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Norway High Speed Rail Assessment Study: Phase III Market, Demand and Revenue Analysis Final Report

25 January 2012

Norway HSR Assessment Study - Phase III Market, Demand and Revenue Analysis, Final Report

Atkins Norway HSR Assessment Study - Phase III: Market, Demand and Revenue Analysis,

Final Report

Notice

This document and its contents have been prepared and are intended solely for Jernbaneverket‟s information and use in relation to the Norway High Speed Rail Study - Phase III.

Atkins assumes no responsibility to any other party in respect of or arising out of or in connection with this document and/or its contents.

This document has 118 pages including the cover.

Document history

Job number: 5101627 Document ref: Final Report

Revision Purpose description Originated Checked Reviewed Authorised Date

Rev 1.0 Phase III Final Report, Draft for Client Review

TH / JA TH / JA MH AJC / WL 17/01/12

Rev 1.1 Updated Appendices TH JA MH AJC / WL 19/01/12

Rev 2.0 Phase III Final Report, incorporating Client comments

TH JA MH AJC / WL 25/01/12

Client signoff

Client Jernbaneverket

Project Norway High Speed Rail Assessment Study - Phase III

Document title Norway High Speed Rail Assessment Study - Phase III: Market Demand and Revenue Analysis, Final Report

Job no. 5101627

Copy no.

Document reference Final Report



Final Report

Table of contents

Chapter Pages

1. Introduction 8 1.1. Background 8 1.2. Overall Context of this Report 8 1.3. Purpose of this Report 9 1.4. Structure of this Report 9

2. Development of Market Forecasts 10 2.1. Introduction 10 2.2. Update of Phase II Forecasting Approach 10 2.3. Main Forecasting Assumptions 11 2.4. Explanation of Model Outputs 17

3. North Corridor Results 19 3.1. Introduction 19 3.2. Alternative G3:Y 19 3.3. Alternative Ø2:P 24 3.4. Summary 28

4. West Corridor Results 29 4.1. Introduction 29 4.2. Alternative N1:Q 29 4.3. Alternative HA2:P 34 4.4. Alternative H1:P 39 4.5. Alternative BS1:P 44 4.6. Summary 48

5. South Corridor Results 49 5.1. Introduction 49 5.2. Alternative S8:Q 49 5.3. Alternative S2:P 55 5.4. Summary 59

6. East Corridor Results 60 6.1. Introduction 60 6.2. Alternative ST5:U 60 6.3. Alternative ST3:R 64 6.4. Alternative GO3:Q 68 6.5. Alternative GO1:S 72 6.6. Summary 75

7. Corridor Comparison 76 7.1. Introduction 76 7.2. Comparison between Corridors 76 7.3. Impact of Alternative Service Assumptions 78 7.4. International Comparison 80

8. Scenario B Results 84 8.1. Introduction and Description of Scenario B Alternatives 84 8.2. Scope of Analysis 84 8.3. Scenario B Market, Demand and Revenue Analysis 85

9. Conclusions 90 9.1. Introduction 90



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9.2. Demand and Revenue Forecasts 90 9.3. Further Development 91

Appendices 92

Appendix A. Alternative Summary Tables 93 A.1. Description of Summary Tables 93 A.2. Alternatives Included in Summary Tables 94 G3:Y, PSS1 97 Ø2:P, PSS1 98 N1:Q, PSS1 99 HA2:P, PSS1 100 H1:P, PSS1 101 BS1:P, PSS1 102 S8:Q, PSS1 103 S2:P, PSS1 104 ST5:U, PSS1 105 ST3:R, PSS1 106 GO3:Q, PSS1 107 GO1:S, PSS1 108 G1:P, PSS1 109 HA1:Q, PSS1 110 N4:P, PSS1 111 S8:T, PSS1 112 S3:Z, PSS1 113 S4:P, PSS1 114 ST1:Q, PSS1 115 ST2:R, PSS1 116

Tables Table 1. HSR Alternatives Considered for Detailed Technical Analysis 14 Table 2. HSR Alternatives Considered for Sensitivity Testing of Demand and Revenue 14 Table 3. Summary of Demand and Revenue – Alternative G3:Y 19 Table 4. Source of HSR Demand – G3:Y 20 Table 5. HSR Demand by Origin/Destination – G3:Y 20 Table 6. Summary of Demand and Revenue – Alternative G3:Y (PSS2) 23 Table 7. Summary of Demand and Revenue – Alternative Ø2:P 24 Table 8. Source of HSR Demand – Ø2:P 24 Table 9. HSR Demand by Origin/Destination – Ø2:P 25 Table 10. Summary of Demand and Revenue – Alternative Ø2:P (PSS2) 28 Table 11. Summary of Demand and Revenue – Alternative N1:Q 29 Table 12. Source of HSR Demand – N1:Q 30 Table 13. HSR Demand by Origin/Destination – N1:Q 30 Table 14. Summary of Demand and Revenue – Alternative N1:Q (PSS2) 32 Table 15. Summary of Demand and Revenue – Alternative HA2:P 34 Table 16. Source of HSR Demand – HA2:P 34 Table 17. HSR Demand by Origin/Destination – HA2:P 35 Table 18. Summary of Demand and Revenue – Alternative HA2:P (PSS2) 37 Table 19. Summary of Demand and Revenue – Alternative H1:P 39 Table 20. Source of HSR Demand – H1:P 39 Table 21. HSR Demand by Origin/Destination – H1:P 40 Table 22. Summary of Demand and Revenue – Alternative H1:P (PSS2) 42



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Table 23. Summary of Demand and Revenue – Alternative BS1:P 44 Table 24. Source of HSR Demand – BS1:P 44 Table 25. HSR Demand by Origin/Destination – BS1:P 45 Table 26. Summary of Demand and Revenue – Alternative BS1:P (PSS2) 47 Table 27. Summary of Demand and Revenue – Alternative S8:Q 49 Table 28. Source of HSR Demand – S8:Q 50 Table 29. HSR Demand by Origin/Destination – S8:Q 50 Table 30. Summary of Demand and Revenue – Alternative S8:Q (PSS2) 53 Table 31. Summary of Demand and Revenue – Alternative S2:P 55 Table 32. Source of HSR Demand – S2:P 55 Table 33. HSR Demand by Origin/Destination – S2:P 56 Table 34. Summary of Demand and Revenue – Alternative S2:P (PSS2) 58 Table 35. Summary of Demand and Revenue – Alternative ST5:U 60 Table 36. Source of HSR Demand – ST5:U 61 Table 37. HSR Demand by Origin/Destination – ST5:U 61 Table 38. Summary of Demand and Revenue – Alternative ST5:U (PSS2) 63 Table 39. Summary of Demand and Revenue – Alternative ST3:R 64 Table 40. Source of HSR Demand – ST3:R 64 Table 41. HSR Demand by Origin/Destination – ST3:R 65 Table 42. Summary of Demand and Revenue – Alternative ST3:R (PSS2) 66 Table 43. Summary of Demand and Revenue – Alternative GO3:Q 68 Table 44. Source of HSR Demand – GO3:Q 68 Table 45. HSR Demand by Origin/Destination – GO3:Q 69 Table 46. Summary of Demand and Revenue – Alternative GO3:Q (PSS2) 70 Table 47. Summary of Demand and Revenue – Alternative GO1:S 72 Table 48. Source of HSR Demand – GO1:S 72 Table 49. HSR Demand by Origin/Destination – GO1:S 73 Table 50. Summary of Demand and Revenue – Alternative GO1:S (PSS2) 74 Table 51. Comparison of International HSR Travel 82 Table 52. Comparison of International HSR Travel (Passenger km) 83 Table 53. Scenario B Summary of Specification 84 Table 54. Scenario B Journey Times 85 Table 55. NTM5 Journey Time Factor 85 Table 56. Summary of Demand and Revenue: Oslo – Trondheim 86 Table 57. Summary of Demand and Revenue: Oslo – Bergen 87 Table 58. Summary of Demand and Revenue: Oslo – Stavanger 88 Table 59. Summary of Demand and Revenue: Oslo – Stockholm 89 Table 60. HSR Alternatives Considered for Detailed Technical Analysis 94 Table 61. HSR Alternatives Considered for Sensitivity Testing of Demand and Revenue 94

Figures Figure 1. HSR Corridors and Route Alternatives 12 Figure 2. HSR Daily Boardings / Alightings by Station – G3:Y 20 Figure 3. Mode Share by HSR Journey Length, 2024 – G3:Y 21 Figure 4. Annual HSR Demand by Originating Zone and Daily Boarding Demand by Station, 2024 –

G3:Y 22 Figure 5. HSR Daily Boardings and Alightings by Station – G3:Y (PSS2) 23 Figure 6. HSR Daily Boardings / Alightings by Station 25 Figure 7. Mode share by HSR Journey Length, 2024 – Ø2:P 26 Figure 8. Annual HSR Demand by Originating Zone and Daily Boarding Demand by Station, 2024 –

Ø2:P 27



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Figure 9. HSR Daily Boardings and Alightings by Station – Ø2:P (PSS2) 28 Figure 10. HSR Boardings / Alightings by Station – N1:Q 30 Figure 11. Mode share by HSR Journey Length, 2024 – N1:Q 31 Figure 12. Annual HSR Demand by Originating Zone and Daily Boarding Demand by Station, 2024 –

N1:Q 32 Figure 13. HSR Boardings and Alightings by Station – N1:Q (PSS2) 33 Figure 14. HSR Boardings / Alightings by Station – HA2:P 35 Figure 15. Mode share by HSR Journey Length, 2024 – HA2:P 36 Figure 16. Annual HSR Demand by Originating Zone and Daily Boarding Demand by Station – HA2:P 37 Figure 17. HSR Boardings and Alightings by Station – HA2:P (PSS2) 38 Figure 18. HSR Boardings / Alightings by Station – H1:P 40 Figure 19. Mode share by HSR Journey Length, 2024 – H1:P 41 Figure 20. Annual HSR Demand by Originating Zone and Daily Boarding Demand by Station – H1:P 42 Figure 21. HSR Boardings and Alightings by Station – H1:P (PSS2) 43 Figure 22. HSR Boardings / Alightings by Station – BS1:P 45 Figure 23. Mode share by HSR Journey Length, 2024 – BS1:P 46 Figure 24. Annual HSR Demand by Originating Zone and Daily Boarding Demand by Station – BS1:P 47 Figure 25. HSR Boardings and Alightings by Station – BS1:P (PSS2) 48 Figure 26. HSR Boardings / Alightings by Station – S8:Q 51 Figure 27. Mode share by HSR Journey Length, 2024 – S8:Q 52 Figure 28. Annual HSR Demand by Originating Zone and Daily Boarding Demand by Station – S8:Q 53 Figure 29. HSR Boardings and Alightings by Station – S8:Q (PSS2) 54 Figure 30. HSR Boardings / Alightings by Station – S2:P 56 Figure 31. Mode share by HSR Journey Length, 2024 – S2:P 57 Figure 32. Annual HSR Demand by Originating Zone and Daily Boarding Demand by Station – S2:P 58 Figure 33. HSR Boardings and Alightings by Station – S2:P (PSS2) 59 Figure 34. HSR Boardings / Alightings by Station – ST5:U 61 Figure 35. Mode share by HSR Journey Length, 2024 – ST5:U 62 Figure 36. HSR Boardings and Alightings by Station – ST5:U (PSS2) 63 Figure 37. HSR Boardings / Alightings by Station – ST3:R 65 Figure 38. Mode share by HSR Journey Length, 2024 – ST3:R 66 Figure 39. HSR Boardings and Alightings by Station – ST3:R (PSS2) 67 Figure 40. HSR Boardings / Alightings by Station – GO3:Q 69 Figure 41. Mode share by HSR Journey Length, 2024 – GO3:Q 70 Figure 42. HSR Boardings and Alightings by Station – GO3:Q (PSS2) 71 Figure 43. HSR Boardings / Alightings by Station – GO1:S 73 Figure 44. Mode share by HSR Journey Length, 2024 – GO1:S 74 Figure 45. HSR Boardings and Alightings by Station – GO1:S (PSS2) 75 Figure 46. Comparison of HSR Passengers by Alternative 76 Figure 47. Comparison of HSR Revenue by Alternative 77 Figure 48. Comparison of HSR Average Occupancy by Alternative 77 Figure 49. Comparison of HSR Passengers by Alternative with PSS2 78 Figure 50. Comparison of HSR revenue by alternative with PSS2 79 Figure 51. Comparison of HSR Average Occupancy by Alternative with PSS2 79 Figure 52. Impact of HSR Fare on Demand and Revenue (Alternative Ø2:P, 2024) 80 Figure 53. Rail-air Market Share (Steer Davies Gleave, 2006) 81 Figure 54. Long Distance Boardings by Station: Oslo – Trondheim 87 Figure 55. Long Distance Boardings by Station: Oslo – Bergen 88 Figure 56. Long Distance Boardings by Station: Oslo – Stavanger 89



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1. Introduction

1.1. Background Jernbaneverket (JBV) has been mandated by the Norwegian Ministry of Transport and Communications to assess the issue of High Speed Rail (HSR) lines in Norway. There is a National Transport Plan covering the period from 2010-2019 which includes relatively minor enhancements to the railway network. The ministry wishes to understand if going beyond this and implementing a step change in rail service provision in the form of higher speed concepts could “contribute to obtaining socio-economically efficient and sustainable solutions for a future transport system with increased transport capacity, efficiency and accessibility”.

Previous studies have been carried out looking into HSR in Norway and there are various conflicting views. The aim of this study is to provide a transparent, robust and evidence based assessment of the costs and benefits of HSR to support investment decisions.

The Norway HSR Assessment Study has been divided into three phases.

In Phase I, which was completed in July 2010, the knowledge base that already existed in Norway was collated, including outputs from previous studies. This included the studies that already were conducted for the National Rail Administration and the Ministry of Transport and Communication, but also publicly available studies conducted by various stakeholders, such as Norsk Bane AS, Høyhastighetsringen AS and Coinco North.

The objective of Phase II was to identify a common basis to be used to assess a range of possible interventions on the main rail corridors in Norway, including links to Sweden. The work in Phase II used and enhanced existing information, models and data. New tools were created where existing tools were not suitable for assessing HSR. Phase II was completed in March 2011.

In Phase III the tools and guiding principles established in Phase II have been used to test scenarios and develop alternatives on the different corridors.

This report is part of the final reporting of Phase III and addresses passenger market, demand and revenue analysis. With regards to demand and revenue forecasting, a number of additional developments have been undertaken to the tools developed during the previous phase, to improve forecasting robustness. A separate analysis of possible feeder services to improve HSR station accessibility is presented in a separate supplementary report to this.

1.2. Overall Context of this Report The purpose of the market analysis exercise is to establish the size of the potential HSR passenger market under different HSR scenarios, and associated revenues. This involves identifying the current market and its projected growth, mode share and the preferences and priorities of those markets. The current market is used as a basis, together with expected willingness to pay for new services, to forecast how much of this market would be attracted to new HSR scenarios, and how much additional demand may be induced.

The outputs from the demand forecasting are used to predict the revenue and socio-economic impacts of the proposed investments. This includes the passengers who would transfer from an existing mode (air, car, existing rail services, coach or ferry) as well as the demand generated as a result of the investment.

The revenue is combined with cost estimates to determine the commercial viability of the investment and together with demand forecasts are used to help specify the infrastructure and rolling stock required. The socio-economic benefits, including environmental impacts, are calculated using outputs from the demand forecasting tool. These benefits are combined with costs and revenue as well as other non-quantified assessments to provide the overall socio-economic assessment of the proposed investments. All of these outputs are used as a basis for making investment decisions and development plans.



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1.3. Purpose of this Report This report presents a summary of the key results for HSR demand and revenue for each of the HSR scenarios examined. The report describes the detailed results of the above tests on a corridor-by-corridor basis, with the exception of modest upgrades to the existing alignments which are presented in a separate chapter.

The analysis presented provides a summary of demand, revenue and passenger km travelled within each route on each corridor, and includes an assessment of the average occupancy of high speed services.

For HSR alternatives the forecasts of incremental rail demand and revenue over the reference case are subdivided into (a) journeys abstracted from air, car, coach and (where appropriate) classic rail, and (b) pure generation, i.e. additional mobility induced by the improved journeys. Additionally the mode share obtained by HSR over trips of varying lengths is shown to assess to competitiveness for different journeys.

In addition, we examine what key conclusions – from a market analysis perspective – can be drawn from the results and discuss the next steps should individual alternatives need to be examined in more detail at a later stage.

Further more detailed model outputs are presented in the appendix to this report.

1.4. Structure of this Report The remainder of this report has the following chapters:

Chapter 2 - Development of Market Forecasts. This chapter sets out a summary of the key assumptions used in developing the market and revenue forecasts in Phase III with relation to both the routes and journey time assumptions and the modelling methodology. Full details of these assumptions can be found in the separate „Journey Time Analysis‟ and „Model Development Report‟, respectively.

The following four chapters present the results for each corridor‟s in turn. These include:

- Chapter 3 - North Corridor. - Chapter 4 - West Corridor - Chapter 5 - South Corridor - Chapter 6 - East Corridor

Chapter 7 - Corridor Comparison. This chapter compares forecasts of demand and revenue by alternative HSR routes and against international HSR usage.

Chapter 8 - Scenario B results, presents the results of analysis examining a development of the existing rail infrastructure generally intended to provide a 20% reduction in travel time whilst maintaining the existing stopping pattern.

Chapter 9 – draws conclusions from the preceding eight chapters.

In addition, Appendix A presents more detailed model outputs for each of the main alternatives presented in the report.



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2. Development of Market Forecasts

2.1. Introduction This chapter provides an overview of the methodology and assumptions used to produce the market and revenue forecasts provided in the latter chapters of this report.

The demand forecasts have been produced using a bespoke modelling framework established to assess Norwegian HSR alternatives during Phase II of this study. This framework has been refined further during Phase III to improve representation of certain journey segments. Sections 2.2 and 2.3 of this report summarise the developments undertaken in this phase of the study and the wider modelling assumptions respectively. For a full description of the model development please refer to the separate report:

“Norway HSR Assessment Study Phase III: Model Development”, Final Report, January 2012

A brief overview of each corridor and alternative is given alongside results in the relevant chapters. For detailed information on the approach adopted for the choice of station locations to be served, stopping patterns adopted, journey time calculation, and more detailed presentation of journey times, please refer to the separate report:

“Norway HSR Assessment Study, Phase III: Journey Time Analysis”, Final Report, January 2012

The demand and market forecasts for each corridor are provided in the latter sections of this report with additional summary outputs provided in Appendix A. A separate report provides a high level assessment of the value of feeder services across the potential high speed network:

“Norway HSR Assessment Study, Phase III: Market Demand and Revenue Analysis - Potential for HSR Feeder Networks”, Supplementary Report, January 2012

2.2. Update of Phase II Forecasting Approach At the end of Phase II it was identified that further model development was desirable. This largely related to the increasing emphasis that has been placed on intermediate trips as the study had developed. The Phase II model had been developed using the NTM5 matrices and consequently only forecast trips of over 100km. In addition there were further gaps in the forecasts due to the modelling structure; for instance where air was not an existing option for travel, HSR forecasts were significantly under-estimated.

Phase III developments have filled in these forecasting gaps as described below. As a result of these modelling improvements the Phase III demand forecasts represent a more complete picture of HSR demand, and correspondingly have increased relative to the previous phase. The improvements provide a better assessment on whether intermediate stations add to the business/economic case.

2.2.1. Improved Representation of Swedish Travel Demand At the end of Phase II the data incorporated into the base matrices for international trips made by highway or rail was sourced from the TransTools model. The granularity of this data was not suitable for considering intermediate demand along these corridors. During Phase III, additional data with relation to Swedish corridors was incorporated into the Mode Choice Model. This was based on Sampers matrices adjusted and calibrated to 2007 and an Intraplan processed matrix for 2005, provided by Kungliga Tekniska Högskolan (KTH).

2.2.2. Improved Mode Choice Model where Air Non-available During the course of this study the emphasis has evolved from concentrating largely on long-distance end-to-end trips (e.g. Bergen-Oslo) to providing a parallel consideration for intermediate movements (e.g. Bergen-Kongsberg, Kongsberg-Oslo). The Phase II model had been calibrated to provide the best mode choice representation for long distance trips where air travel is available; however, on a number of intermediate movements, where air was not a feasible option, HSR movements were correspondingly



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underestimated. During Phase III a dual modelling structure was developed from further analysis of the original passenger survey data, and incorporated into the main forecasting model.

The Phase III model‟s default mode choice remains as per Phase II, whereby an absolute mode choice is made between HSR and demand is incremented from other modes based on the change in the composite cost of fast modes as a result of the introduction of HSR. However, where air is not a feasible option the second model structure is applied. This calculates an absolute mode choice between the existing rail and HSR and increments demand from other modes based on the change in composite cost of rail, following the introduction of high speed services.

2.2.3. Development of Gravity Model As noted in the Phase II report the Mode Choice Model only accounts for trips with a total distance of more than 100km. Giving consideration to the alternatives required for testing within Phase III, this would understate the market for travel between intermediate stations, although generally these are low revenue trips, with shorter time savings over existing modes. In order to infill these missing areas of demand, a separate “gravity” forecasting model was developed.

The Gravity Model is the most commonly used method of deriving trips where no existing trip matrix exists. In this instance the Gravity Model forecasts demand directly based on the population served by each station and the generalised journey time between the stations. It is named from the gravity analogy in that the number of trips between two zones is directionally proportional to their mass (e.g. population/employment) and indirectly proportion to the cost of travel between them. It should be noted that the model does not account for the levels of accessibility, or competition provided, from other modes between stations.

The decay factor is central to the Gravity Model and represents the decrease in trip making associated with increased travel cost. The decay factor has been calibrated through regression of rail trips against rail generalised journey time, the full model development is included in the Norway HSR Assessment Study Phase III: Model Development, Final Report, January 2012. Results have been cross-validated against existing NSB flows and with results from the main Mode Choice Model.

2.3. Main Forecasting Assumptions This section discusses the forecasting assumptions with relation to the results presented later in this report. These can be classified in two categories:

The definition of alternatives for testing. This includes a description of the:

- Specification of alternatives: describing the alternatives developed for testing with consideration to the HSR route and infrastructure;

- HSR passenger service specifications: describing the level of high speed service assumed to be running along each corridor/route; and

Forecasting assumptions with relation to modelling.

2.3.1. Corridor Alternatives

During Phase II four scenarios were considered on each corridor:

Scenario A – a continuation of the current railway policy and planned improvements, with relatively minor works undertaken (the reference case to which the other upgrades listed below are compared);

Scenario B – a more aggressive development of the current infrastructure;

Scenario C – major upgrades to the current infrastructure achieving high-speed concepts; and

Scenario D – building of new separate HSR lines.

As part of the alignment work in Phase III, new scenarios were developed and existing scenarios were adapted. The scenarios have evolved to consider the following alternatives:

Scenario B is conceptually defined as a uniform 20% reduction in travel time, maintaining the current stopping pattern and remaining single track outside of the Inter-City (IC) area;



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Scenario 2* is a new scenario which represents an upgrade of existing lines to double track with a 250 kph design speed;

Scenario D was sub-categorised into two alternatives: - D1: For mixed passenger and freight traffic, design speed 330 kph, gradient 12.5%, double track; - D2: For passenger traffic only, design speed 330 kph, relaxed gradient restrictions, double track; and

Scenario C is defined as a combination of Scenarios D1, D2 and 2*.

In Phase III of the study, these alternatives were considered with respect to four potential corridors and associated routes as presented below in Figure 1. Separate analysis identified potential intermediate station stops – either Category 1 or Category 2, depending on their likely usage.

Figure 1. HSR Corridors and Route Alternatives

As can be seen, each of the corridors within the Phase III alignment studies contains one or more route alternatives. For example, from Oslo to Bergen three different alignment alternatives exist – the Hallingdal alignment (via Hønefoss), the Numedal alignment (via Drammen then north to Geilo) and the Haukeli alignment (the „Y-shaped‟ network which heads more directly west from Drammen via Bø, also serving Stavanger). The full range of routes is shown below.

Corridor North: Oslo – Trondheim - Route: Oslo – Trondheim only;

Hamar

Lillehammer

Kristiansand

Stavanger

Haugesund

Bergen

Trondheim

Oslo

Sarpsborg

Porsgrunn/Skien

Sandnes

Geilo

Arendal

Voss

Stord

Odda

Værnes

Gjøvik

Gardermoen

Mandal

Egersund

Hønefoss

Oppdal

Tynset

Otta

KongsbergSki

Drammen

Lillestrøm

Tønsberg

Kongsvinger

ElverumParkway

Moss

FredrikstadHalden

Myrdal

Torp

LegendCategory 1 StationCategory 2 Station

Note some stations can only be served by 250 kph alignment



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Corridor West: Oslo – Bergen / Bergen – Stavanger; - Route: Bergen – Stavanger - Route: Oslo – Bergen - Route: Oslo – Stavanger (not via Kristiansand);

Corridor South: Oslo – Kristiansand – Stavanger; - Route: Oslo – Kristiansand – Stavanger only;

Corridor East: Oslo – Gothenburg / Oslo – Stockholm; - Route: Oslo – Gothenburg - Route: Oslo – Stockholm.

On the basis of the above classification, a number of specific route alternatives were defined, considered and then shortlisted to provide a manageable set of representative alternatives which have been the primary focus for technical analysis. These fall into two categories:

HSR Alternatives reflecting one of or a combination of D1, D2 (330 kph) and/or 2* (250 kph); and

Scenario B alternatives to HSR.

It should be noted that the primary focus for technical engineering feasibility and development of alternatives has related to HSR alternatives and as a consequence, the scope to undertake a detailed analysis and assessment of these has been greater than for Scenario B. This is reflected in this report, where the primary focus is on the presentation of results for the HSR alternatives, with Scenario B alternatives being summarised in all respects, including alternative specification, within Chapter 8.

2.3.2. Definition of Alternatives

2.3.2.1. Specification of Alternatives

A summary description of the detailed appraisal HSR alternatives is provided in Table 1 below. These are as specified by JBV. Details of the specification of alternatives are provided in the following separate Phase III report:

Høyhastighetsutredningen 2010-12: Vedlegg B - Fastsettelse av alternativer for analyse, 2012-01-22, Railconsult AS

The identification and choice of stops per HSR Alternative is explained in the report “Norway HSR Assessment Study, Phase III: Journey Time Analysis”, Final Report, January 2012. Details of the engineering alignments associated with the above HSR alternatives were developed and reported in detail by each of the four corridor alignment design teams in their Phase III Reports:

High Speed Rail Assessment Project, Corridor North Oslo – Trondheim: Delivery 2 – Phase 3 Alignment study, 2011-11-25, Rambøll

High Speed Rail Assessment 2012-2012: Phase 3 – Corridor West, 25.11.2011, SWECO

High Speed Rail Assessment Phase 3 – South Corridor: Part 1 – technical basis and proposed alignments, 2011-11-25, Multiconsult/WSP

Norwegian High Speed Railway Assessment, Phase 3 corridor east: Corridor specific analysis main report, 2011-11-25, Norconsult



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Table 1. HSR Alternatives Considered for Detailed Technical Analysis

Corridor Ref. HSR Alternative Description

North

G3:Y 250 kph Oslo – Trondheim / Vaernes via Gudsbrandsdalen serving Gardermoen, Hamar, Lillehammer, Otta and, Oppdal

Ø2:P 330 kph Oslo – Trondheim / Vaernes via Østerdalen serving Gardermoen, Elverum Parkway and Tynset

West N1:Q 250 kph Oslo – Bergen via Numedal serving Drammen, Kongsberg, Geilo, Myrdal and Voss

HA2:P 330 kph Oslo – Bergen via Hallingdal serving Hønefoss, Geilo and Voss

H1:P 330 kph Oslo – Bergen via Haukeli serving Drammen, Kongsberg and Odda

330 kph Oslo – Stavanger via Haukeli serving Drammen, Kongsberg, Odda and Haugesund

330 kph Bergen – Stavanger via Roldal serving Haugesund and Odda

BS1:P 330 kph Bergen – Stavanger via coastal route serving Haugesund and Stord

South S8:Q 250 kph Oslo – Stavanger via Vestfold serving Drammen, Tønsberg, Torp, Porsgrunn, Arendal, Kristiansand, Mandal, Egersund and Sandnes

S2:P 330 kph Oslo – Stavanger via direct route serving Drammen, Porsgrunn, Arendal, Kristiansand, Mandal, Egersund and Sandnes

East ST5:U 250 kph Oslo – Stockholm via Ski serving Ski, Karlstad, Örebro and Västerås

ST3:R 330 kph Oslo – Stockholm via Lillestrøm serving Lillestrøm, Karlstad, Örebro and Västerås

GO3:Q 250 kph Oslo – Gothenburg via Moss serving Ski, Moss, Fredrikstad, Sarpsborg, Halden and Trollhättan

GO1:S 330 kph Oslo – Gothenburg via direct route serving Sarpsborg and Trollhättan

A summary description of the sensitivity HSR alternatives is provided in Table 2 below. The results of these sensitivity tests are shown in Appendix A of this report.

Table 2. HSR Alternatives Considered for Sensitivity Testing of Demand and Revenue


North

G1:P 330 kph Oslo – Trondheim / Vaernes via Gudsbrandsdalen serving Gardermoen, Gjøvik, Lillehammer, Otta and Oppdal

West HA1:Q 250 kph Oslo – Bergen via Hallingdal serving Hønefoss, Geilo, Myrdal and Voss

N4:P 330 kph Oslo – Bergen via Numedal serving Drammen, Kongsberg, Geilo and Voss

South S8:T 250 kph Oslo – Stavanger via Vestfold serving Drammen, Tønsberg, Torp, Porsgrunn, Arendal, Kristiansand, Mandal, Egersund and Sandnes

S3:Z 330 kph Oslo – Stavanger via direct route serving Drammen, Porsgrunn, Arendal, Kristiansand, Mandal, Egersund and Sandnes


East ST1:Q 250 kph Oslo – Stockholm via Kongsvinger serving Lillestrøm, Kongsvinger, Karlstad, Örebro and Västerås

ST2:R 330 kph Oslo – Stockholm via Lillestrøm serving Lillestrøm, Karlstad, Örebro and Västerås



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2.3.2.2. HSR Passenger Service Scenarios

Critical to the technical analysis of the implications of HSR are the assumptions made with respect to the type of HSR service that would operate.

At this early stage in project development, there is inevitably a great deal of uncertainty as to the service that might be delivered and operated and consequently it is essential to establish a reasonable basis for “testing” the impact of HSR. To this end, two HSR Passenger Service Scenarios were established, reflecting somewhat different rationales for HSR service provision:

HSR Passenger Service Scenario 1 (PSS1): In the core scenario, the provision of HSR services is specified with the capture of demand and market share in mind. It is assumed that a core hourly HSR service serves all the larger and significant towns and cities on the alignment is provided (approximately 18 trains a day in each direction). This is supplemented by an additional hourly limited stopping, and hence faster, morning and afternoon peak period service targeting the end-to-end market (4 trains a day in each direction in the morning and afternoon). In this scenario it is assumed that rail fare is approximately 60% of air fare, this reflecting the current pricing of existing rail services compared with air services.

HSR Passenger Service Scenario 2 (PSS2): In this alternative scenario, the provision of HSR services is specified with the delivery of commercial operational performance in mind – securing revenue while keeping the associated costs for service delivery down. In this instance it is assumed that only the hourly core HSR service is provided (18 trains a day), reducing the cost of service delivery, while the rail fare is assumed to be higher than in PSS1, equivalent to the competing air fare.

It is fully recognised that each of these scenarios represents a simplification of what might be delivered as an HSR service, and the potential range of service and fare levels that might be offered in practice. However, in order to undertake comparative analysis of a large number of alternatives within the study timescale, and given the detail at which the available tools allow for alternatives to be considered, they provide a reasonable basis and range of service offer for assessment, consistent with this stage of study.

2.3.3. Forecasting Assumptions As described above, for the major interventions – which involve new infrastructure and potentially high specification rolling stock – a specially-developed mode choice modelling framework has been developed to assess Norwegian HSR alternatives is applied. This framework is described in full in the separate model development report (referenced in Section 2.1) the most important modelling assumptions employed in the bespoke demand model are listed as follows:

Zoning: In the main cities, excluding Kristiansand, the model zones are urban districts (bydeler). Elsewhere they are municipalities (kommuner), or in sparsely-populated areas, groups of municipalities with joint population of approximately 60,000. In total the model has 113 zones; this includes 104 area zones within Norway, eight area zones within Sweden and a „point‟ zone for Gardermoen Airport

Mode choice structure: The Mode Choice Model is based on the results of SP/ willingness to pay surveys using survey responses from a large panel of Norwegian volunteers‟. The model considers the mode choice from two automatically selected tiers. Where air is an existing option for travel the model considers the mode choice between air and HSR at an absolute level and increments around the demand from other modes based on a reduced composite cost of fast modes following the introduction of HSR. Where air travel is not an existing option the model considers the mode choice between the current rail service and HSR at an absolute level and increments around the demand from other modes based on the reduced composite cost of rail following the introduction of high speed services. As the main mode model only includes trips of over 100km where HSR journeys are under this distance forecasts are produced directly from a separate Gravity Model.

Mode choice parameters: The results presented in this report use the models and parameters estimated from SP survey analysis. A full description of the surveys and estimated models is included in the „Subjects 2 and 3: Expected Revenue and Passenger Choices Final Report‟.

Access and Egress times:

- HSR and Air: For each zone, the average access/egress time applicable for (a) each major airport and (b) each potential HSR station site is estimated using GIS, allowing for the quality of the highway network („link speeds‟ range between 20kph and 90kph), and the distribution of population within the zone; and



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- Access/egress time penalty weightings: Access/egress time weighting, relative to in-vehicle time, is provided by the SP surveys. Where access times exceed 120 minutes, the maximum access time considered in the SP surveys, an additional access time weighting of 1.5 is applied.

HSR in-vehicle times and service frequencies: are as shown in the Norway HSR Assessment Study Phase III: Journey Time Analysis, Final Report.

Air, coach and classic rail levels of service: are assumed to be the same as the existing situation in Scenarios C and D.

Air, Rail and HSR service frequency penalties: The impact of improvements in air, rail or HSR service frequency is included in the estimated model and considers a set penalty divided by the number of services in a day, this effectively considers service frequency as a headway.

Air, Rail and HSR fares: Average domestic air fares for leisure and business travel between the principal Norwegian airports are based on Avinor‟s survey of air passengers (2009). Rail fares are taken from NTM5. As a default, it is assumed that HSR fares are set to 60% of existing air fares, broadly comparable to current existing rail fare levels. To account for HSR being a high level service, able to compete with air over the length of the corridors, sensitivities have been conducted around the fare to set HSR fares to equal existing air fares.

Air in-vehicle times: are based on a combination of internet research, plus use of NTM5 data for flows to/from minor airports.

Wait Times: wait times for air and HSR have been taken as those stated by existing users in the stated preference surveys, classic rail wait times have been used to approximate the waiting times for a HSR service. The time waiting at airports before take-off has been calculated at approximately 40 minutes in excess of that spent at an HSR station before departure.

Generation: A logsum formulation is used to calculate the change in overall accessibility between zones as a result of introducing HSR. The increased levels of trip making as a result are calculated using an exponential formulation to forecast the increase in trips as a result of the improved levels of accessibility.

HSR intermediate stops: For Scenarios A and B, as there are relatively minor improvements to line speeds and capacity, it is assumed that rail services continue to follow the same stopping pattern, as coded in the NTM5 model. Stopping patterns and the impact of additional intermediate stops on end-to-end journey times for Scenarios C and D are as shown in the Norway HSR Assessment Study Phase III: Journey Time Analysis, Final Report.

Other modes’ monetary costs and journey times: The structure of the Mode Choice Model does not require these „Generalised Cost‟ data for other modes as abstraction from car and coach is based on incremental changes from existing journey volumes.

‘Nesting’ parameters: which reduce the sensitivity of modal shift between HSR and „slow modes‟ (car, classic rail and bus), relative to that between HSR and air are included in the SP model estimation.

Forecasting years: The model is developed to produce forecasts for 2024, 2043 and 2060. In the absence of detailed information on forecasting parameters by mode, growth for future year matrices has been taken from NTM5. Use of NTM5 ensures maximum compatibility with the growth assumptions applied in the appraisal of other Norwegian schemes.

We emphasise the assumption that in the core analysis the existing air and rail services are assumed to be retained after introduction of HSR services. The model is capable of undertaking sensitivity tests around this assumption.

As with assumptions full details of modelling framework development, assumptions and limitations can be found in a separate model development report. Key points to take into account when interpreting results are:

Estimates of individual station usage are limited by zone system and representation of road and rail network access – these could be refined at a next stage of alternative development;

Forecasts do not include origin-destination forecasts where trips are less than 20km or are part of the core inter-city market. There is a potential overlap with the market for intercity rail services, which we have identified in the main report; and

Short distance trips are forecast with relation to journey time aspects only and are not related to fares. Additional survey data would improve estimates of shorter distance travel.



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2.4. Explanation of Model Outputs The purpose of this section is to present an example of the key model outputs, as presented for each scenario examined in Chapters 3 to 6. The objective is to assist in interpretation of results in addition to the text in the following chapters. Standard outputs presented for each alternative are as follows:

2.4.1. Summary of Demand and Revenue For the modelled years of 2024, and 2043 the table gives daily and annual figures of:

Demand in terms of passenger numbers on HSR (including a breakdown by business and leisure travellers);

Demand in terms of passenger km of HSR. In many respects this is a better figure of demand than passenger numbers which does not differentiate between short intermediate trips and end-end trips;

HSR train km. The number of HSR train km assumed to be operated within the scenario;

Average train occupancy, assumed to be passenger km divided by train km: actual loading figures will vary across the length of the route and time of day; and

Total revenue split by business and leisure passengers, in 2009 values.

2.4.2. Source of Forecast HSR Demand Each alternative has a table which describes the assumptions used for modelling demand and revenue, in particular where demand is excluded. Within each table:

„0‟ signifies that the trip is not an option, this should only occur where the origin and the destination are the same;

„M‟ signifies that demand has come from the main Mode Choice Model, this is the default source of HSR demand;

„G‟ indicated that demand has come from the Gravity Model. This can be for one of two reasons: - Firstly no trips of under 100km are included in the Mode Choice Model the Gravity Model has been

used to infill trips on all origin-destination movements of under 100km; and - Secondly the where the origin-destination trip is less than 200km and the Gravity Model forecasts

demand of more than double the mode choice mode the Gravity Model has been used. This Gravity Model has been used in these scenarios as; on some movements of up to 200km the Mode Choice Model under-forecasts demand. This is a rare occurrence within the modelling results and generally happens for one of two reasons either; O-D pairs are not well served by either existing air or rail services and so the Mode Choice Model structure is not well placed to forecast demand, or the amalgamation from smaller zones excludes trips of under 100km where zone centroids are further than 100km apart;

„E‟ indicates that demand between station O-D pairs is not included in the high speed demand. This is due to one of the following reasons: - High speed stations are less than 20km apart. Over these distances the Gravity Model is not

considered reliable; and - The demand is within the Oslo inter-city area and is not considered to be part of the HSR market.

2.4.3. HSR Demand by Origin / Destination and Boarding / Alighting Station For each alternative, we also present a breakdown of daily usage by origin / destination group (based on station to station travel) and total HSR station boardings and alightings.

Figures are given for an average day – different mixes of business and leisure demand will affect the balance of station usage on weekdays and weekends.

We emphasise that forecasts of individual station usage are dependent on zoning definitions in the model, where inadequate information was available on individual station accessibility. Where such issues affect forecasts, they are noted in the text.

As described above, certain of the station to station flows are excluded from forecasting, so all station usage figures need to be interpreted in conjunction with the modelling sources matrices.



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2.4.4. Mode Share by HSR Journey Length Mode share is shown for all trips of a given length where passengers have a total HSR access egress time of less than two hours. The access time limitation is to ensure a defined catchment is set for mode share – which can suffer from wide ranges in definition when compared across different markets. For instance some passengers may travel a large distance to access/egress high speed stations, including these zones within the below analysis would not contain many more HSR trips in the analysis, and would show an increased mode share of car. The figure contains results from the Mode Choice Model only as by definition the mode share cannot be derived from the Gravity Model.

The figures demonstrate the relative competitiveness of HSR by journey length. Generally it would be expected that the HSR mode share will increase with distance over the corridors assessed. It would be anticipated that car would dominate over shorter distances as it is better served to meet ultimate origins/destination and is faster than total rail time over shorter distances. Over longer distances we would expect the air mode share would be expected to increase and obtain a larger share of demand than HSR. This output both provides analysis for results and corridor comparison and a sense check of model results, i.e. large air mode shares shown over short distances would highlight an issue with modelling results.



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3. North Corridor Results

3.1. Introduction There are two alternatives on the North Corridor that have been tested:

G3:Y – Oslo-Trondheim and Værnes via Gudbrandsdalen; and

Ø2:P – Oslo-Trondheim and Værnes via Østerdalen.

Results are shown for the main PSS1 fares and service scenario, with summary results only also presented for the PSS2 higher fares and reduced service sensitivity tests.

3.2. Alternative G3:Y This alternative leaves the existing line just north of Gardermoen Airport and follows the existing rail corridor via Hamar and Gudbrandsdalen to Trondheim and Værnes Airport. It is designed for 330 kph rail passenger and freight traffic between Gardermoen and Trondheim. Table 3 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative.

Table 3. Summary of Demand and Revenue – Alternative G3:Y

Annual Per Day

Demand 2024 2043 2024 2043

Total HSR Passengers (thousands) 4420 5090 12.1 13.9

HSR Business Passengers 1870 2130 5.1 5.8

HSR Leisure Passengers 2550 2960 7.0 8.1

HSR Passenger km (millions) 1610 1870 4.4 5.1

HSR Train km (thousands) 9970 9970 27.3 27.3

Average Train Occupancy1 161 188

Revenue

HSR Total Revenue (MnNOK)2 1480 1710

HSR Revenue from Business Travel 820 950

HSR Revenue from Leisure Travel 650 770

It can be seen that annual HSR journeys in 2024 are estimated at nearly 4.5 million, increasing to 5.1 million in 2043. This demand breaks down as 42% business travel and 58% leisure travel in 2024. Total annual revenue is estimated to be 1.5 BnNOK in 2024 and 1.7 BnNOK in 2043, with 55% of revenue from business travel.

Table 4 below determines which model has been used to forecast HSR demand for each station pairing, where “M” denotes that the Mode Choice Model has been used and “G” indicates that demand has been forecast using the Gravity Model. “E” is shown where demand has been excluded based on the criteria described in Section 2.4.2. Trips between Oslo – Gardermoen and Trondheim – Værnes have been excluded from our analysis as they are expected to be served by local rail services.

1 Average train occupancy refers to passenger km divided by train km

2 Revenue is given in 2009 NOK prices



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Table 4. Source of HSR Demand – G3:Y

Station Oslo S Gardermoen Hamar Lillehammer Otta Oppdal Trondheim Værnes

Oslo S 0 E M M M M M M

Gardermoen E 0 M M M M M M

Hamar M M 0 G M M M M

Lillehammer M M G 0 M M M M

Otta M M M M 0 M M M

Oppdal M M M M M 0 M M

Trondheim M M M M M M 0 E

Værnes M M M M M M E 0

Table 5 below provides a breakdown of daily journeys grouped by type of station, while Figure 2 presents the forecast average daily boardings/alightings for each of the HSR stations.

Table 5. HSR Demand by Origin/Destination – G3:Y

Station A Station B 2024 2043

Daily total (k) % of total Daily total (k) % of total

Oslo S Trondheim 3.36 28% 3.94 28%

Oslo S Other 4.91 41% 5.50 39%

Trondheim Other 2.12 17% 2.54 18%

Other Other 1.73 14% 1.96 14%

Total 12.12 100% 13.95 100%

It can be seen that the highest proportion of demand (41%) in 2024 is for travel between Oslo and intermediate stations along the corridor, with 28% travelling between Oslo and Trondheim. There is also a sizable proportion of demand for trips from Trondheim to intermediate stations and between intermediate stations themselves.

Figure 2. HSR Daily Boardings / Alightings by Station – G3:Y

The highest demand originates from the stations at Oslo and Trondheim, although there is also sizable demand from the intermediate stations, with the exception of Lillehammer. The lower than expected demand

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from Lillehammer is a function of the zoning system in the model, with some demand accessing Otta instead as it is located in a far larger zone. In reality, more passengers would be likely to use Lillehammer station than Otta station.

Figure 3 shows the forecast mode shares for trips of different lengths in 2024. For shorter distance trips of less than 300km, car is the dominant mode. However, for trips of between 400 and 500km HSR commands a majority share of the market, with air contributing fewer than 16% of trips.

Figure 3. Mode Share by HSR Journey Length, 2024 – G3:Y

Figure 4 provides a GIS presentation of the spatial pattern of annual HSR trip-ends, and daily HSR station

boardings. There is significant demand from the regions surrounding Trondheim, accessing the station there

to travel to Oslo. There is also significant demand from intermediate areas.

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Air HSR Classic Rail Coach Car Ferry



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Figure 4. Annual HSR Demand by Originating Zone and Daily Boarding Demand by Station, 2024 – G3:Y

3.2.1. G3:Y PSS2 Sensitivity Test This section presents the key results for the PSS2 sensitivity test for the G3:Y alternative between Oslo and Trondheim, with HSR fares set to 100% of air fares and the peak HSR service removed. Table 6 below



Final Report 23

summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative, while Figure 5 presents the forecast average daily boardings for each of the HSR stations.

Table 6. Summary of Demand and Revenue – Alternative G3:Y (PSS2)

Annual Per Day

Demand 2024 2043 2024 2043






Average Train Occupancy 153 194

Revenue

HSR Total Revenue (MnNOK) 1660 2090



Figure 5. HSR Daily Boardings and Alightings by Station – G3:Y (PSS2)

It can be seen that annual HSR journeys in 2024 are estimated at over 2.9 million, increasing to nearly 3.7 million in 2043, which is a reduction of approximately 1.5 million passengers per year in 2024 compared with the core test. Conversely, the total annual revenue for the sensitivity test is approximately 180 MnNOK higher than the core test, as those travelling on HSR are paying higher fares.

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Trondheim

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2024 2043



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3.3. Alternative Ø2:P This alternative also leaves the existing route 60km north of Gardermoen, via a new station near Elverum, before continuing along the Østerdalen to Trondheim and Værnes Airport. It is designed for 330 kph rail passenger and freight traffic for the majority of the route between Gardermoen and Trondheim. Table 7 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative.

Table 7. Summary of Demand and Revenue – Alternative Ø2:P

Annual Per Day

Demand 2024 2043 2024 2043







Revenue




This alternative attracts similar levels of demand to G3:Y. There are more trips made in Ø2:P between Oslo and Trondheim due to the faster journey time, although this is offset by there being lower intermediate demand and fewer intermediate stations than G3:Y. However, Ø2:P has higher levels of revenue due to the longer average trip length, and hence higher average fares, on this corridor. The higher proportion of longer distance trips also contributes to a higher average train occupancy over the length of the route.

Table 8 below determines which model has been used to forecast HSR demand for each station pairing. The Gravity Model has not been used to forecast demand for any of the station pairings in this alternative. Trips between Oslo – Gardermoen and Trondheim – Værnes have again been excluded.

Table 8. Source of HSR Demand – Ø2:P

Station Oslo Gardermoen Elverum Parkway Tynset Trondheim Værnes

Oslo 0 E M M M M

Gardermoen E 0 M M M M

Elverum Parkway M M 0 M M M

Tynset M M M 0 M M

Trondheim M M M M 0 E

Værnes M M M M E 0




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Table 9. HSR Demand by Origin/Destination – Ø2:P



Oslo Trondheim 5.10 43% 6.00 42%

Oslo Other 3.36 28% 3.84 27%

Trondheim Other 2.50 21% 3.14 22%

Other Other 0.95 8% 1.18 8%

Total 11.89 100% 14.17 100%

It can be seen that in this alternative the highest proportion of demand in 2024 (43%) is for travel between Oslo and Trondheim, which is in contrast with the G3:Y alternative, where the greatest proportion of trips is between Oslo and intermediate stations. This is based on the faster end-to-end journey time achieved in the Ø2:P alternative and fewer intermediate stations being served.

Figure 6. HSR Daily Boardings / Alightings by Station

The highest demand originates from the stations at Oslo and Trondheim, although there is also sizable demand from the intermediate stations. The number of boardings at Trondheim are higher than in G3:Y as there are more trips made to Oslo due to the faster journey time. It can be seen, however, that the total intermediate demand is lower.

Figure 7 shows the forecast mode shares for trips of different lengths in 2024. It can be seen that, compared with G3:Y, in Ø2:P HSR gains a higher share of trips between 200 and 300km, a lower share of trips between 300 and 400km, and a higher share of trips between 300 and 500km. Air contributes fewer than 10% of trips over 400km in this alternative. This is to be expected because Ø2:P offers a more attractive journey time between Oslo and Trondheim than G3:Y.

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Final Report 26

Figure 7. Mode share by HSR Journey Length, 2024 – Ø2:P


boardings. As expected there is less demand accessing the network from the western regions of Norway.

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Final Report 27

Figure 8. Annual HSR Demand by Originating Zone and Daily Boarding Demand by Station, 2024 – Ø2:P



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3.3.1. Ø2:P PSS2 Sensitivity Test This section presents the key results for the PSS2 sensitivity test for the Ø2:P alternative between Oslo and Trondheim. Table 10 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative, while Figure 9 presents the forecast average daily boardings for each of the HSR stations.

Table 10. Summary of Demand and Revenue – Alternative Ø2:P (PSS2)

Annual Per Day

Demand 2024 2043 2024 2043







Revenue




Figure 9. HSR Daily Boardings and Alightings by Station – Ø2:P (PSS2)

It can be seen that annual HSR journeys in 2024 are estimated at over 2.9 million, increasing to 3.8 million in 2043, which is a reduction of approximately 1.3 million passengers per year in 2024 compared with the core test. The total annual revenue for the sensitivity test is approximately 280 MnNOK higher than the core test, which is a larger increase than for the G3:Y sensitivity test.

3.4. Summary Both G3:Y and Ø2:P attract similar levels of demand, though Ø2:P has higher levels of revenue due to the longer average trip length, and hence higher average fares, on this corridor. There are more trips made in Ø2:P between Oslo and Trondheim because of the faster journey time, at the expense of serving fewer communities en-route. G3:Y is able to serve more communities along the corridor and hence has higher levels of intermediate demand.

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4. West Corridor Results

4.1. Introduction There are four alternatives on the West Corridor that have been tested:

N1:Q – Oslo-Bergen via Numedal;

HA2:P – Oslo-Bergen via Hallingdal;

H1:P – Oslo-Bergen, Oslo-Stavanger and Bergen-Stavanger via Haukeli; and

BS1:P – Bergen-Stavanger via coastal route.

As before, results are shown for the core PSS1 scenario specification of fares and services, with summary results also presented for the higher fare PSS2 sensitivity tests.

4.2. Alternative N1:Q This alternative leaves the existing line at Drammen and follows the Numedal to Geilo, with this section designed for 330 kph rail passenger and freight traffic. The line from Geilo to Bergen predominantly follows the existing route and is designed for 250 kph traffic. Table 11 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative.

Table 11. Summary of Demand and Revenue – Alternative N1:Q

Annual Per Day

Demand 2024 2043 2024 2043







Revenue




It can be seen that annual HSR journeys in 2024 are estimated at nearly 4.5 million, increasing to nearly 5.1 million in 2043. This demand breaks down as 47% business travel and 53% leisure travel in 2024. Total annual revenue is estimated to be 1.4 BnNOK in 2024 and 1.6 BnNOK in 2043, with 58% of revenue from business travel.

Table 12 below determines which model has been used to forecast HSR demand for each station pairing. Trips between Oslo and Drammen have been excluded here as they are expected to be served by local rail services.



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Table 12. Source of HSR Demand – N1:Q

Station Oslo S Drammen Kongsberg Geilo Myrdal Voss Bergen

Oslo S 0 E M M M M M

Drammen E 0 G M M M M

Kongsberg M G 0 M M M M

Geilo M M M 0 M M M

Myrdal M M M M 0 G M

Voss M M M M G 0 M

Bergen M M M M M M 0


Table 13. HSR Demand by Origin/Destination – N1:Q



Oslo S Bergen 4.62 38% 5.50 40%

Oslo S Other 4.05 33% 4.48 32%

Bergen Other 2.14 17% 2.26 16%

Other Other 1.44 12% 1.63 12%

Total 12.25 100% 13.87 100%

It can be seen that in this alternative the highest proportion of demand in 2024 (38%) is for travel between Oslo and Bergen, which is in slightly higher than the demand between Oslo and intermediate stations (33%).

Figure 10. HSR Boardings / Alightings by Station – N1:Q

The highest demand originates from Oslo and Bergen, although there is also sizable demand from particularly Drammen, Kongsberg and Voss. There is lower demand at Geilo and Myrdal due to the low population density in these mountainous areas. There is likely in reality to be a variation in the spread of

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Final Report 31

demand between the Voss, Myrdal and Geilo, as these stations are located in large zones in the model. In particular, tourist demand associated with Myrdal station may be understated in these results and associated with Bergen station instead.

Figure 11 shows the forecast mode shares for trips of different lengths in 2024. For shorter distance trips of less than 300km, car is the dominant mode. However, for trips of between 300 and 400km HSR commands a share of just over 50% of the market, with air maintaining a share of approximately 19% of trips.

Figure 11. Mode share by HSR Journey Length, 2024 – N1:Q


boardings. It can be seen that large numbers of passengers are accessing the HSR stations at Bergen and

Voss from relatively long distances.

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Figure 12. Annual HSR Demand by Originating Zone and Daily Boarding Demand by Station, 2024 – N1:Q

4.2.1. N1:Q PSS2 Sensitivity Test This section presents the key results for the PSS2 higher fares sensitivity test for the N1:Q alternative between Oslo and Bergen. Table 14 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative, while Figure 13 presents the forecast average daily boardings for each of the HSR stations.

Table 14. Summary of Demand and Revenue – Alternative N1:Q (PSS2)

Annual Per Day

Demand 2024 2043 2024 2043







Revenue






Final Report 33

Figure 13. HSR Boardings and Alightings by Station – N1:Q (PSS2)

It can be seen that annual HSR journeys in 2024 are estimated at over 3 million, increasing to nearly 3.7 million in 2043, which is a reduction of approximately 1.5 million passengers per year in 2024 compared with the core test. Conversely, the total annual revenue for the sensitivity test is approximately 120 MnNOK higher than the core test, as those travelling on HSR are paying higher fares.

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424

39

988

2652

2806

850

1205

355

40

856

2137

0 1000 2000 3000 4000

Oslo S

Drammen

Kongsberg

Geilo

Myrdal

Voss

Bergen


2024 2043



Final Report 34

4.3. Alternative HA2:P This alternative involves a new direct line between Sandvika and Hønefoss before following the existing rail corridor to Bergen. It is designed for 330 kph rail passenger and freight traffic between Oslo and Geilo, and 330 kph rail passenger traffic only from Geilo to Bergen. Table 15 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative.

Table 15. Summary of Demand and Revenue – Alternative HA2:P

Annual Per Day

Demand 2024 2043 2024 2043







Revenue




This alternative attracts lower demand than N1:Q, but higher average train occupancy and slightly higher revenue due to the greater proportion of long distance trips, which is as a result of the lower journey times. However, the additional demand driven by the faster journey times is relatively small, when considering that the journey time between Oslo and Bergen is 30 minutes faster in HA2:P than N1:Q. The higher overall demand in the N1:Q alternative is driven by shorter distance trips between intermediate stations in larger towns, such as Drammen and Kongsberg.

Table 16 below determines which model has been used to forecast HSR demand for each station pairing.

Table 16. Source of HSR Demand – HA2:P

Station Oslo S Hønefoss Geilo Voss Bergen

Oslo S 0 G M M M

Hønefoss G 0 M M M

Geilo M M 0 M M

Voss M M M 0 M

Bergen M M M M 0




Final Report 35

Table 17. HSR Demand by Origin/Destination – HA2:P



Oslo S Bergen 6.07 53% 7.25 54%

Oslo S Other 3.95 34% 4.71 35%

Bergen Other 1.13 10% 1.10 8%

Other Other 0.38 3% 0.35 3%

Total 11.52 100% 13.40 100%

It can be seen that in this alternative the highest proportion of demand in 2024 (53%) is for travel between Oslo and Bergen; a higher share than with N1:Q. The demand between Oslo and intermediate stations remains at a similar level to N1:Q, with a lower share of demand from Bergen to intermediate stations and between intermediate stations.

Figure 14. HSR Boardings / Alightings by Station – HA2:P

There is higher demand originating from Oslo and Bergen than with the N1:Q alternative, but in the absence of demand from intermediate stations at Drammen and Kongsberg, there is lower level of demand overall.

Figure 15 shows the forecast mode shares for trips of different lengths in 2024. For shorter distance trips of less than 200km, car is again the dominant mode. For trips of between 200 and 300km HSR commands a far higher share of the market (61%) than N1:Q. For trips between 300 and 400km, HSR has a market share of nearly 55%, with an air market share of nearly 20%.

5978

1200

636

1414

4172

5010

1011

585

1319

3598

0 1000 2000 3000 4000 5000 6000 7000

Oslo S

Hønefoss

Geilo

Voss

Bergen


2024 2043



Final Report 36

Figure 15. Mode share by HSR Journey Length, 2024 – HA2:P


boardings. There is less demand accessing zones to the south as the Hallingdal alignment heads further

north than Numedal.

0.7% 0.4%5.1%

19.6%

1.6%8.1%

60.5%

54.4%

3.0%

4.6%

4.3%

7.9%

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4.3%

2.5%

84.9%

82.4%

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0%

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40%

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90%

100%

100 200 300 400 500 600

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de

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Final Report 37

Figure 16. Annual HSR Demand by Originating Zone and Daily Boarding Demand by Station – HA2:P

4.3.1. HA2:P PSS2 Sensitivity Test This section presents the key results for the higher fares sensitivity test for the HA2:P alternative between Oslo and Bergen. Table 18 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative, while Figure 17 presents the forecast average daily boardings for each of the HSR stations.

Table 18. Summary of Demand and Revenue – Alternative HA2:P (PSS2)

Annual Per Day

Demand 2024 2043 2024 2043







Revenue






Final Report 38

Figure 17. HSR Boardings and Alightings by Station – HA2:P (PSS2)

It can be seen that annual HSR journeys in 2024 are estimated at just under 3 million, increasing to 3.7 million in 2043, which is a reduction of approximately 1.2 million passengers per year in 2024 compared with the core test. The total annual revenue for the sensitivity test is approximately 190 MnNOK higher than the core test, which is a larger increase than for the N1:Q sensitivity test.

4537

1110

450

1075

2967

3568

907

379

935

2373

0 1000 2000 3000 4000 5000

Oslo S

Hønefoss

Geilo

Voss

Bergen


2024 2043



Final Report 39

4.4. Alternative H1:P This alternative involves a Y-shaped network linking Oslo with both Bergen and Stavanger with two branches joining at Røldal, enabling services between Oslo – Bergen, Oslo – Stavanger and Bergen – Stavanger. The whole network is designed for 330 kph rail passenger and freight traffic, with the exception of Haugesund – Stavanger which is for passenger traffic only. Table 19 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative.

Table 19. Summary of Demand and Revenue – Alternative H1:P

Annual Per Day

Demand 2024 2043 2024 2043







Revenue




It can be seen that annual HSR journeys in 2024 are estimated at nearly 7.5 million, increasing to over 8.8 million in 2043. This demand breaks down as 53% business travel and 47% leisure travel in 2024. The overall demand is significantly higher than with the other individual alternatives, as this alternative provides 3 separate service routes and links 3 major urban areas in Norway. This is associated with higher levels of revenue when compared with other alternatives with total annual revenue of 2.7 BnNOK in 2024 and 3.2 BnNOK in 2043, with 63% of revenue from business travel. There are a far greater number of vehicle kilometres compared with other alternatives due to the number of services run. Average train occupancy figures are lower than for other West Corridor alternatives, due to lower loading figures on Stavanger – Bergen services. Oslo – Bergen and Oslo – Stavanger services have similar loading figures to the other West Corridor alternatives.

Table 20 below determines which model has been used to forecast HSR demand for each station pairing. Trips between Oslo and Drammen have been excluded here as they are expected to be served by local rail services.

Table 20. Source of HSR Demand – H1:P

Station Oslo S Drammen Kongsberg Odda Bergen Haugesund Stavanger

Oslo S 0 E M M M M M

Drammen E 0 G M M M M

Kongsberg M G 0 M M M M

Odda M M M 0 G M M

Bergen M M M G 0 M M

Haugesund M M M M M 0 G

Stavanger M M M M M G 0



Final Report 40


Table 21. HSR Demand by Origin/Destination – H1:P



Oslo S Bergen 5.46 27% 6.55 27%

Oslo S Stavanger 3.72 18% 4.62 19%

Oslo S Other 3.57 17% 4.00 17%

Bergen Stavanger 2.62 13% 2.96 12%

Bergen Other 1.73 8% 1.96 8%

Stavanger Other 2.31 11% 2.81 12%

Other Other 1.07 5% 1.28 5%

Total 20.48 100% 24.19 100%

It can be seen that in this alternative the highest proportion of demand in 2024 (27%) is for travel between Oslo and Bergen, 18% of demand travelling between Oslo and Stavanger, and 13% of demand between Bergen and Stavanger.

Figure 18. HSR Boardings / Alightings by Station – H1:P

The highest levels of demand are unsurprisingly from the three terminus stations. There is also higher demand from intermediate stations when compared with the majority of alternatives, with the exception of Odda, which is situated in a relatively remote location.

Figure 19 shows the forecast mode shares for trips of different lengths in 2024. For trips of over 200km both HSR and air have an increasing market share with increasing distance. For trips of between 200 and 300km HSR has a market share of 36%, rising to nearly 47% for trips between 400 and 500km. Air has a market share of over 25% for trips of this distance.

7587

1521

1974

218

5739

1952

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1283

1768

195

4903

1629

4327

0 1000 2000 3000 4000 5000 6000 7000 8000

Oslo S

Drammen

Kongsberg

Odda

Bergen

Haugesund

Stavanger


2024 2043



Final Report 41

Figure 19. Mode share by HSR Journey Length, 2024 – H1:P


boardings. The majority of demand is focused around the cities of Oslo, Bergen and Stavanger, with

demand also accessing Drammen and Kongsberg from the south.

0.2% 0.5%5.4%

14.1%

25.5%

5.3%7.3%

36.3%

44.6%

46.6%

8.9%0.8%

0.6%

6.0%

5.7%

6.6%

5.0%

4.2%

3.6%

2.6%

77.6%

78.3%

42.5%

28.6%

19.5%

1.4%

8.1%11.1%

3.2%0.0%

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Final Report 42

Figure 20. Annual HSR Demand by Originating Zone and Daily Boarding Demand by Station – H1:P

4.4.1. H1:P PSS2 Sensitivity Test This section presents the key results for the higher fares sensitivity test for the H1:P alternative between Oslo and Bergen, Oslo and Stavanger, and Bergen – Stavanger. Table 22 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative, while Figure 21 presents the forecast average daily boardings for each of the HSR stations.

Table 22. Summary of Demand and Revenue – Alternative H1:P (PSS2)

Annual Per Day

Demand 2024 2043 2024 2043







Revenue






Final Report 43

Figure 21. HSR Boardings and Alightings by Station – H1:P (PSS2)

It can be seen that annual HSR journeys in 2024 are estimated at over 5 million, increasing to over 6.6 million in 2043, which is a reduction of approximately 2.2 million passengers per year in 2024 compared with the core test. Conversely, the total annual revenue for the sensitivity test is approximately 380 MnNOK higher than the core test, as those travelling on HSR are paying higher fares.

5407

1233

1571

186

4222

1639

3948

4205

989

1316

158

3372

1313

3090

0 1000 2000 3000 4000 5000 6000

Oslo S

Drammen

Kongsberg

Odda

Bergen

Haugesund

Stavanger


2024 2043



Final Report 44

4.5. Alternative BS1:P This alternative follows an different alignment between Stavanger and Bergen along the coast via the towns of Haugesund and Leirvik (Stord), and is designed for 330 kph rail passenger traffic. Table 23 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative.

Table 23. Summary of Demand and Revenue – Alternative BS1:P

Annual Per Day

Demand 2024 2043 2024 2043







Revenue




It can be seen that annual HSR journeys in 2024 are estimated at fewer than 2 million, increasing to over 2.2 million in 2043. This demand breaks down as 64% business travel and 36% leisure travel in 2024. Total annual revenue is estimated to be MnNOK in 2024 and 450 MnNOK in 2043, with 73% of revenue from business travel. The demand and revenue is lower than for the other alternatives on the West corridor, which is to be expected as this alternative does not serve Oslo.

Table 24 below determines which model has been used to forecast HSR demand for each station pairing. A greater proportion of trips are forecast from the Gravity Model on this corridor due to the shorter nature of the trips on average.

Table 24. Source of HSR Demand – BS1:P

Station Bergen Stord Haugesund Stavanger

Bergen 0 M M G

Stord M 0 G G

Haugesund M G 0 G

Stavanger G G G 0




Final Report 45

Table 25. HSR Demand by Origin/Destination – BS1:P



Bergen Stavanger 3.07 59% 3.40 56%

Bergen Other 0.51 10% 0.59 10%


Other Other 0.11 2% 0.14 2%

Total 5.24 100% 6.07 100%

It can be seen that in this alternative the highest proportion of demand in 2024 (59%) is for travel between Bergen and Stavanger, 29% of demand is for travel between Stavanger and intermediate stations.

Figure 22. HSR Boardings / Alightings by Station – BS1:P

The highest levels of demand are again from the terminus stations, although there is intermediate demand, particularly from Haugesund. This alternative would be more effective in terms of demand generation if combined with an HSR line between Oslo – Bergen and/or Oslo – Stavanger.

Figure 23 shows the forecast mode shares for trips of different lengths in 2024. HSR has a market share of over 40% for trips of between 200 and 300km in length, with the remaining air market occupying a share of less than 5%. Car still has a reasonably large market share (39%) for journeys of 200 to 300km. Ferry has a market share of approximately a tenth for trips of all distances on this corridor after introduction of HSR services.

2668

1053

353

1997

2306

858

281

1793

0 500 1000 1500 2000 2500 3000

Stavanger

Haugesund

Stord

Bergen


2024 2043



Final Report 46

Figure 23. Mode share by HSR Journey Length, 2024 – BS1:P


boardings. Demand is focused around the cities of Bergen and Stavanger; accessing these stations from

long distances away.

0.1% 1.7%4.8%

0.0% 0.0%0.0%

5.4%

40.7%

0.0% 0.0%0.0%

0.0%

0.5%

0.0% 0.0%4.7%

5.5%

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0.0% 0.0%

85.1% 71.0%

39.3%

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Final Report 47

Figure 24. Annual HSR Demand by Originating Zone and Daily Boarding Demand by Station – BS1:P

4.5.1. BS1:P PSS2 Sensitivity Test This section presents the key results for the sensitivity test for the BS1:P alternative between Bergen and Stavanger. Table 26 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative, while Figure 25 presents the forecast average daily boardings for each of the HSR stations.

Table 26. Summary of Demand and Revenue – Alternative BS1:P (PSS2)

Annual Per Day

Demand 2024 2043 2024 2043







Revenue






Final Report 48

Figure 25. HSR Boardings and Alightings by Station – BS1:P (PSS2)

It can be seen that annual HSR journeys in 2024 are estimated at fewer than 1.7 million, increasing to over 2 million in 2043, which is a reduction of approximately 120 million passengers per year in 2024 compared with the core test. The total annual revenue for the sensitivity test is approximately 160 MnNOK higher than the core test, as those travelling on HSR are paying higher fares.

4.6. Summary N1:Q attracts higher levels of demand than HA2:P overall, though HA2:P has a higher average train occupancy and slightly higher revenue due to the greater proportion of long distance trips, which is as a result of the lower journey times. The higher overall demand in the N1:Q alternative is driven by shorter distance trips between intermediate stations in larger towns, such as Drammen and Kongsberg.

H1:P has higher levels of demand and revenue than any of the other corridors tested, due to this alternative effectively serving three different corridors. BS1:P has the lowest levels of demand and revenue of any of the corridors, as it is the only corridor to not serve Oslo.

2288

1200

353

1765

1899

955

281

1483

0 500 1000 1500 2000 2500

Stavanger

Haugesund

Stord

Bergen


2024 2043



Final Report 49

5. South Corridor Results

5.1. Introduction There are two alternatives on the South Corridor that have been tested:

S8:Q – Oslo-Stavanger via Vestfold; and

S2:P – Oslo-Stavanger via direct route.

As before, results are presented for the core scenario PSS1, with summary results for the higher fares sensitivity test scenario PSS2.

5.2. Alternative S8:Q This alternative follows the alignment of the existing Vestfoldbanen between Oslo and Porsgrunn before following the south coast to Kristiansand and Stavanger. The line between Drammen and Stavanger is designed for 250 kph rail passenger and freight traffic. Table 27 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative.

Table 27. Summary of Demand and Revenue – Alternative S8:Q

Annual Per Day

Demand 2024 2043 2024 2043







Revenue




It can be seen that annual HSR journeys in 2024 are estimated at greater than 5 million, increasing to nearly 6 million in 2043. This demand breaks down as 51% business travel and 49% leisure travel in 2024. Total annual revenue is estimated to be 1.5 BnNOK in 2024 and 1.7 BnNOK in 2043, with 63% of revenue from business travel.

Table 28 below determines which model has been used to forecast HSR demand for each station pairing. Trips between Oslo – Drammen and Sandnes – Stavanger have been excluded here as they are expected to be served by local rail services. Trips between Torp – Tønsberg, Porsgrunn – Tønsberg and Porsgrunn – Torp have been excluded as they involve trips of less than 20km on HSR services.



Final Report 50

Table 28. Source of HSR Demand – S8:Q

Station Oslo S Drammen Tønsberg Torp Porsgrunn Arendal Kristiansand Mandal Egersund Sandnes Stavanger

Oslo S 0 E M M M M M M M M M

Drammen E 0 M M M M M M M M M

Tønsberg M M 0 E E M M M M M M

Torp M M E 0 E M M M M M M

Porsgrunn M M E E 0 G G M M M M

Arendal M M M M G 0 G G M M M

Kristiansand M M M M G G 0 G M M M

Mandal M M M M M G G 0 M M M

Egersund M M M M M M M M 0 G G

Sandnes M M M M M M M M G 0 E

Stavanger M M M M M M M M G E 0


Table 29. HSR Demand by Origin/Destination – S8:Q




Oslo S Kristiansand 0.94 7% 1.10 7%

Stavanger Kristiansand 0.34 2% 0.37 2%

Oslo S Other 5.28 38% 6.27 38%


Kristiansand Other 1.90 14% 2.49 15%

Other Other 1.45 10% 1.73 11%

Total 13.85 100% 16.38 100%

It can be seen that in this alternative the highest proportion of demand in 2024 (38%) is for travel between Oslo and intermediate stations, with trips between Oslo, Kristiansand and Stavanger accounting for approximately 28% of demand. When compared with North and West corridors, there is a greater proportion of travel here from intermediate areas.



Final Report 51

Figure 26. HSR Boardings / Alightings by Station – S8:Q

HSR service boardings are more evenly spread along the corridor than compared with other corridors, which is due to the greater population density in intermediate areas, particularly between Oslo and Kristiansand. The highest boardings are still at the terminus stations of Oslo and Stavanger, although Kristiansand and Arendal also generate significant levels of demand.

Figure 27 shows the forecast mode shares for trips of different lengths in 2024. It can be seen that HSR has a healthy mode share of trips between 300 and 600km in length. The mode share of air travel increases with distance and for trips of over 500km, air maintains a share of nearly 32% of the market. This reflects the less competitive HSR journey time for end-to-end travel when compared with other corridors.

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Tønsberg

Torp

Porsgrunn

Arendal

Kristiansand

Mandal

Egersund

Sandnes

Stavanger


2024 2043



Final Report 52

Figure 27. Mode share by HSR Journey Length, 2024 – S8:Q


boardings. It can be seen that there is more intermediate demand than the majority of other alternatives,

accessing HSR services throughout the corridor.

0.0% 0.3% 2.6%

13.3% 15.0%

31.7%

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74.1%68.1%

36.4%32.2%

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Final Report 53

Figure 28. Annual HSR Demand by Originating Zone and Daily Boarding Demand by Station – S8:Q

5.2.1. S8:Q PSS2 Sensitivity Test This section presents the key results for the higher fares sensitivity test for the S8:Q alternative between Oslo and Stavanger. Table 30 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative, while Figure 29 presents the forecast average daily boardings for each of the HSR stations.

Table 30. Summary of Demand and Revenue – Alternative S8:Q (PSS2)

Annual Per Day

Demand 2024 2043 2024 2043







Revenue






Final Report 54

Figure 29. HSR Boardings and Alightings by Station – S8:Q (PSS2)

It can be seen that annual HSR journeys in 2024 are estimated at nearly 3.7 million, increasing to over 4.6 million in 2043, which is a reduction of approximately 1.4 million passengers per year in 2024 compared with the core test. Conversely, the total annual revenue for the sensitivity test is approximately 190 MnNOK higher than the core test, as those travelling on HSR are paying higher fares.

3676

438

399

417

974

1456

1775

510

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855

1628

2871

340

301

327

777

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455

659

1363

0 1000 2000 3000 4000

Oslo S

Drammen

Tønsberg

Torp

Porsgrunn

Arendal

Kristiansand

Mandal

Egersund

Sandnes

Stavanger


2024 2043



Final Report 55

5.3. Alternative S2:P This alternative follows a new direct alignment between Drammen and Porsgrunn before following the south coast to Kristiansand and Stavanger. The line between Porsgrunn and Egersund is designed for 330 kph rail passenger and freight traffic, with Drammen – Porsgrunn and Egersund – Stavanger for passenger traffic only. Table 31 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative.

Table 31. Summary of Demand and Revenue – Alternative S2:P

Annual Per Day

Demand 2024 2043 2024 2043







Revenue




This alternative attracts higher demand and revenue than S8:Q, due to the shorter journey times for long distance trips, with the journey time between Oslo and Stavanger being approximately 30 minutes shorter, although serving no intermediate centres between Porsgrunn and Drammen. Correspondingly, there is poorer community access for the Vestfold region; although passengers are still able to access the HSR network at Drammen and Porsgrunn.

Table 32 below determines which model has been used to forecast HSR demand for each station pairing. Trips between Oslo – Drammen and Sandnes – Stavanger have again been excluded here as they are expected to be served by local rail services.

Table 32. Source of HSR Demand – S2:P

Station Oslo S Drammen Porsgrunn Arendal Kristiansand Mandal Egersund Sandnes Stavanger

Oslo S 0 E M M M M M M M

Drammen E 0 G M M M M M M

Porsgrunn M G 0 G G M M M M

Arendal M M G 0 G G M M M

Kristiansand M M G G 0 G M G M

Mandal M M M G G 0 M M M

Egersund M M M M M M 0 G G

Sandnes M M M M G M G 0 E

Stavanger M M M M M M G E 0




Final Report 56

Table 33. HSR Demand by Origin/Destination – S2:P




Oslo S Kristiansand 1.13 7% 1.32 7%

Stavanger Kristiansand 0.35 2% 0.37 2%

Oslo S Other 5.64 37% 6.57 37%


Kristiansand Other 1.68 11% 2.10 12%

Other Other 1.90 12% 2.31 13%

Total 15.22 100% 17.90 100%

It can be seen that in this alternative has a similar proportion of demand in 2024 (37%) is for travel between Oslo and intermediate stations as S8:Q, Trips between Oslo, Kristiansand and Stavanger also make up a similar proportion. This demonstrates that the shorter journey times with this alternative compensate for the loss of demand from Vestfold. It is likely that many passengers from Vestfold are still accessing the HSR network at Porsgrunn

Figure 30. HSR Boardings / Alightings by Station – S2:P

There is higher demand for this alternative at the terminus stations of Oslo and Stavanger when compared with S8:Q. There are no stops at Tønsberg and Torp in this alternative, but there is higher demand at Drammen and in particular Porsgrunn, which illustrates that a number of passengers are likely to be accessing these stations from the Vestfold region.

Figure 31 shows the forecast mode shares for trips of different lengths in 2024. It can be seen that HSR again has a healthy mode share of trips of over 300km in length. HSR has a higher mode share of travel between 400 and 500km than S8:Q, which is due to the more competitive journey time between Oslo and Stavanger.

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Oslo S

Drammen

Porsgrunn

Arendal

Kristiansand

Mandal

Egersund

Sandnes

Stavanger


2024 2043



Final Report 57

Figure 31. Mode share by HSR Journey Length, 2024 – S2:P


boardings. There is more demand accessing the network from between Kristiansand and Stavanger when

compared with S8:Q owing to faster journey times.

0.0% 0.5%5.3%

10.2%

24.4%

1.7%

12.1%

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39.4%

47.2%

7.9%

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4.8%

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2.8%

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73.7%

53.2%

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Final Report 58

Figure 32. Annual HSR Demand by Originating Zone and Daily Boarding Demand by Station – S2:P

5.3.1. S2:P PSS2 Sensitivity Test This section presents the key results for the higher fares sensitivity test for the S2:P alternative between Oslo and Stavanger. Table 34 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative, while Figure 33 presents the forecast average daily boardings for each of the HSR stations.

Table 34. Summary of Demand and Revenue – Alternative S2:P (PSS2)

Annual Per Day

Demand 2024 2043 2024 2043







Revenue






Final Report 59

Figure 33. HSR Boardings and Alightings by Station – S2:P (PSS2)

It can be seen that annual HSR journeys in 2024 are estimated at nearly 4.1 million, increasing to over 5.1 million in 2043, which is a reduction of approximately 1.4 million passengers per year in 2024 compared with the core test. The total annual revenue for the sensitivity test is approximately 260 MnNOK higher than the core test, which is a larger increase than with the S8:Q sensitivity test.

It should be noted that the slightly lower number of intermediate stations in the S2:P test means that a larger proportion of flows are affected by the fares increase (as flows from the Gravity Model are excluded) – hence the differential between alternatives should be viewed with caution.

5.4. Summary The alternative S2:P attracts higher demand and revenue than S8:Q, due to the shorter journey times for long distance trips, with the journey time between Oslo and Stavanger being approximately 30 minutes shorter. S8:Q, however, offers better community access for passengers from the highly populated Vestfold region. There is higher demand on this corridor compared with the Oslo – Trondheim and Oslo – Bergen corridors, regardless of the alignment chosen.

4153

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686

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Drammen

Porsgrunn

Arendal

Kristiansa…

Mandal

Egersund

Sandnes

Stavanger


2024 2043



Final Report 60

6. East Corridor Results

6.1. Introduction There are four alternatives on the West Corridor that have been tested:

ST5:U – Oslo-Stockholm via Ski;

ST3:R – Oslo-Stockholm via Lillestrøm;

GO3:Q – Oslo-Gothenburg via Moss; and

GO1:S – Oslo-Gothenburg via direct route.

Again, results are presented for the core PSS1 fares and service scenario, with outline results presented for the PSS2 scenario – although we advise that the PSS2 results are treated with caution for the reasons outlined below.

6.2. Alternative ST5:U This alternative follows the existing Eastern Østfold Line via Ski and Mysen, before following a new alignment between Mysen and Arvika in Sweden. The majority of the route is designed for 250 kph rail passenger and freight traffic. Table 35 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative.

Table 35. Summary of Demand and Revenue – Alternative ST5:U

Annual Per Day

Demand 2024 2043 2024 2043







Revenue




It can be seen that annual HSR journeys in 2024 are estimated at just over 4.2 million, increasing to over 5.2 million in 2043. This demand breaks down as 53% business travel and 47% leisure travel in 2024. Total annual revenue is estimated to be 1.1 BnNOK in 2024 and 1.3 BnNOK in 2043, with 63% of revenue from business travel.

Table 36 below determines which model has been used to forecast HSR demand for each station pairing. Trips between Oslo and Ski have been excluded here as they are expected to be served by local (intercity) rail services.



Final Report 61

Table 36. Source of HSR Demand – ST5:U

Station Oslo S Ski Karlstad Örebro Västerås Stockholm

Oslo S 0 E G M M M

Ski E 0 G M M M

Karlstad G G 0 G M M

Örebro M M G 0 G M

Västerås M M M G 0 G

Stockholm M M M M G 0


Table 37. HSR Demand by Origin/Destination – ST5:U



Oslo S Stockholm 2.86 25% 3.34 23%

Oslo S Other 2.41 21% 2.91 20%

Stockholm Other 5.52 48% 7.09 50%

Other Other 0.78 7% 0.98 7%

Total 11.58 100% 14.32 100%

It can be seen that in this alternative the highest proportion of demand in 2024 (48%) is for travel between Stockholm and intermediate stations. This is in contrast with the corridors within Norway, where a higher proportion of demand travels either to or from Oslo. There is also significant travel between Oslo and Stockholm (25%), and between Oslo and intermediate areas (21%).

Figure 34. HSR Boardings / Alightings by Station – ST5:U

There is a large proportion of demand between Stockholm and intermediate stations in Sweden. There is also significant demand between Oslo and Stockholm. It should be noted that demand from Ski to Oslo has

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Karlstad

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Västerås

Stockholm

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Final Report 62

been excluded from these figures, as it is assumed to travel on Inter-City services instead. Average train occupancy is lower than other corridors in Norway, which demonstrates the high number of shorter distance trips within Sweden.

Figure 35 shows the forecast mode shares for trips of different lengths in 2024. HSR has a large market share for trips of between 400 and 600km in length, with the remaining air market occupying a share of less than 4% for trips between 400 and 500km, and just over 20% for trips of between 500 and 600km.

Figure 35. Mode share by HSR Journey Length, 2024 – ST5:U

6.2.1. ST5:U PSS2 Sensitivity Test This section presents the key results for the higher fares sensitivity test for the ST5:U alternative between Oslo and Stockholm. Table 38 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative, while Figure 36 presents the forecast average daily boardings for each of the HSR stations.

0.4%5.1%

7.9% 8.4%3.5%

20.5%

0.2%

8.4%

11.1%

20.8%

70.3%

61.7%

11.7%

13.5%

26.4%

17.0%

1.6%

2.9%

9.3%

7.2%

7.9% 8.7%

4.5%

3.8%

78.4%

65.9%

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Table 38. Summary of Demand and Revenue – Alternative ST5:U (PSS2)

Annual Per Day

Demand 2024 2043 2024 2043







Revenue




Figure 36. HSR Boardings and Alightings by Station – ST5:U (PSS2)

It can be seen that annual HSR journeys in 2024 are estimated at just over 3.6 million, increasing to over 4.6 million in 2043, which is a reduction of approximately 0.6 million passengers per year in 2024 compared with the core test. Conversely, the total annual revenue for the sensitivity test is approximately 340 MnNOK higher than the core test, as those travelling on HSR are paying higher fares.

2580

209

1379

1045

2917

4509

2077

171

1061

791

2323

3508

0 1000 2000 3000 4000 5000

Oslo S

Ski

Karlstad

Örebro

Västerås

Stockholm


2024 2043



Final Report 64

6.3. Alternative ST3:R This alternative follows a new alignment between Lillestrøm and Arvika before following existing rail routes to Stockholm. The line between Lillestrøm and Arvika is designed for 330 kph rail passenger traffic, with the remainder of the route designed for 250 kph rail passenger and freight traffic. Table 39 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative.

Table 39. Summary of Demand and Revenue – Alternative ST3:R

Annual Per Day

Demand 2024 2043 2024 2043







Revenue




This alternative attracts a slightly higher demand and revenue than ST5:U. This is partly a function of the shorter journey time between Oslo and Stockholm and the higher number of boardings at Lillestrøm towards Oslo compared with Ski (trips between Lillestrøm and Oslo are assumed to travel on Inter-City services).

Table 40 below determines which model has been used to forecast HSR demand for each station pairing. Trips between Oslo and Lillestrøm have been excluded here as they are expected to be served by local rail services.

Table 40. Source of HSR Demand – ST3:R

Station Oslo S Lillestrøm Karlstad Örebro Västerås Stockholm

Oslo S 0 E G M M M

Lillestrøm E 0 G M M M

Karlstad G G 0 G M M

Örebro M M G 0 G M

Västerås M M M G 0 G

Stockholm M M M M G 0




Final Report 65

Table 41. HSR Demand by Origin/Destination – ST3:R



Oslo S Stockholm 2.83 23% 3.31 22%

Oslo S Other 2.68 22% 3.26 22%

Stockholm Other 5.61 47% 7.18 48%

Other Other 0.94 8% 1.17 8%

Total 12.06 100% 14.91 100%

It can be seen that in this alternative that again the highest proportion of demand in 2024 (47%) is for travel between Stockholm and intermediate stations, with significant travel between Oslo and Stockholm (23%), and between Oslo and intermediate areas (22%).

Figure 37. HSR Boardings / Alightings by Station – ST3:R

Station boardings are similar to those in the alternative ST5:U, but are generally slightly higher due to the faster journey time between Oslo and Stockholm.

Figure 38 shows the forecast mode shares for trips of different lengths in 2024. HSR has a large market share of over 62% for trips of between 400 and 500km in length, with the remaining air market occupying a share of approximately 19%.

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Final Report 66

Figure 38. Mode share by HSR Journey Length, 2024 – ST3:R

6.3.1. ST3:R PSS2 Sensitivity Test This section presents the key results for the higher fares sensitivity test for the ST3:R alternative between Oslo and Stockholm. Table 42 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative, while Figure 39 presents the forecast average daily boardings for each of the HSR stations.

Table 42. Summary of Demand and Revenue – Alternative ST3:R (PSS2)

Annual Per Day

Demand 2024 2043 2024 2043







Revenue




0.5%5.3% 7.2% 9.6%

19.1%9.1%

16.9% 13.2%

62.4%

5.1%

14.1%

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19.5%

2.9%

8.8%

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Figure 39. HSR Boardings and Alightings by Station – ST3:R (PSS2)

It can be seen that annual HSR journeys in 2024 are estimated to be 3.8 million, increasing to over 4.8 million in 2043, which is a reduction of approximately 0.6 million passengers per year in 2024 compared with the core test. The total annual revenue for the sensitivity test is approximately 380 MnNOK higher than the core test, which is a slightly higher increase than with the ST5:U sensitivity.

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Lillestrøm

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Örebro

Västerås

Stockholm


2024 2043



Final Report 68

6.4. Alternative GO3:Q This alternative is principally an upgrade of the existing Western Østfold Line between Oslo and Gothenburg and is designed for 250 kph rail passenger and freight traffic. Table 43 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative.

Table 43. Summary of Demand and Revenue – Alternative GO3:Q

Annual Per Day

Demand 2024 2043 2024 2043







Revenue




It can be seen that annual HSR journeys in 2024 are estimated at nearly 4.7 million, increasing to 5.8 million in 2043. This demand breaks down as 66% business travel and 34% leisure travel in 2024. Total annual revenue is estimated to be 830 MnNOK in 2024 and 1 BnNOK in 2043, with 86% of revenue from business travel.

Table 44 below determines which model has been used to forecast HSR demand for each station pairing. Trips between Oslo – Ski and Oslo – Moss have been excluded here as they are expected to be served by local rail services. Trips between Sarpsborg – Fredrikstad, Sarpsborg – Halden and Fredrikstad – Halden have been excluded as they involve trips of less than 20km on HSR services.

Table 44. Source of HSR Demand – GO3:Q

Station Oslo S Ski Moss Fredrikstad Sarpsborg Halden Trollhättan Göteborg

Oslo S 0 E E G G M M M

Ski E 0 G G G M M M

Moss E G 0 G G M M M

Fredrikstad G G G 0 E E G M

Sarpsborg G G G E 0 E G M

Halden M M M E E 0 M M

Trollhättan M M M G G M 0 G

Göteborg M M M M M M G 0




Final Report 69

Table 45. HSR Demand by Origin/Destination – GO3:Q



Oslo S Göteborg 1.33 10% 1.49 9%

Oslo S Other 6.05 47% 7.56 48%

Göteborg Other 3.36 26% 4.21 27%

Other Other 2.06 16% 2.59 16%

Total 12.81 100% 15.85 100%

It can be seen that in this alternative the highest proportion of demand in 2024 (47%) is for travel between Oslo and intermediate stations. There is significant demand for travel (26%) between Gothenburg and intermediate areas. There is a relatively low proportion of demand between the terminus stations (Oslo and Gothenburg) when compared with the other corridors examined.

Figure 40. HSR Boardings / Alightings by Station – GO3:Q

There are generally far lower levels of long distance demand on this corridor when compared with the other alternatives tested, although this is offset by higher levels of intermediate demand, especially between Oslo and Sarpsborg and Fredrikstad in Norway, and between Trollhättan and Gothenburg within Sweden. The greater proportion of short distance trips is illustrated by the lower average train occupancy and passenger kilometres compared with other alternatives.

Figure 41 shows the forecast mode shares for trips of different lengths in 2024. Car dominates the travel market for trips of all lengths on this corridor. HSR has a market share of just over 23% for trips of between 300 and 400km, with air having a very small market share (2%). In reality the mode share of HSR will be a lot higher for trips of less than 100km due to the additional HSR demand from the Gravity Model.

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Ski

Moss

Fredrikstad

Sarpsborg

Halden

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Final Report 70

Figure 41. Mode share by HSR Journey Length, 2024 – GO3:Q

6.4.1. GO3:Q PSS2 Sensitivity Test This section presents the key results for the higher fares sensitivity test for the GO3:Q alternative between Oslo and Gothenburg. Table 46 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative, while Figure 42 presents the forecast average daily boardings for each of the HSR stations.

Table 46. Summary of Demand and Revenue – Alternative GO3:Q (PSS2)

Annual Per Day

Demand 2024 2043 2024 2043







Revenue




0.1% 0.0% 0.1% 2.0%7.0% 9.1%

4.5%

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0.8%

7.5%14.7%

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Final Report 71

Figure 42. HSR Boardings and Alightings by Station – GO3:Q (PSS2)

It can be seen that annual HSR journeys in 2024 are estimated at over 4.4 million, increasing to over 5.5 million in 2043, which is a reduction of approximately 0.2 million passengers per year in 2024 compared with the core test. Conversely, the total annual revenue for the sensitivity test is approximately 410 MnNOK higher than the core test, as those travelling on HSR are paying higher fares.

It should be noted that a large proportion of HSR demand is calculated by the Gravity Model approach, which does not include fares sensitivity: hence the impact of the PSS2 sensitivity test on demand and revenue is likely to be significantly over-optimistic.

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Moss

Fredrikstad

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Trollhättan

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Final Report 72

6.5. Alternative GO1:S This alternative follows a new direct alignment between Ski and the Swedish border before following the existing alignment to Gothenburg. The line within Norway is designed for 330 kph rail passenger and freight traffic. Table 47 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative.

Table 47. Summary of Demand and Revenue – Alternative GO1:S

Annual Per Day

Demand 2024 2043 2024 2043







Revenue




This alternative attracts a lower demand and revenue than GO3Q. Although there is a vastly quicker journey time between Oslo and Sarpsborg, Trollhättan and Gothenburg, there are far less journey opportunities in the Østfold region within Norway due to the lack of intermediate stations. Base demand for travel between Oslo and Gothenburg, where the greatest journey time savings are made in this alternative, is lower than for end-to-end travel on the other corridors examined.

Table 48 below determines which model has been used to forecast HSR demand for each station pairing.

Table 48. Source of HSR Demand – GO1:S

Station Oslo Sarpsborg Trollhättan Göteborg

Oslo 0 G M M

Sarpsborg G 0 G M

Trollhättan M G 0 G

Göteborg M M G 0




Final Report 73

Table 49. HSR Demand by Origin/Destination – GO1:S



Oslo S Göteborg 1.51 15% 1.70 14%

Oslo S Other 5.17 51% 6.45 51%

Göteborg Other 3.34 33% 4.19 33%

Other Other 0.15 1% 0.19 2%

Total 10.18 100% 12.53 100%

It can be seen that in this alternative the highest proportion of demand in 2024 (51%) is for travel between Oslo and intermediate stations. There is significant demand for travel (33%) between Gothenburg and intermediate areas. There is again a relatively low proportion of demand between the terminus stations (Oslo and Gothenburg) when compared with the other corridors examined. Demand between intermediate stations is very low when compared with other corridors.

Figure 43. HSR Boardings / Alightings by Station – GO1:S

Demand is more evenly spread between stations on this corridor due to the relatively small number of stations and the relatively quick HSR journey times encouraging access to HSR stations from longer distances away.

Figure 44 shows the forecast mode shares for trips of different lengths in 2024. Car again dominates the travel market for trips of all lengths on this corridor. HSR has a higher market share (29%) than GO3:Q for trips of between 300 and 400km.

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Sarpsborg

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Final Report 74

Figure 44. Mode share by HSR Journey Length, 2024 – GO1:S

6.5.1. GO1:S PSS2 Sensitivity Test This section presents the key results for the higher fares sensitivity test for the GO1:S alternative between Oslo and Gothenburg. Table 50 below summarises the forecast demand and revenue for HSR travel for the years 2024 and 2043 under this alternative, while Figure 45 presents the forecast average daily boardings for each of the HSR stations.

Table 50. Summary of Demand and Revenue – Alternative GO1:S (PSS2)

Annual Per Day

Demand 2024 2043 2024 2043







Revenue

HSR Total Revenue (millions) 1060 1300



0.0% 0.0% 0.1% 1.9%

11.7%5.0%

28.5%12.5%

0.8%

8.0%

11.7%

1.9%

2.9%

4.4%

64.1%

97.6%91.1%

57.2%

0%

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Figure 45. HSR Boardings and Alightings by Station – GO1:S (PSS2)

It can be seen that annual HSR journeys in 2024 are estimated to be over 3.5 million, increasing to over 4.4 million in 2043, which is a reduction of approximately 0.2 million passengers per year in 2024 compared with the core test. The total annual revenue for the sensitivity test is approximately 350 MnNOK higher than the core test, which is a slightly lower increase than with the GO3:Q sensitivity. This is likely due to the reduced number of stations served which are calculated by the Gravity Model approach.

6.6. Summary In the Stockholm corridor both ST5:U and ST3:R attract similar levels of demand and revenue, with ST3:R having marginally higher demand due to the slightly faster journey time. The levels of demand and revenue on this corridor are lower than those on the corridors within Norway.

The Gothenburg corridor, in particular the GO3:Q alternative, attracts a lot more short distance trips than the other corridors tested. Although GO1:S has vastly quicker journey times between Oslo and Sarpsborg, Trollhättan and Gothenburg, there are far less journey opportunities in the Østfold region within Norway due to the lack of intermediate stations. Base demand for travel between Oslo and Gothenburg, where the greatest journey time savings are made in this alternative, is lower than for end-to-end travel on the other corridors examined.

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Final Report 76

7. Corridor Comparison

7.1. Introduction The following chapter compares forecasts of demand and revenue across alternative HSR route alternatives. The colouring within the charts reflects the corridor on which the alternative sits, with routes labelled within each corridor group.

It is important to note than demand and revenue are only part of the assessment of route alternatives, with non-financial benefits and costs also impacting on an alternative‟s attractiveness. The cross-comparison between alternatives is given in the separate, overall scheme appraisal report.

7.2. Comparison between Corridors Figure 46 compares the total annual HSR passenger trips for all of the alternatives tested, for the years 2024 and 2043.

Figure 46. Comparison of HSR Passengers by Alternative

The Y-shaped network linking Oslo with both Bergen and Stavanger obtains the highest demand, which includes core and peak services operate to both Bergen and Stavanger. Of the other more comparable single route corridors, the faster route to Stavanger (S2:P) obtains the highest demand. Demand is lowest on the Bergen-Stavanger corridor – as would expected, as it is the only corridor which does not serve Oslo.

Figure 47 compares the total annual HSR revenue for all of the alternatives tested, for the years 2024 and 2043, presented in NOK millions. In general it shows the main Norwegian corridors generating similar levels of revenue. The South corridor alternatives, which generated in the region of 20% more demand than either the North or West corridor alternatives, do not generate a similar increase in revenue due to the larger proportion of short distance trips on the South corridor. Although the East corridor alternatives into Sweden forecast a similar number of passengers to the Norwegian corridors, they offer significantly less revenue; again this is due to a high proportion of relatively short distance trips on these corridors.

G3Y G3Y

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Final Report 77

Figure 47. Comparison of HSR Revenue by Alternative

Figure 48 shows average train occupancy by route alternative. In many ways this is a better indicator of demand than passenger demand as it accounts for corridor length. Average occupancy is similar across the three main Norwegian corridors with the highest occupancy being on route Ø2:P. As with revenue, occupancy is lower on the East corridors due to a large number of short distance trips.

Interestingly although the occupancy on the Bergen-Stavanger corridor (BS1:P) is lower than on other routes, proportionally this is not as significant as either for passengers or revenue; this is due to the shorter distance on this line. The average occupancy on the Y-shaped corridor between Oslo and Bergen/Stavanger is brought down by relatively low occupancy on the Bergen-Stavanger services compared to the Oslo-Bergen and Oslo-Stavanger services.

Figure 48. Comparison of HSR Average Occupancy by Alternative

The graphs show that it is not clear cut whether a slower stopping service or a faster direct service would offer an optimised service on a given corridor, which is an area for further detailed investigation, in conjunction with associated analysis of operating costs and economic benefits.

G3Y G3Y

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Final Report 78

7.3. Impact of Alternative Service Assumptions The presentation of main HSR alternatives in this report has been based on PSS1, as described in Chapter 2. PSS1 assumes a HSR fare equivalent to 60% of the competing air fare. However, over the lengths of the corridors considered, HSR would be very competitive with air, with forecasting showing a clear preference by passengers for HSR over air. Consequently any HSR service will serve a different market to the existing long distance rail service within Norway, attracting passengers with higher values of time who are willing to pay a fare premium for faster more efficient travel. In addition, there might be scope to reduce train service related costs with some reduction in the number of services operated without significantly affecting demand.

The alternative passenger service scenario, PSS2 has also been established, which offers a combination of higher rail fares and a reduction in the number of trains operated, with the additional 4 trains in each direction identified for the peak periods removed. It should be noted however, that, because the forecasting model is an all day model, the removal of “peak” services manifests itself as a reduction in trains per day by 8 to around 18 a day, and hence a reduced overall daily frequency, rather than a specific peak period impact.

The figures below show the alterations to passenger demand, revenue and average occupancy as a percentage of the original demand from the core tests. It should be noted that the methodology for forecasting trips of under 100km in the model does not account for fare increases, therefore these trips are unchanged in the results below. This has not had a large effect on the North, South and West corridors although the effect is more marked on the East corridor alternatives, and hence some caution should be used in using these figures for those alternatives.

Figure 49. Comparison of HSR Passengers by Alternative with PSS2

Figure 49 above shows that, despite a fare increase to the equivalent of the current air fare and a reduction in the number of HSR services by 8 trains per day to around 18 trains per day, demand on the Norwegian corridors remains at approximately 65-70% of those forecast in the core appraisal. A slightly higher proportion of demand can be seen to remain in 2043, due to increasing values of time throughout the appraisal period. The Eastern corridors show over 80% of demand remaining, it is noted that this is inflated due to the shorter distance trips, which are significant on these corridors, not being influenced by the fare within the forecasting approach. Realistically, this figure would be expected to reduce further.

G3Y

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Figure 50. Comparison of HSR revenue by alternative with PSS2

Figure 50 shows that, despite the reduction in demand, the fare increase results in larger revenue than in the core results, despite the reduction in service levels also introduced. Revenue on the Norwegian corridors is typically more than 10% higher than in the original tests. The increase in revenue is higher in 2043, again this is due to the higher value of time which reduce the impact of fare increases on demand. Again, revenues on the East corridors are inflated due to the forecasting not accounting for the impact of increased fare on demand over shorter distances.

Figure 51. Comparison of HSR Average Occupancy by Alternative with PSS2

Figure 51 above shows that, although demand has fallen, occupancies are typically similar to in the original PSS1.

As noted earlier in this report the alternatives tested do not necessarily, and are extremely unlikely to, reflect an optimised service in terms of service provision or fares. For route Ø2:P (Oslo-Trondheim), Figure 52 below shows how changing the HSR fare (as a proportion of the current air fare) impacts on HSR demand and revenue.

G3Y

G3Y

O2P

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Figure 52. Impact of HSR Fare on Demand and Revenue (Alternative Ø2:P, 2024)

Figure 52 shows that, although the HSR appraisals alternatives in PSS1 have been assessed with a HSR fare set to 60% of the current air fare, HSR revenues are not maximised until the HSR reaches approximately 150% of the existing air fare. It can be seen that, although a higher HSR fare maximises revenue, passenger demand is significantly lower with higher fares. Starting with an HSR fare of 60% of the existing air fare, HSR demand falls by around 30% with an HSR fare of 100% air fare, and by over 50% with an HSR fare of 150% air fare. These figures vary corridor-by-corridor and movement-by-movement; typically shorter distance journeys, where HSR does not compete with air, are likely to be more elastic to changes in fare.

It is noted that maximising revenues will not maximise benefits, as a lower number of passengers will receive time savings. In addition, where passengers pay a premium over their existing fare they will experience a disbenefit which will also be included in the economic appraisal.

We also emphasise that the forecasting looks at average fare paid by journey purpose. With appropriate marketing and revenue management systems – similar to those used on airlines – it may be possible to increase overall revenues while maintaining passenger numbers to some extent. Further investigation would follow at a more detailed stage of scheme development.

7.4. International Comparison This section provides an international comparison between forecast Norwegian HSR demand and observed demand from existing HSR services. Two comparisons are provided:

The first examines the forecast rail:air mode share forecast Norway against that from existing routes; and

The second compares the demand from international high speed networks against that forecast for Norway.

7.4.1. Rail-air Mode Share

Previous studies have examined the relationship between travel time by train and the rail-air market share. A strong relationship is observed between the two as the majority of travel time by air is not incurred as in-vehicle time. Figure 53 below shows the relationship as reproduced from Steer Davies Gleave (2006). Modelled results from the NHSRDM are overlaid onto this chart.



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Figure 53. Rail-air Market Share (Steer Davies Gleave, 2006)

From Figure 53 it can be seen that the forecast HSR-Air market share from the NHSRDM fits well with the observed results. Although with lower journey times the HSR market share is at the upper levels of the observed results, the inferred in-vehicle journey time elasticity from the model, at the top of the curve, have been shown to be similar to those presented in other studies.

It is noted that although the above relationship is observed there still exists a significant variation even when

rail journey times are similar. On market shares rail/air Steer Davies Gleave (2006) writes:

“the rail journey time was the single most important factor determining market share, but nonetheless there

could be significant variation even where the journey times were similar: for example, routes with rail journey

times of about 2 hours 30 minutes had rail shares varying from 44% to 85%. This variation arose because:

Other factors related to the schedule offered or the effective journey time, such as the frequencies offered by each mode and average access times, influence market share;

Other factors not related to the schedule, including price and service quality, also influence market share; and

Definitions of the markets varied between routes, and were sometimes different for air and rail on the same route.”

Whilst only journey time is changed in the test results above from the NHSRDM above it is noted that other factors may vary more significantly along with rail journey time on the observed corridors (for instance slower journey times may correlate to longer trips and increased fares or reduced service quality). This may result in the modelled mode rail mode shares being higher than those on observed corridors.

7.4.2. HSR Network Demand

This comparison compares the reported passenger km travelled on international HSR networks to those forecast for Norway. The level of travel for international networks is taken from the International Union of Railways (UIC

3). The level of travel on a high speed network is influenced by a large number of factors

including the; length of the high speed network, the fare policy, the extent of competing modes, the

3 http://www.uic.org/IMG/pdf/201101_hs_traffic__tab_50_-_2009.pdf



Final Report 82

population and spread of the population relative to the high speed network. As a result, it is difficult to produce a definitive comparison between market usage in different countries.

Table 51 gives a high-level comparison of international HSR trips made per head of population against forecasts for Norway.

Table 51. Comparison of International HSR Travel

Country Network/

Operator

Passenger trips (k) (2009)

Population (k) HSR trips per head of population

International comparisons

Sweden SJ 9,071 9,476 0.96

Spain RENFE 28,751 46,162 0.62

Germany DB AG 73,709 81,768 0.90

France SNCF 114,395 65,027 1.76

Italy IFS 33,377 60,742 0.55

Japan JJR 288,836 127,760 2.26

Taiwan THSRC 32,349 23,215 1.39

Norway (2010 demand)

Norway Ø2:P 3,943 4,985 0.79

Norway S8:Q 3,778 4,985 0.76

The usage figures clearly reflect the level of HSR network development and extent of HSR services, with France leading by a significant margin, due to its extensive TGV network of services. However, the table also shows typical levels of HSR usage penetration, and that the forecasts for two of the sample Norway alternatives fall well within expected levels. Even if combined, they would still be of the same order of magnitude as for the Swedish network of high speed services – bearing in mind that Norway HSR services would be competing far more with large, established domestic air markets than existing Swedish high speed services.

To provide a more meaningful comparison with international demand we have also included each country‟s population

4 and HSR network track km

5. This has enabled a comparison to show the number of HSR

passenger km per head of population and per km of high speed track. For instance, on this measure a value of „1‟ is equivalent to each person in a country travelling the length of the high speed network once. The results are shown below in Table 52.

4 Source: Wikipedia

5 Source: The High Speed Rail Revolution: History and Prospects HS2



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Table 52. Comparison of International HSR Travel (Passenger km)

Country Network Passenger km (m) (2009)

Track km (HSR)

Population (k)

HSR Passenger km (per head of population per HSR track km)

International comparisons

Spain RENFE 11,505 1,599 46,162 0.16

Germany DB AG 22,561 1,285 81,768 0.21

France SNCF 51,864 1,872 65,027 0.43

Italy IFS 10,746 744 60,742 0.24

Japan JJR 76,039 2,176 127,760 0.27

Taiwan THSRC 6,863 345 23,214 0.86

Norway (2010 demand)

Norway Ø2:P 1412 483 4,985 0.59

Norway S8:Q 1291 538 4,985 0.48

Table 52 above shows that, per head of population and length of the HSR network, the forecast demand on the Norwegian corridors is high although within the bounds of international comparisons above. The trip rate on the Norwegian corridors shown varies between 0.48 and 0.59 passenger km per head of population per HSR track km. The highest value on existing HSR lines in Europe is 0.43 in France although the value is as high as 0.86 in Taiwan.

This is not to be unexpected previous reports have shown that Norwegians‟ overall long-distance mobility is one of the highest throughout Europe. Statistically in 1999, more than 7000 km had been covered by each Norwegian on long distance traffic (>80 km travel distance). In Germany and France for example overall long-distance mobility averages between 5000 and 5500 km per year

6.

6 Feasibility Study Concerning High-Speed Railway Lines in Norway, VWI, 2006.



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8. Scenario B Results

8.1. Introduction and Description of Scenario B Alternatives As discussed in Chapter 2, the mandate given to JBV for investigation of HSR in Norway has required that the upgrade of existing lines as an alternative be examined. It is recognised that this does not deliver a HSR offer but would indicate the scope to secure benefits in the HSR corridors via existing lines.

For the purposes of this study, Scenario B was conceptually defined by JBV as:

„Delivery of a uniform 20% reduction in travel time, maintaining the current stopping pattern and remaining single track outside of the Inter-City (IC) area’

JBV‟s alignment design teams each examined possible alternatives for delivery of Scenario B and high level specifications were provided to Atkins and F+G, covering each route per corridor, and reflecting the sections of route where the journey time improvement would be secured. This is summarised in Table 53 below:

Table 53. Scenario B Summary of Specification

Corridor Route Section(s) of route where journey time improvement is secured

% Journey Time Assumption

North Oslo-Trondheim Gardermoen-Oppdal 20% reduction in total end-to-end time West Oslo-Bergen Hønefoss-Bergen

South Oslo - Kristiansand -Stavanger Drammen-Sandnes

East Oslo - Stockholm Lillestrøm-Kongsvinger 20% reduction in Olso-Charlottenberg time: equates to a 5% reduction in Oslo-Stockholm time

The exceptional Scenario B alternative is clearly the East corridor alternative between Oslo and Stockholm, where the specification aims only to achieve a 20% reduction in journey time between Oslo and Charlottenberg. Norconsult, the alignment consultants for this corridor, advised that insufficient information was available to determine a specification for Scenario B improvements on Swedish sections of route and consequently specification only aimed to deliver the reduction in journey time within Norway. This should be borne in mind when considering the results presented in this section.

8.2. Scope of Analysis The scope of analysis undertaken for Scenario B alternatives has been significantly less than that for Scenario C/D alternatives as presented in the rest of this report. This reflects the level of scheme development associated with Scenario B, which has been significantly less than that for Scenario C/D HSR alternatives, and the requirements and remit for Scenario B analysis as advised by JBV.

8.2.1. Scenario B Journey Time Analysis Scenario B has been specified to involve a reduction in journey time of 20% on the existing rail corridors in Norway, compared with the current situation. It has been assumed that there is no journey time improvement within Sweden, and that there is no improvement in service frequency. Testing has been carried out on four corridors:

Oslo-Trondheim;

Oslo-Bergen;

Oslo-Kristiansand-Stavanger; and

Oslo-Charlottenberg (Stockholm).



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Atkins calculated the overall change in journey time based on the current fastest timetabled journey times for each route, as shown in Table 54 below. The alignment data for Scenario B provided by the alignment teams was used to determine where the journey time reductions are applied along each corridor. The equivalent HSR alternative journey time is also shown for comparison and highlights the significantly shorter journey times full HSR would offer, albeit for a very different type of service.

Table 54. Scenario B Journey Times

Corridor 2011 Fastest Journey Time

Scenario B Journey Time

HSR Alternative Comparison Time

Oslo-Trondheim 6:36 5:16 2:59 (G3:Y)

Oslo-Bergen 6:28 5:10 2:06 (HA2:P)

Oslo - Kristiansand -Stavanger 7:42 6:09 3:31 (S8:Q)

Oslo-Stockholm

(Oslo-Charlottenberg)

5:55

(1:43)

5:34

(1:22)

2:56 (ST5:U)

To implement these Scenarios in NTM5 (the model used for demand forecasting), corridor specific adjustments were applied to the relevant services on the relevant links. The factors are multiplicative, and were applied within the EMME data repository. The derivation of these factors was based on the current journey times contained within NTM5. These factors were applied to the sections of each route, based on where the line upgrades have been specified in the alignment data, as described in Table 38.

The time saving required from the current NTM5 times to achieve the Scenario B times was calculated, and a reduction factor was then derived to apply to the journey time of the section where the Scenario B improvements have been made. This working is shown in Table 55 below. Note that the journey time was calculated separately for each direction, labelled as “from Oslo” and “to Oslo” in the table.

Table 55. NTM5 Journey Time Factor

Corridor Current NTM5 Journey Time

(Reference Case)

Time Saving Required

NTM5 JT over Upgraded Section

Journey Time Reduction Factor Applied to Section

From Oslo

To Oslo From Oslo

To Oslo From Oslo

To Oslo From Oslo

To Oslo

Oslo-Trondheim 6:22 6:22 1:06 1:06 4:25 4:21 0.75 0.75

Oslo-Bergen 6:12 6:24 1:02 1:14 4:43 5:00 0.78 0.75

Oslo - Kristiansand –Stavanger

6:49 6:39 0:40 0:30 6:13 6:02 0.89 0.92

Oslo-Charlottenberg 1:44 1:48 0:22 0:26 1:01 0:57 0.64 0.54

As a result, the changes modelled in these NTM5 tests represent an improvement in journey time on sections of track where the upgrades are to be implemented, with the end-to-end journey time representing a 20% reduction to the current rail service. This will enable more local demand responses to supply changes at specific locations to be identified.

8.3. Scenario B Market, Demand and Revenue Analysis This section presents a summary of the key results from the testing of the four corridors under Scenario B.

8.3.1. Key Assumptions Scenario B represents a relatively small improvement to the base-case rail network. NTM5 therefore provides a suitable basis for forecasting the demand impacts for each scenario, consistent with testing of similar scale of schemes by JBV. It is important to note that this modelling approach is not consistent with the mode choice and gravity modelling approach used in the main alternative testing described in previous sections – those models were specifically developed to examine larger scale changes to journey times and



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are less applicable for marginal journey time savings such as those in Scenario B alternative tests. It is also important to note that NTM5 does not model trips under 100km, resulting in slight under-forecasting of the effects of the upgrade on rail demand and revenue.

Atkins was supplied with the NTM5 network specifications and associated socio-economic data, as used for the recent National Transport Plan work in Norway. The networks were identical for the two forecast years under scrutiny (2024 and 2043).

The specification for the service improvements under Scenario B is described in the previous section, and involves a reduction in journey time only. It should be noted that these changes were applied to long distance services on each corridor, including “Night Trains”, as well as Oslo-Kristiansand and Kristiansand-Stavanger regional services. All the services mentioned are specified in NTM5, and equally contribute to supply and are available for assignment, as the model is based on aggregate daily demand and supply levels. Each corridor has been tested individually, with Scenario B improvements applied to one corridor for each test, in order to identify the relative effect on demand of Scenario B to each corridor.

Revenue calculations have been made based on the forecast demand in NTM5 and the fare assumptions stored within the NTM5 model.

8.3.2. Demand and Revenue Performance The following tables and charts summarise the demand forecasts from NTM5 for Scenario B tests in 2024 and 2043, and the associated revenue. The demand and revenue shown is the forecast increase in rail passengers as a result of the journey time improvements made under Scenario B, compared with the base scenario in NTM5. The train kilometres shown are for the long distance rail services to which the journey time improvements were applied. The station boardings are shown for the long distance rail services only and for a selection of key stations, with demand for smaller stations merged with the nearest key station. The figures represent the total boardings under Scenario B for long distance services.

8.3.2.1. Oslo – Trondheim

Table 56 below summarises the forecast change in demand due to Scenario B being applied on the Oslo – Trondheim route.

Table 56. Summary of Demand and Revenue: Oslo – Trondheim

Demand and Revenue Annual Per day

2024 2043 2024 2043

Change in Rail Passengers 169,140 216,590 460 590

Change in Rail Passenger km (thousands) 104,950 134,330 290 370

Train km (thousands) 2,530 2,530 6.9 6.9

Change in Revenue (NOK thousands)7 96,670 124,820

It can be seen that there is an increase in annual rail demand of approximately 170,000 passengers in 2024 as a result of the Scenario B improvements, rising to nearly 220,000 in 2043. This increase in demand generates revenue of approximately 95 million NOK in 2024 and 125 million NOK in 2043.

7 Revenue is given in 2009 NOK prices



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The annual increase in demand of 170,000 passengers and 95 million NOK in 2024 compares with annual HSR demand of 4.4 million and revenue of 1.5 billion NOK under the G3:Y scenario

8. Clearly the

implementation of Scenario B has a very minor impact on rail travel on this corridor compared with that of a new HSR line.

Figure 54 summarises total daily boardings for long distance services at key stations along the Oslo – Trondheim corridor.

Figure 54. Long Distance Boardings by Station: Oslo – Trondheim

8.3.2.2. Oslo – Bergen

Table 57 below summarises the forecast change in demand due to Scenario B being applied on the Oslo – Bergen route.

Table 57. Summary of Demand and Revenue: Oslo – Bergen


2024 2043 2024 2043




Change in Revenue (NOK thousands) 75,260 99,050

It can be seen that there is an increase in annual rail demand of approximately 170,000 passengers in 2024 as a result of the Scenario B improvements, rising to nearly 220,000 in 2043, which is very similar to the impact on the Trondheim corridor. This increase in demand generates revenue of approximately 75 million NOK in 2024 and 100 million NOK in 2043. The lower revenue on this corridor compared with Oslo – Trondheim is as a result of a lower average trip length.

8 Please note that figures presented for Scenario B and Scenario C/D alternatives are not directly

comparable. This is principally because (a) the NTM5 demand modelling in Scenario B does not include short distance trips of less than 100km which are estimated using a gravity model approach in Scenario C/D alternatives, and (b) Scenario C/D figures are shown inclusive of extraction from existing classic rail services, hence the net increase to all types of rail services would be slightly lower. However, these effects are relatively small and demand figures for equivalent Scenario C/D alternatives remain 10-20 times as large as for equivalent Scenario B alternatives. This applies to the comparison between Scenario B and Scenario C/D in each of the four corridors.

1186

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Lillehammer

Otta

Dombås

Oppdal

Berkåk

Støren

Heimdal

Trondheim

Boarders per day

2024 2043



Final Report 88

The annual increase in demand of 170,000 passengers and 75 million NOK in 2024 compares with annual HSR demand of 4.2 million and revenue of 1.4 billion NOK under the HA2:P scenario. Again the implementation of Scenario B has a very minor impact on rail travel on this corridor compared with the new HSR line.

Figure 55 summarises total daily boardings for long distance services at key stations along the Oslo – Bergen corridor. Note that due to the assignment of demand to the network within the NTM5 model for this alternative, the majority of demand from the Oslo area boards at Drammen. In reality it is likely that a large proportion of this demand would board at Oslo.

Figure 55. Long Distance Boardings by Station: Oslo – Bergen

8.3.2.3. Oslo – Stavanger

Table 58 summarises the forecast change in demand due to Scenario B being applied on the Oslo – Stavanger route.

Table 58. Summary of Demand and Revenue: Oslo – Stavanger


2024 2043 2024 2043




Change in Revenue (NOK thousands) 32,820 44,810

It can be seen that there is an increase in annual rail demand of approximately 70,000 passengers in 2024 as a result of the Scenario B improvements, rising to nearly 100,000 in 2043. The lower demand increase on this corridor is as a result of the smaller journey time improvement in Scenario B compared with the reference case in NTM5, which has shorter journey times compared with those in the present day timetable. The increase in demand generates revenue of approximately 35 million NOK in 2024 and 45 million NOK in 2043.

The annual increase in demand of 70,000 passengers and 35 million NOK in 2024 compares with annual HSR demand of 5.1 million and revenue of 1.5 billion NOK under the S8:Q scenario. Again the implementation of Scenario B has a very minor impact on rail travel on this corridor compared with the new HSR line.

Figure 56 summarises total daily boardings for long distance services at key stations along the Oslo – Stavanger corridor.

271

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Geilo

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Voss

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Bergen

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2024 2043



Final Report 89

Figure 56. Long Distance Boardings by Station: Oslo – Stavanger

8.3.2.4. Oslo – Charlottenberg (Stockholm)

Table 59 below summarises the forecast change in demand due to Scenario B being applied on the Oslo – Charlottenberg route.

Table 59. Summary of Demand and Revenue: Oslo – Stockholm


2024 2043 2024 2043

Change in Rail Passengers 340 400 1 1

Change in Rail Passenger km (thousands) 40 50 0.1 0.1

Train km (thousands) 200 200 0.6 0.6

Change in Revenue (NOK thousands) 60 70

It can be seen that the demand change in the Stockholm corridor as a result of Scenario B improvements is negligible, reflecting the very small improvement in journey overall journey time this offers. It also reflects the fact that there is no Swedish demand in the NTM5 model, and therefore there is very little demand on the long distance service to Stockholm in the reference case. The increase in rail demand is calculated from a very low base level of demand and hence is almost zero. However, also note that improvements have been made to the Norway side only, so there is a relatively small effect on cross-border demand and demand within Sweden.

8.3.3. Summary The overall demand results are at a similar level to those forecast in Phase II and they demonstrate that the impact of Scenario B on long distance travel in Norway is comparatively very small. For the Stockholm corridor the impact of Scenario B is negligible, which is a result of the very low levels of demand in the reference case in NTM5 and the fact that the overall journey time improvement is only 5%. In contrast, the impact of the step-change provided by full HSR implementation on long distance travel is significant, with the increase in demand typically 10 – 20 times as large.

Further investigation of the impact of Scenario B on shorter distance trips may improve the demand and revenue potential, but it is almost certain that this will still fall very short of the demand and revenue forecasts for full HSR implementation, given the very significantly greater improvement in journey time that HSR alternatives offer in comparison.

1442

498

57

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Drammen

Kongsberg

Bø

Nelaug …

Kristiansand

Egersund

Sandnes

Stavanger

Boarders per day

2024 2043



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9. Conclusions

9.1. Introduction In this section, we set out the key conclusions from the Phase III forecasting work, related to the demand and revenue impacts of the shortlisted alternatives selected by JBV for further testing.

9.2. Demand and Revenue Forecasts Phase III of the Assessment Study has developed demand and revenue forecasts for selected representative alternatives on the four main corridors considered in this phase of work. Further refinements to the forecasting framework have allowed an increased level of accuracy in this phase of work, subject to the assumptions noted in this section.

In particular, our cross-checking of demand model outputs against observed international HSR usage suggest that the forecasting model provides a robust basis for decision-making at this stage of scheme development. This comparison also reflects the nature of the Norwegian long-distance travel market, with significantly higher propensity to travel longer distances than most European countries, as well as a geography that suits the services – if not the engineering – of HSR.

However, it is also important to note that there are some limits to forecasting, especially related to estimation of individual HSR station usage, and the potential for new rail markets – particularly commuting into Oslo – to be developed by introduction of HSR services.

9.2.1. Core HSR Scenario Tests In general, predicted levels of demand are higher than reported in Phase II, through better representation of intermediate trips and forecasting of HSR demand from areas where existing air services do not currently exist. Revenue forecasts have also increased from Phase II, although by a lesser extent.

In terms of the performance of alternatives between corridors, generally, there are between 4.2m and 5.5m trips per year in 2024 on each of the domestic corridors between Oslo and Bergen, Trondheim and Stavanger, apart from the Haukeli alternative which generates around 7.5m trips per year. We note that these levels of patronage appear reasonable when compared to the existing domestic air-market and existing trip rates for medium-distance rail flows into Oslo.

Moreover, there is comfort that the expected levels of loading on HSR services – typically average all-day loadings of 150-200 per service for most alternatives – are at typical European levels, and match the capacity assumptions made in cost modelling (reviewed in separate reports).

Forecast revenues lie in a closer range, typically around 1.4 BnNOK to 1.6 BnNOK in 2024 (in 2010 prices, undiscounted) for the same corridors, with the Haukeli alternative generating around 2.7 BnNOK in 2024. This level of flow corresponds to a typical average fare paid of 280 NOK to 340 NOK, both per one-way trip, for an average journey length of between 280km and 360km.

Equivalent figures for the East corridor alternatives are slightly lower, with demand ranging between 3.7m to 4.7m and revenue between 0.7 BnNOK and 1.2 BnNOK in 2024. While significant improvements have been made to forecasting since Phase II, we stress that further data collection within Sweden would further improve the certainty of forecasting for these corridors. However, our forecasts generally show patronage and revenue on corridors from Oslo to Stockholm and Gothenburg.

However, sensitivity testing has shown that higher HSR fare assumptions could lead to up to 20% additional revenue in 2043 for the same alternatives, albeit with demand reducing by up to 35%. The level of sensitivity varies by flow group according to the strength of competing modes (such as air and car) and the length of the HSR flow. Our analysis of fares sensitivity is limited where a Gravity Model approach has been used, but is applicable to the vast majority of flows on the North, South and West corridors.



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9.2.2. Scenario B Tests Analysis has been undertaken of alternative to HSR alternatives involving upgrading of existing lines to achieve journey time improvements of 20% - Scenario B. Comparison of the improvement in journey times for Scenario B against the equivalent HSR alternatives with the slowest times, shows that the Scenario B journey times are around twice as long as those for full HSR.

Demand and revenue forecasting of Scenario B alternatives shows that the overall demand results are at a similar level to those forecast in Phase II and they demonstrate that the impact of Scenario B on long distance travel in Norway is comparatively very small and for North, West and South corridors is between 2% and 4% of forecast HSR demand, though it is noted that shorter distance trips are not included. For the Stockholm corridor the impact of Scenario B is negligible, which is a result of the very low levels of demand in the reference case in NTM5 and the fact that the overall journey time improvement is only 5%.

Further investigation of the impact of Scenario B on shorter distance trips may improve the demand and revenue potential, but it is almost certain that this will still fall very short of the demand and revenue forecasts for full HSR implementation, reflecting the very significant improvement in journey time that HSR alternatives, and hence step change in transport provision, they offer in comparison to Scenario B.

9.3. Further Development The focus of the analysis work undertaken during Phase III of the assessment project was to forecast levels of passenger demand and revenue for representative alternatives along each of the corridors using consistent forecasting assumptions. We emphasise that actual demand and revenue forecasts will vary according to a number of factors:

All the demand and revenue forecasts are clearly dependent on the fare and journey time assumptions for developed for each alternative. As alternatives are gradually developed, assumptions on both fares and journey times will change to match the objectives of HSR on that corridor. In particular, optimisation of fare levels for intermediate flows may have a significant impact on demand and revenue levels;

Each of the alternatives has been tested individually, and in isolation. When combined, it is reasonable to expect an increased “network effect”, where HSR services provide greater connectivity to other parts of the national rail network, including those served by both high speed and conventional or Inter-City services. In particular, extending services on the West and South corridors through to Gardermoen Airport could significantly increase HSR demand and revenue for those alternatives;

The results presented in this summary report assume no changes to other modes, particularly air and the existing rail services. As described elsewhere – and in Phase II reports – the response of airlines is difficult to predict, but reductions in air service frequency would, in turn, increase the attractiveness of HSR services, with associated increases in demand and revenues for each alternative. A similar situation may occur with competing bus, coach and existing rail services – although there is much greater potential for the “slower” modes to compete on price;

Station accessibility is a critical factor in determining the attractiveness of HSR services, both in terms of station location and catchment area, and connectivity by road, rail and bus. Our analysis has demonstrated different ways that a HSR network could be accessed by different modes, which will in turn have an effect on HSR patronage; and

The train specification used allows for peaks in the demand for services, but further work would be required going forward to optimise the distribution of peak and non-peak services and any other differentials (for example in the calling pattern) between those services.

Finally, we emphasise that all alternatives have been modelled on a consistent basis, reflecting the level of development of each of the alternatives at this stage and the need to cross-compare demand and revenue forecasts between alternatives. Going forward, bespoke approaches would be developed for each corridor to match its individual market potential which will further increase the accuracy of demand and revenue forecasts.

Appendices



Final Report 93

Appendix A. Alternative Summary Tables

A.1. Description of Summary Tables This appendix contains a single summary sheet for each HSR scenario investigated. For 2024 each sheet contains a number of tables/figures:

Firstly the sheets contain a schematic of the corridor under consideration and a table giving stations and journey times/train frequencies as entered into the Mode Choice Model following an incorporation into the roof top model (as described in the Model Development Report).

The remaining tables/figures contain results for each corridor. The first five tables/figures are as presented and described in the main body of this report for the HSR alternatives considered for detailed technical analysis. These show:

Summary of demand and revenue

Source of forecast HSR demand

HSR demand by origin/destination of boarding/alighting station

Mode share by HSR journey length

There are three extra results tables, which include:

A.1.1. Summary of HSR Demand by Originating Mode This figure contains two pie charts showing the proportion of HSR demand as forecast from the Mode Choice Model who originally would have:

Made their journey by air;

Made their journey by the existing rail network;

Made their journey by coach;

Made their journey by car;

Made their journey by ferry; or

Would not have travelled without the HSR service.

Of the two pie charts the pie on the left hand side shows the originating mode by HSR trips whilst the one on the right shows the originating mode by HSR km.

A.1.2. HSR Mode Share by Airport Catchment This figure shows the proportion of demand between two airport catchments (as defined by the Mode Choice Model) that would travel by HSR under the scenario in question. For example where Bergen Airport and Gardermoen Airport intersect the figure is the percentage of all traffic between the catchments around these two airports that would travel by HSR as opposed to by air, car, coach, rail or ferry.

A.1.3. Indicative Station-station Demand The final table contains an estimate of daily demand matrix between high speed stations. Demand is labelled as estimated as individual station usage is limited by the zone system and representation of the road and rail network access. This can result in demand being incorrectly allocated between neighbouring stations when one occupies a significantly larger zone than the other. It is for this reason that this table is presented in the Appendices rather than in the main body of this report.



Final Report 94

A.2. Alternatives Included in Summary Tables Summary tables are presented for each of the alternatives considered for detailed technical analysis, as shown below in Table 60. For each of these alternatives, this includes the HSR passenger service scenarios PSS1 (60% air fare, core and peak HSR services) and PSS2 (100% air fare, core HSR service only).

Further tables are provided for each of the HSR alternatives considered for sensitivity testing of demand and revenue, as shown below in Table 61.

Table 60. HSR Alternatives Considered for Detailed Technical Analysis


North

G3:Y 250 kph Oslo – Trondheim / Vaernes via Gudsbrandsdalen serving Gardermoen,

Hamar, Lillehammer, Otta and, Oppdal

Ø2:P 330 kph Oslo – Trondheim / Vaernes via Østerdalen serving Gardermoen, Elverum

Parkway and Tynset

West N1:Q 250 kph Oslo – Bergen via Numedal serving Drammen, Kongsberg, Geilo, Myrdal and

Voss

HA2:P 330 kph Oslo – Bergen via Hallingdal serving Hønefoss, Geilo and Voss

H1:P 330 kph Oslo – Bergen via Haukeli serving Drammen, Kongsberg and Odda

330 kph Oslo – Stavanger via Haukeli serving Drammen, Kongsberg, Odda and Haugesund

330 kph Bergen – Stavanger via Roldal serving Haugesund and Odda

BS1:P 330 kph Bergen – Stavanger via coastal route serving Haugesund and Stord

South S8:Q 250 kph Oslo – Stavanger via Vestfold serving Drammen, Tønsberg, Torp, Porsgrunn,

Arendal, Kristiansand, Mandal, Egersund and Sandnes


East ST5:U 250 kph Oslo – Stockholm via Ski serving Ski, Karlstad, Örebro and Västerås

ST3:R 330 kph Oslo – Stockholm via Lillestrøm serving Lillestrøm, Karlstad, Örebro and

Västerås

GO3:Q 250 kph Oslo – Gothenburg via Moss serving Ski, Moss, Fredrikstad, Sarpsborg,

Halden and Trollhättan

GO1:S 330 kph Oslo – Gothenburg via direct route serving Sarpsborg and Trollhättan

Table 61. HSR Alternatives Considered for Sensitivity Testing of Demand and Revenue


North

G1:P 330 kph Oslo – Trondheim / Vaernes via Gudsbrandsdalen serving Gardermoen,

Gjøvik, Lillehammer, Otta and Oppdal

West HA1:Q 250 kph Oslo – Bergen via Hallingdal serving Hønefoss, Geilo, Myrdal and Voss

N4:P 330 kph Oslo – Bergen via Numedal serving Drammen, Kongsberg, Geilo and Voss

South S8:T 250 kph Oslo – Stavanger via Vestfold serving Drammen, Tønsberg, Torp, Porsgrunn,

Arendal, Kristiansand, Mandal, Egersund and Sandnes

S3:Z 330 kph Oslo – Stavanger via direct route serving Drammen, Porsgrunn, Arendal,

Kristiansand, Mandal, Egersund and Sandnes

S4:P 330 kph Oslo – Stavanger via direct route serving Drammen, Porsgrunn, Arendal,

Kristiansand, Mandal, Egersund and Sandnes

East ST1:Q 250 kph Oslo – Stockholm via Kongsvinger serving Lillestrøm, Kongsvinger, Karlstad,

Örebro and Västerås

ST2:R 330 kph Oslo – Stockholm via Lillestrøm serving Lillestrøm, Karlstad, Örebro and

Västerås



Final Report 97

G3:Y, PSS1



Final Report 98

Ø2:P, PSS1



Final Report 99

N1:Q, PSS1



Final Report 100

HA2:P, PSS1



Final Report 101

H1:P, PSS1



Final Report 102

BS1:P, PSS1



Final Report 103

S8:Q, PSS1



Final Report 104

S2:P, PSS1



Final Report 105

ST5:U, PSS1



Final Report 106

ST3:R, PSS1



Final Report 107

GO3:Q, PSS1



Final Report 108

GO1:S, PSS1



Final Report 109

G1:P, PSS1



Final Report 110

HA1:Q, PSS1



Final Report 111

N4:P, PSS1



Final Report 112

S8:T, PSS1



Final Report 113

S3:Z, PSS1



Final Report 114

S4:P, PSS1



Final Report 115

ST1:Q, PSS1



Final Report 116

ST2:R, PSS1

© Atkins Ltd except where stated otherwise. The Atkins logo, „Carbon Critical Design‟ and the strapline „Plan Design Enable‟ are trademarks of Atkins Ltd.

Michael Hayes Atkins Highways & Transportation Euston Tower 286 Euston Road London NW1 3AT UK

Email: [email protected] Direct telephone: +44 207 121 2388 Fax: +44 207 121 2333

market, demand and revenue analysis final report · 2015-06-09 · norway hsr assessment study -...

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