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the airport capacity utilisation.
6.3.16 The left side of Figure 82 reports delays and additional times on the inbound traffic flow
(airport arrival ATFM delays + ASMA additional times). The right side shows the offblock
delays and the additional times on the outbound traffic flow (pre-departure and
taxi-out phase).
25 CFMU data are used to determine the level of congestion at the airport and CODA data are used for the taxi time
calculations. The methodology is explained in the ATMAP Framework Edition 2009.
26 Airport CDM European Airport Stakeholder – Survey 2009; EUROCONTROL Airport CDM Coordination
Group. The survey reports about the status of CDM implementation in Europe.
PRR 2009 70 Chapter 6: ANS performance at main airports
0 5 10 15
minutes per departure
Departure Congestion (IATA Codes 89)
Additional time in the taxi-out phase
15 10 5 0
Stockholm (ARN)
Copenhagen (CPH)
Oslo (OSL)
Brussels (BRU)
Milan (MXP)
Paris (ORY)
Amsterdam (AMS)
Barcelona (BCN)
Dusseldorf (DUS)
Athens (ATH)
Munich (MUC)
Zurich (ZRH)
Paris (CDG)
Vienna (VIE)
London (LGW)
Madrid (MAD)
Frankfurt (FRA)
Roma (FCO)
Istanbul (IST)
London (LHR)
minutes per arrival
ATFM - OTHER (Equipment, Special event, Strike, etc.)
ATFM- WEATHER
ATFM - ATC & Aerodrome Capacity
Additional ASMA time (TMA airborne)
Arrivals Departures
Source: CFMU/CODA Period : 2009 / 0600 to 2159
N/A
Figure 82: Estimated total additional time related to airport airside operations in 2009
6.3.17 Figure 83 shows the relationship between additional time and airport slot utilisation for
the top 20 busy airports. Additional times increase exponentially with the airport slot
utilisation. However additional times in winter are higher than in summer, although the
airport utilisation is generally lower. This demonstrates that other factors influence
performance, weather in particular. More work is needed to better understand the relation
between service quality, demand management and other factors such as weather
conditions.
0.0
5.0
10.0
15.0
20.0
25.0
0% 20% 40% 60% 80% 100%
Slot utilisation [%]
Total estimated additional time
[min/mov]
Source: CFMU/CODA Period : 2009 / 0600 to 2159
Figure 83: Airport slot utilisation/additional times
6.4 Conclusions
6.4.1 There have been some improvements in ANS performance at airports in 2009
6.4.2 Air Navigation Services at the top 20 major airports can adequately sustain the declared
airport capacity in daily operations during favourable conditions, with the exception of
Vienna, Athens and partially Madrid.
6.4.3 Overall, the performance in Athens, Istanbul, Madrid and Wien has significantly
deteriorated, mainly due to capacity constraints.
PRR 2009 71 Chapter 6: ANS performance at main airports
6.4.4 There were significant improvements in the approach phase at London LHR (20%
reduction of additional time) without increase in ATFM delay, under slightly reduced
traffic (-2%).
6.4.5 ANS performance at airports is more affected by weather conditions than traffic changes
with the European system of airport slot allocation.
6.4.6 The implementation of operational concepts, systems and procedures to improve ANS
performance during unfavourable weather conditions, especially high winds, should be
expedited. The application of time-based separation in final approach instead of distancebased
separation will certainly improve the situation. This will require, inter alia,
Meteorological infrastructures, AMAN/DMAN tools and CDM.
Chapter 7: Environment
PRR 2009 Chapter 7: Environment
72
7 Environment
KEY MESSAGES OF THIS CHAPTER
Aviation represents 3.5% of man made CO2 emission in Europe. ANS-actionable CO2 emissions are
estimated to be 6% of aviation-related emissions and account therefore for some 0.2% of total CO2
emissions in Europe.
ANS fuel efficiency is already high, close to 94%, and there is therefore limited scope for
improvement from ANS. Due to safety requirements, environmental constraints (noise) and other
factors, ANS fuel efficiency cannot attain 100%.
The main limitations to ANS fuel efficiency are related to horizontal route extension (3.9% of total fuel
burn) and airborne terminal delays mainly related to sequencing arrival traffic into main airports (1.1%
of total fuel burn).
Even higher priority should be given (1) to the optimisation of the route network and (2) to the
implementation of arrival management at main airports. In the longer term (SESAR IP2), the focus
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