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EUROPEAN ORGANISATIONFOR THE SAFETY OF AIR NAVIGATION

EUROCONTROL EXPERIMENTAL CENTRE

The information contained i

The views express

FAP Future ATM Profile

Air

Short - TermCapacity Targets

For the European Navigation Services

EEC Note No. 15/00

Project GEN–4–E2

Issued: October 2000

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

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

RREEPPOORRTT DDOOCCUUMMEENNTTAATTIIOONN PPAAGGEE

ReferenceEEC Note No. 15/00

Security ClassificationUnclassified

OriginatorEEC - PFE(Performance, Flow Management,Economics & Efficiency)

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 2001 for the European Air Navigation Services

AuthorsM. DalichamptJ.C. Hustache

A. Marsden

Date10/00

Pagesiv +103

Figs163

Tables107

Annex1

References7

EATCHIP Taskspecification

-

ProjectGEN-4-E2

Sponsor Task No.-

PeriodJuly - Sept.

2000

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

Descriptors (keywords) :

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).The subject of the study is the target setting of the 2001 en-route capacities for the Europeanair navigation services.Assumption: Medium traffic growth (+5.4%) Airport capacities 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 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 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 2001


iii

Table of Contents

ABBREVIATIONS _______________________________________________________________________IV

1. INTRODUCTION______________________________________________________ 11.1 Short – term Capacity Planning _______________________________________ 21.2 Scope ____________________________________________________________ 31.3 Objectives ________________________________________________________ 41.4 Scenarios _________________________________________________________ 42. FAP METHODOLOGY, DATA AND TOOLS ________________________________ 52.1 ATFM Baseline Simulation ___________________________________________ 62.2 Traffic Growth Forecasts (STATFOR) __________________________________ 72.3 Traffic augmentation within AMOC ____________________________________ 92.4 The Capacity Demand Ratio__________________________________________ 9

2.4.1 An indicator of the demand for ATC services___________________________ 92.4.2 Determination of the capacity demand ratio___________________________ 11

2.5 Economic Evaluation ______________________________________________ 122.6 Course of Simulations _____________________________________________ 152.7 Airports__________________________________________________________ 162.8 Assumptions, Constraints and Risks _________________________________ 183. SUMMER 2000 ______________________________________________________ 203.1 Actual Traffic Growth 1999 - 2000 ____________________________________ 203.2 Delay per Flight ___________________________________________________ 213.3 Capacity Planning versus Actual_____________________________________ 233.4 Delay Forecast versus Actual _______________________________________ 253.5 Optimum operating points __________________________________________ 274. SUMMER 2001 ______________________________________________________ 284.1 Traffic Forecast 2001 ______________________________________________ 284.2 Capacity Targets 2001______________________________________________ 294.3 Return on Investments _____________________________________________ 305. CONCLUSION ______________________________________________________ 31

6. REFERENCES ______________________________________________________ 33




iv

Abbreviations

ACC Area Control CentreAMOC ATFM Modeling CapabilityATC Air Traffic ControlATFM Air Traffic Flow ManagementATM Air Traffic ManagementATS Air Traffic ServiceATSP Air Traffic Service ProvidersCASA Computer Assisted Slot AllocationCFMU Central Flow Management UnitCIM Capacity Indicator ModelCIP Convergence and Implementation ProgrammeCRCO Central Route Charges Officec/d Capacity demand ratioEAM EUROCONTROL Airspace ModelEATMP European Air Traffic Management ProgrammeECAC European Civil Aviation ConferenceMECA Model for the Economic Evaluation of Capacities in the ATM SystemRAMS Reorganized ATC Mathematical SimulatorROI Return on InvestmentSTATFOR Specialist Panel on Air Traffic Statistics and ForecastTACOT TACT Automated Command Tool




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1. Introduction

The year 1999 was unique due to the Kosovo war. Despite the adaptability shown by somecentres in increasing their capacities during the crisis, the average ATFM delay per flightwent up to almost 10 minutes on certain days. After the re-opening of the Balkan airspace(August 1999), the delay level went back to the 1998 level and stayed there until the end ofthe year. The first months of 2000 showed an increase (see yellow curve below)significantly above the 1998 curve. Keeping in mind that the strategic objective was toreturn to the 1997 delay level in the summer season, this situation was quite disconcerting.

However, taking into account the national capacity enhancement plans of the ATSproviders, the delay per flight for the summer 2000 was forecast to stay between the 1997and the 1998 levels (see EEC Note No 03/00, March 2000).

This forecast appears to be globally quite good for the beginning of the summer seasonwhen the delay per flight was significantly lower than in the same period of the year 1998,even if the objective is unlikely to be reached in 2000. Assuming a traffic increase of 5.4%between 2000 and 2001 (STATFOR baseline), the strategic short-term delay target was setto 3.5 minutes per flight for 2001.

To keep the same level of accuracy, it has been decided to focus in the short-term plan tothe year n+1 and to include the year n+2 in the “Medium-Term Capacity Shortfalls” Study.

ATFM Delays in 1997, 1998, 1999 and 2000

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As for previous capacity plans, the 2001 study will be performed in the three followingsteps:

• The first step, represented by this document, identifies the capacity targets to beachieved by the European air navigation services to reach the overall strategic delaytarget.

• 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, identifies remaining bottlenecks andevaluates delays, costs and return on investments.

This document shows where the en-route capacity shortfalls in 2001 are most likely to be,and discusses weekday and weekend scenarios. Capacity shortfalls are evaluated for 72European Air traffic Control Centres. The evaluation included economic aspects, particularlythe most cost effective balance between cost for capacity and cost for delay.

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 overleaf).

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This study concentrates on Short-term planning with a time horizon of 1 year.

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.

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).

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

including:• Air traffic Control Centres and regulated airports (72 ACCs, 64 airports)• Air Traffic Flow Management issues (CFMU slot allocation procedures)

analyzing:• traffic, capacities and delay statistics for more than 500 days in 1999 and 2000• service intervals such as summer/winter, weekday/weekend

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

Mid-termMid-term Long-termLong-term

Best possibleInformation

CurrentCurrent1 Year1 Year 2-5 Years2-5 Years 15 Years15 Years-1 Year-1 Year 10 Years10 Years

ReactionaryReactionaryPowerPower

Information QualityInformation Quality

Planning time horizonPlanning time horizon

Actions:Actions:- Too late to react

Actions:Actions:- Better use of exist. resources

Information (+1 y):- routes used- Sector loads- ACC cap. Shortfalls- Airport cap. planning- ATM Actions planned- Problems envisaged

Actions:Actions:- Airspace Management- ATC technology- ATC staff growth

Information (+5 y):- ACC cap. Shortfalls- Airport cap. planning- ATM Actions planned

Actions:Actions:- Airport capacity planning- Outline ATM planning- Leading to focus R&D

Information (+10 y):- ACC cap. shortfalls- Airport cap shortfalls

EnablerEnabler

Short-termShort-term




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1.3 Objectives

Identification of capacity targets for the years 2000 to enable the European air navigationservices• 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 2000)

• current routes (as used in summer 2000)

• medium traffic growth (according to STATFOR forecast)




5

2. FAP Methodology, Data and Tools

FAP is a methodology that provides a platform to investigate the ATM system behaviourresulting from parameter changes forecast in the short and longer term future. FAP isimplemented as a set of distributed modelling and analysis tools comprising ATFMsimulation facilities as well as Spreadsheet and macro based analysis and reporting tools.The objective within FAP is to provide a consolidated performance prediction in terms ofdelay values and capacity shortfalls of the future ATM system given a number of potentialscenarios concerning the evolution of both capacity and demand.

In order to provide accurate performance predictions pertaining to a future system, it isnecessary to establish a reliable baseline (based on an ATFM simulation) from which thefuture predictions can be calculated. The prediction of future performance is based on trafficgrowth estimates as well as estimates of the airport and airspace capacity growth providedby the National Administrations and ATSPs. An economic analysis is then used to predictthe future performance and ensure that proposed modifications are based on soundfinancial principles.

The following figure illustrates the essential processes and input data in the execution of aFAP study :

This section will describe each of these processes in more detail.

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plans

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prediction)




6

2.1 ATFM Baseline Simulation

The FAP ATFM baseline simulation is based on an analysis of the flow management delaysobserved for a series of given days in order to determine the capacity of an ACC. For thisstudy all days in the AIRAC cycles 205 and 206 were simulated (from 15th of June to9th of August, except 3 days with special events, giving a total number of 53 days).

Within the CFMU, the flow management delay is manifested in terms of the allocation of aCalculated Take Off Time (CTOT) as a means to regulate the flow of aircraft through eachregulated zone. The CTOT represents a deviation from the normal take-off time that wouldhave been achieved by the aircraft had it adhered to its estimated off block time (EOBT) atthe last filed flight plan. The assignment of a CTOT therefore constitutes an ATFM delay.

Delays are determined by the CFMU Tactical system (TACT) according to the declaredsector capacities and any regulations that may be in force during the day in question. Theheart of TACT is the Computer Assisted Slot Allocation (CASA) algorithm which assignsATFM delays to individual aircraft affected by the regulations in force. The delay assignedto an aircraft is the result of its most penalising regulation and it is therefore possible toassign the CFMU delay to an individual ACC or airport.

For any given day, the CFMU archive data includes the filed flight plans (employed byTACT), the declared ACC sector configurations, the regulations in force and theconsequential ATFM delays. All of this data is used within FAP in order to determine theACC centre capacity. For an ACC, the capacity is defined as the number of aircraft whichcould pass through the centre whilst generating the same ATFM delay as was actuallyobserved. In flow management terms it is as though the ACC is considered as a singleelementary sector.

The CFMU hourly sector regulations are simplified within FAP in order to apply a constantregulation throughout the day.

Unfortunately, the CASA algorithm cannot simply be employed using an inverse function i.e.given delay figures it cannot provide capacity values. Hence for each ACC it is necessary toconsider the delay which would have been observed for a hypothetical estimate of capacity.The calculated delay is then compared to that which was observed and an iterativemodification cycle commences until the CASA determined delay within an ACC for anestimated value of capacity converges to that observed for the day in question. Thismethodology is referred to within FAP as the “reverse CASA”.

EstimateCapacity of

ACC

CASA Calculateddelay for ACC




7

When performing this iterative analysis, it is necessary to consider the ATM system as anetwork. This is a common theme within FAP, the idea that there is a close interactionbetween the capacities and demand for each ACC and the way that these parametersaffect the observed delay in other ACCs. Hence the iteration is performed simultaneouslyfor all ACCs and only when each ACC gives the same calculated delay as that observed inthe CFMU is the process considered to be terminated1.

This iterative convergence process within the network is achieved by the AMOC (ATFMModelling Capability) tool which is an integral part of FAP and has an implemented copy ofCASA. The convergence to the correct delays for each ACC within the network can typicallybe the result of several thousand ATFM simulations.

The traffic demand in each ACC, and the consequential determination of delays, is basedon the CFMU TACT profile calculation. Although this profile may not be identical to theactual profile flown by the aircraft (for reasons such as performance, Letters of Agreementor routing schemes not modelled in TACT), it does correspond to the profile that is used inthe assignment of the ATFM departure slot and is therefore considered to be the ideal onefor the purposes of FAP.

The result of this analysis is a set of capacity estimates for each ACC, respecting the CFMUtraffic demand, TACT profile, sectorisation and regulation schemes in force as well as theinteractions of capacity and delay between centres. Had each ACC centre comprised asingle elementary sector with the regulations in force for the day in question, then thecalculated centre capacities would have generated the same ATFM delay as was observedin CFMU.

This method is considered to provide an accurate estimate of the ACC capacity for delayproducing centres. However for those centres not producing delays, there is often areduced knowledge on the part of the ACC concerning the maximum sector capacities andin some cases accurate sectorisation plans are not provided to the CFMU as there is nospecific requirement for flow regulation. Such centres are not included in the shorter term(conducted annually) planning of FAP since the emphasis is placed on the delay producingcentres. A consequence of this is that FAP does not predict the initial transition from “non-delay producing “ to “delay producing” for a given ACC.

In shorter term planning those airports currently regulated by the CFMU are considered inthe analysis. In medium term planning all airports are considered to be flow regulated.

2.2 Traffic Growth Forecasts (STATFOR)

The traffic growth predictions used in the FAP performance forecasting are provided by theEUROCONTROL Specialist Panel on Air Traffic Statistics and Forecasts (STATFOR).

The STATFOR forecasts are based on approximately 2000 “traffic flows”. These trafficflows are described as emanating from a set of origin/destination zones (ODZ), currentlynumbering 68. These zones cover the entire world but are naturally more detailed for theEuropean area.

1 If the correct delay has been found for a given ACC, then the process of changing the estimated capacity in another ACCmay affect the newly observed delay in our previously correct ACC ! – this is the so-called network effect.




8

STATFOR provide a number of growth predictions defined as “Low”, “Medium” and “High”.Each of these predictions is performed in close co-operation with the member states, takinginto account parameters such as the evolution of Gross Domestic Product, airlineproductivity, currency value fluctuations, airport constraints, the increased use of HighSpeed Rail Links and the impact of technology such as “video-conferencing”. The threeSTATFOR forecasts are therefore based on differing assumptions concerning the variationand interaction of these parameters. The FAP Medium Term Capacity Plan for 2003 – 2005(Reference /5/) employs the STATFOR Medium and High growth scenarios in itsperformance predictions.

A shortcoming of the STATFOR predictions (due to the non-availability of the data ratherthan an inherent flaw in the methodology) is that some of the existing ODZ definitions aretoo general and there is a need for some refinement. As an example of this, the traffic flowswhose ODZ is in the UK are defined according to the criteria :

London TMA (EGKK, EGLL, EGGW, EGSS, EGLC)Rest of United Kingdom (all EG* but not including London TMA)

Similar definitions hold true for both France and Germany.

Under certain circumstances this kind of definition can lead to poor forecasting accuracy.For example, it does not allow growth potential in one major airport to be divorced from anear neighbour, a notable case being the Paris airports of Roissy CDG and Orly where bothfall in the same ODZ (Paris TMA) but have very different growth potentials. Theconsequence is that some airports can significantly exceed their forecast capacity at somefuture time when applying the traffic growth forecast for the ODZ.

In order to circumvent this problem, and any potential impact that it may have on theperformance predictions provided by FAP, the FAP team and STATFOR urge states torefine the individual traffic flows (ODZ pairs) for use in the STATFOR predictions in thosecases where “local” data or knowledge may be available. It is the desire of FAP to continueto use the STATFOR predictions since despite the above reservations, the STATFOR datais still considered to be the most reliable available.

It is preferable that the improved ODZ definitions be incorporated directly in STATFORrather than post-hoc in FAP as this ensures the uniformity of both the input data to FAP andthe methods applied in the performance predictions for the different states. In this studythe London TMA ODZ was split into 3 sub-zones and figures provided by NATS wereused instead of the STATFOR figures. Paris TMA was also split into 2 sub-zones.

This area is important as traffic growth scenarios are one of the most crucial input data tothe FAP methodology.




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2.3 Traffic augmentation within AMOC

For each growth prediction determined by STATFOR, the traffic samples which formed thesubject of the baseline ATFM simulations (described above) can be augmented in line withthe growth predictions.

This augmentation is performed within AMOC by “cloning” existing flights for each ODZfamily according to the growth predictions given by STATFOR. The ODZ growth predictionscan be combined to determine the consequential percentage traffic augmentation for eachACC. Once this has been performed, the traffic in each ODZ is augmented in a mannerwhich attempts to conserve in so far as possible the current traffic distribution with timethroughout the day.

FAP therefore assumes that in the absence of contrary information, the characteristics ofthe traffic load curves of each ODZ throughout the day will remain valid during theprediction period.

This type of approach is illustrated in the following figure where the traffic for a hypotheticalODZ is augmented in a manner which reflects the daily distribution and respects theincreased traffic on each ODZ as defined by STATFOR.

2.4 The Capacity Demand Ratio

2.4.1 An indicator of the demand for ATC services

A key notion within FAP is the capacity demand ratio. The capacity demand ratio isimportant since it forms the basis for describing both the current and predicted performanceof ACCs and airports as well as the basis of the economic modelling.

Before describing the capacity demand ratio and its determination in more detail, it isnecessary to consider the notion of “demand”. The demand for ATC services is a fluctuatingparameter characterised in most ACCs and airports by peaks and troughs throughout theday as well as seasonal peaks and troughs throughout the year. These peaks and troughstend to be consistent and predictable.

The graphs below show the hourly traffic distributions from 51 days in summer 1999 forKarlsruhe UAC, London ACC, Madrid ACC and Reims ACC.

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The aim of FAP is to provide a “snapshot” of performance at some future time. It wastherefore desired to quantify the demand as a single parameter and to effectively find anindicator that represented adequately, for prediction purposes, these peaks and troughsthroughout the day. This value of demand should also be sufficiently robust to provide areliable benchmark against which the requirements for future capacity enhancement can bedefined.

This indicator development was performed in the context of the Medium-term CapacityShortfalls 2003 - 2005, the results of which are given below.

A series of more than 1200 ATFM simulations were performed in order to investigate thedelay sensitivity of each ACC to variations in its assumed capacity and a series of differentpotential indicators that could, in theory, be used as a measure of the demand. No networkeffects were simulated i.e. each ACC was simulated individually with all other ACCcapacities set to infinite. Five potential indicators for demand were investigated, namely :

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

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The following main results were observed :

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

As a result, the FAP model employs the 3 hour peak as a measure of the demand becauseit is the most stable indicator in the area of interest where capacity is close to demand i.e.capacity demand ratios close to 1.

2.4.2 Determination of the capacity demand ratio

Once an indicator for demand has been developed it is possible to determine the capacitydemand ratio for each ACC using the calculated ACC centre capacity from the baselineATFM simulations and the demand indicator from the 3 hour peak traffic load.

By performing further ATFM simulations, it is possible to study the way in which delays willvary in each ACC as the assumed capacity varies. In this way, a curve can be built up foreach ACC indicating the variation of delay with the capacity demand ratio. This analysisprovides what can be referred to as the current delay sensitivity for each ACC.

Once combined with the CFMU data of the average delay per flight observed for each ACC,it is possible to determine the point of operation of each ACC in terms of capacity demandratio.

current delay sensitivities

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2.5 Economic Evaluation

It is to the advantage of a common European Capacity Plan that investments in capacitiescan be evaluated using a macro-economic evaluation. This evaluation consists inseparating direct and indirect costs for airspace users, and in assessing total and marginalcosts for ACC capacity. This methodology finally allows the identification of those ACCswhere the best returns on investment can be obtained.

TOTAL COSTFrom the airspace user point of view, the total cost of the Air Navigation Services iscomposed of the direct cost (charges for en-route and airport services) AND the indirectcost (ATFM delays, non-optimum flight profiles).

Direct costEn-route air navigation charges paid byairspace users are considered here. Fromcountry to country they widely vary. Thenumber of kilometres controlled canexplain most of the differences in costlevels. However, the traffic complexity(both density and flight profiles), theaverage route length, and the time (seeEEC note 07/00) have also an importanteffect on the ANSPs cost.FAP takes into account the total capacitycost relative to each ACC.

Indirect costThe cost for ATFM delays only isconsidered in this study. Total delay costper ACC results both from the delay itself(mismatch between demand andcapacity) and from the cost of one-minutedelay (depending on the regional trafficmix, as large aircraft have higheroperating costs).FAP attributes to each ACC a particulardelay cost curves.

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MARGINAL COST• The marginal cost for local capacity increase remains a complex issue. The problem is

to evaluate the extra cost of increasing the capacity by x%. Depending on the degree ofutilisation of existing resources, the marginal cost can be close to zero, or converselycan require large investments. In the short term, the real capacity cost function is mostlikely to be step function, succession of low and high marginal costs. However, in thelong term, historical trends, and a recent investigation of the cost benchmarking of AirNavigation Services show that, at the State level, the relation between the cost and theamount of traffic controlled is almost linear. This linearity actually results from twoopposite phenomena: decreasing economies of density (+ cost), (when the trafficbecomes denser, ACCs have to cope with a higher complexity, requiring more controllerworkload / more airspace optimisation), and productivity gains (- cost).

Then,- The marginal capacity cost within an ACC is supposed constant- Differences between ACC marginal capacity cost are proportional to their actual cost andcapacity.

• Marginal cost for local delay variations is better known. At a given capacity demandratio, the marginal delay cost depends on the hourly traffic distribution and of thenetwork effects. This can be simulated by AMOC. As the delay is a decreasing functionof the capacity, the assessments of capacity growth required in the future lead tonegative marginal delay cost (benefits to the airspace user)

Finally, marginal capacity cost and marginal delay cost allows to compute the return oninvestment that can be expected in each ACC. The theoretical optimum capacity demandratio can also be identified.

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APPLICATION TO CAPACITY PLANNING

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 2001 does nottarget the optimum c/d but an intermediate step at 3.5 min delay per flight. However, thistarget shall be achieved at best return on investments.

The EUROCONTROL Experimental Centre has therefore continued to develop a model toautomate the economic evaluations for all ACCs. The model is called MECA (Model for theEconomic evaluation of the Capacities of the ATM system) and a new version has beenused to run the Short Term Capacity Targets 2001.

The previous MECA version, which was relying on statistical curves to model the delayvariations consecutive to changes in capacity / demand ratio, has been replaced by amodule integrated in ISA (Innovative Slot Allocation).

The major advantage of this upgrade is to increase the reliability of MECA results by takingaccount of the network effects. Capacity targets generated with MECA are, now byconstruction, consistent with the delay forecast computed in AMOC.

MECA runs on daily traffic samples, but is still fast enough to simulate several days. Inputdata that are required in the model for each ACC are:• Capacity cost (CRCO data)• Delay costs (IATA aircraft operating costs * traffic mix)• Current (2000) ACC capacity (ISA used in inverse mode)• Increased traffic sample (AMOC)

AMOCAMOC MECAMECA

Traffic patternTraffic growth

Cost for delaysCost for capacities

Total delaysDelay sensitivity

Delay target

-New Capacity Plan

CFMU delaysCurrent Capacities

MECA simulation process

1. Computes the potential cost savings in all ACCs2. Identifies the ACCs with best return on investment (ROI) and increases their capacity3. Check whether or not the overall delay target is reached, taking into account the

network effect4. Iterates the 3 previous steps until the overall target is reached

MECA final output

• Capacity growth required per ACC, in order to achieve a given delay target at ECAClevel, at minimum cost.




15

2.6 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 all days in AIRAC cycles 205 & 206, except 3 days due to

special events (strike, failure, weather)• validate ATFM simulation (AMOC) against CFMU observed delays• input 2000 CRCO cost data and the delay target into the economic evaluator (MECA)

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

(based on STATFOR medium growth scenario, modified for London and Paris)

4) MECA simulations• The new MECA simulation process has been described in the previous section• For each day the target delay per flight in 2001 should be proportional to the delay

observed the same day in 2000, the ratio is set so that the overall target (3.5 minutes) isreached for the whole summer period.

• The shape of the histogram “monthly ECAC delay” has been assumed to be identicaluntil end of October 2000 to the 1998 distribution.

5) Simulate various scenarios for 2001

• Scenario 1: “weekdays – current routes”traffic volume (city pairs) 2000 weekdays + traffic growth – current routes used

• Scenario 2: “weekends – current routes”traffic volume (city pairs) 2000 weekends + traffic growth – current routes used




16

2.7 Airports

Airport capacities were included in the network if the airport was regulated in summer 2000(June 15th to August 9th ). The 2000-2001 airport capacity increase was taken from theEUROCONTROL data base. The table below shows the actual 2000 airport capacities, andthe forecasted 2001 capacities.

(in red : capacity increase forecast)

ICAO Airport capacity capacityCode 2000 2001

EBBR Brussels 72 72EDDB Berlin/Schonefeld 37 37EDDC Dresden 30 30EDDF Frankfurt 80 80EDDH Hamburg 54 54EDDI Berlin/Tempelhof 30 30EDDK Koln 66 66EDDL Düsseldorf 38 38EDDM Munich 90 90EDDN Nürnberg 30 30EDDP Leipzig - Halle 45 45EDDS Stuttgart 38 38EDDT Berlin/Tegel 40 40EDDV Hannover 50 50EETN Tallin 22 22EFHK Helsinki 48 48EGAA Belfast 40 40EGAC Belfast 14 14EGBB Birmingham 38 38EGCC Manchester 54 54EGGW London/Luton 35 35EGKK London/Gatwick 48 48EGLL London/Heathrow 78 78EGNX Derby 30 30EGSS London/Stansted 38 38EHAM Amsterdam/Schipol 108 108EHBK Maastricht 15 15EHGG Groningen 60 60EHRD Rotterdam 30 30EKBI Billund 50 60EKCH Kobenhavn 91 91ELLX Luxembourg 35 35ENBR Bergen 29 29ENGM Oslo - Gardermoen 80 80ENZV Stavanger 31 31EPWA Warsaw 42 42ESGG Goteborg 35 35ESMS Malmo 30 30ESSA Stockholm - Arlanda 70 70EVRA Riga 50 50EYVI Vilnius 60 60




17

ICAO Airport capacity capacityCode 2000 2001

GCFV Puerto del Rosario 12 12GCLP Las Palmas - Gran Canaria 36 36GCRR Arrecife 22 22GCTS Tenerife Sur 35 35LBSF Sofia 24 24LCLK Larnaca 25 25LDZA Zagreb 29 29LEAL Alicante 30 30LEBL Barcelona 55 55LEGE Gerona 12 12LEIB Ibiza 22 22LEMD Madrid Barajas 75 75LEMG Malaga 35 35LEMH Menorca 18 18LEPA Palma de Mallorca 60 60LFBO Toulouse 42 42LFLL Lyon 50 50LFML Marseille 30 30LFMN Nice 49 49LFPB Paris/Le Bourget 45 45LFPG Paris/Charles de Gaulle 95 95LFPO Paris/Orly 70 70LFQQ Lille 30 30LFRS Nantes 16 16LFSB Bâle-Mulhouse 40 40LFST Strasbourg 20 20LGAT Athens 40 40LGIR Heraklion 14 14LGKO Kos 10 10LGKR Corfu 10 10LGRP Rhodes 13 13LGTS Thessaloniki 27 27LHBP Budapest 40 40LIMF Turino 32 32LIML/C Milan/Malpensa+Linate 102 102LIPZ Venice 30 30LIRF Roma/Fiumicino 84 84LJLJ Ljubljana 20 20LKPR Prague 45 45LMML Valleta 16 16LOWS Salzburg 20 20LOWW Vienna 65 65LPFR Faro 24 24LPFU Funchal 12 12LPPR Porto 30 30LPPT Lisbon 30 30LROP Bucharest 35 35LSGG Geneva 38 38LSZH Zurich 66 66LTBB Istambul/Ataturk 36 36




18

2.8 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 – Size of ODZ (Origin Destination Zones)When a STATFOR Origin Destination Zone is too big and contains many airports (e.g.“all Germany except Frankfurt”), the way AMOC increases the traffic randomly can leadto a wrong prediction for certain airports.Risk: low, particularly in this study where 53 different traffic increases were simulated,giving, for each ODZ, as many traffic growth scenarios.

• 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, even reducedthis year for London and Paris, as already explained)

• 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 simulations made on 53 days insummer 2000. The accuracy could vary around +/- 5% for all those ACCs that produceddelays in summer 2000. However, variation may be higher for ACCs not working at itsmaximum capacity in 2000. 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)




19

• 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 2000 (CRCO forecast). This assumption isconfirmed by observations of the last 16 years CRCO capacity cost data.Risk: low

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




20

3. Summer 20003.1 Actual Traffic Growth 1999 - 2000

The figure below shows the traffic growth in Europe between summer 1999 and summer2000 (June and July).




21

3.2 Delay per Flight

The map below shows the average delays per flight observed in summer 2000(AIRAC 205 & 206).

There are some developments recognizable in comparison with the delays observed insummer 1999 (figure below)

1999




22

The overall situation is better than in 1999, with a similar delay distribution. Prague, Athensand Nicosia seem to have reduced the delay per flight more than in average.

The figure below compares the delays per flight observed in 2000 and 1999 sorted by delayproduced in 1999. The top 1999 delay producers have significantly decreased their delays.

The figure below shows the same data this time sorted by delays produced in 2000.

Even after this significant reduction, Padua, Geneva, Zurich and Marseille are still the topdelay producers.

The next chapter will show that these ACCs did not achieve the capacity targets proposedby FAP for the year 2000 (see also “Short-Term Capacity Targets 2000 & 2001 for theEuropean Air Navigation Services” EUROCONTROL, Nov. 1999).

It can be noticed that a planning process leads to a more homogeneous delay per flight.These efforts have been made mainly in the centres which caused the highest delays in1999.

T o p 3 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

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23

3.3 Capacity Planning versus Actual

The table below compares the FAP 2000 capacity targets and the agreed capacity growth(CFMU Global plan) with the actual capacity increase achieved in summer 2000.

The actual capacity growth was estimated based on daily traffic and delay observations(see annex figure Ax.1) together with the average figure of the hourly centre capacity thatthe 53 day study proved to be stable enough. Unclear observations were indicated with “?”.

Estimation of theACC ICAO FAP Global FAP Global effective increaseNAME code capacity Plan capacity Plan observed in summer

increase States increase States 1999 - 2000target responses target responses

Prague LKAA 21.0% 5.0% 20.0% 5.0% 11%Zurich LSAZ 21.0% 12.0% 23.0% 12.0% 9%Geneva LSAG 19.0% 7.0% 26.0% 7.0% 5%Makedonia LGMD 18.0% 18.0% 27.0% 27.0% out of the study (no delay)Padova LIPP 17.0% 10.0% 20.0% 10.0% 8%Barcelona LECB 16.0% 16.0% 22.0% 22.0% 13%Karlsruhe EDUU 12.0% 5.0% 11.0% 5.0% 14%Maastricht EDYY 10.0% 7.5% 11.0% 7.5% 3%Reims LFEE 10.0% 7.5% 11.0% 7.5% 7%Marseille LFMM 10.0% 8.0% 17.0% 8.0% 6%Madrid LECM 9.0% 9.0% 15.0% 15.0% 6%Warsaw EPWW 8.0% 8.0% 8.0% 8.0% 4%London EGTT 7.0% 6.0% 7.0% 6.0% 4%Paris LFFF 7.0% 7.0% 0.0% 5.0% 7%Milano LIMM 7.0% 15.0% 11.0% 15.0% ? (transfer LIMM/LIRR)Canaries GCCC 5.0% 5.0% 0.0% 0.0% 2%Frankfurt EDFF 4.0% 5.0% 0.0% 5.0% 2%Athinai LGGG 3.0% 3.0% 2.0% 2.0% 20%Bruxelles EBBU 0.0% 0.0% 0.0% 0.0% 0%Berlin EDBB 0.0% 6.0% 0.0% 6.0% 0%Muenchen EDMM 0.0% 2.0% 0.0% 2.0% 4%Tampere EFES 0.0% 0.0% 0.0% 0.0% out of the study (no delay)Manchester EGCC 0.0% 2.0% 0.0% 2.0% 0%Amsterdam EHAA 0.0% 9.3% 0.0% 9.3% 5%Malmo ESMM 0.0% 0.0% 0.0% 0.0% out of the study (no delay)Nicosia LCCC 0.0% 0.0% 0.0% 0.0% 20%Palma LECP 0.0% 10.0% 7.0% 15.0% out of the study (no delay)Sevilla LECS 0.0% 0.0% 11.0% 11.0% 3%Bordeaux LFBB 0.0% 6.0% 4.0% 6.0% 0%Reims LFRR 0.0% 6.0% 5.0% 6.0% 0%Budapest LHCC 0.0% 0.0% 0.0% 0.0% ?Brindisi LIBB 0.0% 0.0% 0.0% 0.0% ?Roma LIRR 0.0% 0.0% 2.0% 2.0% ? (transfer LIMM/LIRR)Lisbon LPPC 0.0% 5.0% 5.0% 5.0% 10%Bratislava LZBB 0.0% 10.0% 0.0% 10.0% out of the study (no delay)

WEEKDAY WEEKEND

Brest




24

The figure below compares the States planning (red) with the actual achievements in 2000(yellow). It appears that three centres have significantly increased their capacities morethan planned (Prague, Karlsruhe and Athinai). Fourteen centres have over-estimated theirability to increase their capacities.

The comparison of the actual achievements (yellow) with the FAP targets for 2000 (blue)shows that most ATSPs had some problems to achieve the FAP targets (see figure below).Only Karlsruhe, Paris and Athinai succeeded.

C F M U G lo b a l P la n v e r s u s A c t u a l 2 0 0 0

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?




25

The figure below shows the average delay per flight observed in summer 2000 compared tothe achievement of the FAP target.

3.4 Delay Forecast versus Actual

The figure below compares the 2000 forecast delay (gray) with the actual delays observedby CFMU (blue). The delay forecast was made by FAP using ATFM models with theassumption of 6.5% and 5.3% traffic growth and the national capacity enhancement plansin March 2000.

These ACCs did not These ACCs . achieve the target achieved the target

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26

Observations:

• The Capacity Plan focuses on the summer period. Therefore a very rough assumptionwas made for the implementation of the capacity enhancements: it was assumed acomplete implementation everywhere on the 15th of April. Obviously the capacityincrease was spread during the first part of the year giving a smoother delay curve.

• In June/July, the observed delay was higher than it was foreseen, as the ATSPs did notachieve their plans early enough (this is confirmed by the observations made on page24 “CFMU Global Plan versus Actual 2000”). In addition, three special events occurredduring this period (strike, failure, generalized bad weather).

• The first days of August (end of the AIRAC cycle 206), the observed delay per flight isclose to the forecast.

Conclusions:

• Unforeseeable effects excluded, the delay forecast model seems to be sufficientlyreliable. If a better forecast for the first part of the year is needed, assumptions wouldhave to be defined for the implementation of the capacity enhancement plans.

• The Capacity Planning Process seems to give good results, even if it appears to bedifficult for the ATSPs to achieve the plan before the summer period.




27

3.5 Optimum operating points

Despite of the efforts made in Europe to increase capacity, there are still some significantcapacity shortfalls observed for 30% of the European air traffic control centres. The figurebelow compares the current operating point (capacity demand ratio in summer 2000) withthe optimum capacity demand ratio computed by FAP under consideration of the cost forcapacity and the cost for the delays.

The centres have been sorted by decreasing order of the differences between optimum andcurrent c/d. Even if the trend (from the same chart in summer 1999) is a reduction, thereare still centres operating significantly below their optimum point.

Current versus Optimum Operating Point

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Current c/d Optimum c/d




28

4. Summer 20014.1 Traffic Forecast 2001

Traffic growth data is based on STATFOR medium growth estimates on 2000 flows inEurope (+5.3%) applied to the traffic pattern of the 53 baseline scenarios representing:

37 weekdays and 16 weekend days in summer 2000




29

4.2 Capacity Targets 2001

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)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).

Centre Code

Average capacity demand

ratio 2000Traffic growth

2000-2001

Capacity ShortfallWeekday

Capacity ShortfallWeekend

Brussels EBBU 1.12 7% 0% 1%Berlin EDBB 1.15 6% 0% 0%Frankfurt EDFF 1.00 4% 3% 0%Dusseldorf EDLL 1.05 8% 2%Munich EDMM 1.02 6% 4% 1%Karlsruhe EDUU 0.97 6% 8% 4%Bremen EDWW 1.10 7% 2%Maastricht EDYY 0.99 6% 9% 7%Manchester EGCC 1.17 6% 0% 0%Scottish EGPX 1.02 5% 2% 1%London EGTT 0.96 6% 8% 9%Amsterdam EHAA 1.07 6% 2% 0%Dublin EIDW 1.09 7% 1%Copenhagen EKDK 1.16 6% 0%Oslo ENOS 0.98 4% 2%Warszawa EPWW 1.01 5% 2% 2%Stockholm ESOS 1.02 5% 0%Canarias GCCC 1.15 7% 0% 0%Nicosia LCCC 1.10 5% 1% 0%Zagreb LDZO 1.08 6% 2% 3%Barcelona LECB 1.03 8% 5% 7%Madrid LECM 0.95 7% 10% 11%Palma LECP 0.99 8% 7% 1%Seville LECS 1.06 7% 1% 4%Bordeaux LFBB 1.03 6% 2% 5%Reims LFEE 0.98 6% 8% 6%Paris LFFF 0.97 4% 5% 3%Marseille LFMM 0.98 6% 6% 11%Brest LFRR 1.03 7% 3% 7%Athinai LGGG 1.04 7% 5% 6%Makedonia LGMD 0.98 9% 6%Budapest LHCC 1.03 6% 3% 3%Brindisi LIBB 1.05 6% 5% 0%Milano LIMM 0.98 7% 7% 5%Padova LIPP 0.94 7% 10% 11%Roma LIRR 1.00 6% 1% 6%Prague LKAA 1.07 5% 1% 0%Wien LOVV 1.07 5% 0%Lisbon LPPC 1.04 7% 3% 2%Geneva LSAG 0.95 6% 11% 9%Zurich LSAZ 0.94 5% 10% 9%FYROM LWSS 1.05 7% 7%Bratislava LZBB 1.11 6% 0%




30

4.3 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 2001considering the traffic, and the resulting delay growth against the cost for the capacityincrease. The ROI compares the whole investment (assuming 2000 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, 2000 )

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.

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31

5. Conclusion

The table below is the synthesis of the simulations of 53 days in summer 2000. It shows, foreach ACC, assuming a medium traffic growth of 5.4% in ECAC, the capacity increaserequired to reduce the ATFM delays to 3.5 min per flight in summer 2001:

% Weekday % Weekend Number of simulated daysGeneva 11% 9% > 50Madrid 10% 11% > 50Padova 10% 11% > 50Zurich 10% 9% > 50Maastricht 9% 7% > 50London 8% 9% > 50Karlsruhe 8% 4% > 50Reims 8% 6% > 50Milano 7% 5% > 50Marseille 6% 11% > 50Paris 5% 3% > 50Munich 4% 1% > 50Bordeaux 2% 5% > 50Barcelona 5% 7% 30< <50Frankfurt 3% 0% 30< <50Brest 3% 7% 30< <50Dusseldorf 2% 30< <50Amsterdam 2% 0% 30< <50Warszawa 2% 2% 30< <50Seville 1% 4% 30< <50Prague 1% 0% 30< <50Brussels 0% 1% 30< <50Palma 7% 1% < 30FYROM 7% < 30Makedonia 6% < 30Brindisi 5% 0% < 30Athinai 5% 6% < 30Lisbon 3% 2% < 30Budapest 3% 3% < 30Scottish 2% 1% < 30Zagreb 2% 3% < 30Oslo 2% < 30Bremen 2% < 30Dublin 1% < 30Roma 1% 6% < 30Nicosia 1% 0% < 30

The right column gives the number of simulations for each centre (i.e. the number of dayswith significant observed delay within the AIRAC Cycles 205 and 206). This information canbe considered as an indicator of reliability of the results.




32

The annex contains a detailed analysis of the main delay producing ACCs to be used toidentify where and how capacity could be increased. It also gives the individual ACC targetdelay per flight, different from one centre to the other due to the different cost functions andin average close to 1.2 minute (3.5/3), as a flight passes approximately through 3 centres inEurope.

A curve giving, for each simulated day, the observed and the required capacities is alsoshown.




33

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,B. Nicolas Number of Flights per Region (1974-2015)B. Dehollander EATMP SDE/SCS Unit - STATFOR

EUROCONTROL, Brussels, 2000

/5/ 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

/6/ M. Dalichampt Short-term Capacity Targets 2000 & 2001J.-C. Hustache for the European Air Navigation ServicesS. Mahlich EEC Note 19/99

EUROCONTROL Experimental CentreBrétigny sur Orge, France, Nov. 1999

/6/ M. Dalichampt Delay Forecast 2000J.-C. Hustache Based on local capacity enhancement plans

EEC Note 03/00EUROCONTROL Experimental CentreBrétigny sur Orge, France, March 2000

/7/ P. Enaud Progress Towards Cost-BenchmarkingG. Nero of the European ATM SystemJ.-C. Hustache PRU Note - EEC Note 07/00

EUROCONTROL Experimental CentreBrétigny sur Orge, France, July 2000




34

Annex 0 – Annex 34 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.

A1 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 EGCC Manchester ACC pg 49A9 EGPX Scottish ACC pg 51A10 EGTT London ACC pg 53A11 EHAA Amsterdam ACC pg 55A12 EPWW Warszawa pg 57A13 GCCC Canarias pg 59A14 LCCC Nicosia pg 61A15 LDZO Zagreb pg 63A16 LECB Barcelona ACC pg 65A17 LECM Madrid ACC pg 67A18 LECS Seville ACC pg 69A19 LFBB Bordeaux ACC pg 71A20 LFEE Reims ACC pg 73A21 LFFF Paris ACC pg 75A22 LFMM Marseille ACC pg 77A23 LFRR Brest ACC pg 79A24 LGGG Athinai ACC pg 81A25 LHCC Budapest ACC pg 83A26 LIBB Brindisi ACC pg 85A27 LIMM Milano ACC pg 87A28 LIPP Padova ACC pg 89A29 LIRR Rome ACC pg 91A30 LKAA Prague ACC pg 93A31 LPPC Lisbon ACC pg 95A32 LSAG Geneve ACC pg 97A33 LSAZ Zürich ACC pg 99A34 LWSS Bratislava ACC pg 101




35

A1 EBBU: Brussels ACC

Figure A1. 1: Traffic and resulting delays in summer 99 and 00 (15th of June - 9th of August)

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Traffic growth 1999 – 2000: 2%Capacity growth 1999 – 2000: 0%

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

− Summer weekdays 99 1800 xxxx∆ Summer weekends 99 xxxx xxxx− Summer weekdays 00 xxxx xxxx∆ Summer weekends 00 xxxx xxxx




36

Traffic evolution Summer 2000 - Economic Information

Top 5 local capacity shortfalls

Reference Location Grouped Yes / No Total delay (min)from 15 june to 9 aug.

Delayed Flights Delay per delayed flight

EBBRMB AZ 15237 463 32.9EBBUHES AS NO 12268 660 18.6EBBULES AS NO 11559 717 16.1EBBULWS AS NO 2565 150 17.1EBBUWS AS YES 3 85 2 42.5

Delay distribution over the day

E B B U d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

3 0 0 0

3 5 0 0

4 0 0 0

4 5 0 0

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

E B B R M B A ZE B B U H E S A SE B B U L E S A SE B B U L W S A SE B B U W S A S

Figure A1.3: Delay distribution over the days (June15th to Aug.9th 2000)Date format (mm/dd/yy); vertical lines indicate MondaysRegulation AS: sector; AD: airport; AZ: group of airports; SP: point;

Conclusion

Capacity Shortfall 2001Weekday Weekend

0% 1%

Current Capacity/Demand ratio 1.12

Optimum Capacity/Demand ratio 1.00Current Repartition of the total cost (% delay cost %capacity cost)

Del:5% Cap:95%

Delay Target summer 2001(min. per flight) 0.83

EBBU Traffic Evolution (1997, 1998, 1999,2000)

1000

1100

1200

1300

1400

1500

1600

1700

1800

j f m a m j j a s o n d

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37

A2 EDBB: Berlin ACC

Figure A2.1: Traffic and resulting delays in summer 99 and 00 (15th of June - 9th of August)

0

20

40

60

80

100

120

140

160

Simulated days

Mou

vem

ents

per

hou

r

simulated capacity 2000 % target 2001

Figure A2. 2: Current versus target capacities (15th of June - 9th of August)

EDBB (Summer 1999 - 2000)

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

dela

y pe

r flig

ht [m

in]



− Summer weekdays 99 xxxx xxxx∆ Summer weekends 99 xxxx xxxx− Summer weekdays 00 xxxx xxxx∆ Summer weekends 00 xxxx xxxx




38





LIDVI SP 13187 547 24.1EDBBUR3O AS NO 302 20 15.1

Main delay producer and their delay distribution in Summer 1999E D B B d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

0

2 0 0

4 0 0

6 0 0

8 0 0

1 0 0 0

1 2 0 0

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L I D V I S PE D B B U R 3 O A S


Conclusion


0% 0%



Del:2% Cap:98%


EDBB Traffic Evolution (1997, 1998, 1999,2000)

500

700

900

1100

1300

1500

1700


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39

A3 EDFF: Frankfurt ACC



EDFF (Summer 1999 - 2000)

0

1

2

3

4

5

0 500 1000 1500 2000 2500 3000 3500

daily traffic

dela

y pe

r flig

ht [m

in]



− Summer weekdays 99 2650 xxxx∆ Summer weekends 99 xxxx xxxx− Summer weekdays 00 2750 xxxx∆ Summer weekends 00 xxxx xxxx

40

60

80

100

120

140

160

180

200

Simulated days

Mou

vem

ents

per

hou

r





40





EDDF AD 156343 8010 19.5EDFFNR2 AS NO 31057 1975 15.7EDFBDP2 AS NO 21740 1294 16.8EDFFN12 AS YES 2 15592 664 23.5EDFFSR16 AS YES 2 14006 738 19.0


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 d u r i n g A I R A C 2 0 5 & 2 0 6

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

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

E D D F A DE D F F N R 2 A SE D F B D P 2 A SE D F F N 1 2 A SE D F F S R 1 6 A S


Conclusion


3% 0%



Del:8% Cap:92%


EDFF Traffic Evolution (1997, 1998, 1999,2000)

1500

1700

1900

2100

2300

2500

2700


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41

A4 EDLL: Düsseldorf ACC



EDLL (Summer 1999 - 2000)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 500 1000 1500 2000 2500

daily traffic

dela

y pe

r flig

ht [m

in]

Traffic growth 1999 – 2000: 4 %Capacity growth 1999 – 2000: 0 %



0

20

40

60

80

100

120

140

Simulated days

Mou

vem

ents

per

hou

r





42


Top 5 local capacity shortfalls Reference Location Grouped Yes / No Total delay (min)

from 15 june to 9 aug.Delayed Flights Delay per delayed flight

EDLLOR1 AS NO 12199 952 12.8EDLLOR2 AS NO 9523 712 13.4EDLLSR3 AS NO 9238 574 16.1EDDL AD 6157 251 24.5EDDK AD 4247 283 15.0


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 d u r i n g A I R A C 2 0 5 & 2 0 6

0

2 0 0

4 0 0

6 0 0

8 0 0

1 0 0 0

1 2 0 0

1 4 0 0

1 6 0 0

1 8 0 0

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

E D L L O R 1 A SE D L L O R 2 A SE D L L S R 3 A SE D D L A DE D D K A D


Conclusion


2%



Del:5% Cap:95%


EDLL Traffic Evolution (1997, 1998, 1999,2000)

1000

1100

1200

1300

1400

1500

1600

1700

1800


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43

A5 EDMM: München ACC

Figure A5.1: Traffic and resulting delays in summer 99and 2000 (15th of June - 9th of August)

Figure A5.2: Current versus target capacities (15th of June - 9th of August)

EDMM (Summer 1999 - 2000)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 500 1000 1500 2000 2500 3000 3500

daily traffic

dela

y pe

r flig

ht [m

in]



− Summer weekdays 99 2650 xxxx∆ Summer weekends 99 xxxx xxxx− Summer weekdays 00 2800 xxxx∆ Summer weekends 00 2500 xxxx

0

50

100

150

200

250

Simulated days

Mou

vem

ents

per

hou

r





44




EDMMSU2 AS YES 2 25154 1246 20.2EDMMHUR1 AS YES 2 19725 932 21.2EDDM AD 19320 882 21.9EDMMSR4 AS YES 3 17003 1032 16.5EDMMCN1 AS YES 3 9604 479 20.1


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 d u r i n g A I R A C 2 0 5 & 2 0 6

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 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

E D M M S U 2 A SE D M M H U R 1 A SE D D M A DE D M M S R 4 A SE D M M C N 1 A S


Conclusion


4% 1%



Del:14% Cap:86%


EDMM Traffic Evolution (1997, 1998, 1999,2000)

1500

1700

1900

2100

2300

2500

2700

2900


Dai

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45

A6 EDUU: Karlsruhe ACC

Figure A6.1

Figure A6

EDUU (Summer 1999 - 2000)

0

1

2

3

4

5

6

7

8

9

10

0

dela

y pe

r flig

ht [m

in]

50

70

90

110

130

150

170

190

210

Mou

vem

ents

per

hou

r



− Summer weekdays 99 2400 2450

∆ Summer weekends 99 2300 xxxx− Summer weekdays 00 2800 2800∆ Summer weekends 00 2650 xxxx


: Traffic and resulting delays in summer 99 and 00 (15th of June - 9th of August)

. 2: Current versus target capacities (15th of June - 9th of August)

500 1000 1500 2000 2500 3000 3500

daily traffic

Simulated days





46




EDUUWUR AS YES 2 156367 9446 16.6EDUUFFM AS YES 2 135723 8282 16.4EDUUSLN AS YES 2 12490 383 32.6EDUUFUL AS YES 2 11904 606 19.6EDUUNTM AS YES 2 7612 306 24.9


E D U U d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

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

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

E D U U W U R A SE D U U F F M A SE D U U S L N A SE D U U F U L A SE D U U N T M A S


Conclusion


8% 4%



Del:27% Cap:73%


EDUU Traffic Evolution (1997, 1998, 1999,2000)

1500

1800

2100

2400

2700

3000


Dai

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47

A7 EDYY: Maastricht ACC


50

100

150

200

250

300

Simulated days

Mou

vem

ents

per

hou

r

simulated capacity 2000 % target 2001Figure A7.2: Current versus target capacities (15th of June - 9th of August)

EDYY (Summer 1999 - 2000)

0

1

2

3

4

5

6

7

8

9

10

0 500 1000 1500 2000 2500 3000 3500 4000 4500

daily traffic

dela

y pe

r flig

ht [m

in]



− Summer weekdays 99 3500 3700∆ Summer weekends 99 3300 xxxx− Summer weekdays 00 3600 3800∆ Summer weekends 00 3450 xxxx




48




EBMAWSL AS NO 144220 7544 19.1EBMALNL AS NO 77620 3628 21.4EBMALUX AS YES 2 55185 2507 22.0EHDELMD AS NO 46574 2635 17.7EDYYSOL AS YES 2 16204 1081 15.0


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 d u r i n g A I R A C 2 0 5 & 2 0 6

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 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

E B M A W S L A SE B M A L N L A SE B M A L U X A SE H D E L M D A SE D Y Y S O L A S


Conclusion


9% 7%



Del:37% Cap:63%


EDYY Traffic Evolution (1997, 1998, 1999,2000)

2000

2200

2400

2600

2800

3000

3200

3400

3600

3800

4000


Dai

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49

A8 EGCC: Manchester ACC



EGCC (Summer 1999 - 2000)

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

daily traffic

dela

y pe

r flig

ht [m

in]

Traffic growth 1999 – 2000: 18 %Capacity growth 1999 – 2000: x %



50

60

70

80

90

100

110

120

130

140

150

Simulated days

Mou

vem

ents

per

hou

r





50




EGCC29G AS NO 1709 147 11.6EGCCIMW AS YES 2 1122 88 12.8EGCC AD 84 6 14.0EGCCW AS NO 2 1 2.0


E G C C d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

0

5 0

1 0 0

1 5 0

2 0 0

2 5 0

3 0 0

3 5 0

4 0 0

4 5 0

5 0 0

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

E G C C 2 9 G A SE G C C I M W A SE G C C A DE G C C W A S


Conclusion


0% 0%



Del:1% Cap:99%


EGCC Traffic Evolution (1997, 1998, 1999,2000)

500

700

900

1100

1300

1500


Dai

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51

A9 EGPX: Scottish ACC



EGPX (Summer 1999 - 2000)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 500 1000 1500 2000 2500

daily traffic

dela

y pe

r flig

ht [m

in]

Traffic growth 1999 – 2000: - 8 %Capacity growth 1999 – 2000: x %



50

70

90

110

130

150

170

Simulated days

Mou

vem

ents

per

hou

r





52



ReferenceLocation

Grouped Yes /No

Total delay (min)from 15 june to 9

aug.

DelayedFlights

Delay per delayedflight

EGPXDCS AS YES 2 7798 414 18.8EGPXSWE AS NO 6296 296 21.3EGPXDXS AS NO 6129 392 15.6EGPXTLA AS NO 4880 300 16.3EGPXANT AS NO 4499 332 13.6


E G P X d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

3 0 0 0

3 5 0 0

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

E G P X D C S A SE G P X S W E A SE G P X D X S A SE G P X T L A A SE G P X A N T A S


Conclusion


2% 1%



Del:3% Cap:97%


EGPX Traffic Evolution (1997, 1998, 1999, 2000)

400

500

600

700

800

900

1000

1100

1200

1300

1400

1500

1600

1700

1800

1900

2000


Dai

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53

A10 EGTT: London ACC



EGTT (Summer 1999 - 2000)

0

1

2

3

4

5

6

7

8

9

10

0 1000 2000 3000 4000 5000 6000

daily traffic

dela

y pe

r flig

ht [m

in]



− Summer weekdays 99 4900 xxxx∆ Summer weekends 99 4750 4800− Summer weekdays 00 5100 xxxx∆ Summer weekends 00 5000 5000

50

100

150

200

250

300

350

400

Simulated days

Mou

vem

ents

per

hou

r





54




EGTTLUE AS NO 117372 4851 24.2EGTTSFD AS NO 70197 2338 30.0EGTTHRN AS YES 2 65270 1481 44.1EGTTS11 AS NO 63103 3419 18.5EGLL AD 60667 2347 25.8


E G T T d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

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

3 0 0 0 0

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

E G T T L U E A SE G T T S F D A SE G T T H R N A SE G T T S 1 1 A SE G L L A D


ConclusionCapacity Shortfall 2001

Weekday Weekend8% 9%



Del:16% Cap:84%


EGTT Traffic Evolution (1997, 1998, 1999,2000)

2500

3000

3500

4000

4500

5000

5500

6000

6500


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55

A11 EHAA: Amsterdam ACC



EHAA (Summer 1999 - 2000)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 200 400 600 800 1000 1200 1400 1600 1800 2000

daily traffic

dela

y pe

r flig

ht [m

in]




50

60

70

80

90

100

110

120

130

140

150

Simulated days

Mou

vem

ents

per

hou

r





56




EHSECT2 AS NO 40503 2272 17.8EHAM AD 16686 837 19.9EHSECT3 AS NO 2493 153 16.3EH4 AZ EHAM 1225 99 12.4EH1+2 AS YES 2 973 85 11.4


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 d u r i n g A I R A C 2 0 5 & 2 0 6

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

3 0 0 0

3 5 0 0

4 0 0 0

4 5 0 0

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

E H S E C T 2 A SE H A M A DE H S E C T 3 A SE H 4 A ZE H 1 + 2 A S


ConclusionCapacity Shortfall 2001

Weekday Weekend2% 0%



Del:7% Cap:93%


EHAA Traffic Evolution (1997, 1998, 1999,2000)

1000

1100

1200

1300

1400

1500

1600

1700


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57

A12 EPWW: Warszawa ACC



EPWW (Summer 1999 - 2000)

0

1

2

3

4

5

6

7

8

9

10

0 100 200 300 400 500 600 700 800 900 1000

daily traffic

dela

y pe

r flig

ht [m

in]



− Summer weekdays 99 xxxx 800∆ Summer weekends 99 xxxx 750− Summer weekdays 00 xxxx xxxx∆ Summer weekends 00 xxxx xxxx

0

10

20

30

40

50

60

70

Simulated days

Mou

vem

ents

per

hou

r





58





EPWWJED AS NO 39749 1863 21.3EPWWSE AS YES 3 8437 334 25.3EPWWDRE AS NO 5228 265 19.7EPWWNW AS YES 2 4231 177 23.9EPWWE AS YES 2 4002 162 24.7


E P W W d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

3 0 0 0

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

E P W W J E D A SE P W W S E A SE P W W D R E A SE P W W N W A SE P W W E A S


Conclusion


2% 2%



Del:14% Cap:86%


EPWW Traffic Evolution (1997, 1998, 1999, 2000)

0

100

200

300

400

500

600

700

800

900

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

Daily

Tra

ffic

(30

days

mov

ing

aver

age)




59

A13 GCCC: Canarias ACC



GCCC (Summer 1999 - 2000)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 100 200 300 400 500 600 700 800 900

daily traffic

dela

y pe

r flig

ht [m

in]

Traffic growth 1999 – 2000: 1 %Capacity growth 1999 – 2000: 2%



0

10

20

30

40

50

60

70

Simulated days

Mou

vem

ents

per

hou

r





60




GCE AS YES 3 3661 176 20.8NELSO SP 2001 151 13.3GCLP AD 1122 49 22.9GCRR AD 528 29 18.2GCAC AS NO 135 8 16.9


G C C C d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

0

2 0 0

4 0 0

6 0 0

8 0 0

1 0 0 0

1 2 0 0

1 4 0 0

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

G C E A SN E L S O S PG C L P A DG C R R A DG C A C A S


Conclusion


0% 0%



Del:2% Cap:98%


GCCC Traffic Evolution (1997, 1998, 1999,2000)

400

450

500

550

600

650

700

750

800

850


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61

A14 LCCC: Nicosia ACC



LCCC (Summer 1999 - 2000)

0

1

2

3

4

5

6

7

8

9

10

0 100 200 300 400 500 600 700 800 900

daily traffic

dela

y pe

r flig

ht [m

in]




0

10

20

30

40

50

60

70

Simulated days

Mou

vem

ents

per

hou

r





62




LCCCWS AS NO 8898 457 19.5LCCCSSW AS YES 2 1630 68 24.0LCCCALL AS YES 4 1568 90 17.4LCCCEST AS YES 2 231 14 16.5LCCCSS AS NO 166 10 16.6


L C C C d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L C C C W S A SL C C C S S W A SL C C C A L L A SL C C C E S T A SL C C C S S A S


Conclusion


1% 0%



Del:9% Cap:91%


LCCC Traffic Evolution (1997, 1998, 1999,2000)

300

350

400

450

500

550

600

650

700

750


Dai

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63

A15 LDZO: Zagreb ACC



LDZO (Summer 1999 - 2000)

0

0.5

1

1.5

2

2.5

3

3.5

4

0 100 200 300 400 500 600 700 800 900

daily traffic

dela

y pe

r flig

ht [m

in]



− Summer weekdays 99 xxxx xxxx∆ Summer weekends 99 xxxx xxxx− Summer weekdays 00 700 xxxx∆ Summer weekends 00 700 xxxx

0

10

20

30

40

50

60

70

Simulated days

Mou

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ents

per

hou

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64




LDULW AS YES 3 4253 212 20.1LDZOALL AS YES 12 1353 61 22.2LDULCRO AS YES 9 1147 59 19.4LDTUCRO AS YES 9 734 31 23.7LDTULW AS YES 4 472 21 22.5


L D Z O d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L D U L W A SL D Z O A L L A SL D U L C R O A SL D T U C R O A SL D T U L W A S


Conclusion


2% 3%



Del:8% Cap:92%


LDZO Traffic Evolution (1997, 1998, 1999, 2000)

0

100

200

300

400

500

600

700

800


Dai

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65

A16 LECB: Barcelona ACC



LECB (Summer 1999 - 2000)

0

2

4

6

8

10

12

14

16

18

20

0 500 1000 1500 2000 2500

daily traffic

dela

y pe

r flig

ht [m

in]



− Summer weekdays 99 1600 xxxx∆ Summer weekends 99 xxxx xxxx− Summer weekdays 00 1750 xxxx∆ Summer weekends 00 2000 xxxx

0

20

40

60

80

100

120

140

160

Simulated days

Mou

vem

ents

per

hou

r





66




LEBL AD 73692 4566 16.1LECBECO AS YES 2 28798 1083 26.6LECBW13 AS YES 2 16483 563 29.3LECBCEN AS YES 2 13649 573 23.8LECBEN AS NO 8368 257 32.6


L E C B d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

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 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L E B L A DL E C B E C O A SL E C B W 1 3 A SL E C B C E N A SL E C B E N A S


Conclusion


5% 7%



Del:12% Cap:88%


LECB Traffic Evolution (1997, 1998, 1999,2000)

500

700

900

1100

1300

1500

1700

1900

2100


Dai

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67

A17 LECM: Madrid ACC



LECM (Summer 1999 - 2000)

0

1

2

3

4

5

6

7

8

9

10

0 500 1000 1500 2000 2500 3000

daily traffic

dela

y pe

r flig

ht [m

in]



− Summer weekdays 99 2000 2200∆ Summer weekends 99 xxxx 2000− Summer weekdays 00 2100 2300∆ Summer weekends 00 xxxx 2200

0

20

40

60

80

100

120

140

160

180

Simulated days

Mou

vem

ents

per

hou

r





68





LECMDGO AS NO 102137 4761 21.5LECMBLC AS YES 3 64810 2994 21.6LEMD AD 55074 3183 17.3LECMZMR AS YES 2 47282 2160 21.9LECMDT AS YES 2 25843 1065 24.3


L E C M d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

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 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L E C M D G O A SL E C M B L C A SL E M D A DL E C M Z M R A SL E C M D T A S


Conclusion


10% 11%



Del:33% Cap:67%


LECM Traffic Evolution (1997, 1998, 1999,2000)

900

1100

1300

1500

1700

1900

2100

2300

2500


Dai

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69

A18 LECS: Seville ACC



LECS (Summer 1999 - 2000)

0

1

2

3

4

5

6

7

8

9

10

0 200 400 600 800 1000 1200

daily traffic

dela

y pe

r flig

ht [m

in]



− Summer weekdays 99 800 xxxx∆ Summer weekends 99 800 850− Summer weekdays 00 800 xxxx∆ Summer weekends 00 850 xxxx

0

10

20

30

40

50

60

70

80

90

Simulated days

Mou

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ents

per

hou

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70





LECSBLN AS YES 2 14728 622 23.7LECSBY AS YES 3 9802 436 22.5LECSRUT AS YES 6 3600 164 22.0LECSBAN AS NO 1136 62 18.3LECSSM AS YES 3 492 33 14.9


L E C S d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

3 0 0 0

3 5 0 0

4 0 0 0

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L E C S B L N A SL E C S B Y A SL E C S R U T A SL E C S B A N A SL E C S S M A S


Conclusion


1% 4%



Del:8% Cap:92%


LECS Traffic Evolution (1997, 1998, 1999,2000)

500

550

600

650

700

750

800

850

900


Dai

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71

A19 LFBB: Bordeaux ACC



LFBB (Summer 1999 - 2000)

0

1

2

3

4

5

6

7

8

9

10

0 500 1000 1500 2000 2500 3000

daily traffic

dela

y pe

r flig

ht [m

in]

Traffic growth 1999 – 2000: 4 %Capacity growth 1999 – 2000: x%


− Summer weekdays 99 xxxx xxxx∆ Summer weekends 99 1950 2200− Summer weekdays 00 xxxx xxxx∆ Summer weekends 00 2050 xxxx

0

20

40

60

80

100

120

140

160

180

Simulated days

Mou

vem

ents

per

hou

r





72





LFBUT2 AS NO 42313 1995 21.2LFBUX2 AS NO 36332 1280 28.4LFBUPV AS YES 4 25586 950 26.9LFBUCR1 AS YES 2 17163 816 21.0LFBUSUD AS YES 8 16438 291 56.5


L F B B d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

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

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L F B U T 2 A SL F B U X 2 A SL F B U P V A SL F B U C R 1 A SL F B U S U D A S


Conclusion


2% 5%



Del:14% Cap:86%


LFBB Traffic Evolution (1997, 1998, 1999,2000)

1200

1400

1600

1800

2000

2200

2400


Dai

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73

A20 LFEE: Reims ACC



LFEE (Summer 1999 - 2000)

0

1

2

3

4

5

6

7

8

9

10

11

12

0 500 1000 1500 2000 2500 3000

daily traffic

dela

y pe

r flig

ht [m

in]

Traffic growth 1999 – 2000: -1 %Capacity growth 1999 – 2000: 7 %


− Summer weekdays 99 xxxx 2000∆ Summer weekends 99 xxxx 1900− Summer weekdays 00 xxxx 2200∆ Summer weekends 00 xxxx 2000

0

20

40

60

80

100

120

140

160

Simulated days

Mou

vem

ents

per

hou

r





74




LFEUE AS NO 88023 4225 20.8LFEURYX AS YES 3 45068 1992 22.6LFEUY AS NO 35411 1458 24.3LFEUHL AS NO 33744 1764 19.1LFEURXR AS YES 2 33519 1433 23.4


L F E E d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

3 0 0 0

3 5 0 0

4 0 0 0

4 5 0 0

5 0 0 0

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L F E U E A SL F E U R Y X A SL F E U Y A SL F E U H L A SL F E U R X R A S


Conclusion


8% 6%



Del:26% Cap:74%


LFEE Traffic Evolution (1997, 1998, 1999,2000)

1200

1400

1600

1800

2000

2200

2400


Dai

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75

A21 LFFF: Paris ACC



LFFF (Summer 1999 - 2000)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

0 500 1000 1500 2000 2500 3000 3500 4000 4500

daily traffic

dela

y pe

r flig

ht [m

in]



− Summer weekdays 99 3250 3750∆ Summer weekends 99 3000 xxxx− Summer weekdays 00 3600 xxxx∆ Summer weekends 00 3100 xxxx

0

50

100

150

200

250

300

Simulated days

Mou

vem

ents

per

hou

r



EEC: Capacity Targets 2001 76





LFPG AD 166595 3978 41.9MERUE SP 123251 3339 36.9LFFUJ AS NO 42112 1607 26.2LFFEGAS AS NO 22888 931 24.6LFFTB AS NO 18464 1145 16.1


L F F F d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

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

3 0 0 0 0

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L F P G A DM E R U E S PL F F U J A SL F F E G A S A SL F F T B A S


Conclusion


5% 3%



Del:15% Cap:85%


LFFF Traffic Evolution (1997, 1998, 1999,2000)

2000

2200

2400

2600

2800

3000

3200

3400

3600

3800

4000


Dai

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77

A22 LFMM: Marseille ACC



LFMM (Summer 1999 - 2000)

0

2

4

6

8

10

12

14

16

18

20

0 500 1000 1500 2000 2500 3000 3500

daily traffic

dela

y pe

r flig

ht [m

in]



− Summer weekdays 99 2600 2750∆ Summer weekends 99 2300 2750− Summer weekdays 00 2800 2750∆ Summer weekends 00 xxxx 2750

0

50

100

150

200

250

Simulated days

Mou

vem

ents

per

hou

r








LFMYY AS YES 2 88505 2693 32.9LFMEK AS YES 4 62970 2403 26.2LFMEK1 AS YES 2 62387 2024 30.8LFMNNR AS NO 55480 3426 16.2LFMB1 AS NO 43476 1448 30.0


L F M M d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

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

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L F M Y Y A SL F M E K A SL F M E K 1 A SL F M N N R A SL F M B 1 A S


Conclusion


6% 11%



Del:23% Cap:77%


LFMM Traffic Evolution (1997, 1998, 1999,2000)

1500

1700

1900

2100

2300

2500

2700

2900

3100

3300


Dai

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79

A23 LFRR: Brest ACC



LFRR (Summer 1999 - 2000)

0

1

2

3

4

5

6

7

0 500 1000 1500 2000 2500 3000

daily traffic

dela

y pe

r flig

ht [m

in]



− Summer weekdays 99 xxxx xxxx∆ Summer weekends 99 2400 xxxx− Summer weekdays 00 xxxx xxxx∆ Summer weekends 00 xxxx xxxx

0

20

40

60

80

100

120

140

160

180

200

Simulated days

Mou

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ents

per

hou

r








LFRZ AS YES 2 37963 1715 22.1LFRN AS YES 2 23426 746 31.4LFRZS AS NO 21893 950 23.0LFRZX AS YES 4 11295 192 58.8LFROQ AS YES 4 7530 274 27.5


L F R R d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

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

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L F R Z A SL F R N A SL F R Z S A SL F R Z X A SL F R O Q A S


Conclusion


3% 7%



Del:10% Cap:90%


LFRR Traffic Evolution (1997, 1998, 1999,2000)

800

1000

1200

1400

1600

1800

2000

2200

2400


Dai

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81

A24 LGGG: Athinai ACC



LGGG (Summer 1999 - 2000)

0

1

2

3

4

5

6

7

8

9

10

11

12

0 200 400 600 800 1000 1200 1400 1600 1800

daily traffic

dela

y pe

r flig

ht [m

in]



− Summer weekdays 99 xxxx xxxx∆ Summer weekends 99 xxxx 1300− Summer weekdays 00 xxxx xxxx∆ Summer weekends 00 xxxx xxxx

0

20

40

60

80

100

120

Simulated days

Mou

vem

ents

per

hou

r








LGAT AD 304308 10235 29.7LGIR AD 51089 1105 46.2LGRP AD 18552 524 35.4LGGSE1 AS YES 2 5658 216 26.2LGSR AD 5525 139 39.7


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 d u r i n g A I R A C 2 0 5 & 2 0 6

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

3 0 0 0 0

3 5 0 0 0

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L G A T A DL G I R A DL G R P A DL G G S E 1 A SL G S R A D


Conclusion


5% 6%



Del:4% Cap:96%


LGGG Traffic Evolution (1997, 1998, 1999,2000)

500

600

700

800

900

1000

1100

1200

1300

1400

1500


Dai

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83

A25 LHCC: Budapest ACC



LHCC (Summer 1999 - 2000)

0

1

2

3

4

5

6

7

8

9

10

0 200 400 600 800 1000 1200 1400 1600 1800 2000

daily traffic

dela

y pe

r flig

ht [m

in]

Traffic growth 1999 – 2000: -6 %Capacity growth 1999 – 2000: x %



0

20

40

60

80

100

120

Simulated days

Mou

vem

ents

per

hou

r








LHB AS YES 2 32071 1762 18.2LHE AS YES 2 434 21 20.7


L H C C d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

3 0 0 0

3 5 0 0

4 0 0 0

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L H B A SL H E A S


Conclusion


3% 3%



Del:11% Cap:89%


LHCC Traffic Evolution (1997, 1998, 1999,2000)

500

700

900

1100

1300

1500

1700

1900


Dai

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85

A26 LIBB: Brindisi ACC



LIBB (Summer 1999 - 2000)

0

2

4

6

8

10

12

14

16

0 100 200 300 400 500 600 700 800 900 1000

daily traffic

dela

y pe

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ht [m

in]




0

10

20

30

40

50

60

70

Simulated days

Mou

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ents

per

hou

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LIBBMS2 AS YES 2 17385 779 22.3LIBBUND AS YES 2 15606 583 26.8LIBBUMS AS YES 4 9313 145 64.2LIBBSD1 AS NO 2919 135 21.6LIBBALL AS YES 6 1414 9 157.1


L I B B d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

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

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L I B B M S 2 A SL I B B U N D A SL I B B U M S A SL I B B S D 1 A SL I B B A L L A S


Conclusion


5% 0%



Del:11% Cap:89%


LIBB Traffic Evolution (1997, 1998, 1999,2000)

200

300

400

500

600

700

800

900

1000


Dai

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87

A27 LIMM: Milano ACC



LIMM (Summer 1999 - 2000)

0

2

4

6

8

10

12

14

0 500 1000 1500 2000 2500 3000

daily traffic

dela

y pe

r flig

ht [m

in]



− Summer weekdays 99 2250 2350∆ Summer weekends 99 2250 2350− Summer weekdays 00 xxxx xxxx∆ Summer weekends 00 xxxx xxxx

0

20

40

60

80

100

120

140

160

180

Simulated days

Mou

vem

ents

per

hou

r








LIMC AD 114396 4281 26.7LIMWS2 AS NO 37161 1619 23.0LIMMWNL AS YES 2 30467 1249 24.4LIMMWS2 AS NO 28326 1079 26.3LIMWNL AS YES 2 22690 969 23.4


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 d u r i n g A I R A C 2 0 5 & 2 0 6

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

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L I M C A DL I M W S 2 A SL I M M W N L A SL I M M W S 2 A SL I M W N L A S


Conclusion


7% 5%



Del:15% Cap:85%


LIMM Traffic Evolution (1997, 1998, 1999,2000)

1200

1400

1600

1800

2000

2200

2400

2600


Dai

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89

A28 LIPP: Padua ACC



LIPP (Summer 1999 - 2000)

0

5

10

15

20

25

30

0 200 400 600 800 1000 1200 1400 1600 1800

daily traffic

dela

y pe

r flig

ht [m

in]

Traffic growth 1999 – 2000: 1 %Capacity growth 1999 – 2000: 4 % ?



0

20

40

60

80

100

120

140

Simulated days

Mou

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ents

per

hou

r








LIPPNU6 AS YES 8 126912 4939 25.7LIPPSL6 AS YES 12 96802 3955 24.5LIPPNL6 AS YES 12 81276 3292 24.7LIPPNT6 AS YES 4 56131 2165 25.9LIPPND2 AS YES 24 12507 756 16.5


L I P P d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

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 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L I P P N U 6 A SL I P P S L 6 A SL I P P N L 6 A SL I P P N T 6 A SL I P P N D 2 A S


Conclusion


10% 11%



Del:41% Cap:59%


LIPP Traffic Evolution (1997, 1998, 1999,2000)

500

700

900

1100

1300

1500

1700

1900


Dai

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91

A29 LIRR: Rome ACC



LIRR (Summer 1999 - 2000)

0

1

2

3

4

5

6

7

0 500 1000 1500 2000 2500 3000 3500

daily traffic

dela

y pe

r flig

ht [m

in]


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

− Summer weekdays 99 2250 xxxx∆ Summer weekends 99 2250 2250− Summer weekdays 00 xxxx xxxx∆ Summer weekends 00 xxxx xxxx

0

50

100

150

200

250

Simulated days

Mou

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ents

per

hou

r








LICC_CZ AZ 18087 994 18.2LIRQ AD 14769 559 26.4LIRRMW AS YES 2 7281 261 27.9LIRRME AS YES 2 6820 240 28.4LIRREW1 AS NO 6617 306 21.6


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 d u r i n g A I R A C 2 0 5 & 2 0 6

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

3 0 0 0

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L I C C _ C Z A ZL I R Q A DL I R R M W A SL I R R M E A SL I R R E W 1 A S


Conclusion


1% 6%



Del:3% Cap:97%


LIRR Traffic Evolution (1997, 1998, 1999,2000)

1200

1500

1800

2100

2400

2700

3000


Dai

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e)




93

A30 LKAA: Prague ACC



LKAA (Summer 1999 - 2000)

0

1

2

3

4

5

6

7

0 200 400 600 800 1000 1200 1400

daily traffic

dela

y pe

r flig

ht [m

in]

Traffic growth 1999 – 2000: -6 %Capacity growth 1999 – 2000: 11 %


− Summer weekdays 99 875 1050∆ Summer weekends 99 825 950− Summer weekdays 00 1000 xxxx∆ Summer weekends 00 875 xxxx

0

10

20

30

40

50

60

70

80

90

Simulated days

Mou

vem

ents

per

hou

r








LKAANWU AS YES 2 13908 881 15.8LKAAEA AS YES 4 4261 324 13.2LKAANW AS YES 6 1930 78 24.7LKAAWL AS YES 2 1854 120 15.5


L K A A d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

0

2 0 0

4 0 0

6 0 0

8 0 0

1 0 0 0

1 2 0 0

1 4 0 0

1 6 0 0

1 8 0 0

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L K A A N W U A SL K A A E A A SL K A A N W A SL K A A W L A S


Conclusion


1% 0%



Del:8% Cap:92%


LKAA Traffic Evolution (1997, 1998, 1999,2000)

400

500

600

700

800

900

1000

1100

1200


Dai

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95

A31 LPPC: Lisbon ACC



LPPC (Summer 1999 - 2000)

0

1

2

3

4

5

6

7

8

9

10

0 200 400 600 800 1000 1200

daily traffic

dela

y pe

r flig

ht [m

in]



− Summer weekdays 99 xxxx xxxx∆ Summer weekends 99 900 950− Summer weekdays 00 xxxx xxxx∆ Summer weekends 00 950 xxxx

0

10

20

30

40

50

60

70

80

Simulated days

Mou

vem

ents

per

hou

r








LPCEN AS YES 4 17490 898 19.5LPSUL AS YES 2 10666 439 24.3LPNOR AS YES 2 2112 96 22.0LPOESTE AS YES 2 808 49 16.5LPLESTE AS YES 8 69 3 23.0


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 d u r i n g A I R A C 2 0 5 & 2 0 6

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L P C E N A SL P S U L A SL P N O R A SL P O E S T E A SL P L E S T E A S


Conclusion


3% 2%



Del:6% Cap:94%


LPPC Traffic Evolution (1997, 1998, 1999,2000)

300

400

500

600

700

800

900

1000

1100


Dai

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97

A32 LSAG: Geneve ACC



LSAG (Summer 1999 - 2000)

0

2

4

6

8

10

12

14

0 200 400 600 800 1000 1200 1400 1600 1800 2000

daily traffic

dela

y pe

r flig

ht [m

in]



− Summer weekdays 99 1400 1600∆ Summer weekends 99 xxxx 1600− Summer weekdays 00 1500 1700∆ Summer weekends 00 xxxx 1700

0

20

40

60

80

100

120

140

Simulated days

Mou

vem

ents

per

hou

r








LSAGISE AS YES 4 95254 3761 25.3LSAGKU3 AS YES 2 72555 2906 25.0LSAGUP6 AS NO 32302 1462 22.1LSAGKU45 AS YES 4 31450 1230 25.6LSAGKIN AS YES 4 27327 1359 20.1


L S A G d e l a y s p e r s e c t o r / a i r p o r t d u r i n g A I R A C 2 0 5 & 2 0 6

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 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L S A G I S E A SL S A G K U 3 A SL S A G U P 6 A SL S A G K U 4 5 A SL S A G K I N A S


Conclusion


11% 9%



Del:48% Cap:52%


LSAG Traffic Evolution (1997, 1998, 1999,2000)

1000

1100

1200

1300

1400

1500

1600

1700

1800

1900


Dai

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99

A33 LSAZ: Zürich ACC



LSAZ (Summer 1999 - 2000)

0

2

4

6

8

10

12

14

0 500 1000 1500 2000 2500 3000

daily traffic

dela

y pe

r flig

ht [m

in]



− Summer weekdays 99 xxxx 2050∆ Summer weekends 99 xxxx xxxx− Summer weekdays 00 2050 2300∆ Summer weekends 00 xxxx 2250

0

20

40

60

80

100

120

140

160

180

Simulated days

Mou

vem

ents

per

hou

r








LSAZUP2 AS NO 163667 7959 20.6LSAZESL AS NO 108645 6096 17.8LSAZUP1 AS NO 70036 3204 21.9LSZH AD 59049 3610 16.4LSAZU23 AS YES 2 33764 1784 18.9



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

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L S A Z U P 2 A SL S A Z E S L A SL S A Z U P 1 A SL S Z H A DL S A Z U 2 3 A S


Conclusion


10% 9%



Del:57% Cap:43%


LSAZ Traffic Evolution (1997, 1998, 1999,2000)

1200

1400

1600

1800

2000

2200

2400

2600


Dai

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101

A34 LWSS: Skopje ACC


LWSS (Summer 1999 - 2000)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

0 100 200 300 400 500 600

daily traffic

dela

y pe

r flig

ht [m

in]




0

5

10

15

20

25

30

35

40

45

Simulated days

Mou

vem

ents

per

hou

r








LWSSALL AS YES 4 7174 395 18.2LWSSCTL AS YES 2 1455 72 20.2



0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

6 / 1 2 / 0 0 6 / 1 9 / 0 0 6 / 2 6 / 0 0 7 / 3 / 0 0 7 / 1 0 / 0 0 7 / 1 7 / 0 0 7 / 2 4 / 0 0 7 / 3 1 / 0 0 8 / 7 / 0 0

daily

del

ays

(min

)

L W S S A L L A SL W S S C T L A S


Conclusion


7%



Del:7% Cap:93%


LWSS Traffic Evolution (1997, 1998, 1999, 2000)

0

50

100

150

200

250

300

350

400


Dai

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103

Contacts

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

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

Co-operations

Domain Contacts Email TelCFMU J.-R. Bauchet [email protected] ++32 2 729 9600CFMU / ENGD P.-O. Jeannet [email protected] ++32 2 729 9700CFMU / FMD J. Byrom [email protected] ++32 2 729 9800CFMU / URB A. Fournie [email protected] ++32 2 729 9820

Related Activities

Domain Contacts Email TelEEC J-M. Garot [email protected] ++33 1 69 887501EEC/PFE P. Ky [email protected] ++33 1 69 887688EATMP / 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 M. Dalichampt [email protected] ++33 1 69 88 7574 7352Economy J.C. Hustache [email protected] ++33 1 69 88 7802 7352OR and Analysis A. Marsden [email protected] ++33 1 69 88 73 61 7352OR and Analysis J.-J. Andrevet [email protected] ++33 1 69 88 73 62 7352Complexity T. Chaboud [email protected] ++33 1 69 88 74 09 7352System Dynamics M. Gibellini [email protected] ++33 1 69 88 75 68 7352ATFM simulator J. Lebreton [email protected] ++33 1 69 88 7604 7352ATFM simulator S. Bourdais [email protected] ++33 1 69 88 7026 7352ATFM simulator H. Kadour [email protected] ++33 1 69 88 7863 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. P. Morignot [email protected] ++33 1 69 88 70 35 7352Applied math. F. Le Huede [email protected] ++33 1 69 88 78 22 7352

mailto:[email protected]















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