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EUROPEAN ORGANISATIONFOR THE SAFETY OF AIR NAVIGATION

EUROCONTROL EXPERIMENTAL CENTRE

EEC Note No. 19/99

Project GEN–4–E2

Issued: November 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

For the EuropeanAir Navigation Services

Short - TermCapacity Targets

RREEPPOORRTT DDOOCCUUMMEENNTTAATTIIOONN PPAAGGEE

ReferenceEEC Note No. 19/99

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 :Short-Term Capacity Targets 2000 & 2001 for the European Air Navigation Services

AuthorsM. DalichamptJ.C. Hustache

S. Mahlich

Date11/99

Pagesiv + 73

Figs196

Tables37

Annex32

References8

EATCHIP Taskspecification

-

ProjectPLC-C-E1

Sponsor Task No.-

PeriodJuly - Oct.

1999

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

Descriptors (keywords) :

2000, 2001, Capacity, ATC, ATFM, delay, CFMU, FAP, Europe, ECAC, performance, capacityshortfall, future scenarios, traffic demand, traffic growth, costs, ROI, ACC

Abstract :The study is performed in support of the European ATFM Group (EAG).Subject of the study is the target setting of the 2000 and 2001 en-route capacities for theEuropean air navigation services.Assumption: Medium traffic growth (2000 : +5.3%; 2001 : +5.5%) Airport capacities 2000/2001 as declared to EUROCONTROLTarget: ATFM delay for all flights is lower or equal to 3.5 min per flightObjective: Identification of potential capacity shortfalls in 2000 and 2001. Evaluation of capacity plans under consideration of economic aspects such as cost for capacity and cost for delay.Methodology: Future ATM Profile (FAP)Results: The report highlights the critical ACCs, estimates the capacity shortfalls in 2000 and 2001, discusses risks and provides capacity targets for the “best trade-off between cost for capacity and delay” solution.

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

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EEC: Capacity Targets 2000 & 2001


iii

Table of Contents

ABBREVIATIONS _______________________________________________________________________ IV

1. INTRODUCTION______________________________________________________ 1

1.1 Short – term Capacity Planning _______________________________________ 21.2 Scope ____________________________________________________________ 31.3 Objectives_________________________________________________________ 31.4 Scenarios _________________________________________________________ 3

2. FAP METHODOLOGY, DATA AND TOOLS ________________________________ 4

2.1 Air Traffic Flow Management Simulations ______________________________ 52.1.1 The Capacity Demand Ratio – ACC level _____________________________ 62.1.2 The Capacity Demand Ratio – Sector level ____________________________ 62.1.3 Hourly Traffic Distribution __________________________________________ 72.1.4 Network Effects _________________________________________________ 8

2.2 Economic Evaluation________________________________________________ 92.3 Course of Simulations______________________________________________ 122.4 Airports__________________________________________________________ 132.5 Assumptions, Constraints and Risks _________________________________ 15

3. SUMMER 1999 ______________________________________________________ 17

3.1 Actual Traffic Growth 1998 - 1999 ____________________________________ 173.2 Delay per Flight ___________________________________________________ 183.3 Capacity Planning versus Actual _____________________________________ 203.4 Delay Forecast versus Actual________________________________________ 223.5 Costs of Capacity Shortfalls_________________________________________ 23

4. SUMMER 2000 AND 2001 _____________________________________________ 24

4.1 Shortest Routes and “Open Sky”_____________________________________ 244.2 Traffic Forecast 2000 and 2001_______________________________________ 26

Scenario: ARN V3 – Current Routes _____________________________________ 26Scenario: ARN V3 - Shortest Routes _____________________________________ 26

4.3 Capacity Targets 2000 & 2001: “weekdays”____________________________ 274.4 Capacity Targets 2000 & 2001: “Weekend” ____________________________ 284.5 Return on Investments _____________________________________________ 29

5. CONCLUSION ______________________________________________________ 30

6. REFERENCES ______________________________________________________ 31

ANNEX 0 EXAMPLE INTERPRETATION ________________________ 33

ANNEX 1 - ANNEX 32 DELAY PRODUCER IN DETAIL _______________________ 35

CONTACTS ____________________________________________________________ 99




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

MECA Model for the Economic 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

Despite increased engagement in pan European ATS capacity planning, the EuropeanATFM delays did not decrease in summer 1999 compared with the same reference periodin summer 1998. The current ATFM delays are still unacceptable for the airspace users.

However, this year was quite particular with regard to unforeseen changes to the planningparameter used by the ATS provider for their capacity planning, such as:- increase of military traffic during the Kosovo crisis- Yugoslavian and adjacent airspace was not available for civil use- Transition phase of ARN V3

Problems on the ATS provider side were:- difficulties in achieving the capacity targets (within one year)- a lack of sufficient capacity buffer to compensate unforeseen growth- a lack of flexibility of the ATS system to react to short term parameter changes

The new capacity plan shall therefore- increase the planning time horizon from one to two years- facilitate the interpretation and thus the implementation of targets by improved (sector

related) capacity shortfall estimates- identify risks for parameter changes

The capacity plan 2000&2001 shall be generated in three steps. The first step, representedby this document, identifies the capacity targets to be achieved by the European airnavigation services to reduce the delays significantly (strategic objective: 3.5 min delay perflight). In the second step, the individual targets are discussed with all parties involved(action: CFMU/EAG), coordinated with regional plans and revised targets are agreed in thecontext of a global capacity plan. The third step reruns the FAP simulations, identifiesremaining bottlenecks and evaluates delays, costs and return on investments.

This document shows where the en-route capacity shortfalls in 2000 and 2001 are mostlikely to be, and discusses weekday and weekend scenarios. Capacity shortfalls areevaluated for 65 European Air traffic Control Centres. Evaluation included economicaspects including the most cost effective balance between cost for capacity and cost fordelay.

ATFM Delays in 1998 and 1999

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1.1 Short – term Capacity Planning

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 in 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 Short-term planning with a time horizon of 1 to 2 years.

The advantage of short-term planning is its high information quality - the reliability of itsassumptions and forecast. The range of future scenarios can be kept low. The grade ofdetail can be higher (break down to microscopic sector planning, differentiation ofweekdays and weekends).

On the other hand, the short planning time horizon incorporates constraints and reducedreactionary power. The enablers of short-term planning are usually limited to a better use ofexisting resources (revised airspace, staff and resource management).

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Medium-term3-5 years

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1.2 ScopeThe evaluation is performed on:• the whole ECAC area

including:• Air traffic Control Centres and regulated airports (65 ACCs, 20 airports)• Air Traffic Flow Management issues (CFMU slot allocation procedures)

analyzing:• traffic, capacities and delay statistics for more than 500 days in 1998 and 1999• service intervals such as summer/winter, weekday/weekend

simulating:• present and future traffic volumes (1999, 2000, 2001)• regional characteristic traffic growth (ca. 1200 traffic flows)• weekend and weekday traffic pattern.

1.3 Objectives

Identification of capacity targets for the years 2000 and 2001 to enable the European airnavigation services• to reduce the average ATFM delays to 3.5 min per flight and• to cope with the forecast traffic growth

The study shall:• identify delay-causing bottlenecks (en-route)• determine which ACCs have the most urgent need for capacity increase

(best trade-off between cost for capacity and cost for delays• identify the problem zones (sectors, traffic nodes)• quantify the capacity shortfalls• estimate the risks• give final recommendations

1.4 Scenarios

The following scenarios shall be examined:

• 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 traffic growth (according to STATFOR forecast)




4

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.




5

• A Model for the Economic 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 operate atminimum cost for the airspace users.

2.1 Air Traffic Flow Management 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.

A new delay model based on constraint programming techniques has been developed bythe EUROCONTROL Experimental Centre. The model is called Innovative Slot Allocation(ISA). It is developed to investigate improved slot allocation algorithm. But, it can be alsoset up to match the CASA algorithm 100%.

The strength of ISA is its adaptability, in particular its capability to run simulations in theinverse mode. In this mode ISA uses the CFMU observed delays as an input and providesthe ACC capacities as an output. This mode was used to improve significantly the reliabilityof the estimation of the current capacity demand ratios.

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2.1.1 The Capacity Demand Ratio – ACC level

The study uses the notion of “capacity demand ratio” (c/d) to describe the operating point ofan air traffic service provider (ATSP). The indicator is simple but sensitive enough torepresent the impact of the various traffic demand curves over the day. Investigations haverevealed that the indicator reaches its best stability using capacity and demand figures ofthe “peak 3 hour” traffic period observed for the air traffic control centre.

The figure below displays the delay versus capacity demand ratio as a result of multipleATFM simulations with various traffic patterns and various capacities.

Observations:• delays converge towards zero at c/d above 1.05• between 1.05 and 0.95 delays start to rise• delays increase progressively at capacity demand ratios below 0.95

2.1.2 The Capacity Demand Ratio – Sector level

In principle, the c/d can be also applied on the sector (or traffic volume) level. The figurebelow shows delays per flight caused by regulations observed during 51 days in summer1999. We decided to use the 2hour peak for this exercise because the regulation timeswere often too short to compute 3hour loads.

Observation:• Delays increase progressively at capacity demand ratios below 1.1Conclusion:• This information was used in short-term planning to estimate capacity shortfalls on

sector level (see annex: Top 5 local capacity shortfalls)

Delay Sensitivities

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c a p a c it y d e m a n d r a t io [ 3 h o u r p e a k ]

delay sensitivities - traffic volumes

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2.1.3 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 as 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

the same over the next few years, as long as no contrary effect is predicted.

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2.1.4 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 modeling these network effects.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. Another set of simulations investigated the delaysensitivity for individual ACCs for the case that all other ACCs have no capacity constraint(no network effects).

The left figure below shows the simulation results of all simulations with network effects(red) and without network effects (blue). On the right: Frankfurt, Barcelona, Amsterdam andParis as examples 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 complex slot allocation.

Conclusions:• Capacity constraints may have positive (protecting) and negative (delay increasing)

effects on the delay caused by other ACCs.• As long as we don’t target the ATM system at very low delays (resulting in marginal

network effects) the network effects remain an important and dynamic parameter forshort term capacity planning.

• The capacity shortfalls published in this document take into account the predictableimpact of network effects on delays in 2000 and 2001, derived from multiple ATFMsimulations.

Delay Sensitivities: "Network" - "No Network"

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2.2 Economic Evaluation

It is to the advantage of a common European Capacity Plan that investments in capacitiescan be evaluated using macro-economic 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 of delay also varies throughout Europe depending onthe regional traffic mix (for ACC: 11 – 27 ECU /min delay; for airports: 10 – 24 ECU/mindelay). Consequently, every ACC (and every airport) has its own curve. In addition, thesecurves interrelate as network effects change with capacity increases elsewhere.

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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 of delay (traffic mix)

Consequently, every ACC has its own cost curves and optimum capacity demand ratio.The optimum capacity demand ratio varies between 0.99 and 1.17 throughout Europe.

The limited reactionary power available in the shorter term to increase significantly ATMcapacity obliges the air traffic service providers to move in incremental steps towards theoptimum capacity demand ratio (c/d). Therefore, the short-term capacity plan 2000 and2001 does not target the optimum c/d but an intermediate step at 3.5 min delay perflight. However, this target shall be achieved at best return on investments.

The figure below describes in economic terms how traffic and capacity increase effects theoverall cost effectiveness of the service and how the most promising capacity targets (withthe best return on investments) are computed.

FAP uses the delay and capacity costs observed in 1999 to identify the operating point ofevery ACC for the year 1999 (step 1).

In the next step the cost for the “do nothing scenario in 2001” are calculated. If capacitystagnates and the traffic grows the capacity/demand ratio decreases (step 2,3) and theoperating point moves up the curve to the left (step4).

In the last step (5) the capacity is increased in incremental steps (at best ROI) until thedelay target for all ACC is reached. With the increased capacity the operating point movesto the right and the total costs of the service decrease.

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The EUROCONTROL Experimental Centre has developed a model to automate thecomputation described above for multiple ACCs. The model is called MECA (Model for theEconomic evaluation of the Capacities of the ATM system).

The economic evaluation (MECA) and ATFM simulations (AMOC) are complementary.MECA uses the precise delay sensitivities derived from AMOC (1999 network), combinesthem with economic aspects and calculates for each ACC the capacity growth needed tofulfill the target with the best ROI. The new plan is entered into AMOC to verify if the delaytarget is really reached within the new 2001 network environment (see figure below).

MECA input data are individual for each ACC:• static delay sensitivity curves (based on 650 ATFM simulations)• traffic mix (CFMU data, CIM pre-processed)• capacity costs (CRCO data)• delay costs (IATA aircraft operating costs * traffic mix)

MECA is• based on the FAP methodology• built into linked EXCEL sheets and macros

MECA allows• automated iterative model runs

MECA provides• ACC capacity growth with highest return on investment (ROI)

required to achieve a given delay target

AMOCAMOC MECAMECA

Capacity increased with best return on investment

Traffic patternTraffic growth

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Total delaysDelay sensitivity

Iterative Process

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2.3 Course of Simulations

1) Analysis of historical data (CFMU and CRCO archives)• compute traffic and delay distributions

(ACCs - airports, summer - winter, week - weekend)

2) Reference and validation• select traffic samples of representative days• validate ATFM simulation (AMOC) against CFMU observed delays• input 1999 CRCO cost data and the delay target into the economic evaluator (MECA)• compute delay sensitivity curves using AMOC and input into MECA

3) Simulation set up for 2000 and 2001• implementation of the 2000 resp. 2001 airport capacities• creation of the 2000 resp. 2001 traffic samples

(based on STATFOR medium growth scenario)

4) Iterative MECA and AMOC simulations• run the economic evaluator (MECA) to identify capacity increase with highest ROIs• run ATFM simulations (AMOC) to estimate the network effects on delays• re-run MECA with fine-tuned set-up• verify results with AMOC• stop iterations if the network effects are incorporated into the MECA planning AND the

delay target is reached.

5) Simulate various scenarios for 2000/2001

• Scenario 1: “weekdays – current routes”traffic volume (city pairs) 1999 weekdays + traffic growth – current routes used

• Scenario 2: “weekends – current routes”traffic volume (city pairs) 1999 weekends + traffic growth – current routes used

• Scenario 3: “weekdays – shortest routes”traffic volume (city pairs) 1999 weekdays + traffic growth – shortest routes applied(described by EUROCONTROL DSA)

• Scenario 4: “weekends – shortest routes”traffic volume (city pairs) 1999 weekends + traffic growth – shortest routes applied(described by EUROCONTROL DSA)




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2.4 Airports

Airport capacities were included in the network if the airport was regulated in summer 1999(June 17th to August 6th ). The 1999-2000-2001 airport capacity increase was taken fromthe EUROCONTROL data base. The table below shows the actual 1999 airport capacities,and the forecasted 2000 capacities.(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




14

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




15

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 this data is the most comprehensiveand consistent in Europe. However, history has shown that air traffic growth is difficult toforecast in some years and/or areas, and capacity shortfalls are very sensitive to theregional traffic growth. We believe therefore, that the traffic growth scenarios are one ofthe 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 (because a family of airport serves usually the same ACC)

• 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 made on 51 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: low (no big influence on short term planning, can be further reduced)

• 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




16

• Airport regulationsAll airports were ATFM regulated in the 2000 and 2001 scenarios if they were regulatedin 1999. There are two risks with opposite effects:1. Additional regulated airports will increase the total (ACC + airport) delays for 2000

and 2001.2. As these airports are not taken into account they will not protect the ACCs and

higher en-route capacity is required. This could compensate, to some extent, thefirst effect.

Risk: low (for the short-term planning)




17

3. Summer 1999

3.1 Actual Traffic Growth 1998 - 1999

The year 1999 was a difficult year for the European air traffic management. The impact ofthe Kosovo crisis, the transition to ARN Version 3 and a high traffic growth in generalresulted in unforeseeable regional traffic growth figures and capacity shortfalls.

The figure below shows the traffic growth in Europe between summer 1998 and summer1999 (June and July).

C A N

L I S

S E V

M A D

B C N

B O R D

B R S TR E M S

M A A SL O NW A R S

B R A T

B U D P

B U C S TA R A D

C L U JB A C

C S T A

P R A G

V I E NP A R

A I X

R O M B R I N

A T H

I S T

V A RS O F

Z A GP A D L J U B

M I L

G E NZ U R

M U N

K A R L

B E R L

A N K

N I C

M A KI Z M

P O Z

M A L T

S H A

S C O T

S T A V

B E O

T A M P

R I G A

T A L L

V I L

O S L O

M L M OC O P N

S T O K

T R O N

T I R

S A R

B O D O R O V

S U N D

/R Z H U �$ LU V S D F H

B O R D

M A N

DU

B

L O N

B R S T P A R

B R U

A M S T

D U S S

B R E M

B E R L

C O P N

F R A N K

M U N

G E NA I X

Z U R V I E NR E M S

M I L P A D




18

3.2 Delay per Flight

The map below shows the average delays per flight observed in summer 1999(June and July).

There are some developments recognizable in comparison with the delays observed insummer 1998 (figure below)

1998

1999




19

The capacity problems in the Greek airspace seem to be widely solved. Makedonia ACCand Athinai ACC both increased their capacity by more than 20%.

The figure below compares the delays per flight observed in 1999 and 1998 sorted by delayproduced in 1998. The top 1998 delay producers have significantly decreased their delays.

The figure below shows the same data this time sorted by delays produced in 1999.

The new top delay producers Padua (Kosovo effect), Zurich, Marseille and Barcelonaincreased their delays by 150 to 300% within one year.

The next chapter will show that these ACCs could not achieve the capacity targetsproposed by FAP for the year 1999 (see also “Capacity Plan 1999 for the European AirNavigation Services” EUROCONTROL, Oct. 1998).

T o p 2 0 D e la y P r o d u c e r in S u m m e r 1 9 9 8

0

2

4

6

8

1 0

1 2

Ath

inai

Mak

edon

ia

Rei

ms

Gen

eva

Kar

lsru

he

Lond

on

Mila

no

Am

ster

dam

Pad

ova

Mar

seill

e

Zur

ich

Par

is

Nic

osia

Bre

st

Mad

rid

Bud

apes

t

Bar

celo

na

Lisb

on

Maa

stric

ht

War

szaw

a

Rom

a

Fra

nkfu

rt

Sev

ille

Bor

deau

x

Dus

seld

orf

Bra

tisla

va

Brin

disi

Mun

ich

Can

aria

s

Bru

ssel

s

Pra

gue

Ber

lin

Del

ay p

er f

ligh

t [

min

] 1 9 9 8 1 9 9 9

T o p 2 0 D e l a y P r o d u c e r i n S u m m e r 1 9 9 9

0

2

4

6

8

1 0

1 2

Pad

ova

Zur

ich

Mar

seill

e

Bar

celo

na

Gen

eva

Rei

ms

Pra

gue

War

szaw

a

Bra

tisla

va

Mad

rid

Mila

no

Kar

lsru

he

Ath

inai

Maa

stri

cht

Lond

on

Bri

ndis

i

Par

is

Lisb

on

Bud

apes

t

Nic

osia

Can

aria

s

Bor

deau

x

Rom

a

Fra

nkfu

rt

Sev

ille

Mak

edon

ia

Bre

st

Ber

lin

Dus

seld

orf

Am

ster

dam

Mun

ich

Bru

ssel

s

Del

ay p

er f

ligh

t [

min

]

1 9 9 9 1 9 9 8




20

3.3 Capacity Planning versus Actual

The table below compares the FAP 1999 capacity targets and the agreed capacity growth(CFMU Global plan) with the actual capacity increase achieved in summer 1999.

ACCName

ICAOCode

FAPCapacityincreasetargets

(Cap 99)

Global PlanCapacity Increase

1998 – 1999States responses

(respectively CFMU interpretation)

Estimation of theeffective increase

observed in summer1998 - 1999

Athinai LGGG 15% 18% 22%

Karlsruhe EDUU 14% Delays kept at the 1998 level - assume 4% 9%

Geneva LSAG 14% 0% 0%

Reims LFEE 13% Will be partially met - assume 5% 8%

Milano LIMM 13% 5 or 6% total 28%

Zurich LSAZ 12% 0% 0%

Makedonia LGMD 11% 18% > 20% ?

London EGTT 11% 10% 11%

Marseille LFMM 10% Feasible in the longer term – assume 8% 4%

Padova LIPP 9% No value available – assume 0% 0?

Barcelona LECB 8% 6% weekday & 8% global, - weekend difficult 0-3%?

Maastricht EDYY 8% target will be met 3%

Madrid LECM 7% Global value of 7% will be met 11%

Brest LFRR 7% Target will be met > 14%

Paris LFFF 6% Assume 1% 8%

Budapest LHCC 5% Target will be met 0% ?

Zagreb LDZO 5% Target will be met ?

Amsterdam EHAA 4% Target will be met 7%

Bodo ENBD 4% Target will be met

Bratislava LZBB 4% Target will be met ?

Nicosia LCCC 4% Target will be met ?

Lisbon LPPC 4% Did not response – assume 0% ?

Oslo EN0S 4% will probably meet the target

Brindisi LIBB 4% 4% total 11%

Warsaw EPWW 3% Target will be met ?

Sarajevo LQSB 3% will probably meet the target

Stockholm ES0S 3% Target will be met

Frankfurt EDFF 3% Target will be met 4%

Dusseldorf EDLL 3% Target will be met 4%

Tampere EFES 2% Target will be met

Malmo ESMM 2% Target will be met

Prague LKAA 2% Target will be met ?

Skopje LW SS 2% will probably meet the target

Roma LIRR 2% 2% global 5%

Muenchen EDMM 1% Target will be met ?

Canaries GCCC 1% 2% for weekdays ?

Seville LECS 1% 5% for weekend 6%

Bordeaux LFBB 1% Target will be met ?

Dublin EIDW 1% Target will be met

The actual capacity growth was estimated based on daily traffic and delay observations(see annex figure Ax.2). Unclear observations were indicated with “?”.




21

The figure below compares the States planning (red) with the actual achievements in 1999(yellow). It appears the notion of ACC capacity growth is well understood. The majority ofthe 20 ACCs made a good estimate of the capacity growth they were going to deliver in1999. Fifteen ACCs out of twenty delivered even more than committed.

The comparison of the actual achievements (yellow) with the FAP targets for 1999 (blue)shows that a number of ATSPs had some problems to achieve the FAP targets (see figurebelow). Some knew it from the very beginning and stated it in the global plan (e.g.Switzerland, France) others may have realised their problems a bit later (e.g. Barcelona,Maastricht). Thirteen out of twenty ACCs delivered a capacity greater than or equal to thetargets proposed by FAP.

Nearly all ACCs that did not achieve the targets caused high delays above 4 min per flight(see figure below).

F A P C a p a c i t y T a r g e t s v e r s u s G l o b a l P l a n n i n g a n d A c t u a l A c h i e v e m e n t s

0 %

5 %

1 0 %

1 5 %

2 0 %

2 5 %

3 0 %

Ath

inai

Kar

lsru

he

Gen

eva

Rei

ms

Mila

no

Zur

ich

Mak

edon

ia

Lond

on

Mar

seill

e

Pad

ova

Bar

celo

na

Maa

stric

ht

Mad

rid

Bre

st

Par

is

Bud

apes

t

Am

ster

dam

Brin

disi

Fra

nkfu

rt

Dus

seld

orf

Rom

a

Sev

ille

Cap

acit

y In

crea

se

F a p 9 9 G lo b a l p l a n A c t u a l 9 9

0

2

4

6

8

10

12

-20% -15% -10% -5% 0% 5% 10% 15% 20%

FAP targets not achieved - FAP targets achieved

Del

ay p

er flig

ht [m

in]

These ACCs didnotachieve the target

These ACCsachieved the target

C F M U G l o b a l P l a n v e r s u s A c t u a l 1 9 9 9

0 %

5 %

1 0 %

1 5 %

2 0 %

2 5 %

3 0 %

Ath

inai

Mak

edon

ia

Lond

on

Mar

seill

e

Bar

celo

na

Maa

stric

ht

Mad

rid

Bre

st

Mila

no

Rei

ms

Bud

apes

t

Sev

ille

Kar

lsru

he

Am

ster

dam

Brin

disi

Fra

nkfu

rt

Dus

seld

orf

Rom

a

Par

is

Gen

eva

Zur

ich

Pad

ova

Cap

acit

y in

crea

se

G l o b a l p l a n A c t u a l 9 9

0% 0% 0%




22

3.4 Delay Forecast versus Actual

The figure below compares the 1999 forecast delay (gray) with the actual delays observedby CFMU (blue). The delay forecast was made by FAP using ATFM models with theassumption of 8, 6 and 4.4% traffic growth and a step wise introduction of the Global ActionPlan in January 1999.

Observations:

January: Actual delays are higher partly because the Global Plan is not yetimplemented.

Feb. - March: Transition phase of ARN V3 caused high delays within the first weeks.

March-June: Crisis in Kosovo, high delays because of blocked airspace,and unforeseeable change in traffic pattern.

June-July: Getting back to normal. The actual delays match well with the delay forecastfor 8% in June and 6% in July.

Aug.-Sept.: The Yugoslavian airspace is still not fully available and the actual delays arebelow the forecast. Obviously the European ATM system has been adaptedto the reduced capacity network.

Conclusions:• Unforeseeable effects excluded, the delay forecast process seems to be sufficiently

reliable.• The European ATM system is on the right way. The actual capacity provided in

August and September 1999 was obviously higher than foreseen in the Global Action Plan (this is confirmed by observations made on page 21

“CFMU Global Plan versus Actual 1999”).

Delay evolution in Europe 1999 - Forecasts against Actual

5.7%5.0%

3.2%

6.2% 6.6% 7.9%5.3% 5.8% 6.2%

0

50,000

100,000

150,000

200,000

250,000

Dai

ly d

elay

s [m

in]

(mov

ing

aver

age

over

30

days

)

J F M A M J J A S O N D

Traffic increase 1999

Forecast delay 99 (4.4% traffic growth)

Forecast delay 99 (6% traffic growth)

Forecast delay 99(8% traffic growth)

Delay 1999

Kosovo

ARN V3

Kosovo

Kosovo

Kosovo

Kosovo




23

3.5 Costs of Capacity Shortfalls

Despite of the efforts made in Europe to increase the capacity, there are still somesignificant capacity shortfalls observed for 30% of the European air traffic control centre.The figure below compares the current operating point (capacity demand ratio in summer1999) with the optimum capacity demand ratio computed by FAP under consideration of thecost for capacity and the cost for the delays.

Some ACCs such as Prague, Bratislava and Padua were surprised by an extraordinarytraffic growth this year. Other ACCs are operating for years below their optimum operatingpoint.

FAP estimates the total cost for En-route ATC operations below the optimum capacitydemand ratio was around 1.4 Million Euro per day in summer 1999. This is around 10% ofthe direct cost for the European Air Navigation Service.

The figure above shows the daily cost of operating at non optimum operating points brokendown by air traffic control centre.

Daily cost of operating at non optimum capacity demand ratio

0

50

100

150

200

250

300

Zu

rich

Pad

ua

Aix

/Mar

seill

e

Bar

celo

na

Rei

ms

Gen

eva

Maa

stri

cht

Lo

nd

on

Mad

rid

Pra

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e

Kar

lsru

he

Par

is

Mila

n

Bra

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va

Ath

ens

Bu

dap

est

War

saw

Nic

osi

a

Po

ssib

le s

avin

gs

[keu

ro/d

ay]

C u r r e n t v e r s u s O p t im u m O p e r a t in g P o in t

0 .8 0

0 .9 0

1 .0 0

1 .1 0

1 .2 0

Pra

gu

e

Zu

rich

Gen

eva

Bra

tisl

ava

Pad

ua

Bu

dap

est

Rei

ms

Aix

/Mar

seill

e

Maa

stri

cht

Bar

celo

na

Ath

ens

War

saw

Mad

rid

Kar

lsru

he

Nic

osi

a

Mila

n

Par

is

Bri

nd

isi

Lis

bo

n

Lo

nd

on

Sev

illa

Fra

nkf

urt

Ro

ma

Can

aria

s

cap

acit

y d

eman

d r

atio

[3

ho

ur

pea

k] c u r re n t c /d o p t im u m c /d




24

4. Summer 2000 and 2001

4.1 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 the constraints areof a short term nature. Consequently, short-term capacity planning cannot rely solely on thecurrent 1999 traffic pattern.

Another possible scenario “shortest routes” assumes that ARN V3 is used without en-routecapacity constraints. The differences of both scenarios (“shortest routes” and “current”) areillustrated by the figure below.

ACCs coloured in blue may loose flights/flows if capacity constraints disappear. TheseACCs seem to have some “artifical traffic” in summer 1999.

ACCs coloured in red may gain flights/flows if capacity constraints disappear. These ACCswere either restricted or protected in summer 1999.




25

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(provided the capacity is available).

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%

Smaller changes of traffic flows can be observed for the core European airspace. However,bigger ACCs such as London and Maastricht may observe some changes on the sectorlevel that may compensate and thus be invisible on the centre level.

It is impossible to forecast which of the scenarios are most likely to take place in 2000 and2001. Nevertheless, ACCs with a high variation of their traffic volume for the two scenariosmay try better to target the higher capacity growth to be on the safe side.

This study investigates both scenarios leading to three possible conclusions• Capacity growth based on scenario “current routes”• Capacity growth based on scenario “shortest route” or• Capacity growth based on the maximum shortfall observed in both scenarios

The final decision can be made for each ACC individually within the context of globalplanning (action CFMU/EAG).




26

4.2 Traffic Forecast 2000 and 2001

Traffic growth data is based on STATFOR medium growth estimates on 2000 flows inEurope (2000 : +5.3%; 2001 : 11.1%) applied to the traffic pattern of the six baselinescenarios representing:- typical weekdays and weekends in summer 1999- actual routes flown and shortest flyable routes

Scenario: ARN V3 – Current RoutesThis scenario takes the traffic between city pairs and the routes observed in summer 1999,and increases the traffic according to STATFOR.

Scenario: ARN V3 - Shortest RoutesThis scenario takes the traffic between city pairs observed in summer 1999, applies theshortest ARN V3 routes and increases the traffic according to STATFOR.

C A N

L IS

S E V

M A D

B C N

B O R D

B R S TR E M S

M A A SL O NW A R S

B R A T

B U D P

B U C S TA R A D

C L U JB A C

C S T A

P R A G

V IE NP A R

A I X

R O M B R IN

A T H

I S T

V A RS O F

Z A GP A D L J U B

M I L

G E NZ U R

M U N

K A R L

B E R L

A N K

N I C

M A KIZ M

P O Z

M A L T

S H A

S C O T

S T A V

B E O

T A M P

R I G A

T A L L

V IL

O S L O

M L M OC O P N

S T O K

T R O N

T IR

S A R

B O D O R O V

S U N D

/ R Z H U �$ LU V S D F H

B O R D

M A N

DU

B

L O N

B R S T P A R

B R U

A M S T

D U S S

B R E M

B E R L

C O P N

F R A N K

M U N

G E NA IX


M I L P A D

C A N

L IS

S E V

M A D

B C N

B O R D

B R S TR E M S

M A A SL O NW A R S

B R A T

B U D P

B U C S TA R A D

C L U JB A C

C S T A

P R A G

V IE NP A R

A I X

R O M B R IN

A T H

I S T

V A RS O F

Z A GP A D L J U B

M I L

G E NZ U R

M U N

K A R L

B E R L

A N K

N I C

M A KIZ M

P O Z

M A L T

S H A

S C O T

S T A V

B E O

T A M P

R I G A

T A L L

V IL

O S L O

M L M OC O P N

S T O K

T R O N

T IR

S A R

B O D O R O V

S U N D

/ R Z H U �$ LU V S D F H

B O R D

M A N

DU

B

L O N

B R S T P A R

B R U

A M S T

D U S S

B R E M

B E R L

C O P N

F R A N K

M U N

G E NA IX


M I L P A D




27

4.3 Capacity Targets 2000 & 2001: “weekdays”

The table below shows the results of the FAP simulations performed as described in the chapters 1 and 2.

Assumptions: medium traffic growth throughout Europe (regional details: STATFOR)2000/2001 airport capacities (EUROCONTROL data base)

Objective: Total ATFM delays shall not be worse than 3.5 min per flight => so that ATFM delays in 2000/2001 are lower than in 1997.Method: Increase ACC capacities successively at best ROI until the target delay is achieved.Parameter: Capacity demand ratio (green); traffic growth (blue); capacity shortfall (yellow).

ParameterCapacity Dem and

R atio

Capacity Dem and

ratio

Traffic growth

Capacity Shortfall

Traffic growth

Capacity Shortfall

Traffic growth

Capacity Shortfall

Traffic growth

Capacity Shortfall

Year / Period 1999 99-00 2000 99-00 2000 99-01 2001 99-01 2001

M odel / M ethodology

MECA optimum operating

point

MEC A Average,

observed in S99

STATFOR +SAAM+ +AMOC +

TAC T

SynthesisSTATFOR +AMOC +

TAC TSy nthesis

STATFO R +SAAM+ +AMO C+

TACT

SynthesisSTATFOR +AMOC +

TAC TSy nthesis

Pattern W eekday Weekday W eekday Weekday W eekday Weekday W eekday WeekdayRoutes Current Shortest Shortest Current Current Shortest Shortest Current CurrentNam e Code % % % % % % % %Bruxelles EBBU 1 1,11 -4 0 7 0 0 0 11 0Berlin EDBB 1,07 1,25 19 7 7 0 26 15 14 3Frankfurt EDFF 1,03 0,99 4 4 3 4 8 8 7 8Dusseldorf EDLL 1,03 1,04 4 0 8 1 13 6 17 10M unchen EDM M 1 1,05 7 0 6 0 15 4 13 4Karlsruhe EDUU 1,04 0,93 8 12 5 12 15 18 11 18Brem en EDW W 1 1,46 6 0 7 0 11 0 12 0M aastricht EDYY 1,08 0,95 8 12 5 10 15 19 12 17Tallin EETT 1,07 1,74 -35 0 6 0 -33 0 9 0Tam pere EFES 1,05 1,70 18 0 4 0 25 4 10 0Rov aniem i EFPS 1,01 3,82 0 0 4 0 11 0 11 0M anchester EGCC 1,07 1,30 7 0 5 0 15 0 13 0Scottish EGPX 1,01 1,19 -2 0 4 0 4 0 11 0London EGTT 1 0,95 5 8 5 7 12 14 11 13Am sterdam EHAA 1,07 1,10 1 0 4 0 9 2 12 4Dublin EIDW 1,08 1,09 3 0 4 0 9 3 10 4Shannon EISN 1,06 1,21 13 0 3 0 20 4 10 0Kobenhavn EKDK 1,05 1,38 1 0 4 0 6 0 10 0Bodo ENBD 1,04 1,84 6 0 6 0 7 0 7 0Oslo ENOS 1,07 1,09 6 3 5 3 10 7 9 7Stav enger ENSV 1,05 1,26 9 0 6 0 13 1 11 0Trondheim ENTR 1,07 1,42 3 0 5 0 8 0 10 0W arsaw EPW W 1,06 0,95 12 11 3 8 16 15 7 12M alm o ESM M 1,05 1,18 6 0 4 0 12 0 9 0Stockholm ESOS 1,07 1,93 10 0 5 0 15 0 9 0Sundswall ESUN 1,03 2,70 2 0 4 0 2 0 11 0Riga EVRR 1,04 2,23 -29 0 2 0 -27 0 4 0Vilnius EYVC 1,05 2,48 37 0 2 0 39 0 3 0Canarias GCCC 1,01 0,99 9 5 7 5 15 10 12 10Sofia LBSR 1,07 1,55 -19 0 7 0 -14 0 14 0Varna LBW R 1,07 1,85 -29 0 4 0 -23 0 13 0Nicosia LCCC 1,06 0,97 -16 0 2 2 -11 4 8 8Zagreb LDZO 1,06 1,76 169 0 4 0 184 0 10 0Barcelona LECB 1,08 0,96 11 19 7 16 19 27 14 24M adrid LECM 1,06 0,97 5 8 6 9 13 16 13 17Palm a LECP 1,03 1,21 4 0 6 0 14 0 16 0Sev illa LECS 1,07 1,16 2 0 5 0 9 0 13 0Bordeaux LFBB 1,05 1,22 8 0 6 0 14 4 12 2Reim s LFEE 1,06 0,91 16 24 5 10 23 32 12 17Paris LFFF 1,01 0,94 4 7 4 7 9 12 9 12Aix /M arseille LFM M 1,06 0,93 10 14 6 10 17 21 13 17Brest LFRR 1,04 1,13 10 0 5 0 19 6 13 3Athens LGGG 1,1 0,98 -3 3 7 10 0 6 10 13M akedonia LGM D 1,17 1,21 33 18 5 3 40 25 11 8Budapest LHCC 1,15 1,02 -19 0 4 9 -13 4 11 16Brindisi LIBB 1,01 0,94 -11 0 6 6 -7 2 10 10M ilan LIM M 1,02 0,95 2 7 5 8 8 13 12 15Padua LIPP 1,06 0,90 6 17 6 19 12 23 12 25Rom a LIRR 1,04 1,04 2 0 5 1 9 6 12 8Lubiana LJLA 1,05 1,63 79 27 4 0 91 40 12 0Prague LKAA 1,16 0,93 10 21 4 19 16 27 10 25M alta LM M M 1,11 2,66 -3 0 3 0 6 0 12 0Vienna LOVV 1,02 1,28 18 0 3 0 26 0 11 0Lisbon LPPC 1,09 1,05 16 8 5 0 29 21 17 10Sarajev o LQSB 1,05 2,67 ??? ??? 5 ??? 431 ??? 10 ???Arad LRAR 1,09 1,20 -35 0 5 0 -32 0 10 0Bucuresti LRBB 1,08 1,13 -39 0 5 0 -35 0 12 0Genev a LSAG 1,12 0,92 18 27 6 19 26 34 13 25Zurich LSAZ 1,07 0,87 0 16 6 21 6 22 12 28Ankara LTAA 1,01 1,29 -5 0 8 0 0 0 14 0Istam bul LTBB 1,02 1,14 -2 0 7 0 5 0 15 2Skopje LW SS 1,02 2,83 ??? ??? 9 ??? 128 ??? 11 ???Beograd LYBA 1,08 ??? ??? ??? 5 ??? 2038 ??? 11 ???Bratislava LZBB 1,17 0,98 -20 0 5 7 -14 0 13 14




28

4.4 Capacity Targets 2000 & 2001: “Weekend”

The table below shows the results of the FAP simulations performed as described in the chapters 1 and 2.

Assumptions: medium traffic growth throughout Europe (regional details: STATFOR)2000/2001 airport capacities (EUROCONTROL data base)

Objective: Total ATFM delays shall not be worse than 3.5 min per flight => so that ATFM delays in 2000/2001 are lower than in 1997.Method: Increase ACC capacities successively at best ROI until the target delay is achieved.Parameter: Capacity demand ratio (green); traffic growth (blue); capacity shortfall (yellow).

ParameterCapacity Demand

Ratio

Capacity Demand

ratio

Traffic growth

Capacity Shortfall

Traffic growth

Capacity Shortfall

Traffic growth

Capacity Shortfall

Traffic growth

Capacity Shortfall

Year / Period 1999 99-00 2000 99-00 2000 99-01 2001 99-01 2001

Model / Methodology

MECA optimum operating

point

MECA Average,

observed in S99

STATFOR +SAAM+ +AMOC+

TACT

AMOC MECA Synthesis

STATFOR +AMOC+

TACT

AMOC MECA

Synthesis

STATFOR +SAAM+ +AMOC+

TACT

AMOC MECA Synthesis

STATFOR +AMOC+

TACT

AMOC MECA Synthesis

Pattern Weekend Weekend Weekend Weekend Weekend Weekend Weekend WeekendRoutes Current Shortest Shortest Current Current Shortest Shortest Current CurrentName Code % % % % % % % %Bruxelles EBBU 1 1 ,1 1 -4 0 7 0 0 0 11 0Berlin EDBB 1,07 1 ,2 5 19 0 7 0 26 8 14 2Frankfurt EDFF 1,03 1 ,0 9 4 0 3 0 8 0 7 0Dusseldorf EDLL 1,03 1 ,0 7 4 0 8 0 13 2 17 4Munchen EDMM 1 1 ,1 3 7 0 6 0 15 0 13 0Karlsruhe EDUU 1,04 0 ,9 5 8 13 5 11 15 19 11 17Bremen EDWW 1 1 ,4 6 6 0 7 0 11 0 12 0Maastricht EDYY 1,08 0 ,9 5 8 13 5 11 15 21 12 18Tallin EETT 1,07 1 ,7 4 -35 0 6 0 -33 0 9 0Tampere EFES 1,05 1 ,7 0 18 0 4 0 25 0 10 0Rovaniemi EFPS 1,01 3 ,8 2 0 0 0 0 11 0 11 0Manchester EGCC 1,07 1 ,3 0 7 0 5 0 15 0 13 0Scottish EGPX 1,01 1 ,1 9 -2 0 4 0 4 0 11 0London EGTT 1 0 ,9 4 5 8 5 7 12 14 11 13Amsterdam EHAA 1,07 1 ,1 0 1 0 4 0 9 0 12 3Dublin EIDW 1,08 1 ,0 9 3 0 4 0 9 4 10 6Shannon EISN 1,06 1 ,2 1 13 0 3 0 20 0 10 0Kobenhavn EKDK 1,05 1 ,3 8 1 0 4 0 6 0 10 0Bodo ENBD 1,04 1 ,8 4 6 0 6 0 7 0 7 0Oslo ENOS 1,07 1 ,0 9 6 0 5 0 10 4 9 3Stavenger ENSV 1,05 1 ,2 6 9 0 6 0 13 4 11 2Trondheim ENTR 1,07 1 ,4 2 3 0 5 0 8 0 10 0Warsaw EPWW 1,06 0 ,9 5 12 16 3 8 16 20 7 12Malmo ESMM 1,05 1 ,1 8 6 0 4 0 12 1 9 0Stockholm ESOS 1,07 1 ,9 3 10 0 5 0 15 0 9 0Sundswall ESUN 1,03 2 ,7 0 2 0 11 0 2 0 11 0Riga EVRR 1,04 2 ,2 3 -29 0 2 0 -27 0 4 0Vilnius EYVC 1,05 2 ,4 8 37 0 2 0 39 0 3 0Canarias GCCC 1,01 1 ,0 4 9 0 7 0 15 5 12 5Sofia LBSR 1,07 1 ,5 5 -19 0 7 0 -14 0 14 0Varna LBWR 1,07 1 ,8 5 -29 0 4 0 -23 0 13 0Nicosia LCCC 1,06 0 ,9 8 -16 0 2 2 -11 0 8 8Zagreb LDZO 1,06 1 ,7 6 169 0 4 0 184 0 10 0Barcelona LECB 1,08 0 ,9 2 11 25 7 22 19 33 14 30Madrid LECM 1,06 0 ,9 2 5 11 6 15 13 19 13 23Palma LECP 1,03 0 ,9 8 4 6 6 7 14 16 16 17Sevilla LECS 1,07 0 ,9 8 2 5 5 11 9 12 13 18Bordeaux LFBB 1,05 0 ,9 7 8 7 6 4 14 13 12 10Reims LFEE 1,06 0 ,9 3 16 19 5 11 23 26 12 18Paris LFFF 1,01 0 ,9 8 4 1 4 0 9 6 9 5Aix/Marseille LFMM 1,06 0 ,8 9 10 21 6 17 17 28 13 24Brest LFRR 1,04 0 ,9 9 10 11 5 5 19 19 13 13Athens LGGG 1,1 0 ,9 9 -3 2 7 10 0 5 10 13Makedonia LGMD 1,17 1 ,1 4 33 27 5 7 40 34 11 12Budapest LHCC 1,15 0 ,9 8 -19 0 4 10 -13 0 11 17Brindisi LIBB 1,01 0 ,9 7 -11 0 6 3 -7 0 10 7Milan LIMM 1,02 0 ,9 1 2 11 5 12 8 17 12 19Padua LIPP 1,06 0 ,8 9 6 20 6 22 12 26 12 28Roma LIRR 1,04 1 ,0 0 2 2 5 4 9 9 12 11Lubiana LJLA 1,05 1 ,6 3 79 17 4 0 91 30 12 0Prague LKAA 1,16 0 ,9 4 10 20 4 17 16 26 10 24Malta LMMM 1,11 2 ,6 6 -3 0 3 0 6 0 12 0Vienna LOVV 1,02 1 ,2 8 18 0 3 0 26 0 11 0Lisbon LPPC 1,09 1 ,0 1 16 17 5 5 29 30 17 17Sarajevo LQSB 1,05 2 ,6 7 ??? ??? 5 ??? 431 ??? 10 0Arad LRAR 1,09 1 ,2 0 -35 0 5 0 -32 0 10 0Bucuresti LRBB 1,08 1 ,1 3 -39 0 5 0 -35 0 12 0Geneva LSAG 1,12 0 ,9 2 18 37 6 26 26 44 13 32Zurich LSAZ 1,07 0 ,8 7 0 16 6 23 6 22 12 30Ankara LTAA 1,01 1 ,2 9 -5 0 8 0 0 0 14 0Istambul LTBB 1,02 1 ,1 4 -2 0 7 0 5 0 15 0Skopje LWSS 1,02 2 ,8 3 ??? ??? 9 ??? 128 0 11 0Beograd LYBA 1,08 ??? ??? ??? 5 ??? 2038 0 11 0Bratislava LZBB 1,17 0 ,9 8 -20 0 5 16 -14 0 13 24




29

4.5 Return on Investments

The return on investment (ROI) was used to identify the regions where capacity increasewould be of most benefit. The ROI describes the potential savings in delays in 2000 and2001 considering the traffic, and the resulting delay growth against the cost for the capacityincrease. The ROI compares the whole investment (assuming 1999 marginal capacity cost)against the total savings in the year 2001.

ROI = total savings possible / total investment

ROI = (Cdelay, do nothing 2001 – Cdelay, plan2001 ) / (Ccap, plan2001 – Ccap, 1999 )

The ROI assumes the return in the year of the investment. The chart below shows the ROIcalculated for all ACCs affected by the capacity targets for 2001.

The return on investment is positive (>1) for all ACCs mentioned in the final conclusion ofthe capacity plan. The highest ROIs are computed for Switzerland, Padova, Maastricht,London and Barcelona.

There are still recognizable differences between weekdays and weekend. However,compared to the year 1998, the differences between capacity demand ratios duringweekdays and weekend capacities have been reduced.

0

10

20

30

40

50

60

Ret

urn

on

In

vest

men

t (R

OI)

Weekdays

Weekends




30

5. Conclusion

A further compression of the 8 scenarios is made based on the assumptions:1. the Yugoslavian airspace will be open 2000/20012. the “Standard Routing Scheme” will be still in place.Hence, for those ACCs being effected by the Kosovo Crisis the capacity shortfalls basedon the shortest routes scenario are the most reliable (orange). For the others, the capacityshortfalls of the current routes scenario was taken (green).

In conclusion, the following capacity increase is required to reduce the ATFM delays to 3.5min per flight in summer 2000 and summer 2001:

The comments on the right side describe possible risks and the alternative targets for thecase of an incorrect assumption.

The annex contains a detailed analysis of the main delay producing ACCs to be used toidentify where and how capacity could be increased.

Comments

Weekday Weekend Weekday WeekendName Code % % % %Makedonia LGMD 18 27 25 34 Att.: 3-8% if Yug. Airspace is not fully available

Ljubljana LJLA 27 17 40 30 Att.: 0% if Yug. Airspace is not fully available

Geneva LSAG 19 26 25 32 reliableZurich LSAZ 21 23 28 30 reliablePrague LKAA 21 20 27 26 reliableBarcelona LECB 16 22 24 30 reliablePadua LIPP 17 20 23 26 reliableAix/Marseille LFMM 10 17 17 24 reliableMadrid LECM 9 15 17 23 reliableKarlsruhe EDUU 12 11 18 17 reliableReims LFEE 10 11 17 18 Att.: 24-32% if shortest routes are applied

Maastricht EDYY 10 11 17 18 reliableMilan LIMM 7 11 13 17 reliableWarsaw EPWW 8 8 12 12 reliableLondon EGTT 7 7 13 13 reliableSevilla LECS 0 11 0 18 reliableParis LFFF 7 0 12 5 reliablePalma LECP 0 7 0 17 reliableAthens LGGG 3 2 6 5 Att.: 10-13% if Yug. Airspave is not fully available

Lisbon LPPC 0 5 10 17 reliableCanarias GCCC 5 0 10 5 reliableBrest LFRR 0 5 3 13 reliableFrankfurt EDFF 4 0 8 0 reliableBordeaux LFBB 0 4 2 10 reliableOslo ENOS 3 0 7 3 reliableRoma LIRR 0 2 6 9 reliableDusseldorf EDLL 1 0 10 4 reliableDublin EIDW 0 0 4 6 reliableAmsterdam EHAA 0 0 4 3 reliableBerlin EDBB 0 0 3 2 Att.: 7-15% if shortest routes are applied

Munich EDMM 0 0 4 0 reliableNicosia LCCC 0 0 4 0 Att.: 2-8% if Yug. Airspave is not fully available

Budapest LHCC 0 0 4 0 Att.: 9-16% if Yug. Airspave is not fully available

Brindisi LIBB 0 0 2 0 Att.: 6-10% if Yug. Airspave is not fully available

Stavanger ENSV 0 0 0 2 reliable

Pattern

Capacity Shortfall

2000 2001Year




31

6. 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/ M. Dalichampt Capacity Plan 1999S. Mahlich for the European Air Navigation Services

EEC Note 23/98EUROCONTROL Experimental CentreBrétigny sur Orge, France, Oct. 1998

/6/ M. Dalichampt Delay Forecast 1999S. Mahlich Based on local capacity enhancement plans

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

/7/ J.-C. Hustache Cost of the En-RouteAir Navigation Services in EuropeEEC Note 8/99EUROCONTROL Experimental CentreBrétigny sur Orge, France, June 1999

/8/ M. Dalichampt Medium-term Capacity Shortfalls 2003 - 2005S. Mahlich including national and supra-national

Capacity Enhancement PlansEEC Note 16/99EUROCONTROL Experimental CentreBrétigny sur Orge, France, Oct. 1999


EEC: Capacity Targets 2000 & 2001 32


Annex 0 – Annex 32 ATFM Delay Producer in Detail

The annex contains a detailed analysis of the main delay producing ACCs (sorted byalphabetic order) to be used in support of problem diagnostics and solution finding.

An example for the interpretation (highlighted in red) is given in Annex 0.

A0 Example ACC pg 33A1 EBBU Brussels ACC pg 35A2 EDBB Berlin ACC pg 37A3 EDFF Frankfurt ACC pg 39A4 EDLL Düsseldorf ACC pg 41A5 EDMM München ACC pg 43A6 EDUU Karlsruhe ACC pg 45A7 EDYY Maastricht ACC pg 47A8 EGTT London ACC pg 49A9 EHAA Amsterdam ACC pg 51A10 EPWW Warszawa pg 53A11 GCCC Canarias pg 55A12 LCCC Nicosia pg 57A13 LECB Barcelona ACC pg 59A14 LECM Madrid ACC pg 61A15 LECS Seville ACC pg 63A16 LFBB Bordeaux ACC pg 65A17 LFEE Reims ACC pg 67A18 LFFF Paris ACC pg 69A19 LFMM Marseille ACC pg 71A20 LFRR Brest ACC pg 73A21 LGGG Athinai ACC pg 75A22 LGMD Makedonia ACC pg 77A23 LHCC Budapest ACC pg 79A24 LIBB Brindisi ACC pg 81A25 LIMM Milano ACC pg 83A26 LIPP Padova ACC pg 85A27 LIRR Rome ACC pg 87A28 LKAA Prague ACC pg 89A29 LPPC Lisbon ACC pg 91A30 LSAG Geneve ACC pg 93A31 LSAZ Zürich ACC pg 95A32 LZBB Bratislava ACC pg 97


EEC: Capacity Targets for 2000 and 2001


33

A00 : Example ACC

Figure A00.1: Traffic and delays in summer and winter, on weekdays and weekends in 1998

Figure A00.2: Traffic and resulting delays in summer 98 and 99 (17th of June - 6th of August)

LFMM (Summer 1998 - 1999)

0

2

4

6

8

10

12

14

16

18

20

0 500 1000 1500 2000 2500 3000 3500

daily traffic

del

ay p

er f

ligh

t [m

in]

Traffic growth 1998 – 1999: 11 %

Capacity growth 1998 – 1999: 4 %

Daily capacity at a given delay per flight: 1 min 4 min

− Summer weekdays 98 2500 2750

∆ Summer weekends 98 2300 2400

− Summer weekdays 99 xxxx 2750

∆ Summer weekends 99 xxxx 2750

LFMM (Summer - Winter 1998)

0

2

4

6

8

10

12

14

16

18

20

0 500 1000 1500 2000 2500 3000 3500

daily traffic

del

ay p

er f

ligh

t [m

in]


− Summer weekdays 2500 2750

∆ Summer weekends 2300 2400

− Winter weekdays xxxx xxxx

∆ Winter weekends xxxx xxxx

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Traffic evolution Operating point

Top 5 local capacity shortfalls

Referencelocation

Grouped Yes / No Total delay (min)from 12 aug. to 8 sept.

Delayed Flights Delay per delayed flight Capacity Shortfallduring the regulation period

LFMMF1 Yes 2 73,002 2183 33.4 46%LFMK1 No 56,082 1923 29.2 33%LFMT2 No 43,169 1395 30.9 39%LFMMF2 Yes 2 34,044 1083 31.4 40%LFMW1 No 30,036 1390 21.6 11%

Main delay producer and their delay distribution over the day

Figure A00.3: Delay distribution over the days (June17th to Aug.6th 1999)Date format (mm/dd/yy); vertical lines indicate MondaysRegulation AS: sector; AD: airport; AZ: group of airports; SP: point;

Conclusion

Capacity Shortfall Comments2000 2001

Weekday Weekend Weekday Weekend

10 % 17% 17 % 24 % reliable

L F M M d e l a y s p e r s e c t o r i n S 9 9

0

4 0 0 0

8 0 0 0

1 2 0 0 0

1 6 0 0 0

2 0 0 0 0

2 4 0 0 0

2 8 0 0 0

3 2 0 0 0

3 6 0 0 0

4 0 0 0 0

4 4 0 0 0

4 8 0 0 0

5 2 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

[m

in]

L F M L Y O N A SL F M A B A SL F M K D A SL F M K 2 A SL F M W 2 A SL F M M 2 A SL F M W T 2 A SL F M Y Y A SL F M B 1 A SL F M M N C O A SL F M B B A SL F M 5 A ZL F M W T 1 A SL F M M N S C A SL F M S T A SL F M M F A SL F M T 2 A SL F M W 1 A SL F M K K A SL F M M F 2 A SL F M K 1 A SL F M M F 1 A S

LFMM Traffic Evolution (1997, 1998, 1999)

1300

1500

1700

1900

2100

2300

2500

2700

2900

3100


Dai

ly T

raffi

c (3

0 da

ys m

ovin

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LFMM operating point in summer 1999 (average weekdays and weekend)

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4

Capacity Demand ratio

Cos

ts (

Eur

o)

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35

A1 EBBU: Brussels ACC



EBBU (Summer - Winter 1998)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 200 400 600 800 1000 1200 1400 1600 1800 2000

daily traffic

del

ay p

er f

ligh

t [m

in]

EBBU (Summer 1998 - 1999)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 200 400 600 800 1000 1200 1400 1600 1800 2000

daily traffic

del

ay p

er f

ligh

t [m

in]


Capacity growth 1998 – 1999: x%


− Summer weekdays 98 1800 xxxx

∆ Summer weekends 98 xxxx xxxx

− Summer weekdays 99 xxxx xxxx



− Summer weekdays 1800 xxxx

∆ Summer weekends xxxx xxxx






Traffic evolution Operating point in summer 1999


Referencelocation



EBBRMB No 42,107 1987 21.2 10%



Conclusion


Weekday Weekend Weekday Weekend0 % 0 % 0 % 0 % reliable

E B B U d e l a y s p e r s e c t o r i n S 9 9

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

s [m

in]

E B B U W S A S

E B B U L W S A S

E B B U L E S A S

E B B U C S S A S

E B B R M B A Z

EBBU Traffic Evolution (1997, 1998, 1999)

1000

1100

1200

1300

1400

1500

1600

1700

1800


Da

ily T

raffi

c (3

0 d

ays

mo

vin

g a

vera

ge

)

EBBU operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Co

sts

(Eu

ro)




37

A2 EDBB: Berlin ACC



EDBB (Summer - Winter 1998)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 200 400 600 800 1000 1200 1400 1600 1800

daily traffic

del

ay p

er f

ligh

t [m

in]

EDBB (Summer 1998 - 1999)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 200 400 600 800 1000 1200 1400 1600 1800

daily traffic

del

ay p

er f

ligh

t [m

in]









− Summer weekdays xxxx xxxx









Referencelocation



LIDVI No 9,573 415 23.1 15%

Main delay producer and their delay distribution in Summer 1999


Conclusion

Capacity Shortfall Comments

2000 2001


0 % 0 % 3 % 2 % Att.: 7-15% if shortest routes are applied

E D B B d e l a y s p e r s e c t o r i n S 9 9

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

s [m

in]

L I D V I S PE D D T A DE D B B U R 3 A SE D B B U R 2 A SE D B B A C C A S

EDBB Traffic Evolution (1997, 1998, 1999)

500

700

900

1100

1300

1500

1700


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

EDBB operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)




39

A3 EDFF: Frankfurt ACC



EDFF (Summer 1998 - 1999)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 500 1000 1500 2000 2500 3000

daily traffic

del

ay p

er f

ligh

t [m

in]

EDFF (Summer - Winter 1998)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 500 1000 1500 2000 2500 3000

daily traffic

del

ay p

er f

ligh

t [m

in]


Capacity growth 1998 – 1999: 4%









− Winter weekdays 2500 xxxx







Referencelocation



EDDF No 51,978 2583 20.1 6%



Conclusion



E D F F d e l a y s p e r s e c t o r / a i r p o r t i n S 9 9

0

5 0 0 0

1 0 0 0 0

1 5 0 0 0

2 0 0 0 0

2 5 0 0 0

6 / 1 4 / 9 9 6 /2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 /9 9 7 / 1 2 / 9 9 7 /1 9 /9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

[m

in]

E D F F S R 1 6 A SE D F F N R 2 A SE D F F N 1 2 A SE D F O R 1 2 A SE D D F A D

EDFF Traffic Evolution (1997, 1998, 1999)

1500

1700

1900

2100

2300

2500

2700


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

EDFF operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)




41

A4 EDLL: Düsseldorf ACC



EDLL (Summer 1998 - 1999)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 500 1000 1500 2000 2500

daily traffic

del

ay p

er f

ligh

t [m

in]

EDLL (Summer - Winter 1998)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 500 1000 1500 2000 2500

daily traffic

del

ay p

er f

ligh

t [m

in]


















Referencelocation



EDLLOR3 No 1,123 34 33.0 45%



Conclusion


2000 2001


1 % 0 % 10 % 4 % reliable

E D L L d e l a y s p e r s e c t o r / a i r p o r t i n S 9 9

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

[m

in]

E D L L O R 1 A SE D L L S C 1 A SN O R S PE D L L S R 3 A S

EDLL Traffic Evolution (1997, 1998, 1999)

900

1000

1100

1200

1300

1400

1500

1600

1700


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

EDLL operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)




43

A5 EDMM: München ACC



EDMM (Summer 1998 - 1999)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 500 1000 1500 2000 2500 3000

daily traffic

del

ay p

er f

ligh

t [m

in]

Traffic growth 1998 – 1999: - 3 %

Capacity growth 1998 – 1999: x %



∆ Summer weekends 98 2400 xxxx



EDMM (Summer - Winter 1998)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 500 1000 1500 2000 2500 3000

daily traffic

del

ay p

er f

ligh

t [m

in]



∆ Summer weekends 2400 xxxx








Referencelocation



EDMMUR2 No 3,597 118 30.5 37%EDDM No 1,837 90 20.4 7%EDMMCN1 Yes 2 484 18 26.9 27%



Conclusion



0 % 0 % 4 % 0 % reliable

E D M M d e l a y s p e r s e c t o r / a i r p o r t i n S 9 9

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

[m

in]

E D M M N R 4 A SE D M M S R 1 A SE D D M A D

EDMM Traffic Evolution (1997, 1998, 1999)

1300

1500

1700

1900

2100

2300

2500

2700


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

EDMM operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)




45

A6 EDUU: Karlsruhe ACC



EDUU (Summer 1998 - 1999)

0

1

2

3

4

5

6

7

0 500 1000 1500 2000 2500 3000

daily traffic

del

ay p

er f

ligh

t [m

in]







∆

EDUU (Summer - Winter 1998)

0

1

2

3

4

5

6

7

0 500 1000 1500 2000 2500 3000

daily traffic

del

ay p

er f

ligh

t [m

in]




− Winter weekdays 1900 2250

∆ Winter weekends 1750 xxxx






Referencelocation



EDUUTGO Yes 2 12,612 599 21.1 9%EDUUNTM Yes 2 2,256 88 25.6 23%EDUUSLN Yes 2 535 26 20.6 8%EDUUFF2 No 364 18 20.2 7%



Conclusion


2000 2001


12 % 11 % 18 % 17 % reliable

E D U U d e l a y s p e r s e c t o r i n S 9 9

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

6 0 0 0

7 0 0 0

8 0 0 0

9 0 0 0

1 0 0 0 0

1 1 0 0 0

1 2 0 0 0

1 3 0 0 0

1 4 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

[m

in]

E D U U N T 2 A S

E D U U S L N A S

E D U U W U 1 A S

E D U U N T M A S

E D U U F U L A S

E D U U E R L A S

E D U U T G O A S

E D U U T G 2 A S

E D U U W U R A S

E D U U F F M A S

EDUU Traffic Evolution (1997, 1998, 1999)

1300

1500

1700

1900

2100

2300

2500


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

EDUU operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)




47

A7 EDYY: Maastricht ACC



EDYY (Summer 1998 - 1999)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 500 1000 1500 2000 2500 3000 3500 4000

daily traffic

del

ay p

er f

ligh

t [m

in]








EDYY (Summer - Winter 1998)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 500 1000 1500 2000 2500 3000 3500 4000

daily traffic

del

ay p

er f

ligh

t [m

in]











Referencelocation



EBMALNL No 52,937 2554 20.7 8%EBMALUX No 38,616 1617 23.9 18%EBMAUCE Yes 3 11,991 585 20.5 7%



Conclusion


2000 2001


10 % 11 % 17 % 18 % reliable

E D Y Y d e l a y s p e r s e c t o r / a i r p o r t i n S 9 9

0

2 0 0 0

4 0 0 0

6 0 0 0

8 0 0 0

1 0 0 0 0

1 2 0 0 0

1 4 0 0 0

1 6 0 0 0

1 8 0 0 0

2 0 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

[m

in]

E D Y Y H A M A S

E D Y M U R W A S

E H D E L M D A S

E D Y Y S O L A S

E D Y Y C S T A S

E B M A U C E A S

E B M A W S L A S

E B M A L N L A S

E B M A L U X A S

EDYY Traffic Evolution (1997, 1998, 1999)

2000

2200

2400

2600

2800

3000

3200

3400

3600


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

EDYY operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)




49

A8 EGTT: London ACC



EGTT (Summer 1998 - 1999)

0

1

2

3

4

5

6

7

8

0 1000 2000 3000 4000 5000 6000

daily traffic

del

ay p

er f

ligh

t [m

in]








EGTT (Summer - Winter 1998)

0

1

2

3

4

5

6

7

8

0 1000 2000 3000 4000 5000 6000

daily traffic

del

ay p

er f

ligh

t [m

in]











Referencelocation



EGTTLUE No 35,515 1770 20.1 6%EGTTHRN Yes 3 29,283 1103 26.5 26%EGTTSFD No 18,700 802 23.3 16%EGTTLUW No 18,032 601 30.0 36%EGTTBHD No 17,146 670 25.6 23%



Conclusion



7 % 7 % 13 % 13 % reliable

E G T T d e la y s p e r s e c to r in S 9 9

0

2 0 0 0

4 0 0 0

6 0 0 0

8 0 0 0

1 0 0 0 0

1 2 0 0 0

1 4 0 0 0

1 6 0 0 0

1 8 0 0 0

2 0 0 0 0

2 2 0 0 0

2 4 0 0 0

2 6 0 0 0

6 / 1 4 /9 9 6 /2 1 /9 9 6 / 2 8 /9 9 7 /5 /9 9 7 /1 2 /9 9 7 /1 9 /9 9 7 / 2 6 /9 9 8 /2 / 9 9

dai

ly d

elay

[min

]

E G T T B C N A S

E G T T S 1 3 A S

E G T T D T N A S

E G L L A D

E G T T L N D A S

E G T T L Y D A S

E G T T B H D A S

E G T T S T U A S

E G T T L M W A S

E G T T S F D A S

E G T T N 2 S A S

E G T T S 7 A S

E G T T 3 C 4 A S

E G T T L U W A S

E G T T H R N A S

E G T T S 1 1 A S

E G T T D V R A S

E G T T S 1 4 A S

E G T T L U E A S

EGTT Traffic Evolution (1997, 1998, 1999)

2500

3000

3500

4000

4500

5000

5500


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

EGTT operating point in summer 1999 (average weekdays and weekend)

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

2,000,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)




51

A9 EHAA: Amsterdam ACC



EHAA (Summer 1998 - 1999)

0

2

4

6

8

10

12

0 200 400 600 800 1000 1200 1400 1600 1800

daily traffic

del

ay p

er f

ligh

t [m

in]








EHAA (Summer - Winter 1998)

0

2

4

6

8

10

12

0 200 400 600 800 1000 1200 1400 1600 1800

daily traffic

del

ay p

er f

ligh

t [m

in]











Referencelocation



ELDIN No 734 6 122.33 313%EH4 No 346 17 20.4 7%



Conclusion


2000 2001


0 % 0 % 4 % 3 % reliable

E H A A d e l a y s p e r s e c t o r / a i r p o r t i n S 9 9

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

6 0 0 0

7 0 0 0

8 0 0 0

9 0 0 0

1 0 0 0 0

1 1 0 0 0

1 2 0 0 0

1 3 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

[m

in] E H 6 A Z

E H S E C T 2 A SE H A M A D

EHAA Traffic Evolution (1997, 1998, 1999)

900

1000

1100

1200

1300

1400

1500

1600


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

EHAA operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)




53

A10 EPWW: Warszawa ACC



EPWW (Summer 1998 - 1999)

0

1

2

3

4

5

6

7

8

9

10

0 100 200 300 400 500 600 700 800 900

daily traffic

del

ay p

er f

ligh

t [m

in]

Traffic growth 1998 – 1999: x %







EPWW (Summer - Winter 1998)

0

1

2

3

4

5

6

7

8

9

10

0 100 200 300 400 500 600 700 800 900

daily traffic

del

ay p

er f

ligh

t [m

in]









Operating point in summer 1999


Referencelocation



EPWWJED No 33,052 1332 24.8 20%EPWWSW Yes 2 18,832 776 24.3 19%EPWWE Yes 2 4,139 199 20.8 8%EPWWDRE No 2,269 94 24.1 18%EPWWNE Yes 3 1,968 64 30.8 38%



Conclusion



8 % 8 % 12 % 12 % reliable

E P W W d e l a y s p e r s e c t o r i n S 9 9

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

6 0 0 0

7 0 0 0

8 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

[m

in]

E P W W N E A S

E P W W D R E A S

E P W W E A S

E P W W J E D A S

E P W W S W A S

EPWW operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)




55

A11 GCCC: Canarias ACC



GCCC (Summer 1998 - 1999)

0

1

2

3

4

5

6

7

8

9

10

0 100 200 300 400 500 600 700 800 900

daily traffic

del

ay p

er f

ligh

t [m

in]








GCCC (Summer - Winter 1998)

0

1

2

3

4

5

6

7

8

9

10

0 100 200 300 400 500 600 700 800 900

daily traffic

del

ay p

er f

ligh

t [m

in]











Referencelocation



GCRR No 15,678 394 39.8 65%GCFV No 1,623 34 47.7 89%GCCCNB No 1,074 47 22.9 15%GCCCGC No 286 12 23.8 18%



Conclusion



5 % 0 % 10 % 5 % reliable

G C C C d e l a y s p e r s e c t o r i n S 9 9

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

6 0 0 0

7 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

[m

in]

G C C C N B A S

G C F V A D

N E L S O S P

G C C C A C A S

G C R R A D

GCCC Traffic Evolution (1997, 1998, 1999)

300

350

400

450

500

550

600

650

700

750


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

GCCC operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)




57

A12 LCCC: Nicosia ACC



LCCC (Summer 1998 - 1999)

0

1

2

3

4

5

6

7

8

9

10

0 100 200 300 400 500 600 700 800

daily traffic

del

ay p

er f

ligh

t [m

in]








LCCC (Summer - Winter 1998)

0

1

2

3

4

5

6

7

8

9

10

0 100 200 300 400 500 600 700 800

daily traffic

del

ay p

er f

ligh

t [m

in]











Referencelocation



LCCCSSW Yes 2 13,955 515 27.1 27%LCCCSS No 11,608 294 39.5 64%LCCCWS No 10,868 361 30.1 36%LCCCALL Yes 4 5,398 225 24.0 18%LCCCEST Yes 2 651 30 21.7 11%



Conclusion


2000 2001


0 % 0 % 4 % 0 % Att.: 2-8% if Yug. Airspave is not fully available

L C C C d e l a y s p e r s e c t o r i n S 9 9

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

[m

in]

L C C C S S W A S

L C C C S S A S

L C C C A L L A S

L C C C W S A S

LCCC Traffic Evolution (1997, 1998, 1999)

300

350

400

450

500

550

600

650


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LCCC operating point in summer 1999 (average weekdays and weekend)

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)




59

A13 LECB: Barcelona ACC



LECB (Summer 1998 - 1999)

0

2

4

6

8

10

12

14

16

18

20

0 500 1000 1500 2000 2500

daily traffic

del

ay p

er f

ligh

t [m

in]








LECB (Summer - Winter 1998)

0

2

4

6

8

10

12

14

16

18

20

0 500 1000 1500 2000 2500

daily traffic

del

ay p

er f

ligh

t [m

in]











Referencelocation



LECBECO Yes 2 44,957 1366 32.9 45%LECBECO Yes 2 44,957 1366 32.9 45%LECBCEN Yes 2 39,365 1339 29.4 34%LECBLEV Yes 2 30,946 1221 25.3 22%LECBW Yes 3 17,872 722 24.8 20%



Conclusion


2000 2001


16 % 22 % 24 % 30 % reliable

L E C B d e l a y s p e r s e c t o r i n S 9 9

0

4 0 0 0

8 0 0 0

1 2 0 0 0

1 6 0 0 0

2 0 0 0 0

2 4 0 0 0

2 8 0 0 0

3 2 0 0 0

3 6 0 0 0

4 0 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

[m

in]

L E C B W D A S

L E C B L D A A S

L E C B _ E A S

L E B L A D

L E C B E M A S

L E C B W 1 3 A S

L E C B L E V A S

L E C B W A S

L E C B E C O A S

L E C B C E N A S

LECB Traffic Evolution (1997, 1998, 1999)

300

500

700

900

1100

1300

1500

1700

1900


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LECB operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)




61

A14 LECM: Madrid ACC



LECM (Summer 1998 - 1999)

0

2

4

6

8

10

12

14

0 500 1000 1500 2000 2500

daily traffic

del

ay p

er f

ligh

t [m

in]








LECM (Summer - Winter 1998)

0

2

4

6

8

10

12

14

0 500 1000 1500 2000 2500

daily traffic

del

ay p

er f

ligh

t [m

in]











Referencelocation



LECMBLV No 39,282 1521 25.8 23%LECMZMR Yes 2 26,814 1160 23.1 15%LECMDGO No 22,167 825 26.9 27%LECMTLD No 20,132 758 26.6 26%LECMSTG No 16,099 749 21.5 10%



Conclusion


2000 2001


9 % 15 % 17 % 23 % reliable

L E C M d e l a y s p e r s e c t o r i n S 9 9

0

2 0 0 0

4 0 0 0

6 0 0 0

8 0 0 0

1 0 0 0 0

1 2 0 0 0

1 4 0 0 0

1 6 0 0 0

1 8 0 0 0

2 0 0 0 0

2 2 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

[m

in] L E C M C J C A S

L E M D A D

L E C M P P N A S

L E C M T L D A S

L E C M D G O A S

L E C M S T G A S

L E C M Z M R A S

L E C M B L V A S

LECM Traffic Evolution (1997, 1998, 1999)

1100

1300

1500

1700

1900

2100


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LECM operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)




63

A15 LECS: Seville ACC



LECS (Summer 1998 - 1999)

0

2

4

6

8

10

12

14

0 100 200 300 400 500 600 700 800 900 1000

daily traffic

del

ay p

er f

ligh

t [m

in]








LECS (Summer - Winter 1998)

0

2

4

6

8

10

12

14

0 100 200 300 400 500 600 700 800 900 1000

daily traffic

del

ay p

er f

ligh

t [m

in]











Referencelocation



LECSBLN No 7,626 298 25.6 23%LECSSM Yes 2 6,133 213 28.8 32%LECSBY Yes 2 6,132 236 26.0 24%LECSBY Yes 2 6,132 236 26.0 24%LECSRUT Yes 4 4,659 211 22.1 12%



Conclusion


2000 2001


0 % 11 % 0 % 18 % reliable

L E C S d e l a y s p e r s e c t o r i n S 9 9

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

6 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

[m

in]

L E C S B L N A SL E C S S M A SL E C S B Y A S

LECS Traffic Evolution (1997, 1998, 1999)

400

450

500

550

600

650

700

750

800


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LECS operating point in summer 1999 (average weekdays and weekend)

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)




65

A16 LFBB: Bordeaux ACC



LFBB (Summer 1998 - 1999)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 500 1000 1500 2000 2500

daily traffic

del

ay p

er f

ligh

t [m

in]








LFBB (Summer - Winter 1998)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 500 1000 1500 2000 2500

daily traffic

del

ay p

er f

ligh

t [m

in]











Referencelocation



LFBUX2 No 20,259 710 28.5 32%LFBBDXN Yes 13 7,608 282 27.0 27%LFBUC2 No 4,097 108 37.9 60%LFBBDXS Yes 11 3,631 125 29.0 33%LFBULR2 Yes 2 1,512 49 30.9 39%



Conclusion



0 % 4 % 2 % 10 % reliable

L F B B d e l a y s p e r s e c t o r i n S 9 9

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

6 0 0 0

7 0 0 0

8 0 0 0

9 0 0 0

1 0 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

[m

in] L F B U N O R A S

L F B O A D

L F B B D X S A S

L F B U C R 2 A S

L F B U X 2 A S

LFBB Traffic Evolution (1997, 1998, 1999)

1000

1200

1400

1600

1800

2000

2200


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LFBB operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)




67

A17 LFEE: Reims ACC



LFEE (Summer 1998 - 1999)

0

2

4

6

8

10

12

0 500 1000 1500 2000 2500

daily traffic

del

ay p

er f

ligh

t [m

in]








LFEE (Summer - Winter 1998)

0

2

4

6

8

10

12

0 500 1000 1500 2000 2500

daily traffic

del

ay p

er f

ligh

t [m

in]


− Summer weekdays xxxx 1850

∆ Summer weekends xxxx 1750

− Winter weekdays xxxx 1800

∆ Winter weekends 1450 1700






Referencelocation



LFEBRUT Yes 4 77,880 3482 22.4 13%LFEUE No 39,375 1678 23.5 16%LFEUY No 34,072 1569 21.7 11%LFEUYUR Yes 2 21,032 771 27.3 28%LFEHXSE Yes 3 7,342 343 21.4 10%



Conclusion



10 % 11 % 17 % 18 % Att.: 24-32% if shortest routes are applied

L F E E d e la y s p e r s e c t o r in S 9 9

0

2 0 0 0

4 0 0 0

6 0 0 0

8 0 0 0

1 0 0 0 0

1 2 0 0 0

1 4 0 0 0

1 6 0 0 0

1 8 0 0 0

2 0 0 0 0

2 2 0 0 0

2 4 0 0 0

6 /1 4 /9 9 6 /2 1 /9 9 6 /2 8 /9 9 7 /5 /9 9 7 /1 2 /9 9 7 /1 9 /9 9 7 /2 6 /9 9 8 /2 /9 9

dai

ly d

elay

[min

]

L F E X N A S

L F E H X S E A S

L F E U Y U R A S

L F E E A S

L F S B A D

L F E U Y A S

L F E U E A S

L F E B R U T A S

LFEE Traffic Evolution (1997, 1998, 1999)

1200

1300

1400

1500

1600

1700

1800

1900

2000

2100

2200


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LFEE operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)




69

A18 LFFF: Paris ACC



LFFF (Summer 1998 - 1999)

0

1

2

3

4

5

6

7

0 500 1000 1500 2000 2500 3000 3500 4000

daily traffic

del

ay p

er f

ligh

t [m

in]








LFFF (Summer - Winter 1998)

0

1

2

3

4

5

6

7

0 500 1000 1500 2000 2500 3000 3500 4000

daily traffic

del

ay p

er f

ligh

t [m

in]







EEC: Capacity Targets for 2000 and 2001 70




Referencelocation



LFFTE No 49,735 2427 20.5 7%MERUE No 26868 1031 26.1 24%LFPG No 11,119 238 46.7 86%LFFTP No 9,618 472 20.4 7%LFFUJ No 3,029 138 21.9 12%



Conclusion



7 % 0 % 12 % 5 % reliable

L F F F d e l a y s p e r s e c t o r i n S 9 9

0

2 0 0 0

4 0 0 0

6 0 0 0

8 0 0 0

1 0 0 0 0

1 2 0 0 0

1 4 0 0 0

1 6 0 0 0

1 8 0 0 0

2 0 0 0 0

2 2 0 0 0

2 4 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

[m

in]

L F F U K A S

L F F T N T B A S

L F F T B A S

L F F S U A S

L F F U T T U A S

L F F T H A S

L F F T S A S

L F F A R A S

L F F T W A S

L F F T C A S

L F P G A D

L F F T P A S

L F F T E A S

LFFF Traffic Evolution (1997, 1998, 1999)

1600

2100

2600

3100

3600


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LFFF operating point in summer 1999 (average weekdays and weekend)

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)




71

A19 LFMM: Marseille ACC



LFMM (Summer 1998 - 1999)

0

2

4

6

8

10

12

14

16

18

20

0 500 1000 1500 2000 2500 3000 3500

daily traffic

del

ay p

er f

ligh

t [m

in]








LFMM (Summer - Winter 1998)

0

2

4

6

8

10

12

14

16

18

20

0 500 1000 1500 2000 2500 3000 3500

daily traffic

del

ay p

er f

ligh

t [m

in]











Referencelocation



LFMMF1 Yes 2 73,002 2183 33.4 46%LFMK1 No 56,082 1923 29.2 33%LFMT2 No 43,169 1395 30.9 39%LFMMF2 Yes 2 34,044 1083 31.4 40%LFMW1 No 30,036 1390 21.6 11%



Conclusion


2000 2001


10 % 17 % 17 % 24 % reliable

L F M M d e l a y s p e r s e c t o r i n S 9 9

0

4 0 0 0

8 0 0 0

1 2 0 0 0

1 6 0 0 0

2 0 0 0 0

2 4 0 0 0

2 8 0 0 0

3 2 0 0 0

3 6 0 0 0

4 0 0 0 0

4 4 0 0 0

4 8 0 0 0

5 2 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

[m

in]

L F M L Y O N A SL F M A B A SL F M K D A SL F M K 2 A SL F M W 2 A SL F M M 2 A SL F M W T 2 A SL F M Y Y A SL F M B 1 A SL F M M N C O A SL F M B B A SL F M 5 A ZL F M W T 1 A SL F M M N S C A SL F M S T A SL F M M F A SL F M T 2 A SL F M W 1 A SL F M K K A SL F M M F 2 A SL F M K 1 A SL F M M F 1 A S

LFMM Traffic Evolution (1997, 1998, 1999)

1300

1500

1700

1900

2100

2300

2500

2700

2900

3100


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LFMM operating point in summer 1999 (average weekdays and weekend)

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)




73

A20 LFRR: Brest ACC



LFRR (Summer 1998 - 1999)

0

1

2

3

4

5

6

7

0 500 1000 1500 2000 2500 3000

daily traffic

del

ay p

er f

ligh

t [m

in]


Capacity growth 1998 – 1999: > 14 %






LFRR (Summer - Winter 1998)

0

1

2

3

4

5

6

7

0 500 1000 1500 2000 2500 3000

daily traffic

del

ay p

er f

ligh

t [m

in]











Referencelocation



LFRN Yes 2 11,728 422 27.8 29%LFRJ Yes 2 2,482 90 27.6 29%LFRNU No 1,671 63 26.5 26%LFRNGA Yes 6 1,042 44 23.7 17%LFRZS No 715 21 34.0 48%



Conclusion


2000 2001


0 % 5 % 3 % 13 % reliable

LFRR Traffic Evolution (1997, 1998, 1999)

800

1000

1200

1400

1600

1800

2000

2200


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LFRR operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)

L F R R d e l a y s p e r s e c t o r i n S 9 9

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

6 0 0 0

7 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

s [m

in]

L F R J A S

L F R Z S A S

L F R N A S




75

A21 LGGG: Athinai ACC



LGGG (Summer 1998 - 1999)

0

5

10

15

20

25

0 200 400 600 800 1000 1200 1400 1600

daily traffic

del

ay p

er f

ligh

t [m

in]








LGGG (Summer - Winter 1998)

0

5

10

15

20

25

0 200 400 600 800 1000 1200 1400 1600

daily traffic

del

ay p

er f

ligh

t [m

in]





∆ Winter weekend s 900 1050






Referencelocation



LGAT No 105,775 3887 27.2 28%LGIR No 58,044 971 59.8 125%LGGR5 Yes 2 36,876 927 39.8 65%LGTS No 25,330 709 35.7 53%LGGR2 Yes 2 15,221 394 38.6 62%


Figure A21.3: Delay distribution over the days (June17th to Aug.6th 1999)

Date format (mm/dd/yy); vertical lines indicate MondaysRegulation AS: sector; AD: airport; AZ: group of airports; SP: point;

Conclusion


2000 2001



LGGG Traffic Evolution (1997, 1998, 1999)

400

500

600

700

800

900

1000

1100

1200

1300

1400


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LGGG operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)

L G G G d e l a y s p e r s e c t o r / a i r p o r t i n S 9 9

0

2 0 0 0

4 0 0 0

6 0 0 0

8 0 0 0

1 0 0 0 0

1 2 0 0 0

1 4 0 0 0

1 6 0 0 0

1 8 0 0 0

2 0 0 0 0

2 2 0 0 0

2 4 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

[m

in] L G K R A D

L G G R 6 A S

L G R P A D

L G G R 2 A S

L G G R 5 A S

L G A T A D

L G I R A D




77

A22 LGMD: Makedonia ACC



LGMD (Summer 1998 - 1999)

0

5

10

15

20

25

30

0 100 200 300 400 500 600 700 800

daily traffic

del

ay p

er f

ligh

t [m

in]

Traffic growth 1998 – 1999: - 1 %







LGMD (Summer - Winter 1998)

0

5

10

15

20

25

30

0 100 200 300 400 500 600 700 800

daily traffic

del

ay p

er f

ligh

t [m

in]





∆ Winter weekends 500 550






Referencelocation



LGMR4 Yes 2 1,429 46 31.1 39%LGMK No 163 4 40.8 68%



Conclusion


2000 2001


18 % 27 % 25 % 34 % Att.: 3-8% if Yug. Airspace is not fully available

LGMD Traffic Evolution (1997, 1998, 1999)

100

200

300

400

500

600

700


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LGMD operating point in summer 1999 (average weekdays and weekend)

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)

L G M D d e la y s p e r s e c to r /a i r p o r t in S 9 9

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

6 0 0 0

6 /1 4 /9 9 6 /2 1 /9 9 6 /2 8 /9 9 7 /5 /9 9 7 /1 2 /9 9 7 /1 9 /9 9 7 /2 6 /9 9 8 /2 /9 9

dai

ly d

elay

[m

in]

L G M R 4 3 A S

L G M R 4 A S

L G T S A D




79

A23 LHCC: Budapest ACC



LHCC (Summer 1998 - 1999)

0

1

2

3

4

5

6

7

8

9

10

0 200 400 600 800 1000 1200 1400 1600 1800

daily traffic

del

ay p

er f

ligh

t [m

in]


Capacity growth 1998 – 1999: 0 % ?






LHCC (Summer - Winter 1998)

0

1

2

3

4

5

6

7

8

9

10

0 200 400 600 800 1000 1200 1400 1600 1800

daily traffic

del

ay p

er f

ligh

t [m

in]











Referencelocation



LHC No 34223 1548 22.11 12.3%LHB No 24582 1159 21.21 9.6%LHA No 5259 236 22.28 12.9%LHD No 1754 75 23.39 16.2%



Conclusion


2000 2001



LHCC Traffic Evolution (1997, 1998, 1999)

100

300

500

700

900

1100

1300

1500

1700


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LHCC operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)

L H C C d e l a y s p e r s e c t o r i n S 9 9

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

6 0 0 0

7 0 0 0

8 0 0 0

9 0 0 0

1 0 0 0 0

1 1 0 0 0

1 2 0 0 0

1 3 0 0 0

1 4 0 0 0

1 5 0 0 0

1 6 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

[m

in]

L H E A S T A S

L H C C N E A S

L H N O R T H A S

L H W E S T A S




81

A24 LIBB: Brindisi ACC



LIBB (Summer 1998 - 1999)

0

2

4

6

8

10

12

14

16

0 100 200 300 400 500 600 700 800 900 1000

daily traffic

del

ay p

er f

ligh

t [m

in]








LIBB (Summer - Winter 1998)

0

2

4

6

8

10

12

14

16

0 100 200 300 400 500 600 700 800 900 1000

daily traffic

del

ay p

er f

ligh

t [m

in]











Referencelocation



LIBBMS2 Yes 2 11,593 401 28.9 33%LIBBUND Yes 2 3,372 120 28.1 30%



Conclusion


2000 2001



LIBB Traffic Evolution (1997, 1998, 1999)

100

200

300

400

500

600

700

800


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LIBB operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)

L I B B d e l a y s p e r s e c t o r i n S 9 9

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

6 0 0 0

7 0 0 0

8 0 0 0

9 0 0 0

1 0 0 0 0

1 1 0 0 0

1 2 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

[m

in]

L I B B S D 1 A S

L I B B U N D A S

L I B B M S 2 A S




83

A25 LIMM: Milano ACC



LIMM (Summer 1998 - 1999)

0

2

4

6

8

10

12

14

0 500 1000 1500 2000 2500

daily traffic

del

ay p

er f

ligh

t [m

in]


Capacity growth 1998 – 1999: 28%






LIMM (Summer - Winter 1998)

0

2

4

6

8

10

12

14

0 500 1000 1500 2000 2500

daily traffic

del

ay p

er f

ligh

t [m

in]











Referencelocation



LIMMWSL Yes 2 31,836 1040 30.6 38%LIMMUE4 Yes 2 3,621 113 32.0 42%LIMMUW4 Yes 2 1,154 38 30.4 37%



Conclusion



7 % 11 % 13 % 17 % reliable

LIMM Traffic Evolution (1997, 1998, 1999)

1000

1200

1400

1600

1800

2000

2200

2400


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LIMM operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)

L I M M d e l a y s p e r s e c t o r / a i r p o r t i n S 9 9

0

2 0 0 0

4 0 0 0

6 0 0 0

8 0 0 0

1 0 0 0 0

1 2 0 0 0

1 4 0 0 0

1 6 0 0 0

1 8 0 0 0

2 0 0 0 0

2 2 0 0 0

2 4 0 0 0

2 6 0 0 0

2 8 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

[m

in]

L I B B S D 1 A S

L I B B U N D A S

L I B B M S 2 A S

L I M M E S L A S

L I M M U E 4 A S

L I M C A D

L I M M E S 2 A S

L I M M W S L A S




85

A26 LIPP: Padua ACC



LIPP (Summer 1998 - 1999)

0

5

10

15

20

25

30

0 200 400 600 800 1000 1200 1400 1600 1800

daily traffic

del

ay p

er f

ligh

t [m

in]


Capacity growth 1998 – 1999: 0 % ?






LIPP (Summer - Winter 1998)

0

5

10

15

20

25

30

0 200 400 600 800 1000 1200 1400 1600 1800

daily traffic

del

ay p

er f

ligh

t [m

in]


− Summer weekdays xxxx 1300









Referencelocation



LIPPNU1 Yes 3 55,253 2020 27.4 28%LIPPSL1 Yes 2 44,321 1762 25.2 21%LIPPNTS Yes 2 39,200 1371 28.6 32%LIPPSTS Yes 2 35,160 1506 23.3 16%LIPPNL1 Yes 2 26,777 1209 22.1 12%



Conclusion


2000 2001


17 % 20 % 23 % 26 % reliable

LIPP Traffic Evolution (1997, 1998, 1999)

500

700

900

1100

1300

1500

1700


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LIPP operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)

LIPP delays per sector/airport in S99

0

4000

8000

12000

16000

20000

24000

28000

32000

36000

40000

44000

48000

6/14/99 6/21/99 6/28/99 7/5/99 7/12/99 7/19/99 7/26/99 8/2/99

dailydelay[min]

LGMR43 AS

LGMR4 AS

LGTS AD

LIPX AD

LIPE AD

LIPPNOR AS

LIPPSU1 AS

LIPPNTS AS

LIPPSUD AS

LIPPNUP AS

LIPPSLS AS

LIPPNLS AS

LIPPSTS AS

LIPPSUP AS

LIPPSL1 AS

LIPPNU1 AS

LIPPNL1 AS




87

A27 LIRR: Rome ACC



LIRR (Summer 1998 - 1999)

0

1

2

3

4

5

6

7

8

9

0 500 1000 1500 2000 2500 3000

daily traffic

del

ay p

er f

ligh

t [m

in]








LIRR (Summer - Winter 1998)

0

1

2

3

4

5

6

7

8

9

0 500 1000 1500 2000 2500 3000

daily traffic

del

ay p

er f

ligh

t [m

in]











Referencelocation



LIRQ No 20,344 908 22.4 13%LIEO No 221 7 31.6 41%



Conclusion


2000 2001


0 % 2 % 6 % 9 % reliable

LIRR Traffic Evolution (1997, 1998, 1999)

1000

1200

1400

1600

1800

2000

2200

2400


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LIRR operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)

L I R R d e l a y s p e r s e c t o r / a i r p o r t i n S 9 9

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

6 0 0 0

7 0 0 0

8 0 0 0

9 0 0 0

1 0 0 0 0

1 1 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

s [m

in]

L I R R U S R A S

L I R R S E 4 A S

L I R F A D

L I R R N E 4 A S

L I R Q A D




89

A28 LKAA: Prague ACC



LKAA (Summer 1998 - 1999)

0

2

4

6

8

10

12

0 200 400 600 800 1000 1200 1400

daily traffic

del

ay p

er f

ligh

t [m

in]








LKAA (Summer - Winter 1998)

0

2

4

6

8

10

12

0 200 400 600 800 1000 1200 1400

daily traffic

del

ay p

er f

ligh

t [m

in]











Referencelocation



LKAAND Yes 2 51,035 2495 20.5 7%LKAAEQ Yes 2 22,247 1059 21.0 9%LKAAWA Yes 2 1,860 88 21.1 9%



Conclusion


2000 2001


21 % 20 % 27 % 26 % reliable

LKAA Traffic Evolution (1997, 1998, 1999)

300

400

500

600

700

800

900

1000

1100


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LKAA operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)

L K A A d e l a y s p e r s e c t o r i n S 9 9

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

6 0 0 0

7 0 0 0

8 0 0 0

9 0 0 0

1 0 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

s [m

in]

L K A A W A A S

L K A A W U 1 A S

L K A A N D A S

L K A A S B A S

L K A A E Q A S




91

A29 LPPC: Lisbon ACC



LPPC (Summer - Winter 1998)

0

1

2

3

4

5

6

7

8

9

0 200 400 600 800 1000 1200

daily traffic

del

ay p

er f

ligh

t [m

in]

LPPC (Summer 1998 - 1999)

0

1

2

3

4

5

6

7

8

9

0 200 400 600 800 1000 1200

daily traffic

del

ay p

er f

ligh

t [m

in]


















Referencelocation



LPCEN Yes 4 19,856 742 26.8 26%LPOESTE Yes 2 16,640 568 29.3 34%LPSUL Yes 2 8,896 319 27.9 30%LPPRTMA No 102 4 25.5 23%



Conclusion



0 % 5 % 10 % 17 % reliable

LPPC Traffic Evolution (1997, 1998, 1999)

300

400

500

600

700

800

900

1000


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LPPC operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)

L P P C d e l a y s p e r s e c t o r / a i r p o r t i n S 9 9

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

6 0 0 0

7 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

s [m

in]

L P F R A D

L P P T T S 2 A S

L P S U L A S

L P O E S T E A S

L P C E N A S




93

A30 LSAG: Geneve ACC



LSAG (Summer 1998 - 1999)

0

2

4

6

8

10

12

14

0 200 400 600 800 1000 1200 1400 1600 1800 2000

daily traffic

del

ay p

er f

ligh

t [m

in]








LSAG (Summer - Winter 1998)

0

2

4

6

8

10

12

14

0 200 400 600 800 1000 1200 1400 1600 1800 2000

daily traffic

del

ay p

er f

ligh

t [m

in]











Referencelocation



LSAGKIN Yes 5 82,131 3788 21.7 11%LSAGSUP Yes 4 66,267 2231 29.7 35%LSAGINS No 39,471 1520 26.0 24%LSAGB4 Yes 8 23,243 1018 22.8 14%LSAGSU2 Yes 2 2,815 140 20.1 6%



Conclusion


2000 2001


19 % 26 % 25 % 32 % reliable

LSAG Traffic Evolution (1997, 1998, 1999)

800

900

1000

1100

1200

1300

1400

1500

1600

1700


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LSAG operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)

L S A G d e l a y s p e r s e c t o r i n S 9 9

0

2 0 0 0

4 0 0 0

6 0 0 0

8 0 0 0

1 0 0 0 0

1 2 0 0 0

1 4 0 0 0

1 6 0 0 0

1 8 0 0 0

2 0 0 0 0

2 2 0 0 0

2 4 0 0 0

2 6 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

daily

del

ays

[min

]

L S A G I N N A S

L S A G S U 2 A S

L S A G B 4 A S

L S A G I N S A S

L S A G S U P A S

L S A G K I N A S




95

A31 LSAZ: Zürich ACC



LSAZ (Summer 1998 - 1999)

0

2

4

6

8

10

12

14

16

18

20

0 500 1000 1500 2000 2500

daily traffic

del

ay p

er f

ligh

t [m

in]








LSAZ (Summer - Winter 1998)

0

2

4

6

8

10

12

14

16

18

20

0 500 1000 1500 2000 2500

daily traffic

del

ay p

er f

ligh

t [m

in]











Referencelocation



LSAZUAC Yes 3 204,698 9740 21.0 9%LSAZWSL No 94,894 4224 22.5 13%LSZH No 33,257 1620 20.5 8%ODINA No 9,893 341 29.0 33%



Conclusion



21 % 23 % 28 % 30 % reliable

LSAZ Traffic Evolution (1997, 1998, 1999)

1000

1200

1400

1600

1800

2000

2200

2400


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LSAZ operating point in summer 1999 (average weekdays and weekend)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)

L S A Z d e l a y s p e r s e c t o r / a i r p o r t i n S 9 9

0

4 0 0 0

8 0 0 0

1 2 0 0 0

1 6 0 0 0

2 0 0 0 0

2 4 0 0 0

2 8 0 0 0

3 2 0 0 0

3 6 0 0 0

4 0 0 0 0

4 4 0 0 0

4 8 0 0 0

5 2 0 0 0

5 6 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

s [m

in] O D I N A S P

L S Z H A D

L S A Z N S L A S

L S A Z E S L A S

L S A Z W S L A S

L S A Z U A C A S




97

A32 LZBB: Bratislava ACC



LZBB (Summer 1998 - 1999)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 500 1000 1500 2000 2500

daily traffic

del

ay p

er f

ligh

t [m

in]








LZBB (Summer - Winter 1998)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 500 1000 1500 2000 2500

daily traffic

del

ay p

er f

ligh

t [m

in]











Referencelocation



LZBB2-4 No 5,930 257 23.1 15%LZBB5-6 No 1,375 57 24.1 18%



Conclusion



LZBB Traffic Evolution (1997, 1998, 1999)

0

100

200

300

400

500

600

700

800

900

1000


Dai

ly T

raffi

c (3

0 da

ys m

ovin

g av

erag

e)

LZBB operating point in summer 1999 (average weekdays and weekend)

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4


Cos

ts (

Eur

o)

L Z B B d e l a y s p e r s e c t o r i n S 9 9

0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

5 0 0 0

6 0 0 0

7 0 0 0

6 / 1 4 / 9 9 6 / 2 1 / 9 9 6 / 2 8 / 9 9 7 / 5 / 9 9 7 / 1 2 / 9 9 7 / 1 9 / 9 9 7 / 2 6 / 9 9 8 / 2 / 9 9

dai

ly d

elay

s [m

in]

L Z B B 5 - 6 A S

L Z B B L M 3 A S

L Z B B U P 3 A S

L Z B B 2 - 4 A S

L Z B B U 2 A S




99

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 TelCFMU D. Duytschaever [email protected] ++32 2 729 9600CFMU / ENG P.-O. Jeannet [email protected] ++32 2 729 9700CFMU / CEU J. Penwarne [email protected] ++32 2 729 9800CFMU / OPS M. Richard [email protected] ++32 2 729 9857

Related Activities

Domain Contacts Email TelEEC J-M. Garot [email protected] ++33 1 69 887501EATMP / DSA G. Paulson [email protected] ++32 2 729 3108PRU 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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