by the aircraft operator and the
take-off slot given by the CFMU.
ATFM Regulation
When traffic demand is anticipated to exceed the declared capacity in en-route control
centres or at the departure/arrival airport, ATC units may call for “ATFM regulations.”
ATM
Air Traffic Management. A system consisting of a ground part and an air part, both of
which are needed to ensure the safe and efficient movement of aircraft during all
phases of operation. The airborne part of ATM consists of the functional capability
which interacts with the ground part to attain the general objectives of ATM. The
ground part of ATM comprises the functions of Air Traffic Services (ATS), Airspace
Management (ASM) and Air Traffic Flow Management (ATFM). Air traffic services are
the primary components of ATM.
ATO
Air Traffic Organization (FAA)
ATS
Air Traffic Service. A generic term meaning variously, flight information service, alerting
P a g e | 107
service, air traffic advisory service, air traffic control service.
AU
Airspace User
AUP
Airspace Use Plan (Europe)
AZ
Aerodrome Zone (Europe)
Bad weather
For the purpose of this report, “bad weather” is defined as any weather condition (e.g.
strong wind, low visibility, snow) which causes a significant drop in the available airport
capacity.
BTS
Bureau of Transportation Statistics (US)
CAA
Civil Aviation Authority
CANSO
Civil Air Navigation Services Organisation (http://www.canso.org)
CBA
Cross-Border Area (Europe)
CCF
Combined Control Facility (US): An air traffic control facility that provides approach
control services for one or more airports as well as en-route air traffic control (center
control) for a large area of airspace. Some may provide tower services along with
approach control and en-route services. Also includes Combined Center Radar
Approach (CERAP) facilities.
CDA
Continuous Descent Approach
CDM
Collaborative Decision Making
CDR
Conditional Route (Europe)
CDR
Coded Departure Route (US)
CFMU
See NMOC
CFR
Call For Release Time (US)
CM
Capacity Management
CO
2
Carbon dioxide
CODA
EUROCONTROL Central Office for Delay Analysis
CONUS
see US CONUS
CTA
Control Area
CTOP
Collaborative Trajectory Options Program
CTOT
Calculated take-off Time
DCB
Demand Capacity Balancing
DP
Departure Procedure
DSP
Departure Spacing (US)
DTW
Departure Tolerance Window (Europe)
EC
European Commission
ECAC
European Civil Aviation Conference.
EDA
European Defence Agency (EU)
EDCT
Estimate Departure Clearance Time. EDCT is a long-term Ground Delay Programme
(GDP), in which the Command Centre (ATCSCC) selects certain flights heading to a
capacity limited destination airport and assigns an EDCT to each flight, with a 15 minute
time window.
EI
Early Intent (US)
ESP
En-route Spacing (US)
ETA
Estimated Time of Arrival
ETD
Estimated Time of Departure
ETFMS
Enhanced Tactical Flow Management System (Europe)
EU
Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland,
France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxemburg, Malta,
Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden and United
Kingdom. All these 28 States are also Members of the ECAC.
EUROCONTROL
The European Organisation for the Safety of Air Navigation. It comprises Member States
and the Agency.
EUROCONTROL
Member States
(2015)
Albania, Armenia, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus,
Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary,
Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Moldova, Monaco, Montenegro,
The Netherlands, Norway, Poland, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain,
Sweden, Switzerland, The former Yugoslav Republic of Macedonia, Turkey, Ukraine and
United Kingdom of Great Britain and Northern Ireland
FAA
US Federal Aviation Administration
FAA-ATO
US Federal Aviation Administration - Air Traffic Organization
FAB
Functional Airspace Block (Europe)
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FCA
Flow Constrained Area (US)
FDP
Flight data processing
FEA
Flow Evaluation Area (US)
FEI
Flight Efficiency Initiative (Europe)
FIR
Flight Information Region. An airspace of defined dimensions within which flight
information service and alerting service are provided.
FL
Flight Level. Altitude above sea level in 100-foot units measured according to a standard
atmosphere. Strictly speaking a flight level is an indication of pressure, not of altitude.
Only above the transition level are flight levels used to indicate altitude; below the
transition level, feet are used.
FL
Level capping scenario (Europe)
FMP
Flow Management Position (Europe). The FMP’s role is, in partnership with the NM, to
act in such a manner so as to provide the most effective ATFCM service to ATC and AOs.
Each FMP area of responsibility is normally limited to the area for which the parent ACC
is responsible including the area(s) of responsibility of associated Air Traffic Services
(ATS) units as defined in the NM Agreement. However, depending on the internal
organisation within a State, some FMPs may cover the area of responsibility of several
ACCs, either for all ATFCM phases or only for part of them. All FMPs within the NM area
have equal status. The size of individual FMPs will vary according to the demands and
complexities of the area served.
FMS
Flight Management System
FMU
Flow Management Unit
FRA
Free Route Airspace (Europe)
FUA
Level 1
Level 2
Level 3
Flexible Use of Airspace
Strategic Airspace Management
Pre-tactical Airspace Management
Tactical Airspace Management
GANP
Global Air Navigation Plan (ICAO)
GAT
General Air Traffic. Encompasses all flights conducted in accordance with the rules and
procedures of ICAO.
The report uses the same classification of GAT IFR traffic as STATFOR:
1. Business aviation: All IFR movements by aircraft types in the list of business aircraft
types (see STATFOR Business Aviation Report, May 2006, for the list);
2. Military IFR: ICAO Flight type = 'M', plus all flights by operators or aircraft types for
which 70%+ of 2003 flights were 'M';
3. Cargo: All movements by operators with fleets consisting of 65% or more all-freight
airframes
4. Low-cost: See STATFOR Document 150 for list.
5. Traditional Scheduled: ICAO Flight Type = 'S', e.g. flag carriers.
6. Charter: ICAO Flight Type = 'N', e.g. charter plus air taxi not included in (1)
GDP
Ground Delay Program (US)
General Aviation
All flights classified as “G” (general aviation) in the flight plan submitted to the
appropriate authorities.
GS
Ground Stop (US)
IATA
International Air Transport Association (www.iata.org)
ICAO
International Civil Aviation Organisation
ICR
Integrated Collaborative Rerouting (US)
IFR
Instrument Flight Rules. Properly equipped aircraft with properly qualified flight crews
are allowed to fly under bad-weather conditions following instrument flight rules.
ILS
Instrument landing System; a lateral and vertical beam aligned with the runway
centreline in order to guide aircraft in a straight line approach to the runway threshold
for landing.
IMC
Instrument Meteorological Conditions
KPA
Key Performance Area
KPI
Key Performance Indicator
M
Million
MDI
Minimum Departure Interval
MET
Meteorological Services for Air Navigation
MIL
Military flights
MINIT
Minutes In Trail
MIT
Miles in Trail
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MTOW
Maximum Take-off Weight
NAS
National Airspace System
NextGen
The Next Generation Air Transportation System (NextGen) is the name given to a new
National Airspace System due for implementation across the United States in stages
between 2012 and 2025.
NM
Nautical mile (1.852 km)
NMOC
Eurocontrol Network Management Operations Centre located in Brussels (formerly
CFMU)
NOP
Network Operations Plan (Europe)
NRP
North American Route Program (US – Canada)
NSP
Network Strategy Plan (Europe)
NSR
National System Review (US)
OEP
Operational Evolution Partnership (a list of 35 US airports that was compiled in 2000,
based on lists from the FAA and Congress and a study that identified the most
congested airports in the US).
OJT
On the Job Training
OP
Operations Plan (US)
OPS
Operational Services
OPSNET
The Operations Network is the official source of NAS air traffic operations and delay
data. The data is used to analyse the performance of the FAA's air traffic control
facilities.
PBFA
DoD Policy Board on Federal Aviation (US)
Percentile
A percentile is the value of a variable below which a certain per cent of observations
fall. For example, the 80th percentile is the value below which 80 per cent of the
observations may be found.
PPS
Purchasing power standard
PRC
Performance Review Commission
Primary Delay
A delay other than reactionary
PRU
Performance Review Unit
Punctuality
On-time performance with respect to published departure and arrival times
PW
Planning Webinar (US)
RAD
Route availability document
Reactionary delay
Delay caused by late arrival of aircraft or crew from previous journeys
RL
Reference Location (Europe)
RR
Rerouteing scenario (Europe)
RR
Required Reroutes TMI (US)
RTCA
Radio Technical Commission for Aeronautics, Inc.
Separation minima
The minimum required distance between aircraft. Vertically usually 1,000 ft below flight
level 290, 2,000 ft. above flight level 290. Horizontally, depending on the radar, 3 NM or
more. In the absence of radar, horizontal separation is achieved through time
separation (e.g. 15 minutes between passing a certain navigation point).
SES
Single European Sky (EU)
http://ec.europa.eu/transport/modes/air/single_european_sky/index_en.htm
SESAR
The Single European Sky implementation programme
Slot (ATFM)
A take-off time window assigned to an IFR flight for ATFM purposes
SP
Special Point (Europe)
STAM
Short Term ATFCM Measure (Europe)
STAR
Standard Terminal Arrival Route
STATFOR
EUROCONTROL Statistics & Forecasts Service
STMP
Special Traffic Management Program (US)
STW
Slot Tolerance Window (Europe)
SUA
Special Use Airspace
Summer period
May to October inclusive
SWAP
Severe Weather Avoidance Plan (US)
Taxi-in
The time from touch-down to arrival block time.
Taxi-out
The time from off-block to take-off, including eventual holding before take-off.
TBFM
Time Based Flow Management (US)
TBM
Time Based Metering (US)
TFMS
Traffic Flow Management System (US)
TMA
Terminal Manoeuvring Area
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TMI
Traffic Management Initiative
TMR
Traffic Management Review (US)
TMS
Traffic Management System
TMU
Traffic Management Unit (US). TMUs use TFMS workstations to participate in traffic
flow management. They are located at Air Route Traffic Control Centers (ARTCCs),
Terminal Radar Approach Control (TRACON) facilities and large/stand-alone Airport
Traffic Control Towers (ATCTs).
TOS
Trajectory Option Set (US)
TRACON
Terminal Radar Approach Control
TSA
Temporary Segregated Area (Europe)
TSD
Traffic Situation Display (US)
TV
Traffic Volume (Europe)
TWR
Tower
UAC
Upper Airspace Area Control Centre
UIR
Upper Information Region
US
United States of America
US CONUS
The 48 contiguous States located on the North American continent south of the border
with Canada, plus the District of Columbia, excluding Alaska, Hawaii and oceanic areas
UUP
Updated Airspace Use Plan (Europe)
VFR
Visual Flight Rules
VMC
Visual Meteorological Conditions
XMAN
Cross-border Arrival Management / Extended Arrival Management (Europe)
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ANNEX IV - REFERENCES
1 Performance Review Commission and FAA-ATO (October 2009). U.S./Europe Comparison of ATM-
Related Operational Performance 2008.
2 Performance Review Commission and FAA-ATO (June 2014). U.S./Europe Comparison of ATM-Related
Operational Performance 2013.
3 European Union (May 2013). Regulation 390/2013 – Laying Down a Performance Scheme for Air
Navigation Services and Network Functions. Official Journal of the European Union. 9.5.2013. p. 1.
4 Federal Aviation Administration (2016) NextGen Advisory Committee Highlights.
https://www.faa.gov/nextgen/update/collaboration/advisory_committee/
5 ICAO ATM Requirements and Performance Panel (2009). Manual on Global Performance of the Air
Navigation System. 1
st
ed. Doc 9883.
6 International Civil Aviation Organization (2016). 2016-2030 Global Air Navigation Plan (Doc 9750 5
th
Ed)
http://www.icao.int/airnavigation/Pages/GANP-Resources.aspx
7 European Union (December 2005) Regulation 2150/2005 – Common Rules for Flexible Use of Airspace
(FUA). Official Journal of the European Union. 24.12.2005.
8 US DOT and Federal Aviation Administration (April 2014). Facility Operation and Administration. Order
JO 7210.3Y. http://www.faa.gov/documentLibrary/media/Order/JO_7210.3Y.pdf.
9 Liu, Y., Hansen, M., & Zou, B. (2013). Aircraft gauge differences between the US and Europe and their
operational implications. Journal of Air Transport Management, 29, 1-10.
10 Chow, A., & Gulding, J. (2013). Capacity Variation Algorithms for Simulation Modeling and Performance
Analysis. AIAA, 4355, 12-14. DOI: 10.2514/6.2013-4355.
11 Performance Review Unit and ATMAP MET working group (May 2011). Algorithm to describe weather
conditions at European airports (ATMAP weather algorithm).
http://www.eurocontrol.int/publications/algorithm-weather-conditions-airports.
12
Klein, A., Jehlen, R., and Liang, D. (July 2007). Weather Index with Queuing Component for National
Airspace System Performance Assessment. USA-Europe ATM R&D Seminar.
http://www.atmseminar.org/seminarContent/seminar7/papers/p_024_AAPM.pdf.
13
U.S. DOT Bureau of Transportation Statistics. Airline On-Time Performance and Causes of Flight Delays.
http://www.rita.dot.gov/bts/help_with_data/aviation/index.html.
14
EUROCONTROL (2016). CODA Publications. https://www.eurocontrol.int/articles/coda-publications.
15
Federal Aviation Administration (1999-2001). Free Flight Performance Metrics Reports.
16
U.S. DOT Bureau of Transportation Statistics. Airline On-Time Statistics and Delay Causes.
http://www.transtats.bts.gov/OT_Delay/ot_delaycause1.asp?type=4&pn=1.
17
Knorr, D., Chen, X., Rose, M., Gulding, J., Enaud, P., and Hegendoerfer, H. (2011). Estimating ATM
Efficiency Pools in the Descent Phase of Flight. 9
th
USA/Europe Air Traffic Management Research and
Development Seminar. Berlin.
18
Idris, H., Clarke, J.P., Bhuva, R., and Kang, L. (2001). Queuing Model for Taxi-Out Time Estimation.
Massachusetts Institute of Technology. Submitted to ATC Quarterly.
19
Performance Review Commission (March 2008). Vertical Flight Efficiency.
http://www.eurocontrol.int/publications/vertical-flight-efficiency.
20
EUROCONTROL (2016). European Route Network Improvement Plan (ERNIP).
http://www.eurocontrol.int/services/european-route-network-improvement-plan-ernip.
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21
Performance Review Commission (May 2013). An Assessment of Air Traffic Management in Europe
during the Calendar Year 2012: Performance Review Report.
https://www.eurocontrol.int/sites/default/files/publication/files/prr-2012.pdf.
2016-AJR-254 • Produced by FAA Communications.
Performance Analysis Office
800 Independence Ave., S.W.
Washington, DC 20591
Tel: 202-267-2768
Performance Review Unit
96 Rue de la Fusée
B-1130 Brussels, Belgium
Tel: +32 2 729 3956
Directorate General for
Mobility and Transport
Directorate E—Aviation and
International Transport Affairs
Unit E2—Single European Sky
Tel: +32 2 299 1915
