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Date 16 April 2020
Aviation Stakeholders Ref.: PRC/20/02
Subject: Draft PRR2019 –for comments
Contact: [email protected]
Direct Line: +32 2 729 3576
Encl.: 1
Dear Aviation Colleague,
As a result of COVID-19, the number of flights operating daily in European airspace declined in March-April by up to -90% compared to the same period in 2019. This dramatic reduction in operations is likely to continue for a number of weeks to come.
The impact on aviation, including airlines, ANSPs and people employed in the industry will be far larger than the 9/11 terrorist attacks and the volcanic ash cloud crisis in 2010. The COVID-19 crisis will hit the aviation industry in Europe particularly hard. There will be a need for action, such as State aid, which most likely will have to go beyond what was done to manage crises in the past.
Unlike previous outbreaks (SARS, MERS) which were more regional in scope, the COVID-19 outbreak is global and nobody can answer the most pressing questions, namely how long will the crisis last, how bad it will be, and how to get back to normality?
While those questions remain unanswered, the only option is to monitor closely the dynamic situation to take coordinated action and to prepare as much as possible for the recovery.
The EUROCONTROL Member States have agreed a financial package enabling airlines to defer the payment of up to €1.1 billion of air traffic control fees due for payment to Europe's ATM industry in the coming months. Additionally, EUROCONTROL was authorised to seek a loan of up to €1.27 billion to help mitigating the impact on Air Navigation Service Providers (ANSPs).
EUROCONTROL has set up an interactive dashboard on its web page at www.eurocontrol.int to provide frequent situation updates. The PRC will also evaluate the situation in complementary high-level analyses at https://ansperformance.eu as more data becomes available.
The road back to normality will be long and the PRC welcomes and supports the measures taken by EUROCONTROL and the Member States to help the industry survive until traffic levels return to normal. As already observed in the past (volcanic ash crisis, capacity crisis), the current crisis once again underlines the need to work together and the importance of a proactive network approach to manage such crisis situations.
Even though the PRC fully understands that - in view of the COVID-19 crisis - the situation observed in 2019 has changed entirely, we would like to get stakeholder feedback on the review of ANS performance in 2019.
2
Reviewing the 2018/19 capacity crisis, PRR 2019 illustrates the importance of balanced performance objectives and the need for improved scalability of the ATM system to adjust to changes in operations. Those points will still be relevant when traffic builds up again. The PRC would also like to get stakeholder feedback on the COVID-19 crisis, with a view to analysing its impact on ATM performance in 2020.
The PRC will use your feedback when it makes recommendations to the decision-makers in the EUROCONTROL Member States.
The draft PRR 2019 is available for download at:
http://www.eurocontrol.int/publication/performance-review-report-prr-2019-consultation
Comments on the draft PRR 2019 and your views on the impact of the COVID-19 crisis in an ATM performance context should be emailed to [email protected] before 13 May 2020.
These are turbulent times for the aviation industry and the PRC is impressed by the response of all stakeholders to address this unprecedented crisis. Although recovery will be lengthy and painful, the industry has shown its strength and resilience before. It will, without a doubt, resume its important role in reconnecting families and business after the COVID19 crisis.
Kind regards
Marc Baumgartner
Chairman Performance Review Commission
PERFORMANCE REVIEW COMMISSION
PERFORMANCE REVIEW REPORT An assessment of Air Traffic Management in Europe during the calendar
year 2019
PRR 2019
Draft Final Report
For consultation with stakeholders
(16 April – 13 May 2020)
COMMENTS SHOULD BE SENT TO:
before 16:00 on Wednesday 13 May 2020
CAVEAT
Some of the data are still provisional.
They will be updated, where necessary, prior to the publication of the final PRR 2019.
NOTICE
The PRC has made every effort to ensure that the information and analysis contained in this document are as accurate and complete as possible. Only information from quoted sources has been used and information relating to named parties has been checked with the parties concerned. Despite these precautions, should you find any errors or inconsistencies we would be grateful if you could please bring them to the PRU’s attention. The PRU’s e-mail address is [email protected].
2020 COPYRIGHT NOTICE AND DISCLAIMER
© European Organisation for the Safety of Air Navigation (EUROCONTROL)
This document is published by the Performance Review Commission in the interest of the exchange of information.
It may be copied in whole or in part providing that the copyright notice and disclaimer are included. The information contained in this document may not be modified without prior written permission from the Performance Review Commission.
The views expressed herein do not necessarily reflect the official views or policy of EUROCONTROL, which makes no warranty, either implied or express, for the information contained in this document, neither does it assume any legal liability or responsibility for the accuracy, completeness or usefulness of this information.
Printed by EUROCONTROL, 96 rue de la Fusée, B-1130 Brussels, Belgium. The PRC’s website address is http://www.eurocontrol.int/prc.
DOCUMENT IDENTIFICATION SHEET
DOCUMENT DESCRIPTION
Document Title
Performance Review Commission
Performance Review Report covering the calendar year 2019 (PRR 2019)
PROGRAMME REFERENCE INDEX: EDITION: EDITION DATE:
PRC Performance Review Report Final draft 16-April-2020
This report of the Performance Review Commission analyses the performance of the European Air Traffic Management System in 2019 under the Key Performance Areas of Safety, Capacity, Environment and Cost-efficiency.
Keywords
Air Traffic Management Performance Measurement
Performance Indicators ATM ANS
CONTACT:
Performance Review Unit, EUROCONTROL, 96 Rue de la Fusée,
B-1130 Brussels, Belgium. Tel: +32 2 729 3956,
E-Mail: [email protected]
Web: http://www.eurocontrol.int/ansperformance
DOCUMENT STATUS AND TYPE
STATUS DISTRIBUTION
Draft General Public
Proposed Issue EUROCONTROL Organisation
Released Issue Restricted
INTERNAL REFERENCE NAME: PRR 2019
EXECUTIVE SUMMARY
EXECUTIVE SUMMARY i PRR 2019
Executive Summary 1
TRAFFIC GROWTH (FLIGHTS) BY AREA CONTROL CENTRE (2019)
SHARE OF EN-ROUTE ATFM DELAYED FLIGHTS BY AREA CONTROL CENTRE (2019)
HORIZONTAL EN-ROUTE FLIGHT EFFICIENCY - ACTUAL TRAJECTORY (2019)
CAN
ANK
LIS
SCO
SHA
TAMSTO
MAD
BRE
BOD
ROM
CAS+AGA
DNI
WARLON
BAR
KAR
MAA
BUC
MAR
KIE
ATH+MAK
MAL
MAL
NIC
BOR
BRI
SEV
ODE
OSL+STV
COP
SOF
LVO
REI
ZAG BEO
TBI
RIG
VIL
MIL
TAL
BUDWIE
PAD
PRA
TIR
TEL
CHIBRAT
ZURYER
GENPAR
SKO
LJU
KOS
BREM
LAN
MUN
AMS
BRU
LON TC
DUB
PAL
Lower Airspace
Lower Airspace
Traffic growth 2019 vs 2018
<-3.0%
-2.9% - -1.0%
-0.9% - 0.0%
0.1% - 1.0%
1.1% - 2.5%
2.6% - 5.0%
5.1% - 7.5%
7.6% - 10.0%
>10%
IST
IST
Lower Airspace
CAN
ANK
LIS
SCO
SHA
TAMSTO
MAD
BRE
BOD
ROM
CAS+AGA
DNI
WARLON
BAR
KAR
MAA
BUC
MAR
KIE
ATH+MAK
MAL
MAL
NIC
BOR
BRI
SEV
ODE
OSL+STV
COP
SOF
LVO
REI
ZAG BEO
TBI
RIG
VIL
MIL
TAL
BUDWIE
PAD
PRA
TIR
TEL
CHIBRAT
ZURYER
GENPAR
SKO
LJU
KOS
BREM
LAN
MUN
AMS
BRU
LON TC
DUB
PAL
Lower Airspace
Lower Airspace
% of en-route delayed flights (2019)
No delay
less than 2.5%
2.5%-5.0%
5.1%-7.5%
7.5% - 10%
>10%
IST
IST
Lower Airspace
EXECUTIVE SUMMARY
EXECUTIVE SUMMARY ii PRR 2019
ATM Performance in 2019 - Synopsis
Key Performance Indicator Data & commentary
TRA
FFIC
IFR flights ECAC area
Variation
2019 11.1 M + 0.8%
Air traffic in the ECAC area continued to grow for the sixth consecutive year in 2019, but with contrasted (and even negative) growth rates at State level.
On average IFR flights increased by 0.8% over 2018 which was below the low scenario forecast (1.2%) by STATFOR in the February 2019 forecast.
Because of the COVID-19 outbreak and the resulting unpredictable challenges for the aviation industry in 2020 and beyond no new traffic forecast was available for this report.
SAFE
TY
Accidents with direct ANS contribution
Eurocontrol area
Variation
Due to data availability, this section can only be updated in before the final version of the report is published
CA
PA
CIT
Y
Share of en-route ATFM delayed flights (%) En-route ATFM delayed flights
Eurocontrol area
Variation
2019 9.9% + 0.3%
Although the average delay per flight decreased from 1.74 to 1.57 minutes per flight in 2019, the number of en-route ATFM delayed flights increased further to 9.9% of all flights (+0.3 percentage points vs. 2018). Total en-route ATFM delays decreased by 9.0% compared to 2018, mainly driven by less delays attributed to adverse weather ATC disruptions/strike.
ENV
IRO
NM
ENT
En-route flight efficiency (actual)
Eurocontrol area
Variation
2019 97.2% -0.1%pt.
The average horizontal en-route flight efficiency at EUROCONTROL level remained relatively constant over the past five years with a slight deterioration in 2019. The significant gap between the efficiency of the filed flight plans (blue line - 95.6%) and the actual flown trajectories (red line - 97.2%) remained also in 2019. The shortest constrained routes (95.9%) are only slightly more efficient than the filed ones which means that the inefficiencies are mainly due to the constraints imposed by the network.
CO
ST-E
FFIC
IEN
CY
En-route ANS costs per TSU (€2018)
Eurocontrol area
Variation
2018 47.8 -4.1%
In 2018, en-route ANS costs increased by +1.8%, however, since the en-route service units also grew by +6.2%, the en-route costs per service unit decreased by -4.1% compared to 2017.
1
7
8
9
10
11
12
20
08
2009
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
20
24
20
25
IFR
flig
hts
(Mill
ions
)
STATFOR (Oct. 2019)7-year forecast
Actual and forecast IFR traffic evolution (ECAC area)
Feb. 2008
forecast
Feb. 2011
forecast
Feb. 2014forecast
3.9% 4.8% 5.3%
9.6% 9.9%
2015 2016 2017 2018 2019
95.6% 95.6%
97.3%97.1%
97.3% 97.3% 97.2%
96.0% 95.9%
95%
96%
97%
98%
JAN
AP
R
JUL
OC
T
JAN
AP
R
JUL
OC
T
JAN
AP
R
JUL
OC
T
JAN
AP
R
JUL
OC
T
JAN
AP
R
JUL
OC
T
2015 2016 2017 2018 2019
effi
cien
cy (%
)
Flight plan - annual Flight plan - monthlyActual trajectory - annual Actual trajectory - monthlyShortest constrained - annual Shortest constrained - monthly
Horizontal en-route flight efficiency (EUROCONTROL area)
Bet
ter
Wo
rse
60.0 56.6 55.2 53.2
49.8 47.8
100 102 102 101 103
106 110
115
122 129
20
30
40
50
60
70
2013(37 CZs)
2014(38 CZs)
2015(38 CZs)
2016(38 CZs)
2017(38 CZs)
2018(38 CZs)
En-r
oute
rea
l cos
t pe
r TS
U (
€201
8)
En-route TSU index (2013)
En-route ANScost index (2013)
Source: PRU analysis
EXECUTIVE SUMMARY
EXECUTIVE SUMMARY iii PRR 2019
This report assesses the performance of Air Navigation Services (ANS) in the EUROCONTROL area for 1
the calendar year 2019 for all key performance areas, except for cost-efficiency, which analyses 2
performance in 2018 as this is the latest year for which actual financial data are available. 3
4
Air traffic in the EUROCONTROL area continued the positive trend observed over the past 5 five years in 2019. Air traffic grew by 0.8% compared to 2018, yet at a notably lower growth 6 rate compared to previous years. Compared to 2013 when the positive trend started, the 7 ANS system controlled an additional 1.5 million flights (+15.4%) in 2019. 8
As in previous years, average aircraft size (+1.7% vs. 2018) and distance (+1.7% vs. 2018) 9 continued to grow at a higher rate than flights in 2019 which consequently resulted also in a higher en-10 route service unit1 growth in 2019 (+2.8% vs. 2018). 11
Similarly, passenger numbers continued to grow at a higher rate (+3.2% vs. 2018) than flights in 2019, 12 yet also with a notable slowdown in the rate of growth. 13
There was however a clear slowdown in traffic growth particularly in the second half of 2019, 14 influenced by the slowing economic growth in Europe; the collapse of Flybmi, Germania and Thomas 15 Cook, and the grounding of the B737 MAX fleet. 16
A total of 32 of the 42 Air Navigation Service Providers (ANSPs) included in the analysis reported a 17 traffic increase in 2019. In absolute terms, ENAV (Italy), CroatiaControl, ENAIRE (Spain), HCAA (Greece), 18 SMATSA (Serbia/ Montenegro), and Austro Control showed the highest year on year growth in 2019. 19 Sakaeronavigatsia (Georgia), LFV (Sweden), Maastricht Upper Area Control Centre (MUAC), Skeyes 20 (Belgium), and ANS CR (Czech Republic) showed the highest decrease in absolute terms in 2019. 21
The traffic growth was to some extent affected by the traffic flow measures implemented by the 22 Network Manager in summer 2019 (eNM/19) to mitigate the effects of the capacity shortfall in the 23 core area. The eNM/19 measures aimed at offloading traffic from congested Area Control Centres 24 (ACCs) such as Karlsruhe and Maastricht which resulted in more traffic in other ACCs. 25
The slowdown in traffic growth, less adverse weather, and the preventive measures taken by all 26 stakeholders to avoid the high delay levels observed in summer 2018 helped to improve overall service 27 quality in 2019. After a continuous deterioration over the past years, arrival punctuality improved again 28 from 75.7% in 2018 to 77.8% in 2019. At the same time, average departure delay in the EUROCONTROL 29 area decreased in 2019 by 1.6 minutes per flight to 12.8 minutes. 30
The COVID-19 outbreak at the beginning of 2020 has sparked an unprecedented crisis in Europe and 31 elsewhere. While the full impact of the COVID-19 outbreak on the aviation industry remains to be seen, 32 preliminary indications in early 2020 suggest an unparalleled reduction in traffic volumes at pan-33 European system level. 34
35
Environment is an important political, economic and societal issue and the entire 36 aviation industry has a responsibility to minimise its impact on the environment. The 37 environmental impact of aviation on climate results from greenhouse gas (GHG) 38 emissions and contrails, generated by aircraft engine exhaust. 39
Based on the figures of the European Environment Agency (EEA) aviation accounted for approximately 40 3.8% of total EU28 GHG emissions in 2017 (the latest year for which information is available). Although 41 this share appears to be comparatively small, aviation is one of the fastest growing sources of GHG 42 emissions in Europe. The relative share of aviation is expected to further increase as aviation activity 43
1 Used for charging purposes based on aircraft weight factor and distance factor.
EXECUTIVE SUMMARY
EXECUTIVE SUMMARY iv PRR 2019
based on fossil fuels is forecast to continue to grow while other industrial sectors (including road 1 transport) increasingly decarbonise over time. 2
The challenge in reducing aviation emissions is well known. To reduce GHG emissions from aviation 3 the plans are essentially based on four pillars: (1) Aircraft technology (airframes and engines), (2) 4 Sustainable aviation fuels, (3) Economic measures, and (4) Improved infrastructure and operations 5 (operational efficiency). 6
The Air Traffic Management (ATM)-related impact on climate is closely linked to operational 7 performance (fuel efficiency) which is largely driven by inefficiencies in the flight trajectory and 8 associated fuel burn (and emissions). ATM deploys a number of projects and initiatives aimed at 9 improving operational efficiency, including performance based navigation (PBN), free route airspace 10 (FRA), collaborative decision making (CDM), and continuous climb and descent operations (CCO/CDO). 11 Additional efficiency gains are expected to come from the further digitalisation and automation of the 12 industry which will enable the better use of data-driven technologies like Artificial Intelligence (AI). 13
The current best estimate is that ATM can influence approximately 6% of the total gate-to-gate fuel 14 burn (“benefit pool”). In this context it is important to highlight that the estimated inefficiencies are 15 based on a comparison of actual flight trajectories to theoretical reference trajectories which, due to 16 operational restriction, interdependencies, etc., cannot in practice be reached at system level. 17
Although the ANS contribution towards reducing CO2 emissions is with around 6% of the total 18 emissions from aviation comparatively limited in relation to total European CO2 emissions (0.2% of 19 total GHG emissions), there is still ample scope for further improvement. It goes without saying that 20 the strong focus on improving flight efficiency needs to be maintained. However, considering the 21 forecast traffic growth over the next 20 years the task for ATM will be challenging and the margins for 22 improvement are likely to narrow as the ongoing improvement initiatives such as Free Route Airspace 23 (FRA) are fully deployed. 24
25
The total economic assessment reviews performance over time but also by combining 26 cost-efficiency with service quality. The lower traffic levels following the economic 27 crisis starting in 2008 reduced the pressure on capacity to some extent and provided 28 a suitable environment to change the economic models for ANSPs in preparation of 29 the start of the Single European Sky performance scheme (SES-PS) in 2012. 30
Total en-route ANS costs remained almost flat between 2008 and 2018, mainly due to cost 31 containment measures implemented following the crisis in 2008 and the binding SES-PS cost-efficiency 32 targets as of 2012. 33
At the same time, flights increased by +7.4% while en-route service units grew at a notably higher rate 34 (+32.6% vs. 2008) due to a continuous increase in average flight length and aircraft weight. Air Traffic 35 Controller (ATCO) productivity increased significantly over time and ANS unit costs decreased by -25% 36 between 2008 and 2018. At the same time, ANSPs reduced ATCO recruitment most likely as a reaction 37 on the traffic downturn in 2008 or as part of the cost containment measures. 38
With traffic continuing to grow again in 2013, also ATFM en-route delays increased first gradually and 39 then soared in 2018 which suggests substantial shortcomings in proactive capacity planning and 40 deployment. Despite an increase in ATCO recruitment between 2014 and 2018, which appears to be a 41 reaction on the increasing traffic and delay levels since 2013, the number of trainees in 2018 was still 42 25% below the 2008 level. 43
EXECUTIVE SUMMARY
EXECUTIVE SUMMARY v PRR 2019
The total economic review of en-route ANS performance combines the direct ANS provision costs and 1 the estimated costs of en-route ATFM delay to airspace users. The analysis illustrates that the 2 substantial increase in en-route ATFM delay and associated costs in 2018 and 2019 clearly outweigh 3 the notable cost efficiency gains over the past years. This underlines the importance of finding a 4 balanced approach in performance management which considers all key performance areas equally 5 instead of focusing entirely on one area. 6
In a dynamic interconnected system such as the ATM network, the ability to adapt to changing 7 conditions (flexibility/ scalability) and to mitigate effects of unexpected events (resilience) becomes 8 more and more important. This will also help to better adjust the ATM network to economic and 9 political turbulences, possible demand changes following the environmental debate, and not least the 10 effect of the COVID-19 outbreak in December 2019 on aviation. 11
In today’s performance based environment it is important to ensure that cost-efficiency measures 12 consider effective capacity planning and deployment to avoid exponential increases in delays and 13 related costs to airspace users. 14
15
16
Due to data availability, this section can only be updated with preliminary 2019 data in April 17 2020 before the final version of the report is published 18
19
20
The review of operational en-route ANS performance in the EUROCONTROL area in 2019 21 evaluates ANS-related flight efficiency constraints on airspace users’ trajectories. It 22 addresses several performance areas including ATFM delay, capacity, horizontal flight 23 efficiency and vertical flight efficiency. 24
En-route capacity: Following the disproportional increase in 2018 and the subsequent 25 major disruptions for passengers, en-route ATFM delays fell by 9.0% in 2019 to reach 26
17.2 million minutes; the second highest level since 2010. 27
The average en-route delay decreased from 1.74 to 1.57 minutes per flight in 2019: flights were 28 delayed more frequently, albeit with shorter average delays than in 2018. The number of flights that 29 were delayed by en-route ATFM regulations increased to 9.9% of all flights (+0.3 percentage points vs. 30 2018). Even though approximately one flight in six was subject to en-route ATFM regulations, only one 31 flight in twenty five was delayed for more than 15 minutes. However, just those 4% of flights account 32 for 70% of all en-route ATFM delays. 33
According to the ANSPs, ATC Capacity (43.9%) attributed delays remain the main portion of en-route 34 ATFM delays, followed by ATC Staffing (24.3%), Weather attributed delays (21.2%), and ATC 35 disruptions/industrial actions (7.2%). 36
The observed performance improvement in 2019 was mainly driven by less delays attributed to 37 adverse weather (-24.1% vs 2018) and ATC disruptions/strike (-13.5% vs 2018). Delays attributed to 38 ATC staffing decreased slightly over 2018 (-3.8%) while ATC Capacity attributed en-route ATFM delays 39 increased further compared to 2018 (+6.6%). 40
Previous PRRs highlighted the inconsistent manner in which ANSPs attribute ATFM delays either 41 according to the delay cause or according to the geographical location to where the delay is being 42 assigned. The analysis showed that the share of ATC staffing related delay would increase from 24% to 43 approximately 77% of all en-route ATFM delays which suggests that ATC staffing is much more of a 44 problem, and therefore a possible solution to capacity constraints, than is currently being 45 acknowledged by ANSPs. 46
EXECUTIVE SUMMARY
EXECUTIVE SUMMARY vi PRR 2019
In 2019, DFS (Germany) generated 25.9% of all en-route ATFM delays in the EUROCONTROL area, 1 followed by DSNA (France) (22.9%), Austro Control (10.1%) and HungaroControl (8.3%). 2
The most delay generating ACCs in 2019 were Karlsruhe (17.7%), Marseille (11.7%), Vienna, Budapest, 3 Langen, and Barcelona (4.4% respectively). Karlsruhe UAC, Marseille, Vienna and Budapest together 4 generated half of all en-route ATFM delays in 2019. 5
In order to mitigate the effects of the serious capacity shortfall in the core area observed in summer 6 2018, the Network Manager, in cooperation with a number of ANSPs, implemented again flow 7 measures (re-routing or level-capping ) in summer 2019 (eNM/19) to offload traffic from constrained 8 ACCs. 9
The collaborative, network-centric approach avoided even higher en-route AFM delays in 2019 but at 10 the cost of lower flight efficiency and additional CO2 emissions for a considerable number of flights. It 11 is likely that the Network Manager will continue to have a role in coordinating similar mitigation 12 exercises in the future, considering the existing capacity shortfalls in some locations and the current 13 capacity plans of the ANSPs. 14
A review of the evolution of the declared capacity for the most constraining sectors during the period 15 2012 – 2019 highlights that several ANSPs have not been implementing capacity where it is most 16 needed, indeed some sectors declare less capacity in 2019 than they were providing in 2012. In 17 addition, the high number of collapsed sectors among the most constraining sectors reinforces that 18 point that ATC staffing needs to be properly addressed. 19
En-route flight efficiency has a horizontal (distance) and vertical (altitude) component, both with a high 20 relevance for fuel efficiency and environmental sustainability. 21
Average horizontal en-route flight efficiency at EUROCONTROL level remained relatively constant over 22 the past five years with a slight deterioration in 2019. The efficiency of actual trajectories decreased 23 by 0.1 percentage points to 97.2% in 2019. 24
The significant gap between the efficiency of the filed flight plans (95.6%) and the actual flown 25 trajectories (97.2%) remained also in 2019. In 2019, the actual trajectory is on average 1.6% more 26 efficient than the filed flight plan and still 1.3% more efficient than the shortest constrained route. Or 27 expressed differently if airspace users would have flown the filed flight plans they would have flown 28 an additional 156 million kilometres in 2019. 29
The efficiency of the shortest constrained routes (95.9%) is only slightly more efficient than the filed 30 ones which means that the inefficiencies are mainly due to the constraints imposed by the network. 31
En-route flight efficiency is comparatively low in the core area where traffic density is highest and 32 where Free Route Airspace (FRA) is not yet implemented. The envisaged FRA implementation by 2022 33 and optimised cross border implementation is expected to be one of the main contributors towards 34 improving flight efficiency, as airspace users will have more choices to optimise their flight plans. 35
Although FRA implementation will help to file more efficient trajectories to avoid segregated areas, it 36 will not improve the information flows necessary to optimise the use of segregated airspace in Europe. 37 As shown in previous analyses, there is further scope to improve the civil/military cooperation and 38 coordination. 39
Another important factor affecting horizontal en-route flight efficiency is the lack of capacity. 40 Horizontal flight efficiency is worse in summer due to imposed ATFM constraints, resulting from 41 capacity shortfalls. Days with high en-route ATFM delays tend to have notably lower flight efficiency. 42
The vertical component of en-route flight efficiency has been gaining more attention over the past 43 years, not least because of the impact of measures implemented by the Network manager to mitigate 44 delays at network level during summer 2018 (4ACC) and 2019 (eNM/19). Although difficult to quantify 45 at network level in terms of additional fuel burn and CO2 emissions, the analysis shows a considerably 46 higher number of altitude constrained flights since spring 2018. 47
There are numerous initiatives aimed at improving flight efficiency underway but even with traffic 48
EXECUTIVE SUMMARY
EXECUTIVE SUMMARY vii PRR 2019
growth slowing down, it appears to be challenging to meet the Master Plan ambitions within the given 1 timeframes, considering the existing lack of capacity and the negative effect on flight efficiency in some 2 parts of the core area. 3
4
The average traffic growth at the top 30 European airports in 2019 was of 1.7%, the 5 lowest increase since 2013. The growth is distributed unevenly among the top 30 6 airports, with 9 airports showing less traffic than the previous year. 7
The opening of Istanbul Grand airport in April 2019 was the biggest airport 8 development in Europe in years consequently replacing Istanbul Atatürk in the list of 9
the top 30, which otherwise remains unchanged. 10
The level of inefficiencies on both arrival and departure flows slightly increased in 2019. 11
Overall, 6.1% of all arrivals at the top 30 airports in 2019 were delayed by arrival ATFM regulations, 0.3 12 percentage points more than in 2018. Average arrival ATFM delay increased from 1.09 to 1.14 minutes 13 per arrival in 2019, with airports showing heterogeneous trends. The most important deterioration in 14 terms of arrival ATFM delay was observed at Athens associated to ATC capacity problems, followed by 15 Amsterdam that became the airport with the highest arrival ATFM delay per flight of the top 30 airports 16 and is the biggest contributor to European airport ATFM delays. 17
The average additional ASMA times at the top 30 airports also increased from 2.07 to 2.17 minutes per 18 arrival in 2019 with London airports, Heathrow and Gatwick, still showing the highest additional ASMA 19 times (7 and 4.6 minutes per arrival respectively). 20
The average additional taxi-out times increased from 4.21 to 4.22 minutes per departure in 2019, 21 where some significant variations at airport level are directly related to special circumstances. ATC pre-22 departure delays, where measurable, showed as well noteworthy increases at some airports. 23
Most important performance deteriorations observed in the different indicators are associated to 24 specific events, mainly works on the runway systems or implementation of new systems or procedures. 25 Mitigation measures and careful planning are to be considered to avoid this detrimental impact on 26 performance. 27
Lisbon was the only airport that implemented A-CDM in 2019, but other projects like Advanced Towers 28 allow airports to share departure information with the Network Manager (currently almost 45% of 29 departures share DPIs with NM) which helps improve slot adherence and network predictability. 30
Vertical flight efficiency during climb and descent at the top 30 airports has in general increased very 31 slightly. The biggest increase of average time flown level during descent in 2019 with compared to 32 2018 was observed for flights arriving at Munich (MUC) and Milan (MXP), each having an increase of 33 almost 1 minute per flight. Flights arriving at Frankfurt (FRA), Paris Charles de Gaulle (CDG), London 34 (LHR), London Gatwick (LGW) and Paris Orly (ORY) showed the highest inefficiencies with more than 5 35 minutes of level flight on average in 2019. 36
37
In 2018, the latest year for which actual financial data is available, en-route ANS cost 38 per en-route service unit (TSU) at pan-European system level amounted to 47.8 €2018, 39 which is -4.1% lower than in 2017. This performance improvement, recorded for the 40 sixth year in a row, results from the fact that the growth in TSUs (+6.2%) more than 41 compensated the increase in en-route ANS costs (+1.8%). 42
At the same time, in November 2019 the Member States have provided forecast cost and TSU figures 43 covering period 2019-2024. Undoubtedly, the COVID-19 crisis in 2020 will have a massive impact on 44 future traffic growth. Therefore it is likely that there will be a need to revise the TSUs plans made by 45 States in November 2019. A thorough analysis of the impact of this crisis on the ANS industry will be 46 provided in future PRR reports when updated States/ANSPs forward-looking plans in terms of costs 47
EXECUTIVE SUMMARY
EXECUTIVE SUMMARY viii PRR 2019
and traffic will be available. 1
In 2018, the European terminal ANS unit costs amounted to 174.3 €2018 per terminal service unit 2 (TNSU), which was -3.3% lower than in 2017. This performance improvement results from the 3 significant growth in TNSUs (+4.7%), which compensated the increase in terminal ANS costs (+1.3%). 4
Detailed benchmarking analysis of pan-European ANSPs indicates that, in 2018, gate-to-gate ATM/CNS 5 provision costs increased by +2.1% over the preceding year and amounted to some €8.4 Billion at 6 system level. At the same time traffic, expressed in composite flight-hours, rose by +5.4%, which 7 resulted in a reduction in gate-to-gate unit ATM/CNS provision costs (-3.1%). 8
An indicator of economic costs is also used to account for the quality of service provided by the ANSPs 9 by combining the ATM/CNS provision costs and the estimated costs of en-route and airport ATFM 10 delays. This analysis shows that, at pan-European system level, unit economic costs grew by +6.4% in 11 2018. As a result, the relative share of ATFM delay costs also increased and represented, on average, 12 24% of total economic costs for pan-European ANSPs, compared to some 16% in 2017. 13
14
PRC Recommendations 2019 15
The PRC recommendations will be included in the final report to be published in May/June 2020. 16 17 18
T A B L E O F C O N T E N T S
EXECUTIVE SUMMARY ................................................................................................................................ I
PRC RECOMMENDATIONS 2019 .............................................................................................................. VIII
1 INTRODUCTION AND CONTEXT ............................................................................................. 1
1.1 ABOUT THIS REPORT ..................................................................................................................... 1
1.2 PRC/PRU TASKS ......................................................................................................................... 2
1.3 EUROPEAN AIR TRANSPORT KEY INDICES ........................................................................................... 5
1.4 AIR TRANSPORT PUNCTUALITY ......................................................................................................10
1.5 ENVIRONMENT ..........................................................................................................................11
1.6 TOTAL ECONOMIC ASSESSMENT (EN-ROUTE) ...................................................................................19
2 SAFETY ............................................................................................................................... 23
2.1 INTRODUCTION ..........................................................................................................................23
2.2 SAFETY PERFORMANCE SNAP SHOT ................................................................................................24
2.3 RISK EXPOSURE – COMPOSITE RISK INDEX ......................................................................................26
2.4 SAFETY DATA AFTER 2020 ...........................................................................................................27
2.5 CHALLENGES TO SAFETY AFTER 2020.............................................................................................27
2.6 CONCLUSIONS ...........................................................................................................................27
3 OPERATIONAL EN-ROUTE ANS PERFORMANCE .................................................................... 28
3.1 INTRODUCTION ..........................................................................................................................28
3.2 ANS-RELATED OPERATIONAL EN-ROUTE EFFICIENCY .........................................................................29
3.3 CONCLUSIONS ...........................................................................................................................45
4 OPERATIONAL ANS PERFORMANCE @ AIRPORTS ................................................................ 47
4.1 INTRODUCTION ..........................................................................................................................47
4.2 TRAFFIC EVOLUTION @ THE TOP 30 EUROPEAN AIRPORTS .................................................................49
4.3 CAPACITY MANAGEMENT (AIRPORTS).............................................................................................51
4.4 ANS-RELATED OPERATIONAL EFFICIENCY AT AND AROUND AIRPORTS ...................................................54
4.5 CONCLUSIONS ...........................................................................................................................62
5 ANS COST-EFFICIENCY (2018) .............................................................................................. 64
5.1 INTRODUCTION ..........................................................................................................................64
5.2 EN-ROUTE ANS COST-EFFICIENCY PERFORMANCE ............................................................................65
5.3 TERMINAL ANS COST-EFFICIENCY PERFORMANCE.............................................................................71
5.4 ANSPS GATE-TO-GATE ECONOMIC PERFORMANCE ...........................................................................74
5.5 CONCLUSIONS ...........................................................................................................................78
L I S T O F F I G U R E S
Figure 1-1: EUROCONTROL States (2019) ................................................................................................ 4 Figure 1-2: European air traffic indices (2008-2019) ............................................................................... 5 Figure 1-3: Year on year change versus 2018 .......................................................................................... 6 Figure 1-4: Traffic evolution by ANSP (2019/2018) ................................................................................. 6 Figure 1-5: Traffic growth by ACC (2019) ................................................................................................. 7 Figure 1-6: Daily traffic levels in the EUROCONTROL area (2019) ........................................................... 7 Figure 1-7: Evolution of daily traffic levels (EUROCONTROL area) .......................................................... 7 Figure 1-8: Traffic variability by ACC (2019) ............................................................................................. 8 Figure 1-9: Traffic complexity by ACC (2019) ........................................................................................... 8 Figure 1-10: Traffic complexity by ANSP (2019) ....................................................................................... 8 Figure 1-11: Evolution of arrival punctuality .........................................................................................10 Figure 1-12: ANS contribution towards departure total departure delays............................................10 Figure 1-13: Radiative forcing components ...........................................................................................11 Figure 1-14: Evolution of GHG emissions between 1990 and 2017 by sector (EU28) ...........................12 Figure 1-15: Environmental IATA and ICAO goals ..................................................................................12 Figure 1-16: Alternative propulsion systems .........................................................................................13 Figure 1-17: Distribution of flights and estimated CO2 emissions by distance category (2019) ............13 Figure 1-18: Basket of measures aimed at meeting aviation’s environmental goals ............................15 Figure 1-19: Gate-to-gate efficiency by phase of flight .........................................................................16 Figure 1-20: Estimated ANS-related gate-to-gate benefit pool (CO2 emissions) ...................................16 Figure 1-21: Estimated share of GHG emissions that can be influenced by trajectory optimisation ....17 Figure 1-22: Estimated ANS-related benefit pool by flight phase (CO2 emissions) ...............................17 Figure 1-23: Population exposed to noise above 55dB in Europe (in millions) [14] ..............................18 Figure 1-24: Long term evaluation of en-route ANS performance (2008-2019) ...................................19 Figure 1-25: Network wide evolution of staff related key figures (2013-2018) ....................................20 Figure 1-26: Evolution of ATCOs and trainees in the EUROCONTROL area (2008-2018) ......................20 Figure 1-27: En-route ANS provision costs and estimated costs of en-route ATFM delays (B€ 2018) ..21 Figure 2-1: Total air traffic accidents (2014-2019P) - TBU .....................................................................24 Figure 2-2: Accidents with ATM contribution (2014-2019P) - TBU ........................................................24 Figure 2-3: Incidents reported via AST in EUROCONTROL area (2019 preliminary data) -TBU .............24 Figure 2-4: Total reported incidents (2014-2019P) - TBU ......................................................................25 Figure 2-5: Occurrence rates EUROCONTROL area (2019) - TBU ...........................................................25 Figure 2-6: Composite Risk Index components and their parameters...................................................26 Figure 3-1: Evolution of ATFM delays ....................................................................................................29 Figure 3-2: En-route ATFM delays in the EUROCONTROL area .............................................................29 Figure 3-3: En-route ATFM delays by attributed delay category (Overview) ........................................30 Figure 3-4: En-route ATFM delay by attributed delay category ............................................................30 Figure 3-5: Monthly evolution of en-route ATFM delay by attributed cause ........................................30 Figure 3-6: Temporal distribution of ATFM delays (2019) .....................................................................31 Figure 3-7: Share of total en-route ATFM delay in 2019........................................................................31 Figure 3-8: Days with average en-route ATFM delay >1 min per flight .................................................32 Figure 3-9: Share of en-route ATFM delayed flights by ACC ..................................................................32 Figure 3-10: Peak throughput and en-route ATFM delayed flights at the most constraining ACCs ......32 Figure 3-11: En-route ATFM delay per flight by most constraining ACC ...............................................33 Figure 3-12: ATFM delay attributed by ANSP and revised attribution (2019) .......................................34 Figure 3-13: Evolution of declared capacity (2012-2019) in bottleneck locations (Germany) ..............35 Figure 3-14: Evolution of declared capacity (2012-2019) in bottleneck locations (France) ..................35 Figure 3-15: Evolution of declared capacity (2012-2019) in bottleneck locations (Other) ....................36 Figure 3-16: eNM initiative shifting of traffic flows to offload congested ACCs ....................................37 Figure 3-17: Horizontal en-route flight efficiency (EUROCONTROL area) .............................................39 Figure 3-18: Daily horizontal en-route flight efficiency and ATFM en-route delays (2019) ..................39 Figure 3-19: Horizontal en-route flight efficiency by airport pair (2019) ..............................................40 Figure 3-20: Map of horizontal en-route flight efficiency by State (2019) ............................................40
Figure 3-21: Implementation status of Free Route Airspace (FRA) – End 2019 ....................................41 Figure 3-22: Horizontal en-route flight efficiency by State (2019) ........................................................41 Figure 3-23: Illustration of routes impacted by military training areas .................................................42 Figure 3-24: Edinburgh to London city airport (plan and actual) ...........................................................42 Figure 3-25: Horizontal en-route flight efficiency by State (actual trajectories – 2019) .......................43 Figure 3-26: Evolution of total en-route vertical flight inefficiency during summer .............................44 Figure 3-27: Evolution of vertically RAD constrained airport pairs ........................................................44 Figure 3-28: Top 20 airports pairs with respect to total VFI ..................................................................44 Figure 4-1: ANS-related operational performance at airports (overview) ............................................48 Figure 4-2: Airport initiatives implementation status (2019) ................................................................48 Figure 4-3: Traffic variation at the top 30 European airports (2019/2018) ...........................................50 Figure 4-4: Top 30 airports and airport systems (2019) ........................................................................50 Figure 4-5: Arrival throughput at the top 30 airports (2019) .................................................................51 Figure 4-6: Total throughput at the top 30 airports (2019) ...................................................................52 Figure 4-7: Evolution of hourly movements at the top 30 airports (2009-2019) ..................................52 Figure 4-8: Max. hourly movements for different runway configurations – T30 airports (2019) .........53 Figure 4-9: Share of total en-route ATFM delay in 2019........................................................................54 Figure 4-10: Repartition arrival ATFM delay 2019 .................................................................................54 Figure 4-11: ANS-related inefficiencies on the arrival flow at the top 30 airports in 2019 ...................55 Figure 4-12: Combined inefficiencies on the arrival flow at the top 30 airports in 2018-2019 .............56 Figure 4-13: ATFM slot adherence at the top 30 airports in 2019 .........................................................58 Figure 4-14: ANS-related inefficiencies on the departure flow at the top 30 airports in 2019 .............59 Figure 4-15: Combined inefficiencies on the departure flow at the top 30 airports in 2018-2019 .......60 Figure 4-16: Average time flown level in descent/climb at the top 30 airports ....................................61 Figure 4-17: Median CDO/CCO altitude vs. Average time flown level per flight (2019) ........................61 Figure 5-1: SES and non-SES States ........................................................................................................65 Figure 5-2: Real en-route ANS cost per TSU for EUROCONTROL Area (€2018) ........................................66 Figure 5-3: Long-term trends in en-route ANS cost-efficiency (€2018) ....................................................66 Figure 5-4: Trends in en-route costs, TSUs and unit costs for SES States ..............................................67 Figure 5-5: Trends in en-route costs, TSUs and unit costs for SES States ..............................................67 Figure 5-6: Breakdown of en-route costs by type ..................................................................................68 Figure 5-7: Changes in en-route cost categories - 2018 vs 2013 - SES & non-SES States (€2018) ...........68 Figure 5-8: 2018 Real en-route ANS costs per TSU by charging zone (€2018) .........................................69 Figure 5-9: Pan-European en-route cost-efficiency outlook 2018-2024 (€2018) (To be updated) ..........71 Figure 5-10: Geographical scope of terminal ANS cost-efficiency analysis ...........................................71 Figure 5-11: Real terminal ANS cost per TNSU at European System level (€2018) ..................................72 Figure 5-12: Breakdown of changes in terminal cost categories between 2017-2018 (€2018) ...............72 Figure 5-13: 2018 Real terminal ANS costs per TNSU by charging zone (€2018) .....................................73 Figure 5-14: Breakdown of gate-to-gate ATM/CNS provision costs 2018 (€2018)...................................74 Figure 5-15: Economic gate-to-gate cost-effectiveness indicator, 2018 ...............................................75 Figure 5-16: Changes in economic cost-effectiveness, 2013-2018 (€2018) .............................................76 Figure 5-17: Long-term trends in traffic, ATM/CNS provision costs and ATFM delays ..........................77 Figure 5-18: ANSPs contribution to ATFM delays increase at pan-European system level in 2018 ......77 Figure 5-19: Breakdown of changes in cost-effectiveness, 2017-2018 (€2018) .......................................78
L I S T O F T A B L E S
Table 2-1: Occurrence rates (SMI, RI, UPA) in the EUROCONTROL area (2018P) ..................................25
Chapter 1: Introduction
PRR 2019- Chapter 1: Introduction 1
1 Introduction and context 1
2
3
4
5
6
7
8
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10
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12
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21 22 23 24
25 26 27 28 29 30 31
32 33 34 35
36 37 38
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1.1 About this report
Air Navigation Services (ANS) are essential for the safety, efficiency and sustainability of Civil and Military aviation, and to meet wider economic, social and environmental policy objectives.
The purpose of the independent Performance Review Commission (PRC) is “to ensure the effective management of the European Air Navigation Services (ANS) system through a strong, transparent and independent performance review system”, per Article 1 of its Terms of Reference [1]. More information about the PRC is given on the inside cover page of this report.
This Performance Review Report (PRR 2019) has been produced by the PRC with its supporting unit the Performance Review Unit (PRU). It gives an independent holistic view of ANS performance in all EUROCONTROL Member States across all key performance areas. Its purpose is to provide policy makers and ANS stakeholders with objective information and independent advice concerning the performance of European ANS in 2019, based on analysis, consultation and information provided by relevant parties. PRR 2019 also describes other activities undertaken by the PRC in 2019 as part of its work-programme.
Through its PRRs, the PRC seeks to assist all stakeholders in understanding why, where, when, and possibly how, ATM performance should be improved, in knowing which areas deserve special attention, and in learning from past successes and mistakes. The spirit of these reports is neither to praise nor to criticise, but to help everyone involved in effectively improving performance in the future.
Stakeholders are consulted on the draft Final Report and are invited to provide comments for the PRC’s consideration before the report is finalised and the PRC prepares its recommendations arising out of PRR 2019. The consultation phase is from 16 April – 13 May 2020.
On the basis of PRR 2019 and stakeholders’ comments, the PRC will develop and provide independent advice on ANS performance and propose recommendations to the EUROCONTROL States. 40
41
2
PRR 2019 - Chapter 1: Introduction 2
1.2 PRC/PRU Tasks 1
The EUROCONTROL States and the European Union have a long-standing relationship and history of 2 cooperation in ATM and in the implementation of the SES and other related policies. 3
Of central importance is the contribution that the EUROCONTROL Performance Review System and the 4 Performance Scheme of the SES jointly make towards improving the overall performance of air 5 navigation services and network functions. 6
The Performance Review Body (PRB) is an independent group of experts established under EU 7 legislation. It assists the Commission in the implementation of the Performance Scheme of the SES. 8
The PRC, supported by the PRU, was designated as the PRB until 31 December 2016. Although this 9 designation has ended, the ongoing co-operation and close links continue and are fostered. 10
The PRC and the PRB ensure that their tasks complement each other, and avoid overlaps. 11
12
1.2.1 Performance Analysis 13
The PRC, supported by the PRU, collects and analyses performance-related data and makes 14 recommendations to the EUROCONTROL States. In particular, it: 15
conducts research to improve long-term performance measurement and to test new indicators;16
benchmarks operational stakeholders;17
provides in-depth analysis and independent ad-hoc studies on PRC initiative and at the request of18 interested parties;19
ensures widespread circulation of best practices for ATM performance.20
The PRC publishes an annual Performance Review Report (PRR) and an annual ATM Cost-Effectiveness 21 (ACE) Benchmarking report. The ACE report presents yearly factual data and analysis on cost-22 effectiveness and productivity for Air Navigation Service Providers (ANSPs) in Europe. 23
In addition, the PRC also researches and analyses specific topics. Over the years, the PRC and PRU have 24 expanded this activity, building on their extensive knowledge database and technical expertise. 25
The PRC’s most recent publications can be found on its webpages: 26
https://www.eurocontrol.int/air-navigation-services-performance-review 27
28
1.2.2 Dashboards 29
The PRC, in consultation with stakeholders, has adapted its outputs to make them more relevant and 30 timely. It has created web-based Performance dashboards for its key PRR and ACE data, as well as key 31 metrics for ANSP revenues such as en-route and terminal service units. 32
The web-based dashboards contain up-to-date performance data, in a user-friendly format: 33
The ANS performance data portal provides monthly updates of performance information covering all 34 41 States. Customised reports can also be requested online, e.g. vertical flight efficiency. 35
The ACE dashboard gives easy access to data, indicators and interactive graphics on European ANS 36 cost-effectiveness. 37
The Service Units Dashboard allows monitoring and interacting with en-route service units billed by 38 the CRCO or forecasted by STATFOR and States. 39
The PRU also supports the official Single European Sky SES Performance Dashboard. 40
41
2
PRR 2019 - Chapter 1: Introduction 3
1.2.3 Performance Benchmarking for non-EUROCONTROL States 1
The PRU works closely with a number of non-EUROCONTROL States (e.g. Brazil, China, Japan and 2 Singapore) at their request, to develop performance benchmarking. 3
4
1.2.4 Support to the European Commission 5
The PRU provides support to the EC under a service contract, signed in 2017, which runs for four years. 6 Activities include support to monitoring, target setting and assessment of performance plans. 7
The PRU also supports the EC, its Agencies and Advisory Bodies under a longer-term co-operation 8 agreement. Activities include: 9
assessment of technical compliance of unit rates and reporting on costs exempt from cost sharing;10
work on US-EU performance comparisons and ad hoc analyses at the EC’s request in support of11 performance and charging schemes;12
promotion of global and regional performance measurement within ICAO and other world regions;13
secondment of performance experts to the EC.14
Areas of common interest can be added to this co-operation. 15
16
17
18
19
20
21
22
visit us @ https://ansperformance.eu
2
PRR 2019 - Chapter 1: Introduction 4
1.2.5 Geographical scope 1
Unless otherwise indicated, this 2 report relates to the calendar year 3 2019 and refers to ANS performance 4 in the airspace controlled by the 41 5 Member States of EUROCONTROL 6 (see Figure 1-1), here referred to as 7 “EUROCONTROL area”. 8
In 2016, EUROCONTROL signed an 9 agreement with Israel and Morocco 10 with a view to fully integrate both 11 States into its working structures. 12
Where available, data for those two 13 States have been included in the PRR 14 2019 analysis. 15
Note that the constitutional name of 16 the FYROM is the Republic of North 17 Macedonia, with effect from 12 18 February 2019. 19
1.2.6 Report structure 20
The report is structured in five chapters: 21
22
23
Introduction and contextChapter 1
• To provide you with the "bigger picture", Chapter 1 informs you about the latest traffic and punctuality trends in the EUROCONTROL area.
• The second part takes a look at the environmental responsability of aviation and the role of ANS
• The chapter closes with a review of performance trends observed over the past years in order develop a wider economic evaluation of performance and to identify future challenges.
SafetyChapter 2• Chapter 2 revies ANS safety performance in terms of accidents, ATM-related incidents and the level of
safety occurence reporting in the EUROCONTROL area.
• It also highlights the need to address issues in the changing safety data reporting environment.
En-route ANS performanceChapter 3
• In this chapter you will find a detailed analysis of en-route ATFM delays including a breakdown by most constraining en-route locations and attributed delay causes in 2019.
• In the second part looks at the horizontal and the vertical dimension of en-route flight efficiency in 2019.
ANS performance @ airportsChapter 4• Airport capacity is one of the key challenges for future growth. Chapter 4 reviews ANS related
performance at the top 30 airports in 2019 for the arrival (terminal holdings, airport ATFM delays) and the departure (taxi-out and pre departure delays) flows.
ANS Cost-efficiencyChapter 5• The final chapter evaluates ANS cost efficiency performance for 2018 (which is the latest year for which
actual financial data is available).
•The analyses are provided for en-route and terminal services and by Air Navigation Service Provider.
Figure 1-1: EUROCONTROL States (2019)
2
PRR 2019 - Chapter 1: Introduction 5
1.3 European air transport key indices 1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
The evolution of the European air traffic indices2 in Figure 1-2 shows that the positive trend observed 18 since 2013 also continued in 2019, yet at a notably lower rate. The impact of the COVID-19 outbreak 19 on the aviation industry remains to be seen but indications in early 2020 suggest an unprecedented 20 reduction in traffic as a reaction on the drop in demand. 21
Controlled flights in the ECAC 22 area3 increased by +0.8% in 2019 23 which was below the low growth 24 scenario (+1.2%) in the STATFOR 25 7-year forecast published in 26 February 2019 [2]. 27
The lower growth rate in 2019 28 was influenced by the slowing 29 economic growth in Europe; the 30 collapse of Flybmi, Germania and 31 Thomas Cook, and the grounding 32 of the B737 MAX fleet. 33
Although airlines managed to find 34 alternative aircraft, the 35 grounding of the B737 MAX is 36 estimated to amount to nearly 37 0.2 percentage points lost from 38 Europe wide growth in 2019 [3]. 39
Since 2013, flights in the ECAC area have grown by 15.4% which corresponds to 1.5 million additional 40
2 Note that the individual indices can refer to slightly different geographical areas. 3 The European Civil Aviation Conference (ECAC) is an intergovernmental organization which was established by
ICAO and the Council of Europe. ECAC now totals 44 members, including all 28 EU, 31 of the 32 European Aviation Safety Agency member states, and all 41 EUROCONTROL member states.
IFR traffic continued to grow for the sixth consecutive year in 2019
+0.8% vs 2018
Figure 1-2: European air traffic indices (2008-2019)
+ 0.8% IFR flights (ECAC)
+3.2% Passengers (ACI)
+ 1.7% Avg. weight (MTOW)
+ 1.7% Distance (ECAC)
+ 2.8% En-route Service
Units (CRCO area)
90
100
110
120
130
140
150
160
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
IND
EX 1
00
= 2
00
8
Sources: Performance Review UnitACI; STATFOR; CRCO
Pan-European key air traffic indices
2
PRR 2019 - Chapter 1: Introduction 6
flight in 2019 compared to 2013. 1
As in previous years, average aircraft size (+1.7% vs. 2018) and distance (+1.7% vs. 2018) continued to 2 grow at a higher rate than flights in 2019. This resulted in a higher en-route service unit4 growth in 3 2019 (+2.8% vs. 2018). 4
Similarly, passenger numbers continued to grow at a higher rate (+3.2% vs. 2018) than flights in 2019, 5 yet also with a notable slowdown in the rate of growth. 6
Figure 1-3 provides a year on year comparison in terms of flight type, traffic segment, distance and 7 flight hours compared to 2018. The main driver of growth in 2019 was scheduled traffic to and from 8 Europe while intra-European traffic growth confirms the slowdown of traffic growth in Europe in 2019. 9
Source: STATFOR
Figure 1-3: Year on year change versus 2018 10
Figure 1-4 shows the number of average daily flights by Air Navigation Service Provider (ANSP) in 2019 11 at the bottom and the change compared to 2018 in absolute (grey bars) and relative (red lines) terms 12 at the top. The figure is sorted according to the absolute change versus 2018. 13
14 Figure 1-4: Traffic evolution by ANSP (2019/2018) 15
In absolute terms, ENAV (Italy), CroatiaControl, ENAIRE (Spain), HCAA (Greece), SMATSA (Serbia/ 16 Montenegro), and Austro Control showed the highest year on year growth in 2019. 17
Sakaeronavigatsia (Georgia), LFV (Sweden), Maastricht Upper Area Control Centre (MUAC), Skeyes 18 (Belgium), and ANS CR (Czech Republic) showed the highest decrease in absolute terms in 2019. 19
4 Used for charging purposes based on aircraft weight factor and distance factor.
0.1%
3.2%
1.4%
0 200
Pan-European
From/toEurope
Overflights
Average daily flights
Change by flight type (2019 vs 2018)
+1.5%
+0.7%
+2.9%
-3.0%
-2.8%
-1.0%
-100 100 300
Trad. Scheduled
Low-Cost
Charter
Business Aviation
Cargo
Other (incl. military)
Average daily flights
Change by traffic segment (2019 vs 2018)
IFR flights
11.1 M (+0.8% )
Flight hours 18.7 M (2.0% )
Flight distance 13 528 M (+1.7% )
Avg. flight duration 101.3min (+1.2%)
Avg. speed722.8 Km/h (-0.3%)
Avg. flight length
1,220 km (+0.9%)
Change 2019 vs. 2018
4.5%
10.6
%
2.5
% 6.2% 6.8%
4.2
%
1.4%
4.5%
11.8
%
15.5
%
7.3%
0.9%
1.3% 4.
5%
2.7
%
7.3%
1.8
%
2.7%
1.2%
0.9
%
2.7% 3.
8%
0.5
% 4.5%
0.5%
0.2% 1.
4%
1.4%
0.1
%
0.7%
0.4
%
-1.4
%
-0.3
%
-1.3
%
-0.7
%
-0.9
%
-0.8
%
-1.5
%
-0.5
%
-1.6
%
-9.3
%
-10%
-5%
0%
5%
10%
15%
20%
25%
-100
-50
0
50
100
150
200
250
chan
ge in
flig
hts
vs 2
018
(%)
chan
ge in
avg
. dai
ly f
light
s
change in average daily flights vs. 2018
0
2,000
4,000
6,000
8,000
10,000
ENA
V (
Ital
y)
Cro
atia
Co
ntr
ol (
Cro
atia
)
ENA
IRE
(Spa
in)
HCA
A (G
recc
e)
SMA
TSA
(Se
rbia
/…
Au
stro
Co
ntro
l (A
ust
ria)
DSN
A (F
ran
ce)
PA
NSA
(Pol
and)
UkS
ATS
E (U
krai
ne)
M-N
AV
(No
rth
Mac
edon
ia)
Slo
ven
ia C
on
tro
l (Sl
ove
nia)
BH
AN
SA
NA
TS-C
ont
inen
tal (
UK)
DH
MI (
Turk
ey)
DCA
C C
ypru
s
NA
V-C
ont
inen
tal (
Port
uga
l)
Alb
con
tro
l (A
lban
ia)
IAA
(Ir
elan
d)
ON
DA
(Mo
rocc
o)
RO
MA
TSA
(R
oman
ia)
BU
LAT
SA (B
ulg
aria
)
LGS
(Lat
via)
Isra
el A
A (
Isra
el)
Skyg
uide
(Sw
itze
rla
nd)
MA
TS (
Mal
ta)
Hun
garo
Co
ntro
l
DFS
(G
erm
any)
Oro
Nav
igac
ija (L
ith
uan
ia)
AN
S Fi
nlan
d (
Fin
lan
d)
NA
VIA
IR (
Den
mar
k)
AN
A L
UX
(Lux
embo
urg
)
MO
LDA
TSA
(Mo
ldo
va)
AR
MA
TS (
Arm
eni
a)
LVN
L (N
eth
erla
nds)
EAN
S (E
ston
ia)
Avi
no
r (N
orw
ay)
LPS
(Slo
vaki
a)
AN
S C
R (
Cze
ch R
epu
blic
)
Skey
es (
Bel
giu
m)
MU
AC
(Maa
stri
cht)
LFV
(Sw
eden
)
Saka
ero
navi
gats
ia (
Ge
orgi
a)Avg
. dai
ly fl
igh
ts (2
019)
Source: NM; PRC analysis
Average daily flights (2019)
2
PRR 2019 - Chapter 1: Introduction 7
Figure 1-5 zooms in at Area Control 1 Centre (ACC) level. The map shows 2 that traffic decreased notably in 3 Scandinavia due to less domestic and 4 tourist travel as a result of a 5 slowdown in economic growth. 6
ACCs in the Balkan region and eastern 7 Italy again had high traffic growth in 8 2019 due to continued tourist flows to 9 Greece and Turkey and traffic shifting 10 from more northern routes to avoid 11 regulated airspace. 12
In order to mitigate the capacity 13 crunch observed in summer 2018, the 14 Network Manager implemented 15 Enhanced NM/ANSPs Network 16 Measures for Summer 2019 (eNM/S19) to offload traffic from congested ACCs. The initiative involved 17 a number of ACCs which – as a result – had more or less traffic. 18
For instance, there was a reduction of traffic in the upper airspace in the core area (Maastricht and 19 Karlsruhe UAC) but an increase of traffic for Langen and Munich ACCs in lower airspace. 20
More information on the eNM/S19 initiative is provided in Chapter 3 Section 3.2.7. 21
22
Traffic characteristics 23
Figure 1-6 shows the daily 24 number of flights in the 25 EUROCONTROL area in 2019. 26
The significant difference 27 between summer and winter 28 season but also between 29 weekday and weekend is 30 clearly visible. 31
Figure 1-7 shows the evolution 32 of the average daily flights in 33 the EUROCONTROL area 34 between 2008 and 2019. 35
The box plots show that the 36 peak traffic load and all 37 quartiles increased notably 38 between 2015 and 2018, but 39 with only a slight increase in 40 2019. 41
The peak day in 2019 (and the 42 highest level on record) was on 43 Friday 28 June 2019 when the 44 system handled 37,228 45 controlled flights. The peak day 46 in 2019 was 22.5% higher than 47 an average day. 48
49
Figure 1-5: Traffic growth by ACC (2019)
CAN
ANK
LIS
SCO
SHA
TAMSTO
MAD
BRE
BOD
ROM
CAS+AGA
DNI
WARLON
BAR
KAR
MAA
BUC
MAR
KIE
ATH+MAK
MAL
MAL
NIC
BOR
BRI
SEV
ODE
OSL+STV
COP
SOF
LVO
REI
ZAG BEO
TBI
RIG
VIL
MIL
TAL
BUDWIE
PAD
PRA
TIR
TEL
CHIBRAT
ZURYER
GENPAR
SKO
LJU
KOS
BREM
LAN
MUN
AMS
BRU
LON TC
DUB
PAL
Lower Airspace
Lower Airspace
Traffic growth 2019 vs 2018
<-3.0%
-2.9% - -1.0%
-0.9% - 0.0%
0.1% - 1.0%
1.1% - 2.5%
2.6% - 5.0%
5.1% - 7.5%
7.6% - 10.0%
>10%
IST
IST
Lower Airspace
Figure 1-6: Daily traffic levels in the EUROCONTROL area (2019)
Figure 1-7: Evolution of daily traffic levels (EUROCONTROL area)
Christmas day
10
15
20
25
30
35
40
Dai
ly f
ligh
tsTh
ou
san
ds
Weekday Weekend Average 2019
Daily traffic levels in the EUROCONTROL area in 2019
2
PRR 2019 - Chapter 1: Introduction 8
Figure 1-8 shows the level of seasonal 1 variation between peak and average 2 week by ACC in 2019. In some ACCs, 3 traffic in the peak week is more than 4 50% higher than in the average week. 5
Typically this is the case for ACCs with 6 a high share of holiday traffic peaking 7 in summer. 8
Although the relationship between 9 “traffic complexity” and ANS 10 performance is in general not 11 straightforward, traffic complexity is a 12 factor for consideration when 13 analysing performance. 14
High density (concentration of traffic 15 in space and time) can lead to a better 16 utilisation of resources. High 17 structural complexity (intensity of 18 potential interactions between traffic) 19 entails higher ATCO workload and 20 potentially less traffic. 21
The map in Figure 1-9 shows the 22 annual complexity scores by ACC in 23 2019. As can be expected, the highest 24 complexity levels are observed in the 25 core area where traffic is most dense. 26
Figure 1-10 shows the complexity 27 score in 2019 by ANSP, broken down 28 into adjusted traffic density (y-axis) 29 and structural complexity (x-axis). 30
As can be expected the picture is 31 contrasted with Skyguide showing the 32 highest level, followed by Maastricht, 33 DFS, NATS (continental), Skeyes and 34 Slovenia Control. 35
Please note that the figures shown in 36 this section represent annual averages 37 which may differ substantially 38 depending on the time period 39 analysed (e.g. daily, peak period, etc.). 40
Traffic complexity is therefore a factor 41 that needs to be carefully managed as 42 it may have an impact on productivity, 43 cost-efficiency, and the service quality 44 provided by air navigation service 45 providers. 46
More information on the 47 methodology and more granular data 48 are available from the ANS 49 performance data portal. 50
51
Figure 1-8: Traffic variability by ACC (2019)
Figure 1-9: Traffic complexity by ACC (2019)
Figure 1-10: Traffic complexity by ANSP (2019)
CAN
ANK
LIS
SCO
SHA
TAMSTO
MAD
BRE
BOD
ROM
CAS+AGA
DNI
WARLON
BAR
KAR
MAA
BUC
MAR
KIE
ATH+MAK
MAL
MAL
NIC
BOR
BRI
SEV
ODE
OSL+STV
COP
SOF
LVO
REI
ZAG BEO
TBI
RIG
VIL
MIL
TAL
BUDWIE
PAD
PRA
TIR
TEL
CHIBRAT
ZURYER
GENPAR
SKO
LJU
KOS
BREM
LAN
MUN
AMS
BRU
LON TC
DUB
PAL
Lower Airspace
Lower Airspace
Traffic seasonality 2019 (peak week vs avg. week)
<= 1.14
1.14 - 1.19
1.19 - 1.26
1.26 - 1.35
1.35 - 1.49
> 1.49
IST
IST
Lower Airspace
CAN
ANK
LIS
SCO
SHA
TAMSTO
MAD
BRE
BOD
ROM
CAS+AGA
DNI
WARLON
BAR
KAR
MAA
BUC
MAR
KIE
ATH+MAK
MAL
MAL
NIC
BOR
BRI
SEV
ODE
OSL+STV
COP
SOF
LVO
REI
ZAG BEO
TBI
RIG
VIL
MIL
TAL
BUDWIE
PAD
PRA
TIR
TEL
CHIBRAT
ZURYER
GENPAR
SKO
LJU
KOS
BREM
LAN
MUN
AMS
BRU
LON TC
DUB
PAL
Lower Airspace
Lower Airspace
Traffic complexity 2019
<= 2
2 - 4
4 - 6
6 - 8
> 8
IST
IST
Lower Airspace
2
PRR 2019 - Chapter 1: Introduction 9
Traffic forecast 1
With the 2019–20 coronavirus pandemic sweeping its way across the globe, the entire aviation 2 industry is facing an unprecedented challenge. 3
Unlike previous outbreaks (SARS, MERS) which were more regional in scope, the COVID-19 outbreak is 4 global and nobody can answer the most pressing question, namely how long will the crisis last, how 5 bad it will be, and how to get back to normality? 6
While those questions remain unanswered at the time of writing, the only option is to monitor closely 7 the dynamic situation to take coordinated action and to prepare as much as possible for the long road 8 back to normality. 9
The new STATOR forecast was due to be published at the end of February 2020 but in view of the 10 COVID-19 outbreak in 2020 and the uncertainties attached the forecast has been postponed. 11
The PRC will monitor the situation and produce dedicated analyses as soon as more information 12 becomes available. 13
2
PRR 2019 - Chapter 1: Introduction 10
1.4 Air transport punctuality 1
From a passenger perspective, punctuality is a commonly used service quality indicator. It is defined 2 as the percentage of flights arriving (or departing) within 15 minutes of the scheduled time. 3
After a continuous deterioration over the 4 past years, arrival punctuality improved 5 again from 75.7% in 2018 to 77.8% in 2019. 6
Previous analyses have shown that arrival 7 punctuality is primarily driven by departure 8 delay at the origin airport with only 9 comparatively small changes once the 10 aircraft is airborne. 11
Figure 1-12 provides a causal breakdown of 12 the delays reported by airlines to better 13
understand the drivers of departure delays5 and the 14 contribution of ANS towards operational performance. 15
Average departure delay in 16 the EUROCONTROL area 17 decreased in 2019 by 1.6 18 minutes per flight to 12.8 19 minutes. 20
As in 2018, air traffic flow 21 measures (mainly en-route 22 related) contributed to the 23 observed level in overall 24 arrival punctuality in 2019. 25 The relative share of ANS 26 related departure delay increased slightly from 19.4% in 2018 to 20.6% in 2019. 27
Reactionary delay from previous flight legs accumulate throughout the day, and in 2019 were by far 28 the largest delay category (44.4% in 2019), followed by local turn around delays (32.6%). 29
The network sensitivity to primary delays6 decreased in 2019 from 0.87 to 0.80 leading to the observed 30 decrease in the relative share of reactionary delays. 31
A thorough analysis of non-ANS-related delay causes is beyond the scope of this report. A more 32 detailed analysis of departure delays reported by airlines is available from the Central Office for Delay 33 Analysis (CODA)7. 34
5 Departure delays can be further classified as “primary” delay (directly attributable) and “reactionary” delay (carried over from previous flight legs). Different from ATFM delays, which are based on the last filed flight plans, the departure delays are based on published airline schedules. Therefore the delays are not directly comparable.
6 Measured as minutes of reactionary delay for each minute of primary delay. 7 The Central Office for Delay Analysis (CODA) publishes detailed monthly, quarterly, and annual reports on more
delay categories (see https://www.eurocontrol.int/articles/coda-publications). The data is collected from airlines and the coverage is 70% of the commercial flights in the ECAC region for 2019.
Arrival punctuality in 2019 improved again from 75.7% in 2018 to 77.8%
Figure 1-11: Evolution of arrival punctuality
75.7%
77.8%
70%
75%
80%
85%
90%
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
% o
f flig
hts
Source: CODA
Share of arrivals within 15 min of scheduled arrival time (%)
Figure 1-12: ANS contribution towards departure total departure delays
77.8%
of flights arrived within 15 min of scheduled time
in 2018
(+2.1% pt. vs 2018)
2
PRR 2019 - Chapter 1: Introduction 11
1.5 Environment 1
Environmental is an important political, economic and societal issue and the entire aviation industry 2 has a responsibility to minimise its impact on the environment. 3
ANS performance clearly affects the environmental impact of aviation which can be broadly divided 4 into the impact on (i) global climate, (ii) local air quality (LAQ), and (iii) noise. 5
Whilst the focus of the PRR is on ATM performance, it is helpful to first set out the overall context with 6 regard to aviation and the environment as a whole, before discussing ATM and environmental 7 performance. 8
1.5.1 Aviation impact on climate 9
What is the problem? 10
The environmental impact of aviation on 11 climate results from greenhouse gas (GHG) 12 emissions8 (CO2, NOx, etc.) and contrails, 13 generated by aircraft engine exhaust. 14
Figure 1-13 provides an overview of the 15 radiative forcing9 of different contributors to 16 climate change, as detailed in the 5th IPCC 17 report [6]. 18
Whereas CO2 emissions are directly 19 proportional to the fuel burn, NOx emissions 20 are more difficult to quantify as they depend on 21 engine settings and prevailing atmospheric 22 conditions. Moreover, the radiative forcing 23 effect of non-CO2 emissions depends on 24 altitude, location, and time of the emission. 25
The remainder of this section focuses on GHG 26 emissions of which CO2 is considered to be the 27 most important one. 28
Based on the figures of the European Environment Agency (EEA) aviation accounted for approximately 29 3.8% of total EU28 GHG emissions (4.5% of CO2 emissions) in 2017 (the latest year for which 30 information is available) [7]. Although this share appears to be comparatively small, aviation is one of 31 the fastest growing sources of GHG emissions in Europe. 32
Whereas anthropogenic GHG emissions in Europe decreased by 20.6% between 1990 33 and 2017 (mainly due to reductions in electricity and heat production), total GHG 34 emissions from the transport sector (incl. aviation) grew by +28% vs 1990, resulting in 35 an increase of its relative share of total EU28 GHG from 16.7% in 1990 to 27% in 2017. 36
At the same time, total GHG emissions from aviation increased by 110% compared to 37 1990 levels to reach 174 million tonnes in the EU28 area in 2017 (+91 million tonnes 38 per annum compared to 1990 levels). As a result, the relative share of aviation grew 39 from 1.4% in 1990 to 3.8% of total GHG in 2017. 40
8 GHG are the gases against which emission reduction targets were agreed under the Kyoto Protocol (carbon dioxide, methane, nitrous oxide, hydrofluorocarbons, perfluorocarbons and sulphur hexafluoride. Global warming factors are applied to each gas in order to present the emissions in terms of CO2 equivalent. For example: 1 kg of N2O is equivalent to 298 kg of CO2 in terms of global warming effect. An important natural GHG that is not covered by the protocol is water vapour.
9 Radiative forcing is a measure of the influence a factor has in altering the balance of incoming and outgoing energy in the Earth-atmosphere system and is an index of the importance of the factor as a potential climate change mechanism.
3.8%
approx. GHG emissions from
European aviation
Figure 1-13: Radiative forcing components
2
PRR 2019 - Chapter 1: Introduction 12
The distribution by mode on the right side of Figure 1-14 shows that aviation was accountable for 1 13.9% of the GHG emissions from transport in 2017. By far the most important source of transport 2 GHG emissions was road transport, which accounted for 71.7% of all emissions from transport. 3
The relative share of aviation is expected to further increase as aviation activity based on fossil fuels is 4 forecast to continue to grow while other industrial sectors (including road transport) increasingly 5 decarbonise over time. 6
7 Figure 1-14: Evolution of GHG emissions between 1990 and 2017 by sector (EU28) 8
What are the expectations? 9
Recognising the need to address aviation’s impact on climate, the International Air Transport 10 Association (IATA), set three global goals for civil aviation in 2009 to address its impact on climate: 11
(1) Short-term (2009-2020): average fuel efficiency improvement of 1.5% per annum; 12 (2) mid-term: carbon neutral growth from 2020; and, 13 (3) long term: Reduce CO2 emissions from aviation by 50% by 2050 compared with 2005 levels. 14
In 2010, the International Civil Aviation Organization (ICAO) adopted a comprehensive agreement to 15 reduce the impact of international aviation emissions on climate change with the following goals: 16
(1) annual average fuel efficiency improvement rate of 2% up to 2050; and, 17 (2) Carbon neutral growth of international aviation from 2020 onwards. 18
To achieve these goals, ICAO adopted in 2016 a global carbon offsetting scheme (CORSIA10) for CO2 19 emissions above 2020 levels to address emissions from international aviation. 20
Additionally, ICAO agreed in 2017 on a CO2 efficiency standard for new aircraft which limits the CO2 21 emissions form aircraft in relation to their size and weight (applicable to new aircraft as of 2020 and to 22 in production aircraft as of 2028). 23
24 Figure 1-15: Environmental IATA and ICAO goals 25
10 Carbon Offsetting and Reduction Scheme for International Aviation.
Aviation13.9%
Road transport71.7%
Railways0.5%
Other transport 0.5%
Maritime13.4%
Transport GHG by mode of transport (EU-28)
201716.7%
Transport (incl. int. aviation & maritime)
27.0%
1.4%
Aviation3.8%
0%
5%
10%
15%
20%
25%
30%
35%
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
199
0
199
3
199
6
199
9
200
2
200
5
200
8
201
1
201
4
201
7
rela
tive
sh
are
by
sect
or
(%)
GH
G e
mis
sio
ns
(EU
-28
) in
mill
ion
to
nn
es
Energy (Industries) Energy (Residental/ Commercial)Transport (incl. int. aviation & maritime) Energy (Manufacturing and Construction)Agriculture Industrial processesAviation
Evolution of GHG emissions by sector (EU-28)
Source: EEA
2
PRR 2019 - Chapter 1: Introduction 13
According to the Air Transport Action Group (ATAG), commercial aviation fuel efficiency improved by 1 21.4% between 2009 and 2019 which corresponds to an average annual improvement of 2% [9]. 2
In Europe, much focus has been on environment. Emissions from aviation were included in the EU 3 trading system (EU ETS) already in 2012 and the new European Green Deal for the European Union 4 [10] lays out a much more ambitious plan to address climate change. One of the overarching goals is 5 to achieve climate neutrality by 2050. To achieve this, a 90% reduction in transport emissions is needed 6 by 2050. 7
How to get there? 8
The challenge in reducing aviation emissions is well known. Essentially there are four pillars to reduce 9 GHG emissions from aviation: (1) Aircraft technology (airframes and engines), (2) Sustainable aviation 10 fuels, (3) Economic measures, and (4) Improved infrastructure and operations (operational efficiency). 11 12
Aircraft technology: With the relative share of fuel cost in airline operational costs increasing, there 13 has historically been a strong focus on increasing fuel efficiency. Hence the pressure to reduce fuel 14 burn is not new but gained additional momentum through the environmental focus. 15
The Clean Sky Joint Undertaking is, for instance, a public-private partnership between the European 16 Commission and the European aeronautics industry which was formed in 2008 to deliver significantly 17 quieter and more environmentally friendly aircraft. 18
The latest generation of aircraft burn about 15-20% less fuel than the previous generation. Overall, 19 fuel burn per 100 passenger kilometres fell by 24% between 2005 and 2017. The latest generation of 20 jet aircraft has a fuel consumption of around 3 litres per 100 passenger kilometre [11]. 21
The increasing environmental focus 22 has added to this pressure and aircraft 23 manufacturers find it increasingly 24 difficult to deliver efficiency gains from 25 design and engine improvements. 26 Moreover, aircraft generally have a 27 lifespan of 20-30 years which means 28 that efficiency gains through fleet 29 replacements will take time to filter 30 through the entire aircraft fleet. 31
Electric or hybrid aircraft are an option 32 to help decarbonising aviation but 33 only make sense if the process is 34 based on renewable sources. This 35 technology is considered to be an 36 option for short haul flights up to 37 1000-1500 km after which the 38 necessary batteries will become too 39 heavy. 40
Whereas the potential to substitute 41 flights with electric or hybrid aircraft 42 is high (70% of the departures in 2019 43 were below 1500 km), the impact on 44 reducing GHG emissions is in the best 45 case scenario limited to one quarter 46 of the total GHG emissions from 47 aviation (see Figure 1-17). 48
49
50
51
Figure 1-17: Distribution of flights and estimated CO2 emissions by
distance category (2019)
Figure 1-16: Alternative propulsion systems
24
.1%
29
.8%
16
.3%
11
.7%
12
.9% 5.
1%
3.8%
11
.3%
10
.2%
10
.2%
20
.2%
44
.2%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
0-500 500-1k 1-1.5k 1.5-2k 2-5k >5k
% o
f to
tal
flight distance in km (full trajectory)
% of total departures (2019)
% of total CO2 emissions
-> 70% of total departures-> 25% of total CO2 emissions
Distribution of flights and CO2 emissions by distance and potential enablers to reduce emissions
(departures from Europe in 2019)
Possible substitution by batterypowered or hybrid driven aircraft
(market entry 2030+)
Sustainable Aviation Fuels (SAF)
Improved fuel efficiency and operational efficiency
2
PRR 2019 - Chapter 1: Introduction 14
Hence, sustainable aviation fuels (SAF) appear to be a real option to help decarbonising aviation, 1 especially on medium to long haul flights which cannot be substituted by electric or hybrid powered 2 aircraft. 3
Biofuels11 and electrofuels12 provide the necessary energy density and can be used to power the 4 existing aircraft fleet with only minor modifications to engines and existing infrastructure. 5
However, the production processes are energy intensive and blending of or switching to SAF only helps 6 decarbonising aviation if the production is fully based on renewable sources. 7
As a result, SAF uptake in the aviation sector is still very limited today due to the price gap between 8 SAF and traditional kerosene. The gap is expected to narrow with commercialisation and higher 9 demand from the industry. 10
Economic measures: Essentially economic measures aim to introduce a price for carbon emissions to 11 incentivise innovative technologies to bring down CO2 emissions from aviation but also to curb demand 12 (price elasticity of air tickets). There is a vast portfolio of possible economic measures including, global 13 or regional emission trading schemes, specific taxes on fuel13 or tickets14, or the application or 14 adjustment charges to incentivise the decarbonisation of aviation. 15
As international aviation is not part of the Kyoto Protocol, the EU decided to include emissions from 16 aviation in the EU emissions trading system (EU ETS) in 2012, after earlier consideration of taxation. 17
The initial legislation covered all flights in and out of the European Economic Area (EEA), with a cap 18 based on average emissions in 2004-2006. Following resistance from the industry and non EU States, 19 the EU decided to support the development of a global scheme by ICAO for reducing aviation emissions 20 and to limit the EU ETS obligations to flights within the EEA. The EU has decided to maintain the 21 geographic scope of the EU ETS limited to intra-EEA flights from 2017 onwards and to review the 22 scheme to reflect progress on the development of a global scheme through ICAO [12]. 23
In 2016, the ICAO 39th Assembly approved CORSIA as the global market-based measure to limit and 24 offset emissions from international aviation. Domestic CO2 emissions from aviation will be addressed 25 under the Paris Agreement which enters into force in 2020. 26
The CORSIA emissions baseline is the average of 2019-2020 emissions from international air traffic. 27 Although the EU ETS baseline can be considered as more constraining as it was set when emissions 28 were lower, CORSIA does have the advantage of being applied worldwide. 29
CORSIA requires monitoring of fuel consumption on international flights starting on 1st January 2019. 30 By 2027, CORSIA will be mandatory for all international flights between States not exempted from the 31 scheme. To compensate for CO2 emissions growth above 2020 levels in international aviation airlines 32 will have to buy emission units from green projects to compensate excess CO2 levels. 33
While the EU ETS and CORSIA encourage airlines to reduce emissions, contrary to the EU ETS, which is 34 a “cap and trade” scheme, CORSIA is an “offsetting” scheme implying that emissions can grow as long 35 as they are compensated by offsets which may reduce CO2 emissions elsewhere rather than directly in 36 the sector, but does count towards net reductions for aviation. 37
Even though the schemes are expected to generate large amounts of climate financing through the 38 offsetting process (CORSIA is estimated to generate some $40 billion by 2035), it is not foreseen that 39 the money will be used to directly support the costly transition to SAF which would directly reduce 40 aviation’s net CO2 emissions. 41
Figure 1-18 provides an overview of the expected contribution from the respective areas to achieve 42 aviation’s environmental goals [11]. 43
11 Fuels of biological origin (biomass: crops but also from waste and residues). 12 Fuels of non-biological origin (hydrogen from water is usually combined with CO2). 13 International aviation is generally exempt from excise duty on jet fuel. 14 In Europe there is a large variety of country specific environmental taxes on tickets.
2
PRR 2019 - Chapter 1: Introduction 15
1 Figure 1-18: Basket of measures aimed at meeting aviation’s environmental goals 2
Whereas more fuel efficient aircraft and operational improvements will further reduce CO2 emissions 3 per passenger kilometre, it is obvious that those improvements alone will not be sufficient to offset 4 the increase in CO2 emissions from increased aviation activity. Sustainable aviation fuels clearly is a 5 major factor in the industry meeting its goals as the widespread adoption by airlines has the potential 6 to substantially reduce aviation’s net emissions once the key hurdles of price and supply can be 7 overcome. 8
What can ATM do to help? 9
Optimised flights & operations: 10
The ATM-related impact on climate is closely linked to operational performance (fuel efficiency) which 11 is largely driven by inefficiencies in the flight trajectory and associated fuel burn (and emissions). For 12 every tonne of fuel reduced, an equivalent amount of 3.15t of CO2 is avoided. 13
Hence, the focus has been traditionally on the monitoring of ANS-related operational efficiency by 14 flight phase which served as a proxy for environmental performance since the distance or time saved 15 by operational measures can be converted into estimated fuel and CO2 savings. 16
ATM deploys a number of projects and initiatives aimed at improving operational efficiency, including 17 performance based navigation (PBN), free route airspace (FRA), collaborative decision making (CDM), 18 and continuous climb and descent operations (CCO/CDO). 19
Additional efficiency gains are expected to come from the further digitalisation and automation of the 20 industry (especially as drones proliferate) which will enable the better use of data-driven technologies 21 like Artificial Intelligence (AI). Although there is already a significant collection of data sets in European 22 aviation today it is not efficiently used and shared. 23
There is a need to further speed up digitalisation and the development of a common infrastructure for 24 data sharing including agreed standards which is a crucial enabler for efficiency gains and the way 25 stakeholders in the ATM system interact with each other at operational level. This is increasingly being 26 done through system wide information management (SWIM) protocols and business to business (B2B) 27 interfaces, including a better use of ADS-B data. 28
Although it is understood that there are safety and confidentiality issues that need to be solved, the 29 better sharing of trajectory and airspace data and the increase in predictability, resulting both from 30 the use of AI and the use of additional source of data clearly has the potential to bring further benefits 31 for all involved parties in terms of planning (utilisation of resources) and decision making. 32
2
PRR 2019 - Chapter 1: Introduction 16
Figure 1-19 provides an overview of the gate-to-gate efficiency by phase of flight including an 1 indication of the supporting ATM related projects/ enablers. In view of the aforementioned close links 2 between ANS related operational efficiency and to avoid duplication the environmental perspective is 3 directly addressed in the respective chapters of the report. 4
5 Figure 1-19: Gate-to-gate efficiency by phase of flight 6
To get a high level estimate of the possible contribution of ATM towards reducing CO2 emissions, Figure 7 1-20 provides a breakdown of estimated gate-to-gate excess CO2 emissions as a percentage of the 8 unimpeded gate-to-gate trajectory in the EUROCONTROL area. 9
The estimated “benefit pool” that can be influenced by ANS is based on a comparison of actual flight 10 trajectories to theoretical reference trajectories15. It is important to highlight that the calculated 11 inefficiencies are not entirely attributable to ANS. In fact, the inefficiencies (separation minima, 12 adverse weather, avoidance of ‘Danger Areas’, interdependencies) cannot and should not be reduced 13 to zero (shortest is not automatically the wind optimum route) so that the reference trajectory can in 14 practice not be achieved at system level. 15
The current best estimate16 suggests that ATM can influence approximately 6% of the total gate-to-16 gate fuel burn (emissions) in the ECAC area17 through the optimisation of trajectories. 17
It is important to point out that the percentage is the ECAC average. The average masks notable 18
15 The theoretical (unachievable) reference trajectory is characterised by: zero additional taxi-out time, no level-off during climb (full fuel CCO), no sub-optimal cruise level, en-route actual distance equal to great circle distance (ISA conditions), no level-off during descent (full fuel CDO), no additional time in the Arrival Sequencing and Metering Area (ASMA), zero additional taxi-in time. The EUROCONTROL area average is slightly lower than the sum of the individual flight phases as some of the inefficiencies take place outside of the EUROCONTROL area.
16 The calculation is the current best estimate and is being continuously refined and improved to reflect new or additional data as it becomes available.
17 Only the share of the trajectories in the ECAC area are considered.
Figure 1-20: Estimated ANS-related gate-to-gate benefit pool (CO2 emissions)
2
PRR 2019 - Chapter 1: Introduction 17
differences in performance at city pair or airline level across the network. On short haul flights 1 operating on airport pairs with complex terminal manoeuvring areas (TMA), the inefficiency can be in 2 some cases above 25%. Therefore, inefficiencies for individual flights, airport pairs or airlines can be 3 notably different from this system-wide average. 4
If the “ANS related benefit pool” that can be actioned by ANS is related to the total GHG emissions in 5 Europe the share that can be influenced by ANS corresponds to 0.2% of the total GHG emissions (6% 6 of 3.8% = 0.2% of total GHG emissions). 7
8 Figure 1-21: Estimated share of GHG emissions that can be influenced by trajectory optimisation 9
As visualised in Figure 1-18, by far the main contribution to decouple CO2 emissions growth from air 10 traffic growth is expected to come from the use of sustainable aviation fuels, aircraft technology 11 (airframes, engines) and to a lesser extent from economic measures. 12
Although the ANS contribution towards 13 reducing CO2 emissions is with around 14 6% of the total emissions from aviation 15 comparatively limited in relation to 16 total European CO2 emissions (0.2% of 17 total GHG emissions), the absolute 18 numbers are significant and there is still 19 ample scope for further ANS 20 improvement (see Figure 1-22). 21
Horizontal en-route flight efficiency is 22 the largest single component and also 23 the only component subject to target 24 setting in the Single European Sky, 25 followed by inefficiencies during the 26 arrival phase. 27
Considering the ATM effort required 28 and the fact that a certain level of 29 inefficiency will not be recoverable due 30 to necessary operational constraints 31 (separation minima, etc.) and 32 interdependencies (capacity vs. flight efficiency) the ambition of the 2020 European ATM Master Plan 33 [13] to reduce the share of excess gate-to-gate CO2 emissions to 2.3% by 2035 appears to be 34 challenging. 35
It goes without saying that the strong focus on improving flight efficiency needs to be maintained. 36 However, considering the forecast traffic growth over the next 20 years the task for ATM will be 37 challenging and the margins for improvement are likely to narrow as the ongoing improvement 38 initiatives such as Free Route Airspace (FRA) are fully deployed. 39
Energy (Industries)
25%
Energy (Manufacturing and Construction)
11%
27%
Energy (Residental/ Commercial)
16%
Industrial processes
8%
Agriculture9%
Waste3%
% of total GHG emissions by sector (EU-28)
2017
Transport
Aviation
13.9%
Road transport71.7%
Railways0.5%
Other transport…Maritime
13.4%
% of total transport GHG emissions by mode
2017 6.0%2017
ANS-related inefficiency
Figure 1-22: Estimated ANS-related benefit pool by flight phase
(CO2 emissions)
Surface:
Taxi-in0.474%
Surface:
Taxi-out1.029%
Terminal airspace:
departure path extension
0.00%
Climb: inefficiency due to intermediate
level off0.11%
En route airspace:
horizontal inefficiency
4.539%
En route airspace:
vertical inefficiency
2.118%
Terminal airspace: arrival path extension
2.420%
Descent:
inefficiency due to intermediate
level-off1.1
9%
11.6Mtonnes
Source: EUROCONTROL/PRU
2019
Estimated ANS-related benefit pool by phase of flight (CO2 emissions)
2
PRR 2019 - Chapter 1: Introduction 18
1.5.2 Noise and ANS performance 1
Although perceived noise reduced by 75% since the first jets [11], aircraft noise is generally recognised 2 as the single largest environmental issue at airports. 3
Airports face the challenge to balance the need to increase capacity in order to accommodate future 4 air traffic growth with the need to limit negative 5 effects on the population in the airport vicinity. 6 Political decisions on environmental constraints 7 can impact operations in terms of the number of 8 movements, route design, runway configuration 9 and usage and aircraft mix (engine types etc.). 10
The European Environment Agency estimates 11 that around 3 million people are exposed to 12 aircraft noise above 55dB [14]. 13
Regulation (EU) No 598/2014 lays down rules on the process to be followed for the introduction of 14 noise-related operating restrictions in a consistent manner on an airport-by-airport basis, in 15 accordance with the ICAO Balanced Approach (Doc 9829) which breaks down the affecting factors into 16 (1) land use planning, (2) reduction of noise at source, (3) aircraft operational restrictions and (4) noise 17 abatement operational procedures [15]. 18
Noise emissions from aircraft operations are airport-specific and depend on a number of factors 19 including aircraft type, number of take-offs and landings, route structure, runway configuration, and a 20 number of other factors. Moreover, there can also be interdependencies and trade-offs for instance 21 between capacity and noise, or when different flight paths reduce noise exposure but result in less 22 efficient trajectories and hence increased emissions. 23
Accordingly a collaborative approach is of paramount importance and ANS has an important active 24 supporting role in this process. The noise management at airports is usually, but not always, under the 25 responsibility of the airport operators which coordinate and cooperate with all parties concerned to 26 reduce the population’s exposure to noise while optimising the use of scarce airport capacity. 27
Generally the management of noise is considered to be a local issue which is best addressed through 28 local airport-specific agreements developed in coordination and cooperation with all relevant parties 29 including ANS. Due to the complexity of those local agreements, there are presently no commonly 30 agreed Europe-wide indicators specifically addressing ANS performance in the noise context. 31
Noise restrictions are then usually imposed by Governments or local authorities and the level of 32 compliance is monitored at local level. 33
Apart from the active support in noise management decisions, the areas where ANS can contribute to 34 the reduction of aircraft noise are mainly related to operational procedures. Continuous climb (CCO) 35 and descent operations (CDO), noise preferential routes and runways are all in the ANS portfolio18 and 36 help to avoid unnecessary exposure to aircraft noise. CCO/CDO is addressed in more detail in Section 37 4.4.3 in Chapter 4. 38
Although the main contributions for reducing noise usually come from measures with long lead times 39 outside the control of ANS (land use planning, reduction of noise at source), there is clearly an active 40 role for ANS to help reducing the noise exposure of the population. 41
18 In some States arrival and departure procedures are owned by airports, not the ANSP, and Government policy is that noise is the primary consideration when making changes below 7,000 ft.
Figure 1-23: Population exposed to noise above 55dB
in Europe (in millions) [14]
2
PRR 2019 - Chapter 1: Introduction 19
1.6 Total economic assessment (en-route) 1
Under the full cost recovery regime, the focus was largely 2 on the provision of sufficient capacity to accommodate 3 anticipated traffic demand. 4
With the advent of the Single European Sky (SES) 5 performance scheme, and the application of binding 6 targets, ANSPs are mandated to provide sufficient capacity 7 to meet the Network delay target whilst providing value for 8 money for the airspace users. 9
The lower traffic levels following the economic crisis starting in 2008 reduced the pressure on capacity 10 to some extent and provided a suitable environment to change the economic models for ANSPs in 11 preparation of the start of the performance scheme in 2012. 12
However, with traffic continuing to grow again in 2013, also ATFM en-route delays increased first 13 gradually and then soared in 2018 which suggests substantial shortcomings in proactive capacity 14 planning and deployment. 15
While traffic in the EUROCONTROL area increased by 14.4% between 2013 and 2018, en-route ATFM 16 delays increased by 279% to reach 19 million minutes in 2018. An increasing share of the en-route 17 ATFM delay was attributed to Air Traffic Control (ATC) capacity and staffing issues which provides a 18 clear indication that capacity is either not available, or not deployed, when and where needed. 19 Previous PRC research suggests that the ATC staffing issue is bigger than currently visible in the delay 20 statistics due to delay cause attribution issues (see also Chapter 3 for more information). 21
The previously described trends are clearly visible in Figure 1-24 which provides an overview of the 22 evolution between 2008 and 2019. While the data on en-route ANS cost for 2019 is not yet available, 23 the actual 2019 traffic and en-route ATFM delay figures are included. The ANS costs in this section 24 were derived from Chapter 5, where a more detailed analysis of ANS cost-efficiency is available. 25
Although average en-route delay per flight decreased in 2019, compared to 2018, the decrease is 26 mainly in delays attributed to adverse weather and ATC disruptions (strikes). 27
Despite the reduction in average en-route ATFM delay, the number of flights delayed by en-route 28 ATFM regulations continued to increase to 1.1 million in 2019, which corresponds to 9.9% of all flights 29 and the outlook for the coming years is not promising. A comprehensive analysis of the operational 30 en-route performance in 2019 is provided in Chapter 3. 31
Figure 1-24: Long term evaluation of en-route ANS performance (2008-2019)
Flights +7.4% vs 2008
ANS Costs -0.7% vs 2008
Unit costs: -25.1% vs 2008
En-route SU+32.6% vs 2008
1.43
0.93
2.02
1.14
0.62 0.53 0.60
0.73 0.86 0.88
1.74 1.57
-
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
0
20
40
60
80
100
120
140
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
Avg
. en-
rou
te A
TFM
de
lay
(min
)
Tra
ffic
an
d C
ost
ind
ex
[ba
se 2
00
8]
ATC Capacity [C] ATC Staffing [S] ATC Disruptions [I,T]
Weather [W] Other Flights [Index 2008]
En-route ANS costs [Index 2008] Unit ANS costs [TSU] En-route SU
?
Long term evolution of en-route ANS costs and en-route ATFM delays(EUROCONTROL area)
2
PRR 2019 - Chapter 1: Introduction 20
Total en-route ANS costs (black line) remained almost flat between 2008 and 2018 mainly due to cost 1 containment measures implemented following the crisis in 2008 and the binding cost-efficiency targets 2 put in place as part of the Single European Sky Performance Scheme as of 2012. 3
At the same time, flights increased by +7.4% (blue line) while en-route service units grew at a notably 4 higher rate (+32.6%) due to a continuous increase in average flight length and aircraft weight. As a 5 result, ANS unit costs decreased by -25% between 2008 and 2018. 6
So what has happened? 7
With the advent of the Single European Sky (SES) performance scheme in 2012 and the application of 8 binding targets many Air Navigation Service Providers (ANSPs) shifted their focus towards cost-9 efficiency, especially as the lower traffic levels resulting from the economic crisis provided a suitable 10 environment to adopt these measures. 11
The data in the ATM Cost-Effectiveness 12 (ACE) benchmarking reports [16] shows 13 that between 2013 and 2018 14 (provisional data), controlled flight 15 hours at EUROCONTROL level (37 16 ANSPs) increased by +19.5% while 17 ATCO hours on duty grew by only 18 +2.2%. 19
Hence, overall cost efficiency and ATCO 20 productivity (+15.3% vs 2013) improved 21 notably during that time. 22
At the same time, ANSPs reduced ATCO 23 recruitment over the past years, most 24 likely as a reaction to the traffic 25 downturn or as part of cost 26 containment measures. 27
Figure 1-26 shows a continuous 28 decrease in ATCO trainees levelling off 29 in 2014/2015 with a slow change of the 30 trend in the following two years. 31
Despite the increase between 2014 and 32 2018, which appears to be a reaction on 33 the increasing traffic and delay levels 34 since 2013, the number of trainees in 35 2018 was still 25% below the 2008 level. 36
The rising delay levels since 2013 suggests that the ATC capacity plans (including staffing) were not 37 sufficient to cope with the traffic growth leading to substantial capacity shortages in some areas. 38
What’s the problem? 39
The European aviation system is a dynamic interconnected network with continuously changing 40 capacity and demand levels. Air Traffic Management (ATM) has to safely, and effectively, 41 accommodate airspace users’ demand in a cost-efficient manner. 42
In addition to the increasing pressure to reduce costs, the challenges ANSPs are facing with regard to 43 capacity planning and deployment include (1) traffic demand evolution, (2) the comparatively long lead 44 times to recruit and train new air traffic controllers (ATCOs), and (3) potentially substantial investments 45 to upgrade or implement new systems. 46
Traffic demand, in time and space, is largely determined by airspace users’ choices but increasingly 47 influenced by constraints, due to a lack of capacity. 48
Accommodating this demand requires capacity, which is determined by factors including airspace 49
Figure 1-25: Network wide evolution of staff related key figures
(2013-2018)
Figure 1-26: Evolution of ATCOs and trainees in the EUROCONTROL
area (2008-2018)
+19.5%
+0.8%
+2.2%
+15.3%
+1.4%
-11.1%
Total flight hours controlled
Total ATCO in Ops (FTE)
Total ATCO hours on duty
ATCO Productivity
ATCO hours on duty per ATCO
ATM/CNS costs per composite flight-hour
Source: ATM Cost Benchmarking Report (ACE)
% change 2018 (provisional) vs. 2013(EUROCONTROL area - 37 ANSPs)
13
.9%
13
.1%
13
.6%
11
.2%
10
.5%
9.7
%
8.7
%
7.8
%
7.1
%
8.2
%
9.8
%
0%
2%
4%
6%
8%
10%
12%
14%
16%
0
20
40
60
80
100
120
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
(P)
Ab-initio / on-the-job trainees as % of ATCOs in OPS
Index - Number of ATCO in OPS (base 2008)
Index - Number of ab-initio / on-the-job trainees (base 2008)
Evolution of ATCOs in OPS and trainees (EUROCONTROL area)
2
PRR 2019 - Chapter 1: Introduction 21
design, ATM infrastructure, ATM system capabilities, and the availability of qualified staff. 1
Depending on demand levels, the airspace configuration can be changed during the day. Sectors may 2 be split when demand and hence controller workload increases or collapsed when workload decreases. 3 The ability of an ANSP to build and deploy capacity depends on the planning horizon available. 4
Strategic planning defines system capabilities, available airspace configurations and the number of 5 available ATCOs. Decisions are usually based on annual traffic forecasts for the next 5 years. 6
Inadequate strategic planning can lead to situations which are difficult to solve in the short term as 7 ATCO recruitment and training requires a considerable lead time. For some extreme cases, the 8 Network Operations Plan (NOP) [17] estimates capacity gaps of up to 25% in 2019, mainly due to the 9 lack of ATCOs to open sectors during peak hours. 10
Although it is likely that it will take some time to close the existing capacity gaps, ATC staffing is clearly 11 an issue which needs to be urgently addressed and managed (see also Section 3.2.5 in Chapter 3) – 12 even more so considering the demographic profile in some ANSPs and the reduced or postponed level 13 of ATCO recruitment over the past years. 14
It underlines the importance of dynamic and proactive capacity planning processes with sufficient 15 flexibility to adjust the capacity plans as necessary and also the need to monitor that the capacity will 16 be deployed as foreseen (i.e. availability of sufficient ATCOs to deploy full capacity when required). 17
Tactical planning is more concerned with the deployment of existing resources as determined by 18 previous strategic decisions on the day of operations. The ability to deploy maximum capacity when 19 needed depends on the number of available ATCOs, qualifications (licensing, rating) and prevailing 20 staffing policies. 21
The introduction of dynamic shift management and a flexible roster with an appropriate planning 22 process can simultaneously improve capacity and cost-efficiency. The ANSP challenge is to ensure 23 adequate numbers of qualified ATCOs to meet requirements – meet current demand (including 24 sickness, retention, and competency); prepare for future demand (including staff replacement and 25 implementation of projects, e.g. SESAR). 26
Insufficient capacity will have a significant impact on airspace users and passengers in terms of delays 27 and associated costs. 28
Figure 1-27 aims at providing a more complete 29 economic picture of the en-route ANS 30 performance by combining the ANS costs and 31 the estimated costs of en-route ATFM delay to 32 airspace users. 33
The analysis clearly shows the substantial 34 increase in en-route ATFM delay and 35 associated costs in 2018 and 2019 which 36 outweigh the unit cost improvements over the 37 past years (see also Figure 1-24). 38
It is estimated that en-route ATFM delay costs 39 to airspace users were equivalent to 25% of the 40 total en-route ANS provision costs in 2018. This 41 underlines the importance of finding a 42 balanced approach in performance 43 management which considers all key 44 performance areas equally instead of focusing 45 entirely on one area. 46
As also detailed in Chapter 5, the en-route ANS cost figures up to 2018 reflect actuals whereas the 47 costs for 2019 are based on the latest available planned/forecasted figures, which might change. 48
Figure 1-27: En-route ANS provision costs and estimated
costs of en-route ATFM delays (B€ 2018)
7.28
€
7.38
€
7.40
€
7.36
€
7.50
€
7.93
€
0.59 € 0.73 € 0.88 € 0.95 €1.93 €
1.76 €
€ -
€ 2
€ 4
€ 6
€ 8
€ 10
€ 12
2014 2015 2016 2017 2018 2019
Bill
ion
Estimated en-route ATFM delay costs (€2018)
Total en-route ANS costs (€2018)
En-route ANS provision costs and estimated costs
of en-route ATFM delays (€ 2018)
Fore
cast
2
PRR 2019 - Chapter 1: Introduction 22
The estimated delay costs19 to airspace users are based on a study from the University of Westminster 1 [18]. This estimate does not consider costs for on-board equipment nor does it provide a full societal 2 impact assessment which would include, for instance, also the cost of delay to passengers and 3 environmental costs. Inevitably, there are margins of uncertainty in delay costs estimates, which 4 should therefore be handled with caution. The full University of Westminster report is available for 5 download on the PRC website. 6
It is acknowledged that the analysis only considers costs of en-route ATFM delays. A holistic approach 7 will also need to consider costs related to operational inefficiencies in the gate-to-gate phase. The PRC 8 is working to establish a more complete picture in future editions of this report. 9
What to focus on? 10
In a dynamic interconnected system such as the ATM network, the ability to adapt to changing 11 conditions (flexibility/ scalability) and to mitigate effects of unexpected events (resilience) becomes 12 more and more important. This will also help to better adjust the ATM network to economic and 13 political turbulences, possible demand changes following the environmental debate, and not least the 14 effect of the COVID-19 outbreak in December 2019 on aviation. 15
In today’s performance based environment it is important to ensure that cost-efficiency measures 16 consider effective capacity planning and deployment to avoid exponential increases in delays and 17 related costs to airspace users. 18
The planning and deployment of adequate capacity levels may infer a cost to be borne by the ANSP 19 but failing to deploy sufficient staffing levels results in disproportionally higher costs to airspace users 20 and passengers which are in most cases sufficiently high to justify adding extra ATCOs. 21
In the short to medium term, an increase in tactical flexibility at local ANSP level (dynamic shift 22 management, review and optimisation of current rostering practices, etc.) could help to 23 simultaneously improve capacity and cost-efficiency and avoid delay during peak periods. 24
Although en-route ATFM delay levels remain unacceptably high in 2019, close collaboration between 25 the Network Manager, ANSPs and airspace users adds flexibility and helps to find a balanced network 26 wide solution to mitigate the effects of the serious capacity shortfall in some areas today and in the 27 foreseeable future. The coordinated effort of the eNM/19 measures is clearly a good step in the right 28 direction (see also Chapter 3 for more information on operational en-route performance). 29
As there are limits to how many times a sector can be split to increase capacity, there is also a need to 30 rethink the way the system is presently operated, particularly with a view to the still existing 31 considerable level of fragmentation. 32
In simple terms what is needed is an increased predictability of the traffic demand on the one side 33 (digitalisation, etc.) and a better scalability and flexibility in the deployment of capacity and staff on 34 the supply side. 35
A future enabler with considerable synergy effects and benefits for the entire ATM network will be the 36 increased digitalisation with common standards to enhance data accuracy and sharing. Data sharing 37 will increase predictability and enable the use of new technologies such as artificial intelligence to 38 support decision making and planning while making the best use of available resources. 39
19 The estimated costs due to ATFM en-route delays are based on the University of Westminster study. Cost assumptions include direct costs (fuel, crew, maintenance, etc.) the network effect (i.e. cost of reactionary delays) and airline related passenger costs (rebooking, compensation, etc.). Costs related to the EU emission trading scheme are not included.
Chapter 2: Safety
PRR 2019 - Chapter 2: Safety 23
2 Safety 1
2
SYSTEM TREND (AST REPORTING) 2018 2019(P) Trend % change
Accidents and incidents
Total number of reported Accidents with ATM Contribution 1
Total number of reported ATM incidents 62 125
Total number of reported OPS incidents 43 889
Total number of reported Severity A+B 930
Separation Minima Infringements (SMI)
Total number reported 2542
Total number of reported Severity A+B 341
Runway incursions (RI)
Total number reported 1704
Total number of reported Severity A+B 99
Unauthorised penetration of airspace (UPA)
Total number reported 6531
Total number of reported Severity A+B 92
ATM Specific Occurrences
Total number reported 18 236
Total number of reported Severity AA+A+B 298
3
4
5
6
7
8
9
2.1 Introduction 10
Please note that this chapter is still based on 2018 data as the provisional 2019 was not yet available when 11 this draft report was made available for consultation. The data will be updated in prior to the publication of 12 the final PRR 2019. 13
14 This chapter reviews the Air Navigation Services (ANS) safety performance of the EUROCONTROL 15 Member States between 2008 and 2018 (note that 2019 data is only preliminary). 16
The review of ANS safety performance in this chapter is based on safety occurrence (accident and 17 incidents) data reported to EUROCONTROL via the Annual Summary Template (AST) reporting 18 mechanism and complemented with additional sources of information when necessary. 19
This section shows the safety performance in the EUROCONTROL area between 2013 and 2019(P), 20 based on AST data (reported occurrences) submitted by the EUROCONTROL Member States. The data 21 was cross checked and supplemented with the available information from the ICAO Accident/Incident 22 Data Reporting (ADREP). 23
24
Notwithstanding the further increase in traffic, Safety in the EUROCONTROL area remains high
3
PRR 2019 - Chapter 2: Safety
24
2.2 Safety performance snap shot 1
The analysis of accidents covers accidents involving 2 aircraft above 2250 kg Maximum Take-Off Weight 3 (MTOW), irrespective of whether the ATM domain 4 contributed to the event or not. 5
As opposed to the accident analysis, there is no 6 MTOW limit (2250 kg) for the ATM-related incidents. 7
8
Accidents 9
Based on preliminary data, there were X air 10 traffic accidents in the EUROCONTROL area in 11 2019, of which X were fatal accidents (X%). 12
There was X reported accident with direct20 13 ATM contribution and X with indirect21 ATM 14 contribution in 2019. 15
[commentary to be added when data is 16 available] 17
18
19
20
21
22
Incidents 23
The PRC has made use, with gratitude, of the 24 data provided by EUROCONTROL 25 DECMA/ACS/SAS Unit. 26
Figure 2-3 shows share of incidents reported 27 via AST in 2019, based on preliminary data. 28
[commentary to be added when data is 29 available] 30
31
32
20 Where at least one ATM event or item was judged to be DIRECTLY in the causal chain of events leading to an accident or incident. Without that ATM event, it is considered that the occurrence would not have happened.
21 Where no ATM event or item was judged to be DIRECTLY in the causal chain of events leading to an accident or incident, but where at least one ATM event potentially increased the level of risk or played a role in the emergence of the occurrence encountered by the aircraft. Without such ATM event, it is considered that the accident or incident might still have happened.
Figure 2-1: Total air traffic accidents (2014-2019P) - TBU
Figure 2-2: Accidents with ATM contribution (2014-2019P) -
TBU
17 18 16 8 9
6873
66
63
75
20% 20% 20% 11% 11%
2014 2015 2016 2017 2018 2019 (P)
Fatal accidents Non fatal accidents % of Fatal Accidents
Total air traffic accidents - fixed wing, weight >2250kg MTOW) (EUROCONTROL area)
1 1 2 1 1
1.2% 1.1%
2.4%
1.4%1.2%
2014 2015 2016 2017 2018 2019 (P)
Accidents with indirect ATM contribution
Accidents with direct ATM contribution
% with direct or indirect ATM contribution
Accidents with ATM contribution - fixed wing, weight >2250kg MTOW)
(EUROCONTROL area)
Figure 2-3: Incidents reported via AST in EUROCONTROL
area (2019 preliminary data) -TBU
OPS4379371%
Technical (ATM Specific)1823629.4%
Other39.0%
Deviation from ATC clearance
10.7%
UPAs10.5%
SMI4.1%
RI2.7%
Level Bust. 1.8%
PLOC1.4%
Runway Excursion0.1%
Near CFIT0.2%
2018Incidents
EUROCONTROL area
reported via AST
3
PRR 2019 - Chapter 2: Safety
25
Figure 2-4 shows the evolution of the 1 number of reported occurrences 2 between 2014 and 2019(P), including 3 a breakdown by operational and 4 technical occurrences. 5
The increase in the number of 6 reported occurrences as of 2017 is 7 mainly due to alignment of the AST 8 reporting with the Occurrences 9 Reporting Regulation 376/2014 (i.e. 10 more types of occurrences became 11 mandatory to report). 12
13
14
Zooming in on the key risk occurrence types, 15 namely: separation minima infringements (SMIs), 16 runway incursions (RIs), airspace infringements 17 (AIs)/unauthorised penetrations of airspace (UPAs), 18 and ATM Specific Occurrences (ATM-S), Table 2-1 19 shows the EUROCONTROL area overall occurrence 20 rates (as reported by all 37 reporting States) for 21 SMI, RI and UPAs in 2019. 22
[commentary to be developed when data is 23 available]. 24
25
26
Complementary to Table 2-1, Figure 2-5 shows the underlying distribution of occurrence rates of all 37 27 reporting EUROCONTROL Member States for the three categories of occurrences SMI, RI and UPAs 28 compared to the EUROCONTROL area overall rate. 29
30
31
Figure 2-5: Occurrence rates EUROCONTROL area (2019) - TBU 32
33
Figure 2-4: Total reported incidents (2014-2019P) - TBU
SMI
X.X (13.4 in 2018)
Separation Minima Infringements
per 100 thousand flight hours
RIs
0.X (0.9 in 2018)
Runway incursions per 10 thousand movements
UPAs X.X (34.7 in 2018)
Unauthorised penetration of airspace
per 100 thousand flight hours Table 2-1: Occurrence rates (SMI, RI, UPA) in the
EUROCONTROL area (2018P)
3
PRR 2019 - Chapter 2: Safety
26
2.3 Risk exposure – Composite Risk Index 1
In PRR 2018, the PRC presented a methodology that could be used to measure the performance of the 2 European ATM system as a whole and its individual entities, using the principle of the Composite Risk 3 Index (CRI). 4
The initial calculation of the CRI was mainly based on reported safety occurrences. More specifically, 5 the CRI was represented as a cumulative risk value calculated aggregating all reported, assessed and 6 severity classified key safety-related incidents to form an index. This measure of risk exposure was 7 based on probability and severity that considers the human perception of equivalent risk. The overall 8 idea behind the CRI was that the performance of the safety system can be analysed in three important 9 broad categories: the quality of the reporting system with reporting entity, measured risks within the 10 system, and the human perception of risk (https://ansperformance.eu/methodology/cri-pi/). 11
The PRC has highlighted that there could be possibilities to further improve the CRI by considering 12 specific local operating conditions, airspace size, capacity and/or complexity. For this reason the PRC 13 expanded the methodology which now includes four distinct components (Figure 2-6): 14
1. Safety data (with the following parameters: number and type of safety occurrences, severity 15 classification, and human perception of risk), 16
2. Traffic / exposure data (consisting of the following parameters: flight hours and airport 17 movements), 18
3. Complexity (namely adjusted density, structural index and Complexity index), and 19 4. Reporting practices (described through parameters of reporting rate and reporting culture). 20
21
Figure 2-6: Composite Risk Index components and their parameters 22
Different statistical methods and expert opinions were used to form Weights for various components 23 and parameters, ranging from optimisation methods, Principal Component Analysis and Cluster 24 Analysis. Overall, the updated CRI methodology should now allow a better representation of the local 25 conditions as specific parameters, such as airspace environment and its local characteristics are better 26 captured through the inclusion of complexity, reporting culture and traffic. 27
The detailed technical report will be published later in 2020. Besides a detailed explanation of the 28 methodology it will include uncertainty and sensitivity analyses which should further improve the 29 transparency of the method. 30
31
[to be developed when data is available] 32
3
PRR 2019 - Chapter 2: Safety
27
2.4 Safety data after 2020 1
To the PRC’s knowledge, the AST reporting mechanism is likely to be discontinued from 2020 onwards. 2 Should this happen, it would jeopardise the PRC’s continued assessment of the KPAs from the Safety 3 perspective. Accordingly, the PRC has held discussions with the Agency and other relevant parties with 4 a view to ensuring: 5
(i) continued access to a reliable source of ATM-related safety data for its work post-2020 6 and identified a possibility and; 7
(ii) a suitable reporting mechanism post-2020 for non-EU States is being discussed by the 8 Agency (DECMA/ACS/ECP) with those States. In addition, it should be noted that PC/50 9 (November 2018) “agreed to keep pan-European needs under review in further discussions 10 between EASA and the Agency.” 11
[to be developed when data is available] 12
2.5 Challenges to Safety after 2020 13
The ATM system goal is to facilitate the flow of air traffic in a seamless, efficient and expeditious way, 14 in as safe a manner as possible by controlling safety risks, although in practice developments are often 15 dominated by changes made to improve capacity, efficiency and cost of the system. 16
ATM safety has improved over the past decades for many reasons, including better equipment, more 17 efficient operations and additional safety defences and mitigation tools. However, any further 18 improvement of current safety performance, and even maintaining the current levels, will be 19 extremely demanding due to numerous technological and institutional changes in the future and rising 20 levels of traffic. Moreover, recent political priorities and calls for action raise several concerns and 21 questions that would need to be addressed before they are officially accepted and implemented in any 22 shape and form. 23
As safety in aviation and in ATM is a priority; the challenge for the European ATM stakeholders is to 24 determine what (potential) threats to safety are of concern, how these can be measured and what 25 analyses are needed to ensure acceptable safety levels with any new developments and changes in 26 operational concepts. Therefore, it is important to identify challenges to future ATM safety, and 27 besides identifying how to assure the safety of the systems, processes and procedures, to also define 28 methods and tool which would allow monitoring of the safety performance of the new system. 29
The ATM community will have to look deeper into questions related to the future challenges to ATM 30 safety. For that reason, the Performance Review Commission proposes to engage with the ATM 31 community in a discussion and work in order to (1) properly identify the key risks to ATM safety in the 32 light of the changing ATM environment and (2) to formulate a performance approach in a risk 33 environment, which could help in assessing safety performance in the future. This is an extremely 34 complex undertaking, however, a prerequisite for measurement of safety performance in the future 35 ATM environment. 36
The Workshop, originally planned for 16th March 2020, had to be postponed because of the COVID-19 37 outbreak. The PRC is looking into alternative ways on how to gather the input from ATM Bodies and 38 Stakeholder and organise a dialogue on this matter. 39
40
2.6 Conclusions 41
[due to data issues, the conclusions will be only available in the final version] 42
43
Chapter 3: Operational En-route ANS Performance
PRR 2019 - Chapter 3: Operational En-route ANS Performance 28
3 Operational en-route ANS 1
Performance 2
3
SYSTEM TRENDS 2019 Trend change vs. 2018
IFR flights controlled 11.1M +0.8%
Capacity
En-route ATFM delayed flights 9.9% +0.3 %pt.
Average en-route ATFM delay per flight (min.) 1.57 -0.17 min
Total en-route ATFM delay (min.) 17.2M -9.0%
Environment/ Efficiency
Average horizontal en-route efficiency (flight plan) 95.6% +/-0.0%pt
Average horizontal en-route efficiency (actual) 97.2% -0.1%pt.
4
5
6
7
8
9
10
11
3.1 Introduction 12
This chapter reviews operational en-route ANS performance in the EUROCONTROL area in 2019. 13
En-route ATFM delays decreased by 9.0% in 2019: reaching 17.2 million minutes while traffic increased 14 by 0.8% over the same period. 2018 & 2019 had the highest amount of en route 15 delays since 2010. 16
The European ANS system operated with an average en-route delay above 1 minute 17 per flight on 206 days in 2019 which is equivalent to 56% of the days. 18
Together with the 6.5 million minutes airport ATFM delay (+1.8% vs. 2018), the 19 total ATFM delay reached 23.8 million minutes (-6.3% vs. 2018). Airport ATFM 20 delays are addressed in Chapter 4 of this report. 21
Section 3.2 analyses ANS-related operational en-route efficiency by evaluating 22 constraints on airspace users’ flight trajectories, including en-route ATFM delays 23 and horizontal and vertical flight efficiency. 24
25
206days
with averageen-route ATFM delay > 1minute (+22 vs. 2018)
En-route ATFM delays decreased by 9.0% in 2019 but more delayed flights
4
PRR 2019 - Chapter 3: Operational En-route ANS Performance 29
3.2 ANS-related operational en-route efficiency 1
This section evaluates ANS-related flight efficiency constraints on airspace users’ trajectories. It 2 addresses several performance areas including ATFM delay, capacity, horizontal flight efficiency and 3 vertical flight efficiency. 4
ANS-related constraints are not the only constraints on the airspace users’ trajectories. For example, 5 ATFM delays will not be the only delays affecting a flight: turnaround delays due to ground services, 6 reactionary delays and passenger-related delays can also impact the departure time. Therefore, it is 7 not surprising that the average departure delays in section 1.4 are significantly higher than the figures 8 reported in this chapter. 9
3.2.1 ATFM delay: the high-level picture 10
Figure 3-1 shows the total minutes of ATFM 11 delay (y-axis) together with the number of 12 ATFM delayed flights (x-axis) in the 13 EUROCONTROL area by reference location 14 type (airport vs. en-route). 15
En-route ATFM delays: Following the 16 significant increase from 2017 to 2018, 17 total en-route ATFM delays decreased 18 slightly in 2019. 19
Airport delays: Airport ATFM delays have 20 stayed at a similar level over the past 5 21 years. 22
[Airport ATFM delays are addressed in 23 more detail in Chapter 4 of this report.] 24
25
Total en-route ATFM delays decreased by 26 9.0% compared with 2018, to 17.2 million 27 minutes, while traffic increased by +0.8% over 28 the same period. 29
Although the total en-route delays were less 30 than 2018, they were significantly higher than 31 the total annual delays from 2013 to 2017. 32
Although the average en-route delay 33 decreased from 1.74 to 1.57 minutes per flight 34 in 2019, the number of flights that were 35 delayed by en-route ATFM regulations 36 increased to 9.9% of all flights (+0.3 37 percentage points vs. 2018). 38
At the same time, the average en-route ATFM 39 delay per delayed flight decreased from 18.2 40 to 15.8 minutes per flight. 41
In summary: more flights were delayed, but 42 for a shorter average delay period than in 43 2018. 44
45
46
47
Figure 3-1: Evolution of ATFM delays
20132014
20152016
2017
2018
2019
2013
2014
2015
20162017
20182019
2
4
6
8
10
12
14
16
18
20
0 500,000 1,000,000
Min
ute
s o
f ATF
M d
elay
M
illio
ns
ATFM delayed flights
En-route and airport ATFM delays
En-route
Airport
17.2 M min en-route ATFM delay
(-9.0% vs. 2018)
9.9 % en-route ATFM delayed flights
( +0.3% pt. vs. 2018)
1.57 min average en-route ATFM delay per flight
( -0.17 vs. 2018)
15.8 min ATFM delay per en-route delayed flight
( -2.3 min vs. 2018)
Figure 3-2: En-route ATFM delays in the EUROCONTROL area
7.2 8.7 9.3
19.0 17.2
2015 2016 2017 2018 2019
3.9% 4.8% 5.3%
9.6% 9.9%
2015 2016 2017 2018 2019
0.73 0.86 0.88
1.74 1.57
2015 2016 2017 2018 2019
18.818.0
16.5
18.2
15.8
2015 2016 2017 2018 2019
4
PRR 2019 - Chapter 3: Operational En-route ANS Performance 30
3.2.2 Reasons given for the en-route ATFM delays 1
This section reviews ATFM delays from en-route ATFM regulations and the delay cause as attributed 2 by the local Flow Management Position (FMP), when the regulation was implemented. An alternative 3 perspective on en-route ATFM delay attribution is presented in section 3.2.5 below. 4
As was the case in previous years, delays attributed to ATC Capacity (43.9%) remain the 5 main portion of en-route ATFM delays, followed by ATC Staffing (24.3%), Weather 6 attributed delays (21.2%), and ATC disruptions/industrial actions (7.2%). 7
8
Figure 3-3: En-route ATFM delays by attributed delay category (Overview) 9
10
In 2019, 5.2% of all flights were 11 delayed by en-route ATFM 12 regulations attributed to ATC 13 capacity; 2.4% of all flights by 14 regulations attributed to ATC 15 staffing, and 1.6% by regulations 16 attributed to adverse weather. 17
The evolution of en-route ATFM 18 delay categories as attributed by 19 the ANSPs, year on year, is shown 20 in Figure 3-4. 21
Total en-route delay decreased in 22 2019, compared to 2018, mainly 23 in delays attributed to adverse 24 weather (-24.1%) and ATC 25 disruptions/strikes (-13.5%). 26
27
Delays attributed to ATC 28 Staffing decreased slightly, by 29 3.8%, while delays attributed to 30 ATC capacity increased by 6.6%. 31 [Section 3.2.5 gives a 32 significantly different 33 perspective.] 34
The critical period is clearly the 35 summer period when traffic 36 levels are highest. 37
The summer months combine a 38 high level of en-route ATFM 39 delay, attributed to ATC 40 capacity and ATC staffing, with 41 significant delay attributed to 42 bad weather conditions. 43
44
2019 vs 2018 2019 vs 2018 2019 % of total vs 2018
ATC Capacity [C] 5.2% 0.9% 13.3 -1.7 7.6 M 43.9% 0.5 M
ATC Staffing [S] 2.4% 0.1% 16.2 -1.2 4.2 M 24.3% -0.2 M
ATC Disruptions [I,T] 0.4% 0.0% 27.3 -5.2 1.2 M 7.2% -0.2 M
Weather [W,D] 1.6% -0.3% 20.9 -2.5 3.6 M 21.2% -1.2 M
Other [all other codes] 0.4% -0.3% 15.0 -2.8 0.6 M 3.5% -0.7 M
Total 9.9% 0.3% 15.8 17.2 M 100%
delayed flights delay per delayed flight Total delay minutes
Figure 3-4: En-route ATFM delay by attributed delay category
ATC Capacity [C]+6.6% vs 2018
ATC Staffing [S]-3.8% vs2018
ATC Disruptions [I,T]
-13.5% vs 2018
Weather [W] -24.1% vs. 2018
0
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Figure 3-5: Monthly evolution of en-route ATFM delay by attributed cause
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hts
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ATC Disruptions [I,T] ATC Staffing [S]
ATC Capacity [C] Cumulative min. [2018]
Cumulative min. [2019]
En-route ATFM delayed flights by attributed delay category
Source: EUROCONTROL, Performance Review Unit
worst day in 2019 with 28.8% of all flights delayed by
en-route ATFM delays (ATC
system failure in France and
adverse weather)
01 Sep
2019
4
PRR 2019 - Chapter 3: Operational En-route ANS Performance 31
3.2.3 Delays experienced by airspace users. 1
Figure 3-6 below shows a graphical representation of the ATFM regulations in the EUROCONTROL area 2 in 2019. Almost 4 in every 5 flights operate without need for ATFM regulation. 1 flight in 20 is subject 3 to an airport ATFM regulation and 1 in 6 is subject to an en-route ATFM regulation. 4
Being subject to a regulation does not necessarily mean that the flight is delayed - 40% of regulated 5 flights are regulated without being delayed. 78% of all en-route regulated flights receive ATFM delays 6 of 15 minutes or less. 7
However, the 22% of en-route regulated flights (just under 4% of all traffic) that are delayed by more 8 than 15 minutes, account for 70% of all ATFM delays. 9
10
11
Figure 3-6: Temporal distribution of ATFM delays (2019) 12
13
3.2.4 Most constraining ANSPs and en-route locations in 2019 14
In 2019, DFS (Germany) generated 15 25.9% of all en-route ATFM delays in 16 the EUROCONTROL area, followed by 17 DSNA (France) (22.9%), Austro Control 18 (10.1%) and HungaroControl (8.3%). 19
The most delay generating ACCs in 20 2019 were Karlsruhe (17.7%), Marseille 21 (11.7%), Vienna, Budapest, Langen, 22 and Barcelona (4.4% respectively). 23
Karlsruhe UAC, Marseille, Vienna and 24 Budapest together generated half of all 25 en-route ATFM delays in 2019. 26
As in previous Performance Review 27 Reports, Figure 3-8 below shows the 28 ACCs in the EUROCONTROL area with 29 more than 30 days of an average en-30 route ATFM delay > 1 minute per flight, 31 in 2019. 32
33
6.9%
6.3%
2.5%
1.0%
0.2%
30.3%
34.4%
25.5%
9.8%
0%
5%
10%
15%
20%
25%
30%
35%
40%
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
Regulatedwithout delay
1-15 min 15-30 min 30-60 min >60 min
Cu
mu
lati
ve %
of
en-r
oute
ATF
M d
elay
% A
TFM
en
-ro
ute
regu
late
d fl
igh
ts
Distribution of en-route regulated flights in 2019
ATC capacity/ staffing ATC Disruptions [I,T]
Weather [W,D] Other [all other codes]
% of total en-route ATFM delay
Unregulated flights78.1%
Airport ATFM
regulated5.0%
En-route ATFM
regulated16.9%
ATFM regulations imposed on flights
in 2019
EUROCONTROLarea
Figure 3-7: Share of total en-route ATFM delay in 2019
DFS25.9%
DSNA22.9%
Austro Control10.1%
HungaroControl (EC)
8.3%
ENAIRE8.0%
skeyes3.7%
Croatia Control3.6%
DCAC Cyprus2.8%
Other14.5%
Karlsruhe UAC17.7%
Langen4.3%
Marseille AC11.7%
Reims3.2%
Barcelona AC+AP4.4%
Madrid2.7%
17.2 million minutes en-route
ATFM delay
( -9.0% vs. 2018)
Source: EUROCONTROL/ PRU@ https://ansperformance.eu
Distribution of en-route ATFM delay (%) EUROCONTROL Member States (2019)
4
PRR 2019 - Chapter 3: Operational En-route ANS Performance 32
Within the EUROCONTROL area there 1 were 206 days when the network en-2 route delay per flight was above 1 3 minute (+22 vs. 2018). 4
As in 2018, Karlsruhe UAC remains the 5 main bottleneck with 254 days when 6 the average en-route ATFM delay per 7 flight >1 min. 8
Brest, Maastricht, Prague, Canarias, 9 Zurich and Beograd are no longer on 10 the list, having appeared in 2018. 11
Kosovo, Bremen and Bordeaux appear 12 on the list for the first time with 139 13 days, 105 days and 31 days 14 respectively. 15
Figure 3-9 shows the share of flights 16 delayed by en-route ATFM regulations 17 within each ACC in 2019. 18
At least one flight in every ten passing 19 through either Karlsruhe UAC or 20 Budapest ACC were delayed by en-21 route ATFM regulations in 2019. 22
Figure 3-10 shows the evolution of the 23 peak throughput in terms of hourly 24 flights and the share of en-route 25 ATFM delayed flights for the 10 most 26 constraining ACCs, when taken from 27 the perspective of total ATFM delay. 28
In 2019, notable decreases in peak 29 throughput can be observed in 30 Karlsruhe UAC, Reims ACC and Budapest ACC. 31
32
33
Figure 3-10: Peak throughput and en-route ATFM delayed flights at the most constraining ACCs 34
35
36
37
12.9%
8.7%
9.9%
11.5%
3.8%
5.4%6.3%
6.1%
5.4%
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Karlsruhe UAC
Marseille AC Wien Budapest Langen Barcelona AC+AP
Brussels Zagreb Bremen Reims
en-r
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del
ayed
flig
hts
(%
)
Pea
k h
ou
rly
thro
ugh
pu
t (9
9 p
erce
nti
le) Peak throughput and share of en-route ATFM delayed flights at the most constraining ACCs
Figure 3-8: Days with average en-route ATFM delay >1 min per flight
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2019Karlsruhe UAC 10 17 22 23 29 30 31 28 29 20 4 11 254
Budapest 1 0 0 5 30 30 31 31 29 5 0 0 162
Kosovo 0 0 0 1 18 30 31 30 27 2 0 0 139
Marseille AC 2 1 1 11 9 21 29 28 10 11 3 11 137
Wien 0 0 5 9 25 30 29 27 5 5 0 0 135
Nicosia 1 1 4 8 3 16 27 21 12 10 6 4 113
Brussels 0 2 13 17 10 12 12 16 12 13 0 4 111
Bremen 2 0 1 3 27 21 9 16 13 9 3 1 105
Zagreb 0 0 0 1 0 19 29 28 16 5 0 0 98
Barcelona AC+AP 1 0 4 4 3 16 18 18 13 10 3 5 95
Reims 2 3 5 4 3 16 11 2 2 3 0 4 55
Langen 0 0 6 1 23 13 4 3 1 1 0 0 52
Athinai+Macedonia 0 0 0 0 0 2 11 19 9 1 0 0 42Bordeaux 0 0 1 1 9 1 7 3 1 2 0 6 31
EUROCONTROL area 2 2 10 18 28 30 31 31 29 14 1 10 206
Figure 3-9: Share of en-route ATFM delayed flights by ACC
CAN
ANK
LIS
SCO
SHA
TAMSTO
MAD
BRE
BOD
ROM
CAS+AGA
DNI
WARLON
BAR
KAR
MAA
BUC
MAR
KIE
ATH+MAK
MAL
MAL
NIC
BOR
BRI
SEV
ODE
OSL+STV
COP
SOF
LVO
REI
ZAG BEO
TBI
RIG
VIL
MIL
TAL
BUDWIE
PAD
PRA
TIR
TEL
CHIBRAT
ZURYER
GENPAR
SKO
LJU
KOS
BREM
LAN
MUN
AMS
BRU
LON TC
DUB
PAL
Lower Airspace
Lower Airspace
% of en-route delayed flights (2019)
No delay
less than 2.5%
2.5%-5.0%
5.1%-7.5%
7.5% - 10%
>10%
IST
IST
Lower Airspace
4
PRR 2019 - Chapter 3: Operational En-route ANS Performance 33
Figure 3-11 provides an 1 overview of en-route ATFM 2 delay in each ACC and the 3 evolution compared to 4 2018. 5
Budapest, Brussels, and 6 Bremen ACCs had a notable 7 increase in delay despite 8 lower traffic levels in 2019. 9
Performance at Karlsruhe 10 UAC improved compared to 11 2018 but with less traffic 12 and a notable reduction of 13 delay attributed to adverse 14 weather. 15
The decrease in traffic at 16 Karlsruhe UAC was a direct 17 result of efforts to reduce 18 demand in light of the 19 significant reduction in available capacity at Karlsruhe UAC over recent years. 20
Such efforts include the eNM measures which are discussed in section 3.2.7 below. 21
3.2.5 Alternative perspective on en-route ATFM delay attribution 22
Delays attributed to ATC Staffing are a significant portion of en route ATFM delays in the network (24% 23 of total delays). Simply put, capacity constraints due to ATC staffing are when there is a lack of suitably 24 qualified ATC staff to provide the required capacity. 25
Unavailability of suitably qualified ATC staff may be planned in advance – for example a corporate 26 decision not to employ and roster ATC staff; it may be last-minute (e.g. sickness) or it may possibly be 27 due to training purposes where an ATCO-in training is not yet able to safely handle the declared 28 capacity of the elementary sector. 29
In the first two cases the ANSP operates a collapsed sector (two or more elementary sectors combined) 30 which provides less capacity to airspace users than would be available if the sectors were open 31 individually. In the final instance the ANSP would operate the elementary sector at a reduced capacity. 32
The PRC decided to review the ATFM delays attributed to ATC staffing throughout the network in 33 general, and at the 10 ACCs listed in Figure 3-11 above, to identify the deployed sector configurations 34 (i.e. collapsed sectors) where the ANSPs admit that additional capacity could have been deployed if 35 qualified ATC staff were available. 36
The relative allocations of delays both network-wide and within the 10 ACCs is very consistent: ATC 37 staffing is 24-25% of the total delays. ATC capacity accounts for 46-47% of delays and 22-23% of delays 38 were attributed to adverse weather. 39
The PRC then reviewed the ATFM delays attributed to adverse weather and to ATC capacity in the 40 same ACCs. The PRC observed that a significant portion of those delays occurred in the same sector 41 configurations where the ANSPs have already flagged that the capacity constraints are due to the 42 unavailability of qualified ATC staff (ATC staffing). 43
It would therefore appear that ATC staffing and deployment, an issue under the full control of the 44 ANSP, is often an aggravating factor in capacity constraints that are totally attributed to ATC capacity 45 and even to adverse weather. 46
The PRC recalls the contention (from some ANSPs) that capacity constraints due to adverse weather 47 are outside the responsibility of the ANSP. The PRC recalls the principles provided in the rationale of 48 its recommendations from PRR 2017 and PRR 2016 where “Attribution of delays to external causes 49 (e.g. weather or 3rd party strike) should only be used in cases where no ANSP-internal capacity 50
Figure 3-11: En-route ATFM delay per flight by most constraining ACC
-1,7
%
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%
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%
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%
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%
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%
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%)
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ange
(m
inu
tes
per
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ht)
ATC Capacity [C] ATC Staffing [S] ATC Disruptions [I,T]
Weather [W,D] Other [all other codes] Traffic change
Year on year change vs 2018
1,7 1,7 1,9 1,7
0,80,6
1,0 0,9 0,90,5
0,0
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lsru
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n
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celo
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AC
+AP
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s
Zagr
eb
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men
Rei
ms
Ave
rage
de
lay
pe
r fl
igh
t (m
in) Average en route ATFM delay per flight - 2019 (min)
4
PRR 2019 - Chapter 3: Operational En-route ANS Performance 34
constraints prevent the deployment of maximum capacity”, and “Attribution of delays to ATC capacity 1 should not be used for collapsed sectors or when the regulated capacity is less than the maximum 2 declared capacity of the sector.” 3
Identifying those instances where ATC staffing was responsible, in whole or in part, for the capacity 4 constraints leading to the ATFM delays presents a very different picture of ATFM performance at the 5 list of ACCs, and for the network, as shown in Figure 3-12. 6
3.2.6 Evolution of declared capacity (2012-2019) in bottleneck locations 7
The PRC has previously highlighted the need to plan and implement additional capacity to mitigate or 8 resolve capacity bottlenecks within the Network. 9
Every year the ANSPs publish capacity plans in the Network Operations Plan (NOP) following dialogue 10 with the Network Manager in light of future traffic growth and, where relevant, required levels of 11 capacity performance. 12
The PRC reviewed the en route ATFM regulations from 2012 to the end of 2019. The individual ATFM 13 delays (all delay causes) attributed to each reference location over the 8 year period were aggregated 14 and ranked in terms of the overall total, from highest to lowest. The top 20 reference locations with 15 the greatest amount of ATFM delays over the 8 year period can be grouped as follows: Belgium 16 (Brussels UIR -2 sectors [Maastricht UAC]); Germany (Langen FIR – 1 sector), (Rhein UIR – 6 sectors); 17 Netherlands (Amsterdam FIR – 1 sector); Cyprus (Nicosia FIR – 2 sectors); Croatia (Zagreb FIR – 1 18 sector); Spain (FIR/UIR Barcelona – 2 sectors); France (UIR France- 5 sectors [Reims ACC-1; Paris ACC-19 1; Brest ACC-3]) 20
The declared capacity of an ATC sector is the nominal amount of traffic that can be safely handled 21 during normal activities. ANSPs provide the Network Manager with the declared capacities of 22 individual sectors in a static format. 23
Declared capacities can be viewed as the level of capacity that airspace users can expect within an ATC 24 sector. When an ANSP increases capacity in a sector (for example by redesigning airspace, reducing 25 ATCO workload, or improving technical equipment (e.g. implementation of radar)) the declared 26 capacity of the sector should increase accordingly. 27
The PRC reviewed the declared capacity for the top 20 reference locations (total ATFM delay) published 28 annually over the 8 year period. The findings and PRC observations are as follow: 29
Germany: One elementary sector - Wurzburg 325-355, high traffic demand but only 2% growth in 30 declared capacity over entire period. 31
Figure 3-12: ATFM delay attributed by ANSP and revised attribution (2019)
24.6%
76.7%
45.8%
18.2%22.0%
6.9%
0%
20%
40%
60%
80%
100%
ANSP attributed Revised attribution
Network wide (2019)
ATC Staffing [S] ATC Capacity [C]ATC Strike [I] Weather [W]Other [all other codes]
24.0%
83.9%47.0%
7.1%22.7%
2.8%
0%
20%
40%
60%
80%
100%
ANSP attributed Revised attribution
Top 10 ACCs (2019)
ATC Staffing [S] ATC Capacity [C]ATC Strike [I] Weather [W]Other [all other codes]
4
PRR 2019 - Chapter 3: Operational En-route ANS Performance 35
Six collapsed sectors (2/3 sectors combined): with exception of EDGG1 sector, no increase in declared 1 capacity after delays became a problem for any sector. Significant capacity gains can be realised from 2 operating the separate sectors – indicating that staffing is significant contributory factor. 3
France: Two elementary sectors – Paris UJ sector, high traffic demand, low-medium sector throughput 4 9% reduction in sector capacity over period; Brest NU sector, high traffic demand, low-medium sector 5 throughput 9% increase in sector capacity over period. 6
Three collapsed sectors (2/4 sectors combined): no increase in declared capacity over period, 7 reduction of 3% in Brest MZU, despite implementation of ERATO project. Capacity gains can be realised 8 from operating the separate sector – indicating that staffing is significant contributory factor. 9
Belgium (MUAC): Two collapsed sectors (Brussels East High & Olno), high throughput, high traffic 10 demand, frequently impacted by activation of segregated airspace – both showed significant growth 11 over period (+13% and +10% respectively). 12
Netherlands: EHAACBAS sector: not an operational sector. Used to regulate traffic landing at Schiphol 13 whilst attributing delays to entire Amsterdam FIR, including part controlled by MUAC. Impossible to 14 identify specific sector where capacity constraint required application of ATFM regulation. 15
Spain: Two collapsed sectors, medium throughput. No growth in declared capacity over period. 16 Significant capacity gain can be realised by operating separate sectors, indicating that staffing is 17 contributory factor. 18
Croatia: Collapsed sector – 3% growth in declared capacity over period but 2019 level is lower than 19
Figure 3-13: Evolution of declared capacity (2012-2019) in bottleneck locations (Germany)
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(Dusseldorf) -
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Fulda Global -
collapsed
Havel Global -
collapsed
Wurzburg
245-355 -
collapsed
Wurzburg
325-355 -
elementary
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)
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lare
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aci
tyGermany
Langen ACC Karlsruhe UAC
+6%vs . 2012
+13%vs . 2012
+4%vs . 2012 +4%
vs . 2012
+4%vs . 2012
+2%vs . 2012
+4%
vs . 2012
Figure 3-14: Evolution of declared capacity (2012-2019) in bottleneck locations (France)
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4
PRR 2019 - Chapter 3: Operational En-route ANS Performance 36
2014. Medium throughput. 1
Cyprus: Two collapsed sectors, low throughput. LCCCS1 shows 4% growth in declared capacity over 2 entire period but none since delays became a problem. LCCCES0 shows 11% reduction in declared 3 capacity over period. 4
5
PRC observations on evolution of declared capacity. 6
The PRC is disappointed to observe that so many of the most constraining sectors over the period 2012 7 – 2019 are collapsed sectors, where additional capacity could have been deployed if qualified ATC staff 8 had been available. 9
The PRC does not understand how five of the most constraining sectors have not had any increases in 10 capacity over the last 8 years, and how three of those are declaring less capacity in 2019 than was 11 already existing in 2012. 12
The PRC notes that Amsterdam ACC routinely allocates ATFM delays to airspace that lies partially 13 outside its area of responsibility, EHAACBAS, making it impossible to identify where the actual capacity 14 constraints exist. 15
The PRC finds it difficult to correlate the above situation with the capacity plans that are published 16 annually by the ANSPs. The question that arises is: how can ANSPs claim to increase capacity if they do 17 not increase the capacity of the sectors that are causing the capacity constraints? 18
19
Figure 3-15: Evolution of declared capacity (2012-2019) in bottleneck locations (Other)
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tyBelgium - Brussels UIR
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4
PRR 2019 - Chapter 3: Operational En-route ANS Performance 37
3.2.7 eNM initiative – Network Manager perspective 1
The Enhanced NM/ANSPs Network Measures for summer 2019 (eNM/S19) were implemented at the 2 end of April. 3
This set of ATFM measures was 4 aimed at reducing summer delays 5 by removing traffic from 6 congested areas, either by re-7 routing or level-capping flights. 8 The traffic re-routing objective of 9 the initiative was well achieved 10 and airlines were spared much of 11 the disruption of the previous 12 summer. The constrained ACCs 13 benefited from a decrease in 14 traffic complexity. Since 15 implementation, traffic has been 16 reduced in Karlsruhe (-3%), 17 Maastricht (-1%) UACs and 18 Bremen (-3%). 19
Without the eNM measures, NM estimated that en-route delay per flight in summer 2019 could have 20 reached twice the level of 2018. 21
Re-routes and flight level caps on traffic meant that the eNM/S19 initiative had an impact on fuel burn 22 on the city pairs affected by the RAD measures since the start of the summer. 23
An impact analysis of the eNM/S19 initiative was performed by the operations planning unit of NM. 24 The city pairs subject to each category of RAD measures were identified. The fuel burn estimations 25 were based on operational flight data. 26
According to NM estimates, the routes potentially impacted by horizontal re-routing measures (3% of 27 the daily traffic in the network) resulted on an additional route length per flight of 1.62 NM and an 28 average increase in fuel burn of 11 kg per flight. 29
As for level capping measures, there were two main types of restrictions: departures / arrivals subject 30 to a level cap before they exit airspace where the level cap existed (typically only for 10-15NM); and 31 city-pair restrictions where the change in level could be in the region of 4000 feet. The routes 32 potentially affected by flight-level measures represented 4% of the traffic and had a higher average 33 increase in fuel burn of 12 kg per flight throughout the summer. However, when looking at certain city-34 pairs affected by FL measures (e.g. some German domestic routes), the fuel increase was significantly 35 higher but these represented only a fraction of the traffic affected by the measures. 36
The total environmental impact for the 6 months of the initiative was estimated by the NM to be over 37 16 thousand tonnes of additional C02. Some airspace users expresses their concerns that the estimated 38 numbers computed by NM are too conservative with a higher fuel burn due to the implemented 39 measures. 40
Although the initiative prevented an even higher increase in en-route ATFM delays in 2019, from an 41 ANS performance point of view, it is important to consider the bigger picture including the substantial 42 flight inefficiencies and related costs imposed on airspace users. 43
With European airspace being congested in many areas and solutions some time from deployment, a 44 collaborative, network centric, approach will be an important enabler to manage the forecast rising 45 demand levels over the coming years. 46
Instead of taking a limited local view, capacity, traffic flows and the application of ATFM regulations 47 need to be managed from a network perspective with the Network Manager, ANSPs and airspace users 48 working collaboratively together to find the best solution for the network as a whole. 49
50
Figure 3-16: eNM initiative shifting of traffic flows to offload congested ACCs
4
PRR 2019 - Chapter 3: Operational En-route ANS Performance 38
3.2.8 En-route flight efficiency 1
This section evaluates en-route flight efficiency in the 2 EUROCONTROL area. En-route flight efficiency has a 3 horizontal (distance) and vertical (altitude) component. 4
As fuel is a big share in airlines’ operating costs, there has 5 been a considerable focus on reducing fuel burn over the 6 past years. Those economic considerations are now 7 supplemented and reinforced by the additional 8 environmental focus, which makes reducing fuel burn 9 even more important. 10
ANS has clearly a role to play because of the constraints 11 which are imposed due to capacity and safety 12 considerations. These constraints limit the choices 13 available to the airspace users. 14
15
3.2.8.1 Horizontal en-route flight efficiency 16
The horizontal en-route flight efficiency indicator is 17 computed for three different trajectories: (1) the actual 18 flown trajectory, (2) the planned trajectory according to 19 the flight plan and (3) the shortest constrained route22 20 provided by the Network Manager. It is expressed as a 21 ratio of distances and is therefore an average per distance 22 within a given airspace (distance achieved per distance 23 flown). 24
The shortest constraint route (SCR) reflects the effect of 25 the constraints imposed by ANSPs (route structure, 26 airspace availability, etc.) on flight planning. It is not 27 influenced by weather conditions or specific airline 28 considerations, and it sets the limits within which the airlines can optimise. 29
The filed flight plan takes into consideration not only those constraints, but also other factors which 30 are linked to airlines’ preferences (including cost considerations and trade-offs). The filed flight plan 31 must always be at least as long as, if not longer than, the SCR. 32
Finally, the actual flown trajectory is based on the flight plan but is influenced by unforeseen or 33 unplannable factors at the time of filing, including weather and tactical ATC routings. Some of these 34 modifications might lead to a lengthening of the trajectory, while others will lead to a shortening of it. 35 36
The high level picture 37
Figure 3-17 shows that the average horizontal en-route flight efficiency at EUROCONTROL level23 38 remained relatively constant over the past five years with a slight deterioration in 2019. 39
As pointed out in previous PRRs, there is a significant gap between the efficiency of the filed flight plans 40 (95.6%) and the actual flown trajectories (97.2%). 41
The shortest constrained routes (95.9%) are only slightly more efficient than the filed ones. This means 42 that the flight planning processes of airspace users don’t add much inefficiencies to the ones created 43 by the imposed constraints. 44
The flown trajectories are notably shorter than the ones airspace users file. They are also notably 45
22 The SCRs are the shortest trajectories which could be filed by a flight, taking into consideration the restrictions in the Route Availability Document (RAD) and conditional routes (CDRs) availability.
23 The airspace analysed in this section refers to the NMOC area.
Direct routes are not the shortest The indicator adopts a gate-to-gate perspective by using as reference for the entire flight the great circle distance between its origin and destination (which for most of the flights correspond to the location of the two airports).
A direct route is no guarantee of shorter distance overall because the direction might not be the correct one.
It is possible to break down the additional distance for a specific airspace into a local component (additional distance with respect to the direct between entry and exit) and an interface component (additional distance due to the direct between entry and exit).
When airspaces are aggregated, the additional distance is the same but the local component increases because part of the interface becomes local.
More information on methodologies (approach, limitations) and data for monitoring the ANS-related performance is available at: http://ansperformance.eu/.
4
PRR 2019 - Chapter 3: Operational En-route ANS Performance 39
shorter than the ones they could have filed given the existing constraints in the network (shortest 1 constrained route). 2
In 2019, the actual trajectory 3 is on average 1.6% more 4 efficient than the filed flight 5 plan and still 1.3% than the 6 shortest constrained route. 7 Or expressed differently if 8 airspace users would have 9 flown the filed flight plans 10 they would have flown an 11 additional 156 million 12 kilometres in 2019 (approx. 13 3897 trips around the world). 14
As already mentioned, the 15 gap between the actual 16 trajectory and the flight plan 17 is affected by a number of 18 factors. The SCR explains most of the flight plan inefficiencies, as the values of SCR and flight plan are 19 very close so that airspace users add only marginally to the overall value. This marginal contribution 20 has slightly decreased between 2018 and 2019. 21
The gap between flight plan and actual trajectory reflects a trade-off between conservative constraints 22 in flight planning and flexibility in operations. Actual trajectories which are much shorter than the SCR 23 imply that those same trajectories would be rejected as flight plans and indicate that flexibility more 24 than compensate the effect of conservative constraints. 25
In order to better understand the EUROCONTROL wide high level annual average, Figure 3-18 26 provides a breakdown of the horizontal en-route flight efficiency (actual trajectories) by day and en-27 route ATFM delay in 2019. Each dot represents a day in 2019. 28
Horizontal flight efficiency is clearly worse in summer. The analysis suggests that the lack of capacity 29 and the resulting ATFM constraints have a notable negative effect on flight efficiency. Days with high 30 en-route ATFM delays tend to have notably lower flight efficiency. 31
32
Figure 3-18: Daily horizontal en-route flight efficiency and ATFM en-route delays (2019) 33
Figure 3-18 also provides an indication of the ambition of the Master plan by 2035 [13]. Although 34
French ATC strike on 09 May 2019
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Figure 3-17: Horizontal en-route flight efficiency (EUROCONTROL area)
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4
PRR 2019 - Chapter 3: Operational En-route ANS Performance 40
there are numerous initiatives aimed at improving flight efficiency underway and even with traffic 1 growth slowing down, it appears to be challenging to meet the set ambitions within the given 2 timeframes considering the existing lack of capacity in some parts of the core area (see next section 3 of this chapter). 4
Figure 3-19 provides a 5 distribution of the 6 horizontal en-route flight 7 efficiency on airport pairs 8 in 2019. 9
It shows that the vast 10 majority of airport pairs 11 have already a relatively 12 good flight efficiency 13 (measured in distance, 14 not in flights). 15
Approximately 58% of 16 the traffic was more 17 efficient than the 18 EUROCONTROL average 19 in 2019. 20
Some 3% of the traffic 21 had a flight efficiency of 22 90% or worse. 23
24
Horizontal en-route flight efficiency by State 25
Figure 3-20 provides a map showing level of horizontal en-route flight efficiency (actual trajectories) 26 by state in 2019. 27
En-route flight efficiency 28 is comparatively low in 29 the core area where 30 traffic density is highest 31 and where Free Route 32 Airspace (FRA) is not yet 33 implemented (see Figure 34 3-21). 35
FRA is expected to be one 36 of the main contributors 37 towards improving 38 horizontal en-route flight 39 efficiency as it provides 40 airspace users with more 41 choices to optimise their 42 flight plans. 43
According to the 44 European ATM Master 45 Plan [13] and supported 46 by Commission implementing regulation (EU) No 716/2014 [19], Free Route Airspace on a H24 basis 47 should be implemented above flight level 305 throughout the entire EUROCONTROL area by 2022. 48
Figure 3-20: Map of horizontal en-route flight efficiency by State (2019)
Figure 3-19: Horizontal en-route flight efficiency by airport pair (2019)
4
PRR 2019 - Chapter 3: Operational En-route ANS Performance 41
In view of the high traffic 1 volume, expected savings in 2 terms of fuel burn and CO2 3 emissions following the FRA 4 implementation in the entire 5 core area are high. 6
Nonetheless it is important to 7 point out that the overall 8 benefits that can be expected 9 vary by airspace depend, inter 10 alia, on traffic volume and 11 growth, complexity and other 12 factors. 13
Figure 3-22 shows the level of 14 flight efficiency in actual 15 trajectories (x-axis) and filed 16 flight plans (y-axis) by State in 17 2019. States in which FRA is fully or partly implemented are shown as a red dot. 18
The benefits are clearly visible. On average, States were FRA has been implemented show a 0.5% higher 19 flight efficiency compared to the other states. 20
Moreover, it can be seen that the gap between the flight plan efficiency and the efficiency in actual 21 flown trajectories (vertical distance between a dot and the diagonal arrow) is narrower than for the 22 other States. Actual operations closer to plan improves the level of predictability for all players involved 23 with a positive impact on capacity and resource utilisation. The gap is clearly more prominent in those 24 States where FRA has not been fully implemented all day. 25
Although the continued FRA implementation is expected to bring further benefits over the next years 26 there are also other areas that need to be addressed. 27
Figure 3-21: Implementation status of Free Route Airspace (FRA) – End 2019
Figure 3-22: Horizontal en-route flight efficiency by State (2019)
Albania
Armenia
Austria
Belgium
Bosnia and Herzegovina
Bulgaria
Croatia
Denmark
Estonia
Finland
North Macedonia
Georgia
Germany
HungaryIreland
Italy
Latvia
LithuaniaMalta
Moldova
Netherlands
Norway
Poland
Portugal (Continental)
Romania
Serbia and Montenegro
Slovakia
SloveniaSweden
Ukraine
Cyprus
Czech Republic
France
Greece
Spain (Canarias)
Spain (Continental)
Switzerland
UK (Continental)
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4
PRR 2019 - Chapter 3: Operational En-route ANS Performance 42
Figure 3-23 shows a map of the 10 most penalising airport pairs in 2019 in terms of total additional 1 flown distance (driven by the level of inefficiency and the number of flights on the airport pair). 2
The light green areas in Figure 3-23 indicate FRA airspace and the brown areas represent 3 restricted/segregated airspace. The analysis shows that flight efficiency is not only affected by the level 4 of FRA implementation but also 5 by the need to integrate military 6 objectives and requirements in 7 the ATM system. 8
To meet their national security 9 and training requirements, while 10 ensuring the safety of other 11 airspace users, it is occasionally 12 necessary to restrict or segregate 13 airspace for exclusive use which 14 may conflict with civilian 15 objectives to improve flight 16 efficiency as flights must then 17 detour around these areas. 18
Through the implementation of 19 the Flexible Use of Airspace (FUA) 20 concept – which is included in EU 21 legislation since 2005 [20] – civil and military requirements should be coordinated through a dynamic 22 CDM process which culminates in the publication of the daily European Airspace Use Plan (AUP) on D-23 1 and Updated Airspace Use Plans (UUP) on the day of operations. The AUP and UUP activate 24 Conditional Routes and allocate Temporary Segregated Areas and Cross-Border Areas for specific 25 periods of time. 26
Previous PRC research on the level of civil military cooperation and coordination [21] suggested that 27 there is still ample scope for optimisation in terms of processes and information flows. Although FRA 28 implementation will help to file more efficient trajectories to avoid segregated areas, it will not 29 improve the information flows necessary to optimise the use of segregated airspace in Europe. 30 Previous analysis showed that horizontal flight efficiency is better on weekends when no military 31 training activities take place and therefore more airspace is available to be used for civil traffic. The 32 PRC hasn’t observed any improvement in the application of FUA, providing benefits for civil traffic. 33
Figure 3-24 shows flights between Edinburgh and London City airport which is Europe’s least efficient 34 airport pair in terms of total additional distance (inefficiency 35 and volume combined). The computed level of efficiency in 36 2019 was 78.3% and due to the high frequency the 37 estimated total additional distance amounted to some 38 476,000 kilometres in 2019. 39
The example illustrates nicely how the various factors affect 40 flight efficiency. The filed flight plans (blue lines) are 41 affected by military airspace but also by the terminal 42 interface (make a big detour to enter the London TMA from 43 the south-east). 44
The actual flown trajectories (red lines) were then improved 45 on a tactical basis in coordination with ATC resulting in the 46 observed gap efficiency gap between filed flight plans and 47 actual trajectories. 48
As the flight efficiency indicator is expressed as a ratio of 49 distances, the results do not allow to get an understanding of the total additional distance flown 50 (inefficiency and traffic combined) in the respective airspace which is proportional to the impact on 51 system wide performance. 52
Figure 3-23: Illustration of routes impacted by military training areas
Figure 3-24: Edinburgh to London city airport
(plan and actual)
4
PRR 2019 - Chapter 3: Operational En-route ANS Performance 43
To provide an additional perspective and a more complete picture of the contribution of the individual 1 states, the total additional distance flown (top) and the average additional distance per flight (bottom) 2 in 2019 is shown in Figure 3-25. 3
4 Figure 3-25: Horizontal en-route flight efficiency by State (actual trajectories – 2019) 5
Moreover the analysis provides a breakdown in a local component (additional distance within a given 6 airspace) and an interface component (additional distance related to the whole flight). The local 7 component is always shown in dark red at the bottom of each bar. 8
France and Spain combine a below average flight efficiency with long average flight segments and a 9 high traffic volume which consequently results in substantial amounts of total additional kilometers. 10
The four states with the highest amount of additional distance flown in 2019 (France, Spain 11 Continental, UK Continental, and Germany) accounted for more than half (53%) of total additional 12 distance which corresponds to 143 million kilometers (approx. 3 576 trips around the world). 13
As illustrated in this section, the efficiency of actual flown trajectories is affected by a number of 14 factors. Once FRA is fully implemented in the EUROCONTROL area (targeted data is 2022), further 15 improvements will be needed to reach the ambitions laid out in the ATM Master Plan. 16
The additional improvements will need to come from cross-border FRA implementation, 17 improvements in airspace availability, the avoidance of capacity shortfalls, less TMA entry/exit point 18 penalisation, and airlines making different route choices themselves. 19
An overarching enabler to achieve performance improvements will be improved information flows (in 20 terms of accuracy and timeliness) on trajectories and airspace availability in the planning and tactical 21 phase. This will help improving the level of predictability for the benefit of all stakeholders involved 22 with a positive impact on capacity and resource utilisation. 23
Work is ongoing to better understand and quantify the individual factors affecting horizontal flight 24 efficiency (flight planning, awareness of route availability, Civil/Military coordination, etc.) in order to 25 identify and formulate strategies for future improvements. 26
27
3.2.8.2 Vertical en-route flight efficiency 28
The vertical component of flight efficiency gets more and more attention. Since years, the PRC has 29 been using its methodologies to assess vertical flight efficiency [22]. More information on the 30 methodology is available on the ANS performance data portal. 31
96.7
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4
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4
PRR 2019 - Chapter 3: Operational En-route ANS Performance 44
The vertical en-route flight 1 inefficiency (VFI) follows a 2 cyclical trend with higher 3 inefficiency levels in 4 summer. 5
Figure 3-26 shows the total 6 VFI (in terms of total 7 additional feet) for AIRAC 8 cycles 06 to 09 9 (representing the summer 10 periods) since 2015. 11
The amount of vertical en-12 route flight inefficiency 13 during the summer of 2019 14 was again higher than previous years. Part of this increase is due to the higher number of movements. 15
Figure 3-27 shows the number of airport pairs and flights that are impacted by a level capping 16 constraint detailed in the Route Availability Document (RAD). 17
It can be seen that there is a considerably higher number of altitude constrained flights since spring 18 2018. Since then, this number 19 stayed quite stable, even 20 though the number of RAD 21 constraints decreased again in 22 the beginning of 2019. 23
The analysis shows a notable 24 increase in vertically RAD 25 constrained airport pairs 26 which appears to be the result 27 of the measures implemented 28 by the 4ACC initiative in 2018 29 and the eNM initiatives in 30 2019 (see also Section 3.2.7). 31
The top 20 airport pairs with 32 the highest amount of total 33 vertical flight inefficiency during AIRAC cycle 1907 (June-July 2019) are shown in Figure 3-28. 34
Figure 3-28: Top 20 airports pairs with respect to total VFI
Figure 3-27: Evolution of vertically RAD constrained airport pairs
Figure 3-26: Evolution of total en-route vertical flight inefficiency during summer
4
PRR 2019 - Chapter 3: Operational En-route ANS Performance 45
The flight levels next to the arrows connecting the departure and arrival airports indicate the altitudes 1 of the RAD constraints on these airport pairs. Fourteen (14) out of the 20 airport pairs were completely 2 or partially below the Karlsruhe UAC and MUAC. 3
Flights on these airport pairs were restricted in terms of their cruising altitude, which allows handling 4 other flights at higher altitudes. This is an example of the trade-off between flight efficiency and 5 capacity, which again indicates the need to take into account all aspects of performance. 6
To enable stakeholders to get the en-route VFE results for an airport pair of their choice, an online 7 report request tool is online and can be accessed through the ANS performance data portal. It provides 8 interested parties with a tailor made report for a specific airport pair and AIRAC cycle. 9
10
3.3 Conclusions 11
The headline figure for capacity: en-route delay was lower in 2019 than in 2018 (-9.0%), although still 12 higher than every year since 2013. The proportion of flights that incurred ATFM delays increased to 13 almost one in ten although the delay per delayed flight decreased from 18.2 minutes to 15.8 minutes. 14 In summary, aircraft were delayed less but more frequently than in 2018. 15
Approximately one flight in six was subject to ATFM regulations and only one flight in twenty five was 16 delayed for more than 15 minutes. However, those flights account for 70% of all ATFM delays, so there 17 is room for lots of improvement. 18
As usual, the majority of ANSPs provide good capacity performance. Just four ANSPs were responsible 19 for two thirds of en route ATFM delay in 2019: DFS (25.9%), DSNA (22.9%), Austro Control (10.1%) and 20 Hungarocontrol (8.3%). 21
Individual ACCs had varied performance: some had higher delays with less traffic, others had lower 22 delays with increased traffic. Not all ANSPs needed to add capacity, but there are still ACCs that cannot 23 handle the existing traffic demand, never mind future growth. 24
As in 2018, the Network Manager coordinated, with participating ANSPs, a range of measures to re-25 route traffic (vertically or laterally) from congested areas, primarily MUAC Brussels sectors and 26 Karlsruhe UAC – which has significantly reduced available capacity in recent years. 27
Until ANSPs actually address the capacity deficits within their airspace, it appears that the Network 28 Manager will have an increasing role in coordinating such measures in the future. It must be noted 29 however, that re-routing aircraft, either vertically or laterally, penalises the airspace users. 30
In effect the aviation industry is being asked to make a choice: take a long delay, or take a shortened 31 delay with a horizontal (and vertical) penalty, when what they are really expecting is to be able to fly 32 the desired route according to their prescribed schedule free of ATM constraints. 33
Previous PRRs highlighted the inconsistent manner in which ANSPs attribute ATFM delays either 34 according to the delay cause or according to the geographical location to where the delay is being 35 assigned. These inconsistencies make it difficult to identify the actual delay causes and therefore 36 impede effective mitigation or resolution. 37
2019 was no different and the PRC analysis highlights that ATC staffing is much more of a problem, and 38 possible solution, to capacity constraints than is currently being acknowledged by ANSPs. 39
Through the review of declared capacity of the most penalising locations, it is apparent that some 40 ANSPs are failing to deploy available capacity, by relying on collapsed sectors, and are failing to add 41 capacity in significant capacity bottlenecks – indeed there are several instances where capacity in 2019 42 was less than was already declared in 2012. 43
The issues raised in regards to ATC staffing, combined with the reluctance of certain ANSPs to add, or 44 deploy, capacity raises significant questions about the plausibility of ANSP capacity plans and on future 45 capacity performance. 46
47
48
4
PRR 2019 - Chapter 3: Operational En-route ANS Performance 46
En-route flight efficiency has a horizontal (distance) and vertical (altitude) component, both with a high 1 relevance for fuel efficiency and the environment. 2
Horizontal en-route flight efficiency at EUROCONTROL level remained relatively constant over the past 3 five years with a slight deterioration in 2019. The efficiency of actual trajectories decreased by 0.1 4 percentage points to 97.2% in 2019. 5
The significant gap between the efficiency of the filed flight plans (95.6%) and the actual flown 6 trajectories (97.2%) remained also in 2019. In 2019, the actual trajectory is on average 1.6% more 7 efficient than the filed flight plan and still 1.3% more efficient than the shortest constrained route. Or 8 expressed differently if airspace users would have flown the filed flight plans they would have flown 9 an additional 156 million kilometres in 2019. 10
The efficiency of the shortest constrained routes (95.9%) is only slightly more efficient than the filed 11 ones which means that the inefficiencies are mainly due to the constraints imposed by the network. 12
En-route flight efficiency is comparatively low in the core area where traffic density is highest and 13 where Free Route Airspace (FRA) is not yet implemented. The envisaged implementation of Free Route 14 Airspace on a H24 basis above flight level 305 throughout the entire EUROCONTROL area by 2022 and 15 optimised cross border implementation is expected to be one of the main contributors towards 16 improving horizontal en-route flight efficiency as it provides airspace users with more choices to 17 optimise their flight plans. Although expected benefits can vary by airspace, States where FRA has been 18 implemented show a 0.5% higher flight efficiency compared to the other states and the gap between 19 filed flight plans and the actual flown trajectory narrows which in turn improves the level of 20 predictability for all players involved. 21
The average flight efficiency masks notable differences in performance at airport pair level. While the 22 vast majority of airport pairs have already a relatively good flight efficiency there is a comparatively 23 small share with high inefficiencies. On short haul flights operating on airport pairs with complex 24 terminal manoeuvring areas (TMA) and affected by segregated airspace, the inefficiency can be in 25 some cases above 25%. 26
Although FRA implementation will help to file more efficient trajectories to avoid segregated areas, it 27 will not improve the information flows necessary to optimise the use of segregated airspace in Europe. 28 As shown in previous analyses, there is further scope to improve the civil/military cooperation and 29 coordination. Generally flight efficiency is better on weekends when no military training activities take 30 place and therefore more airspace is available to be used for civil traffic. 31
Another important factor affecting horizontal en-route flight efficiency is the lack of capacity. 32 Horizontal flight efficiency is clearly worse in summer. The more granular analysis shows that the lack 33 of capacity in summer and the resulting ATFM constraints have a clear negative impact on horizontal 34 flight efficiency. Days with high en-route ATFM delays tend to have notably lower flight efficiency. 35
The vertical component of en-route flight efficiency has been gaining more attention over the past 36 years, not least because of the impact of measures implemented by the Network manager to mitigate 37 delays at network level during summer 2018 (4ACC) and 2019 (eNM/19). Although difficult to quantify 38 at network level in terms of additional fuel burn and CO2 emissions, the analysis shows a considerably 39 higher number of altitude constrained flights since spring 2018. 40
Although there are numerous initiatives aimed at improving flight efficiency underway and even with 41 traffic growth slowing down, it appears to be challenging to meet the set ambitions within the given 42 timeframes considering the existing lack of capacity in some parts of the core area. 43
44
Chapter 4: Operational ANS Performance at Airports
PRR 2019 - Chapter 4: Operational ANS Performance - Airports 47
4 Operational ANS 1
Performance @ Airports 2
3
SYSTEM TREND (TOP 30 AIRPORTS IN TERMS OF TRAFFIC) 2019 Trend change vs. 2018
Average daily movements (arrivals + departures) 23 649 +1.7%
Arrival flow management (per arrival)
Average Airport Arrival ATFM Delay 1.14 +0.1 min
Average Additional ASMA Time (without Turkish airports) 2.20 +0.16 min
Average time flown level during descent (without Turkish airports) 3.2 +0.1 min
Departure flow management (per departure)
Average additional Taxi-out Time (without Turkish airports) 4.26 +0.1 min
Average time flown level during climb (without Turkish airports) 0.6 +/-0.0 min
4
5
6
7
8
9
4.1 Introduction 10
The provision of sufficient airport capacity is one of the key challenges for future air transport growth. 11 This chapter provides a review of operational ANS performance at major European airports. The 12 evaluation of future airport capacity requirements (e.g. new runways, taxiways, etc.) is beyond the 13 scope of this report. 14
This chapter evaluates the top 30 airports in terms of IFR movements in 2019, which 15 have the strongest impact on network-wide performance. Together the top 30 airports 16 accounted for 43.8% of all arrivals in the EUROCONTROL area in 2019. 17
The new Istanbul Grand airport became fully operational in April 2019, joining the Top 18 30 as the 8th busiest airport in Europe (6th in the period April-December). While all 19 scheduled passenger traffic has been transferred from Istanbul Atatürk to Istanbul 20 Grand airport (IATA code: IST; ICAO code: LTFM), business aviation and military flights 21 still operate from Atatürk. It is the most important airport development in Europe 22 since the opening of the new airport in Munich in 1992, and significant growth is 23 expected to be observed at Istanbul Grand in line with the development plans that include up to 6 24 independent runways and the capacity to accommodate 200 million passengers a year, which would 25 potentially position this airport as the busiest in the wold. The new airport should also help reduce the 26 congestion at Istanbul Sabiha Gökçen, but at the same time it will have an impact in the surrounding 27 airspace. 28
Any atypical performance observed at an airport not included in the top 30 airports is commented on 29 in the respective sections of the chapter. Despite repeated attempts to implement the 30 EUROCONTROL’s standard Airport Operator Data Flow at the main Turkish airports, the data is still not 31 available and consequently these airports could not be reflected in all analyses throughout this 32 chapter. 33
43.8%
share of total arrivals at the
top 30 airports in the
EUROCONTROL area
The slowest growth at the top 30 since 2013 does not prevent most indicators from slightly deteriorating.
6
PRR 2019 - Chapter 4: Operational ANS Performance - Airports
48
Further information on the underlying methodologies and data for monitoring the ANS-related 1 performance at the top 30 and all other reviewed airports is available online on the ANS performance 2 data portal. 3
The following sections evaluate ANS-related inefficiencies on the departure and arrival traffic flow at 4 the top 30 airports. The performance indicators used in this chapter are summarised in Figure 4-1. 5
6
Arrival flow management Departure flow management
Related indicators
• Airport ATFM arrival delay [ICAO GANP KPI 12]
• Additional Arrival Sequencing and Metering Area (ASMA) time [ICAO GANP KPI 08]
• Average level time in descent
• ATC-pre departure delay
• Additional taxi-out time [ICAO GANP KPI 02]
• ATFM slot adherence [ICAO GANP KPI 03]
• Average level time in climb
Expected benefits
• Reduction of airborne terminal holdings
• Support to fuel efficient descent trajectory
• Maximise airport throughput
• Minimise ANS-related departure delays
• Optimise push back time sequencing
• Optimum taxi routing (distance & time)
• Adherence to ATFM departure slots
Supporting projects/ initiatives
• Continuous descent operation (CDO)
• Performance based navigation (PBN)
• Arrival manager (AMAN/XMAN)
• Time Based Separation (TBS)
• Wake vortex recategorisation (RECAT EU)
• Airport Operations Plan (AOP)-Network Operations Plan (NOP) integration (AOP-NOP)
• Airport Collaborative Decision Making (A-CDM) / Advanced Tower
• Departure manager (DMAN)
• Continuous climb operations (CCO)
• Airport Operations Plan (AOP)-Network Operations Plan (NOP) integration (AOP-NOP)
Figure 4-1: ANS-related operational performance at airports (overview) 7
Figure 4 2 shows the implementation status (end of 2019) of some of the initiatives listed in Figure 4-1. 8
9 Figure 4-2: Airport initiatives implementation status (2019) 10
Several projects and initiatives, whether or not part of the SESAR Pilot Common Project, have 11 contributed to improve efficiency and predictability. Still to be implemented widely, the Airport 12
Capacity management (throughput)
Arrival flow management Departure flow management
Approach (ASMA)
Airport arrival ATFM delay
ATC-related departure
delay
Taxi-out additional
time
Optimisation of departure
sequencing
Balancing ATFM delays at origin airport vs. local
airborne holdings
6
PRR 2019 - Chapter 4: Operational ANS Performance - Airports
49
Operations Plan-Network Operations Plan integration (AOP-NOP), a collaborative concept developed 1 under the SESAR programme), together with Total Airport Manager (TAM) /Airport Operations Center 2 (APOC) implementation are expected to allow airport stakeholders to better handle their local capacity 3 and bring local and network benefits in terms of predictability enhancement. The AOP is a rolling plan 4 that covers the pre-tactical and tactical phases by providing dynamic data updates as an operational 5 situation evolves. Through the timely two-way exchange of relevant airport and network information 6 between airports and the Network Manager, AOP-NOP integration is expected to improve both the 7 airport’s and the network’s operational performance through enhanced situational awareness 8 facilitating decision making. In 2020, airports like Frankfurt, Amsterdam, Madrid, Gatwick and 9 Heathrow are working on the establishment of this AOP-NOP integration. 10
Improving operational performance at airports requires the joint effort of all involved stakeholders, 11 therefore for the interpretation of the analyses in this chapter it should be borne in mind that the 12 results are driven by complex interactions between these stakeholders (airlines, ground handlers, 13 airport operator, ATC, slot coordinator, etc.), which make a clear identification of underlying causes 14 and attribution to specific actors sometimes difficult. 15
While ANS at airports is not often the root cause for a capacity/demand imbalance (e.g. adverse 16 weather, policy decisions in the airport scheduling phase, traffic demand variation, airport layout), the 17 way traffic is managed has an effect on airspace users (time, fuel burn, costs), the utilisation of 18 available capacity and the environment. 19
Hence, the analyses in the respective sections of this chapter should not be interpreted in isolation, 20 but as an integral part of the overall operational performance observed at the airport concerned. 21
22
4.2 Traffic evolution @ the top 30 European airports 23
Average daily movements (arrival + departure) at the top 30 airports in 2019 increased by 1.7% 24 compared to 2018, without taking into account the new Istanbul airport. This corresponds to 379 25 additional movements each day. At the same time, the number of passengers at the top 30 airports 26 in 2019 increased by 3.8% compared to the previous year. According to ACI Europe the highest year 27 on year passenger growths are observed at Vienna (+17.1%), Milan (+16.6%) and Antalya (+13.6%) 28 [23]. This goes in line with the evolution observed on the number of flights, where only three of the 29 top 30 airports showed a traffic growth above 5% in 2019: Vienna (VIE), Milan Malpensa (MXP) and 30 Antalya (AYT). Figure 4-3 shows the evolution of average daily IFR movements at the top 30 airports 31 in absolute and relative terms24. 32
The most significant traffic increase is observed at Milan Malpensa (+20.3%) due to the closure of 33 Milan Linate from the 27th of July to the 26th of October for maintenance works of the runway and 34 terminal, when Malpensa absorbed Linate’s traffic. Nevertheless, outside of that period, Malpensa 35 still shows an important increase of almost 10% in traffic. In Vienna, Wizzair opened a base in June 36 2018, which together with new Anisec operation has resulted in a notable increase in traffic during 37 2019 (+9.9%). 38
On the other hand nine airports observed a decrease in traffic in 2019, five of them above 1%: Paris 39 (ORY), Stockholm (ARN), Oslo (OSL), Palma (PMI) and Copenhagen (CPH). Airlines’ bankruptcies/cease 40 of operations, runway works and a slowdown in domestic travel in Scandinavia are the main 41 contributors to these reductions in traffic. 42
43
24 The ranking is based on IFR movements, which is different from commercial movements (ACI Europe statistics).
6
PRR 2019 - Chapter 4: Operational ANS Performance - Airports
50
1
Figure 4-3: Traffic variation at the top 30 European airports (2019/2018) 2
Airport systems 3
Thirteen of the top 30 airports 4 are part of a larger airport 5 system. 6
Figure 4-4 shows the 7 distribution of traffic per cities 8 where the top 30 airports are 9 located. 10
London, Paris, and Istanbul are 11 the most significant airport 12 systems, representing a much 13 higher share of the traffic than 14 the airports individually show. 15
When capacity at an airport has 16 reached limits and expansion is 17 not possible, additional 18 airports in the same area 19 provide extra capacity, 20 although there is a potential 21 impact in TMA complexity. 22
On the other hand, some cities 23 with only one airport, like 24 Dublin, Lisbon and Athens 25 show high saturation levels and limit the traffic increase to/from these destinations. 26
27
28
Figure 4-4: Top 30 airports and airport systems (2019)
6
PRR 2019 - Chapter 4: Operational ANS Performance - Airports
51
4.3 Capacity management (airports) 1
Airport capacity is one of the major constraints to future traffic growth in Europe. Some major 2 European airports are already operating close to their maximum capacity throughout most of the day. 3 If capacity decreases (due to exogenous events such as adverse weather, etc.) the impact on such 4 airports becomes more severe in terms of operational inefficiencies. 5
Airport operations depend upon a number of factors which all affect airport and runway capacity to 6 some degree. In addition to physical constraints, such as airport layout, there are “strategic” factors 7 such as airport scheduling and “tactical” factors which include, inter alia, the sequencing of aircraft and 8 the sustainability of throughput during specific weather conditions. 9
Safe operations of aircraft on the runway and in surrounding 10 airspace is the dominant constraint of runway throughput. Airport 11 layout and runway configuration, traffic mix, runway occupancy 12 time of aircraft during take-off and landing, separation minima, 13 wake vortex, ATC procedures, weather conditions and 14 environmental restrictions - all affect the throughput at an airport. 15
A number of initiatives to further increase airport capacity 16 including, inter alia, time based separation and improved wake 17 vortex separation standards, are being implemented at a number 18 of capacity-constrained airports across Europe. 19
It is acknowledged that the analysis in this section only provides a 20 high-level indication of operations at the top 30 airports. This 21 analysis does not allow direct comparisons to be made between 22 those airports. A more detailed analysis would need to consider 23 factors such as, among other things, runway layout, mode of 24 operation, and available runway configurations and societal 25 factors such as noise and environmental policies. 26
As in previous reports Figure 4-5, compares the declared peak 27 arrival capacities (red mark) to actual throughput at the top 30 28 airports in 2018 (06:00-22:00 local time) to provide an 29 understanding of the distribution of the arrival throughput. 30
31 Figure 4-5: Arrival throughput at the top 30 airports (2019) 32
The “peak service rate25” is used as a proxy to evaluate the peak throughput that can be achieved in 33 ideal conditions and with a sufficient supply of demand. The box plots give an indication of the degree 34
25 The peak service rate (or peak throughput) is a proxy for the operational airport capacity provided in ideal
conditions. It is based on the cumulative distribution of the movements per hour, on a rolling basis of 5 minutes.
60
68
64
45
48
58
38
54
36
48
40
48
40
30
40
4248
24
33
38
23 22
33 30
33
28
36
2630
0
10
20
30
40
50
60
70
Fran
kfur
t* (
FRA
)
Am
ster
dam
* (A
MS)
Pari
s* (
CD
G)
Lon
do
n*
(LH
R)
Mad
rid
* (M
AD
)
Mun
ich
* (M
UC)
Bar
celo
na*
(B
CN
)
Ista
nbu
l Int
. (IS
T)
Ro
me*
(FC
O)
Lon
do
n*
(LG
W)
Vie
nna
(VIE
)
Zuri
ch*
(ZR
H)
Cop
enha
gen
* (C
PH)
Osl
o* (
OSL
)
Du
blin
(D
UB
)
Mila
n*
(MXP
)
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ckho
lm*
(AR
N)
Bru
ssel
s* (
BR
U)
Ista
nb
ul (
SAW
)
Due
ssel
dorf
* (D
US)
Pari
s O
rly*
(O
RY)
Lisb
on
* (L
IS)
Ath
ens
(ATH
)
Pal
ma*
(P
MI)
An
taly
a (A
YT)
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ches
ter^
(M
AN
)
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on^
(ST
N)
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sin
ki*
(HEL
)
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saw
(W
AW
)
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lin T
egel
(TX
L)
Arr
ival
s p
er h
ou
r
Arrival throughput at the top 30 airports in 2019
Source: PRU analysis
* A-CDM implemented airport^Advanced Tower airport
Performance trade off - capacity vs noise @ airports
Noise emissions are generally recognised as the most significant environmental impact at airports. Noise levels are automatically monitored at many airports in compliance with the noise indicators and contour maps specified in the EU Environmental Noise Directive [33].
From a capacity management perspective, airports face the challenge of balancing the need for increased capacity with the need to limit negative effects on the population in the vicinity of the airport. This can include trade-offs between environmental restrictions when different flight paths reduce noise exposure but result in less efficient trajectories and hence increased emissions.
While ANS clearly has a role to play, the main influencing factors such as quieter engines, land use planning or political decisions are outside the control of ANS.
Noise management at airports is therefore generally considered to be a local issue with limited scope for ANS- related performance improvements.
6
PRR 2019 - Chapter 4: Operational ANS Performance - Airports
52
of dispersion of the total throughput at the airport. The wider the ranges, the more spread out the 1 distribution of the throughput. 2
Looking only at Figure 4-5 might give the idea that, although several airports show a narrow 3 distribution relatively close to the maximum arrival capacity, most of the top 30 airports still could 4 accommodate extra arrivals in the hours analysed. However, at airports where the schedule and 5 runway configuration (i.e. single runway mixed mode) result in a balanced mix between arrivals and 6 departures along the day (instead of alternate arrival and departure peaks), the maximum arrival or 7 departure capacities might not be the limiting factor, but the capacity for the total number of 8 movements. Therefore Figure 4-6 offers the same view for total throughput, where it is very clear that 9 several airports are working at the limits of their declared total capacity (in red). 10
11 Figure 4-6: Total throughput at the top 30 airports (2019) 12
London Heathrow (LHR), Gatwick (LGW), Dublin (DUB), Istanbul Sabiha (SAW), Lisbon (LIS), and 13 Warsaw (WAW) show a comparatively narrow distribution with a compact interquartile range (blue 14 box) very close to the declared peak capacity, which suggests a constant high traffic demand 15 throughout most of the day, leaving little room for recovery from situations when no optimal 16 conditions apply. 17
Figure 4-7 shows the historic evolution of the total hourly throughputs between 2009 and 2019 18 (median and peak service rate). The narrow gap between peak and median throughput indicates again 19 a narrow distribution or a continuous operation close to the peak capacity. 20
21 Figure 4-7: Evolution of hourly movements at the top 30 airports (2009-2019) 22
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40
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140
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kfu
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(FR
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ster
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* (A
MS)
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is*
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G)
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do
n*
(LH
R)
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rid
* (M
AD
)
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ich
* (M
UC)
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celo
na*
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CN
)
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nbu
l Gra
nd (
IST)
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me*
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O)
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W)
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nna
(VIE
)
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ch*
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H)
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en*
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H)
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o* (
OSL
)
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lin (D
UB
)
Mila
n*
(MXP
)
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ckh
olm
* (A
RN
)
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ssel
s* (B
RU
)
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nb
ul (
SAW
)
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esse
ldo
rf*
(DU
S)
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s O
rly*
(O
RY)
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on*
(LIS
)
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ens
(ATH
)
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ma*
(P
MI)
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taly
a (A
YT)
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ches
ter^
(M
AN
)
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on^
(ST
N)
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lsin
ki*
(HE
L)
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saw
(W
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)
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lin T
egel
(TX
L)
Arr
ival
s p
er h
ou
r
Total throughput (arr + dep) at the top 30 airports in 2019
Source: PRU analysis
* A-CDM implemented airport^Advanced Tower airport
0
20
40
60
80
100
120
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Frankfurt*(FRA)
Amsterdam*(AMS)
Paris* (CDG) London*(LHR)
Madrid*(MAD)
Munich*(MUC)
Barcelona*(BCN)
IstanbulGrand (IST)
Rome* (FCO) London*(LGW)
Vienna (VIE) Zurich* (ZRH)Copenhagen*(CPH)
Oslo* (OSL) Dublin(DUB)
Mo
vem
ents
per
ho
ur
Evolution of hourly throughput (arr. + dep.) at the top 30 airports in 2019
Median Peak ServiceSource: PRU analysisSource: PRU analysis
* A-CDM implemented airport ^Advanced Tower airport
Peak Service Rate (99th percentile)
Median (50th percentile)
0
20
40
60
80
100
120
200
9
201
4
201
9
200
9
201
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9
Milan* (MXP) Stockholm*(ARN)
Brussels*(BRU)
Istanbul(SAW)
Duesseldorf*(DUS)
Paris Orly*(ORY)
Lisbon* (LIS) Athens (ATH) Palma* (PMI) Antalya (AYT) Manchester^(MAN)
London^(STN)
Helsinki*(HEL)
Warsaw(WAW)
BerlinTegel(TXL)
Mo
vem
ents
per
ho
ur
6
PRR 2019 - Chapter 4: Operational ANS Performance - Airports
53
Frankfurt, Amsterdam, Istanbul Sabiha and Antalya showed the highest increase in hourly throughput 1 over the past few years. At airports like Lisbon, Warsaw or Dublin the gap between peak and median 2 throughput is closing, showing once more the saturation some airports are reaching. 3
Airport capacity imbalance 4
In saturation conditions, it is important to understand the different limitations that the airports have 5 to deliver their maximum capacity. Among all factors affecting the runway system capacity, the runway 6 layout and configuration is considered as the most relevant. While some airport layouts allow for very 7 similar operation conditions in one runway configuration or another, resulting in equal or similar 8 capacities for all possible runway configurations, other layouts do not offer that balance as the 9 different configurations might have very different operating conditions, dependencies and limitations. 10
To assess this potential capacity imbalance for the top 30 airports, their runway configurations during 11 2019 have been analysed together with their maximum hourly throughput and percentage of usage. 12
Figure 4-8 shows a view of this analysis per airport. Each bubble represents a runway configuration 13 (only those used at least 3% of the time are considered) with the corresponding maximum hourly 14 throughput (vertical axis) and share of utilization (size of the bubble). 15
16 Figure 4-8: Max. hourly movements for different runway configurations – T30 airports (2019) 17
While most airports show very similar throughput for all runway configurations, some airports present 18 configurations with a significant share that reaches a maximum throughput significantly lower than the 19 best configuration. 20
This analysis is based on the actual throughput which is also driven by demand, therefore it is not 21 possible to conclude that these configurations are limiting the delivery of required capacity. However, 22 it was also observed that in many cases the runway configuration had a clear impact on the 23 performance indicators. 24
The runway configuration and its probability is therefore an important factor in the particular 25 monitoring of performance for each airport, and should be considered for the planning as far ahead as 26 possible. Runway configuration prediction models can support the decision making to minimize the 27 impact on operations. 28
What is presented above constitutes only a part of the results of this study. The PRC will publish further 29 details and results, including the impact on performance, in a Technical Note available online in 30 summer 2020 on the ANS performance data portal. 31
0
20
40
60
80
100
120
140
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Maximum total throughput (arr + dep) at the top 30 airports in 2019per runway configuration
* A-CDM implemented airport ^Advanced Tower airport0
5
10
15
20
25
30
Fran
kfu
rt*
(FR
A)
Am
ster
dam
* (A
MS)
Pari
s* (C
DG
)
Lond
on*
(LH
R)
Mad
rid*
(MAD
)
Mun
ich*
(MU
C)
Bar
celo
na*
(BCN
)
Rom
e* (F
CO
)
Lond
on*
(LG
W)
Vie
nna
(VIE
)
Zuri
ch*
(ZRH
)
Co
pe
nh
age
n*
(CP
H)
Osl
o*
(OSL
)
Dub
lin (D
UB)
Mila
n* (M
XP)
Sto
ckh
olm
* (A
RN
)
Brus
sels
* (B
RU)
Du
ess
eld
orf
* (D
US)
Pari
s O
rly*
(OR
Y)
Lisb
on
* (L
IS)
Ath
ens
(ATH
)
Palm
a* (P
MI)
Ma
nch
est
er^
(MA
N)
Lond
on ̂(S
TN)
Hel
sink
i* (H
EL)
War
saw
(W
AW
)
Berl
in T
egel
(TXL
)
Maximum total throughput (arr + dep) at the top 30 airports in 2019per runway configuration
Bubble size: Rwy configuration share
6
PRR 2019 - Chapter 4: Operational ANS Performance - Airports
54
4.4 ANS-related operational efficiency at and around airports 1
4.4.1 Arrival flow management 2
ANS-related inefficiencies on the arrival flow are measured in 3 terms of arrival ATFM delay and additional time in the arrival 4 sequencing and metering area (ASMA time). Whereas ATFM 5 delays have an impact in terms of delay on the ground, additional 6 ASMA time (airborne holdings) has also a direct impact in terms 7 of fuel burn and emissions. 8
In 2019, the top 30 airports generated 75.5% 9 of all airport arrival ATFM delay in the 10 EUROCONTROL area, with only the first three 11 in terms of generated delay accounting for 12 more than 30% (Amsterdam, Lisbon and 13 London Heathrow). 14
Overall, 6.1% of the arrivals at the top 30 15 airports were delayed by airport arrival ATFM 16 regulations, while the EUROCONTROL 17 average is lower (4% of arrivals) 18
One more year, and despite a 10 points 19 reduction, the main reason for airport ATFM 20 regulations was still adverse weather (47.6%), 21 followed by insufficient Airport Capacity 22 (23%) and ATC attributed (17.4%). The delay 23 due to ATC related regulations or those 24 associated to other reasons like special 25 events, have increased significantly in 2019. 26
The constant efforts to increase capacity through new 27 infrastructure, systems and procedures take an 28 important toll during the works or the implementation 29 phases, resulting in a detrimental effect that can be 30 observed in both arrival metrics during those periods. 31 It is only through careful planning, coordination and 32 mitigation measures that airports can minimize the 33 impact of these events on performance. 34
Figure 4-11 shows the breakdown and evolution per 35 airport of arrival ATFM delay (left of figure) and the 36 additional ASMA time (right of figure) per arrival at the 37 top 30 European airports in 2019. 38
Both average airport ATFM delays and average 39 additional ASMA time at the top 30 European airports 40 increased by 0.05 and 0.1 minutes per arrival respectively compared to 2018, with arrival ATFM delays 41 reaching the 1.14 minutes per arrival in 2019 and the additional ASMA times 2.17 minutes per arrival. 42
The most important deterioration in terms of arrival ATFM delay is observed at Athens associated to 43 ATC capacity problems due to traffic growth combined with lack of ATCOs and other systems/tools (i.e. 44 A-CDM) to maximize capacity and improve planning. Measures to improve the situation are planned 45 for 2021-2022. 46
Figure 4-9: Share of total en-route ATFM delay in 2019
Amsterdam/ Schiphol
16.5%
Lisbon7.0%
London/ Heathrow
6.8%
London/ Gatwick
6.4%
Athens6.0%
Madrid/ Barajas4.2%
Zürich4.0%
Barcelona3.6%
Frankfurt3.4%
Porto2.3%
Other39.8% 6.5 million
minutes airport ATFM delay
(+1.8% vs. 2018)
Source: EUROCONTROL/ PRU
Distribution of airport arrival ATFM delay (%) EUROCONTROL Member States (2019)
Figure 4-10: Repartition arrival ATFM delay 2019
ATC
attributed [C,S,I,T]17.4%
Airport
Capacity [G]23.0%
Weather
[W,D]47.6%
Other12.0%
Top 30 airports
Source: EUROCONTROL/ PRU
Distribution of airport arrival ATFM delay by attributed delay cause (2019)
6
PRR 2019 - Chapter 4: Operational ANS Performance - Airports
55
1 Figure 4-11: ANS-related inefficiencies on the arrival flow at the top 30 airports in 2019 2
Amsterdam (AMS) also shows a significant increase in arrival ATFM delays (mainly due to strong winds 3 in March, works on taxiways in May and June, and the Electronic Flight Strip system implementation 4 at the tower in April and May), becoming the airport with the highest arrival ATFM delay in the top 30. 5 Additional ASMA times also showed a deterioration in this period from March to July. 6
Milan Malpensa (MXP) shows an important increase in the additional ASMA times (+1.2 min. vs 2018) 7 due to two main factors: the additional traffic transferred from Linate from August to October due to 8 the closure of Linate for works, and the implementation of the trombone approaches at the end of 9 March. The closure of Linate also impacted the arrival ATFM delays, resulting in an increase of +0.3 10 min/arr. 11
The second airport with the highest ATFM delays per arrival in 2019 is Lisbon (LIS), where airspace 12 management issues due to military activity contributed to a further increase in these delays. 13
At London (LGW) ATC Capacity regulations had an important effect in summer (also partially due to 14 Staffing). A clear deterioration can also be observed in the additional ASMA times until the month of 15 October, when XMAN started operating in cooperation with MUAC with a notable reduction of the 16 holdings. 17
Avg per arrival (min) change vs 2018 Avg per arrival (min) change vs 2018
Source: EUROCONTROL PRU
ANS-related inefficiencies on the arrival flow at the top 30 aiports in 2019
1.14
0.87
4.230.31
1.85
1.29
0.25
1.35
0.30
0.16
2.92
0.91
1.94
0.07
0.31
0.17
0.33
0.32
0.90
0.21
0.68
1.384.13
3.57
1.08
0.14
0.12
0.37
0.37
0.86
0.19
0 5
Top 30
Frankfurt* (FRA)
Amsterdam* (AMS)
Paris* (CDG)
London* (LHR)
Madrid* (MAD)
Munich* (MUC)
Barcelona* (BCN)
Istanbul Grand (IST)
Rome* (FCO)
London* (LGW)
Vienna (VIE)
Zurich* (ZRH)
Copenhagen* (CPH)
Oslo* (OSL)
Dublin (DUB)
Milan* (MXP)
Stockholm* (ARN)
Brussels* (BRU)
Istanbul (SAW)
Duesseldorf* (DUS)
Paris Orly* (ORY)
Lisbon* (LIS)
Athens (ATH)
Palma* (PMI)
Antalya (AYT)
Manchester^ (MAN)
London ̂(STN)
Helsinki* (HEL)
Warsaw (WAW)
Berlin Tegel (TXL)
ATC attributed [C,S,I,T] Airport Capacity [G]
Weather [W,D] Other
0.1
0.0
1.8
0.0
0.0
0.5
-0.2
-1.6
0.3
0.1
0.2
0.3
0.1
0.0
-0.1
-0.1
0.2
-0.1
0.0
-0.6
0.2
0.0
0.3
2.1
-1.1
0.0
0.0
-0.9
0.0
0.2
0.0
0.75
0.79 1.
26
1.23
1.18
1.09
1.14
0
1
2
3
2013
2014
2015
2016
2017
2018
2019
AIRPORT ARRIVAL ATFM DELAYS
2.17
2.2
1.8
0.97.0
1.1
2.1
2.6
4.6
2.1
2.9
1.1
1.0
3.3
2.6
1.1
1.0
1.9
1.0
1.3
1.3
1.6
1.7
1.2
2.1
0.9
0 2 4 6 8
Top 30
Frankfurt* (FRA)
Amsterdam* (AMS)
Paris* (CDG)
London* (LHR)
Madrid* (MAD)
Munich* (MUC)
Barcelona* (BCN)
Istanbul Grand (IST)
Rome* (FCO)
London* (LGW)
Vienna (VIE)
Zurich* (ZRH)
Copenhagen* (CPH)
Oslo* (OSL)
Dublin (DUB)
Milan* (MXP)
Stockholm* (ARN)
Brussels* (BRU)
Istanbul (SAW)
Duesseldorf* (DUS)
Paris Orly* (ORY)
Lisbon* (LIS)
Athens (ATH)
Palma* (PMI)
Antalya (AYT)
Manchester^ (MAN)
London ̂(STN)
Helsinki* (HEL)
Warsaw (WAW)
Berlin Tegel (TXL)
No data available
No data available
No data available
0.1
0.2
0.3
0.1-0.6
0.5
0.1
-0.1
0.7
0.4
0.1
0.1
-0.3
0.2
1.2
0.0
0.1
0.1
-0.2
0.1
-0.2
0.0
-0.2
0.1
0.6
-0.2
2.13
2.05 2.2
1
2.15
2.18
2.0
7
2.1
7
0
1
2
3
2013
2014
2015
2016
2017
2018
2019
ASMA ADDITIONAL TIME
* A-CDM implemented airport^ Advanced Tower airport
6
PRR 2019 - Chapter 4: Operational ANS Performance - Airports
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Madrid (MAD) arrivals observed no change in additional ASMA time, but higher arrival ATFM delay 1 mainly due to regulations associated with ATC capacity, runway works in March, and the 2 implementation of the new approach procedures in June-July. 3
In Barcelona (BCN), the absence of regulations due to special event (compared to the implementation 4 of new approach last year) and a reduction of weather delays results in an improvement of the arrival 5 ATFM delays in 2019. 6
Arrival ATFM delays at the new airport Istanbul Grand (0.30 min/arr) represent only a small part of the 7 delays that arrivals at Istanbul Atatürk suffered in 2018 (1.84 min/arr) thanks to the increased capacity 8 but also slightly lower traffic than Atatürk (-4% in the May-Dec period) 9
In both Palma (PMI) and London Stansted (STN) weather delays have significantly decreased and 10 aerodrome capacity issues have practically disappeared, resulting in a reduction of the overall arrival 11 ATFM delay. 12
There was also in 2019 a further reduction of airport capacity delays at Istanbul Sabiha (SAW) where 13 the ATFM capacity attributed delays have evolved from almost 10 minutes per arrival in 2016 to 14 practically zero in 2019. 15
Paris Orly (ORY), despite heavy works and closing one runway in August-November period, observed 16 almost no negative impact on the performance indicators. This was achieved through careful planning 17 and coordination which included a reduction in the schedules. 18
As was the case already last year, London (LHR) shows a further reduction of the additional ASMA times 19 in 2019, mainly due to the improvement in Jan-Feb 2019 compared to Jan-Feb 2018 before the 20 implementation of the enhance Time Based Separations using RECAT-EU. 21
Warsaw (WAW) had a runway closed for works during part of the year, which resulted in longer arrival 22 ATFM delay and ASMA times. 23
Figure 4-12 shows the combined delays affecting the arrival flow at the top 30 airports, due to both 24 the additional ASMA times and arrival ATFM delay, and the evolution with respect to 2018. The figure 25 can only show those airports where both metrics could be calculated (27 airports). The size of the 26 bubble represents both delays combined. 27
Most of these 27 airports maintain a 28 combined delay below the 4 minutes 29 per arrival. However, there are 30 several outliers that reach up to 31 almost 9 minutes per arrival. 32
Amsterdam (AMS), Lisbon (LIS) and 33 Athens (ATH) manage their flows with 34 arrival regulations resulting in very 35 high arrival ATFM delays, while 36 keeping similar additional ASMA 37 times as other airports. On the other 38 hand, Heathrow (LHR) and Gatwick 39 (LGW) stand out with very high 40 additional ASMA times, having at the 41 same time a higher than average 42 arrival ATFM delay. 43
Overall, the most significant increases 44 of the combined inefficiencies with 45 respect to 2018 are observed at 46 Amsterdam (AMS) and Athens (ATH) 47
(above 2 min/arr in both cases) and Milan Malpensa (MXP) (above 1 min/arr). At the same time, 48 Stansted (STN), Barcelona (BCN) and Palma (PMI) show combined reductions above 1 min/arr. 49
50
Figure 4-12: Combined inefficiencies on the arrival flow at the top 30
airports in 2018-2019
DUS
LGW
LHR
STN
AMS
DUB
WAW
BCN
MAD
PMI
ATH
MXP
LISZRH
BRU
FRA
DUSMUC
TXL
HEL
MAN
LGW
LHR
STN AMS
DUB
CPH
OSL
WAW
ARN
BCN
MAD
PMI
CDG
ORY ATH
MXP
FCO VIE
LISZRH
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
0.0 1.0 2.0 3.0 4.0 5.0
ASM
A a
dd
itio
nal
tim
e (m
in p
er a
rriv
al)
Airport arrival ATFM delay (min per arrival)
2018
2019
© EUROCONTROL/PRU
6
PRR 2019 - Chapter 4: Operational ANS Performance - Airports
57
Regional Greek airports 1
Annual traffic to the Greek regional airports decreased by 1.8% compared to 2018. However the high 2 delays remain, with Mikonos reaching the highest arrival ATFM delay in Europe (10.58 min/arrival 3 yearly average in 2019 and up to 22.6 min/arrival in July 2019). 4
Heraklion, Khania and Kos have significantly reduced their average arrival ATFM delay in 2019, but the 5 positive effect is neutralised by the increases observed mainly at Rodos, Santorini and Mikonos. 6
Although the capacity constraints and the layout limitations at most of these airports are well known 7 (in terms of runway exits, parallel taxiways and parking stands, and also terminal building capacity), 8 the ATFM delays attributed to Airport Capacity only represent 3% of the total, whereas most of them 9 are attributed to ATC Capacity (68%) and ATC Staffing (25%) issues. Improvement is expected from 10 2020 onwards with additional ATCOs. 11
The situation at Athens airport, with heavy arrival regulations and departure delays, has an adverse 12 knock-on effect impacting the predictability at the regional airports. 13
The Network Manager, in cooperation with 14 of these regional airports is developing a system to 14
foster the integration in the ATFM process. The plan includes the deployment of ADS-B receivers 15
and the data exchange between the airports and NM during the summer of 2020, with the 16
objective of improving the operational performance through common situation awareness 17
enhancement. 18
19
4.4.2 Departure flow management 20
This section analyses ANS-related operational inefficiencies 21 on the departure flow at the top 30 European airports in terms 22 of ATFM departure slot adherence, additional taxi-out time, 23 and, ATC pre-departure delays at the gate. 24
25
4.4.2.1 ATFM departure slot adherence 26
ATFM regulated flights are required to take off at a calculated 27 time (ATC has a 15 minute slot tolerance window [-5 min, +10 min] to sequence departures). 28 Adherence to ATFM slots helps to ensure that traffic does not exceed regulated capacity and increases 29 overall traffic flow predictability. 30
After 3 years of significant increase, the share of ATFM regulated departures at the top 30 European 31 airports in 2019 (24.3%) slightly decreases with respect to 2018 (25.2%) (brown bar in Figure 4-13). 32
On the other hand, the share of regulated flights departing outside the ATFM slot tolerance window at 33 these airports continues to decrease, from 6.1% in 2018 to 5.5% in 2019 which is positive in terms of 34 network predictability. 35
Turkish airports, together with Paris Orly, continue to show the highest share of departures outside 36 the ATFM slot tolerance window in 2019. However, the new Istanbul airport shows better slot 37 adherence (11.7% departures outside of the ATFM window) than the previous Atatürk (19.3% in 2018). 38 There is also a notable improvement at Istanbul Sabiha (-5 pp). 39
The sharing of Departure Planning Information (DPI) messages with the Network Manager by A-CDM 40 airports or Advanced ATC Towers, helps improve the predictability of the network through more 41 accurate take-off information. 42
In 2019, a total of 50 airports (27 A-CDM and 23 Advanced Tower) provided DPI messages to the 43 Network Manager (44.8% of the departures in the EUROCONTROL area). Although London Gatwick 44 and Stockholm Arlanda are A-CDM implemented, the DPI messages were temporarily suspended. Once 45 they re-connect to the network, the provision of DPI messages will reach 47.4%. 46
6
PRR 2019 - Chapter 4: Operational ANS Performance - Airports
58
1 Figure 4-13: ATFM slot adherence at the top 30 airports in 2019 2
Lisbon became an A-CDM airport in April 2019, showing a reduction in departures outside of the ATFM 3 window (-5%). More airports (i.e. Warsaw) are planning implementation in the near future, which will 4 further improve local performance and network predictability. 5
6
4.4.2.2 ANS-related inefficiencies on the departure flow 7
Figure 4-14 shows the local ATC departure delays and the taxi-out additional time at the top 30 airports 8 in 2019. The average additional taxi-out time only increased by 0.01 minutes per departure in 2019 9 (excluding the Turkish airports for which no data was available), resulting in 4.22 min per departure. 10
Overall, 12 of the 27 airports for which data was available reported an increase in additional taxi-out 11 time in 2019. As in 2018, the highest levels of average additional taxi-out times were observed at 12 London (LHR), London (LGW), Rome (FCO) and Dublin (DUB), while the most notable year on year 13 deterioration was observed at Malpensa (MXP) (+0.9 min vs. 2018), directly associated with the events 14 impacting also the arrival flow performance (closure of Linate airport and implementation of new 15 approach procedures) 16
London (LGW) and Rome (FCO), where additional taxi-out times were already high, registered also a 17 significant increase. The runway closure at Warsaw (WAW) from March to June due to the construction 18 of the new Rapid Exit Taxiway (RET) and double holding points had a strong impact in the additional 19 taxi-out times, with an average increase of 1.1 minutes during those months. 20
Notable year on year improvements were observed at Palma (PMI) (-1.0 min vs 2018) followed by 21 Stockholm (ARN), Berlin (TXL) and Barcelona (BCN) 22
As can be seen on the left side of Figure 4-14, data for the computation of local ATC pre-departure 23 delay is still either not available or does not reach the minimum quality threshold for 7 of the top 30 24 airports. Despite multiple efforts and the new EUROCONTROL specification for the collection of 25 operational data at airports, there has been no improvement in the reporting of most of the concerned 26 airports [17]. 27
Lisbon (LIS) remains the airport with the highest ATC pre-departure delays (4.2 min/dep). Gatwick 28 (LGW), Zurich (ZRH) and Warsaw (WAW) observe each an increase of 0.5 min in ATC departure delay 29 in 2019. 30
6.5%
2.9% 5.
1%
2.3% 4.
5% 5.2%
3.3%
11.7
%
5.2% 7.
3%
1.9% 4.
9%
1.4%
0.9%
3.8% 3.3%
2.1%
3.7%
19.1
%
4.8%
13.7
%
3.1%
6.7%
3.9%
27.7
%
8.4%
7.1%
6.1%
3.2%
7.4%
Fran
kfu
rt*
(FR
A)
Am
ster
dam
* (
AM
S)
Par
is*
(C
DG
)
Lon
do
n*
(LH
R)
Mad
rid
* (
MA
D)
Mu
nic
h*
(MU
C)
Bar
celo
na*
(B
CN
)
Ista
nb
ul I
nt.
(IS
T)
Ro
me*
(FC
O)
Lon
do
n*
(LG
W)
Vie
nn
a (V
IE)
Zuri
ch*
(ZR
H)
Co
pen
hag
en
* (
CP
H)
Osl
o*
(OSL
)
Du
blin
(D
UB
)
Mila
n*
(M
XP
)
Sto
ckh
olm
* (
AR
N)
Bru
sse
ls*
(BR
U)
Ista
nb
ul (
SAW
)
Du
ess
eld
orf
* (D
US)
Par
is O
rly*
(O
RY
)
Lisb
on
* (
LIS)
Ath
ens
(ATH
)
Pal
ma*
(P
MI)
An
taly
a (
AY
T)
Man
che
ste
r^ (
MA
N)
Lon
do
n^
(ST
N)
He
lsin
ki*
(HEL
)
War
saw
(W
AW
)
Ber
lin T
egel
(T
XL)
% of ATFM regulated departures
% of ATFM regulated departures outside the ATFM tolerance window
Average (ATFM regulated departures outside the ATFM tolerance window)
* A-CDM implemented airport^ Advanced Tower airport
Source: NM; PRU analysis
ATFM slot adherence at the top 30 airports (2019)
24.3% of the flights departing at the top 30 airports were ATFM regulated (- 0.9% pt. vs 2018)
5.5% of the ATFM regulated flights departed outside the slot tolerance window (-0.7% pt. vs 2018)
17
.6%
25
.2%
24
.3%
7.2%
6.1%
5.5%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
20
13
20
14
20
15
20
16
20
17
20
18
20
19
Top 30 average
6
PRR 2019 - Chapter 4: Operational ANS Performance - Airports
59
1
Figure 4-14: ANS-related inefficiencies on the departure flow at the top 30 airports in 2019 2
Figure 4-15 shows the combined delays affecting the departure flows, due to the additional taxi-out 3 time together with the ATC pre-departure delay, and their evolution with respect to 2018. The figure 4 can only show those airports where both metrics could be calculated (23 airports). The size of the 5 bubble represents both delays combined. 6
ANS-related inefficiencies on the departure flow at the top 30 aiports in 2019
4.2
3.9
3.1
3.8 9.0
3.8
4.5
7.9
8.9
3.1
3.7
2.6
3.9
7.1
4.8
2.0
2.2
2.6
4.0
2.6
2.2
3.9
4.4
3.0
3.4
0 2 4 6 8 10
Top 30
Frankfurt* (FRA)
Amsterdam* (AMS)
Paris* (CDG)
London* (LHR)
Madrid* (MAD)
Munich* (MUC)
Barcelona* (BCN)
Istanbul Grand (IST)
Rome* (FCO)
London* (LGW)
Vienna (VIE)
Zurich* (ZRH)
Copenhagen* (CPH)
Oslo* (OSL)
Dublin (DUB)
Milan* (MXP)
Stockholm* (ARN)
Brussels* (BRU)
Istanbul (SAW)
Duesseldorf* (DUS)
Paris Orly* (ORY)
Lisbon* (LIS)
Athens (ATH)
Palma* (PMI)
Antalya (AYT)
Manchester^ (MAN)
London ̂(STN)
Helsinki* (HEL)
Warsaw (WAW)
Berlin Tegel (TXL)
No data available
No data available
No data available
No data available
0.0
-0.1
0.2
-0.2
-0.1
0.2
0.0
-0.5
0.7
0.6
0.3
0.1
-0.4
0.3
0.0
0.9
-0.6
-0.2
0.3
0.3
0.0
0.0
-1.0
0.2
-0.3
-0.1
0.6
3.7
4
3.54
3.7
0
3.91
3.8
5
4.21
4.22
0
2
4
20
13
20
14
20
15
20
16
20
17
20
18
20
19
ADDITIONAL TAXI-OUT TIME
Avg per departure (min) change vs 2018 Avg per departure (min) change vs 2018
Source: EUROCONTROL PRU
0.6
0.1
1.5
1.5
1.6
1.6
0.1
0.1
0.9
0.9
0.1
0.8
0.5
0.6
4.2
1.0
0.9
0.4
0.9
0 1 2 3 4 5
Top 30
Frankfurt* (FRA)
Amsterdam* (AMS)
Paris* (CDG)
London* (LHR)
Madrid* (MAD)
Munich* (MUC)
Barcelona* (BCN)
Istanbul Grand (IST)
Rome* (FCO)
London* (LGW)
Vienna (VIE)
Zurich* (ZRH)
Copenhagen* (CPH)
Oslo* (OSL)
Dublin (DUB)
Milan* (MXP)
Stockholm* (ARN)
Brussels* (BRU)
Istanbul (SAW)
Duesseldorf* (DUS)
Paris Orly* (ORY)
Lisbon* (LIS)
Athens (ATH)
Palma* (PMI)
Antalya (AYT)
Manchester^ (MAN)
London ̂(STN)
Helsinki* (HEL)
Warsaw (WAW)
Berlin Tegel (TXL)
No data available
0.1
0.0
-0.1
0.5
-0.1
0.5
0.0
0.0
0.2
0.2
0.0
0.0
-0.2
0.0
0.5
ATC PRE-DEPARTURE DELAY
No consistent time series available
No data available
No data available
Data quality issue
Data quality issue
Data quality issue
Data quality issue
* A-CDM implemented airport^ Advanced Tower airport
Data quality issue
Data quality issue
Data quality issue
Data quality issue
6
PRR 2019 - Chapter 4: Operational ANS Performance - Airports
60
In the absence of London 1 Heathrow (LHR) (the data 2 quality does not allow the 3 calculation of the ATC pre-4 departure delay), it is clear that 5 most airports form a cloud 6 where the combined delays sit 7 below 6 minutes per departure. 8 However, 4 airports (London 9 Gatwick (LGW), Rome (FCO), 10 Dublin (DUB) and Lisbon (LIS)) 11 stand out with a total delay of 8 12 to 10 minutes per departure. 13
There is a general deterioration 14 with respect to 2018. Gatwick 15 (LGW), Warsaw (WAW) and 16 Milan (MXP) have increased 17 their combined delays by more 18 than 1 min/dep with respect to 19 2018, while the best reductions 20 (Stockholm (ARN) and 21 Copenhagen (CPH)) are well 22 below the min/dep. 23
24
25
26
4.4.3 Vertical flight efficiency during climb and descent 27
Vertical flight efficiency during climb and descent is calculated by 28 using a methodology developed by the PRC [24]. 29
Free tailored analyses for many European airports are available 30 from the online reporting tool accessible through the ANS 31 performance data portal. 32
Figure 4-16 shows the average time flown level per flight within a 33 200NM radius around the airport. Generally, climb-outs (right side) 34 were less subject to level-offs than descents (left side). 35
On average, the time flown level during descent is around five times 36 higher than the time flown level during climb. At system level 37 average time flown level stays relatively constant over time with 3.2 minutes per arrival compared to 38 0.6 minutes per climb out. 39
Flights arriving at Frankfurt (FRA), Paris Charles de Gaulle (CDG), London Heathrow (LHR), London 40 Gatwick (LGW) and Paris Orly (ORY) showed the highest amounts of time flown level with more than 5 41 minutes of level flight on average in 2019. 42
Vertical flight efficiency during descent deteriorated significantly at Munich (MUC) and Milan (MXP) in 43 2019. Traffic and the additional ASMA times increased for Milan Malpensa (MXP) since April 2019 due 44 to the implementation of the trombone approaches and also an increase in traffic diverted from Milan 45 Linate (LIN) during its closure from 27th of July until the 27th of October. Notable efficiency 46 improvements were observed at London Heathrow (LHR). 47
Vertical flight efficiency during climb stayed relatively stable in 2019 with increases at Zurich (ZRH) and 48 Vienna (VIE). 49
Environmental impact Reducing intermediate level-offs and diversions during climb and descent can save substantial amounts of fuel and CO2 and also reduce noise levels in the vicinity of airports. The lower the level segment, the higher the additional fuel consumption.
Figure 4-15: Combined inefficiencies on the departure flow at the top 30
airports in 2018-2019
BRU
FRA
HEL
LGW
DUB
CPH
OSL
WAWARN
MXP
FCO
VIE
LISZRH
BRU
FRA
DUS
MUC
HEL
MAN
LGW
DUB
CPH
OSL
WAW
ARN
ORYATH
MXP
FCO
VIE LIS
ZRH
0.0
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6.0
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dit
ion
al t
axi-
ou
t tim
e (m
in p
er d
epa
rtu
re)
ATC pre-departure delay (min per departure)
2018
2019
© EUROCONTROL/PRU
6
PRR 2019 - Chapter 4: Operational ANS Performance - Airports
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1 Figure 4-16: Average time flown level in descent/climb at the top 30 airports 2
Figure 4-17 shows the median altitudes at which continuous descent operations (CDO) started and at 3 which continuous climb operations (CCO) ended versus the average time flown level per flight. The 4 circles (climb) and triangles (descent) indicate the type of operation. Airports with good vertical flight 5 efficiency results are located in the top left corner while efficiency deteriorates towards the bottom 6 right corner of Figure 4-17. 7
8
Figure 4-17: Median CDO/CCO altitude vs. Average time flown level per flight (2019) 9
6
PRR 2019 - Chapter 4: Operational ANS Performance - Airports
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As usual, the analysis of climb-outs shows that all airports but London Heathrow (LHR) are located in 1 the top left corner confirming that vertical flight efficiency during the climb phase is good. The results 2 for climbs out of London Heathrow have however improved, mainly in terms of median CCO altitude. 3
Vertical flight efficiency during descent at Helsinki (HEL), Oslo (OSL) and Lisbon (LIS) is clearly above 4 average (close to the top left corner). Most other airports have a median CDO altitude below 10,000 5 feet which means that at least 50% of their flights start a continuous descent below this altitude. Paris 6 Orly (ORY), Frankfurt (FRA), Paris Charles de Gaulle (CDG), London Heathrow (LHR) and London Gatwick 7 (LGW) are the airports with the worst vertical flight efficiency results during the descent. 8
4.5 Conclusions 9
The situation at airports appears stable but uneven with some airports at, or close to, saturation levels. 10 Traffic growth in 2019 was the weakest (+1.7%) since 2013 with 9 of the top 30 airports actually 11 showing reduction in traffic. In the current airline market, the biggest traffic fluctuations at airports 12 are in fact driven by airlines pulling out or going bankrupt. The impact of the COVID-19 outbreak on 13 the aviation industry remains to be seen but indications in early 2020 suggest an unprecedented 14 reduction in traffic as a reaction on the drop in demand. 15
The list of the top 30 remains unchanged with respect to 2018, except for Istanbul Grand replacing 16 Istanbul Atatürk as operations were transferred from one airport to the other in April 2019. 17
Once more the number of passengers in 2019 grew faster than movements (+3.8% vs 2018) and 18 although 9 airports showed less traffic, only 3 airports saw fewer passengers. Vienna had 17.1% more 19 passengers than in 2018 thanks to new airlines’ operations, and Milan Malpensa saw an annual 20 increase in passengers (+16.6%) due to the transfer of operations during the closing of Linate. Antalya, 21 as in 2018, continued experiencing high passenger growth (+13.6%). 22
In 2019, 6.1% of all arrivals into these top 30 airports were delayed by arrival ATFM regulations, 0.3 23 percentage points more than in 2018. Although in average there was a slight deterioration in arrival 24 ATFM delay (increasing from 1.09 to 1.14 minutes per arrival in 2019), the trend is heterogeneous 25 among the airports. The most significant increases were observed at Athens (due to ATC capacity 26 issues) and Amsterdam, that now shows the highest arrival ATFM delay per flight in the top 30 and is 27 the biggest contributor to European airport ATFM delays. 28
Arrivals into Istanbul airports, that four years ago suffered the highest airport ATFM delays in the top 29 30, have now some of the lowest delays. However, the tactical holdings and delays both on the ground 30 and on the air cannot be analysed. There are three Turkish airports in the top 30 representing 9% of 31 the traffic. Due to the missing flow of airport operational data, the performance review at these 32 airports is very limited. High level intervention is expected in this concern by the PRC. 33
The average additional ASMA times at the top 30 airports also increased in 2019 from 2.07 to 2.17 34 minutes per arrival. London airports Heathrow and Gatwick, despite improvements observed at 35 Heathrow, still stand out with the highest additional ASMA times (7.0 and 4.6 minutes per arrival 36 respectively), associated with a high TMA complexity at the biggest airport system in Europe and a 37 maximization of runway capacity usage. 38
Although Lisbon was the only airport which implemented A-CDM in 2019, there are different projects 39 to integrate airports in the network besides the full A-CDM implementation, like Advanced Towers or 40 some specific projects planned in collaboration between NM and the airport operators aimed at 41 sharing departure information based on ADSB data. As a result, each year more airports share their 42 departure information with NM (currently almost 45% of departures share DPIs with NM) which helps 43 improve slot adherence and network predictability. Turkish airports still show poor performance in this 44 sense. 45
The average additional taxi-out time at the top 30 airports increased from 4.21 to 4.22 minutes per 46 departure in 2019. Significant variations at airport level were directly related to special circumstances. 47 ATC pre-departure delays, where measurable, showed as well noteworthy increases at some airports. 48
In this respect, the most significant deteriorations observed in the different performance indicators 49
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PRR 2019 - Chapter 4: Operational ANS Performance - Airports
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were associated to specific events, mainly works on the runway systems or implementation of new 1 systems or procedures. Mitigation measures and careful planning are to be considered to avoid this 2 detrimental impact on performance, following examples like Paris Orly, where runway 08/26 was 3 completely rebuilt in the second half of the year and several measures were put in place to guarantee 4 the level of service, including a reduction of the available slots. This resulted in no impact on arrival 5 delays although there was a significant increase in additional taxi-out times during those months. 6
Planned developments during summer 2020 at already saturated airports (i.e. new runway 7 construction at Dublin) are to be monitored carefully. Airports (like Dublin) implementing their own 8 performance frameworks in collaboration with ANSPs and airlines in order to provide constant 9 performance monitoring, together with the AOP-NOP integration and introduction of APOC/TAM, are 10 expected to improve coordination and mitigation in the future. 11
Increasing predictability both at network and airport level however will not be enough to solve the 12 capacity problem at some saturated airports where the infrastructure is the main constraint for growth 13 and, at the same time, the development of new runways is extremely restricted due to environmental 14 issues. 15
With respect to vertical flight efficiency during climb and descent, only slight increases were observed 16 except for the arrivals into Munich (MUC) and Milan (MXP). Arrivals and departures into/from London 17 (LHR) have on average experienced less level flight than in 2018. 18
19
Chapter 5: ANS Cost-efficiency
PRR 2019 - Chapter 5: ANS Cost-efficiency 64
5 ANS Cost-efficiency (2018) 1
2
3
SYSTEM TREND 2018 Trend change vs. 2017
En-route ANS cost-efficiency performance (38 charging zones)
Total en-route ANS costs (M€2018) 7 501 +1.8%
En-route service units (M) 157 +6.2%
En-route ANS costs per service unit (€2018) 47.8 -4.1%
Terminal ANS cost-efficiency performance (36 charging zones)
Total terminal ANS costs (M€2018) 1 258 +1.3%
Terminal service units (M) 7.2 +4.7%
Terminal ANS costs per terminal service unit (€2018) 174.3 -3.3%
Air Navigation Service Provider gate-to-gate economic performance (38 ANSPs)
Gate-to-gate ATM/CNS provision costs (M€2018) 8 416 +2.1%
Composite flight-hours (M) 21.6 +5.4%
Gate-to-gate ATM/CNS provision costs per composite flight-hour (€2018) 390 -3.1%
Gate-to-gate unit costs of ATFM delays (€2018) 120 +56.1%
Gate-to-gate economic costs per composite flight-hour (€2018) 510 +6.4%
4
5
6
7
5.1 Introduction 8
This chapter analyses ANS cost-efficiency performance in 2018 (i.e. the latest year for which actual 9 financial data are available) and presents a performance outlook, where possible. 10
It provides a pan-European view, covering 39 States26 operating 38 en-route charging zones27 that are 11 part of the multilateral agreement for Route Charges. This includes the 30 States which are subject to 12 the requirements of the Single European Sky (SES) Performance Scheme (“SES States”) and also 9 13 EUROCONTROL Member States which are not bound by SES regulations (see section 5.2 below). 14
The cost-efficiency performance of SES States in 2018 has already been scrutinised in accordance with 15 the SES Regulations and the results have been reflected in the Performance Review Body (PRB) 2018 16 monitoring report28. The PRC’s annual PRR does not seek to duplicate this analysis nor assess 17 performance against SES targets. Indeed, the focus in this PRR is on the changes in terms of cost-18 effectiveness performance from one year to another and not on the comparison of actual against 19 planned performance as in the PRB reports. In addition, this chapter takes into account the SES data 20 and aggregates it with the information provided by the non-SES States to present a pan-European view. 21
26 This is different from the 41 EUROCONTROL Member States in 2018 since: (1) Ukraine is a EUROCONTROL Member State which is not yet integrated into the Multilateral Agreement relating to Route Charges, and (2) Monaco en-route costs are included in the French cost-base.
27 Note that in the Route Charges system, two en-route charging zones include more than one State (Belgium-Luxembourg and Serbia-Montenegro). Similarly, there are two charging zones for Spain (Spain Continental and Spain Canarias).
28 2018 Annual Monitoring Report is available online on EU SES Performance website
En-route ANS cost-efficiency performance improved for the sixth consecutive year in 2018
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PRR 2019 - Chapter 5: ANS Cost-efficiency
65
Section 5.2 presents a detailed analysis of en-route cost-efficiency performance at pan-European 1 system level. Section 5.3 gives an evaluation of terminal ANS costs-efficiency within the SES area. 2
Finally, section 5.4 provides a factual benchmarking analysis of ANSPs’ 2018 gate-to-gate economic 3 performance focusing on ATM/CNS costs which are under ANSPs direct responsibility, and including 4 the estimated costs of total ATFM delays (en-route and airport) attributable to the respective service 5 providers. 6
Since the focus of this chapter is the analysis of cost-efficiency for the year 2018, the financial indicators 7 presented in sections 5.2, 5.3 and 5.4 are expressed in Euro 201829. 8
5.2 En-route ANS cost-efficiency performance 9
The analysis of en-route ANS cost-efficiency in this section refers to the 38 en-route charging zones 10 which were part of EUROCONTROL's Route Charges System in 2018 (with the exception of Portugal 11 Santa Maria). 12
As shown in Figure 5-1, the “SES States” 13 refer to the 28 Member States of the 14 European Union (EU), plus Switzerland 15 and Norway. These States operate under 16 the “determined costs” method which 17 includes specific risk-sharing 18 arrangements, defined in the Charging 19 Regulation [25] aiming at incentivising 20 economic performance and driving cost-21 efficiency improvements. 22
The “non-SES States“ refer to nine States 23 which are not bound by SES regulations 24 but which were part of the 25 EUROCONTROL Multilateral Route 26 Charges System in 2018 (i.e. Albania, 27 Armenia, Bosnia-Herzegovina, Georgia, 28 Moldova, North Macedonia, Serbia, 29 Montenegro and Turkey). For these nine 30 States, the “full cost-recovery method” applied in 2018. 31 32
5.2.1 Trends in en-route cost-efficiency performance at pan-European system level 33
The trend analysis presented in this sub-section focuses on the 37 en-route charging zones that 34 consistently provided en-route costs data over the 2013-2018 period30. 35
Figure 5-2 shows that in 2018, at pan-European level, en-route ANS costs increased by +1.8%. This cost 36 increase was compensated by robust TSU growth (+6.2%), resulting in a reduction of en-route unit 37 costs (-4.1%) compared to 2017. This is the sixth consecutive year of reducing en-route unit costs at 38 pan-European system level (-22.8% overall compared to 2012). 39
29 More information on the treatment and presentation of financial values for the purpose of this Report is available on p. 56 of PRR 2018 [26].
30 Details on the changes in scope and the impact of adjustments implemented on the historical cost efficiency data, in particular for the Croatian and Hungarian en-route charging zones, are provided on pg. 52-53 of PRR 2016 [28]. In addition, it should be noted that Georgia, which started to provide actual en-route costs data as of 2014, is not included in the trend analysis for the years 2013-2018 presented in this section. On the other hand, Georgia data is reflected in analysis of changes between 2017 and 2018.
Figure 5-1: SES and non-SES States
TR
RS
GE
BA
AM
MK
MD
AL
ME
FR
FI
ES
SE
IT
DE
PL
NO
RO
GB
BG
IE
GR
PT
AT HU
CZ
LT
LV
SK
EE
CH
BE
HR
NL
SI
DK
CY
LU
MT
RP2 SES States
Non SES States
SES and non SES States
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PRR 2019 - Chapter 5: ANS Cost-efficiency
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1 Figure 5-2: Real en-route ANS cost per TSU for EUROCONTROL Area (€2018) 2
3
Between 2013 and 2018, en-route unit costs for pan-European system reduced by -4.4% p.a., on 4 average. This is a result of a slight increase of en-route ANS costs (+0.6% p.a.), in the context of 5 continuous TSU growth over the entire period (+5.3% p.a.). 6
Figure 5-3 shows the 7 long-term trends31 in 8 terms of en-route 9 costs32, en-route 10 service units and en-11 route costs per TSU 12
between 2003 and 2018. 13
Over the whole period, en-route TSUs 14 grew much faster (+3.6% p.a.) than en-15 route costs (+0.6% p.a.). 16
As a result, the en-route costs per TSU 17 decreased by -2.9% p.a. between 2003 18 and 2018 (or -36.0% over the entire 19 period). 20
A more detailed analysis on historical 21 cost-efficiency performance is available in PRR 2018 on p. 57 [26]. 22
These long-term trends, however, mask the different dynamics observed for SES and non-SES States. 23
The analysis provided below focuses on the 2013-2018 period and highlights the observed differences 24 in en-route cost-efficiency performance between the SES and non-SES States. 25
Figure 5-4 and Figure 5-5 present the cost-efficiency performance for SES and non-SES States. The map 26 of en-route charging zones on the left hand side of these figures shows the annual changes in real en-27 route unit costs between 2013 and 2018 for each individual charging zone. At the same time, the index 28 chart on the right-hand side provides an aggregated trends in TSUs, costs and en-route unit costs over 29 this period. 30
31 Consistent time-series is not available for Armenia, Bosnia-Herzegovina, Estonia, Georgia, Latvia, Lithuania, Poland and Serbia and Montenegro en-route charging zones. These States are therefore excluded from the long-term analysis presented in Figure 5-3.
32 Due to data availability, the en-route cost data presented in the long-term analysis also includes costs for exempted VFR flights. This presentation differs from that in the remainder of this chapter, which focuses on en-route costs excluding the costs for exempted VFR flights.
2013
(37 CZs)
2014
(38 CZs)
2015
(38 CZs)
2016
(38 CZs)
2017
(38 CZs)
2018
(38 CZs)
2018 vs
2017
(38 CZs)
2013-18
CAGR
(37 CZs)
Total en-route ANS costs (M€2018) 7 247 7 283 7 384 7 402 7 364 7 501 1.8% 0.6%
SES States (EU-28+2) 6 859 6 846 6 924 6 904 6 842 6 927 1.2% 0.2%
Other 9 States in the Route Charges System 388 437 460 498 523 574 9.7% 7.4%
Total en-route service units (M TSUs) 121 129 134 139 148 157 6.2% 5.3%
SES States (EU-28+2) 107 112 115 120 127 134.0 5.6% 4.6%
Other 9 States in the Route Charges System 14 17 19 19 21 23 9.7% 9.7%
En-route ANS costs per TSU (€2018) 60.0 56.6 55.2 53.2 49.8 47.8 -4.1% -4.4%
SES States (EU-28+2) 64.2 61.3 60.2 57.5 53.9 51.7 -4.1% -4.2%
Other 9 States in the Route Charges System 27.8 25.6 24.5 26.2 24.9 24.9 -0.002% -2.1%
Figure 5-3: Long-term trends in en-route ANS cost-efficiency (€2018)
reduction in en-route ANS
unit costs between 2003
and 2018
-36.0%
60
80
100
120
140
160
180
Total en-route service units Total en-route ANS costs (€2018)
En-route real unit cost per TSU (€2018)
Source: PRU analysis
TSU index(2003=100)+3.6% p.a.
Cost index(2003=100)+0.6% p.a.
Unit cost index(2003=100)-2.9% p.a.
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PRR 2019 - Chapter 5: ANS Cost-efficiency
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Figure 5-4: Trends in en-route costs, TSUs and unit costs for SES States
Figure 5-4 shows that en-route unit costs for SES States decreased continuously at an annualised rate 1 of -4.2% over the 2013-2018 period. This cost efficiency improvement was achieved by maintaining 2 en-route costs mostly stable (+0.2% p.a.) in the context of significant TSU growth (+4.6% p.a.). 3
As also shown in the Figure 5-4, eight of these charging zones have managed to reduce their en-route 4 unit costs by more than -6.0% per annum over this period (coloured light blue in the map). This includes 5 Greece (-7.2% p.a.), Spain Continental (-7.0% p.a.), Lithuania (-6.9% p.a.), United Kingdom (-6.6% p.a.), 6 Hungary (-6.3% p.a.), Germany (-6.1% p.a.), Norway (-6.1% p.a.) and Spain Canaries (-6.0% p.a.). The 7 observed performance improvements for these States reflect a combination of reducing en-route ANS 8 costs and TSU growth, with the exception of Hungary, for which the increase in en-route costs base 9 was compensated by growth in TSUs. 10
On the other hand, for Poland (+3.7 % p.a.), Estonia (+3.4% p.a.) and Malta (+1.8% p.a.), highlighted in 11 dark blue in the map above, the growth in TSUs was not sufficient to compensate the increase in en-12 route costs. 13
Figure 5-533: Trends in en-route costs, TSUs and unit costs for SES States
Figure 5-5 indicates that en-route unit costs also decreased for non-SES States (-2.1% p.a.) over the 14 2013-2018 period. This is primarily the result of a substantial TSU growth (+9.7% p.a.), while costs also 15 rose by +7.4% p.a. over the period. It is noteworthy, that these results are heavily influenced by trends 16 for Turkey, since it represents some 67% of total costs and 76% of TSUs recorded for non-SES States. 17
It should be noted that, over this period, the TSUs grew for all non-SES States, with the exception of 18 Moldova, which experienced a significant traffic downturn between in 2013 and 2016 (-75.1% overall 19 over this period). 20 21
33 For the sake of completeness, the data for Georgia reflected in the left-hand side of Figure 5-5 refers to the changes in unit costs for the period 2014 to 2018, since Georgia only started providing data in 2014. However, Georgia is excluded from the index chart on the right side of the figure.
60
80
100
120
140
160
2013 2014 2015 2016 2017 2018
Unit cost index (2013=100) Cost index (2013=100)
TSU index (2013=100)
60
80
100
120
140
160
2013 2014 2015 2016 2017 2018
Unit cost index (2013=100) Cost index (2013=100)
TSU index (2013=100)
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PRR 2019 - Chapter 5: ANS Cost-efficiency
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5.2.2 Breakdown of en-route costs by type 1
As shown in Figure 5-6, en-route costs in 2018 can be broken down into the following main 2 components: 3
Staff costs – the largest category 4 representing some 59% of the en-route cost-5 base; 6
The second largest category, other operating 7 costs account for 23% of the total; 8
Capital-related costs which represent 18% of 9 total en-route costs can be further broken 10 down into depreciation (12%) and cost of 11 capital (6%); 12
Finally, exceptional costs recorded in 2018 13 are negative and represent less than 0.1% of 14 total costs. 15
16
Figure 5-7 presents the breakdown of en-route ANS costs into main components as well as the changes 17 in these cost categories over 2013-2018 period for SES and non-SES States. 18
19 Figure 5-7: Changes in en-route cost categories - 2018 vs 2013 - SES & non-SES States (€2018) 20
Figure 5-7 shows that, over this period, en-route costs for non-SES States grew by +43.2% (+167.3 21 M€2018) resulting from increases across all cost categories: staff costs (+51.3 M€2018), other operating 22 costs (+75.5 M€2018), depreciation costs (+19.1 M€2018) and the cost of capital (+21.3 M€2018). 23
On the other hand, for SES states, the increase in en-route ANS costs (+67.4 M€2018) mostly reflect 24 increases in staff costs (+179.3 M€2018) and cost of capital (+18.2 M€2018), which were slightly 25 compensated by reductions in other operating costs (-86.6 M€2018) and exceptional item costs (-41.3 26 M€2018). At the same time, the depreciation costs remained mostly stable over this period (-0.3%). 27
When interpreting this result for SES States, it is important to recall that Germany reported negative 28 components in its en-route cost-base between 2016 and 2018 (some -50 M€2018 annually), mostly 29 reflecting a contribution of the German State in DFS equity. It should also be noted, that as of 2017, 30 part of the administrative and regulatory costs, including EUROCONTROL contribution to Part I of the 31 budget (some 43 M€2018), are now financed by the Ministry of Transport and therefore are no longer 32 reflected in the en-route cost-base for Germany. While these two elements allow to significantly 33 reduce the unit rate charged to airspace users, they affect the trend analysis of cost-efficiency 34 performance for Germany and the pan-European system. Should these two items be taken into 35 account in this analysis, the 2018 en-route costs for SES States would be some +2.9% higher than in 36 2013 (instead of +1.0% as currently shown). 37
4.5
%
-5.4
%
-0.3
%
5.0
%
1.0
%
29
.9%
56
.5%
42
.4%
57
.1%
43
.2%
-150
-100
-50
-
50
100
150
200
250
Staff costs Other operatingcosts
Depreciation Cost of capital Exceptionalitems
Total costs
€2
01
8 (
mill
ion
)
Source: PRU analysis
SES
No
n-S
ES
Figure 5-6: Breakdown of en-route costs by type
Staff costs59%
Other operating
costs23%
Depreciation12%
Cost of capital
6%
Exceptional items -0.11%
Costs bynature (2018)
Source: PRU analysis
7
PRR 2019 - Chapter 5: ANS Cost-efficiency
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1
5.2.3 Actual en-route unit costs at charging zone level 2
The bottom part of Figure 5-8 presents the level of en-route unit costs34 for each individual charging 3 zone in 2018. En-route unit costs ranged from 83.1 €2018 for Switzerland to 22.0 €2018 for Turkey, a 4 factor of almost four between these two charging zones. It is important to recognise the effect of 5 currency exchange rate fluctuations, in particular for CZs which are outside the Euro zone. Substantial 6 changes of the national currency against the Euro may significantly affect the level of en-route unit 7 costs when expressed in Euros35. 8 9
10 Figure 5-8: 2018 Real en-route ANS costs per TSU by charging zone (€2018) 11
Figure 5-8 also presents the changes in en-route unit costs, TSUs and costs compared to 2017 (upper 12 part). It indicates that in 2018, en-route unit costs increased for nine en-route CZs out of the 38 13 included in the analysis. 14
Figure 5-8 indicates, that for five charging zones en-route ANS costs increased by more than 10%. In 15 2018, including Turkey (+14.7%), Greece (+13.0%) and Portugal Continental (+12.0%): 16
Turkey (+49.2 M€2018), reflecting increases in other operating costs (+18.8%) and the cost of 17 capital (+83.9%). The growth in other operating costs is understood to result from significant 18 depreciation of Turkish Lira, which adversely affected the prices of goods and services 19 purchased in foreign currency. At the same time, significant increase in cost of capital mostly 20 reflects the use of higher rate of return on equity (7.9%) than in the previous year (4.5%). 21
Greece (+15.6 M€2018), resulting from increases in other operating costs (+44.2%), 22 depreciation (+40.1%) and the cost of capital (+129.6%). The observed increase in other 23 operating costs reflects changes in accounting procedures applicable to the main ANSP 24 introduced in the previous years, which resulted in exceptionally low other operating costs 25 reported in 2017. At the same time, the increases in depreciation costs and cost of capital are 26 understood to reflect the implementation of the investment programme. 27
Portugal Continental (+15.1 M€2018), stemming primarily from significant increase in staff costs 28
34 The actual unit costs reflected in Figure 5-8 only refer to the ratio of actual en-route costs and TSUs recorded for 2018 and should not be confused with chargeable unit rate.
35 This is, for example, the case of Turkey which experienced a depreciation of Turkish Lira vis-à-vis the Euro as of 2013. The Turkish en-route unit costs would amount to some 49.4 € in 2018 (instead of 22.0 €), assuming that the Turkish Lira had remained at its 2013 level. Further details on the variations in exchange rates can be found in Annex 7 of the ACE 2018 Report [ref].
83
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-4%
-30%
-20%
-10%
0%
10%
20%
30%Actual unit cost (% change vs. 2017) Service units (% change vs. 2017) Actual en-route costs (% change vs. 2017)
% c
ha
ng
e v
s. 2
01
7A
ctu
al 2
018
un
it
cost
(€2
01
8)
Source: PRU analysis
7
PRR 2019 - Chapter 5: ANS Cost-efficiency
70
(+15.3%), which are understood to reflect i) actuarial losses related to pensions and ii) 1 additional payments to operational staff aimed at mitigating the effects of capacity shortage. 2
Other sizeable increases, in relative terms, can be observed for Armenia (+16.8%, or + 0.8 M€2018) and 3 Moldova (+12.2%, or +0.5 M€2018), resulting from higher staff costs and other operating costs in 2018. 4
On the other hand, Figure 5-8 indicates that for 7 CZs, en-route unit costs decreased by more than -5 10% in 2018, with substantial unit costs reductions observed for Lithuania (-17.1%), Serbia and 6 Montenegro (-14.8%), Switzerland (-12.1%), Sweden (-11.8%), Finland (-11.1%), Italy (-10.6%) and 7 Norway (-10.3%). Detailed analysis shows, that for most of these CZs, the reduction in unit costs 8 reflects the combination of lower en-route costs and higher TSUs, with the exception of Norway, which 9 managed to reduce its en-route cost base in the context of slight decrease of TSUs. 10 11
5.2.4 Pan-European en-route cost-efficiency outlook for 2019-2024 12
In November 2019, EUROCONTROL Member States submitted information related to planned en-route 13 costs and service units in the context of the Enlarged Committee for Route Charges36 for the period 14 2019-2024. These forecast figures are presented in Figure 5-9. 15
It is important to note that in parallel, the States 16 bound by the SES regulation are preparing the draft 17 performance plans for the RP3, including planned TSU 18 and determined cost figures. These plans will be 19 reviewed by the European Commission and the PRB. 20
It should also be noted that the forecast data 21 presented in this sub-section might slightly differ from 22 the information submitted in the draft RP3 23 performance plans. These figures are therefore 24 subject to change pending the adoption of the final 25 RP3 performance plans later in 2020. 26
In addition, due to the changes in the method of 27 calculating en-route TSUs (see grey box above), the 28 figures reflected for the period 2020-2024 are not 29 entirely consistent with previous years’ data. 30
Finally, the COVID-19 outbreak at the beginning of 31 2020 has sparked an unprecedented crisis in Europe 32 and elsewhere. While the full impact of the COVID-19 33 outbreak on the aviation industry remains to be seen, preliminary indications in early 2020 suggest an 34 unprecedented reduction in traffic volumes at pan-European system level. 35
Figure 5-9 shows the planned en-route costs and TSU data as reported in November 2019 in the context 36 of the Enlarged Committee for Route Charges. Figure 5-9 also shows actual TSU figures for 2018 and 37 2019. 38
Undoubtedly, the COVID-19 outbreak will have a massive impact on future traffic growth and the TSUs 39 plans made by States in November 2019 (see red line in Figure 5-9) are likely to be revised. A thorough 40 analysis of the impact of this crisis on the ANS industry will be provided in future PRR reports when 41 updated States/ANSPs forward-looking plans in terms of costs and TSUs will be available. 42
36 It should be noted that Germany did not report any forecast figures for 2019 in the November submission of the en-route reporting tables. For 2019, the forecast data for Germany reflects the data submitted to the Enlarged Committee for Route Charges in the June submission of en-route reporting tables.
Changes in calculation of service units As of 1st January 2020 a new methodology was put in place to establish the distance factor used for the calculation of route charges. The new method uses actual route flown as recorded by the Network Manager, whereby previously the distance factor was calculated based on the last filed flight plan.
This change, driven by increasing availability of flight and aircraft data, also affects the distance factor used in calculating the en-route service units. Preliminary analysis done by STATFOR shows that, while the impact of this change on the service units at pan-European system level is limited (-0.29%) the changes for individual States range from -5.87% for Denmark, to +1.47% for Moldova.
More information on this change and detailed impact on individual States can be found in Annex 4 to the “EUROCONTROL Seven-Year Forecast” published by STATFOR on February 2019.
7
PRR 2019 - Chapter 5: ANS Cost-efficiency
71
1
Figure 5-9: Pan-European en-route cost-efficiency outlook 2018-2024 (€2018) (To be updated) 2
3
5.3 Terminal ANS cost-efficiency performance 4
The analysis of terminal ANS cost-efficiency 5 in this section refers to the SES States (see 6 Figure 5-10) which are required to provide 7 terminal ANS costs and unit rates 8 information in accordance with EU 9 legislation [25]. As detailed in section 5.1, 10 the financial figures are expressed in Euro 11 2018 throughout this analysis. As for en-12 route, the SES States refers to the 28 13 Member States of the European Union 14 (EU), plus Switzerland and Norway. These 15 States report on 38 Terminal Charging 16 Zones (TCZs), generally one per State, but 17 two for Italy, UK, Poland and France, and 18 five for Belgium. 19
2018 is the fourth year for which the 20 “determined costs” method is applied for 21 terminal ANS. 22
The terminal cost-efficiency KPI is computed as the ratio of terminal ANS costs with terminal navigation 23 service units (TNSUs). 24
TNSUs are computed as a function of the maximum take-off weight ((MTOW/50)α). Since 2015, in 25 accordance with the Charging Scheme Regulation [27], all States use a common formula (MTOW/50)0.7 26 to compute TNSUs. This allows for a better comparison of the level of unit terminal costs per TNSU 27 which is achieved by the different charging zones. 28
This analysis includes 36 TCZs comprising 165 airports. It should be noted that the two UK TCZs have 29 been excluded from this analysis since: 30
a) information relating to UK TCZ B, which refers to nine airports where terminal ANS are 31 provided on a contractual basis, is not publicly available; and, 32
b) UK TCZ C (London Approach) is not directly comparable with other TCZs since the service 33 provided is of a different nature. Indeed, London Approach is making the transition between 34 the en-route and terminal phases for the five London Airports which are also part of TCZ B. 35
In addition, for four States (i.e. Cyprus, Greece, Belgium and Spain) the unit costs presented in this 36 analysis do not consider other revenues which are used to subsidise all or part of terminal ANS costs 37 charged to the users of terminal airspace. 38 39
7.5 7.9
8.4 8.5 8.6 8.6 8.7
103
106 109
113
116
119
103
0
1
2
3
4
5
6
7
8
9
10
2018 2019 2020 2021 2022 2023 2024
Tota
l en
-ro
ute
AN
S co
sts
(B€
20
18
)
Total en-route ANS costs (B€2018) Total TSU index (2018=100)
2019 actuals and STATFOR TSU forecast (February 2020) Source: PRU analysis
Figure 5-10: Geographical scope of terminal ANS cost-efficiency analysis
FR
FI
ES
SE
IT
DE
PL
NO
RO
GB
BG
IE
GR
PT
AT HU
CZ
LT
LV
SK
EE
CH
BE
HR
NL
SI
DK
CY
LU
MT
RP2 SES States
SES States
7
PRR 2019 - Chapter 5: ANS Cost-efficiency
72
5.3.1 Trends in actual terminal ANS cost-efficiency performance at European system 1
Figure 5-11 below provides a summary of actual terminal ANS performance at European system level 2 for the period 2015-2018. As explained in PRR 2016 [28], no consistent dataset is available at system 3 level prior to 2015 due to a) introduction of a common formula to compute TNSUs (described above), 4 and b) a number of changes in reporting scope introduced with at start of second reference period. As 5 a result, the data recorded prior to 2015 for both terminal ANS costs and terminal ANS service units is 6 not directly comparable at charging zone and European system level. 7
Figure 5-11 shows the changes in terminal ANS costs, TNSUs and unit costs between 2015 and 2018 at 8 European system level. It is expected that with the availability of additional actual terminal ANS data 9 in the future, this figure will be developed to show a five years trend analysis. 10
11 Figure 5-11: Real terminal ANS cost per TNSU at European System level (€2018) 12
In 2018, terminal ANS costs increased (+1.3%) in the context of continuous TNSU growth (+4.7%), As a 13 result, the terminal costs per TNSU reduced by -3.3% compared to 2017. This is the third consecutive 14 year of decreasing terminal unit costs at European level (-3.7% p.a. over the period covering 2015 to 15 2018). 16
As detailed on p.68 of this report, these results are significantly affected by the negative exceptional 17 items reported by Germany in its terminal cost-base for the years 2016-2018 (amounting to some -12 18 M€2018, -46 M€2018 and -45 M€2018 respectively). Excluding these amounts arising from the German 19 State intervention, the European system actual terminal ANS costs for 2018 would be +5.6% above 20 those in 2015 (instead of +1.9% as currently presented). 21
Figure 5-12 below shows how the main 22 components of terminal ANS costs 23 changed between 2017 and 2018. 24
An increase in terminal ANS costs 25 observed in 2018 (+1.3%, or +16.1 26 M€2018) results mainly from increases in 27 most of cost categories: staff costs 28 (+1.3%, or +11.5 M€2018), other 29 operating costs (+3.3%, or +6.9 M€2018), 30 cost of capital (+0.7%, or +0.5 M€2018) 31 and exceptional item costs (+0.4 32 M€2018). At the same time, these 33 increases were slightly compensated by 34 a reduction in depreciation costs (-35 2.4%, or -3.1 M€2018). 36
5.3.2 Terminal ANS 2018 cost-efficiency performance at terminal charging zone level 37
Figure 5-13 presents a composite view of the changes in terminal ANS unit costs for the 36 TCZs 38 included in this analysis. Upper part of the figure shows the changes in terminal costs, TNSUs and 39 terminal unit costs between 2017 and 2018, while the lower part provides information on the level of 40 terminal ANS unit costs in 2018. For the sake of completeness, the bottom chart of Figure 5-13 also 41 shows the number of airports included in each of the charging zone (see number in brackets). 42
Figure 5-13 indicates that in 2018, the average terminal ANS costs per TNSU amounted to 174.3 €2018 43 at system level. Figure 5-13 also shows that the terminal unit costs ranged from 1 564 €2018 for Belgium 44 Antwerpen TCZ, to 98 €2018 for Poland TCZ 1, a factor of more than 15. 45
195.3 188.4 180.2 174.3
101 101 102
105
109
114
-
50
100
150
200
250
2015Actuals
2016Actuals
2017Actuals
2018Actuals
TNSU index (2015)
Terminal ANScost index
(2015)
Source: PRU analysis
Re
al t
erm
ina
l co
st p
er
TN
SU
(€
20
18
)
2015
Actuals
2016
Actuals
2017
Actuals
2018
Actuals
2018 vs
2017
2015-18
CAGR
Total terminal ANS costs (M€2018) 1 234 1 248 1 242 1 258 1.3% 0.6%
Total terminal service units ('000 TNSUs) 6 319 6 622 6 891 7 215 4.7% 4.5%
Real terminal unit cost per TNSU (€2018) 195.3 188.4 180.2 174.3 -3.3% -3.7%
Figure 5-12: Breakdown of changes in terminal cost categories between 2017-2018 (€2018)
1.3%
3.3%
-2.4%
0.7%
1.3%
-5
-
5
10
15
20
Staff costs Otheroperating
costs
Depreciation Cost ofcapital
Exceptionalitems
Total costs
€ 2
01
8 (m
illio
n)
Source: PRU analysis
7
PRR 2019 - Chapter 5: ANS Cost-efficiency
73
1
2
Figure 5-13: 2018 Real terminal ANS costs per TNSU by charging zone (€2018) 3
Caution is needed when interpreting these results since several factors on top of performance-related 4 issues can affect the level of terminal unit costs in a specific TCZ. These factors include the number and 5 size of aerodromes included in the charging zone, the use of different cost-allocation between en-route 6 and terminal ANS, differences in number of TNSUs across TCZs and the scope of ANS provided. 7
For instance, Figure 5-13 shows that the two Belgian TCZs (Belgium Antwerpen and Oostende-Brugge) 8 with the highest unit terminal costs in 2018 only include one airport each and, together, represent 9 0.7% of the total terminal ANS costs at European system level. Similarly, while the French TCZ 2 reflects 10 the information relating to 58 airports (including regional airports), only the five main airports are 11 included in the two Italian TCZs. 12
The upper half of Figure 5-13 indicates that terminal unit costs reduced for 24 TCZs. Seven of these 13 TCZs managed to reduce unit costs by more than -10% in 2018: Switzerland (-20.4%), Lithuania (-14 18.7%), Latvia (-17.3%), Italy TCZ 1 (-13.2%), Belgium Liege (-12.1%), Finland (-10.2%) and Belgium 15 Oostende-Brugge (-10.1%). Detailed analysis indicates that the performance improvements observed 16 for majority of these TCZs in 2018 reflect a reduction in terminal ANS costs in the context of TNSU 17 growth. This is different for Belgium Liege and Belgium Oostende-Brugge TCZs, for which the growth 18 in TNSUs more than compensated slight increase in terminal costs. 19
On the other hand, Figure 5-13 also indicates that, in 2018, terminal unit costs increased for 12 TCZs, 20 with unit costs growing by more than +10% for Greece (+31.2%) and Estonia (+13.5%). Detailed analysis 21 shows that for these TCZs, the increases reflect substantially higher terminal ANS costs in 2018. 22
23
24
25
26
27
1564
(1)
504
(1)
310
(3)
298
(1)
286
(2)
275
(58)
274
(4)
250
(1)
240
(1)
236
(1)
232
(1)
228
(2)
228
(6)
224
(1)
223
(1)
194
(4)
179
(2)
178
(14)
174
(4)
170
(1)
162
(4)
149
(1)
147
(4)
144
(16)
143
(1)
143
(3)
143
(1)
139
(10)
139
(1)
138
(2)
135
(2)
134
(1)
133
(3)
123
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122
(5)
98
(
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174
(1
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Bel
giu
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ne
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Pol
and
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the
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ds
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ia
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y -
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e 2
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Zon
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Lith
uan
ia
Ge
rman
y
Gre
ece
Latv
ia
Den
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k
Por
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Esto
nia
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ta
Irel
and
Swe
den
Spai
n
Pol
and
- Z
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e 1
Euro
pean
Sys
tem
leve
l
+2%
-10%
-9%
-1%
-20
%
-1%
-3%
+8%
-12
%
-10%
-7%
-7%
-0.1
%
+1%
+2
%
-2%
+0.1
%
-2%
+7%
-8%
-9%
-13%
-19
%
+1%
+31%
-17%
-0.1
%
+9%
-10%
+9
%
+1
4%
+3
%
-5%
-8%
-7%
-4%
-3%
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%Actual terminal unit cost (% change vs. 2017) Terminal navigation service units (% change vs. 2017) Actual terminal costs (% change vs. 2017)
% c
han
ge v
s. 2
017
Act
ual
201
8 u
nit
co
st, €
2018
an
d n
um
be
r o
f a
irp
ort
s in
TC
Z
Source: PRU analysis
7
PRR 2019 - Chapter 5: ANS Cost-efficiency
74
5.4 ANSPs gate-to-gate economic performance 1
Note that the ACE data included in this section is still provisional at the time when this draft report is made 2 available for consultation. The data will be updated in May 2020 prior to the publication of the final PRR 3 2019 to ensure consistency with the data that will be presented in the final ACE 2018 Benchmarking Report. 4
The ATM Cost-Effectiveness (ACE) 5 benchmarking analysis is a pan-European 6 review and comparison of ATM cost-7 effectiveness for 38 Air Navigation Service 8 Providers (ANSPs). This includes 30 ANSPs 9 which were at 1st January 2018 part of the SES, 10 and hence subject to relevant SES regulations 11 and obligations. Detailed analysis is given in the 12 ACE 2018 Benchmarking Report [29]. 13
Along the annual ACE Working Group meetings, 14 the PRU has currently also embarked on a 15 programme of bilateral visits to participating ANSPs. The objective of these visits is to raise the 16 awareness of the ACE benchmarking activity to ANSPs top-management. The programme foresees two 17 to three visits annually. The most recent of these bilateral meetings were organised with PANSA, 18 Sakaeronavigatsia and skeyes. 19
The ACE 2018 data analysis presents information on performance indicators relating to the 20 benchmarking of cost-effectiveness and productivity performance for the year 2018, and shows how 21 these indicators changed over time (2013-2018). It examines both individual ANSPs and the pan-22 European ATM/CNS system as a whole. It is important to note that the year under review (2018) is the 23 latest year for which actual financial data are currently available. 24
Some elements of ANS provision are outside the control of individual ANSPs. These elements include 25 the costs of aeronautical MET services, the costs of the EUROCONTROL Agency and costs associated to 26 regulatory and governmental authorities. Therefore, from a methodological point of view, the ACE 27 Benchmarking analysis focuses on the specific costs of providing gate-to-gate ATM/CNS services which 28 are under the direct responsibility of the ANSP. 29
The analysis developed in the ACE Reports allows identifying best practices in terms of ANSPs economic 30 performance and to infer a potential scope for future performance improvements. This is a useful 31 complement to the analysis of the en-route and terminal KPIs which are provided in the previous 32 sections of this chapter. 33
34
Figure 5-14: Breakdown of gate-to-gate ATM/CNS provision costs 2018 (€2018) 35
Figure 5-14 shows a detailed breakdown of gate-to-gate ATM/CNS provision costs. Since there are 36
Total ATM/CNS provision costs: € 8 416 M
€ M % € M % € M %
Staff costs 4 244 64.2% 1 242 68.8% 5 486 65.2%
ATCOs in OPS employment costs 2 070 n/appl 631 n/appl 2 701 n/appl
Other staff employment costs 2 175 n/appl 611 n/appl 2 786 n/appl
Non-staff operating costs 1 054 16.0% 297 16.4% 1 351 16.1%
Depreciation costs 794 12.0% 155 8.6% 949 11.3%
Cost of capital 440 6.7% 100 5.5% 540 6.4%
Exceptional Items 77 1.2% 13 0.7% 90 1.1%
6 610 100.0% 1 806 100.0% 8 416 100.0%Total ATM/CNS provision costs
En-route Terminal Gate-to-gate
49.2%
50.8%
Staff costs65.2%
Non-staff operating
costs16.1%
Depreciation costs
11.3%
Cost of capital6.4%
Exceptional Items
1.1%ATCOs in OPS
employment costs
Other staff employment costs
7
PRR 2019 - Chapter 5: ANS Cost-efficiency
75
differences in cost-allocation between en-route and terminal ANS among ANSPs, it is important to keep 1 a “gate-to-gate” perspective when benchmarking ANSPs cost-effectiveness performance. 2
Figure 5-14 indicates that in 2018, at pan-European system level, gate-to-gate ATM/CNS provision 3 costs amount to some €8.4 Billion. Operating costs (including staff costs, non-staff operating costs and 4 exceptional cost items) account for some 82% of total ATM/CNS provision costs, and capital-related 5 costs (cost of capital and depreciation) amount to some 18%. 6
The analysis presented in this section is factual. It is important to note that local performance is 7 affected by several factors which are different across European States, and some of these are typically 8 outside (exogenous) an ANSP’s direct control while others are endogenous. Indeed, ANSPs provide 9 ANS in contexts that differ significantly from country to country in terms of environmental 10 characteristics (e.g. the size of the airspace), institutional characteristics (e.g. relevant State laws), and 11 of course in terms of operations and processes. 12
A genuine measurement of cost inefficiencies would require full account to be taken of the exogenous 13 factors which affect ANSPs economic performance. This is not straightforward since these factors are 14 not all fully identified and measurable. Exogenous factors related to operational conditions are, for the 15 time being, those which have received greatest attention and focus. Several of these factors, such as 16 traffic complexity and seasonal variability, are now measured. 17
The quality of service provided by ANSPs has an impact on the efficiency of aircraft operations, which 18 carry with them additional costs that need to be taken into consideration for a full economic 19 assessment of ANSP performance. The quality of service associated with ATM/CNS provision by ANSPs 20 is, for the time being, assessed only in terms of ATFM delays, which can be measured consistently 21 across ANSPs, can be attributed to ANSPs, and can be expressed in monetary terms. The indicator of 22 “economic” cost-effectiveness is therefore the ATM/CNS provision costs plus the costs of ATFM delay, 23 all expressed per composite flight-hour. Further details on the methodology used to compute 24 economic costs are available in the ACE 2018 Benchmarking Report [ref] 37. 25
5.4.1 Economic cost-effectiveness performance (2013-2018) 26
Figure 5-15 shows the comparison of ANSPs gate-to-gate economic cost per composite flight-hour 27 (“unit economic costs” thereafter) in 2018. The economic cost-effectiveness indicator at pan-European 28 level amounts to €510 per composite flight-hour in 2018, and, on average, ATFM delays represent 29 23.5% of the total economic costs. 30
31 Figure 5-15: Economic gate-to-gate cost-effectiveness indicator, 2018 32
37 The ACE 2018 Benchmarking Report is available online at www.eurocontrol.int/prc/publications.
868832 815 810
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200
300
400
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700
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Financial gate-to-gate cost-effectiveness Unit cost of en-route ATFM delays Unit cost of airport ATFM delays
European system average for economic cost-effectiveness: €510
European system average for financial cost-effectiveness: €390
815668
485 466 458
0
200
400
600
800
1000
DFS
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7
PRR 2019 - Chapter 5: ANS Cost-efficiency
76
Figure 5-15 indicates that in 2018 unit economic costs ranged from €868 for skeyes to €211 for MATS, 1 a factor of more than four. Figure 5-15 also indicates that DFS had the highest unit economic costs 2 amongst the five largest ANSPs. 3
Figure 5-16 displays the trend 4 at pan-European level of the 5 unit economic costs between 6 2012 and 2017 for a 7 consistent sample of 37 8 ANSPs for which data for a 9 time-series analysis was 10 available38. The upper part of 11 the Figure 5-16 shows the 12 changes in unit economic 13 costs, while the lower part 14 provides complementary 15 information on the year-on-16 year changes in ATM/CNS 17 provision costs, composite 18 flight-hours and unit costs of 19 ATFM delays. 20
Between 2013 and 2018, 21 economic costs per composite 22 flight-hour increased by 23 +0.9% p.a. in real terms. Over 24 this period, ATM/CNS 25 provision costs grew by +0.9% 26 p.a. while the number of 27 composite flight-hours also 28 increased (+3.3% p.a.). At the 29 same time, the unit costs of 30 ATFM delays increased by 31 +19.6% p.a., on average, over 32 the period, primarily due to 33 the significant increases recorded in 2014 (+11.1%), 2015 (+39.0%) and 2018 (+56.1%). More details 34 on the development of the ATFM delays can be found in chapter 3 of this report. 35
In 2018, composite flight-hours rose much faster (+5.4%) than ATM/CNS provision costs (+2.1%). As a 36 result, unit ATM/CNS provision costs reduced by -3.1% in 2018. In the meantime, the unit costs of 37 ATFM delays increased by +56.1% and therefore unit economic costs grew by +6.4% compared to 2017. 38
Figure 5-17 shows the long-term trends in terms of ATM/CNS provision costs, composite flight-hours, 39 ATFM delays and unit economic costs39. 40
The trend of decreasing ATFM delays, which began in 2011, stopped in 2014, when a new cycle 41 characterised by higher delays started (+23.6% p.a. on average between 2013 and 2018). 42
38 Sakaeronavigatsia which provided data for the first time as part of the ACE 2015 cycle is not included in this analysis.
39 Consistent time-series data for the entire period is not available for ARMATS, PANSA and SMATSA, these ANSPs are therefore excluded from the long term analysis.
Figure 5-16: Changes in economic cost-effectiveness, 2013-2018 (€2018)
-0.6% +3.6% -0.9% -3.6%+6.4%
0
100
200
300
400
500
600
700
2013 2014 2015 2016 2017 2018
€ p
er c
om
po
site
flig
ht-
ho
ur
(20
18
pri
ces)
ATM/CNS provision costs per composite flight-hour Unit costs of en-route ATFM delays Unit costs of airport ATFM delays
+0.4% +0.8% +0.5% +0.9% +2.1%+2.3% +1.7% +2.5% +4.8% +5.4%+11.1%
+39.0%
+5.3%
-3.4%
+56.1%
-60%
-40%
-20%
0%
20%
40%
60%
2013-14 2014-15 2015-16 2016-17 2017-18
ATM/CNS provision costs Composite flight-hours Unit costs of ATFM delays
7
PRR 2019 - Chapter 5: ANS Cost-efficiency
77
The most recent 1 available data 2 shows that, while 3 the ATFM delays 4 have reduced by -5 5.2% in 2019, they 6 still remain very 7 high in absolute 8 terms compared 9 to previous years’ 10 levels. 11
Figure 5-18 shows 12 the contribution of 13 each of the 38 14 ANSPs to the 15 change in ATFM 16 delays observed in 17 2018 at pan-18 European system level. 19
Right-hand side of the Figure 5-18 indicates that the increase in ATFM delays observed at system level 20 in 2018 mainly reflects increases for a few ANSPs (DFS, DSNA, ENAIRE, Austro Control and HCAA). The 21 right-hand side of Figure 5-18 shows that, as a result, for most of these ANSPs the share of ATFM delays 22 in economic costs in 2018 is higher than the European average (24%). This is particularly the case for 23 HCAA (43.0%), MUAC (49.6%) and DCAC Cyprus (56.1%). 24
25
26 Figure 5-18: ANSPs contribution to ATFM delays increase at pan-European system level in 2018 27
28
Left-hand side of the Figure 5-18 also shows that three ANSPs significantly contributed to the increase 29 in ATFM delays observed at system level in 2018. Indeed, DFS, DSNA and ENAIRE generated some 6.6 30 million additional minutes of ATFM delays in 2018. The higher ATFM delays recorded for these ANSPs 31 in 2018 were mainly associated to en-route ATC capacity/staffing issues, as well as adverse weather 32 conditions at airports for ENAIRE. 33
12.7%25.8%
3.9%0.2%
0.0%0.1%0.2%
1.5%1.3%
5.7%6.3%
9.5%1.9%2.8%
20.2%12.3%
56.1%2.6%
4.9%5.3%
16.3%12.5%
21.9%20.0%
49.6%16.5%
28.5%25.8%
32.3%23.8%
43.0%29.1%
24.1%32.8%
34.0%
0% 10% 20% 30% 40% 50% 60%
Share of ATFM delays in economic costs for each ANSP (2018)
-546-298
-38-1
0.040.324111316191921283133384771103
169188
229250277
318340364390
475513
926
-750 -500 -250 0 250 500 750 1 000 1 250 1 500
DHMILVNL
Avinor (Continental)MATS
AlbcontrolARMATS
SakaeronavigatsiaMOLDATSA
Oro NavigacijaBULATSA
Slovenia ControlNAVIAIR
ANS FinlandLGS
EANSUkSATSE
IAAM-NAVskeyes
DCAC CyprusENAV
LFVROMATSA
LPSPANSA
SMATSASkyguide
MUACNATS (Continental)
Croatia ControlHungaroControl
NAV Portugal (Continental)ANS CR
HCAAAustro Control
ENAIREDSNA
DFS
Contribution to change in ATFM delays (2017-2018, thousands of min.)
2 9702 750
Figure 5-17: Long-term trends in traffic, ATM/CNS provision costs and ATFM delays
40
60
80
100
120
140
160
180
200
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Ind
ex (
10
0 in
20
04
)
Composite flight-hours ATM/CNS provision costs ATFM delays Unit economic costs
7
PRR 2019 - Chapter 5: ANS Cost-efficiency
78
Figure 5-19 shows how the unit ATM/CNS provision costs (see blue part of the bar in Figure 5-19) can 1 be broken down into three main key economic drivers: (1) ATCO-hour productivity, (2) employment 2 costs per ATCO-hour and (3) support costs per composite flight-hour. Figure 5-19 also shows how these 3 various components contributed to the overall change in cost-effectiveness between 2017 and 2018. 4
Figure 5-19 indicates that in 2018, ATCO-hour productivity rose faster (+5.3%) than ATCO employment 5 costs per ATCO-hour (+0.9%). As a result, ATCO employment costs per composite flight-hour 6 substantially decreased (-4.2%). In the meantime, unit support costs fell by -2.6% since the number of 7 composite flight-hours increased by +5.4% while support costs were +2.6% higher than in 2017. As a 8 result, in 2018 unit ATM/CNS provision costs reduced by -3.1% at pan-European system level. 9
10 Figure 5-19: Breakdown of changes in cost-effectiveness, 2017-2018 (€2018) 11
More details on the changes in unit ATM/CNS provision costs at ANSP and pan-European system levels 12 are available in the ACE 2018 Benchmarking Report. 13
In addition, time-series of ANSPs cost-effectiveness performance data for the period 2002-2018 are 14 available online in the ATM cost-effectiveness dashboard. 15
5.5 Conclusions 16
PRR 2019 analyses performance in 2019 for all key performance areas, except for cost-efficiency, which 17 focuses on performance in 2018 as it is the latest year for which actual financial data are available. 18
In 2018, the en-route ANS cost-efficiency performance of the pan-European system improved for the 19 sixth consecutive year, since real en-route unit cost per service unit (TSU) reduced by -4.1% to reach 20 an amount of 47.8 €2018. This performance improvement is driven by a significant growth in TSUs 21 (+6.2%), which more than compensated the increase in en-route ANS costs (+1.8%). 22
Over the six year period covering 2013-2018, en-route unit costs reduced continuously at an average 23 rate of -4.4% annually, reflecting cost-efficiency performance improvements achieved by both SES (-24 4.2% p.a.) and non-SES (-2.1% p.a.) States. The decrease in unit costs observed for SES States over this 25 period was achieved by maintaining ANS costs mostly stable (+0.2% p.a.) in the context of robust TSU 26 growth (+4.6% p.a.). This is slightly different for non-SES States, for which en-route cost-efficiency 27 improvement was entirely driven by substantial TSU growth (+9.7% p.a.), which outweighed the 28 increase in en-route ANS costs (+7.4% p.a.). 29
Real terminal ANS costs per terminal navigation service unit (TNSU) decreased by -3.3% compared to 30 2017 and amounted to 174.3 €2018. The drivers for this improvement are similar to those observed for 31 en-route ANS, since TNSUs increased at a higher rate (+4.7%) than terminal ANS costs (+1.3%). 32
Detailed benchmarking analysis focusing on ANSPs cost-efficiency shows that in 2018 the gate-to-gate 33 unit costs of the pan-European system reduced by -3.1% since the increase in ATM/CNS provision costs 34 (+2.1%) was more than compensated by the traffic growth (+5.4% in terms of composite flight-hours). 35 In the meantime, the ATFM delays generated by the ANSPs rose for the fifth consecutive year in 2018 36 (+64.5%). As a result, the unit economic cost-effectiveness indicator for pan-European system 37 increased by +6.4% in 2018. 38
39
+5.3%
+0.9%
-4.2%-3.1% -2.6%
+2.6%
+5.4%
"Traffic effect"
ATCO-hour productivity
Decrease inunit ATM/CNS provision costs
2017-2018
"Support costs effect"
Employment costs per
ATCO-hour
ATCO employment costs per composite
flight-hour
Support costs per composite flight-
hour
Weight 68%
Weight 32%
79
[1] PRC, Performance Review Commission, PRC Terms of Reference.
[2] STATFOR, “EUROCONTROL STATFOR 7-year forecast,” Feb. 2019.
[3] EUROCONTROL, “EUROCONTROL Think Paper #5 - Effects on the network of extra standby aircraft and Boeing 737 MAX grounding,” October 2019. [Online]. Available: https://www.eurocontrol.int/publication/effects-network-extra-standby-aircraft-and-boeing-737-max-grounding.
[4] Intergovernmental Panel on Climate Change (IPCC), “Fifth Assessment Report (AR5),” 2017.
[5] EEA, “Greenhouse gas - data viewer,” 2018. [Online]. Available: https://www.eea.europa.eu/data-and-maps/data/data-viewers/greenhouse-gases-viewer.
[6] Air Transport Action Group (ATAG), “Fact sheet 3: Tracking aviation efficiency,” January 2020. [Online]. Available: https://aviationbenefits.org/media/166901/fact-sheet_3_tracking-aviation-efficiency.pdf.
[7] European Commission, “The European Green Deal - COM(2019) 640 final,” 12 2019. [Online]. Available: https://ec.europa.eu/info/publications/communication-european-green-deal_en.
[8] EUROCONTROL, “Think Paper #4 - The aviation network - Decarbonisation issues,” September 2019. [Online]. Available: https://www.eurocontrol.int/publication/aviation-network-decarbonisation-issues.
[9] European Commission, “Reducing emissions from aviation,” [Online]. Available: https://ec.europa.eu/clima/policies/transport/aviation_en.
[10] SESAR JU, “European ATM Master Plan - Edition 2020,” 2019. [Online]. Available: https://www.sesarju.eu/masterplan.
[11] European Environment Agency, “Exposure to environmental noise in Europe,” 2014.
[12] EC, European Commission, “Regulation (EU) No 598/2014 on the establishment of rules and procedures with regard to the introduction of noise-related operating restrictions at Union airports within a Balanced Approach and repealing Directive 2002/30/EC,” 2014.
[13] EUROCONTROL Performance Review Commission, “ATM Cost-effectiveness (ACE) 2017 Benchmarking Report. Report commissioned by the Performance Review Commission,” Available online at http://www.eurocontrol.int/prc/publications, May 2019.
[14] EUROCONTROL, EUROPEAN NETWORK OPERATIONS PLAN (NOP) 2019-2024, March 2019.
[15] University of Westminster, “European airline delay cost reference values – updated and extended values,” December 2015.
[16] European Commission (EC), “Commission Implementing Regulation (EU) No 716/2014 of 27 June 2014 on the establishment of the Pilot Common Project supporting the implementation of the European Air Traffic Management Master Plan,” 2014.
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[17] European Commission (EC), Commission Regulation (EC) No 2150/2005 of 23 December 2005 laying down common rules for the flexible use of airspace, 2005.
[18] Performance Review Commission (PRC), “Performance Review Report (PRR) 2017,” June 2018.
[19] EUROCONTROL Performance Review Unit, “Analysis of En-Route Vertical Flight Efficiency,” 2017.
[20] ACI, Airports Council International Europe, “ACI EUROPE Airport Traffic Report - December Q4 H2 FY 2018,” 2019.
[21] EUROCONTROL Performance Review Unit, “Analysis of Vertical Flight Efficiency during Climb and Descent,” 2017.
[22] European Commission (EC), “Commission Regulation (EC) No 1794/2006 of 6 December 2006 laying down a common charging scheme for air navigation services amended by Commission Regulation (EC),” 2006.
[23] EUROCONTROL Performance Review Commission, “"Performance Review Report (PRR) 2018",” June 2019.
[24] European Commission (EC), “Commission Implementing Regulation (EU) No 391/2013 of 3 May 2013 laying down a common charging scheme for air navigation services.,” 2013.
[25] Performance Review Commission, “Performance Review Report (PRR) 2016,” June 2016.
[26] EUROCONTROL Performance Review Commission, “ATM Cost-effectiveness (ACE) 2017 Benchmarking Report. Report commissioned by the Performance Review Commission,” May 2019.
[27] NATS, “Reports,” [Online]. Available: http://www.heathrow.com/file_source/HeathrowNoise/Static/LHR-FP-REPORT-Q32015-FINAL.pdf. [Accessed 14 January 2016].
[28] SESAR, “SESAR,” 2015. [Online]. Available: https://www.atmmasterplan.eu/.
[29] ICAO, “ICAO Performance Objective Catalogue,” ICAO, 2017. [Online]. Available: https://www4.icao.int/aid/ASBU/PerformanceObjective.
[30] European Commission (EC), “Directive 2002/49/EC of the European Parliament and of the Council of 25 June 2002 relating to the assessment and management of environmental noise,” 2002.
[31] EUROCONTROL Performance Review Commission, “ATM Cost-effectiveness (ACE) 2016 Benchmarking Report. Report commissioned by the Performance Review Commission,” Available online at http://www.eurocontrol.int/prc/publications, May 2018.
[32] EUROCONTROL Performance Review Commission, Study on ANSPs Pension Schemes and their Costs - Review of changes over the 2010-2016 period. Report commissioned by the Performance Review Commission, Available online at http://www.eurocontrol.int/prc/publications, October 2018.
[33] European Commission (EC), Commission Implementing Decision (EU) 2018/2021 of 17 December
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2018, amending Implementing Decision (EU) 2015/348 as regards the consistency of the revised targets in the key performance area of cost-efficiency included in the amended national or functional airspace block plans submitted by Portugal and Romania, December 2018.
[34] EUROCONTROL, “Vertical Flight Efficiency Workshop,” 2018. [Online]. Available: https://www.eurocontrol.int/events/vertical-flight-efficiency-workshop.
[35] Performance Review Commission, “Complementary analysis of most constraining en route ATFM regulations, attributed to ATC capacity, during 2017,” 2018. [Online]. Available: https://eurocontrol.int/publications/performance-review-report-prr-2017.
[36] Air Transport Action Group (ATAG), “Facts & Figures,” 2018. [Online]. Available: https://www.atag.org/facts-figures.html.
[37] SESAR JU, “Master Plan Level 3 2018 Implementation Plan,” January 2018. [Online]. Available: https://www.atmmasterplan.eu/downloads/.
[38] EUROCONTROL, “Specification for Operational ANS Performance Monitoring - Airport Operator Data Flow (APDF),” 2018.
[39] European Commission, Report of the Wise Persons Group on the future of the single European Sky, April 2019.
[40] STATFOR, “EUROCONTROL STATFOR 7-year forecast,” Feb. 2020.
[41] EUROCONTROL, “EUROCONTROL Aviation Outlook 2020-2040 (Main Report),” February 2020. [Online].
[42] EUROCONTROL, “Aviation Outlook 2020-2040 (Main Report),” February 2020.
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