Brisa Traffic. Medium Term Analysis ( ) presented by Prof. José Viegas. Transportes, Inovação e Sistemas, S.A.

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Transportes, Inova

Transportes, Inovaçção e Sistemas, S.A.ão e Sistemas, S.A.

Brisa Traffic

Medium Term Analysis (2009-2013)

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UNDER THE CURRENT MACRO UNCERTAINTY

Brisa asked TIS to perform a study on

Traffic Growth Prospects

for the

medium term plan 2009-2013.

The Study gives an

overall view

of traffic trends (organic growth) on Brisa´s

Main Concession

over the next 5 years.

The traffic study

does not cover

other Portuguese road concessions owned

by Brisa, namely

AE

Atlântico

,

Brisal

and

Douro

Litoral

.

The traffic study also

does not cover

Short Term Impacts

from

cannibalization among the Brisa networks or specific impacts from current

competition.

We also present some reflections on the

Medium Term Traffic Impacts

of

the new Portuguese Road Program

, the High Speed Rail (HSR) and the New

Lisbon Airport (NLA), coming out of other studies for Brisa.

Traffic Study Contents

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Brisa Traffic

I. Traffic study approach

II. Portuguese road & traffic systems

III. Traffic medium term analysis

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DRIVERS OF MOBILITY & MOTORWAY TRAFFIC

Traffic Study Contents

M

O

B

IL

IT

Y

M

O

B

IL

IT

Y

M

O

B

IL

IT

Y

M

O

T

O

R

W

A

Y

T

R

A

F

IC

M

O

T

O

R

W

A

Y

T

R

A

F

IC

M

O

T

O

R

W

A

Y

T

R

A

F

IC

+

-Economic Growth

• Internal consumption and exports (freight and business travel)

• Satisfaction of social functions requiring mobility

• Car ownership

Value of Time (VoT)

Fuel Prices

Energy Efficiency & Fleet Renewal

Policies for Sustainability

+

-+

+

-+

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Quantitative

Factors

Quantitative

Factors

Qualitative

Factors

Qualitative

Factors

Reduce Time Time Avoid Tolls Tolls Safety Safety Easy Easy & Direct Direct Reduce Distance Distance Scenic Scenic Comfort Comfort Save Petrol Petrol ROUTE ROUTE CHOICE CHOICE

TRAFFIC STUDIES FOR EACH CONCESSION

Traffic Study Contents

A traffic study for a particular concession requires careful consideration of the factors explaining the generation and geographical distribution of mobility, as well as the choice of transport mode, but especially those

governing ROUTE CHOICEROUTE CHOICE .

In Toll Road Modeling, qualitativequalitative factors are added to the quantitativequantitative ones.

Different valuations of these factors

according to type, purpose and time of type, purpose and time of journey.

journey.

Travel Time AdvantagesTravel Time Advantages (including a reliability bonus) & TollsTolls are the key route choice criteria for toll road modeling.

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In this aggregate study on Organic Traffic Growth in the mid-term (2009-2013), the following factors were taken into account:

Prospects for Economic GrowthEconomic Growth, and in particular the current slowdown and threat of recession

The evolution of Fuel PricesFuel Prices for the consumer in Portugal

The evolution of Car OwnershipCar Ownership in Portugal and also

Development of policies towards sustainablepolicies towards sustainable mobilitymobility

Technological innovationTechnological innovation in the automobile sector.

Traditional

Factors

in the models

Traditional

Factors

in the models

New Framework,

affecting the

play of the

Traditional Factors

New Framework,

affecting the

play of the

Traditional Factors

STUDY FOR AGGREGATE ORGANIC TRAFFIC GROWTH

Traffic Study Contents

Strong

interdependenciesinterdependencies

among these factors exist,

and have been partially built into the model.

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Brisa Traffic

I. Traffic study approach

II. Portuguese road & traffic systems

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Tolled Non Tolled

Portuguese Roads & Traffic Systems

TRAFFIC AND TRANSPORT

NATIONAL MOTORWAY NETWORK

NATIONAL MOTORWAY NETWORK NATIONAL MOTORWAY NETWORK

Private Operators

Private Operators STATE (EP)STATE (EP)

Shadow Tolls Real Tolls Non Tolled

2 860 km 14% 86% 31% 4% 51%

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0 250 500 750 1.000 1.250 1.500 1.750 2.000 2.250 2.500 2.750 3.000 1970 1980 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Other Private Oper. Shadow Toll Brisa Network Total Motorways 1985 158km 2000 1500km 2005 2300km 1995 690km 2008 2800km

Over the last decade, the National Motorway Network has doubled (+126%).

Today we have an even distribution between Tolled and sections Non Tolled motorways.

kms in Operation

Until 2002 BRISA represented more than 50% of national motorway network.BRISA

Shadow Tolls have largely contributed for changes in national motorway market share.

MOTORWAY NETWORK EXTENSION

Portuguese Roads & Traffic Systems

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0 50 100 150 200 250 300

Portugal Spain Austria Germany Sweden EU 15

0 0,01 0,02 0,03 0,04 0,05 0,06 0,07

Portugal Belgium Netherlands Germany EU 15

0 100 200 300 400 500 600 700

Portugal Spain France Italy Austria EU 15

In comparison with EU15 (average), Portugal has a higher index of Motorway Km per inhabitant.

• Only Spain, Austria and Sweden also have above average Motorway indexes.

On Motorway km per area, Portugal is also above EU15 average.

• In this case, the other countries above average are Belgium, Netherlands and Germany.

For both indices, all these other

countries have a majority (or totality) of non tolled motorways.

Portugal has, nevertheless, a lower level of Car Ownership when compared to the same EU countries, which suggests a potential for growing demand on the existing motorway network.

Motorway km / 1.000.000 inh. (2007)

Motorway km / 1.000.000

Motorway km / 1.000.000 inhinh. . (2007)

Motorway km / km2 (2007) Motorway km / km Motorway km / km22 (2007) Car Ownership (2006) Car Ownership Car Ownership (2006)

MOTORWAY NETWORK COMPARISON

Portuguese Roads & Traffic Systems

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BRISA holds aBRISA Market ShareMarket Share over 50% in Portuguese road concessions (through 4 concessions).

93%93% of BRISA network isBRISA TolledTolled, leaving the access to Lisbon and Oporto metropolitan areas free of charge (by law).

AE Atlântico Brisal BRISA

D.Litoral

Linking the most populated urban centers and allowing connection to the TEN, BRISA has a competitive advantagecompetitive advantage

in the national motorway market.

Strong weight of commuting traffic

Average Trip → 34 km (short distance)

Low weight of heavy vehicles (5.7% in 2007)

Weekdays have stronger traffic than Weekends

LESS LESS VOLATILITY VOLATILITY to exogenous factors MAIN CHARACTERISTICS MAIN CHARACTERISTICS MAIN CHARACTERISTICS

Portuguese Roads & Traffic Systems

BRISA MAIN CONCESSION

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BRISA (veh.xkm) 0 5.000.000 10.000.000 15.000.000 20.000.000 25.000.000 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2007 T R A F F IC T R A F F IC G R O W T H G R O W T H

Over the years, BRISA has gone through different Stages of Evolution (until 2007):

2007 2007 2003 2003 to 2006 2006 ... 20022002 Recovery

Recovery → improved performance of the economy + lower

competition effects + new openings (A10) STAGE 3

STAGE 3

New motorways

New motorways → Cannibalization (from A1 to A10 & A13) and

Competition (from A1, A3 and A4 to AE Atlântico & Shadow Tolls)

Economy & Fuel Prices

Economy & Fuel Prices → overall mobility decrease

STAGE 2

Network expansion/building

Network expansion/building, with a strong multiplier → induced

demand and car ownership growth STAGE 1

STAGE 1

stage1

stage1 stage2stage2

stage3

BRISA MAIN CONCESSION

Portuguese Roads & Traffic Systems

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Brisa Traffic

I. Traffic study approach

II. Portuguese road & traffic systems

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40.000 50.000 60.000 70.000 80.000 90.000 100.000 110.000 120.000 130.000 140.000 19 78 1979 1980 1981 1982 1983 1984 8519 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 0020 2001 2002 2003 2004 2005 2006 2007 R ea l G D P ( M ill io n € )

GDP

Car Ownership

Fuel Prices

Competition / Cannibalization

ECONOMIC RECESSION

:

CyclicalCyclical, approx. every 10 years.

NegativeNegative growth rates of about -1%. Followed by Accelerated Economic Accelerated Economic Growth

Growth periods.

Source: Banco de Portugal; INE; TIS.pt

1993

2003

1984

Traffic Medium Term Analysis

MAJOR EXPLANATORY VARIABLES

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0 2.000.000 4.000.000 6.000.000 8.000.000 10.000.000 12.000.000 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 P as se n g er C ar s / P o p u la ti o n 0 50 100 150 200 250 300 350 400 450 C ar O w n er s h ip

Passenger Cars Population Car Ownership

GDP

Car Ownership

Fuel Prices

Competition / Cannibalization

1993 to 2007: annual average growth 6.4%6.4%

2003 to 2007: annual average growth 4.6%4.6%

Growth Rates Slowed DownSlowed Down in recent past, in line with GDP

Traffic Medium Term Analysis

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0 100 200 300 400 500 600 1983 1988 1993 1998 2003 2008 2013 C a r O w n e rs h ip ( lig .v e h / 1 0 0 0 in h .) E U 1 5

Car Ownership (lig.veh/ 1000 inh.) EU15 Estimated Car Ownership (veh.km) EU15 Car Ownership (lig.veh/ 1000 inh.) PT Estimated Car Ownership (veh.km) PT

PORTUGAL VS EUROPE (EU15)

The national Car Ownership Car Ownership rate level in 2007 reached the UE15 level of 1993 → Potential for Growth In Future Years.

Traffic Medium Term Analysis

Considering the recent evolution, it is expected that Portuguese Car Ownership will continue growing at above 1% rates until 2013.

CAR OWNERSHIP estimated 2006 EU 15 → 508 PT → 395 2006 EU 15 → 508 PT → 395

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1993 to 2007: annual average growth 1.6%1.6%

1993 to 2002: annual average growth --0.8%0.8%

2003 to 2007: annual average growth 6.3%6.3%

GDP

Car Ownership

Fuel Prices

Competition / Cannibalization

0,00 0,20 0,40 0,60 0,80 1,00 1,20 1,40 1,60 € / lit re ( e q u iv a le n t y e a r 2 0 0 0 €) Gas Diesel Weight Average Stable Prices

2003

Constant prices €2000 Max. between Sept.04 to Sept. 05 +20,0% Max. between Sept.04 to Sept. 05 +20,0%

Traffic Medium Term Analysis

Steady G

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GDP

Car Ownership

Fuel Prices

Competition/Cannibalization

The Portuguese State has ongoing tenders for

7 New Concessions

→

Over

400km

of new tolled motorways

Traffic Medium Term Analysis

New Tolled motorways 94 km 19 km 184 km 22 km 65 km 14 km Tolled Tolled AE Centro AE Centro Litoral Oeste Litoral Oeste Pinhal Interior Pinhal Interior Baixo Alentejo Baixo Alentejo Baixo Tejo Baixo Tejo AE AE Transmontana Transmontana

Other Challenges/Opportunities

→ _{New Lisbon} Airport & High Speed Rail

These will have both

Positive

(traffic induction) and

Negative Impact

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Traffic GDP Fuel Price -5,00% -3,00% -1,00% 1,00% 3,00% 5,00% 7,00% 9,00% 11,00% 13,00% 15,00% 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 %

KEY EXPLANATORY VARIABLES & TRAFFIC

annual growth rates

-1.3% Decrease +11.0% Increase TRAFFIC TRAFFIC +13.8% Increase +12.4% Increase FUEL PRICES FUEL PRICES Stagnant High Growth +0.5% +3.9% GDP GDP 2000 2000 2005 2005 ∆_% organic growth

Traffic Medium Term Analysis

For 2008, the evolution

pattern of

GDP

GDP and Fuel PricesFuel Prices is similar to that of 2005.

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Traffic GDP Fuel Price Car Ownership 0 50 100 150 200 250 300 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 1 9 9 2 i n d e x = 1 0 0

With Low GDP and constant Fuel Prices, Car OwnershipCar Ownership drives traffic growth.

Constant prices €2000

KEY EXPLANATORY VARIABLES & TRAFFIC

index

index (1992 = 100)(1992 = 100)

Traffic Medium Term Analysis

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TRAFFIC BEHAVIOUR TOWARDS

EXPLANATORY VARIABLES

EXPLANATORY VARIABLES -- CONCLUSIONSCONCLUSIONS

In general, TRAFFIC DEMANDTRAFFIC DEMAND growth is supported by GDPGDP growth alone.

However, the effect of

FUEL PRICE

_{FUEL PRICE}

on

TRAFFIC DEMANDTRAFFIC DEMAND increases as soon

as GDPGDP annual growth rates fall under a threshold of approximately 2%.

The recent combination of low GDPGDP growth rates and medium FUELFUEL PRICEPRICE

growth has highlighted the role of CAR OWNERSHIPCAR OWNERSHIP as driver for TRAFFIC TRAFFIC

DEMAND

DEMAND growth.

Indeed CAR OWNERSHIPCAR OWNERSHIP has been steadily increasing at average annual

rates around 4.6% in the last years.

Traffic Medium Term Analysis

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EXPLANATORY VARIABLES -- PROJECTIONPROJECTION

GDP growth rates based on Bank of Bank of

Portugal

Portugal (*) latest estimates for the short

term (2008-2009), and from IMF(*)IMF(*) to the

medium term (2010-2013).

GDP

Fuel Prices

Car Ownership

1.0% 2010 1.7% 2011 1.8% 2012 0.8% 2008 BoP BoP 1.8% 0.6% GDP GDP ∆_% 2009 2013 YEAR YEAR IMF IMF SOURCE SOURCE 0 20.000 40.000 60.000 80.000 100.000 120.000 140.000 160.000 180.000 200.000 1975 1985 1995 2005 2015 2025 Year R e a l G D P ( M ill io n €) (*) October 2008

Traffic Medium Term Analysis

These are official estimates, but our model

includes an economic slowdown in 2009,

incorporating stochastic elements for its depth (risk of recession) and duration.

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GDP

Fuel Prices

Car Ownership

Fuel Prices

evolution are very

Dependent

on 3 major factors:

1. A “Baseline Growth” of crude prices of 3% per year

2. The range of variation of crude fuel prices (global demand largely

dependent on World GDP & global supply)

3. Random component due to geopolitical, climate, environmental changes, ...

Traffic Medium Term Analysis

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GDP

Fuel Prices

Car Ownership

An unlimited growth for the Car OwnershipCar Ownership

is not conceivable, so a Logistic CurveLogistic Curve is

adopted.

An historic trend along this logistic curve is considered, with future impacts from: • GDP growth;

• Policies towards sustainable mobility, initially affecting car use, but also touching on car ownership on the mid- to long-term;

• Technological Innovation, offering cleaner cars and allowing some “recovery” of car use.

Traffic Medium Term Analysis

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TRAFFIC DEMAND

Economy (GDP)

Oil / Fuel Pric e

Technology Factor Competing M odes Fiscal Policy Car Ownership Global Warming Related Pressures Global Energy Supply

Related Pressures

R&D / Deployment Rates

Several factorsSeveral factors should be considered to establish new standards in traffictraffic demand demand

forecasting forecasting.

Addressing such complexitycomplexity requires heightened attention to the systemic systemic relationships

relationships between variables and feedback cycles to assess crossed consequences.

TRAFFIC MODEL

Traffic Medium Term Analysis

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The model was built based on a

Stochastic Methodology

Stochastic Methodology, using a Risk

assessment approach (@RISK).

The explanatory variables are represented by a Probability DistributionProbability Distribution across the range of possible results.

The model produces many Iterations Iterations

(Monte Carlo simulation), and for each iteration

@RISK generates different combinations of the values of Explanatory VariablesExplanatory Variables

(according to the defined distributions) , thus leading to a range of Demand ForecastsDemand Forecasts

(OUTPUT).

Interpretation/

Validation

TRAFFIC MODEL TRAFFIC MODEL variable

variable AA variable variable BB variable variable CC

Traffic Model

Relations..

OUTPUT OUTPUT

Traffic Medium Term Analysis

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In order to forecast traffic demand, the

3 Main Explanatory

Variables

(GDP, Fuel Prices and Motorization)

were modelled

considering

2 different groups of factors

:

Economic Slow Down

Modelled in terms of its Duration,Duration,

directly affecting the GDPGDP and the

Fuel Price. Car Ownership

Fuel Price. Car Ownership growth is affected via GDP

Environmental Policies & Tech.

Innovation

Affecting sensitivity to Fuel PriceFuel Price

and (later)

Car Ownership

TRAFFIC MODEL

Traffic Medium Term Analysis

The elasticity of traffic volumes to GDP is positive but not symmetrical:

in

growth, traffic expands more easily than it contracts in recessi

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0 2.500.000 5.000.000 7.500.000 10.000.000 12.500.000 15.000.000 17.500.000 20.000.000 22.500.000 25.000.000 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 A A D T ( v e h .x k m ) AADT (veh.xkm) TRAFFIC FORECASTS TRAFFIC FORECASTS

Traffic decrease in 2008decrease in 2008 (9 months -4.1%) largely affected by the economic crisis & external network developments effects.

Over the next five years (20092009--20132013) an average annual growth rate ofannual growth rate of 1.8%1.8% is expected.

After the present economic crisis and its (negative) impact on traffic levels, a (slow) recovery of traffic demand in BRISA’s main concession is expected.

-4.1%* 2008 2008 +2.31% 2011 2011 +2.20% 2012 2012 +2.09% 2013 2013 +1.81% +0.67% ORGANIC ORGANIC Growth Growth 2010 2010 2009 2009 PERIOD PERIOD

Traffic Medium Term Analysis

estimated

observed

Only organic growth All effects combined

* 9 months, includes a 1.9% decrease due to external network effects

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FINAL REMARKS (I)

For the medium term (2009 - 2013) our model foresees a robust

Organic

Annual Traffic Growth

(AADT) of 1.81%, even under difficult macro

conditions (average annual GDP growth around 1.5%, with a strong slowdown

in 2009-2010).

For the same 5 years period, the expected organic annual growth of traffic is about 2.6% for the Optimistic (percentile 75) scenario, and 1.0% for the Pessimistic one (percentile 25).

Besides this growth, Brisa should benefit from the foreseen introduction of

Real Tolls On Shadow Tolls

which should have a positive impact (some lost

traffic will be recovered on BRISA main concession) estimated by the

company to be around 3% (full year impact).

Traffic Medium Term Analysis

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FINAL REMARKS (II)

There is still uncertainty regarding the

New Road Plan

execution & their

timings. All ongoing tenders will not be in place before 2013, so no major

impact on the medium term is expected:

Some new roads will have a Positive ImpactPositive Impact due to traffic induction (and feeding), while others will have a Negative Effect Negative Effect due to traffic detours.

Based on previous studies for Brisa, and assuming some uncertainty associated to these (at least in their calendar), the Total Estimated Impact Total Estimated Impact will be -2.2%.

Other Future ChallengesOther Future Challenges

are expected for the mid- to long term, including

the High Speed Rail (HSR) and the New Lisbon Airport (NLA).

The global impact of the HSRHSR projects in Brisa’s main concession is expected to be very low, around -0.3%.

The impact of the NLANLA on the existing network will be positive +0.9%. This does not include the extra 27 km to be built, regarding the link to the NLA, which will represent, according to the Company, an extra 4% in traffic.

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Portugal has a long tradition in Tolled motorways, Shadow Tolls are an Tolled motorways exception and Government policy defines them as temporary (while regions crossed are below national average wealth).

• Shadow Tolls becoming Real Toll motorways will allow BRISA to recover some (lost) traffic.

Portuguese Car OwnershipCar Ownership rate is bellow the EU15 average (-22% in 2006),

offering a good potential for traffic growth for a reasonable period.

BRISA holds a Market ShareMarket Share in Portuguese road concessions close to 50%,

through 4 road concessions.

The Portuguese highway NetworkNetwork is Deeply Consolidated, so future major Deeply Consolidated

impacts in BRISA main concession are not expected.

Linking the most populated urban centers and allowing connection to the TEN,

BRISA has a Competitive AdvantageCompetitive Advantage in the national motorway market.

Less volatility to exogenous factors

Strengths and Opportunities

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Transportes, Inova

Transportes, Inovaçção e Sistemas, S.A.ão e Sistemas, S.A.