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airports in terms of movements in 2009 (see also Chapter 6).
0%
5%
10%
15%
20%
25%
30%
35%
40%
Paris (CDG)
London (LHR)
Frankfurt (FRA)
Madrid (MAD)
Amsterdam (AMS)
Munich (MUC)
Rome (FCO)
Barcelona (BCN)
Istanbul (IST)
Vienna (VIE)
London (LGW)
Zurich (ZRH)
Copenhagen (CPH)
Brussels (BRU)
Paris (ORY)
Oslo (OSL)
Dusseldorf (DUS)
Athens (ATH)
Stockholm (ARN)
Milan (MXP)
Arrivals Departures
50%
60%
70%
80%
90%
100%
Coverage of data submitted by
arilines (% of total flights) CODA
% of flights delayed by more than
15 min.
Source: CODA
Punctuality at the top 20 airports in terms of movements in 2009
2008
2009
Yearly
average
Source: CODA/ PRC analysis
Figure 40: Punctuality at the top 20 airports in terms of movements
4.2.13 At some airports (most notably Rome (FCO), there is a notable difference between the
punctuality of the inbound and outbound flights. The operations at those airports have a
delay-amplifying effect on the European air transport network.
4.2.14 The difference between the arrival and the departure punctuality is predominantly driven
by local turn-around performance. For example, if the facilities (i.e. ground handling,
security, etc.) at an airport are not dimensioned to handle the corresponding traffic
volume the resulting departure delays will also have an impact on overall ANS
performance. More work is required to better understand the interrelation between
operational performance at individual airports and the air transport network.
4.3 Scheduling of air transport operations
4.3.1 Due to the high level of public interest, it is in an airline’s best interest to operate flights
within the 15-minute punctuality window.
4.3.2 However, from an analytical point of view, the monitoring of punctuality alone is not
sufficient as airlines build their schedules for the next season to some extent on historic
block time distributions which may already include a “buffer” for expected delay.
PRR 2009 Chapter 4: Air Transport Network Performance
32
Airline scheduling
Airlines build their schedules for the next season
on airport slot allocation, crew activity limits,
airport connecting times and the distribution of
historic block times.
Increases in the observed block time in a given
year will most often be reflected in the schedule
for the next season. Increased block times
contribute to preserve punctuality and schedule
integrity but result in lower utilisation of
resources (e.g. aircraft, crews) and higher
overall costs.
Schedule
Early
arrival
OUT OFF ON IN
Buffer
Late
arrival
Taxi Out Airborne Taxi In
Behind
schedule
Ahead of
schedule
Delay
ON Time
4.3.3 Changes in arrival punctuality may be the
result of improvements in actual travel times
or adjustments of scheduled block times.
4.3.4 Hence, operational improvements do not
automatically result in better punctuality in the
next season.
4.3.5 Figure 41 shows the evolution of scheduled
and actual block times on Intra European
flights between 2003 and 2009. Additionally,
the departure delay at origin and the arrival
delay at destination are shown.
4.3.6 The changes observed are relative to the
average for the entire period (2003-2009) and
enable to visualise changes in performance
over time (DLTA Metric) but without
identifying underlying drivers.
4.3.7 Figure 41 shows that overall the scheduled
(red line) and actual (blue line) block times
remained relatively stable between 2003 and
2009. The low level of variation in the actual
block times is partly due to the air traffic flow
management in Europe. While in the US flow
management strategies focus more on the
gate-to-gate phase, in Europe flights are
usually held at the gates with only
comparatively few constraints once they have
left the gate [Ref. 16].
DLTA Metric
The Difference from Long-Term Average
(DLTA) metric is designed to measure relative
change in time-based performance (e.g. flight
time) normalised by selected criteria (origin,
destination, aircraft type, etc.) for which
sufficient data are available. The analysis
compares actual performance for each flight of a
given city pair with the long term average (i.e.
average between 2003 and 2009) for that city
pair.
-2.5
-2.0
-1.5
-1.0
　
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