EUROPEAN ORGANISATION FOR THE SAFETY OF AIR NAVIGATION EUROCONTROL EXPERIMENTAL CENTRE EEC Note No. 16/99 Project PLC–C–E1 Issued: October 1999 The information contained in this document is the property of the EUROCONTROL Agency and no part should be reproduced in any form without the Agency’s permission The views expressed herein do not necessarily reflect the official views or policy of the Agency.. FAP Future ATM Profile Medium-term Capacity Shortfalls Including national and supra-national Capacity Enhancement Plans
45
Embed
Medium-term Capacity Shortfalls - Eurocontrol › sites › default › files › ... · (ACCs instead of sectors, average days instead of weekday/weekend). 0.60 0.50 0.40 ... (ARN
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
EUROPEAN ORGANISATIONFOR THE SAFETY OF AIR NAVIGATION
EUROCONTROL EXPERIMENTAL CENTRE
EEC Note No. 16/99
Project PLC–C–E1
Issued: October 1999
The information contained in this document is the property of the EUROCONTROL Agency and no part should bereproduced in any form without the Agency’s permission
The views expressed herein do not necessarily reflect the official views or policy of the Agency..
FAP Future ATM Profile
Medium-termCapacity Shortfalls
Including national and supra-nationalCapacity Enhancement Plans
RREEPPOORRTT DDOOCCUUMMEENNTTAATTIIOONN PPAAGGEE
ReferenceEEC Note 16/99
Security ClassificationUnclassified
OriginatorEEC - PRF(Performance and Economy Research)
Abstract :The study is performed by the EUROCONTROL Experimental Centre in close co-operationwith the DSA Directorate in support of the medium term capacity enhancement planning forEuropean air navigation services.Objective: Forecast of remaining capacity shortfalls and resulting delays in the medium term future (2003-2005) after implementation of national and supra-national capacity plans.Assumption: Air traffic in Europe grows as forecast by STATFOR (medium and high traffic growth scenario were tested); ACC capacities increase as planned by ATS providers and EUROCONTROL; Airport capacities increase as declared to EUROCONTROL;Methodology: Future ATM Profile (FAP) plus additional elements developed for long-term studies.
This document has been collated by mechanical means. Should there be missing pages,please report to:
AcknowledgementsThe FAP Team would like to thank the experts from EEC – FDR and CFMU namely Leila Zerrouki, Dominique Latge, Nicolas Dufour,Philippe Lecomte, Johannes Koolen, Marcel Richard and Etienne de Muelenaere for their assistance, co-operation and patience in
particular during the critical summer months at the end of this study.
3.1 SHORTEST ROUTES AND “OPEN SKY”_____________________________________ 143.2 AIR TRAFFIC CONTROL CENTRE_________________________________________ 163.3 AIRPORTS _________________________________________________________ 18
The study is performed by the EUROCONTROL Experimental Centre in close co-operationwith the DSA Directorate in support of the medium term capacity enhancement planning forEuropean air navigation services.Objective: Forecast of remaining capacity shortfalls and resulting delays in the medium term future (2003-2005) after implementation of national and supra-national capacity plans.Assumption: Air traffic in Europe grows as forecast by STATFOR (medium and high traffic growth scenario were tested); ACC capacities increase as planned by ATS providers and
EUROCONTROL; Airport capacities increase as declared to EUROCONTROL;Methodology: Future ATM Profile (FAP) plus additional elements developed for long-term studies.
1.1 General Context
Historical data indicate that current capacity management is mainly driven by indicatorsdescribing the past system performance (retro-active). Evolution within the last 15 yearshas shown that investments on capacity rise if delays increase to non-acceptable levels anddiminish in the years of lower delays.
The key towards a more pro-active capacity management is a thorough impact analysis ofpotential costs and benefits related to future ATM actions and a better understanding of theinterrelations of the European capacity network, the traffic growth and the resulting delays.
Short- medium- and long-term planning have to adapt to the various constraints andpotential of their planning time horizons (see figure below).
This study concentrates on Medium-term planning with a time horizon of 3 to 5 years.
The information quality - the reliability of its assumptions and forecast - is lower comparedto the short-term planning. Consequently, the range of future scenarios must be wider(high-medium growth; various traffic pattern), whereas the grade of detail can be lower(ACCs instead of sectors, average days instead of weekday/weekend).
0 .6 0
0 .5 0
0 .4 0
E n -ro u te C h a rg es v s. D e la y p er flig h t
On the other hand, the reactionary power is significantly higher. The enablers of short-termplanning are usually limited to a better use of existing resources. Whereas, the 5 yearsplanning time of the medium-term planning enables consideration of complementary actionssuch as revised airspace management, improved ATC technology and increased ATC staffgrowth.
1.2 Scope
The evaluation is performed on:• the whole ECAC areaincluding:• air traffic control centres and airports (65 ACCs, 91 airports)• Air Traffic Flow Management issues (CFMU slot allocation procedures)simulating:• present and future traffic loads (1999, 2003, 2005)• regional characteristic traffic growth (ca. 2000 traffic flows).
1.3 Scenarios
The following scenarios shall be examined:• “do nothing”
(capacity stagnates at 1999 level, traffic grows as forecast)• “existing plans”
(capacity increases according to the existing national and supra-national capacityenhancement plans)
Both scenarios shall be examined with:
• weekday and weekend traffic pattern (observed in summer 1999)• current routes (ARN V3 as used in summer 1999)• shortest flyable routes (ARN V3 without effects from Kosovo and capacity constraints)• medium and high traffic growth (according to STATFOR forecast)
The Future ATM Profile is a methodology that provides a platform to investigate the ATMsystem behaviour resulting from parameter changes foreseen or forecast in the shortand longer term future.
A ir p o r t C o s t s
C a p a c i t y
D e la yD e m a n d
T e c h n o lo g y
T r a in in g
I n v e s t m e n t
A ir lin e C o s t s
A T C C o s t s
L a n d in g F e e s R o u t e C h a r g e s
F l ig h t P e n a l t i e s
S t a f f F u e l
F le e t o th e r s
ATM capacity is provided by the airports and the en-route ATS providers. The direct cost ofthe capacity is borne by the airspace users, charged via landing/departure and routecharges. The (charged) cost of the European ATM system was around 9.5 billion euro in1998 (airports: 5.5 billion euro; en-route ATS: 4 billion euro).
Delays are the consequence of the inability of the ATM system to provide the capacityneeded to satisfy the demand. Delays increase the operating cost of an aircraft. Theestimated cost of European ATFM delays was around 500 million euro in 1998.
A significant part of the delay cost could have been saved through a more pro-active ATMcapacity management. The Future ATM Profile (FAP) is developed to support the Europeancapacity management. FAP identifies future capacity shortfalls in the air (en-route) and onthe ground (airports). FAP estimates the capacity growth required to optimise the cost ofproviding the service against the cost of the delays and other flight penalties.
This study used the following macro-elements of the FAP methodology:
• An Air Traffic Flow Management (ATFM) simulation tool (AMOC/CASA) with animplemented copy of the CFMU slot allocation algorithm (CASA), using trafficsectorisation and capacity data as an input to identify flights subject to ATFM delays,and the ACCs or airports being the root causes for the delay (bottlenecks). AMOC isused to model the system behaviour of the European capacity network and to quantifythe delay development in the future, based on regional capacity and traffic growthestimates.
• A Model for the Economical Evaluation of the Capacities of the ATM system (MECA),uses capacity cost data recorded by CRCO, aircraft operating data recorded by IATAand traffic/delay data recorded by CFMU to compute the best trade-off between cost forcapacity and cost for delays. The study used the results of this model to identify theoptimum capacity demand ratios and the resulting delays at which the ACCs andairports operate at minimum cost for the airspace users.
2.1 Air Traffic Flow Management (ATFM) simulations
This study is based on a sequence of ATFM simulations using AMOC to investigate theimpact of traffic and capacity growth on the delays.
AMOC (ATFM model capability) is our most realistic delay model. It simulates the CFMUoperations (see figure below).
The heart of AMOC is the slot allocation algorithm that converts overload into delays. Thisalgorithm is a direct copy of the CFMU Computer Assisted Slot Allocation algorithm(CASA). Thorough fine tuning is required prior to every set of simulations using AMOC toguarantee a good model representation for each individual configuration and regulationscheme applied.
ATFM simulations are used:• to investigate the sensitivity of delays on traffic and capacity increase• to anticipate the network effects (e.g. airport protects ACC etc.)
AMOC uses capacities as an input and gives delays as an output. It cannot run in theinverse mode, using delays as an input and giving capacities as an output. Iterativesimulation steps are required to find the capacities needed to reach the delay targetdefined.
A i r p o r t N e e d s
A i r l in e N e e d s
A T C N e e d s
D e la y sD e la y s
D e m a n dD e m a n d
P o s s ib l eR e r o u t in g
C a p a c i t i e sC a p a c i t ie sC o n f i g u r a t i o nC o n f ig u r a t io n
This chapter investigates the sensitivity of delays on the capacity demand ratio. Theobjective is to determine the best traffic indicator to be used for the capacity demand ratio.The indicator should be simple but sensitive enough to represent the impact of the varioustraffic demand curves over the day.
Network effects must not disturb the observation in this exercise. Therefore, each ACC wassimulated individually with all other ACC capacities set to infinite (“no network”). Each ACCwas simulated with various traffic patterns and various capacities. In total, the results ofmore than 1200 ATFM simulations were used to select the best traffic indicator.
5 indicators were investigated:• 1 hour peak (peak hourly traffic load of the ACC)• 2 hour peak (average load per hour during the peak 2 hour period)• 3 hour peak (average load per hour during the peak 3 hour period)• av. 6-18h (average load per hour between 6:00 and 18:00)• av. 0-24h (average load per hour between 0:00 and 24:00)
Observations:• the av. 6-18h load is the most stable indicator at low capacity demand ratios• the 3 hour peak is the best indicator at capacity demand ratios (c/d) around 1• delay sensitivities are rather linear at capacity demand ratios below 0.8.
Conclusion:• we select the 3 hour peak, because it is the most reliable indicator in the area of
current and optimum capacity demand ratios (between 0.85 and 1.2).• the “noise” caused by the variation of hourly traffic distributions on c/d has a maximum
amplitude of +/- 5% throughout Europe (significantly lower for individual ACCs).
The hourly traffic distribution has an influence on the delays caused by the sector or centre.The “noise” can be in the same order of magnitude than the network effects (see nextchapter).
However, investigations have shown that each centre has its individual traffic curve. Themain characteristics of the curve remain rather stable for each ACC. The graphs belowshow the hourly traffic distributions from 51 days in summer 1999 for Karlsruhe UAC,London ACC, Madrid ACC and Reims ACC.
Observation:• Overall, it appears that the phenomena of hourly traffic variation is rather systematic,
repetitive and predictable. Even the most significant variations of the traffic curves(usually observed on Sundays) seem to repeat every week.
Conclusion:• FAP assumes that the characteristics of the traffic load curves of each ACC will remain
within the next 3 to 5 years, as long as no contrary effect is predicted.
• Currently, we don’t know the stability of the traffic load curves over a longer timeframe.However, the impact of a changed traffic load curve on the optimum capacity demandratio (using the peak 3 hour load) is +/-5% at maximum.
Currently, Air Traffic Flow Management is affected by interrelations between regulatedairports and sectors/centres being “bottlenecks” in the European ATS capacity network.These interrelations are called “network effects”. The consequence is that comparablecapacity shortfalls in different regions may have diverse impact on the ATFM delays inEurope.
The ATFM simulation tools used by FAP are capable of modelling these network effects.However, there is an astronomic number of scenarios with different network effectsimaginable for the years 2003 and 2005. This chapter investigates therefore, the impact ofnetwork effects on the optimum capacity demand ratio and the risk of non-foreseenchanges to the network.
The investigation focused on the current (Summer 1999) capacity network. A series of 15ATFM simulations was performed for each ACC with varying capacities for one ACC andconstant 1999 capacities for the others. This was done for all ACC using the three differentbaseline scenarios selected to represent summer 1999 conditions.
The left figure below shows the simulation results of all simulations with (red) and without(blue) network effects. On the right: Frankfurt, Barcelona, Amsterdam and Paris asexamples to demonstrate the variety of network effects.
Observations:• Network effects add another “noise” to the delay curves• Some ACC showed reduced, others increased delays with 1999 network effects• Surprisingly: the majority of ACCs showed increased delays with the 1999 capacity
network due to a more complicated slot allocation.
Conclusions:• The current network effects incorporate a low risk for medium term capacity planning.• The risk of under-estimated capacity shortfalls due to network effects is below 3%.• This is compensated by the positive effect of improved slot allocation within a “quasi no
network” (optimum c/d ratio) target of medium term capacity planning.
It is to the advantage of a common European Capacity Plan that investments in capacitiescan be evaluated using macro-economical aspects - such as the return on investment (ROI)from the airspace user point of view.
The real costs of the en route ATC service and the airport service (from the airspace userpoint of view) include route charges, landing fees AND the indirect costs such as delaycosts and cost for non-optimum flight profiles. Minimum costs are achieved at the pointwhere the total costs, the sum of capacity AND indirect costs is lowest (see figure below).
The unit costs for the en-route charges show that the marginal cost for capacity variesthroughout Europe with the factor 3-4 (see also EEC Note 8/99 “Cost of the En-Route AirNavigation Services in Europe”).
Conversely, the cost for one minute delay also varies throughout Europe depending on theregional traffic mix (for ACC: 11 – 27 ECU /min delay; for airports: 10 – 24 ECU/min delay).Consequently, every ACC (and every airport) has its own curve. In addition, these curvesinterrelate as network effects change with capacity increases elsewhere.
Both the cost for capacity and the cost for delay are regional parameters.They depend on:• total capacity provided• marginal capacity cost (ATC complexity, price index, equipment, etc)• total delay caused• delay sensitivity (network effects, hourly traffic distribution)• cost per minute delay (traffic mix)
Consequently, every ACC has its own cost curves and optimum capacity demand ratio.The 6 figures below show the variety of cost curves observed in Europe ( Frankfurt ACC,London ACC, Barcelona ACC, Marseille ACC, Prague ACC and Zurich ACC ).
In blue: ACC operating point observed for summer 1999.
The optimum capacity demand ratio varies between 0.99 and 1.17 throughout Europe.However, the majority of ACCs have an optimum c/d between 1.03 and 1.08.
Medium-term capacity planning targets the optimum c/d for every ACC. Capacity shortfallsare derived from the computation of current and future capacity demand ratios (C/D) andtheir deviation from the optimum.
The results of this study are derived from more than 6000 various ATFM simulations plus anumber of economic model runs. This chapter briefly explains the course of the simulationswithin the various phases of the study.
2.4.1 Model CalibrationEach simulation with a new traffic sample requires a complete new set up based on theparameters used by CFMU on that day. This is our first check point for validation.
In the next phase, sector regulations are transferred to centre regulations. The centrecapacities are derived from an iterative simulation process. The process is finished whenthe centre produces the same delays per day as observed by CFMU. This is our calibrationphase.
2.4.2 Scenario 1999 “baseline”
The next phase analyses the current situation in summer 1999.
Delay sensitivities and network effects are investigated for each ACC by a number ofATFM simulations with various centre capacities (more than 5000 simulations in total).The outcome is an average delay curve based on the 1999 network effects for each centreindividually. These curves are used in MECA to identify the capacity demand ratio of acentre in 1999 at a given delay per flight.
ATFMSimulation
19991999“baseline”
Model calibration
CFMU
19991999
0
1
2
3
4
5
6
7
0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4
Capacity / Demand ra tio
Del
ay p
er fl
ight
[min
]
ATFMSimulation
19991999
CFMU
19991999
Delay Sensitivities influenced by the current Capacity Network
The delay curves are also translated into delay cost curves using the IATA estimates for thecost of delays in air transportation. We add the CRCO capacity cost data and MECAcalculates the optimum capacity demand ratio at the point where the sum of cost forcapacity and cost for delay is lowest.
MECA uses the 1999 capacity demand ratio and the optimum capacity demand ratio tocalculate the capacity shortfall in 1999.
The estimated cost for the capacity at the optimum point and the real capacity cost 1999 atthe 1999 capacity demand ratio is used to estimate the cost for the extra capacity.
The estimated cost for the delay at the optimum point and the real cost for the delay in 1999is used to estimate the potential benefits in delays.
The cost for the extra capacity and the potential benefits in delays are used to estimate thereturn on investment (ROI).
2.4.3 Scenarios 2003 and 2005 “do nothing”
Future scenarios are based on a 1999 baseline with a modified traffic sample according tothe STATFOR traffic growth scenarios between 70 families of airports.
2003 and 2005 scenarios are simulated with all airports regulated by the CFMU. We used“global” airport capacities (instead of arrival and/or departure capacities) as they aredeclared by the airports (see also chapter “Airport Capacities”).
All future scenarios are simulated with 6 different traffic pattern:
1. July 3rd 1999 / current routes2. July 3rd 1999 / shortest routes3. July 7th 1999 / current routes4. July 7th 1999 / shortest routes5. July 23rd 1999 / current routes6. July 23rd 1999 / shortest routes
Every traffic pattern is simulated with high and medium traffic growth.We end up with 12 “do nothing” scenarios per year.
In the next phase, the new 2005 ACC capacities are calculated based on the currentcapacities and the capacity growth foreseen in the national and supra-national ATMcapacity planning.
Again, all 12 scenarios were simulated and analysed.
The forecast delays are directly derived from the ATFM simulations.Additional outputs are derived from the economic evaluation:• cost for the delays,• remaining capacity shortfalls and• return on investment (ROI)
2.5 Assumptions, Constraints and Risks
• Traffic forecast – generalThis study uses traffic-growth data provided by the Specialist Panel on Air TrafficStatistics and Forecast (STATFOR). We believe these data are the mostcomprehensive and consistent in Europe. However, history has shown that air trafficgrowth is difficult to forecast in some years and/or areas, and capacity shortfalls arevery sensitive to the regional traffic growth. We believe therefore, that the traffic growthscenarios are one of the most crucial input data for this study.Risk: high (difficult to control)
• Traffic forecast – Scheduling CommitteeThe traffic forecast is performed in close co-operation with the member States, brokendown into 2000 traffic flows and under consideration of capacity constraints at airports.Nevertheless, many airports are grouped in the same family. We observed someairports that exceed significantly their forecast capacity in 2005 when applying the trafficgrowth forecast for the family. This problem may be either solved by the creation of anew family (STATFOR) or by a new traffic generator that smoothes or reallocates trafficautomatically to neighbouring airports (FAP).Risk: medium (can be further reduced)
• ATFM RegulationThe CFMU variable sector regulation per hour was simplified. FAP applies constantACC regulation throughout a single day. As a result, there may be local effects whichhave not been covered here. Validation was made against the observed CFMU delays.Differences in delays are usually below 3%.Risk: low
• Current operating pointsThe current ACC capacities are computed based on observations of multiple days insummer 1999. The accuracy could vary around +/- 5% for all those ACCs that produceddelays in summer 1999. However, variation may be higher for ACCs not working at itsmaximum capacity in 1999. Some of these centres have little knowledge of theirmaximum sector capacities, and/or do not provide CFMU with up to date sectorisationplans. We also observed that the description of some routes passing “zero delaycentres” do not always include all sectors within the ACC. We observed these problemsonly for ACCs operating at very high capacity demand ratios. With the current trafficgrowth rates, we hope that a repetitive capacity analysis will discover unforeseenshortfalls latest 2 years before they become really urgent.Risk: medium (can be controlled (in limits), can be further reduced)
• Existing plans – capacity growthCapacity growth due to capacity enhancing projects are estimated by ATS providers inco-operation with Eurocontrol (see also “ATC Capacity Assessment – Review ofexisting national plans”). The growth figures are taken as an input. They were notsubject to investigation within the frame of this study. However, the results of this studyseem to indicate that medium term capacity planning is performed with varying qualityand reliability throughout Europe.Risk: high (can be controlled/reduced by the ATSPs and Eurocontrol)
• Marginal capacity costThe Capacity Plan identifies the optimum capacity demand ratio for each individualACC. This ratio is derived from the cost of the capacity increase versus the cost of thedelays. We assume the marginal capacity costs to be proportional to the total costs andcapacities provided by the states/ACCs in 1999 (CRCO forecast). This assumption isconfirmed by observations of the last 16 years CRCO capacity cost data.Risk: low
• Airport regulationsAll airports were ATFM regulated in the 2003 and 2005 scenarios. This is confirmed bythe current trend and logical arguments. Positive side effect: airport regulations cause asmoothing of the traffic demand curves comparable with the effect of the flight plan co-ordination conducted by the scheduling committee.Risk: low
Traffic growth data are based on STATFOR growth estimates on 2000 flows in Europeapplied to the traffic pattern of the six baseline scenarios representing:- typical weekdays and weekends in summer 1999- actual routes flown and shortest flyable routes
3.1 Shortest Routes and “Open Sky”
The traffic pattern observed in summer 1999 is still influenced by the consequences of theKosovo crisis and some regional capacity constraints. It is assumed that these constraintsare of a short term nature. Consequently, medium term capacity planning cannot rely solelyon the current 1999 traffic pattern.
The objective of medium term ATS capacity planning is to achieve the optimum capacitydemand ratio for every ACC in Europe. The average ATFM delay per flight shall be reducedto 1-2 minutes per average flight. Delays in this order of magnitude should significantlyreduce re-routing due to capacity constraints and we move hopefully more and moretowards an unconstrained route network with a traffic pattern based on the shortest routes.
The effect of an unconstrained capacity network on the traffic pattern is shown by the figurebelow.
It appears, the biggest changes can be expected in the South East part of Europe. A re-opening of the Yugoslavian airspace for civil use will significantly re-arrange traffic flows.
ACC Name TrafficLBSR Sofia -24%LBWR Varna -32%LCCC Nicosia -18%LDZO Zagreb 159%LGGG Athens -10%LGMD Makedonia 26%LHCC Budapest -22%LIBB Brindisi -15%LIMM Milan -3%LIPP Padua 0%LIRR Roma -3%LJLA Lubiana 71%LKAA Prague 5%LMMM Malta -6%LOVV Vienna 14%LQSB Sarajevo 385%LRAR Arad -38%LRBB Bucuresti -42%LTAA Ankara -12%LTBB Istambul -9%LWSS Skopje 106%LYBA Beograd 1825%LZBB Bratislava -24%
Other capacity constraints such as those in Switzerland and France appear to have lowerimpact on the traffic through neighbouring ACCs:
ACC Name TrafficEBBU Bruxelles -10%EDFF Frankfurt 1%EDLL Dusseldorf -3%
EDMM Munchen 1%EDUU Karlsruhe 3%EDWW Bremen -1%EDYY Maastricht 3%LFBB Bordeaux 2%LFEE Reims 10%LFFF Paris 0%LFMM Aix/Marseille 3%LFRR Brest 5%LIMM Milan -3%LIPP Padua 0%LIRR Roma -3%LSAG Geneva 11%LSAZ Zurich -6%
The STATFOR growth estimates are estimated for 70 families of airports. Significantcapacity constraints at airports were taken into account (for example Frankfurt). However,regional differences for airports belonging to the same airports were not made.
The table below shows the planned evolution of ACC capacities between 1999 and 2005.
The future capacity growth (in yellow) is estimated by ATS providers and Eurocontrol basedon the existing national and supra-national capacity plans. The growth figures are taken asan input. They were not subject to investigation within the frame of this study.
The current ACC capacities are estimated by FAP based on multiple days traffic pattern,sector capacities, traffic load and delays. Note that ACC capacities are not a constant. Theydepend on the traffic pattern and the actual sectorisation. The 1999 capacities, shown inthis table below, represent an average observed at multiple days in summer 1999. Theaccuracy could vary around +/- 5% (reliability: H). However, variation may be higher forACCs not working at their maximum capacities in 1999. Some of these centres have littleknowledge of their maximum sector capacities, and/or do not provide CFMU with up to datesector configurations. We also observed that the description of some routes passing “zerodelay centres” do not always include all sectors within the ACC (reliability: L).
A more detailed explanation of the capacity enhancing projects and the resulting capacityincreases and the target dates of implementation broken down by ACC is given in anotherdocument issued by Eurocontrol:
“ATC Capacity Assessment – Review of existing national plans” Brussels, Aug. 1999.
The table below shows the global capacities of the airports and their future development.(in black: capacity declared by the airport; in grey: estimated by EUROCONTROL)
Note: Capacity shortfalls above 5% (red/orange) are leading to very high delays.
Figures in green indicate a capacity forecast in the area of the best trade-off between cost for capacity and cost for delays. Due to the characteristics of the cost curve a tolerance between 5% capacity shortfall and -15% (spare capacity) is acceptable.
Negative capacity shortfalls (grey) indicate that the maximum capacity provided is probably above the capacity needed to operate at the optimum operating point (sufficient technical capacity). However, it can be assumed that some sectors are closed or combined at some time and thus the operating point is in fact closer to the optimum. Consequently, staff levels may have to be increased to open existing sectors for longer time intervals in the future.
Scenario
Traffic growth medium high medium highRoutes current current shortest shortest
Note: Capacity shortfalls above 5% (red/orange) are leading to very high delays.
Figures in green indicate a capacity forecast in the area of the best trade-off between cost for capacity and cost for delays. Due to the characteristics of the cost curve a tolerance between 5% capacity shortfall and -15% (spare capacity) is acceptable.
Negative capacity shortfalls (grey) indicate that the maximum capacity provided is probably above the capacity needed to operate at the optimum operating point (sufficient technical capacity).
Scenario
Traffic growth medium high medium highRoutes current current shortest shortest
Note: Capacity shortfalls above 5% (red/orange) are leading to very high delays.
Figures in green indicate a capacity forecast in the area of the best trade-off between cost for capacity and cost for delays. Due to the characteristics of the cost curve a tolerance between 5% capacity shortfall and -15% (spare capacity) is acceptable.
Negative capacity shortfalls (grey) indicate that the maximum capacity provided is probably above the capacity needed to operate at the optimum operating point (sufficient technical capacity). However, it can be assumed that some sectors are closed or combined at some time and thus the operating point is in fact closer to the optimum. Consequently, staff levels may have to be increased to open existing sectors for longer time intervals in the future.
Scenario
Traffic growth medium high medium highRoutes current current shortest shortest
Note: Capacity shortfalls above 5% (red/orange) are leading to very high delays.
Figures in green indicate a capacity forecast in the area of the best trade-off between cost for capacity and cost for delays. Due to the characteristics of the cost curve a tolerance between 5% capacity shortfall and -15% (spare capacity) is acceptable.
Negative capacity shortfalls (grey) indicate that the maximum capacity provided is probably above the capacity needed to operate at the optimum operating point (sufficient technical capacity).
Scenario
Traffic growth medium high medium highRoutes current current shortest shortest
The figures below shows the future trend of ATFM delays in Europe for each of the fourscenarios individually.
All scenarios show the same trend. The ATFM delays will significantly increase in 2003 and2005 if no complementary actions are taken.
The significant differences between 2003 and 2005 results highlight a lack of longer termcapacity planning throughout Europe.
The surprising difference between “current” and “shortest” routes scenarios indicate that thefuture European capacity network is probably better adapted to the constraints of thecurrent “avoidance scheme” than to the capacity needs of the airspace user.
Another significant growth of ATFM delays and the resulting costs is forecast for theairports. This may indicate that capacity shortfalls on the ground are getting an increasingimportance in the future. However, some of these delays (and costs) may be hidden by themeans of flight plan co-ordination: Traffic peaks may be pre-smoothed over the day. Un-accommodated demand may move to adjacent airports.
The European ATM system is not yet prepared for the years 2003 and 2005.
The existing capacity enhancement plans are not sufficient to reduce the ATFM delays inthe medium term future. On the contrary, delays may increase significantly. The cost fordelays in Europe may reach the same order of magnitude as the cost for fuel or the cost ofthe entire European Air Navigation Services.
The majority of the European Air Traffic Control Centre (39 out of 65) risk to havecapacity shortfalls in at least one of the scenarios tested (see table below).
Compared to the capacity shortfalls experienced during Summer 1999, from the figuresavailable it can be seen that few of the current high delay producing ACCs will improve theireffectiveness in coming years.
The significant increase in ACC related delays forecast in the present study, for the years2003 and 2005, highlights the urgent need for effective pan-European medium termcapacity planning.
The higher delays observed with the “shortest routes” scenarios indicate that the Europeancapacity network for the years 2003 and 2005 adapts better to the constraints of the current“avoidance scheme” than to the more user-friendly “shortest routes” scenario.
The spare capacity of European airports is getting tighter in 2003 and 2005.Consequently, ATFM delays may significantly increase. However, the capacity of overloaded airports will be co-ordinated by the Scheduling Committee. Consequently, some ofthe delays observed here may be in future hidden by the means of flight plan co-ordination:Traffic peaks may be pre-smoothed over the day. Unaccommodated demand may move toadjacent airports.
/4/ C. Vandenbergh Air traffic Statistics and Forecasts,Number of Flights per Region (1974-2015)EATMP Development DirectorateEUROCONTROL, Brussels, 1999
/5/ J.-L. Renteux ATC Capacity AssessmentP. Molinari Review of existing National plans
EATMP - DSAEUROCONTROL, Brussels, Aug. 1999
/6/ M. Dalichampt Capacity Plan 1998S. Mahlich for the European Air Navigation Services
EEC Note 3/98EUROCONTROL Experimental CentreBrétigny sur Orge, France, Jan. 1998
/7/ M. Dalichampt Capacity Plan 1999J.C. Hustache for the European Air Navigation ServicesS. Mahlich EEC Note 23/98
EUROCONTROL Experimental CentreBrétigny sur Orge, France, Oct. 1998
/8/ M. Dalichampt Delay Forecast 1999S. Mahlich Based on local capacity enhancement plans
EEC Note 6/99EUROCONTROL Experimental CentreBrétigny sur Orge, France, April 1999
/9/ J.-C. Hustache Cost of the En-RouteAir Navigation Services in EuropeEEC Note 8/99EUROCONTROL Experimental CentreBrétigny sur Orge, France, June 1999