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EUROPEAN ORGANISATIONFOR THE SAFETY OF AIR NAVIGATION

EUROCONTROL EXPERIMENTAL CENTRE

EEC Note No. 16/99

Project PLC–C–E1

Issued: October 1999

The information contained in this document is the property of the EUROCONTROL Agency and no part should bereproduced in any form without the Agency’s permission

The views expressed herein do not necessarily reflect the official views or policy of the Agency..

FAP Future ATM Profile

Medium-termCapacity Shortfalls

Including national and supra-nationalCapacity Enhancement Plans

RREEPPOORRTT DDOOCCUUMMEENNTTAATTIIOONN PPAAGGEE

ReferenceEEC Note 16/99

Security ClassificationUnclassified

OriginatorEEC - PRF(Performance and Economy Research)

Originator (Corporate author) Name/Location :EUROCONTROL Experimental CentreB.P.15F-91222 Brétigny-sur-Orge CEDEXFRANCE.Telephone: +33 (0) 1 69 88 75 00

Sponsor Sponsor (Contract Authority) Name/LocationEUROCONTROL AgencyRue de la Fusée, 96B-1130 BRUXELLESTelephone: +32-(0)2-729 90 11

Title :Medium-term Capacity Shortfalls 2003 - 2005

AuthorsM. Dalichampt

S. Mahlich

Date10/99

Pagesiv + 40

Figs51

Tables21

Annex0

References9

EATCHIP Taskspecification

-

ProjectPLC-C-E1

Sponsor Task No.-

PeriodJune-Sept.

1999

Distribution Statement :(a) Controlled by : Dir. DSA(b) Special Limitations (if any) : None(c) Copy to NTIS : No

Descriptors (keywords) :

2003, 2005, Capacity, ATC, ATFM, delay, FAP, capacity shortfall, ACC, traffic demand, trafficgrowth, do nothing

Abstract :The study is performed by the EUROCONTROL Experimental Centre in close co-operationwith the DSA Directorate in support of the medium term capacity enhancement planning forEuropean air navigation services.Objective: Forecast of remaining capacity shortfalls and resulting delays in the medium term future (2003-2005) after implementation of national and supra-national capacity plans.Assumption: Air traffic in Europe grows as forecast by STATFOR (medium and high traffic growth scenario were tested); ACC capacities increase as planned by ATS providers and EUROCONTROL; Airport capacities increase as declared to EUROCONTROL;Methodology: Future ATM Profile (FAP) plus additional elements developed for long-term studies.

This document has been collated by mechanical means. Should there be missing pages,please report to:

EUROCONTROL Experimental CentrePublications Office

B.P. 1591222 – BRETIGNY-SUR-ORGE CEDEX

France

AcknowledgementsThe FAP Team would like to thank the experts from EEC – FDR and CFMU namely Leila Zerrouki, Dominique Latge, Nicolas Dufour,Philippe Lecomte, Johannes Koolen, Marcel Richard and Etienne de Muelenaere for their assistance, co-operation and patience in

particular during the critical summer months at the end of this study.

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Medium Term Capacity Shortfalls: 2003 - 2005


iii

Table of Contents

ABBREVIATIONS __________________________________________________________ IV

1. INTRODUCTION _______________________________________________________ 1

1.1 GENERAL CONTEXT ___________________________________________________ 11.2 SCOPE ____________________________________________________________ 21.3 SCENARIOS _________________________________________________________ 2

2. FAP METHODOLOGY, DATA AND TOOLS ________________________________ 3

2.1 AIR TRAFFIC FLOW MANAGEMENT (ATFM) SIMULATIONS _______________________ 42.2 THE CAPACITY DEMAND RATIO __________________________________________ 5

2.2.1 Indicator Development ____________________________________________ 52.2.2 Hourly Traffic Distribution __________________________________________ 62.2.3 Network Effects _________________________________________________ 7

2.3 ECONOMIC EVALUATION _______________________________________________ 82.4 COURSE OF SIMULATIONS _____________________________________________ 10

2.4.1 Model Calibration _______________________________________________ 102.4.2 Scenario 1999 “baseline” _________________________________________ 102.4.3 Scenarios 2003 and 2005 “do nothing”_______________________________ 112.4.4 Scenarios 2003 and 2005 “existing plans” ____________________________ 12

2.5 ASSUMPTIONS, CONSTRAINTS AND RISKS _________________________________ 12

3. TRAFFIC GROWTH 1999 – 2005________________________________________ 14

3.1 SHORTEST ROUTES AND “OPEN SKY”_____________________________________ 143.2 AIR TRAFFIC CONTROL CENTRE_________________________________________ 163.3 AIRPORTS _________________________________________________________ 18

4. EXISTING PLANS - CAPACITY GROWTH ________________________________ 20

4.1 AIR TRAFFIC CONTROL CENTRE_________________________________________ 204.2 AIRPORT CAPACITY __________________________________________________ 22

5. CAPACITY SHORTFALLS IN THE YEAR 2003 ____________________________ 24

5.1 TRAFFIC GROWTH: MEDIUM____________________________________________ 245.2 TRAFFIC GROWTH: HIGH ______________________________________________ 255.3 ALL SCENARIOS: EN-ROUTE ___________________________________________ 265.4 AIRPORTS _________________________________________________________ 28

6. CAPACITY SHORTFALLS IN THE YEAR 2005 ____________________________ 30

6.1 TRAFFIC GROWTH: MEDIUM ___________________________________________ 306.2 TRAFFIC GROWTH: HIGH______________________________________________ 316.3 ALL SCENARIOS: EN-ROUTE ___________________________________________ 326.4 AIRPORTS _________________________________________________________ 34

7. TREND ANALYSIS: DELAYS AND COSTS _______________________________ 36

8. CONCLUSION ______________________________________________________ 37

9. REFERENCES ______________________________________________________ 39

CONTACTS ____________________________________________________________ 40




iv

Abbreviations

ACC Area Control Centre

AMOC ATFM Modeling Capability

ATC Air Traffic Control

ATFM Air Traffic Flow Management

ATM Air Traffic Management

ATS Air Traffic Service

ATSP Air Traffic Service Providers

CASA Computer Assisted Slot Allocation

CFMU Central Flow Management Unit

CIM Capacity Indicator Model

CIP Convergence and Implementation Programme

CRCO Central Route Charges Office

c/d Capacity demand ratio

EAM EUROCONTROL Airspace Model

EATMP European Air Traffic Management Programme

ECAC European Civil Aviation Conference

IATA International Air Transportation Association

IROI Instantaneous Return on Investment

MECA Model for the Economical Evaluation of Capacities in the ATM System

RAMS Reorganized ATC Mathematical Simulator

ROI Return on Investment

STATFOR Specialist Panel on Air Traffic Statistics and Forecast

TACOT TACT Automated Command Tool




1

1. Introduction

The study is performed by the EUROCONTROL Experimental Centre in close co-operationwith the DSA Directorate in support of the medium term capacity enhancement planning forEuropean air navigation services.Objective: Forecast of remaining capacity shortfalls and resulting delays in the medium term future (2003-2005) after implementation of national and supra-national capacity plans.Assumption: Air traffic in Europe grows as forecast by STATFOR (medium and high traffic growth scenario were tested); ACC capacities increase as planned by ATS providers and

EUROCONTROL; Airport capacities increase as declared to EUROCONTROL;Methodology: Future ATM Profile (FAP) plus additional elements developed for long-term studies.

1.1 General Context

Historical data indicate that current capacity management is mainly driven by indicatorsdescribing the past system performance (retro-active). Evolution within the last 15 yearshas shown that investments on capacity rise if delays increase to non-acceptable levels anddiminish in the years of lower delays.

The key towards a more pro-active capacity management is a thorough impact analysis ofpotential costs and benefits related to future ATM actions and a better understanding of theinterrelations of the European capacity network, the traffic growth and the resulting delays.

Short- medium- and long-term planning have to adapt to the various constraints andpotential of their planning time horizons (see figure below).

This study concentrates on Medium-term planning with a time horizon of 3 to 5 years.

The information quality - the reliability of its assumptions and forecast - is lower comparedto the short-term planning. Consequently, the range of future scenarios must be wider(high-medium growth; various traffic pattern), whereas the grade of detail can be lower(ACCs instead of sectors, average days instead of weekday/weekend).

0 .6 0

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On the other hand, the reactionary power is significantly higher. The enablers of short-termplanning are usually limited to a better use of existing resources. Whereas, the 5 yearsplanning time of the medium-term planning enables consideration of complementary actionssuch as revised airspace management, improved ATC technology and increased ATC staffgrowth.

1.2 Scope

The evaluation is performed on:• the whole ECAC areaincluding:• air traffic control centres and airports (65 ACCs, 91 airports)• Air Traffic Flow Management issues (CFMU slot allocation procedures)simulating:• present and future traffic loads (1999, 2003, 2005)• regional characteristic traffic growth (ca. 2000 traffic flows).

1.3 Scenarios

The following scenarios shall be examined:• “do nothing”

(capacity stagnates at 1999 level, traffic grows as forecast)• “existing plans”

(capacity increases according to the existing national and supra-national capacityenhancement plans)

Both scenarios shall be examined with:

• weekday and weekend traffic pattern (observed in summer 1999)• current routes (ARN V3 as used in summer 1999)• shortest flyable routes (ARN V3 without effects from Kosovo and capacity constraints)• medium and high traffic growth (according to STATFOR forecast)

RReeaaccttiioonnaarryy PPoowweerrIInnffoorrmmaattiioonn QQuuaalliittyy

PPllaannnniinngg ttiimmee hhoorriizzoonn

Short-term1-2 years

Medium-term3-5 years

Long-term5-20 years

EEnnaabblleerr




3

2. FAP Methodology, Data and Tools

The Future ATM Profile is a methodology that provides a platform to investigate the ATMsystem behaviour resulting from parameter changes foreseen or forecast in the shortand longer term future.

A ir p o r t C o s t s

C a p a c i t y

D e la yD e m a n d

T e c h n o lo g y

T r a in in g

I n v e s t m e n t

A ir lin e C o s t s

A T C C o s t s

L a n d in g F e e s R o u t e C h a r g e s

F l ig h t P e n a l t i e s

S t a f f F u e l

F le e t o th e r s

ATM capacity is provided by the airports and the en-route ATS providers. The direct cost ofthe capacity is borne by the airspace users, charged via landing/departure and routecharges. The (charged) cost of the European ATM system was around 9.5 billion euro in1998 (airports: 5.5 billion euro; en-route ATS: 4 billion euro).

Delays are the consequence of the inability of the ATM system to provide the capacityneeded to satisfy the demand. Delays increase the operating cost of an aircraft. Theestimated cost of European ATFM delays was around 500 million euro in 1998.

A significant part of the delay cost could have been saved through a more pro-active ATMcapacity management. The Future ATM Profile (FAP) is developed to support the Europeancapacity management. FAP identifies future capacity shortfalls in the air (en-route) and onthe ground (airports). FAP estimates the capacity growth required to optimise the cost ofproviding the service against the cost of the delays and other flight penalties.

This study used the following macro-elements of the FAP methodology:

• An Air Traffic Flow Management (ATFM) simulation tool (AMOC/CASA) with animplemented copy of the CFMU slot allocation algorithm (CASA), using trafficsectorisation and capacity data as an input to identify flights subject to ATFM delays,and the ACCs or airports being the root causes for the delay (bottlenecks). AMOC isused to model the system behaviour of the European capacity network and to quantifythe delay development in the future, based on regional capacity and traffic growthestimates.




4

• A Model for the Economical Evaluation of the Capacities of the ATM system (MECA),uses capacity cost data recorded by CRCO, aircraft operating data recorded by IATAand traffic/delay data recorded by CFMU to compute the best trade-off between cost forcapacity and cost for delays. The study used the results of this model to identify theoptimum capacity demand ratios and the resulting delays at which the ACCs andairports operate at minimum cost for the airspace users.

2.1 Air Traffic Flow Management (ATFM) simulations

This study is based on a sequence of ATFM simulations using AMOC to investigate theimpact of traffic and capacity growth on the delays.

AMOC (ATFM model capability) is our most realistic delay model. It simulates the CFMUoperations (see figure below).

The heart of AMOC is the slot allocation algorithm that converts overload into delays. Thisalgorithm is a direct copy of the CFMU Computer Assisted Slot Allocation algorithm(CASA). Thorough fine tuning is required prior to every set of simulations using AMOC toguarantee a good model representation for each individual configuration and regulationscheme applied.

ATFM simulations are used:• to investigate the sensitivity of delays on traffic and capacity increase• to anticipate the network effects (e.g. airport protects ACC etc.)

AMOC uses capacities as an input and gives delays as an output. It cannot run in theinverse mode, using delays as an input and giving capacities as an output. Iterativesimulation steps are required to find the capacities needed to reach the delay targetdefined.

A i r p o r t N e e d s

A i r l in e N e e d s

A T C N e e d s

D e la y sD e la y s

D e m a n dD e m a n d

P o s s ib l eR e r o u t in g

C a p a c i t i e sC a p a c i t ie sC o n f i g u r a t i o nC o n f ig u r a t io n

S y s t e m l o a dS y s t e m lo a d

I te r a t iv eP ro c e s s

G lo b a l A i r s p a c e

C F M U

I t e ra t iv eP r o c e s s

It e ra t iv eP r o c e s s




5

2.2 The Capacity Demand Ratio

2.2.1 Indicator Development

This chapter investigates the sensitivity of delays on the capacity demand ratio. Theobjective is to determine the best traffic indicator to be used for the capacity demand ratio.The indicator should be simple but sensitive enough to represent the impact of the varioustraffic demand curves over the day.

Network effects must not disturb the observation in this exercise. Therefore, each ACC wassimulated individually with all other ACC capacities set to infinite (“no network”). Each ACCwas simulated with various traffic patterns and various capacities. In total, the results ofmore than 1200 ATFM simulations were used to select the best traffic indicator.

5 indicators were investigated:• 1 hour peak (peak hourly traffic load of the ACC)• 2 hour peak (average load per hour during the peak 2 hour period)• 3 hour peak (average load per hour during the peak 3 hour period)• av. 6-18h (average load per hour between 6:00 and 18:00)• av. 0-24h (average load per hour between 0:00 and 24:00)

Observations:• the av. 6-18h load is the most stable indicator at low capacity demand ratios• the 3 hour peak is the best indicator at capacity demand ratios (c/d) around 1• delay sensitivities are rather linear at capacity demand ratios below 0.8.

Conclusion:• we select the 3 hour peak, because it is the most reliable indicator in the area of

current and optimum capacity demand ratios (between 0.85 and 1.2).• the “noise” caused by the variation of hourly traffic distributions on c/d has a maximum

amplitude of +/- 5% throughout Europe (significantly lower for individual ACCs).

Delay Sensitivities: All ACC "no network"

0

100

200

300

400

0 0.5 1 1.5 2

capacity demand ratio (3 hour peak)

del

ay p

er f

ligh

tDelay Sensitivities: All ACC "no network"

0

100

200

300

400

0 0.5 1 1.5 2capacity demand ratio (2 hour peak)

del

ay p

er f

ligh

t


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del

ay p

er f

ligh

t


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capacity demand ratio (av. 6-18h)

del

ay p

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ligh

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capacity demand ratio (av. 0-24h)

del

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t

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0.8 0.9 1 1.1 1.2

0

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0.9 1 1.1 1.2 1.3




6

2.2.2 Hourly Traffic Distribution

The hourly traffic distribution has an influence on the delays caused by the sector or centre.The “noise” can be in the same order of magnitude than the network effects (see nextchapter).

However, investigations have shown that each centre has its individual traffic curve. Themain characteristics of the curve remain rather stable for each ACC. The graphs belowshow the hourly traffic distributions from 51 days in summer 1999 for Karlsruhe UAC,London ACC, Madrid ACC and Reims ACC.

Observation:• Overall, it appears that the phenomena of hourly traffic variation is rather systematic,

repetitive and predictable. Even the most significant variations of the traffic curves(usually observed on Sundays) seem to repeat every week.

Conclusion:• FAP assumes that the characteristics of the traffic load curves of each ACC will remain

within the next 3 to 5 years, as long as no contrary effect is predicted.

• Currently, we don’t know the stability of the traffic load curves over a longer timeframe.However, the impact of a changed traffic load curve on the optimum capacity demandratio (using the peak 3 hour load) is +/-5% at maximum.

EDUU (traffic load 0 to 24h)

0

204060

80100

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fic

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ur

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SundaysSundays

Sundays




7

2.2.3 Network Effects

Currently, Air Traffic Flow Management is affected by interrelations between regulatedairports and sectors/centres being “bottlenecks” in the European ATS capacity network.These interrelations are called “network effects”. The consequence is that comparablecapacity shortfalls in different regions may have diverse impact on the ATFM delays inEurope.

The ATFM simulation tools used by FAP are capable of modelling these network effects.However, there is an astronomic number of scenarios with different network effectsimaginable for the years 2003 and 2005. This chapter investigates therefore, the impact ofnetwork effects on the optimum capacity demand ratio and the risk of non-foreseenchanges to the network.

The investigation focused on the current (Summer 1999) capacity network. A series of 15ATFM simulations was performed for each ACC with varying capacities for one ACC andconstant 1999 capacities for the others. This was done for all ACC using the three differentbaseline scenarios selected to represent summer 1999 conditions.

The left figure below shows the simulation results of all simulations with (red) and without(blue) network effects. On the right: Frankfurt, Barcelona, Amsterdam and Paris asexamples to demonstrate the variety of network effects.

Observations:• Network effects add another “noise” to the delay curves• Some ACC showed reduced, others increased delays with 1999 network effects• Surprisingly: the majority of ACCs showed increased delays with the 1999 capacity

network due to a more complicated slot allocation.

Conclusions:• The current network effects incorporate a low risk for medium term capacity planning.• The risk of under-estimated capacity shortfalls due to network effects is below 3%.• This is compensated by the positive effect of improved slot allocation within a “quasi no

network” (optimum c/d ratio) target of medium term capacity planning.

Delay Sensitivities: "Network" - "No Network"

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capacity demand ratio (3 hour peak)

dela

y p

er fl

igth

[min

]

EDFF

y = 0.6947x-22.07

R2 = 0.7913

0

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4

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8

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0.7 0.8 0.9 1 1.1 1.2capacity demand ratio

del

ay p

er f

lig

ht

EHAA

y = 1.186x-15.837

R2 = 0.9933

0

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4

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8

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del

ay p

er f

ligh

t

LECB

y = 1.7518x-21.143

R2 = 0.9697

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ht

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y = 0.4577x-20.651

R2 = 0.6139

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dela

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r fli

ght




8

2.3 Economic Evaluation

It is to the advantage of a common European Capacity Plan that investments in capacitiescan be evaluated using macro-economical aspects - such as the return on investment (ROI)from the airspace user point of view.

The real costs of the en route ATC service and the airport service (from the airspace userpoint of view) include route charges, landing fees AND the indirect costs such as delaycosts and cost for non-optimum flight profiles. Minimum costs are achieved at the pointwhere the total costs, the sum of capacity AND indirect costs is lowest (see figure below).

The unit costs for the en-route charges show that the marginal cost for capacity variesthroughout Europe with the factor 3-4 (see also EEC Note 8/99 “Cost of the En-Route AirNavigation Services in Europe”).

Conversely, the cost for one minute delay also varies throughout Europe depending on theregional traffic mix (for ACC: 11 – 27 ECU /min delay; for airports: 10 – 24 ECU/min delay).Consequently, every ACC (and every airport) has its own curve. In addition, these curvesinterrelate as network effects change with capacity increases elsewhere.

0.7 0.8 0.9 1 1.1 1.2 1.3

Costs

Capacity

Capacity Cost

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0.9

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Cost per min delay

36 ECU

27 ECU

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9 ECU

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Cost

Capacity/DemandCapacity/Demand

Costs

Capacity / demand ratio

0

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Operating point 1999

Delay cost function

Capacity cost function

Total cost function

1999 Delay cost (CFMU)

1999 Capacity cost (CRCO)

Minimum cost




9

Both the cost for capacity and the cost for delay are regional parameters.They depend on:• total capacity provided• marginal capacity cost (ATC complexity, price index, equipment, etc)• total delay caused• delay sensitivity (network effects, hourly traffic distribution)• cost per minute delay (traffic mix)

Consequently, every ACC has its own cost curves and optimum capacity demand ratio.The 6 figures below show the variety of cost curves observed in Europe ( Frankfurt ACC,London ACC, Barcelona ACC, Marseille ACC, Prague ACC and Zurich ACC ).

In blue: ACC operating point observed for summer 1999.

The optimum capacity demand ratio varies between 0.99 and 1.17 throughout Europe.However, the majority of ACCs have an optimum c/d between 1.03 and 1.08.

Medium-term capacity planning targets the optimum c/d for every ACC. Capacity shortfallsare derived from the computation of current and future capacity demand ratios (C/D) andtheir deviation from the optimum.

LECB

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o)

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Eur

o)

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ts (

Eur

o)

LSAZ

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Cos

ts (

Eur

o)

Optimum Capacity Demand Ratio

0.90

0.95

1.00

1.05

1.10

1.15

1.20

LGM

D

LKAA

LSAG

LGG

G

LRAR

EID

W

LYBA

LBSR

LBW

R

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ENO

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LSAZ

LCCC

LECM

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LRBB

EPW

W

LFBB

ENSV

EKDK

LJLA

ENBD

LIRR

ESUN

LEC

P

LWSS

LTBB

LFFF

LIBB

GCC

C

EG

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LOVV




10

2.4 Course of Simulations

The results of this study are derived from more than 6000 various ATFM simulations plus anumber of economic model runs. This chapter briefly explains the course of the simulationswithin the various phases of the study.

2.4.1 Model CalibrationEach simulation with a new traffic sample requires a complete new set up based on theparameters used by CFMU on that day. This is our first check point for validation.

In the next phase, sector regulations are transferred to centre regulations. The centrecapacities are derived from an iterative simulation process. The process is finished whenthe centre produces the same delays per day as observed by CFMU. This is our calibrationphase.

2.4.2 Scenario 1999 “baseline”

The next phase analyses the current situation in summer 1999.

Delay sensitivities and network effects are investigated for each ACC by a number ofATFM simulations with various centre capacities (more than 5000 simulations in total).The outcome is an average delay curve based on the 1999 network effects for each centreindividually. These curves are used in MECA to identify the capacity demand ratio of acentre in 1999 at a given delay per flight.

ATFMSimulation

19991999“baseline”

Model calibration

CFMU

19991999

0

1

2

3

4

5

6

7

0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4

Capacity / Demand ra tio

Del

ay p

er fl

ight

[min

]

ATFMSimulation

19991999

CFMU

19991999

Delay Sensitivities influenced by the current Capacity Network

0

2

4

6

8

10

12

0.8 0.9 1 1.1 1.2 1.3 1.4

Capacity / Demand ratio

Del

ay p

er fl

ight

[min

]

1999 delay sensitivities

ACC1 - n

1999 capacity demand ratios

ACC1

15 simulationsper ACC withvariouscapacities

For each ACC:

Average delay

per flight

CRCOCRCO

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1,000,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)

Optimum capacity demand ratio

For each ACC:

Capacity cost

ACC1

“current situation”Capacity Shortfall 1999

Cost of delays, IROI

IATAIATAAircraft operating cost

Cost per min. ground delay

MECA




11

The delay curves are also translated into delay cost curves using the IATA estimates for thecost of delays in air transportation. We add the CRCO capacity cost data and MECAcalculates the optimum capacity demand ratio at the point where the sum of cost forcapacity and cost for delay is lowest.

MECA uses the 1999 capacity demand ratio and the optimum capacity demand ratio tocalculate the capacity shortfall in 1999.

The estimated cost for the capacity at the optimum point and the real capacity cost 1999 atthe 1999 capacity demand ratio is used to estimate the cost for the extra capacity.

The estimated cost for the delay at the optimum point and the real cost for the delay in 1999is used to estimate the potential benefits in delays.

The cost for the extra capacity and the potential benefits in delays are used to estimate thereturn on investment (ROI).

2.4.3 Scenarios 2003 and 2005 “do nothing”

Future scenarios are based on a 1999 baseline with a modified traffic sample according tothe STATFOR traffic growth scenarios between 70 families of airports.

2003 and 2005 scenarios are simulated with all airports regulated by the CFMU. We used“global” airport capacities (instead of arrival and/or departure capacities) as they aredeclared by the airports (see also chapter “Airport Capacities”).

All future scenarios are simulated with 6 different traffic pattern:

1. July 3rd 1999 / current routes2. July 3rd 1999 / shortest routes3. July 7th 1999 / current routes4. July 7th 1999 / shortest routes5. July 23rd 1999 / current routes6. July 23rd 1999 / shortest routes

Every traffic pattern is simulated with high and medium traffic growth.We end up with 12 “do nothing” scenarios per year.

ATFMSimulation

19991999 Capacity Shortfall 2005Cost of delays

ATFMSimulation

20052005

Traffic ForecastTraffic Forecast

20052005

+

AirportAirportCapacity PlansCapacity Plans

+

20052005

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1,000 ,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4

Capacity Demand ra tio

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MECA“do nothing”




12

2.4.4 Scenarios 2003 and 2005 “existing plans”

In the next phase, the new 2005 ACC capacities are calculated based on the currentcapacities and the capacity growth foreseen in the national and supra-national ATMcapacity planning.

Again, all 12 scenarios were simulated and analysed.

The forecast delays are directly derived from the ATFM simulations.Additional outputs are derived from the economic evaluation:• cost for the delays,• remaining capacity shortfalls and• return on investment (ROI)

2.5 Assumptions, Constraints and Risks

• Traffic forecast – generalThis study uses traffic-growth data provided by the Specialist Panel on Air TrafficStatistics and Forecast (STATFOR). We believe these data are the mostcomprehensive and consistent in Europe. However, history has shown that air trafficgrowth is difficult to forecast in some years and/or areas, and capacity shortfalls arevery sensitive to the regional traffic growth. We believe therefore, that the traffic growthscenarios are one of the most crucial input data for this study.Risk: high (difficult to control)

• Traffic forecast – Scheduling CommitteeThe traffic forecast is performed in close co-operation with the member States, brokendown into 2000 traffic flows and under consideration of capacity constraints at airports.Nevertheless, many airports are grouped in the same family. We observed someairports that exceed significantly their forecast capacity in 2005 when applying the trafficgrowth forecast for the family. This problem may be either solved by the creation of anew family (STATFOR) or by a new traffic generator that smoothes or reallocates trafficautomatically to neighbouring airports (FAP).Risk: medium (can be further reduced)

ATFMSimulation

20052005

ATFMSimulation

19991999

Traffic ForecastTraffic Forecast

20052005 ExistingExisting

Capacity PlansCapacity PlansATSPATSP

++

AirportAirportCapacity PlansCapacity Plans

20052005

+Capacity Shortfall 2005

Cost of delays, ROI

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1,000,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4

C apa city D ema nd ratio

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13

• ATFM RegulationThe CFMU variable sector regulation per hour was simplified. FAP applies constantACC regulation throughout a single day. As a result, there may be local effects whichhave not been covered here. Validation was made against the observed CFMU delays.Differences in delays are usually below 3%.Risk: low

• Current operating pointsThe current ACC capacities are computed based on observations of multiple days insummer 1999. The accuracy could vary around +/- 5% for all those ACCs that produceddelays in summer 1999. However, variation may be higher for ACCs not working at itsmaximum capacity in 1999. Some of these centres have little knowledge of theirmaximum sector capacities, and/or do not provide CFMU with up to date sectorisationplans. We also observed that the description of some routes passing “zero delaycentres” do not always include all sectors within the ACC. We observed these problemsonly for ACCs operating at very high capacity demand ratios. With the current trafficgrowth rates, we hope that a repetitive capacity analysis will discover unforeseenshortfalls latest 2 years before they become really urgent.Risk: medium (can be controlled (in limits), can be further reduced)

• Existing plans – capacity growthCapacity growth due to capacity enhancing projects are estimated by ATS providers inco-operation with Eurocontrol (see also “ATC Capacity Assessment – Review ofexisting national plans”). The growth figures are taken as an input. They were notsubject to investigation within the frame of this study. However, the results of this studyseem to indicate that medium term capacity planning is performed with varying qualityand reliability throughout Europe.Risk: high (can be controlled/reduced by the ATSPs and Eurocontrol)

• Marginal capacity costThe Capacity Plan identifies the optimum capacity demand ratio for each individualACC. This ratio is derived from the cost of the capacity increase versus the cost of thedelays. We assume the marginal capacity costs to be proportional to the total costs andcapacities provided by the states/ACCs in 1999 (CRCO forecast). This assumption isconfirmed by observations of the last 16 years CRCO capacity cost data.Risk: low

• Airport regulationsAll airports were ATFM regulated in the 2003 and 2005 scenarios. This is confirmed bythe current trend and logical arguments. Positive side effect: airport regulations cause asmoothing of the traffic demand curves comparable with the effect of the flight plan co-ordination conducted by the scheduling committee.Risk: low




14

3. Traffic Growth 1999 – 2005

Traffic growth data are based on STATFOR growth estimates on 2000 flows in Europeapplied to the traffic pattern of the six baseline scenarios representing:- typical weekdays and weekends in summer 1999- actual routes flown and shortest flyable routes

3.1 Shortest Routes and “Open Sky”

The traffic pattern observed in summer 1999 is still influenced by the consequences of theKosovo crisis and some regional capacity constraints. It is assumed that these constraintsare of a short term nature. Consequently, medium term capacity planning cannot rely solelyon the current 1999 traffic pattern.

The objective of medium term ATS capacity planning is to achieve the optimum capacitydemand ratio for every ACC in Europe. The average ATFM delay per flight shall be reducedto 1-2 minutes per average flight. Delays in this order of magnitude should significantlyreduce re-routing due to capacity constraints and we move hopefully more and moretowards an unconstrained route network with a traffic pattern based on the shortest routes.

The effect of an unconstrained capacity network on the traffic pattern is shown by the figurebelow.




15

It appears, the biggest changes can be expected in the South East part of Europe. A re-opening of the Yugoslavian airspace for civil use will significantly re-arrange traffic flows.

ACC Name TrafficLBSR Sofia -24%LBWR Varna -32%LCCC Nicosia -18%LDZO Zagreb 159%LGGG Athens -10%LGMD Makedonia 26%LHCC Budapest -22%LIBB Brindisi -15%LIMM Milan -3%LIPP Padua 0%LIRR Roma -3%LJLA Lubiana 71%LKAA Prague 5%LMMM Malta -6%LOVV Vienna 14%LQSB Sarajevo 385%LRAR Arad -38%LRBB Bucuresti -42%LTAA Ankara -12%LTBB Istambul -9%LWSS Skopje 106%LYBA Beograd 1825%LZBB Bratislava -24%

Other capacity constraints such as those in Switzerland and France appear to have lowerimpact on the traffic through neighbouring ACCs:

ACC Name TrafficEBBU Bruxelles -10%EDFF Frankfurt 1%EDLL Dusseldorf -3%

EDMM Munchen 1%EDUU Karlsruhe 3%EDWW Bremen -1%EDYY Maastricht 3%LFBB Bordeaux 2%LFEE Reims 10%LFFF Paris 0%LFMM Aix/Marseille 3%LFRR Brest 5%LIMM Milan -3%LIPP Padua 0%LIRR Roma -3%LSAG Geneva 11%LSAZ Zurich -6%




16

3.2 Air Traffic Control Centre

The tables below show the minimum and maximum traffic growth observed by the fourtraffic scenarios:

• Medium growth – current routes• Medium growth – shortest routes• High growth – current routes• High growth – shortest routes

Years

Min Max Min Max

Traffic growth 99-03 99-03 99-05 99-05

CENTRE Name [%] [%] [%] [%]EBBU Bruxelles 9% 26% 18% 38%EDBB Berlin 26% 50% 37% 70%EDFF Frankfurt 16% 22% 24% 32%EDLL Dusseldorf 29% 38% 43% 57%EDMM Munchen 26% 33% 39% 49%EDUU Karlsruhe 26% 32% 37% 46%

EDWW Bremen 26% 31% 38% 48%EDYY Maastricht 23% 33% 33% 48%EETT Tallin -30% 15% -24% 24%EFES Tampere 18% 40% 29% 58%EFPS Rovaniemi 37% 42% 53% 79%EGCC Manchester 29% 37% 49% 60%EGPX Scottish 15% 28% 26% 46%EGTT London 20% 26% 31% 40%EHAA Amsterdam 20% 28% 32% 44%EIDW Dublin 23% 28% 39% 49%EISN Shannon 20% 37% 27% 52%EKDK Kobenhavn 16% 25% 25% 36%ENBD Bodo 13% 20% 21% 34%ENOS Oslo 21% 27% 31% 43%ENSV Stavenger 24% 30% 34% 46%ENTR Trondheim 14% 21% 22% 32%

EPWW Warsaw 21% 29% 29% 40%ESMM Malmo 17% 26% 26% 38%ESOS Stockholm 19% 30% 30% 43%ESUN Sundswall 15% 28% 25% 35%EVRR Riga -21% 17% -19% 30%EYVC Vilnius 15% 52% 24% 59%GCCC Canarias 29% 36% 42% 53%LBSR Sofia -7% 29% 3% 45%LBWR Varna -13% 27% -5% 42%LCCC Nicosia -4% 19% 5% 29%LDZO Zagreb 20% 214% 28% 247%LECB Barcelona 32% 42% 47% 59%LECM Madrid 24% 33% 36% 49%LECP Palma 30% 36% 46% 56%LECS Sevilla 20% 32% 31% 46%

2003 2005




17

YearsMin Max Min Max

Traffic growth 99-03 99-03 99-05 99-05CENTRE Name [%] [%] [%] [%]

LFBB Bordeaux 20% 29% 28% 40%LFEE Reims 19% 41% 29% 54%LFFF Paris 13% 18% 18% 26%LFMM Aix/Marseille 24% 35% 34% 48%LFRR Brest 20% 33% 29% 46%LGGG Athens 7% 21% 16% 33%LGMD Makedonia 28% 60% 39% 77%LHCC Budapest -5% 26% 5% 40%LIBB Brindisi -1% 23% 8% 36%LIMM Milan 19% 27% 29% 40%LIPP Padua 24% 30% 36% 45%LIRR Roma 20% 29% 28% 40%LJLA Lubiana 20% 113% 27% 140%LKAA Prague 23% 33% 30% 48%LMMM Malta 11% 29% 20% 37%LOVV Vienna 18% 42% 28% 58%LPPC Lisbon 31% 52% 41% 69%LQSB Sarajevo 12% 492% 19% 550%LRAR Arad -26% 26% -19% 40%LRBB Bucuresti -29% 27% -22% 41%LSAG Geneva 24% 44% 37% 59%LSAZ Zurich 16% 29% 25% 43%LTAA Ankara 8% 28% 15% 41%LTBB Istambul 16% 31% 27% 46%LWSS Skopje 6% 166% 6% 206%LYBA Beograd 13% 2300% 13% 2638%LZBB Bratislava -5% 29% 4% 42%

2003 2005




18

3.3 Airports

The STATFOR growth estimates are estimated for 70 families of airports. Significantcapacity constraints at airports were taken into account (for example Frankfurt). However,regional differences for airports belonging to the same airports were not made.

Years

Min Max Min Max

Traffic growth 99-03 99-03 99-05 99-05

Airport Name [%] [%] [%] [%] EBBR Brussels 22% 28% 32% 43% EDDB Berlin/Schonefeld 53% 59% 65% 106% EDDC Dresden 20% 30% 30% 43% EDDF Frankfurt 0% 5% 0% 7% EDDH Hamburg 32% 39% 45% 59% EDDI Berlin/Tempelhof 27% 36% 39% 52% EDDK Koln 33% 45% 48% 65% EDDL Düsseldorf 40% 49% 58% 71% EDDM Munich 36% 43% 49% 63% EDDN Nürnberg 31% 40% 45% 57% EDDP Leipzig - Halle 52% 70% 70% 96% EDDS Stuttgart 36% 43% 53% 69% EDDT Berlin/Tegel 29% 44% 43% 60% EDDV Hannover 38% 45% 60% 72% EETN Tallin 30% 50% 50% 60% EFHK Helsinki 16% 24% 29% 38% EGAA Belfast 23% 42% 45% 55% EGAC Belfast 12% 24% 20% 36% EGBB Birmingham 35% 42% 47% 62% EGCC Manchester 28% 37% 48% 61% EGGW London/Luton 17% 26% 24% 38% EGKK London/Gatwick 11% 19% 18% 28% EGLL London/Heathrow 13% 20% 20% 30% EGNX Derby 19% 34% 31% 50% EGSS London/Stansted 15% 22% 26% 34% EHAM Amsterdam/Schipol 24% 30% 36% 46% EHBK Maastricht 50% 75% 63% 100% EHGG Groningen 50% 50% 58% 83% EHRD Rotterdam 32% 41% 50% 64% EKBI Billund 16% 24% 28% 44% EKCH Kobenhavn 22% 29% 29% 41% ELLX Luxembourg 12% 19% 14% 24% ENBR Bergen 21% 30% 32% 45% ENGM Oslo - Gardermoen 19% 25% 28% 41% ENZV Stavanger 23% 31% 37% 46% EPWA Warsaw 17% 28% 30% 40% ESGG Goteborg 21% 29% 32% 45% ESMS Malmo 12% 12% 24% 53% ESSA Stockholm - Arlanda 21% 28% 30% 39% EVRA Riga 9% 18% 9% 18% EYVI Vilnius 0% 8% 8% 8% GCFV Puerto del Rosario 29% 46% 42% 58% GCLP Las Palmas - Gran Canar 33% 40% 47% 58% GCRR Arrecife 38% 48% 52% 62% GCTS Tenerife Sur 31% 35% 44% 56%

2003 2005




19

Years

Min Max Min MaxTraffic growth 99-03 99-03 99-05 99-05

Airport Name [%] [%] [%] [%] LBSF Sofia 14% 18% 18% 36% LCLK Larnaca 20% 24% 28% 56% LDZA Zagreb 5% 10% 10% 29% LEAL Alicante 27% 32% 38% 49% LEBL Barcelona 26% 37% 40% 54% LEGE Gerona 25% 25% 25% 38% LEIB Ibiza 26% 33% 42% 51%

LEMD Madrid Barajas 24% 29% 39% 48% LEMG Malaga 27% 31% 41% 53% LEMH Menorca 41% 50% 50% 66% LEPA Palma de Mallorca 33% 39% 50% 59% LFBO Toulouse 14% 22% 22% 36% LFLL Lyon 17% 24% 28% 37% LFML Marseille 21% 29% 30% 41% LFMN Nice 22% 27% 29% 39% LFPB Paris/Le Bourget 8% 14% 11% 19% LFPG Paris/Charles de Gaulle 9% 14% 12% 19% LFPO Paris/Orly 4% 9% 5% 12% LFQQ Lille 23% 23% 32% 45% LFRS Nantes 20% 30% 33% 43% LFSB Bâle-Mulhouse 21% 29% 34% 43% LFST Strasbourg 19% 24% 30% 38% LGAT Athens 17% 23% 24% 34% LGIR Heraklion 33% 42% 49% 63% LGKO Kos 27% 27% 33% 53% LGKR Corfu 23% 27% 36% 45% LGRP Rhodes 18% 25% 29% 46% LGTS Thessaloniki 38% 47% 50% 62% LHBP Budapest 16% 24% 22% 33% LIMF Turino 27% 33% 37% 50% LIML Milan/Malpensa+Linate 24% 30% 34% 43% LIPZ Venice 20% 30% 30% 36% LIRF Roma/Fiumicino 19% 28% 25% 40% LJLJ Ljubljana 7% 7% 7% 7% LKPR Prague 11% 18% 14% 21% LMML Valleta 22% 22% 35% 48% LOWS Salzburg 16% 26% 26% 39% LOWW Vienna 15% 22% 21% 30% LPFR Faro 33% 46% 49% 69% LPFU Funchal 18% 45% 18% 45% LPPR Porto 25% 34% 31% 41% LPPT Lisbon 29% 41% 40% 53% LROP Bucharest 9% 13% 13% 30% LSGG Geneva 19% 27% 27% 39% LSZH Zurich 20% 26% 26% 34% LTBA Istambul/Ataturk 35% 42% 44% 61%

2003 2005




20

4. Existing Plans - Capacity Growth

4.1 Air Traffic Control Centre

The table below shows the planned evolution of ACC capacities between 1999 and 2005.

The future capacity growth (in yellow) is estimated by ATS providers and Eurocontrol basedon the existing national and supra-national capacity plans. The growth figures are taken asan input. They were not subject to investigation within the frame of this study.

The current ACC capacities are estimated by FAP based on multiple days traffic pattern,sector capacities, traffic load and delays. Note that ACC capacities are not a constant. Theydepend on the traffic pattern and the actual sectorisation. The 1999 capacities, shown inthis table below, represent an average observed at multiple days in summer 1999. Theaccuracy could vary around +/- 5% (reliability: H). However, variation may be higher forACCs not working at their maximum capacities in 1999. Some of these centres have littleknowledge of their maximum sector capacities, and/or do not provide CFMU with up to datesector configurations. We also observed that the description of some routes passing “zerodelay centres” do not always include all sectors within the ACC (reliability: L).

1999 1999 2000 2001 2002 2003 2004 2005 2005reliability capacity capacity capacity capacity capacity capacity

ICAO capacity cap. estimate increase increase increase increase increase increase capacityName code L/M/H [%] [%] [%] [%] [%] [%]Bruxelles EBBU 138 M 10% 152Berlin EDBB 123 M 10% 15% 15% 179Frankfurt EDFF 166 M/H 15% 5% 5% 20% 253Dusseldorf EDLL 132 M/L 6% 6% 10% 5% 20% 206Munchen EDMM 180 M 5% 18% 10% 5% 256Karlsruhe EDUU 150 H 5% 5% 20% 15% 5% 240Bremen EDWW 115 M/L 10% 15% 145Maastricht EDYY 218 H 7% 14% 7% 7% 7% 326Tallin EETT 30 H 10% 33Tampere EFES 60 M 10% 10% 73Rovaniemi EFPS 25 H 10% 28Manchester EGCC 108 H 2% 110Scottish EGPX 138 M 6% 6% 6% 164London EGTT 304 H 4% 4% 4% 6% 5% 3% 392Amsterdam EHAA 118 H 4% 5% 5% 5% 142Dublin EIDW 39 M/H 5% 41Shannon EISN 88 L 15% 5% 106Kobenhavn EKDK 126 M/L 5% 15% 15% 175Bodo ENBD 44 M 15% 51Oslo ENOS 65 L 15% 75Stavenger ENSV 41 M 15% 47Trondheim ENTR 31 M/H 15% 36Warsaw EPWW 55 H 10% 10% 10% 73Malmo ESMM 103 L 10% 15% 130Stockholm ESOS 125 L/M 10% 15% 158Sundswall ESUN 40 M 40Riga EVRR 51 M 3% 53Vilnius EYVC 40 M 40Canarias GCCC 50 H 15% 58




21

A more detailed explanation of the capacity enhancing projects and the resulting capacityincreases and the target dates of implementation broken down by ACC is given in anotherdocument issued by Eurocontrol:

“ATC Capacity Assessment – Review of existing national plans” Brussels, Aug. 1999.

1999 1999 2000 2001 2002 2003 2004 2005 2005reliability capacity capacity capacity capacity capacity capacity

ICAO capacity cap. Estimate increase increase increase increase increase increase capacityName code L/M/H [%] [%] [%] [%] [%] [%]Sofia LBSR 99 L 5% 5% 15% 10% 5% 5% 152Varna LBWR 59 L 3% 5% 15% 5% 2% 2% 80Nicosia LCCC 40 M 5% 15% 48Zagreb LDZO 33 L 5% 15% 40Barcelona LECB 105 H 3% 15% 124Madrid LECM 126 H 3% 15% 149Palma LECP 72 M 72Sevilla LECS 60 H 3% 15% 71Bordeaux LFBB 146 M 15% 168Reims LFEE 121 M/H 10% 10% 15% 168Paris LFFF 208 M/H 10% 229Aix/Marseille UAC LFMM 137 H 10% 10% 15% 191Brest LFRR 146 M/H 10% 15% 185Athens LGGG 70 H 10% 10% 10% 93Makedonia LGMD 40 L 10% 10% 10% 53Budapest LHCC 95 H 10% 15% 30% 156Brindisi LIBB 49 H 5% 15% 59Milan LIMM 142 H 10% 15% 180Padua LIPP 90 H 10% 15% 114Roma LIRR 142 M/H 5% 15% 171Lubiana LJLA 31 L/M 8% 8% 10% 30% 52Prague LKAA 64 H 15% 74Malta LMMM 32 M/H 5% 34Vienna LOVV 120 M 10% 5% 15% 5% 167Lisbon LPPC 57 H 5% 5% 10% 69Sarajevo LQSB 25 M 25Arad LRAR 110 L/M 15% 127Bucuresti LRBB 106 L 15% 122Geneva LSAG 104 H 15% 10% 132Zurich LSAZ 137 H 10% 10% 166Ankara LTAA 70 L 5% 10% 5% 85Istambul LTBB 80 L 5% 15% 5% 5% 107Skopje LWSS 27 L/M 5% 10% 31Beograd LYBA 45 L 45Bratislava LZBB 59 H 5% 10% 68




22

4.2 Airport Capacity

The table below shows the global capacities of the airports and their future development.(in black: capacity declared by the airport; in grey: estimated by EUROCONTROL)

ICAO Airport Capacity capacity capacity capacity

Code 1999 2000 2003 2005

EBBR Brussels 68 72 72 80

EDDB Berlin/Schonefeld 30 37 37 37

EDDC Dresden 30 30 30 30

EDDF Frankfurt 76 76 80 80

EDDH Hamburg 45 45 48 48

EDDI Berlin/Tempelhof 16 16 16 16

EDDK Koln 52 66 66 66

EDDL Düsseldorf 38 38 38 40

EDDM Munich 80 90 90 90

EDDN Nürnberg 30 30 30 30

EDDP Leipzig - Halle 30 43 43 43

EDDS Stuttgart 38 38 38 38

EDDT Berlin/Tegel 36 36 36 36

EDDV Hannover 50 50 50 50

EETN Tallin 22 22 22 22

EFHK Helsinki 48 48 48 48

EGAA Belfast 18 18 18 18

EGAC Belfast 14 14 14 14

EGBB Birmingham 38 38 38 38

EGCC Manchester 47 54 54 63

EGGW London/Luton 16 16 16 16

EGKK London/Gatwick 48 48 48 48

EGLL London/Heathrow 78 78 78 78

EGNX Derby 30 30 30 30

EGSS London/Stansted 38 38 40 40

EHAM Amsterdam/Schipol 104 105 108 115

EHBK Maastricht 15 15 15 15

EHGG Groningen 60 60 60 60

EHRD Rotterdam 30 30 30 30

EKBI Billund 45 45 45 60

EKCH Kobenhavn 81 81 91 91

ELLX Luxembourg 35 35 35 35

ENBR Bergen 29 29 30 30

ENGM Oslo - Gardermoen 60 60 80 80

ENZV Stavanger 29 31 31 31

EPWA Warsaw 35 35 35 35

ESGG Goteborg 30 30 30 30

ESMS Malmo 30 30 30 30

ESSA Stockholm - Arlanda 70 70 90 90

EVRA Riga 40 50 50 50

EYVI Vilnius 60 60 60 60

GCFV Puerto del Rosario 10 12 20 30

GCLP Las Palmas - Gran Canaria 34 36 38 40

GCRR Arrecife 15 17 20 30

GCTS Tenerife Sur 35 35 35 35

LBSF Sofia 20 20 20 20

LCLK Larnaca 25 25 30 30




23

ICAO Airport Capacity capacity capacity capacity

Code 1999 2000 2003 2005

LDZA Zagreb 29 29 29 29

LEAL Alicante 18 30 30 35

LEBL Barcelona 47 52 55 70

LEGE Gerona 12 12 12 12

LEIB Ibiza 18 22 22 30

LEMD Madrid Barajas 60 75 75 100

LEMG Malaga 35 35 40 40

LEMH Menorca 16 18 20 30

LEPA Palma de Mallorca 60 60 70 75

LFBO Toulouse 35 35 35 35

LFLL Lyon 50 50 50 55

LFML Marseille 30 30 30 35

LFMN Nice 49 49 49 49

LFPB Paris/Le Bourget 45 45 45 45

LFPG Paris/Charles de Gaulle 95 95 95 110

LFPO Paris/Orly 70 70 70 70

LFQQ Lille 30 30 30 30

LFRS Nantes 15 15 15 15

LFSB Bâle-Mulhouse 20 20 20 20

LFST Strasbourg 20 20 20 20

LGAT Athens 32 40 40 60

LGIR Heraklion 12 14 14 14

LGKO Kos 5 10 10 10

LGKR Corfu 10 10 10 10

LGRP Rhodes 13 13 13 13

LGTS Thessaloniki 13 13 20 20

LHBP Budapest 40 40 40 40

LIMF Turino 32 32 32 32

LIML/C Milan/Malpensa+Linate 58 58 61 70

LIPZ Venice 24 28 28 30

LIRF Roma/Fiumicino 68 70 72 80

LJLJ Ljubljana 20 20 23 23

LKPR Prague 45 45 45 45

LMML Valleta 15 15 15 15

LOWS Salzburg 20 20 20 20

LOWW Vienna 65 65 65 65

LPFR Faro 19 24 24 32

LPFU Funchal 6 12 12 12

LPPR Porto 15 30 30 30

LPPT Lisbon 30 30 30 30

LROP Bucharest 35 35 35 35

LSGG Geneva 38 38 38 38

LSZH Zurich 60 60 60 60

LTBB Istambul/Ataturk 36 36 40 40




24

5. Capacity Shortfalls in the Year 2003

5.1 Traffic Growth: medium

Legend

0.7 0.8 0.9 1 1.1 1.2 1.3


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Europe 1999-2003Traffic growth: 21%

Scen.: Shortest RoutesDelay per flight: 15 min

Scen.: Current RoutesDelay per flight: 5 min




25

5.2 Traffic Growth: high

Legend

0.7 0.8 0.9 1 1.1 1.2 1.3


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5.3 All Scenarios: En-route

Scenario

Traffic growth medium high medium highRoutes current current shortest shortest

Name Regioncapacity shortfall

capacity shortfall

capacity shortfall

capacity shortfall

Bruxelles EBBU -2% 3% -12% -7%Berlin EDBB -19% -13% -3% 2%Frankfurt EDFF -6% -1% -6% -1%Dusseldorf EDLL 6% 10% 3% 8%Munchen EDMM -21% -15% -19% -14%Karlsruhe EDUU 5% 10% 5% 10%Bremen EDWW -47% -41% -47% -41%Maastricht EDYY 7% 11% 11% 15%Tallin EETT -68% -60% -165% -145%Tampere EFES -69% -61% -48% -42%Rovaniemi EFPS -206% -194% -206% -194%Manchester EGCC 6% 10% 4% 9%Scottish EGPX -15% -10% -23% -18%London EGTT 6% 10% 6% 10%Amsterdam EHAA -1% 3% -3% 2%Dublin EIDW 20% 24% 20% 24%Shannon EISN -10% -5% 0% 4%Kobenhavn EKDK -34% -27% -38% -32%Bodo ENBD -84% -77% -80% -73%Oslo ENOS 4% 8% 3% 8%Stavenger ENSV -12% -6% -12% -6%Trondheim ENTR -34% -29% -36% -31%Warsaw EPWW 11% 15% 13% 17%Malmo ESMM -22% -17% -18% -13%Stockholm ESOS -97% -89% -88% -80%Sundswall ESUN -132% -117% -122% -109%Riga EVRR -99% -92% -188% -173%Vilnius EYVC -110% -98% -65% -58%Canarias GCCC 12% 16% 13% 17%Sofia LBSR -68% -60% -125% -114%Varna LBWR -91% -83% -170% -161%Nicosia LCCC 3% 8% -16% -11%Zagreb LDZO -70% -64% 36% 38%Barcelona LECB 23% 26% 25% 29%Madrid LECM 18% 21% 15% 19%Palma LECP 24% 27% 24% 27%Sevilla LECS 9% 13% 4% 9%

"existing plans" 2003

Capacity Shortfall(above 5%)

Optimum Capacity(5 to –15%)

Spare Capacity(below –15%)Legend




27

Note: Capacity shortfalls above 5% (red/orange) are leading to very high delays.

Figures in green indicate a capacity forecast in the area of the best trade-off between cost for capacity and cost for delays. Due to the characteristics of the cost curve a tolerance between 5% capacity shortfall and -15% (spare capacity) is acceptable.

Negative capacity shortfalls (grey) indicate that the maximum capacity provided is probably above the capacity needed to operate at the optimum operating point (sufficient technical capacity). However, it can be assumed that some sectors are closed or combined at some time and thus the operating point is in fact closer to the optimum. Consequently, staff levels may have to be increased to open existing sectors for longer time intervals in the future.

Scenario



capacity shortfall

capacity shortfall

capacity shortfall

Bordeaux LFBB 2% 7% 5% 9%Reims LFEE -1% 3% 11% 15%Paris LFFF 10% 14% 10% 14%Aix/Marseille LFMM 3% 7% 7% 11%Brest LFRR -5% -1% 1% 5%Athens LGGG -3% 2% -11% -6%Makedonia LGMD -5% 0% 15% 19%Budapest LHCC -21% -16% -58% -51%Brindisi LIBB 4% 9% -13% -9%Milan LIMM 5% 9% 3% 8%Padua LIPP 15% 19% 15% 18%Roma LIRR 4% 8% 2% 6%Lubiana LJLA -122% -114% -28% -23%Prague LKAA 29% 33% 32% 36%Malta LMMM -121% -106% -139% -127%Vienna LOVV -49% -43% -29% -24%Lisbon LPPC 14% 18% 23% 27%Sarajevo LQSB -135% -127% 58% 60%Arad LRAR -5% 0% -77% -69%Bucuresti LRBB 2% 6% -74% -66%Geneva LSAG 27% 30% 35% 39%Zurich LSAZ 30% 33% 24% 28%Ankara LTAA -20% -15% -36% -30%Istambul LTBB -8% -3% -16% -11%Skopje LWSS -205% -197% -27% -21%Beograd LYBA -1533% -1533% 28% 31%Bratislava LZBB 25% 29% -2% 3%








28

5.4 Airports

Scenario



capacity shortfall

capacity shortfall

capacity shortfall

Brussels EBBR 11% 15% 12% 16%Berlin/Schonefeld EDDB -306% -306% -322% -322%Dresden EDDC -145% -132% -138% -126%Frankfurt EDDF 4% 8% 5% 9%Hamburg EDDH -11% -6% -10% -5%Berlin/Tempelhof EDDI -9% -4% -7% -2%Koln EDDK -85% -77% -93% -84%Düsseldorf EDDL 33% 35% 35% 37%Munich EDDM 11% 14% 9% 13%Nürnberg EDDN -59% -50% -56% -48%Leipzig - Halle EDDP -244% -226% -264% -244%Stuttgart EDDS 0% 5% 0% 5%Berlin/Tegel EDDT -6% -3% 0% 5%Hannover EDDV -98% -90% -100% -92%Tallin EETN -403% -403% -335% -335%Helsinki EFHK -30% -24% -33% -27%Belfast EGAA -37% -30% -27% -18%Belfast EGAC -45% -45% -35% -30%Birmingham EGBB -4% 1% -3% 2%Manchester EGCC 11% 15% 9% 13%London/Luton EGGW 9% 14% 7% 13%London/Gatwick EGKK 20% 23% 18% 21%London/Heathrow EGLL 23% 27% 25% 28%Derby EGNX -115% -104% -132% -120%London/Stansted EGSS 0% 4% 2% 6%Amsterdam/Schipol EHAM 9% 13% 9% 13%Maastricht EHBK -62% -56% -83% -83%Groningen EHGG -895% -895% -895% -895%Rotterdam EHRD -205% -205% -195% -185%Billund EKBI -361% -345% -330% -330%Kobenhavn EKCH -44% -37% -43% -36%Luxembourg ELLX -118% -105% -118% -105%Bergen ENBR -50% -43% -53% -45%Oslo - Gardermoen ENGM -63% -56% -62% -55%Stavanger ENZV -111% -102% -106% -97%Warsaw EPWA -86% -76% -79% -70%Goteborg ESGG -91% -79% -91% -79%Malmo ESMS -369% -369% -369% -369%Stockholm - Arlanda ESSA -71% -64% -69% -63%Riga EVRA -1145% -1145% -1049% -1049%Vilnius EYVI -1280% -1280% -1395% -1395%Puerto del Rosario GCFV -89% -77% -77% -66%Las Palmas - Gran Canar GCLP -43% -38% -45% -39%Arrecife GCRR -41% -35% -45% -38%Tenerife Sur GCTS -62% -57% -62% -57%








29



Negative capacity shortfalls (grey) indicate that the maximum capacity provided is probably above the capacity needed to operate at the optimum operating point (sufficient technical capacity).

Scenario



capacity shortfall

capacity shortfall

capacity shortfall

Toulouse LFBO -54% -47% -49% -43%Lyon LFLL -39% -33% -38% -31%Marseille LFML -10% -6% -13% -9%Nice LFMN -5% 0% -5% 0%Paris/Le Bourget LFPB -233% -216% -233% -216%Paris/Charles de Gaulle LFPG 8% 12% 8% 12%Paris/Orly LFPO -14% -9% -14% -9%Lille LFQQ -228% -228% -228% -228%Nantes LFRS -20% -13% -17% -10%Bâle-Mulhouse LFSB 50% 53% 49% 52%Strasbourg LFST -31% -25% -31% -25%Athens LGAT -9% -4% -8% -3%Heraklion LGIR 34% 36% 31% 35%Kos LGKO -53% -53% -53% -53%Corfu LGKR -6% -2% -6% -2%Rhodes LGRP -13% -6% -13% -6%Thessaloniki LGTS -23% -15% -23% -15%Budapest LHBP -106% -95% -102% -92%Turino LIMF -148% -135% -148% -135%Milan/Malpensa+Linate LIML 35% 38% 35% 38%Venice LIPZ -53% -45% -51% -42%Roma/Fiumicino LIRF -5% 0% -1% 3%Ljubljana LJLJ -326% -326% -326% -326%Prague LKPR -109% -100% -113% -103%Valleta LMML -56% -56% -56% -56%Salzburg LOWS -62% -53% -57% -49%Vienna LOWW -34% -29% -36% -30%Faro LPFR -26% -21% -33% -28%Funchal LPFU -120% -120% -172% -172%Porto LPPR -120% -109% -120% -104%Lisbon LPPT 8% 11% 3% 6%Bucharest LROP -299% -299% -315% -315%Geneva LSGG 7% 12% 5% 10%Zurich LSZH 30% 33% 30% 34%Istambul/Ataturk LTBA -235% -219% -235% -219%








30

6. Capacity Shortfalls in the Year 2005

6.1 Traffic Growth: Medium

Legend

0.7 0.8 0.9 1 1.1 1.2 1.3


Cos

ts (

Eur

o)







31

6.2 Traffic Growth: High

Legend

0.7 0.8 0.9 1 1.1 1.2 1.3


Cos

ts (

Eur

o)







32

6.3 All Scenarios: En-route

Scenario



capacity shortfall

capacity shortfall

capacity shortfall

Bruxelles EBBU 6% 11% -3% 3%Berlin EDBB -26% -18% -7% 0%Frankfurt EDFF -20% -13% -20% -13%Dusseldorf EDLL -7% 0% -10% -4%Munchen EDMM -10% -3% -9% -2%Karlsruhe EDUU -5% 1% -6% 1%Bremen EDWW -34% -26% -33% -24%Maastricht EDYY 1% 7% 6% 12%Tallin EETT -57% -47% -145% -128%Tampere EFES -54% -45% -34% -25%Rovaniemi EFPS -148% -134% -174% -156%Manchester EGCC 19% 24% 18% 23%Scottish EGPX -3% 3% -12% -5%London EGTT 7% 12% 7% 12%Amsterdam EHAA 9% 15% 6% 12%Dublin EIDW 26% 31% 26% 31%Shannon EISN -9% -2% 4% 10%Kobenhavn EKDK -44% -35% -48% -39%Bodo ENBD -71% -60% -65% -54%Oslo ENOS 13% 19% 12% 17%Stavenger ENSV -1% 5% -4% 3%Trondheim ENTR -25% -17% -27% -19%Warsaw EPWW 8% 14% 10% 16%Malmo ESMM -13% -6% -10% -3%Stockholm ESOS -80% -70% -74% -63%Sundswall ESUN -109% -97% -113% -101%Riga EVRR -85% -72% -178% -163%Vilnius EYVC -95% -85% -60% -51%Canarias GCCC 20% 25% 21% 26%Sofia LBSR -68% -57% -124% -111%Varna LBWR -81% -70% -157% -141%Nicosia LCCC 9% 15% -6% 1%Zagreb LDZO -60% -48% 41% 45%Barcelona LECB 31% 36% 33% 37%Madrid LECM 25% 30% 23% 28%Palma LECP 32% 37% 33% 37%Sevilla LECS 17% 23% 13% 19%








33



Negative capacity shortfalls (grey) indicate that the maximum capacity provided is probably above the capacity needed to operate at the optimum operating point (sufficient technical capacity). However, it can be assumed that some sectors are closed or combined at some time and thus the operating point is in fact closer to the optimum. Consequently, staff levels may have to be increased to open existing sectors for longer time intervals in the future.

Scenario



capacity shortfall

capacity shortfall

capacity shortfall

Bordeaux LFBB 9% 15% 12% 17%Reims LFEE 6% 12% 17% 23%Paris LFFF 14% 19% 14% 19%Aix/Marseille LFMM 11% 16% 14% 20%Brest LFRR 2% 8% 8% 14%Athens LGGG 6% 12% -2% 4%Makedonia LGMD 4% 11% 23% 28%Budapest LHCC -11% -3% -42% -33%Brindisi LIBB 12% 17% -4% 3%Milan LIMM 12% 18% 11% 16%Padua LIPP 22% 27% 23% 28%Roma LIRR 11% 16% 8% 14%Lubiana LJLA -109% -95% -15% -8%Prague LKAA 34% 39% 39% 44%Malta LMMM -106% -92% -121% -106%Vienna LOVV -38% -30% -18% -11%Lisbon LPPC 21% 26% 30% 35%Sarajevo LQSB -119% -106% 61% 64%Arad LRAR 4% 10% -61% -50%Bucuresti LRBB 10% 16% -57% -47%Geneva LSAG 27% 32% 34% 39%Zurich LSAZ 29% 33% 23% 28%Ankara LTAA -17% -10% -34% -26%Istambul LTBB -9% -2% -18% -10%Skopje LWSS -205% -180% -13% -5%Beograd LYBA -1533% -1533% 36% 41%Bratislava LZBB 32% 37% 8% 15%








34

6.4 Airports

Scenario



capacity shortfall

capacity shortfall

capacity shortfall

Brussels APT EBBR 9% 15% 10% 16%Berlin/Schonefeld APT EDDB -242% -212% -291% -265%Dresden APT EDDC -120% -104% -126% -109%Frankfurt APT EDDF 4% 10% 5% 11%Hamburg APT EDDH -1% 6% 2% 8%Berlin/Tempelhof APT EDDI 1% 7% 3% 9%Koln APT EDDK -66% -55% -73% -63%Düsseldorf APT EDDL 38% 42% 39% 43%Munich APT EDDM 20% 25% 18% 23%Nürnberg APT EDDN -43% -33% -40% -31%Leipzig - Halle APT EDDP -202% -182% -226% -202%Stuttgart APT EDDS 12% 17% 15% 21%Berlin/Tegel APT EDDT 4% 9% 10% 15%Hannover APT EDDV -71% -60% -71% -60%Tallin APT EETN -335% -335% -308% -308%Helsinki APT EFHK -19% -11% -18% -11%Belfast APT EGAA -15% -8% -15% -8%Belfast APT EGAC -35% -22% -26% -19%Birmingham APT EGBB 6% 12% 8% 15%Manchester APT EGCC 10% 16% 9% 15%London/Luton APT EGGW 18% 22% 13% 18%London/Gatwick APT EGKK 24% 29% 23% 28%London/Heathrow APT EGLL 28% 33% 30% 34%Derby APT EGNX -95% -83% -109% -95%London/Stansted APT EGSS 9% 15% 9% 15%Amsterdam/Schipol APT EHAM 12% 17% 12% 18%Maastricht APT EHBK -45% -36% -68% -56%Groningen APT EHGG -795% -713% -842% -795%Rotterdam APT EHRD -168% -145% -168% -145%Billund APT EKBI -458% -409% -440% -395%Kobenhavn APT EKCH -36% -28% -32% -24%Luxembourg APT ELLX -114% -101% -109% -97%Bergen APT ENBR -36% -27% -40% -31%Oslo - Gardermoen APT ENGM -47% -37% -51% -41%Stavanger APT ENZV -89% -77% -89% -77%Warsaw APT EPWA -64% -54% -67% -57%Goteborg APT ESGG -68% -59% -75% -65%Malmo APT ESMS -270% -241% -324% -324%Stockholm - Arlanda APT ESSA -59% -49% -60% -50%Riga APT EVRA -1145% -1145% -1049% -1049%Vilnius APT EYVI -1280% -1280% -1280% -1280%Puerto del Rosario APT GCFV -160% -145% -145% -132%Las Palmas - Gran Canar APT GCLP -38% -30% -36% -28%Arrecife APT GCRR -100% -86% -100% -86%Tenerife Sur APT GCTS -47% -37% -45% -35%








35



Negative capacity shortfalls (grey) indicate that the maximum capacity provided is probably above the capacity needed to operate at the optimum operating point (sufficient technical capacity).

Scenario



capacity shortfall

capacity shortfall

capacity shortfall

Toulouse APT LFBO -43% -33% -37% -28%Lyon APT LFLL -38% -30% -40% -31%Marseille APT LFML -23% -14% -22% -13%Nice APT LFMN 3% 9% 1% 8%Paris/Le Bourget APT LFPB -224% -202% -224% -202%Paris/Charles de Gaulle APT LFPG -5% 2% -5% 2%Paris/Orly APT LFPO -14% -7% -13% -6%Lille APT LFQQ -205% -176% -205% -185%Nantes APT LFRS -7% 0% -7% 0%Bâle-Mulhouse APT LFSB 55% 58% 55% 58%Strasbourg APT LFST -20% -13% -20% -13%Athens APT LGAT -56% -46% -54% -44%Heraklion APT LGIR 39% 44% 40% 45%Kos APT LGKO -38% -25% -45% -38%Corfu APT LGKR 5% 11% 5% 11%Rhodes APT LGRP -3% 5% 2% 10%Thessaloniki APT LGTS -10% -4% -13% -6%Budapest APT LHBP -95% -85% -89% -80%Turino APT LIMF -129% -113% -124% -108%Milan/Malpensa+Linate APT LIML 31% 35% 30% 35%Venice APT LIPZ -53% -45% -53% -45%Roma/Fiumicino APT LIRF -12% -5% -6% 1%Ljubljana APT LJLJ -326% -326% -326% -326%Prague APT LKPR -103% -94% -106% -94%Valleta APT LMML -40% -27% -40% -27%Salzburg APT LOWS -49% -38% -45% -35%Vienna APT LOWW -28% -20% -29% -22%Faro APT LPFR -50% -40% -61% -50%Funchal APT LPFU -120% -120% -172% -172%Porto APT LPPR -109% -95% -109% -95%Lisbon APT LPPT 13% 18% 10% 16%Bucharest APT LROP -299% -257% -284% -245%Geneva APT LSGG 13% 19% 12% 17%Zurich APT LSZH 34% 38% 34% 38%Istambul/Ataturk APT LTBA -214% -193% -199% -180%








36

7. Trend Analysis: Delays and Costs

The figures below shows the future trend of ATFM delays in Europe for each of the fourscenarios individually.

All scenarios show the same trend. The ATFM delays will significantly increase in 2003 and2005 if no complementary actions are taken.

The significant differences between 2003 and 2005 results highlight a lack of longer termcapacity planning throughout Europe.

The surprising difference between “current” and “shortest” routes scenarios indicate that thefuture European capacity network is probably better adapted to the constraints of thecurrent “avoidance scheme” than to the capacity needs of the airspace user.

Another significant growth of ATFM delays and the resulting costs is forecast for theairports. This may indicate that capacity shortfalls on the ground are getting an increasingimportance in the future. However, some of these delays (and costs) may be hidden by themeans of flight plan co-ordination: Traffic peaks may be pre-smoothed over the day. Un-accommodated demand may move to adjacent airports.

Current Routes - Medium Growth

0

10

20

30

40

5060

70

1999 2003 2005

dela

y pe

r fligh

t [m

in]

En-Route

Airport

Current Routes - High Growth

010203040506070

1999 2003 2005de

lay

per

fligh

t [m

in]

En-Route

Airport

Shortest Routes - Medium Growth

0

10

20

30

40

50

60

70

1999 2003 2005

delay

per fligh

t [m

in] En-Route

Airport

Shortest Routes - High Growth

0

10

20

30

40

50

60

70

1999 2003 2005

dela

y pe

r fligh

t [m

in] En-Route

Airport

Year Routes Traffic Growth DELAY DELAY COST DELAY DELAY COST DELAY DELAY COST[min/flight] [Meuro/day] [min/flight] [Meuro/day] [min/flight] [Meuro/day]

1999 current 5.7 3.7 0.9 0.6 6.5 4.3Medium 5.2 4.2 13.9 11.2 19.0 15.3

High 10.7 8.9 19.2 16.1 29.8 24.9Medium 15.4 12.4 10.4 8.3 25.7 20.6

High 21.2 17.7 14.1 11.8 35.2 29.4Medium 17.1 14.9 16.5 14.3 33.5 29.1

High 28.7 26.6 22.8 21.1 51.5 47.6Medium 27.4 23.8 12.4 10.8 39.8 34.5

High 36.8 33.9 18.5 17.0 55.2 50.9

En-route Airports TOTAL

2003 current

shortest

2005 current

shortest




37

8. Conclusion

The European ATM system is not yet prepared for the years 2003 and 2005.

The existing capacity enhancement plans are not sufficient to reduce the ATFM delays inthe medium term future. On the contrary, delays may increase significantly. The cost fordelays in Europe may reach the same order of magnitude as the cost for fuel or the cost ofthe entire European Air Navigation Services.

The majority of the European Air Traffic Control Centre (39 out of 65) risk to havecapacity shortfalls in at least one of the scenarios tested (see table below).

Scenario

Year 1999

Name Min Max Min MaxBruxelles EBBU -11% -12% 3% -3% 11%Dusseldorf EDLL -1% 3% 10% -10% 0%Karlsruhe EDUU 10% 5% 10% -6% 1%Maastricht EDYY 13% 7% 15% 1% 12%Manchester EGCC -23% 4% 10% 18% 24%London EGTT 5% 6% 10% 7% 12%Amsterdam EHAA -3% -3% 3% 6% 15%Dublin EIDW -1% 20% 24% 26% 31%Shannon EISN -15% -10% 4% -9% 10%Oslo ENOS -2% 3% 8% 12% 19%Stavenger ENSV -21% -12% -6% -4% 5%W arsaw EPW W 11% 11% 17% 8% 16%Canarias GCCC 1% 12% 17% 20% 26%Nicosia LCCC 9% -16% 8% -6% 15%Zagreb LDZO -70% -70% 38% -60% 45%Barcelona LECB 13% 23% 29% 31% 37%Madrid LECM 11% 15% 21% 23% 30%Palma LECP 0% 24% 27% 32% 37%Sevilla LECS 3% 4% 13% 13% 23%Bordeaux LFBB -2% 2% 9% 9% 17%Reims LFEE 14% -1% 15% 6% 23%Paris LFFF 7% 10% 14% 14% 19%Aix/Marseille LFMM 14% 3% 11% 11% 20%Brest LFRR 0% -5% 5% 2% 14%Athens LGGG 12% -11% 2% -2% 12%Makedonia LGMD -1% -5% 19% 4% 28%Budapest LHCC 15% -58% -16% -42% -3%Brindisi LIBB 7% -13% 9% -4% 17%Milan LIMM 9% 3% 9% 11% 18%Padua LIPP 16% 15% 19% 22% 28%Roma LIRR 2% 2% 8% 8% 16%Prague LKAA 23% 29% 36% 34% 44%Lisbon LPPC 6% 14% 27% 21% 35%Sarajevo LQSB -162% -135% 60% -119% 64%Arad LRAR -11% -77% 0% -61% 10%Bucuresti LRBB -5% -74% 6% -57% 16%Geneva LSAG 20% 27% 39% 27% 39%Zurich LSAZ 20% 24% 33% 23% 33%Beograd LYBA -1738% -1533% 31% -1533% 41%Bratislava LZBB 19% -2% 29% 8% 37%

"existing plans"

2003 2005Capacity ShortfallsEn Route







38

Compared to the capacity shortfalls experienced during Summer 1999, from the figuresavailable it can be seen that few of the current high delay producing ACCs will improve theireffectiveness in coming years.

The significant increase in ACC related delays forecast in the present study, for the years2003 and 2005, highlights the urgent need for effective pan-European medium termcapacity planning.

The higher delays observed with the “shortest routes” scenarios indicate that the Europeancapacity network for the years 2003 and 2005 adapts better to the constraints of the current“avoidance scheme” than to the more user-friendly “shortest routes” scenario.

The spare capacity of European airports is getting tighter in 2003 and 2005.Consequently, ATFM delays may significantly increase. However, the capacity of overloaded airports will be co-ordinated by the Scheduling Committee. Consequently, some ofthe delays observed here may be in future hidden by the means of flight plan co-ordination:Traffic peaks may be pre-smoothed over the day. Unaccommodated demand may move toadjacent airports.

Scenario

Year 1999

Name Min Max Min MaxBrussels EBBR -3% 11% 16% 9% 16%Frankfurt EDDF 9% 4% 9% 4% 11%Hamburg EDDH -39% -11% -5% -1% 8%Berlin/Tempelhof EDDI -40% -9% -2% 1% 9%Düsseldorf EDDL 4% 33% 37% 38% 43%Munich EDDM -10% 9% 14% 18% 25%Stuttgart EDDS -38% 0% 5% 12% 21%Berlin/Tegel EDDT -39% -6% 5% 4% 15%Birmingham EGBB -41% -4% 2% 6% 15%Manchester EGCC -2% 9% 15% 9% 16%London/Luton EGGW -9% 7% 14% 13% 22%London/Gatwick EGKK 8% 18% 23% 23% 29%London/Heathrow EGLL 13% 23% 28% 28% 34%London/Stansted EGSS -10% 0% 6% 9% 15%Amsterdam/Schipol EHAM -10% 9% 13% 12% 18%Nice LFMN -29% -5% 0% 1% 9%Paris/Charles de Gaulle LFPG -1% 8% 12% -5% 2%Bâle-Mulhouse LFSB 38% 49% 53% 55% 58%Heraklion LGIR 21% 31% 36% 39% 45%Corfu LGKR -31% -6% -2% 5% 11%Rhodes LGRP -34% -13% -6% -3% 10%Milan/Malpensa+Linate LIML 22% 35% 38% 30% 35%Lisbon LPPT -27% 3% 11% 10% 18%Geneva LSGG -14% 5% 12% 12% 19%Zurich LSZH 15% 30% 34% 34% 38%

"existing plans"

2003 2005Capacity ShortfallsAirports



Spare Capacity(below –15%)Legend:




39

9. References

/1/ CODA Delays to Air Transport in EuropeMonthly reportsCentral Office for Delay Analysis (CODA)EUROCONTROL, Brussels

/2/ CFMU ATFM SummariesDaily, Weekly and Monthly reports

Central Flow Management Unit (CFMU)EUROCONTROL, Brussels

/3/ CRCO Executive Information ReportMonthly reportsCentral Route Charges Office (CRCO)EUROCONTROL, Brussels

/4/ C. Vandenbergh Air traffic Statistics and Forecasts,Number of Flights per Region (1974-2015)EATMP Development DirectorateEUROCONTROL, Brussels, 1999

/5/ J.-L. Renteux ATC Capacity AssessmentP. Molinari Review of existing National plans

EATMP - DSAEUROCONTROL, Brussels, Aug. 1999

/6/ M. Dalichampt Capacity Plan 1998S. Mahlich for the European Air Navigation Services

EEC Note 3/98EUROCONTROL Experimental CentreBrétigny sur Orge, France, Jan. 1998

/7/ M. Dalichampt Capacity Plan 1999J.C. Hustache for the European Air Navigation ServicesS. Mahlich EEC Note 23/98

EUROCONTROL Experimental CentreBrétigny sur Orge, France, Oct. 1998

/8/ M. Dalichampt Delay Forecast 1999S. Mahlich Based on local capacity enhancement plans

EEC Note 6/99EUROCONTROL Experimental CentreBrétigny sur Orge, France, April 1999

/9/ J.-C. Hustache Cost of the En-RouteAir Navigation Services in EuropeEEC Note 8/99EUROCONTROL Experimental CentreBrétigny sur Orge, France, June 1999




40

Contacts

European Organisation EUROCONTROLfor the Safety of Rue de la Fusée, 96Air Navigation B-1130 Brussels___________________ _________________

Organisation EUROCONTROLeuropéenne pour la Experimental Centresécurité de la B.P. 15navigation aérienne F-91222 Brétigny s/Orge Cedex

Co-operations

Domain Contacts Email TelEATMP / DSA G. Paulson [email protected] ++32 2 729 3108EATMP / DSA S. Hockaday [email protected] ++32 2 729 3785EATMP / DSA G. McAuley [email protected] ++32 2 729 3387EATMP / DSA J.-L. Renteux [email protected] ++32 2 729 3407

Related Activities

Domain Contacts Email TelEEC J-M. Garot [email protected] ++33 1 69 887501CFMU D. Duytschaever [email protected] ++32 2 7299600PRU X. Fron [email protected] ++32 2 7293778IATA P. Hogge [email protected] ++32 2 62618 00

The FAP team

Domain Contacts Email Tel FaxFAP Project S. Mahlich [email protected] ++33 1 69 88 7634 7352ATC/ATFM M. Dalichampt [email protected] ++33 1 69 88 7574 7352Complexity T. Chaboud [email protected] ++33 1 69 88 74 09 7352System Dynamics M. Gibellini [email protected] ++33 1 69 88 75 68 7352Economy J.C. Hustache [email protected] ++33 1 69 88 7802 7352ATFM simulator J. Lebreton [email protected] ++33 1 69 88 7604 7352ATFM simulator H. Kadour [email protected] ++33 1 69 88 7863 7352OR and Analysis A. Marsden [email protected] ++33 1 69 88 73 61 7352ATFM simulator E. Petit [email protected] ++33 1 69 88 73 95 7352Applied math. L. Saîntigny [email protected] ++33 1 69 88 78 36 7352Applied math. F. Le Huede [email protected] ++33 1 69 88 78 22 7352

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