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Low-cost share of IFR movements in Europe
Earlier years are estimated from data for a smaller geographical region.
Low-cost market share (lefthand
scale)
Low-cost traffic (righthand
scale)
Figure 19: Distribution of IFR flights by type6 Figure 20: Evolution of “low-cost” flight
movements
2.6.2 After 3 years of strong growth (up to +10% in 2007), Business Aviation traffic showed a
gradual decline in 2008 and the highest decrease of all market segments in 2009 (-14%).
As a consequence, the business aviation market share fell from 7.5% in 2008 to 6.9% in
2009.
2.7 Complexity and Traffic Variability
2.7.1 Traffic characteristics vary considerably across Europe. Traffic complexity and variability
are two factors that can influence service provision costs and quality of service.
COMPLEXITY
2.7.2 Traffic complexity is generally regarded as a factor to be considered when analysing
ATM performance. In 2006, the PRC produced a specific report on air traffic complexity
indicators, prepared in close collaboration with ANSPs [Ref. 15].
2.7.3 An aggregated complexity has been defined, which is the product of adjusted density and
structural complexity.
Adjusted Density measures the volume of traffic in a given volume of airspace taking
into account the concentration of the traffic in space and in time.
Structural Complexity reflects the structure of traffic flows. It is defined as the sum of
interactions between flights: horizontal interactions (different headings), vertical
interactions (climb/descend) and interactions due to different speeds.
6 See STATFOR classification of GAT in glossary. Note that the “Military IFR” segment does not include a
substantial portion of military traffic under military control. Similarly, “Other” does not include General Aviation
flying purely under Visual Flight Rules (VFR).
2
PRR 2009 Chapter 2: Traffic
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PAR
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AMS
BRU
DUB MAN
LON TC
PAL
Lower Airspace
Lower Airspace
Traffic complexity score 2009
< 2
> 2
> 4
> 6
> 8
source : EUROCONTROL
Figure 21: Aggregated complexity scores at ATC-Unit level
2.7.4 Figure 21 shows the aggregated complexity scores for the different area control centres
(ACCs) for the year 2009. London TC (40) has the highest score, followed by Langen
ACC (14), Brussels ACC (13) and Manchester ACC (12). In other words, for each hour
flown within the respective airspace, there were on average in Langen 14 minutes of
potential interactions with other aircraft. The average European aggregated complexity
score is close to 6 minutes of interaction per flight-hour.
2.7.5 Updated complexity indicators for ANSPs and some more information on complexity
indicators can be found in Annex IV.
TRAFFIC DEMAND VARIABILITY
2.7.6 Variability in traffic demand makes it more difficult to make best use of resources while
providing the required capacity. A distinction is made between seasonal, within-week and
hourly variability. Figure 22 presents the seasonal variability indicator, which is
computed as the ratio between the peak weekly traffic demand and the average weekly
traffic demand over the year.
2.7.7 Higher variability is observed in South-East Europe, especially in Greek airspace where
the relatively low number of flights in winter contrasts sharply with high demand in
summer.
2
PRR 2009 Chapter 2: Traffic
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ROV
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SOF
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ZAG BEO
OSL
RIG
VIL
TAL
WIE BUD
REI PRA ODE
PAD
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Performance Review Report 2009(20)
