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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 units 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%.
ScenariosTo 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 transportpolicies and user costs
o Favourable Scenario: favourable development of transport policies and user costswith 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 theBasic Scenario
o Environmental Scenario: a favourable development of transport policies (with regardto rail traffic) with strong interventions in road traffic in light of an increasingly ecologi-cal orientation of transport policies
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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
Rseau Europen
Grande Vitesse
Europisches
Hochgeschwindigkeitsnetz
new lines
lignes nouvelles
Neubaustrecken
upgraded lines
lignes amnages
Ausbaustrecken
High-Speed Division0 500 km Version 01.03.2002
All rights reserved. UIC 2002
Tallinn
Kbenhavn
Ankara
Bucuresti
Sundsvall
Chisinau
Mnchen Wien
Nrnberg
ZrichLjubljana
Zagreb
Oslo
London
Amsterdam
Brux.
GlasgowEdinburgh
Hamburg
Hann.
Kln
Berlin
Fr.Lux.
Istanbul
Bratislava
Budapest
Skopje
Tirana
Sarajevo
BariNapoli
Roma
Sevilla
Lisboa
Mlaga
Madrid
Porto
Bordeaux
Rennes
Lyon
Genve
Paris
Praha
Stockholm
Gteborg
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).
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Figure 3-2 shows the average com-
mercial speed in the rail networks
(weighted by demand). In 2020, the
average commercial speed in theW.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 amaximum 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 - Gteborg/Malm).
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 shareof 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
FinlandSweden
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
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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 addsup 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 intercontinentaltraffic) 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 railways market share
will increase only slightly by 0.5 points due to air traffics 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
BillionPassenger-km
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 ofTransport (Basic Scenario; without Intercontinen-tal Traffic and Airport Feeder Traffic)
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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 railways 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 of392 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
905859 857 805 807
315292258161163158
170 174
0
500
1000
1500
2000
2500
3000
3500
Unfavo
urable
Tariff
Basic
Favo
urable
Environm
ental
BillionPassenger-km
0
500
1000
1500
2000
2500
3000
3500
TotalAir
P.C.
Bus
Rail
Figure 3-4: Transport Performance in Long-distance Passen-ger Traffic in 2020 for all Means of Transport forDifferent Scenarios (without Intercontinental Trafficand Airport Feeder Traffic)
392
315292
416
160
189
135145
258281
100
150
200
250
300
350
400
450
1970 1980 1990 1999 2010 2020
BillionPassenger-km
Environmental ScenarioFavourable Scenario
Past / Basic ScenarioTariff ScenarioUnfavourable Scenario
GDP-Scenario
Figure 3-5: Development of Long-distance Rail Traffic inDifferent Scenarios (Passenger-km; without Air-port Feeder Traffic)
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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 railways 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 Networkon Selected Relations (Basic Scenario, 2020)
Without Extension With ExtensionOD-Relation
Per Cent1 2 3
InternationalParis - 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%
NationalRegion of Berlin4- Mnchen 11% 34%Berlin - Mnchen 12% 41%Paris - Marseille 32%* 44%Kln - Stuttgart 27% 44%Madrid - Barcelona 12% 49%Stockholm - Malm 25% 51%
* state of 1999; before opening of the TGV Provence / Cte dAzur
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
2Region of Bruxelles: Bruxelles, Halle-Vilvoorde, Leuven, Brabant and Wallon3Region of London: London, Kent, Surrey, East and West Sussex, Berkshire, Bucks and Oxfordshire, Bed-
fordshire, Herfordshire and Essex,4Region of Berlin: Berlin, Barnim, Oberhavel, Uckermark, Frankfurt (Oder), Mrkisch-Oderland, Oder-Spree,
Brandenburg, Potsdam, Havelland, Ostprignitz-Ruppin, Potsdam-Mittelmark, Prignitz, Teltow-Flming,Cottbus, Dahme-Spreewald, Elbe-Elster, Oberspreewald-Lausitz, Spree-Neie
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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 railways
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 -
Kln).
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, broughtabout 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.
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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
AL
MK
MD
EE
Spain5635
France
5186
Sweden6211
Italy4387
Finland5763
Poland2254
Norway7064
Germany5446
Austria6349
Greece3377
Ireland4092
United Kingdom3996
Portugal4280
Czech. Republic2324
Belgium3949
Switzerland
5699
Netherlands4390
Denmark4367
Luxembourg5912
0 100 20 050Kilometres
Feasibility StudyHigh-Speed Traffic Norway
Overall Mobility(Long-distance Traffic)1999
source: UICStudy
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
France
528
Italy479
Sweden1479
Finland1141
Poland50
Norway2623
Germany648
Austria631
Greece1556
Ireland1311
United Kingdom1069
Portugal1357
Czech. Republic167
Belgium685
Switzerland
1040
Netherlands821
Denmark1084
Luxembourg1149
0 100 20050Kilometres
Feasibility StudyHigh-Speed Traffic Norway
Air Traffic Mobility(Long-distance Traffic)1999
source: UICStudy
Kilometersper Annum and Inhabitant1999
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. Norwegians air traffic mobility isthe 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, railways market share in Norway
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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
AL
MK
MD
EE
Spain325
France886
Italy512
Sweden629
Finland493
Poland508
Norway339
Germany456
Austria535
Greece113
Ireland151
United Kingdom322
Portugal224
Czech. Republic324
Belgium314
Switzerland875
Netherlands436
Denmark437
Luxembourg698
0 1 00 20 050Kilometres
Feasibility StudyHigh-Speed Traffic Norway
Rail Traffic Mobility(Long-distance Traffic)1999
source: UICStudy
Kilometersper Annum and Inhabitant1999
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
AL
MK
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%
0 100 20050Kilometres
Feasibility StudyHigh-Speed Traffic Norway
Market Share of Rail(Long-distance Traffic)1999
source: UICStudy
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 passengerdemand figures from Kbenhavn 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.
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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 tobe 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 inthe 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
Rendalen
2105
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 ogUvdal2635
Vik2881
Tokke2463
Ringerike28079
Bygland1327
Alvdal2416
Surnadal6160
Nesset3181
Aardal5631
Ringebu4586
Elverum18844
Fyresdal1353
Ulvik1163
Etne3904
Gausdal6175
Flaa1014
Sigdal3537
Holtaalen2132
MidtreGauldal5797
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
NordreLand6847
Grue5275
Nord-Aurdal6442
Fjaler2916
Hemsedal1909
Soer-Fron3271
Nome6565
Rollag1441
OeystreSlidre3114
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
OevreEiker15633
Marnardal2167
Siljan2372
Sarpsborg49753
Vaaler_Hedmark3924
Vanylven3693
Gjerstad2500
VestreSlidre2245
Skaun6063
Sokndal3309
Iveland1154
Kongsvinger17279
Fet9567
Haegebostad1594
Sykkylven7446
Sandnes57618
Evjeog 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
Enebakk
9297
Ski26800
Time14461
Audnedal1575
Soerum12925
Risoer6909
Boemlo10830
Jevnaker6335 Nittedal
19578
Hurum8799
Andebu5083 Fredrikstad
70418
Fitjar2895
Samnanger2322
Eidsberg10203
Klepp14536
Kristiansand76066
VestreToten12546
Ulstein6795
Troegstad4962
Stord16516
Hornindal1197
Songdalen5556
Vestby12990
Karmoey37567
Averoey5448
Hoboel4557
Lillesand9043
Boe_Telemark5249
Leikanger2209
Soegne9547
Skodje3597
Roemskog
667
Solund875
Stokke10014
Rissa6433
Rygge13712
Askim14089
Fjell20043
Tvedestrand5889
Oerskog2121
Sund5537
Porsgrunn33407
Tustna1006
Sola19832
Radoey4656
Frei5301
Raade6465
Skiptvet3355
Askoey22020
Sula7453
NedreEiker21522
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 Sr-Trndelag
3052 Sogn og Fjordane
3041 Rogaland
3053 Mre og Romsdal
3043 Aust-Agder
3042 Vest-Agder
2031 Gteborg
3082 Nord-Trndelag
3021 stfold
3012 Akershus
3033 Vestfold
2062 Karlstad
2063 Borlnge
Oslo529846
Ullensaker24556
Baerum104690
Aas14472
Vaaler_Oestfold4020
Asker50858
Spydeberg4798
Frogn13358
Drammen57148
Roeyken17280
Gjerdrum5064
Skedsmo42094
Svelvik6441
Raelingen14797
Loerenskog30675
Nesodden16231
Oppegaard23586
i3508
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
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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 Gteborg
o Bergen - Trondheim
o Bergen Stavanger
o Bergen Kristiansand
o
Stavanger - KristiansandOutside the agglomeration area of Oslo (stlandet), 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 Karmy (38 000 inhabitants*)
In the agglomeration area of Oslo (stlandet), the following potentials could be attendedby High-Speed Services on the way between the above mentioned centres:
o Oslo Vestfold South (Tnsberg/Sandefjord/Larvik)
o Oslo Telemark East (Skien/Porsgrunn)
o Oslo Hamar/Ringsaker
o Oslo stfold South (Fredrikstad/Sarpsborg/Halden)
*in 2005
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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
Aurskog-Hoeland13275
Loeten7271
SoendreLand6008
Lier21725
Soer-Odal7623
Nord-Odal5073
Drangedal4143
Eidskog6499
Lardal2419
Nes_Akershus18025
Oestre Toten14604
Hurdal2602
Horten24768
Lunner8505
Rakkestad7284
Hole5229
Bamble14154
OevreEiker15633
Siljan2372
Nes_Buskerud3485
Aremark1425
Fet9567
Lillehammer25075
Vaaler_Hedmark
3924
Sauherad4323
Hof
3048
Nannestad10141
Kroedsherad2151
Enebakk9297
Ski26800
Soerum12925
Nittedal19578
Hurum8799
Andebu5083
Ullensaker24556
Eidsberg10203
VestreToten12546
Krageroe10529
Jevnaker6335
Baerum104690
Troegstad4962
Vestby12990
Hoboel
4557
Roemskog667
Stokke10014
Aas14472
Rygge13712
Vaaler_Oestfold4020
Askim14089
Asker50858
Spydeberg4798
Raade6465
Skiptvet3355
Frogn13358
NedreEiker21522
Sande_Vestfold
7690
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 stfold
3012 Akershus
3032 Telemark
3033 Vestfold
2031 Gteborg
2062 Karlstad
3011 Oslo
0 12.5
Kilometers
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
Aurskog-Hoeland13275
Loeten7271
SoendreLand6008
Lier21725
Soer-Odal7623
Nord-Odal5073
Drangedal4143
Eidskog6499
Lardal2419
Nes_Akershus18025
Oestre Toten14604
Hurdal2602
Horten24768
Lunner8505
Rakkestad7284
Hole5229
Bamble14154
OevreEiker15633
Siljan2372
Nes_Buskerud3485
Aremark1425
Fet9567
Lillehammer25075
Vaaler_Hedmark
3924
Sauherad4323
Hof
3048
Nannestad10141
Kroedsherad2151
Enebakk9297
Ski26800
Soerum12925
Nittedal19578
Hurum8799
Andebu5083
Ullensaker24556
Eidsberg10203
VestreToten12546
Krageroe10529
Jevnaker6335
Baerum104690
Troegstad4962
Vestby12990
Hoboel
4557
Roemskog667
Stokke10014
Aas14472
Rygge13712
Vaaler_Oestfold4020
Askim14089
Asker50858
Spydeberg4798
Raade6465
Skiptvet3355
Frogn13358
NedreEiker21522
Sande_Vestfold
7690
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 stfold
3012 Akershus
3032 Telemark
3033 Vestfold
2031 Gteborg
2062 Karlstad
3011 Oslo
0 12.5
Kilometers
Toensberg36452
Halden27582
Bergen239209
Oslo529846
Skien50676
Arendal39676
Hamar27439
Trondheim156161
Fredrikstad70418
Kristiansand76066
Moss28040
Drammen57148
Stavanger113991
Larvik41142
Sarpsborg49753
Sandefjord41289
Porsgrunn33407
OSLO/GARDERMOEN
Vinje3758
Trysil
6882
Luster4927
Lesja2184
Tinn6380
Lom2467
Hol4557
Rendalen2105
Oppdal
6473
Skjaak2394
Vang1613
Odda7378
Voss13850
Tynset5405
Roeros5636
Sirdal1760
Bykle
857
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
Fyresdal
1353
Ulvik
1163
Etne3904
Gausdal
6175
Flaa1014
Aasnes7779
Meraaker2560
Sigdal3537
Holtaalen2132
MidtreGauldal5797
Gran13010
Aaseral907
Verdal13815
Gol4375
Oeyer4840
Nissedal1408
Rennebu
2660
Hjartdal1633
Rindal2101
Flora11364
Elverum18844
Ringsaker31824
Gloppen5793
Norddal1817Oersta
10233
Hitra4025
Nord-Fron5896
Stjoerdal19562
Kvam8334
Stranda4605
Ullensvang3517
Drangedal4143
Seljord
2919
Stange
18427
Volda8351
Kvinesdal5582
Rissa6433
Melhus13977
Meldal3934
Forsand1102
Notodden12359
Eid5766
Hjelmeland2736
Soer-Aurdal3265
Hemne4277
Orkdal10512
Gjoevik27648
Gaular2749
Joelster2918
Froland4672
Sauda
4819
Foerde11151
Kongsvinger17279
Kvinnherad13122
Lund3129
Gjesdal9273
Gulen2459
Eidskog6499
Os_Hedmark2087
Kviteseid2598
NordreLand6847
Birkenes4340
Fusa3709
Nord-Aurdal6442
Fjaler2916
Hemsedal1909
Soer-Fron3271
Sogndal6794
Kongsberg23244
Nome6565
Rollag1441
Modum12541
Bjerkreim2463
Oeystre Slidre3114
Levanger18001
Flesberg2517
Etnedal1397
Snillfjord1026
Bremanger4031
Eidsvoll18637
Marker
3439
Aure2620
Lindaas13043
Leksvik3508
Aurskog-Hoeland13275
Nes_Buskerud3485
Loeten7271
Halsa1697
Molde24124
Fraena
9023
SoendreLand6008
Lier21725
Soer-Odal7623
Lyngdal7244
Nord-Odal5073
Masfjorden1693 Vaksdal
4154
Modalen361
Balestrand1431
Flekkefjord8878
Vestnes6390
Hoeyanger4502
Lardal2419
Sveio
4672
Selje2999
Nes_Akershus18025
Oestre Toten14604
Hurdal2602
Vindafjord4700
Horten
24768
Vaaler_Hedmark3924
Gjemnes2700
Tingvoll3105
Lunner8505
Vennesla12427
Naustdal2682
Haa14784
Rakkestad7284
Eigersund13408
Hole5229
Lillehammer25075
Bamble14154
OevreEiker
15633
Marnardal2167
Siljan2372
Vanylven3693
Gjerstad
2500
Aremark
1425
Askvoll3229
Vestre Slidre2245
Skaun6063
Sokndal3309
Iveland1154
Agdenes1799
Fet9567
Haegebostad1594
Sykkylven7446
Sandnes57618
Evje og Hornnes3305
Sauherad4323
Stordal1007
Jondal1078
Grimstad18885
Strand
10441
Osteroey7207
Vegaarshei1854
Haram8715
Oelen
3420
Eide3304
Lindesnes4484
Smoela2195
Kroedsherad2151
Krageroe10529
Tysnes2825
Tysvaer9370
Hyllestad1526
Malvik12095
Mandal14010
Klaebu5279
Granvin1008
Farsund
9479
Enebakk
9297
Time14461
Risoer6909
Boemlo10830
Jevnaker6335
Andebu
5083
Fitjar2895
Samnanger2322
Eidsberg10203
Froeya
4114
Klepp14536
Vestre Toten
12546
Ulstein6795
Stord16516
Hornindal1197
Songdalen5556
Vestby12990
Karmoey37567
Averoey5448
Hoboel4557
Lillesand
9043
Boe_Telemark5249
Leikanger2209
Soegne9547
Skodje3597
Roemskog667
Solund875
Fjell20043
Tvedestrand5889
Sund5537
Tustna1006
Sola19832
Frei5301
Raade6465
Askoey22020
Frosta2493
Sula7453
Vaagsoey6218
Midsund1939
Haugesund31530
Os_Hordaland14908
Aukra
3050
Oerland5136
Mosvik
888
Bokn
769
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
3081 Sr-Trndelag
2031 Gteborg
3043 Aust-Agder
2062 Karlstad
3052 Sogn og Fjordane
3041 Rogaland
3053 Mre og Romsdal
3042 Vest-Agder
2072 stersund
3021 stfold
3012 Akershus
3082 Nord-Trndelag
2063 Borlnge
3033 Vestfold
0 25 5012.5
Kilometers
Figure 3-13: Main Markets for High-Speed Rail Services in Norway
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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 railand 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 ModelThe basic structure of the model can be seen in Figure 3-14.
2005
European PassengerTransport 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 CostsAir/Rail/Road Transport policies
Car Availability
User CostsAir/Rail/Road
Inhabitants
Income / GDP
Transport policies
Car Availability
Figure 3-14: The European Passenger Transport Model
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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-economicinformation, 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
transferedinto
generalisedcosts
INES
route 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 generationand
distribution
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The demand model itself consists of:
o An integrated trip generation and distribution model based on socio-demographic andsocio-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 tothe railways. Therefore, transport chains including air traffic and airport choice wereconsidered.
The 'exogenous' variables used in the model are:
o Population per zone
o Employment per zone
o GDP development per zoneo Cars per 1000 inhabitants per zone, transferred into car availability
o Factors representing the border 'resistance', calibrated according to the ratio betweeninternational 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 shareof 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].
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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 Sr-Trndelag (Trondheim) are above average.
Fylke 2005 2020 GrowthTotal 4606363 5045056 9.5%01 stfold 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 Mre og Romsdal 244689 252287 3.1%16 Sr-Trndelag 272567 303287 11.3%17 Nord-Trndelag 128444 135010 5.1%18 Nordland 236825 237161 0.1%19 Troms 152741 160292 4.9%20 Finnmark Finnmrku 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 ofyoung 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.
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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. Economyshall 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
2005-2010
p.a.
2010-2015
p.a.
2015-2020
p.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
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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% 100.3Train -0.1% 0.6% 0.9% 0.5% 107.2Express Bus -1.1% 0.1% -0.1% -0.3% 94.5Plane -0.9% 0.0% 0.1% -0.2% 95.8Source: [TI-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 ServicesOverall 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%.
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2005abs. 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 - stfold South 8 100 9 625 18.8%Oslo - Gteborg 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-SpeedServices
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 railways 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) Railways 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 andlower 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 todays 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.
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Minimum Running Time on Main Markets(assuming direct lines and direct services)
Line Length of line Minimum running
timeOslo 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 Gteborg 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 - stfold South 9 625 10 250 6.6% 32.6% 3 350Oslo - Gteborg 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-SpeedServices
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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 ogUvdal2635
Vik2881
Grue5275
Tokke2463
Ringerike28079
Bygland1327
Alvdal2416
Surnadal
6160
Nesset3181
Aardal5631
Ringebu4586
Fyresdal1353
Ulvik1163
Etne3904
Gausdal6175
Flaa1014
Aasnes7779
Meraaker2560
Sigdal3537
Holtaalen2132
MidtreGauldal5797
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
NordreLand6847
Birkenes4340
Fusa3709
Nord-Aurdal6442
Fjaler2916
Hemsedal1909
Soer-Fron3271
Sogndal6794
Kongsberg23244
Nome6565
Rollag1441
Modum12541
Bjerkreim2463
OeystreSlidre3114
Levanger18001
Flesberg2517
Etnedal1397
Snillfjord1026
Bremanger4031
Eidsvoll18637
Marker3439
Aure2620
Lindaas13043
Leksvik3508
Aurskog-Hoeland13275
Nes_Buskerud3485
Loeten7271
Halsa1697
Molde24124
Fraena9023
SoendreLand6008
Lier21725
Soer-Odal7623
Lyngdal7244
Nord-Odal5073
Masfjorden1693 Vaksdal
4154
Modalen361
Balestrand1431
Flekkefjord8878
Vestnes6390
Hoeyanger4502
Lardal2419
Sveio4672
Selje2999
Nes_Akershus18025
OestreToten14604
Hurdal2602
Vindafjord4700
Horten24768
Vaaler_Hedmark3924
Gjemnes2700
Tingvoll3105
Lunner8505
Vennesla12427
Naustdal2682
Haa14784
Rakkestad7284
Eigersund13408
Hole5229
Lillehammer25075
Bamble14154
OevreEiker15633
Marnardal2167
Siljan2372
Vanylven3693
Gjerstad2500
Aremark1425
Askvoll3229
VestreSlidre2245
Skaun6063
Sokndal3309
Iveland1154
Agdenes1799
Fet9567
Haegebostad1594
Sykkylven7446
Sandnes57618
Evjeog 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
Stord
16516
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
Aukra
3050
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
3081 Sr-Trndelag
2031 Gteborg
3043 Aust-Agder
2062 Karlstad
3052 Sogn og Fjordane
3041 Rogaland
3053 Mre og Romsdal
3042 Vest-Agder
2072 stersund
3021 stfold
3012 Akershus
3082 Nord-Trndelag
2063 Borlnge
3033 Vestfold
0 25 5012.5
Kilometers
Demand on Individual MarketsLong-distance TrafficNumber of passengers/day (both directions)
1,000
5,000ca.5,0
00
ca.1
,500
ca.3
,500 ca
.3,5
00
ca
.
3,500
ca.4,00
0
ca.5,500
ca.2,000
ca.3,500
ca.5,0
00
ca.1
,500
ca.3
,500 ca
.3,5
00
ca
.
3,500
ca.4,00
0
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 ViewThe 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 couldnt
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
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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 - Gteborg 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 trafficand 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 Osloto 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 railways 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.
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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)IndividualMarkets
Oslo - Vestfold South 3000Oslo - Telemark East 1000Oslo - Kristiansand/Arendal 3500Kristiansand - Stavanger 2000Oslo - Stavanger 4000Stavanger - Bergen 3500Oslo - Bergen 5500Bergen - Trondheim 1500Oslo - Lillehammer (Oppland N) 1500Oslo - Hamar/Ringsaker 2000Oslo - Trondheim 5000
Oslo - Stockholm 3500Oslo - stfold South 3500Oslo - Gteborg 3500
Legend:
Average Section Load (passengers per day, both directions)for Different Variants of High-speed Lines
(Forecast 2020)
5000
60004500
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 changedue to longer travel times
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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, Norwegians 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 Gteborg 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.
Recommended