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partner/sponsor:

The New Airport for Berlin –

Has Willy-Brandt (BBI) Airport enough Capacity for the

Future?A Simulation Approch

Dipl. Wirt.-Ing. Branko Bubalo

GAP Research Project

branko.bubalo@googlemail.com

German Aviation Research Society Workshop (G.A.R.S.) at ILA 2010

Berlin, June 10th 2010

GARS Workshop at ILA Berlin – 10.06.2010 – Branko Bubalo Page 1

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Personal Background

• Graduate in Business Administration and Engineering from Berlin

School of Economics and Law (BSEL) and University of Applied

Sciences Berlin

• Diploma Thesis (2009): “Benchmarking Airport Productivity and the Role

of Capacity Utilization”

• 4th year in German Airport Performance Research Project at Berlin

School of Economics and Law

• Conducted Benchmarking studies of European Airports and Airlines (75

Airports, 50 Airlines)

• Consultation in MIME Project, funded by EU Commission, studying

Noise mitigation around airports, by market-based instruments

• Looking for new opportunities in Air Transportation Research &

Development and PhD position

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Contents

1. Berlin Airports Today and Tomorrow

2. European Comparisons to BBI

3. SIMMOD Study on Airside Capacity of BBI

4. Extension Plans for similar Airports

5. Vision

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Plan of Berlin-Brandenburg International Airport

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Why is Airport Runway Capacity so important

Economical Perspective:

• Global Competition of Regions which makes air interface vital

• Attracting Airlines and Business, Tourists and Transit Passengers

• Reducing Costs for Flying, by providing enough slots for demand and

increasing future demand

Technical Perspective:

• Operating at Practical/Sustainable Capacity is important for Airport

System Stability, Reliability (Scheduling!) and Service Quality (level-of-

service (LOS) of e.g. 4 minutes of mean delay/flight)

• Operating at Maximum capacity leaves no margin-of-safety for

unscheduled random events, e.g. weather (snow!), unscheduled flights

(Business jets, Charter, General Aviation (GA)), emergency landing etc.)

• Delays increase exponentially at high utilization rates

• Knock-on delay effects make operating at airport costly for airlines

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Relationship between Demand, Capacity and Delay

• As airport demand

increases towards its

(ultimate) capacity,

the airport system

becomes congested

and average delays

per flight increase

exponentially.

Modified from Horonjeff 2010, p.488

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Berlin Airports today and tomorrow

• From 3 (Tegel (TXL), Schoenefeld (SXF), Tempelhof (THF) airports)

make 1 (Willy-Brandt Airport (BBI))

• Existing airports (SXF and TXL) will be replaced fully by BBI on

October 2011

• Political Dimension and Relevance for a study of BBI capacity and

future development:

“When the Number of Runways at the existing Airports will

be reduced from 4 to 2, Bottlenecks are bound to occur.”

(German Federal Transport Minister Peter Ramsauer,

Tagesspiegel, June 7th, 2010)

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European Comparisons

• Peer group of independent, far parallel-runway airports

(Separation of at least 1500 meters and alternate-mode operations)

• Best Practices with similar runway layout and highest productivity (2008)

in comparison to BBI (SXF + TXL): London-Heathrow and Munich

• BBI has the potential to rank third-biggest European airport (before

Palma-de-Mallorca (PMI) and Oslo (OSL)) in peer group by 2012

Airport Annual PAX (million)

Annual Flights (thousand)

PAX per Flight

Peak Hour Throughput

London-Heathrow 67.2 473 142 103

Munich 34.5 409 84 93

BBI (TXL+SXF 2008) 21.2 212 100 48

BBI Planned Final 2023 (Planning Permission 2004)

30 301-329 100-91 90

BBI Sustainable Final at 2024 (60% growth from 2008 at 3%; SIMMOD study 2010)

28.1 303 93 76

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BBI Study Background

• Study prepared for International Conference on Operations Research

"Mastering Complexity“ Munich 2010 with Prof. Joachim Daduna (BSEL)

• Full study available in September 2010

• Software: GUI: Visual SIMMOD

Engine: FAA SIMMOD Engine

• Flight schedule data: Flightstats.com

• Basic Information: Official Planning Permission Documents for BBI

• Sensitivity Analysis towards 6 Scenarios with different traffic mixes

(shares of aircraft wake turbulence classes: Small (<7 tons MTOW),

Large (7-136 tons MTOW) and Heavy (>136 tons MTOW))

• 11 growth scenarios (-20%,0%,20%,40%,…,200%) at 10 iterations each

• Based on predefined Design Peak Day 2008 (June 26th 2008)

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Simulated Scenarios and Traffic Mixes

Scenario 0 Scenario I Scenario II Scenario III

Airport SXF TXL BBI BBI BBI BBITurbulence Class Share Flights Share Flights Share Flights Share Flights Share Flights Share FlightsHEAVY 0% 3% 12 2% 12 5% 32 15% 95 5% 32LARGE 100% 155 95% 454 96% 609 95% 603 80% 508 84% 533SMALL 0% 3% 14 2% 14 0% 0 5% 32 11% 70Sum 100% 155 100% 480 100% 635 100% 635 100% 635 100% 635

Mix Index (MI) 100% 102% 102% 110% 125% 99%MI = 3 x (% HEAVY) + (% LARGE)

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BBI SIMMOD Study Assumptions

• Realistic assumptions resulting from combined actual traffic data of

Berlin Tegel (TXL) and Schoenefeld (SXF) airports and Planning

Permission documents:

• Delay per Flight will be

Key Performance Indicator

• Schedule

• Traffic Mix

• Runway Layout

• Parking Stands

• Punctuality & other

Random Distributions

• 20 Additional Business Jet

or GA Flights in morning & evening Peaks (7-12 & 15-20; Scenario 0a)

17.79%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0%1%2%3%4%5%6%7%8%9%

10%11%12%13%14%15%16%17%18%19%

-50 -40 -30 -20 -10 0 10 20 30 40 50 60 70

Cu

mu

lati

ve O

ccu

rre

nce

in P

erc

en

t

Occ

ure

nce

in P

erc

en

t o

f To

tal

dai

ly F

ligh

ts

Punctuality (Actual minus Scheduled Time in Minutes

Distribution and cumulative Distribution of Punctuality

for Berlin Tegel and Schoenefeld Airports on Peak Day 2008

Punctuality Punctuality Distribution

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Presenting Interim Results

• Scenario 0 (baseline, 2 independent parallel runways, current

SXF+TXL=BBI traffic mix):

• Simulated throughput Capacity of 82-84 Flights per hour at 120-

140%! Further increase of traffic resulted in grid locking queues and

therefore flight cancellations.

• Sustainable long-term throughput/capacity of 76 Flights per hour!

(1 operation every 47 seconds) The animation proves this.

• Demand higher than Capacity at 60% Growth, based on 2008 busy

day schedule and Level-of-Service of 5-6 minutes of Avg. Delay per

flight.

• Delays will increase exponentially beyond 60% growth.

• Upper and lower boundaries of historic growth rates at TXL and SXF

underline the assumption that demand=capacity reached between

2016-2024 (at 3-6% annual growth).

• Each 20% growth increment results in doubling of daily delays

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Results Scenario 0: Simulated Peak Throughput at BBI

48

54

71

7680 82 82 84 83

48

55

69

78

9490

96 98

110

0

10

20

30

40

50

60

70

80

90

100

110

120

Scenario 0 0% Scenario 0

20%

Scenario 0

40%

Scenario 0

60%

Scenario 0

80%

Scenario 0

100%

Scenario 0

120%

Scenario 0

140%

Scenario 0

160%

De

man

d a

nd

Cap

acit

y in

Flig

hts

pe

r h

ou

r

Projected Growth of Traffic at Berlin -Brandenburg Airport in Comparison to Runway Capacity (Scenario 0:"Nullszenario")

Hourly Capacity

Hourly Demand

Maximum Capacity SIMMOD

Maximum Capacity Planning Permission 2023

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Results: Simulated Delays at BBI:

• Scenario 0 (*Note: 42€ cost per minute of delay (Eurocontrol 2009: “Standard Inputs for CBA Analyses”)

Growth from Baseline

Daily Flights

Peak Hour Demand

Hourly Capacity

Mean Delay per Flight

Daily Delay Minutes

Daily Delay Costs at 42€ * (without cancellation costs) Cancellations

-20% 511 40 40 1.1 543 EUR 22,806 0

0% 635 48 48 1.4 887 EUR 37,254 020% 758 55 54 2.3 1760 EUR 73,920 040% 886 69 71 3.7 3287 EUR 138,054 060% 1012 78 76 5.9 5955 EUR 250,110 080% 1145 94 80 11.5 13223 EUR 555,366 0

100% 1270 90 82 21.2 26968 EUR 1,132,656 1120% 1400 96 82 26.8 37501 EUR 1,575,042 134140% 1517 98 84 27.4 41538 EUR 1,744,596 440160% 1639 110 83 58.2 95364 EUR 4,005,288 807

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Results: Simulated Delays at BBI

1.1 1.42.3

3.7

5.9

11.5

21.2

26.8 27.4

Avg Delay = 0.1624e0.0036x

R² = 0.9883

Avg. Delay = 0.0906e0.0572x

R² = 0.96340 20 40 60 80 100 120 140

0

5

10

15

20

25

30

35

0 200 400 600 800 1000 1200 1400 1600 1800 2000

Peak Hourly Flights

Ave

rage

De

lay

pe

r Fl

igh

t

Daily Flights

Average Delay per Flight from Simulation of BBI Baseline Scenario 0

Average Delay per Daily Flights

Average Delay per Peak Hour Flights

Expon. (Average Delay per Daily Flights)

Expon. (Average Delay per Peak Hour Flights)

sustainable

Not sustainable

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Capacity limits reached at BBI

• The Sustainable Capacity of 76 Flights per Hour and 60% growth can

be reached after:

• 16 years at 3% average growth of traffic (2008-2024)

• 12 years at 4% average growth of traffic (2008-2020)

• 10 years at 5% average growth of traffic (2008-2018)

• 8 years at 6% average growth of traffic (2008-2016)

• The planning timeline for a third runway at BBI depends on the

economic developments in the region in the next months and years.

• We must start discussing a 3rd runway at BBI in 2014 the latest, as a

result from Scenario 0 (3% growth), if 10 years until realisation are

considered.

• Legal, political and environmental approval time will increase further in

the future, so do the opportunity costs from building a runway too late.

%591)06.01(1)1( 8nr

%631)05.01(1)1( 10nr

%601)04.01(1)1( 12nr

%601)03.01(1)1( 16nr

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Growth of Berlin Airports

Source: Eurostat

100%107%

120% 125% 128%136%

121%

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

0

40,000

80,000

120,000

160,000

200,000

240,000

280,000

2003 2004 2005 2006 2007 2008 2009

Ind

ex

(In

de

x 1

= 2

00

3)

An

nu

al fl

igh

ts

Year

Growth of Traffic at Berlin Airports and Develoment of Index between 2003 and 2009

BERLIN-TEGEL BERLIN-SCHONEFELD BERLIN-TEMPELHOF

BERLIN-TEGEL INDEX BERLIN-SCHONEFELD INDEX BERLIN-TEMPELHOF INDEX

BERLIN AIRPORTS INDEX

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Trends of Annual and Peak Hour Flights in Europe

y = 32.99x0.976

R² = 0.918

y = 0.376x0.917

R² = 0.914

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

0 50 100 150 200 250 300 350 400 450 500 550 600

Des

ign

Pea

k H

ou

r Fl

igh

ts

Des

ign

Pe

ak H

ou

r P

ass

en

ge

rs

Annual Flights in thousands

Trends of Annual flights to Design Peak Hour Flights and Passengers

(Data from 60 European Airports in 2007

Peak Hour Passengers Peak Hour Flights Power (Peak Hour Passengers) Power (Peak Hour Flights)

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Trends of Peak Hour and Annual Flights in Europe

y = 0.303x1.046

R² = 0.780y = 4.059x0.996

R² = 0.914

0

100

200

300

400

500

600

0

10

20

30

40

50

60

70

80

0 20 40 60 80 100 120

An

nu

al F

ligh

ts in

th

ou

san

ds

An

nu

al P

ass

en

ge

r in

mil

lio

ns

Design Peak Hour Flights

Trends of Peak Hour Flights to Annual Passengers and Flights

Annual Passengers Annual Flights Power (Annual Passengers) Power (Annual Flights)

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Approximation Functions established from 60 EU airports

If Annual Flights are known from forecasts:

1. Peak Hour Flights = 0.376*Annual Flights (in thousand)^0.917

2. Peak Hour Passengers = 32.99*Annual Flights (in thousand)^0.976

If Peak Hour Flights are known from forecasts or simulations:

3. Annual Flights in thousand = 4.059*Peak Hour Flights^0.996

4. Annual Passengers in million = 0.303*Peak Hour Flights^1.046

Source: Bubalo 2010

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Testing Assumptions Using Approximation Equations

• BBI Planning Permission Forecast 2023 (2004):

– 355.000 Flights per year and 90 Movements per hour.

With 1. & 2.: 355.000 annual flights -> 82 Peak Hour Flights and

10173 Peak Hour Passengers

With 3. & 4.: 90 Peak Hour Flights -> 359.000 Annual Flights and

33.5 million Passengers

• BBI SIMMOD Study (2010):

– Sustainable Capacity at 76 Flights per hour after 60%

growth

With 3. & 4.:76 Peak hour flights->303.000 flights and 28.1 million

PAX

– Capped Capacity at 82-84 Flights per hour after 100%

With 3. & 4.:82 Peak Hour flights -> 327.000 Annual Flights and

30.4 million annual Passengers

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Resulting BBI Forecasts based on 2008 traffic schedule

Airport Annual PAX (million)

Annual Flights (thousand)

PAX per Flight

Peak Hour Flights

Capacity

BBI 2008 (TXL+SXF 2008) 21.2 212 100 48 48

BBI Planned Capacity 2023 (Planning Permission 2004, at 3%)

30 301-355 100-85 90 90

BBI Sustainable Capacity 2024 (60% growth from 2008 at 3%; this study)

28.1 303 93 76 82

BBI Sustainable Capacity2018 (60% growth from 2008 at 5%, this study)

28.1 303 93 76 82

BBI Maximum Capacity (100%)

33.5 359 93 90 82

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How far should we think for the future of BBI?

• Should we develop a vision for an ultimate stage of development for

BBI, despite of political concerns?

• Runway configuration Master plan of London-Heathrow after 1945

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The World Busiest Airport

• Atlanta Int. (2008):

88 million passengers

970,235 flights

• How far is too far?

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How far should we think for the future of BBI?

• Regional and airport planners must be creative and realistic in their

forecasts and long term views.

• Runways take 10 years of planning, approval and construction time

(economical life 40-100 years)

• Noise and local air quality increasingly important, but very difficult to

quantify & monetize for compensations

• The future is uncertain, but market outlooks predict further growth of air

transportation

• We can learn from other airports already:

– London Heathrow in 2010: saturated most of the day

– Munich in 2010: congested during daily peak hours

• Possible near Future for BBI? ->

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Current Plan London Heathrow (2020?)

• Strong public opposition against 3rd runway

• Environmental and Legal battle will be endless

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Current Plans for Munich (2020?)

• Munich 2 had been under restrictions from 1993 on.

• 3rd runway is strongly opposed, but will be built

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Vision

• Commitment and Continuing Discussion towards aviation needs

(including General and Business Aviation).

• Neighbours of airports should be kept well informed about future

developments, even if 20 years or more in advance.

• Master plans must present long-term views with high and low growth

forecasts.

• Runways are long-term investments, which are depreciated over 50 to

100 years, so long-sided visions needed.

• Apart from looking at runway capacity, environmental capacity could

become more critical in the future.

• Simulation is the tool to make airport capacity planning and (noise)

impact forecasts, realistic, transparent, safe and fair.

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Thank you for your attention! Questions?

Suggestions and Comments are welcome.

Full study available after:

International Conference

OPERATIONS RESEARCH

MUNICH 2010

branko.bubalo@googlemail.com

www.gap-projekt.de

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GARS Workshop at ILA Berlin –

10.06.2010 – Branko Bubalo