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Feasibility Study Concerning High-Speed Railway Lines in Norway

- 3-1 -

3 Market Study

3.1 Objectives and Background

The aim of the market study in Phase 1 was to identify the most important demand poten-

tials for High-Speed Rail Services in Norway, to compare these potentials among each

other and with the European potentials of already realised or planned High-Speed-

Networks. First priorities for the selection of corridors from the market point of view had to

be set.

The methods and models used in this market study are well proofed in several high-speed

rail and air traffic studies which were conducted by Intraplan in the last two decades, as

for example the forecasts on passenger transport for the German transport master plans,

which include the extension of the German high-speed rail network as well as studies for

the maglev rail system and a European-wide study for UIC, building a common and com-

parable transport database for evaluating High-Speed Rail Traffic on a European level.

The approved passenger transport models used for the above mentioned studies were

adapted to the Norwegian transport market by feeding the model with actual transport

demand data on a high disaggregated spatial level. For each origin-destination relation

between municipalities in Norway, demand data for road, rail, air and boat traffic were

collected by using information from the Norwegian Transport Model (NTM5), from NSB,

from air traffic statistics and own estimations. The model had been calibrated with these

data and was then applied to the various scenarios on socio-economic development and

network extensions.

Before identifying and calculating the demand potentials for High-Speed Rail Services in

Norway, a comparison of key figures of traffic demand on a European level had been car-

ried out (see Chapter 3.2).

For this comparison, data were taken from a study which was conducted in 2002/03 on

behalf of UIC by a German-French consortium, as this study is the only comprehensive

and consistent database of traffic demand which comprises all means of transport

throughout Western Europe on a high disaggregated level [UIC-PTS]. The UIC-Passenger

Traffic Study will be described in chapter 3.2.1 in some more detail, as the methods and

assumptions made in this study are quite comparable to that of the study at hand and the

comparison of demand key figures refer to the results of the UIC-Study.

In chapter 3.3, the potential for High-Speed Rail Services in Norway are identified and

calculated. In chapter 3.4, then, a Basic Network with the most promising High-Speed Rail

Projects is created from the market point of view. This Basic Network is subject of further

examinations with respect to technical and costs aspects in the following main chapters.


- 3-2 -

3.2 Passenger Traffic in Europe

The definition of long-distance traffic in this study belongs to the definition by UIC and

differs from that what is used usual in Norway. The long-distance traffic in the UIC study

comprises trips with a travel distance of more than 80 km, whereas in the Norwegian

Transport Model, long-distance traffic comprises only trips of more than 100 km travel

distance.

3.2.1 Outline UIC Passenger Traffic Study

The „Passenger Traffic Study 2010/2020,” conducted by the consortium INTRAPLAN-

IMTRANS-INRETS on behalf of the International Union of Railways (UIC), delivered es-

sential information on traffic market to the European Railways and political decision-

makers:

o Passenger traffic flows for 1999 throughout Europe

o The development of long-distance passenger traffic in the Western European Coun-tries up till the year 2020 for alternative scenarios

o The impact of the ongoing extension of the High-speed Network in railways transport demand

The study aimed to show the prospects of High-Speed Rail Traffic within Europe. The

advantage of this comprehensive study over national studies was the summary presenta-

tion and the compatibility of the results, stemming from common methods of analyses and

forecasts for all Western European Countries.

The study concerned the domestic and international long-distance passenger traffic of the

15 member states of the European Union plus Switzerland and Norway, denoted as "Wes-

tern European Countries (W.E. Countries)“ and the international traffic to and from the

other European Countries (denoted as C.E.E.C.) and countries outside Europe.

Task Force and Group of Experts

The study was accompanied by a Task Force composed of representatives of the UIC

(High-Speed Division), the European Commission (Directorate-General for Energy and

Transport), German Railways (Deutsche Bahn AG), Austrian Railways (Österreichische

Bundesbahnen) and French Railways (Société Nationale des Chemins de fer Français)

and the consultants. The procedure, the main assumptions of the study and intermediate

results were presented to the “Group of Experts,” made up of representatives of all Euro-

pean UIC members.

Methodology of Forecast

As a basis for the traffic forecasts, the existing traffic flows in Western Europe were ana-

lysed and differentiated by means of transport, trip purpose and by origin and destination

of trips. This was conducted for both, domestic and international traffic within the Western


- 3-3 -

European countries as well as for international traffic affecting the C.E.E.C. and countries

outside of Europe (intercontinental traffic).

The forecasts were designed to reflect the interaction between the different means of

transport, i.e. train, private car, bus and plane, generated by external influencing factors

such as growth in GDP, the development of population and employment, car ownership,

market regularities, user costs, transport policies and the extension of road, rail and air

infrastructure as well as new air and rail services. Special focus was placed on modelling

the attractiveness of rail services. In addition to the traditional supply factors, i.e. travel

time, transport cost, frequency of services and changes in public transport, factors specific

to high-speed train service were considered, e.g. train-user costs based on speed and

travel distance.

A complex forecasting approach was applied, based on data that were broadly differenti-

ated. To reach a higher degree of validation, different forecast methods were used. The

effects of new rail infrastructure and new rail services were calculated with two different

models: the transport model of INTRAPLAN and the M.A.T.I.S.S.E. model, which was

developed at INRETS and applied by IMTrans. Various crosschecks between the different

approaches were undertaken and, as necessary, results were adjusted or combined.

Spatial Differentiation

The core area of the study, i.e. the 17 W.E. Countries, was divided into 354 traffic zones.

The Central and Eastern European Countries were divided into 42 traffic zones. Together

with the six zones outside of Europe, the total number of traffic zones adds up to 402.

The zoning division was based on the administrative regions as most input data were

available at this level only. The zoning division respects the borders of the NUTS (No-

menclature des unités territoriales statistiques) level 1 and 2. The traffic zones were fairly

homogeneous in terms of physical geography and socio-economic conditions and the

zoning system had been formed in accordance with the traffic flows as well as with the

present and future railway network.

A visual overview of all zones within the territory of the study is given in Annex 3.1.

Means of Transport and Trip Purposes

The objectives of the study required a multimodal approach to the forecast of long-

distance passenger traffic. Explicit consideration was given to the following means of

transport:

o Rail transport

o Private car (p.c.) transport

o Air transport

o Bus transport


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Long-distance passenger traffic was differentiated by travel purpose:

o Business

o Trips from home to work or home to place of education (school, university etc.)

o Leisure day trips

o Leisure trips of more than one day of absence

o Holiday trips involving five or more days of absence

Socio-economic Development

The forecasts for population had shown only relatively slight changes between 1999 and

2020 for most of the Western European countries. Only in Ireland, Luxembourg and Nor-

way, the population was expected to grow by more than 10%. France and Switzerland

were also expected to have a considerable increase in population (6% and 8% respec-

tively), too. In all other countries, the growth rates range between -4% and +4%, with the

average amounting to 2%.

Gross domestic product (GDP) which is the key variable describing the economic growth

was expected to increase by 2020 by 70% totally or 2.6% p.a.

The car stock for the 17 Western European countries totalled 178 million vehicles in 1999.

Average car density in the Western European countries amounts to 462 cars per 1000

inhabitants. In all of Western Europe, car stock was expected to grow by 24%.

Scenarios

To show the effects of different basic assumptions on development of user costs and

transport policies, five alternative scenarios, characterised as follows, had been exam-

ined:

o „Basic Scenario“: continuation of the observable development with respect to transport policies and user costs

o „Favourable Scenario“: favourable development of transport policies and user costs with regard to rail traffic

o „Unfavourable Scenario“: unfavourable development of transport policies and user costs with regard to rail traffic

o „Tariff Scenario“: assuming an increase of rail tariffs by 0.5% p.a. in comparison to the „Basic Scenario“

o “Environmental Scenario”: a favourable development of transport policies (with regard to rail traffic) with strong interventions in road traffic in light of an increasingly ecologi-cal orientation of transport policies


- 3-5 -

Rail Network and Services

The rail network model for the analysis comprised about 76 685 km (47%) of the rail net-

work in the W.E. Countries that totalled to 162 714 km in 1999 and 1.1 billion train-km

(37%) of 3.0 billion train-km in total. Of that, 9 340 km of the infrastructure had been de-

fined to be new or upgraded lines and more than 200 million train-km had been assigned

to the category of High-Speed Rail Services.

The High-Speed Rail Network for the time horizon 2020 was defined by the UIC in co-

ordination with the railways. Until 2020, a yearly average of more than 1200 km of new or

upgraded lines in the W.E. Countries and about 500 km of upgraded lines in the C.E.E.C.

were supposed to be constructed. Between 1999 and 2010 the European High-Speed

Network length will more than double and unitl 2020 it will nearly quadruple. Figure 3-1

shows the anticipated High-Speed Network for the year 2020.

2020

European

High-Speed Network

Réseau Européen

à Grande Vitesse

Europäisches

Hochgeschwindigkeitsnetz

new lines

lignes nouvelles

Neubaustrecken

upgraded lines

lignes aménagées

Ausbaustrecken

High-Speed Division0 500 km Version 01.03.2002

All rights reserved. UIC 2002

Tallinn

København

Ankara

Bucuresti

Sundsvall

Chisinau

München Wien

Nürnberg

ZürichLjubljana

Zagreb

Oslo

London

Amsterdam

Brux.

GlasgowEdinburgh

Hamburg

Hann.

Köln

Berlin

Fr.Lux.

Istanbul

Bratislava

Budapest

Skopje

Tirana

Sarajevo

BariNapoli

Roma

Sevilla

Lisboa

Málaga

Madrid

Porto

Bordeaux

Rennes

Lyon

Genève

Paris

Praha

Stockholm

Göteborg

Milano

Kiev

Moskva

Marseille

BolognaBeograd

Genova

Sofia

LvivKatowice

Valencia

Helsinki

Riga

Warszawa

Minsk

Barcelona

Vilnius

St.Petersburg

Dublin

Athinai

Gdansk

Figure 3-1: European High-Speed Network 2020

Travel Times 2010 and 2020

Travel times in rail passenger traffic are falling drastically as a result of the ongoing exten-

sion of the High-Speed Network and new High-Speed Services. Travel times are not only

shorten due to higher speeds on the new and upgraded lines but also due to new direct

services causing a reduction of transfer times and shorter travel times with High-Speed

Trains on conventional and not upgraded lines (e.g. utilisation of tilting trains).


- 3-6 -

Figure 3-2 shows the average com-

mercial speed in the rail networks

(weighted by demand). In 2020, the

average commercial speed in the

W.E. Countries increases to 127 kph

(on average for every traveller) com-

pared to about 100 kph in 1999. The

highest figures appear for France with

an average commercial speed of 160

kph and for Spain and Portugal with

148 kph and 146 kph respectively.

These values correspond to a high

percentage of high-speed lines with a

maximum speed of 300 kph and more.

Sweden will also join this group once

the newly constructed high-speed sys-

tem between the most important re-

gions of the country will be imple-

mented (Stockholm - Gøteborg/Malmö).

Determining the extension of High-

Speed Rail Services was not feasible,

because of the many differing definitions of high-speed traffic and services in the W.E.

Countries. For this reason, the share of traffic on new and upgraded lines was used to

characterise the expansion of high-speed traffic within the different European countries.

The share of long-distance rail traffic on High-Speed Lines (new and upgraded) reaches a

value of about 77% on average for the W.E. Countries in 2020, compared with 33% in

1999 (Figure 3-3). In several countries, almost all long-distance traffic will occur on High-

Speed Lines as defined by the railways.

Not all services on new or upgraded lines will be High-Speed in nature. A significant share

of night trains will remain in particular for passengers travelling very long distances and

conventional services will run for short-distance travellers on journeys of 80 to 150 km. In

contrast, High-Speed Rail Services will continue to expand on the remaining conventional

network.

Mobility and Modal-Split in 1999

Data mining led to the overall result of transport performance (passenger-kilometres) in all

W.E. Countries, including short-distance traffic, amounting to approx. 5100 billion pkm in

1999. This means an average per capita figure of 13200 pkm. The key value of mobility in

40 60 80 100 120 140 160 180

Finland

Sweden

Norway

Denmark

Ireland

United Kingdom

Netherlands

Belgium

Luxembourg

Germany

Austria

Switzerland

France

Spain

Portugal

Italy

Greece

W.E.Countries

C.E.E.C.

kph

1999 2010 2020

Figure 3-2: Average Commercial Speed for Long-

distance Rail Traffic in 1999, 2010 and 2020


- 3-7 -

long-distance traffic (only trips with a travel distance of more than 80 km) for the national

and international traffic of the W.E. Countries amounts to approx. 2000 billion pkm if inter-

continental traffic is not considered and about 2350 billion pkm if it is considered. Car

transport performance in long-distance traffic comprises 62% of all modes, rail traffic adds

up to about 10%, bus transport about 8% and air traffic (without intercontinental traffic)

about 20%.

Long-distance rail transport performance is about 189 billion pkm in 1999. The share of

high-speed traffic out of that is slightly above 30% or 60 billion passenger-kilometres..

Development of Transport Demand until 2020

In the Basic Scenario, the total

long-distance transport per-

formance for all means of trans-

port will increase by 58% from

1967 billion pkm in 1999 to 3111

billion pkm in 2020 (see Figure

3-4). While the growth rates for

private cars (+45%) and bus

traffic (+5%) are below average,

rail transport volume increases

by 67% and for air traffic the

figure (without intercontinental

traffic) will more than double.

Air traffic will significantly in-

crease its market share from

20.4% in 1999 to 23.9% in 2010

and 27.5% in 2020. Although transport performance for all other modes grows as well, as

seen above, private car and bus traffic will loose market share. The railway’s market share

will increase only slightly by 0.5 points due to air traffic’s massive growth in transport vol-

ume. Intercontinental air traffic grows from 457 billion pkm in 1999 to 785 billion pkm in

2010 and 1130 billion pkm in 2020.

1967

2553

3111

258

1527

189 315

161154 157

1223

1778

401

857

611

0

500

1000

1500

2000

2500

3000

3500

1999 2010 2020

Billio

n P

ass

en

ge

r-k

m

0

500

1000

1500

2000

2500

3000

3500

Total

Air

P.C.

Bus

Rail

Figure 3-3: Development of Transport Performance in Long-

distance Passenger Traffic for all Means of Transport (Basic Scenario; without Intercontinen-tal Traffic and Airport Feeder Traffic)


- 3-8 -

In the other scenarios (see Fig-

ure 3-5), total transport per-

formance differs from the Basic

Scenario to a fairly small extent.

The lowest growth rate can be

found in the Environmental

Scenario, where cost increases

reduce total mobility. Transport

performance ranges 4.5% be-

low the Basic Scenario, but still

grows by 51% over 1999.

Growth in total mobility is the

highest (60%) in the Unfavour-

able Scenario, amounting to 1%

above the Basic Scenario.

Figure 3-6 shows the develop-

ment of the railway’s transport

performance (passenger-

kilometres) assuming the reali-

sation of the planned High-

Speed Network. In the Basic

Scenario, rail transport perform-

ance grows by two-thirds from

189 billion pkm in 1999 to 315

billion pkm in 2020. If transport

policies and user costs follow a

favourable development path for

rail traffic, the transport per-

formance of railways will more

than double, reaching a value of

392 billion pkm. In the Environ-

mental Scenario, the increase to

416 billion pkm (+120%) is even higher. If railways raise prices by 0.5% p.a. (real terms),

they will loose about 7% of transport performance compared with the Basic Scenario.

However, compared to 1999, this is still an increase in transport performance by more

than 100 billion pkm or 55%. Even if the assumptions in the Unfavourable Scenario prove

true, the railways attain an increase in transport performance by 36% or 69 billion pkm

stemming from the ongoing extension of the High-Speed Network.

3142 3100 3111 3033 2972

392 416

1821 1786 1778 1666 1575

905 859 857 805 807

315292258

161163158170 174

0

500

1000

1500

2000

2500

3000

3500

Unfa

voura

ble

Tariff

Basi

c

Favoura

ble

Enviro

nmen

tal

Billio

n P

as

se

ng

er-

km

0

500

1000

1500

2000

2500

3000

3500

Total

Air

P.C.

Bus

Rail

Figure 3-4: Transport Performance in Long-distance Passen-ger Traffic in 2020 for all Means of Transport for Different Scenarios (without Intercontinental Traffic and Airport Feeder Traffic)

392

315

292

416

160

189

135145

258281

100

150

200

250

300

350

400

450

1970 1980 1990 1999 2010 2020

Billio

n P

assen

ger-

km

Environmental ScenarioFavourable Scenario

Past / Basic ScenarioTariff ScenarioUnfavourable Scenario

GDP-Scenario

Figure 3-5: Development of Long-distance Rail Traffic in Different Scenarios (Passenger-km; without Air-port Feeder Traffic)


- 3-9 -

As shown in Figure 3-7, the

railway’s market share in the

Basic Scenario will slightly in-

crease by 0.5 points to 10.1%.

Clear share gains over the

1999 level (9.6%) occur in the

Environmental (14%) and Fa-

vourable (almost 13%) Sce-

nario. In the Tariff Scenario

(9.4%), only slight changes

take place. In contrast, the Un-

favourable Scenario shows a

considerable loss to a level of

8.2%.

Effect of the Extension of the

High-Speed Network 2020

The extension of the High-

Speed Network leads to a significant increase in rail demand. Without High-Speed Net-

work extensions (network remains in the state of 1999), long-distance passenger traffic in

the W.E. railway networks would amount to 228 billion pkm in the Basic Scenario 2020

(see Figure 3-7). The extensions would produce an increase in demand to about 319 billion

pkm p.a., a gain of about 40% or 91 billion pkm1.

from

Bus

1.3%

from

Private

Car

32.2%

from Air

27.6%

new

Airport

Feeder

Traffic

6.1%

induced

Traffic

32.8%

180.7227.9

296.3

84.4

91.0

97.0

0.0

100.0

200.0

300.0

400.0

Unfavourable

Scenario

Basic Scenario Favourable

Scenario

Billio

n P

asse

ng

er-

km

Effect of the Extension of the High-Speed Network

Without Furhter Extension of the High-Speed Network

97.0

318.9

(+40%)

393.3

(+33%)

265.1

(+47%)

Figure 3-7: Growth of Long-distance Rail Traffic in the W.E. Countries and Origin of Additional Rail Traffic (without Intercontinental Traffic but with Airport Feeder Traffic)

1 The definition used here differs from that of the previous section with the Figures, primarily by considera-

tion of airport feeder traffic in this section (an exact definition is given in the detailed report). The corre-sponding figures in the definition of the previous section are 227 billion pkm without and 315 billion pkm with extensions of the High-Speed Rail Network

14.0%

12.9%

10.1%10.7%

9.6%

12.8%

18.5%

10.2%

9.4%

8.2%

6%

8%

10%

12%

14%

16%

18%

20%

1970 1980 1990 1999 2010 2020

Environmental ScenarioFavourable

Past / Basic ScenarioTariff ScenarioUnfavourable Scenario

GDP-Scenario

Figure 3-6: Development of Railways’ Market Share in Long-distance Traffic in Different Scenarios (Related to Passenger-kilometres; without Intercontinental Traffic and Airport Feeder Traffic)


- 3-10 -

As already shown above, the levels of railway transport volume in 2020 range from 258

billion pkm in the Unfavourable Scenario to 392 billion in the Favourable and 416 billion

pkm in the Environmental Scenario. This span in relation to the Basic Scenario (315 billion

pkm) with a deviation of about 18% in the Unfavourable Scenario and 24% in the Favour-

able Scenario is also apparent in Figure 3-7. But growth in railway demand as an effect of

High-Speed Network extensions only ranges between -7% (88.4 billion pkm) in the Unfa-

vourable Scenario and +7% (97.0 billion pkm) in the Favourable Scenario as compared to

the Basic Scenario (91.0 billion pkm). That means that the political context for transport

will play a major role in absolute volume of rail transport demand, but the considerable

advantages of the High-Speed Network extension remain rather uninfluenced.

About 60% of the railway’s gains in transport demand stem from substitutions from private

car and air traffic.

Table 3-1 shows the rail market share in the cases with and without further extension of

the High-Speed Network for several OD-Relations.

Rail Market Share in the Cases with and without Further Extension of the High Speed Network on Selected Relations (Basic Scenario, 2020)

Without Extension With Extension OD-Relation

Per Cent 1 2 3

International Paris - Region of Bruxelles2 37% 38% Paris - Bruxelles 43% 44% Madrid - Lisboa 6% 48% Region of London3 - Region of Bruxelles 39% 52% Paris - Milano 18% 54% London - Bruxelles 48% 65%

National Region of Berlin4 - München 11% 34% Berlin - München 12% 41% Paris - Marseille 32%* 44% Köln - Stuttgart 27% 44% Madrid - Barcelona 12% 49% Stockholm - Malmö 25% 51%

* state of 1999; before opening of the TGV Provence / Côte d’Azur

Table 3-1: Rail Market Share in the Cases with and without Further Extension of the High-Speed Network on Selected Relations

It is noteworthy, that on individual origin-destination links, railways can reach market sha-

res of up to 50% in the case of high quality rail supply, i.e. very short travel times, direct

2 Region of Bruxelles: Bruxelles, Halle-Vilvoorde, Leuven, Brabant and Wallon 3 Region of London: London, Kent, Surrey, East and West Sussex, Berkshire, Bucks and Oxfordshire, Bed-

fordshire, Herfordshire and Essex, 4 Region of Berlin: Berlin, Barnim, Oberhavel, Uckermark, Frankfurt (Oder), Märkisch-Oderland, Oder-Spree,

Brandenburg, Potsdam, Havelland, Ostprignitz-Ruppin, Potsdam-Mittelmark, Prignitz, Teltow-Fläming, Cottbus, Dahme-Spreewald, Elbe-Elster, Oberspreewald-Lausitz, Spree-Neiße


- 3-11 -

services and quick access. If conditions for other means of transport a comparatively bad,

market share grows up to 65%.

As there are no further extensions on the Paris - Bruxelles line, the increase in railway’s

market shares on the relation Paris - Bruxelles only attributes to a higher frequency of

services due to extensions on connected lines (Bruxelles - Amsterdam and Bruxelles -

Köln).

The extent to which access and egress aspects influences market share becomes appar-

ent by comparing the results for the city and the region of Bruxelles (see Footnote 2): a

loss of 6 points by comparing the relations Paris - Bruxelles and Paris - region of Bruxelles

or a loss of 13 points by comparing the relations London - Bruxelles and region of London

(see Footnote 3) - region of Bruxelles respectively.

The growth rates of rail demand for individual sections of the railway network, brought

about by the extension of the High-Speed Network exceed 100% in several parts of the

network, where major projects of the planned High-Speed Network will be realised.

The section loads in the core areas of the High-Speed Rail Network reach a value of 50

million passengers per year, i.e. more than 100 000 passengers per day. As minimum

value, a section load of 2 million passengers per year, i.e. about 6 000 passengers per

day, can be observed.


- 3-12 -

3.2.2 Long-distance mobility

Norwegians’ overall long-distance mobility is one of the highest throughout Europe. Statis-

tically In 1999, more than 7 000 km had been covered by each Norwegian on long-

distance traffic (more than 80 km travel distance). This figure is 40% higher than the

Western European average. In Germany and in France, for example, overall long-distance

mobility averages between 5 000 and 5 500 km per year (see Figure 3-8, left side).

HU

SK

HRSI

RU

RO

BY

UA

YU

LT

LV

BG

BA

EE

ALMK

MD

EE

Spain5635

France5186

Sweden6211

Italy4387

Finland5763

Poland2254

Norway7064

Germany5446

Austria6349

Greece3377

Ireland4092

United Kingdom3996

Portugal4280

Czech. Republic2324

Belgium3949

Switzerland5699

Netherlands4390

Denmark4367

Luxembourg5912

0 100 20050Kilometres

Feasibility StudyHigh-Speed Traffic Norway

Overall Mobility(Long-distance Traffic)1999

source: UIC Study

Kilometersper Annum and Inhabitant1999

not specified

< 3000

3000 - 4000

4000 - 5000

5000 - 6000

6000 - 7000

> 7000

HU

SK

HRSI

RU

RO

BY

UA

YU

LT

LV

BG

BA

EE

ALMK

MD

EE

Spain2211

France528

Italy479

Sweden1479

Finland1141

Poland50

Norway2623

Germany648

Austria631

Greece1556

Ireland1311

United Kingdom1069

Portugal1357

Czech. Republic167

Belgium685

Switzerland1040

Netherlands821

Denmark1084

Luxembourg1149



Air Traffic Mobility(Long-distance Traffic)1999

source: UIC Study


not specified

< 500

500 - 1000

1000 - 1500

1500 - 2000

2000 - 2500

> 2500

Figure 3-8: Overall Long-distance Mobility and Air Traffic Mobility

Long-distance mobility in Norway is air traffic orientated. Norwegian’s air traffic mobility is

the highest throughout Europe. In 1999, it was more than twice as high as the Western

European average. Compared to France and Germany it was even four times higher (see

Figure 3-8, right side).

In opposite to that, long-distance rail traffic mobility is one of the lowest. In 1999, only 339

km had been covered by a Norwegian on average. The European average amounted to

about 500 km per year and in countries with quite good rail services, like in France and in

Switzerland, rail traffic mobility per inhabitant summed up to more than 800 km per year

(see Figure 3-9, left side). In consequence to that, railway’s market share in Norway


- 3-13 -

ranged with 4.8% far below the European average, that amounted in 1999 to 9.6% (see

Figure 3-9, right side).

HU

SK

HRSI

RU

RO

BY

UA

YU

LT

LV

BG

BA

EE

ALMK

MD

EE

Spain325

France886

Italy512

Sweden629

Finland493

Poland508

Norway339

Germany456

Austria535

Greece113

Ireland151

United Kingdom322

Portugal224

Czech. Republic324

Belgium314

Switzerland875

Netherlands436

Denmark437

Luxembourg698



Rail Traffic Mobility(Long-distance Traffic)1999

source: UIC Study


not specified

< 300

300 - 400

400 - 500

500 - 600

600 - 700

> 700

HU

SK

HRSI

RU

RO

BY

UA

YU

LT

LV

BG

BA

EE

ALMK

MD

EE

Spain5.8%

France17.1%

Sweden10.1%

Finland8.6%

Poland22.5%

Norway4.8%

Italy11.7%

Germany8.4%

Austria8.4%

Greece3.3%

Ireland3.7%

United Kingdom8.1%

Portugal5.2%

Czech. Republic13.9%

Belgium8.0%

Switzerland15.4%

Netherlands9.9%

Denmark10.0%

Luxembourg11.8%



Market Share of Rail(Long-distance Traffic)1999

source: UIC Study

Market Share1999

not specified

< 4%

4% - 8%

8% - 12%

12% - 16%

16% - 20%

> 20%

Figure 3-9: Rail Traffic Mobility and Rail Market Share

Due to relative low population, passenger demand potentials in Norway are relative low,

too. Overall long-distance passenger mobility of Oslo is one of the lowest among the capi-

tals of the Western European Countries (see Figure 3-10). Passenger mobility of other

capital cities, like Amsterdam, Berlin and Roma is about three times higher and passenger

demand figures from København and Stockholm are about 30% higher than that of the

Oslo region. Traffic demand potentials from the other major Norwegians cities, i.e. Ber-

gen, Trondheim and Stavanger, are about a half to a fourth of that of Oslo (see Figure

3-11). On the other side, traffic demand in Norway is concentrated on a few larger cities

with very low potentials in between.


- 3-14 -

Long-distance Passenger MobilitySelcted Capitals

100 94 9380

67

45 4333

18

0

20

40

60

80

100

Ber

lin

Am

ster

dam

Rom

a

Mad

rid

Lisb

oa

Kø

benh

avn

Sto

ckho

lm

Osl

o

Luxe

mbo

urg

Ind

ex (

Berl

in =

100)

.

Figure 3-10: Rail mobility by regions

Long-distance Passenger Mobility

100

4633 26

0

20

40

60

80

100

Osl

o

Ber

gen

Tro

ndhe

im

Sta

vang

er

Ind

ex (

Oslo

= 1

00)

.

Figure 3-11: Rail mobility by regions

That will say that only Oslo can act as an origin for new High-Speed Lines and that new

High-Speed Services have to be adjusted to relative small traffic potentials. It has to be

reached high cost efficiency by combining the relative small potentials, by integration of

existing services and by minimising of investment and operation costs. On the other side,

rail services have to be competitive to air traffic, the most important competitor for railways

on long-distance passenger traffic in Norway. That means, travel times between the cen-

tres of the major cities should not exceed three hours, the travel time of a domestic jour-

ney using air services including access, egress and terminal times.


- 3-15 -

3.3 Potentials for High-Speed Rail Services

3.3.1 Identification of Main Markets Segments

The main focus of High-

Speed Rail Services has to

be set on heavy markets

segments with a high

concentration of demand

potentials on long distances.

To reach high market shares

on these market segments,

the number of stops has to

be limited to reach a high

commercial speed.

Additional stops have to be

considered, if the demand

potentials of the additional

markets are higher than the

loss of demand potentials as

an effect of longer travel

times on the main markets.

The balance is depending on

the potentials of the different

markets segments, the loss

of time due to additional

stops and the attendant

circumstances on the

markets such as

concurrence by air traffic and

the level of rail services in

the situation without the

additional stop.

Figure 3-12 shows the repartition of population in the southern parts of Norway.

Vinje3758

Luster4927

Lesja2184

Tinn6380

Lom2467

Hol4557

Rendalen2105

Oppdal6473

Skjaak2394

Vang1613

Odda7378

Voss13850

Tynset5405

Roeros5636

Sirdal1760

Bykle857

Stryn6843

Trysil6882

Valle1384

Suldal3901

Tydal902

Engerdal1499

Aal4670

Dovre2875

Selbu3988

Tolga1778

Rauma7336

Sunndal7370

Aamot4398

Aamli1801

Stor-Elvdal2832

Eidfjord914

Folldal1717

Aurland1783

Sel6059

Laerdal2158

Vaagaa3773

Nore og Uvdal2635

Vik2881

Tokke2463

Ringerike28079

Bygland1327

Alvdal2416

Surnadal6160

Nesset3181

Aardal5631

Ringebu4586

Elverum18844

Fyresdal1353

Ulvik1163

Etne3904

Gausdal6175

Flaa1014

Sigdal3537

Holtaalen2132

Midtre Gauldal5797

Ringsaker31824

Skien50676

Meraaker2560

Gloppen5793

Gran13010

Norddal1817

Oersta10233

Aaseral907

Nord-Fron5896

Gol4375

Kvam8334

Stranda4605

Ullensvang3517

Oeyer4840

Nissedal1408

Rennebu2660

Drangedal4143

Hjartdal1633

Seljord2919

Rindal2101

Stjoerdal19562

Flora11364

Kvinesdal5582

Melhus13977

Meldal3934

Forsand1102

Notodden12359

Eid5766

Hjelmeland2736

Soer-Aurdal3265

Hemne4277 Orkdal

10512

Halden27582

Gaular2749

Larvik41142

Joelster2918

Froland4672

Sauda4819

Kvinnherad13122

Lund3129

Hitra4025

Gjesdal9273

Os_Hedmark2087

Kviteseid2598

Aasnes7779

Nordre Land6847

Grue5275

Nord-Aurdal6442

Fjaler2916

Hemsedal1909

Soer-Fron3271

Nome6565

Rollag1441

Oeystre Slidre3114

Flesberg2517

Etnedal1397

Eidsvoll18637

Marker3439

Eidskog6499

Aure2620

Aurskog-Hoeland13275

Soendre Land6008

Modalen361

Hoeyanger4502

Aremark1425

Askvoll3229

Sauherad4323

Stange18427

Volda8351

Gjoevik27648

Foerde11151

Gulen2459

Birkenes4340

Fusa3709

Sogndal6794

Kongsberg23244

Modum12541

Bjerkreim2463

Bremanger4031

Snillfjord1026

Lindaas13043

Nes_Buskerud3485

Bergen239209

Loeten7271

Halsa1697

Molde24124

Hamar27439

Fraena9023

Lier21725

Soer-Odal7623

Lyngdal7244

Nord-Odal5073

Masfjorden1693

Vaksdal4154

Balestrand1431

Flekkefjord8878

Vestnes6390

Lardal2419

Sveio4672

Selje2999

Nes_Akershus18025

Oestre Toten14604

Hurdal2602

Vindafjord4700

Horten24768

Gjemnes2700

Tingvoll3105

Lunner8505

Vennesla12427

Naustdal2682

Haa14784

Rakkestad7284

Eigersund13408

Hole5229

Lillehammer25075

Bamble14154

Oevre Eiker15633

Marnardal2167

Siljan2372

Sarpsborg49753

Vaaler_Hedmark3924

Vanylven3693

Gjerstad2500

Vestre Slidre2245

Skaun6063

Sokndal3309

Iveland1154

Kongsvinger17279

Fet9567

Haegebostad1594

Sykkylven7446

Sandnes57618

Evje og Hornnes3305

Trondheim156161

Hof3048

Stordal1007

Jondal1078

Grimstad18885

Strand10441

Osteroey7207

Vegaarshei1854

Nannestad10141

Haram8715

Oelen3420

Eide3304

Lindesnes4484

Smoela2195

Kroedsherad2151

Arendal39676

Krageroe10529

Tysnes2825

Agdenes1799

Tysvaer9370

Hyllestad1526

Malvik12095

Mandal14010

Klaebu5279

Granvin1008

Farsund9479

Enebakk9297

Ski26800

Time14461

Audnedal1575

Soerum12925

Risoer6909

Boemlo10830

Jevnaker6335 Nittedal

19578

Hurum8799

Andebu5083 Fredrikstad

70418

Fitjar2895

Samnanger2322

Eidsberg10203

Klepp14536

Kristiansand76066

Vestre Toten12546

Ulstein6795

Troegstad4962

Stord16516

Hornindal1197

Songdalen5556

Vestby12990

Karmoey37567

Averoey5448

Hoboel4557

Lillesand9043

Boe_Telemark5249

Leikanger2209

Soegne9547

Skodje3597

Roemskog667

Solund875

Stokke10014

Rissa6433

Rygge13712

Askim14089

Fjell20043

Tvedestrand5889

Oerskog2121

Sund5537

Porsgrunn33407

Tustna1006

Sola19832

Radoey4656

Frei5301

Raade6465

Skiptvet3355

Askoey22020

Sula7453

Nedre Eiker21522

Sande_Vestfold7690

Vaagsoey6218

Sandefjord41289

Toensberg36452

Midsund1939

Haugesund31530

Os_Hordaland14908

Aukra3050

Moss28040

Holmestrand9604

Aalesund40295

Bokn769

Meland5861

Austevoll4451

Sande_Moere2576

Finnoey2772

Hvaler3773

Heroey_Moere8386

Noetteroey20022

Hareid4658

Rennesoey3350

Tjoeme4582

Giske6591

Randaberg9099

Austrheim2527

Sandoey1274

Fedje661

Kristiansund17026

Oeygarden3975

Utsira213

Frosta2493

Kvitsoey511

FLORO

LISTA

ROROS

FYRESDAL

NOTODDEN

MOLDE/ARO

BERGEN/FLESLAND

SKIEN/GEITERYGGEN

KRISTIANSUND/KVERNBERGET

3063 Oppland

3064 Hedmark

3032 Telemark

3031 Buskerud

3051 Hordaland

3081 Sør-Trøndelag

3052 Sogn og Fjordane

3041 Rogaland

3053 Møre og Romsdal

3043 Aust-Agder

3042 Vest-Agder

2031 Göteborg

3082 Nord-Trøndelag

3021 Østfold

3012 Akershus

3033 Vestfold

2062 Karlstad

2063 Borlänge

Oslo529846

Ullensaker24556

Baerum104690

Aas14472

Vaaler_Oestfold4020

Asker50858

Spydeberg4798

Frogn13358

Drammen57148

Roeyken17280

Gjerdrum5064

Skedsmo42094

Svelvik6441

Raelingen14797

Loerenskog30675

Nesodden16231

Oppegaard23586

Leksvik3508

Stavanger113991

0 2512.5Kilometers

Feasibility StudyHigh-Speed Rail Traffic Norway

Population in Norway

Population 2005

Airports

Fylke

Municipalities

Railway

Figure 3-12: Repartition of Population


- 3-16 -

As already seen in chapter 3.2.2, the main potentials in Norway can be found on the rela-

tions from and to the major cities in the southern parts of Norway, i.e.:

o Oslo (530 000 inhabitants**)

o Bergen (239 000 inhabitants*)

o Trondheim (156 000 inhabitants*)

o Stavanger (114 000 inhabitants*)

o Kristiansand (76 000 inhabitants*)

Thus, the following main markets of Norway up to 500 km travel distance can be defined,

including the international markets to the major cities of Sweden:

o Oslo – Trondheim

o Oslo – Bergen

o Oslo – Stavanger

o Oslo – Kristiansand

o Oslo – Stockholm

o Oslo – Gøteborg

o Bergen - Trondheim

o Bergen – Stavanger

o Bergen – Kristiansand

o Stavanger - Kristiansand

Outside the agglomeration area of Oslo (Østlandet), additional potentials are relatively

rare. Depending on the alignment, the following potentials should be attended by high-

speed services.

o Arendal (40 000 inhabitants*)

o Sandnes (58 000 inhabitants*)

o Haugesund (32 000 inhabitants*)

o Karmøy (38 000 inhabitants*)

In the agglomeration area of Oslo (Østlandet), the following potentials could be attended

by High-Speed Services on the way between the above mentioned centres:

o Oslo – Vestfold South (Tønsberg/Sandefjord/Larvik)

o Oslo – Telemark East (Skien/Porsgrunn)

o Oslo – Hamar/Ringsaker

o Oslo – Østfold South (Fredrikstad/Sarpsborg/Halden)

* in 2005


- 3-17 -

As the line between Oslo and Trondheim might take course via Lillehammer, the market

between Oslo and Oppland (Northern parts) was considered as well.

Other stops have to be evaluated separately as the balance between the additional de-

mand potentials through the additional stop and the losses of demand on the main mar-

kets due to longer travel times may not be positive in all cases. Even if the balance is

positive, the benefits might be less than the costs which are caused by an additional stop.

Figure 3-13 shows the main markets which were examined in the market study.

Toensberg36452

Halden27582

Oslo529846

Skien50676

Hamar27439

Fredrikstad70418

Moss28040

Drammen57148

Larvik41142

Sarpsborg49753

Sandefjord41289

Porsgrunn33407

OSLO/GARDERMOEN

Ringerike28079

Elverum18844

Flaa1014

Sigdal3537

Gran13010

Ringsaker31824

Stange18427

Soer-Aurdal3265

Gjoevik27648

Nordre Land6847

Kongsberg23244

Nord-Aurdal6442

Notodden12359

Rollag1441

Modum12541

Flesberg2517

Etnedal1397

Eidsvoll18637

Marker3439

Gol4375

Nome6565


Loeten7271

Soendre Land6008

Lier21725

Soer-Odal7623

Nord-Odal5073

Drangedal4143

Eidskog6499

Lardal2419

Nes_Akershus18025

Oestre Toten14604

Hurdal2602

Horten24768

Lunner8505

Rakkestad7284

Hole5229

Bamble14154

Oevre Eiker15633

Siljan2372

Nes_Buskerud3485

Aremark1425

Fet9567

Lillehammer25075

Vaaler_Hedmark3924

Sauherad4323

Hof3048

Nannestad10141

Kroedsherad2151

Enebakk9297

Ski26800

Soerum12925

Nittedal19578

Hurum8799

Andebu5083

Ullensaker24556

Eidsberg10203

Vestre Toten12546

Krageroe10529

Jevnaker6335

Baerum104690

Troegstad4962

Vestby12990

Hoboel4557

Roemskog667

Stokke10014

Aas14472

Rygge13712

Vaaler_Oestfold4020

Askim14089

Asker50858

Spydeberg4798

Raade6465

Skiptvet3355

Frogn13358

Nedre Eiker21522

Sande_Vestfold7690

Roeyken17280

Boe_Telemark5249

Gjerdrum5064

Skedsmo42094

Svelvik6441

Holmestrand9604

Raelingen14797

Loerenskog30675

Nesodden16231

Gjerstad2500

Hvaler3773

Noetteroey20022

Tjoeme4582

Oppegaard23586

TORP

RYGGE

KJELLER

NOTODDEN

OSLO/FORNEBU

SKIEN/GEITERYGGEN

3063 Oppland

3031 Buskerud

3064 Hedmark

3021 Østfold

3012 Akershus

3032 Telemark

3033 Vestfold

2031 Göteborg

2062 Karlstad

3011 Oslo

0 12.5Kilometers

Toensberg36452

Halden27582

Oslo529846

Skien50676

Hamar27439

Fredrikstad70418

Moss28040

Drammen57148

Larvik41142

Sarpsborg49753

Sandefjord41289

Porsgrunn33407

OSLO/GARDERMOEN

Ringerike28079

Elverum18844

Flaa1014

Sigdal3537

Gran13010

Ringsaker31824

Stange18427

Soer-Aurdal3265

Gjoevik27648

Nordre Land6847

Kongsberg23244

Nord-Aurdal6442

Notodden12359

Rollag1441

Modum12541

Flesberg2517

Etnedal1397

Eidsvoll18637

Marker3439

Gol4375

Nome6565


Loeten7271

Soendre Land6008

Lier21725

Soer-Odal7623

Nord-Odal5073

Drangedal4143

Eidskog6499

Lardal2419

Nes_Akershus18025

Oestre Toten14604

Hurdal2602

Horten24768

Lunner8505

Rakkestad7284

Hole5229

Bamble14154

Oevre Eiker15633

Siljan2372

Nes_Buskerud3485

Aremark1425

Fet9567

Lillehammer25075

Vaaler_Hedmark3924

Sauherad4323

Hof3048

Nannestad10141

Kroedsherad2151

Enebakk9297

Ski26800

Soerum12925

Nittedal19578

Hurum8799

Andebu5083

Ullensaker24556

Eidsberg10203

Vestre Toten12546

Krageroe10529

Jevnaker6335

Baerum104690

Troegstad4962

Vestby12990

Hoboel4557

Roemskog667

Stokke10014

Aas14472

Rygge13712

Vaaler_Oestfold4020

Askim14089

Asker50858

Spydeberg4798

Raade6465

Skiptvet3355

Frogn13358

Nedre Eiker21522

Sande_Vestfold7690

Roeyken17280

Boe_Telemark5249

Gjerdrum5064

Skedsmo42094

Svelvik6441

Holmestrand9604

Raelingen14797

Loerenskog30675

Nesodden16231

Gjerstad2500

Hvaler3773

Noetteroey20022

Tjoeme4582

Oppegaard23586

TORP

RYGGE

KJELLER

NOTODDEN

OSLO/FORNEBU

SKIEN/GEITERYGGEN

3063 Oppland

3031 Buskerud

3064 Hedmark

3021 Østfold

3012 Akershus

3032 Telemark

3033 Vestfold

2031 Göteborg

2062 Karlstad

3011 Oslo

0 12.5Kilometers

Toensberg36452

Halden27582

Bergen239209

Oslo529846

Skien50676

Arendal39676

Hamar27439

Trondheim156161

Fredrikstad70418

Kristiansand76066

Moss28040

Drammen57148

Stavanger113991

Larvik41142

Sarpsborg49753

Sandefjord41289

Porsgrunn33407

OSLO/GARDERMOEN

Vinje3758

Trysil6882

Luster4927

Lesja2184

Tinn6380

Lom2467

Hol4557

Rendalen2105

Oppdal6473

Skjaak2394

Vang1613

Odda7378

Voss13850

Tynset5405

Roeros5636

Sirdal1760

Bykle857

Stryn6843

Valle1384

Suldal3901

Tydal902

Engerdal1499

Aal4670

Dovre2875

Selbu3988

Tolga1778

Rauma7336

Sunndal7370

Aamot4398

Aamli1801

Stor-Elvdal2832

Eidfjord914

Folldal1717

Aurland1783

Sel6059

Laerdal2158

Vaagaa3773

Nore og Uvdal2635

Vik2881

Grue5275

Tokke2463

Ringerike28079

Bygland1327

Alvdal2416

Surnadal6160

Nesset3181

Aardal5631

Ringebu4586

Fyresdal1353

Ulvik1163

Etne3904

Gausdal6175

Flaa1014

Aasnes7779

Meraaker2560

Sigdal3537

Holtaalen2132

Midtre Gauldal5797

Gran13010

Aaseral907

Verdal13815

Gol4375

Oeyer4840

Nissedal1408

Rennebu2660

Hjartdal1633

Rindal2101

Flora11364

Elverum18844

Ringsaker31824

Gloppen5793

Norddal1817Oersta

10233

Hitra4025

Nord-Fron5896

Stjoerdal19562

Kvam8334

Stranda4605

Ullensvang3517

Drangedal4143

Seljord2919

Stange18427

Volda8351

Kvinesdal5582

Rissa6433

Melhus13977

Meldal3934

Forsand1102

Notodden12359

Eid5766

Hjelmeland2736

Soer-Aurdal3265

Hemne4277

Orkdal10512

Gjoevik27648

Gaular2749

Joelster2918

Froland4672

Sauda4819

Foerde11151

Kongsvinger17279

Kvinnherad13122

Lund3129

Gjesdal9273

Gulen2459

Eidskog6499

Os_Hedmark2087

Kviteseid2598

Nordre Land6847

Birkenes4340

Fusa3709

Nord-Aurdal6442

Fjaler2916

Hemsedal1909

Soer-Fron3271

Sogndal6794

Kongsberg23244

Nome6565

Rollag1441

Modum12541

Bjerkreim2463

Oeystre Slidre3114

Levanger18001

Flesberg2517

Etnedal1397

Snillfjord1026

Bremanger4031

Eidsvoll18637

Marker3439

Aure2620

Lindaas13043

Leksvik3508


Nes_Buskerud3485

Loeten7271

Halsa1697

Molde24124

Fraena9023

Soendre Land6008

Lier21725

Soer-Odal7623

Lyngdal7244

Nord-Odal5073

Masfjorden1693 Vaksdal

4154

Modalen361

Balestrand1431

Flekkefjord8878

Vestnes6390

Hoeyanger4502

Lardal2419

Sveio4672

Selje2999

Nes_Akershus18025

Oestre Toten14604

Hurdal2602

Vindafjord4700

Horten24768

Vaaler_Hedmark3924

Gjemnes2700

Tingvoll3105

Lunner8505

Vennesla12427

Naustdal2682

Haa14784

Rakkestad7284

Eigersund13408

Hole5229

Lillehammer25075

Bamble14154

Oevre Eiker15633

Marnardal2167

Siljan2372

Vanylven3693

Gjerstad2500

Aremark1425

Askvoll3229

Vestre Slidre2245

Skaun6063

Sokndal3309

Iveland1154

Agdenes1799

Fet9567

Haegebostad1594

Sykkylven7446

Sandnes57618

Evje og Hornnes3305

Sauherad4323

Stordal1007

Jondal1078

Grimstad18885

Strand10441

Osteroey7207

Vegaarshei1854

Haram8715

Oelen3420

Eide3304

Lindesnes4484

Smoela2195

Kroedsherad2151

Krageroe10529

Tysnes2825

Tysvaer9370

Hyllestad1526

Malvik12095

Mandal14010

Klaebu5279

Granvin1008

Farsund9479

Enebakk9297

Time14461

Risoer6909

Boemlo10830

Jevnaker6335

Andebu5083

Fitjar2895

Samnanger2322

Eidsberg10203

Froeya4114

Klepp14536

Vestre Toten12546

Ulstein6795

Stord16516

Hornindal1197

Songdalen5556

Vestby12990

Karmoey37567

Averoey5448

Hoboel4557

Lillesand9043

Boe_Telemark5249

Leikanger2209

Soegne9547

Skodje3597

Roemskog667

Solund875

Fjell20043

Tvedestrand5889

Sund5537

Tustna1006

Sola19832

Frei5301

Raade6465

Askoey22020

Frosta2493

Sula7453

Vaagsoey6218

Midsund1939

Haugesund31530

Os_Hordaland14908

Aukra3050

Oerland5136

Mosvik888

Bokn769

Austevoll4451

Sande_Moere2576

Finnoey2772

Hvaler3773

Heroey_Moere8386

Hareid4658

Giske6591

Austrheim2527

Sandoey1274

Kristiansund17026

FLORO

LISTA

ROROS

ORLAND

FYRESDAL

NOTODDEN

TROLLHATTAN

3064 Hedmark3063 Oppland

3032 Telemark

3031 Buskerud

3051 Hordaland


2031 Göteborg

3043 Aust-Agder

2062 Karlstad


3041 Rogaland


3042 Vest-Agder

2072 Östersund

3021 Østfold

3012 Akershus


2063 Borlänge

3033 Vestfold

0 25 5012.5Ki lometers

Figure 3-13: Main Markets for High-Speed Rail Services in Norway


- 3-18 -

3.3.2 Database of Passenger Traffic Demand

Passenger demand figures for 2005 were deduced from several data sources and studies

for the Norwegian passenger traffic market. The analyses and forecasts were made to

reflect passenger demand potentials for High-Speed Services in Norway. Therefore, the

segmentation of the markets had been done with respect to these demand potentials.

The model used for the calculation of the traffic demand potentials for High-Speed Rail

Services does not differ between long-distance traffic and regional traffic, as all traffic

segments could be of interest for High-Speed Services.

The model calculates traffic flows between the municipalities on the basis of number of

inhabitants, the centrality of the municipalities and as far as available on empirical data of

traffic demand differentiated by means of transport. Empirical data were available for rail

and air traffic on all relevant relations. Rail data were delivered by NSB, air traffic data

were derived from statistics made by Avinor.

As no empirical data on traffic flows for car traffic were available, the information on exist-

ing car traffic flows was taken from the actual Norwegian Transport Model (NTM5) which

was erected for the workgroup for transport analysis in the National Transport Plan in

Norway [NTM5].

3.3.3 European Passenger Transport Model

The basic structure of the model can be seen in Figure 3-14.

2005

European Passenger

Transport Model

OD-MatricesAir/Rail/Road/Bus

DemandSupply

InfrastructureAir/Rail/Road

ServicesAir/Rail/Bus

Inhabitants

Basic Conditions

2020

OD-MatricesAir/Rail/Road/Bus

InfrastructureAir/Rail/Road

ServicesAir/Rail/Bus

Income / GDP

User Costs Air/Rail/Road Transport policies

Car Availability

User Costs Air/Rail/Road

Inhabitants

Income / GDP

Transport policies

Car Availability

Figure 3-14: The European Passenger Transport Model


- 3-19 -

The model is sensitive to changes in population, growth of income, car-availability, the

development of infrastructure of all means of transport, services by air, rail and bus user

costs and transport policy-related variables.

In combination with traffic demand data of the analysis year, the model is calibrated and

then applied to the various scenarios on socio-economic development and network exten-

sions for the year 2020. As result, the model produces new OD-tables of traffic demand

for the different variants of network extensions on the forecast time horizon.

The model calculated the effects of all these variables on mobility ('induced traffic'), the

spatial distribution of traffic and the modal-split. Rail traffic flows are assigned to the rail-

way network.

Figure 4-3 shows the structure of the European Passenger Transport Model. Input con-

sists of a zonal databank with geographical (used also for graphics) and socio-economic

information, a network handling system as input for a route choice/route split model for all

modes and relevant transport chains, tariff/user cost model and input concerning policy

variables transferred either to network-related or user cost-related factors.

'empirical' OD-matrix for thebase year (p.c., rail, bus, air)

O/D factors/elasticities

induced traffic

distribution

modal-split

assignment

UTILITY

FUNCTIONS

supplycharacteristics

per mode

transfered into

generalisedcosts

INESroute choice/splitmodel (incl. route chains)

zonal databank

- geographic- socio economic

ZONDAT

network handlingsystem

INES

tariff model(user costs)

PEP

networkrelated

user costrelated

policy variables

POLDAT

DEMOdemand model

value of time,

changes

access

access

GIS based graphics

road

rail

integratedairport choice/access splitmodel

air

Figure 3-15: Structure of the European Passenger Transport Model

trip generation

and

distribution


- 3-20 -

The demand model itself consists of:

o An integrated trip generation and distribution model based on socio-demographic and socio-economic factors per zone and level of services (travel time and costs) between origin and destination

o A sophisticated multi-nominal modal-split model

o A multi-route assignment procedure which also assign airport ground access per rail to the railways. Therefore, transport chains including air traffic and airport choice were considered.

The 'exogenous' variables used in the model are:

o Population per zone

o Employment per zone

o GDP development per zone

o Cars per 1000 inhabitants per zone, transferred into car availability

o Factors representing the border 'resistance', calibrated according to the ratio between international and national traffic

The level-of-service variables used in the model are:

o Travel time per main mode and trip purpose as specified by

o In vehicle time

o Access and egress time

o Transfer time (dependent on time tables)

o Adaption time (difference between desired departure/desired arrival and real de-parture/real arrival due to the possible connections)

o Travel costs per mode and trip purpose

o Transfer penalties per trip purpose

o Comfort factors per mode, system (type of train) dependent on trip purpose and share of the modes/systems used on the entire trip

o Access/egress comfort per mode dependent on trip purpose and type of zone

Transport policy related variables are considered by transferring into cost or time variables

(fuel costs -> user costs, access restrictions -> time surcharges etc.).

The model is the international extension of the passenger model for the German Federal

Master Plan of Transport [BVWP 2015].


- 3-21 -

3.3.4 Development of Population

Traffic demand will change significantly until 2020, the time horizon of traffic forecasts,

due to changes in population and growth of income. The assumptions on the development

of socio-economic figures and user costs are made in coherence with the assumptions of

other studies made for the New Norwegian Transport Master Plan.

Table 3-2 shows the development of number of inhabitants between 2005 and 2020.

Overall growth is expected to be 9.5% over this period. Growth rates in the today already

densely populated area such as the Oslo area, in Hordaland (Bergen), in Rogaland (Sta-

vanger), Vest-Agder (Kristiansand) and Sør-Trøndelag (Trondheim) are above average.

Fylke 2005 2020 GrowthTotal 4606363 5045056 9.5%

01 Østfold 258542 289631 12.0%02 Akershus 494218 569982 15.3%03 Oslo 529846 609197 15.0%04 Hedmark 188376 194967 3.5%05 Oppland 183174 187657 2.4%06 Buskerud 243491 266435 9.4%07 Vestfold 220736 243782 10.4%08 Telemark 166289 173816 4.5%09 Aust-Agder 103596 110855 7.0%10 Vest-Agder 161276 179871 11.5%11 Rogaland 393104 445131 13.2%12 Hordaland 448343 502605 12.1%14 Sogn og Fjordane 107032 108819 1.7%15 Møre og Romsdal 244689 252287 3.1%16 Sør-Trøndelag 272567 303287 11.3%17 Nord-Trøndelag 128444 135010 5.1%18 Nordland 236825 237161 0.1%19 Troms 152741 160292 4.9%20 Finnmark Finnmárku 73074 74271 1.6%

Source: SSB, Alternative MMMM, medium national growth

Development of Population by Regions 2005-2020

Table 3-2: Development of population by regions

Table 3-3 shows the development of population by age classes. While the number of

young people (less than twenty years old) will decline slightly, the number of elder people

(67 years or older) will rise by about 30%. For transport demand that means a higher

growth rate on average as long-distance mobility of young people is less than that of eld-

erly people.


- 3-22 -

Age classes 2005 2020 Index0-19 years 1198361 1191183 -0.6%20-66 years 2804068 3063557 9.3%67 years or more 603934 790316 30.9%Total 4606363 5045056 9.5%

Source: SSB, Alternative MMMM, medium national growth

Development of Population by Age Classes 2005-2020

Table 3-3: Development of population by age classes

From Annex 3.2 the development of population differentiated by region and age classes

can be taken.

3.3.5 Economic Growth and Development of Car Availability and User Costs

Figure 3-16 and Table 3-4 show the development of GDP and car availability. Economy

shall grow by about 40% between 2005 and 2020 with respect to a study made for the

finance department [FIN-DEP]. In the same period, it is expected that car availability will

grow by about 23%. Again, the assumptions are made in coherence with the assumptions

of other studies made for the New Norwegian Transport Master Plan.

Development of GDP and Car Availability

39.3

23.2

50 100 150

GDP

Car availability

2005 2005 -2020

Figure 3-16: Economic growth (GDP) and development of car availability

2005-2010p.a.

2010-2015p.a.

2015-2020p.a.

2005-2020

p.a.

Index 2020(2005=100)

GDP 2.1% 2.3% 2.3% 2.2% 139.3

Car availability 1.8% 1.2% 1.2% 1.4% 123.2Source:

2) [FIN-DEP] Tabell 5.5, total and constricted car availabilty (FK=1, B>=hfk and FK=1, B<hfk)

1) [FIN-DEP] Tabell 5.1, Bruttoprodukt, Fastland (Kilde: Finansdepartementet v/ Svein Sæterdahl)

Input for HSR Norway Study

Development of Gross Domestic Product1

Development of Car Availability2

Table 3-4: Economic growth (GDP) and development of car availability


- 3-23 -

The development of user costs is depicted in Figure 3-17.

Development of user costs 2005 - 2020

-8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0

Private Car

Train

Express Bus

Plane

Figure 3-17: Development of user costs

2005-2010 2010-2015 2015-2020 2005-2020 Index 2020

(2005=100)Private Car -0.7% 0.9% -0.2% 0.1% 100.3

Train -0.1% 0.6% 0.9% 0.5% 107.2

Express Bus -1.1% 0.1% -0.1% -0.3% 94.5Plane -0.9% 0.0% 0.1% -0.2% 95.8

Source: [TØI-1891]

Development of user costs (real prices, anual rates )

Input for HSR Norway Study

Table 3-5: Development of user costs

Compared with the assumptions made for the UIC-Passenger Traffic Study, the develop-

ment in Norway will follow a more unfavourable path for railways. Economic growth shall

be less and growth of car availability higher than in the main scenarios of the UIC study.

The prediction of user costs in Norway is most similar to the assumption of the Unfavour-

able Scenario of the UIC study with constant prices for car and bus, an average annual

decrease in air fares of 0.5% and an average annual increase in rail fares of 1.0%. The

Basic Scenario assumed an increase in using the private car and constant user costs for

all other means of transport.

3.3.6 Development of Traffic Demand without High-Speed Rail Services

Overall traffic demand will grow strongly due to growth of population and economic pros-

perity. The growth rates are different with respect to travel distances as not only the total

number of trips will grow but also travel distances (see Table 3-6). On short and medium

distances up to 300 km overall traffic demand will grow by up to 23% whereas on longer

distances within Norway traffic demand growth rate will range between 33% and 42%.


- 3-24 -

2005

abs. abs. Growth rate

Oslo - Vestfold South 7 950 9 325 17.3%

Oslo - Telemark East 3 550 3 950 11.5%Oslo - Kristiansand/Arendal 5 100 6 300 23.4%

Kristiansand - Stavanger 3 250 3 825 17.9%

Oslo - Stavanger 4 400 6 150 39.8%Stavanger - Bergen 3 900 5 050 29.4%

Oslo - Bergen 6 200 8 675 40.0%Bergen - Trondheim 1 700 2 400 41.7%

Oslo - Lillehammer (Oppland N) 2 750 2 950 7.0%Oslo - Hamar/Ringsaker 4 450 4 850 9.2%

Oslo - Trondheim 6 600 8 825 33.7%

Oslo - Stockholm 6 950 8 575 23.5%Oslo - Østfold South 8 100 9 625 18.8%

Oslo - Gøteborg 5 700 6 725 17.9%

Development of Overall Traffic Demand

(passengers per day, both directions)

in the Main Markets

- without High-Speed Services -

Market segment (OD)2020 w/o HSS

Table 3-6: Development of overall passenger traffic in main relations without High-Speed Services

Higher incomes will favour air traffic, as people will switch to the faster traffic mode de-

spite higher costs. In addition, it is expected that air fares will decline further on by about

4% until 2020 on average whereas rail fares will grow by about 7%, the market share of

the railway will fall considerably. In total, it can be assumed, that rail traffic will grow only

slightly if the railway’s level of service will be the same in 2020 as today.

3.3.7 Development of Traffic Demand with High-Speed Rail Services

If high-speed services are established, traffic demand in all mentioned market segments

will grow significantly. There are two main effects on the transport market:

a) Railway’s market share will grow strongly up to about 55% of total traffic demand

in the corridors

b) Overall traffic demand will grow due to shorter land bound travel time today and

lower user costs in comparison to air traffic

Especially the last mentioned effect is quite considerable and in the expected dimension a

specific observation on the Norwegian transport market as today’s travel times on both

land-bound modes, road and rail, range on an extreme bad level compared to land-bound

travel times in other European countries. Overall traffic demand on several market seg-

ments will grow by up to 30% when high-speed rail services will be established.

The following travel times had been assumed for the first estimation of traffic demand with

an established High-Speed Network.


- 3-25 -

Minimum Running Time on Main Markets (assuming direct lines and direct services)

Line Length of line Minimum running time

Oslo –Trondheim ca. 500 km ca. 2h45

Oslo – Bergen ca. 400 km ca. 2h15

Oslo – Stavanger ca. 400 km ca. 2h15

Oslo – Kristiansand ca. 300 km ca. 2h00

Oslo – Gøteborg ca. 300 km ca. 2h00

Oslo – Stockholm ca. 500 km ca. 4h00*

Bergen – Trondheim ca. 500 km ca. 3h00

Bergen – Stavanger ca. 200 km ca. 1h30

Stavanger – Kristiansand ca. 200 km ca. 1h00

* on Swedish side, only an upgraded line had been assumed

Table 3-7: Basic Network – Minimum Running Time on Main Markets (direct services)

Traffic demand would develop as shown in Table 3-8.

2020w/o HSS

abs. abs. Growth rate Market Share abs.

Oslo - Vestfold South 9 325 9 775 4.7% 28.4% 2 775Oslo - Telemark East 3 950 4 175 5.4% 22.1% 925Oslo - Kristiansand/Arendal 6 300 7 075 12.3% 52.2% 3 700Kristiansand - Stavanger 3 825 4 525 18.3% 49.1% 2 225Oslo - Stavanger 6 150 7 900 28.6% 53.3% 4 225Stavanger - Bergen 5 050 6 425 27.2% 55.0% 3 525Oslo - Bergen 8 675 10 925 25.8% 52.1% 5 700Bergen - Trondheim 2 400 2 950 22.8% 54.7% 1 625Oslo - Lillehammer (Oppland N) 2 950 3 075 4.3% 42.7% 1 300Oslo - Hamar/Ringsaker 4 850 5 025 3.3% 42.9% 2 150Oslo - Trondheim 8 825 10 225 16.0% 48.2% 4 925Oslo - Stockholm 8 575 10 775 25.5% 33.9% 3 650Oslo - Østfold South 9 625 10 250 6.6% 32.6% 3 350Oslo - Gøteborg 6 725 7 825 16.5% 46.1% 3 600

2020 with HSS 2020 with HSSMarket segment (OD)

Development of Overall Traffic Demand

(passengers per day, both directions)

in the Main Markets

- with and without High-Speed Services -

Overall Traffic Demand Rail Traffic

Table 3-8: Development of overall passenger traffic in main relations with High-Speed Services


- 3-26 -

Toensberg36452

Halden27582

Bergen239209

Oslo529846

Skien50676

Arendal39676

Hamar27439

Trondheim156161

Fredrikstad70418

Kristiansand76066

Moss28040

Drammen57148

Stavanger113991

Larvik41142

Sarpsborg49753

Sandefjord41289

Porsgrunn33407

OSLO/GARDERMOEN

Vinje3758

Trysil6882

Luster4927

Lesja2184

Tinn6380

Lom2467

Hol4557

Rendalen2105

Oppdal6473

Skjaak2394

Vang1613

Odda7378

Voss13850

Tynset5405

Roeros5636

Sirdal1760

Bykle857

Stryn6843

Valle1384

Suldal3901

Tydal902

Engerdal1499

Aal4670

Dovre2875

Selbu3988

Tolga1778

Rauma7336

Sunndal7370

Aamot4398

Aamli1801

Stor-Elvdal2832

Eidfjord914

Folldal1717

Aurland1783

Sel6059

Laerdal2158

Vaagaa3773

Nore og Uvdal2635

Vik2881

Grue5275

Tokke2463

Ringerike28079

Bygland1327

Alvdal2416

Surnadal6160

Nesset3181

Aardal5631

Ringebu4586

Fyresdal1353

Ulvik1163

Etne3904

Gausdal6175

Flaa1014

Aasnes7779

Meraaker2560

Sigdal3537

Holtaalen2132

Midtre Gauldal5797

Gran13010

Aaseral907

Verdal13815

Gol4375

Oeyer4840

Nissedal1408

Rennebu2660

Hjartdal1633

Rindal2101

Flora11364

Elverum18844

Ringsaker31824

Gloppen5793

Norddal1817Oersta

10233

Hitra4025

Nord-Fron5896

Stjoerdal19562

Kvam8334

Stranda4605

Ullensvang3517

Drangedal4143

Seljord2919

Stange18427

Volda8351

Kvinesdal5582

Rissa6433

Melhus13977

Meldal3934

Forsand1102

Notodden12359

Eid5766

Hjelmeland2736

Soer-Aurdal3265

Hemne4277

Orkdal10512

Gjoevik27648

Gaular2749

Joelster2918

Froland4672

Sauda4819

Foerde11151

Kongsvinger17279

Kvinnherad13122

Lund3129

Gjesdal9273

Gulen2459

Eidskog6499

Os_Hedmark2087

Kviteseid2598

Nordre Land6847

Birkenes4340

Fusa3709

Nord-Aurdal6442

Fjaler2916

Hemsedal1909

Soer-Fron3271

Sogndal6794

Kongsberg23244

Nome6565

Rollag1441

Modum12541

Bjerkreim2463

Oeystre Slidre3114

Levanger18001

Flesberg2517

Etnedal1397

Snillfjord1026

Bremanger4031

Eidsvoll18637

Marker3439

Aure2620

Lindaas13043

Leksvik3508


Nes_Buskerud3485

Loeten7271

Halsa1697

Molde24124

Fraena9023

Soendre Land6008

Lier21725

Soer-Odal7623

Lyngdal7244

Nord-Odal5073


4154

Modalen361

Balestrand1431

Flekkefjord8878

Vestnes6390

Hoeyanger4502

Lardal2419

Sveio4672

Selje2999

Nes_Akershus18025

Oestre Toten14604

Hurdal2602

Vindafjord4700

Horten24768

Vaaler_Hedmark3924

Gjemnes2700

Tingvoll3105

Lunner8505

Vennesla12427

Naustdal2682

Haa14784

Rakkestad7284

Eigersund13408

Hole5229

Lillehammer25075

Bamble14154

Oevre Eiker15633

Marnardal2167

Siljan2372

Vanylven3693

Gjerstad2500

Aremark1425

Askvoll3229

Vestre Slidre2245

Skaun6063

Sokndal3309

Iveland1154

Agdenes1799

Fet9567

Haegebostad1594

Sykkylven7446

Sandnes57618

Evje og Hornnes3305

Sauherad4323

Stordal1007

Jondal1078

Grimstad18885

Strand10441

Osteroey7207

Vegaarshei1854

Haram8715

Oelen3420

Eide3304

Lindesnes4484

Smoela2195

Kroedsherad2151

Krageroe10529

Tysnes2825

Tysvaer9370

Hyllestad1526

Malvik12095

Mandal14010

Klaebu5279

Granvin1008

Farsund9479

Enebakk9297

Time14461

Risoer6909

Boemlo10830

Jevnaker6335

Andebu5083

Fitjar2895

Samnanger2322

Eidsberg10203

Froeya4114

Klepp14536

Vestre Toten12546

Ulstein6795

Stord16516

Hornindal1197

Songdalen5556

Vestby12990

Karmoey37567

Averoey5448

Hoboel4557

Lillesand9043

Boe_Telemark5249

Leikanger2209

Soegne9547

Skodje3597

Roemskog667

Solund875

Fjell20043

Tvedestrand5889

Sund5537

Tustna1006

Sola19832

Frei5301

Raade6465

Askoey22020

Frosta2493

Sula7453

Vaagsoey6218

Midsund1939

Haugesund31530

Os_Hordaland14908

Aukra3050

Oerland5136

Mosvik888

Bokn769

Austevoll4451

Sande_Moere2576

Finnoey2772

Hvaler3773

Heroey_Moere8386

Hareid4658

Giske6591

Austrheim2527

Sandoey1274

Kristiansund17026

FLORO

LISTA

ROROS

ORLAND

FYRESDAL

NOTODDEN

TROLLHATTAN


3032 Telemark

3031 Buskerud

3051 Hordaland


2031 Göteborg

3043 Aust-Agder

2062 Karlstad


3041 Rogaland


3042 Vest-Agder

2072 Östersund

3021 Østfold

3012 Akershus


2063 Borlänge

3033 Vestfold

0 25 5012.5Kilometers

Demand on Individual Markets

Long-distance TrafficNumber of passengers/day (both directions)

1,000

5,000

ca. 5

,000

ca. 1

,500

ca. 3

,500

ca. 3

,500

ca. 3

,500

ca. 4,000

ca. 5,500

ca. 2,000

ca. 3,500

ca. 5

,000

ca. 1

,500

ca. 3

,500

ca. 3

,500

ca. 3

,500

ca. 4,000

ca. 5,500

ca. 2,000

ca. 3,500

Figure 3-18: Traffic demand on Main Markets for High-Speed Rail Services

3.4 Creating a Basic Network from the Market Point of View

The section load is one indicator for the profitability of an infrastructure project. A higher

section load normally leads to a better benefit-cost ratio.

Traffic demand figures for High-Speed Services in Norway are very low in comparison to

existing or planned HS Project in other European countries.

As mentioned in chapter 3-2, demand figures in the core areas of the HS Networks of

France, Germany or Italy are higher than 50,000 passengers per day. There will be only a

few new HS sections with traffic loads less than 10,000 passengers per day. At least, they

will have a section load of 6,000 passengers per day.

For definition of a basic network, a minimum daily section load of 4,000 passengers per

day was set.

This minimum traffic load would be reached by the three main city-pairs in Norway: Oslo –

Bergen (circa 5,500 passengers per day), Oslo – Trondheim (circa 5,000 passengers per

day) and Oslo – Stavanger (circa 4,000 passengers per day). All other relations couldn’t

be served by direct lines, due to less demand.

To heighten traffic demand, variants of high-speed lines serving several markets seg-

ments were examined by


- 3-27 -

� extension of another High-Speed-Line,

� branching from another High-Speed-Line or

� serving regional markets by additional stops.

3.4.1 Including the Main Regional Markets

By adding regional market segments to long-distance corridors it is possible to raise the

average load factor on several corridors.

For the Oslo - Gøteborg line and the Oslo – Kristiansand line, serving the regional mar-

kets will raise the average load factor significantly to a higher range and would jack the

lines into the category of the basic network. In both cases, the minimum travel time on the

main markets to Kristiansand and Goteborg would be prolonged by about 30 minutes, but

total travel time would not exceed 2h30m. Railways would still be competitive to air traffic

and the loss of demand potentials on the main markets due to longer travel times are less

than the additional potential of the regional markets.

Serving the region of Hamar would raise the average load factor of the Oslo – Trondheim

line by about 500 passengers per day.

3.4.2 Combining the Markets of the North- and West-Corridor

An alternative of serving the West-Corridor by a branch from the North-Corridor will have

no advantages in terms of average section loads compared to the separate line from Oslo

to Bergen and Oslo and Trondheim. Although, the demand potentials between Bergen

and Trondheim could be served better in this variant, overall rail traffic demand will be

less, as the loss of traffic demand on the Oslo – Bergen market due to longer travel times

is higher than the additional potential on the regional markets.

3.4.3 Combining the Markets of the South- and West-Corridor

The market potential between Oslo and Stavanger alone will not justify the construction of

a relative expensive new high-speed infrastructure next to a line between Oslo and Ber-

gen or Oslo and Kristiansand.

Linking this market segment to the Oslo – Kristiansand or the Oslo – Bergen corridor

would raise the average load factors of both lines significantly although the railway’s share

on the Oslo – Stavanger market would decrease due to longer travel times. By serving

Stavanger with a branch from the Bergen line, it would be possible to establish additional

services between Bergen and Stavanger. In the alternative via Kristiansand it would be

possible to serve the regional market between Stavanger and Kristiansand directly by the

Oslo – Kristiansand – Stavanger services. The average load factors so would rise by

about 1,500 and 3,000 passengers per day respectively.


- 3-28 -

Combining the markets in the Western parts of Norway will be a better solution than serv-

ing the potentials separately, as load factor is rising and the length of necessary new lines

will be shorter. Which variant of combined market will be the better one can only be an-

swered with a closer look to investment and operational costs.

Table 3-9 shows the traffic demand figures for the examined variants. The demand poten-

tials for High-Speed Rail Services on the individual markets, i.e. the demand between a

city pair, ranges between 1,000 and 5,500 passengers per day (both directions). For the

line variants which serve combined markets, the average demand (average section load)

amounts between 4,500 and 8,500 passenger per day.5

Market segment (OD)Individual Markets

Oslo - Vestfold South 3000Oslo - Telemark East 1000Oslo - Kristiansand/Arendal 3500Kristiansand - Stavanger 2000Oslo - Stavanger 4000Stavanger - Bergen 3500Oslo - Bergen 5500

Bergen - Trondheim 1500Oslo - Lillehammer (Oppland N) 1500Oslo - Hamar/Ringsaker 2000Oslo - Trondheim 5000Oslo - Stockholm 3500Oslo - Østfold South 3500Oslo - Göteborg 3500

Legend:

Average Section Load (passengers per day, both directions)

for Different Variants of High-speed Lines(Forecast 2020)

5000

6000

4500

5500

4500

CombinedMarkets

"high potential"; alternative variants

"high potential"; better variant available

"high potential"

8500

"low traffic demand"

Table 3-9: Average Section Load for Different Variants of High-speed Lines

5 Combining the markets will not result in an addition of the section loads of the individual mar-

kets as demand is weighted by trip length and demand on the individual markets might change due to longer travel times


- 3-29 -

3.4.4 The Basic Network

Figure 3-19 shows the basic network with the two alternatives for serving the Oslo – Sta-

vanger market.

Toensberg36452

Halden27582

Bergen239209

Oslo529846

Skien50676

Arendal39676

Hamar27439

Trondheim156161

Fredrikstad70418

Kristiansand76066

Moss28040

Drammen57148

Stavanger113991

Larvik41142

Sarpsborg49753

Sandefjord41289

Porsgrunn33407

OSLO/GARDERMOEN

Vinje3758

Trysil6882

Luster4927

Lesja2184

Tinn6380

Lom2467

Hol4557

Rendalen2105

Oppdal6473

Skjaak2394

Vang1613

Odda7378

Voss13850

Tynset5405

Roeros5636

Sirdal1760

Bykle857

Stryn6843

Valle1384

Suldal3901

Tydal902

Engerdal1499

Aal4670

Dovre2875

Selbu3988

Tolga1778

Rauma7336

Sunndal7370

Aamot4398

Aamli1801

Stor-Elvdal2832

Eidfjord914

Folldal1717

Aurland1783

Sel6059

Laerdal2158

Vaagaa3773

Nore og Uvdal2635

Vik2881

Grue5275

Tokke2463

Ringerike28079

Bygland1327

Alvdal2416

Surnadal6160

Nesset3181

Aardal5631

Ringebu4586

Fyresdal1353

Ulvik1163

Etne3904

Gausdal6175

Flaa1014

Aasnes7779

Meraaker2560

Sigdal3537

Holtaalen2132

Midtre Gauldal5797

Gran13010

Aaseral907

Verdal13815

Gol4375

Oeyer4840

Nissedal1408

Rennebu2660

Hjartdal1633

Rindal2101

Flora11364

Elverum18844

Ringsaker31824

Gloppen5793

Norddal1817Oersta

10233

Hitra4025

Nord-Fron5896

Stjoerdal19562

Kvam8334

Stranda4605

Ullensvang3517

Drangedal4143

Seljord2919

Stange18427

Volda8351

Kvinesdal5582

Rissa6433

Melhus13977

Meldal3934

Forsand1102

Notodden12359

Eid5766

Hjelmeland2736

Soer-Aurdal3265

Hemne4277

Orkdal10512

Gjoevik27648

Gaular2749

Joelster2918

Froland4672

Sauda4819

Foerde11151

Kongsvinger17279

Kvinnherad13122

Lund3129

Gjesdal9273

Gulen2459

Eidskog6499

Os_Hedmark2087

Kviteseid2598

Nordre Land6847

Birkenes4340

Fusa3709

Nord-Aurdal6442

Fjaler2916

Hemsedal1909

Soer-Fron3271

Sogndal6794

Kongsberg23244

Nome6565

Rollag1441

Modum12541

Bjerkreim2463

Oeystre Slidre3114

Levanger18001

Flesberg2517

Etnedal1397

Snillfjord1026

Bremanger4031

Eidsvoll18637

Marker3439

Aure2620

Lindaas13043

Leksvik3508


Nes_Buskerud3485

Loeten7271

Halsa1697

Molde24124

Fraena9023

Soendre Land6008

Lier21725

Soer-Odal7623

Lyngdal7244

Nord-Odal5073


4154

Modalen361

Balestrand1431

Flekkefjord8878

Vestnes6390

Hoeyanger4502

Lardal2419

Sveio4672

Selje2999

Nes_Akershus18025

Oestre Toten14604

Hurdal2602

Vindafjord4700

Horten24768

Vaaler_Hedmark3924

Gjemnes2700

Tingvoll3105

Lunner8505

Vennesla12427

Naustdal2682

Haa14784

Rakkestad7284

Eigersund13408

Hole5229

Lillehammer25075

Bamble14154

Oevre Eiker15633

Marnardal2167

Siljan2372

Vanylven3693

Gjerstad2500

Aremark1425

Askvoll3229

Vestre Slidre2245

Skaun6063

Sokndal3309

Iveland1154

Agdenes1799

Fet9567

Haegebostad1594

Sykkylven7446

Sandnes57618

Evje og Hornnes3305

Sauherad4323

Stordal1007

Jondal1078

Grimstad18885

Strand10441

Osteroey7207

Vegaarshei1854

Haram8715

Oelen3420

Eide3304

Lindesnes4484

Smoela2195

Kroedsherad2151

Krageroe10529

Tysnes2825

Tysvaer9370

Hyllestad1526

Malvik12095

Mandal14010

Klaebu5279

Granvin1008

Farsund9479

Enebakk9297

Time14461

Risoer6909

Boemlo10830

Jevnaker6335

Andebu5083

Fitjar2895

Samnanger2322

Eidsberg10203

Froeya4114

Klepp14536

Vestre Toten12546

Ulstein6795

Stord16516

Hornindal1197

Songdalen5556

Vestby12990

Karmoey37567

Averoey5448

Hoboel4557

Lillesand9043

Boe_Telemark5249

Leikanger2209

Soegne9547

Skodje3597

Roemskog667

Solund875

Fjell20043

Tvedestrand5889

Sund5537

Tustna1006

Sola19832

Frei5301

Raade6465

Askoey22020

Frosta2493

Sula7453

Vaagsoey6218

Midsund1939

Haugesund31530

Os_Hordaland14908

Aukra3050

Oerland5136

Mosvik888

Bokn769

Austevoll4451

Sande_Moere2576

Finnoey2772

Hvaler3773

Heroey_Moere8386

Hareid4658

Giske6591

Austrheim2527

Sandoey1274

Kristiansund17026

FLORO

LISTA

ROROS

ORLAND

FYRESDAL

NOTODDEN

TROLLHATTAN


3032 Telemark

3031 Buskerud

3051 Hordaland


2031 Göteborg

3043 Aust-Agder

2062 Karlstad


3041 Rogaland


3042 Vest-Agder

2072 Östersund

3021 Østfold

3012 Akershus


2063 Borlänge

3033 Vestfold

0 25 5012.5Kilometers

2h

30

-3h

00

ca. 3

h30

ca.2h

30

ca. 2h15

ca. 1h

30 ca. 3h00

ca. 2h30

ca. 1h00

2h

30

-3h

00

ca. 3

h30

ca.2h

30

ca. 2h15

ca. 1h

30 ca. 3h00

ca. 2h30

ca. 1h00

Basic Networkwith two alternatives of serving Stavanger

Main lines

Alternative lines for serving Oslo - Stavanger

Figure 3-19: Basic network

Up to this point, only market aspects were considered in classifying the different transport

corridors. Passenger demand figures alone are only one indicator of profitability of a spe-

cific project. For evaluation of the efficiency of a project, demand figures have to be dis-

cussed on a more detailed level and other aspects like investment and operational costs

have to be considered.


- 3-30 -

3.5 Conclusion

Long-distance passenger traffic is object of ongoing growth. Due to this, railways’ traffic

demand in long-distance traffic can grow likewise if basic conditions develop on a favour-

able path for railways and service quality is improved by extension of high-speed rail ser-

vices. If no upgrading of the existing rail services is planned, Norwegian’s rail market

share will diminish, while the European average will be constant or will increase in coun-

tries where new high-speed services are established.

As traffic demand potentials are relatively low, planning, construction and operation has to

be adapted to this constraint.

� Traffic demand from Oslo to Trondheim and Bergen are the highest one; from the

market point of view these corridors should be given a high priority

� Traffic demand potentials from Oslo to Gøteborg and Kristiansand should be com-

bined with demand potentials of regional markets. In doing so, these corridors can

also be given a high priority from the market point of view

� Traffic demand potentials between Oslo and Stavanger have to be combined with

demand potentials of the Oslo – Bergen or Oslo – Kristiansand corridor

Due to relative small traffic demand, investments and operation costs have to be mini-

mised, for example by integration of existing infrastructure in agglomeration areas, by de-

signing single tracked high-speed lines outside the agglomeration areas and by integra-

tion of existing services, esp. in the Oslo area.

Feasibility Study Concerning High-Speed Railway Lines in ... · Warszawa Minsk Barcelona Vilnius...

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