Jernbaneverket Norwegian High Speed Railway Assessment Project Contract 5: Market Analysis Subject 1: Demand Forecasting Final Report 04/03/2011 (With additional information) /Market Analysis Demand Forecasting Final Report_UPDATED_040311.docx
Contract 5, Subject 1: Demand Forecasting 2 Notice This document and its contents have been prepared and are intended solely for Jernbaneverket s information and use in relation to The Norwegian High Speed Rail Assessment Project. WS Atkins International Ltd assumes no responsibility to any other party in respect of or arising out of or in connection with this document and/or its contents. Document History JOB NUMBER: 5096833 DOCUMENT REF: Market Analysis Demand Forecasting Final Report_UPDATED_040311.docx Revision Purpose Description Originated Checked Reviewed Authorised Date 1 Skeleton of Final Report MH LMG JD MH 29/10/10 2 Interim Report TH/JM LMG MH WL 12/01/11 3 Draft Final Report TH/JM LMG MH WL 02/02/11 4 Final Report TH AB MH WL 18/02/11 5 Additional Information TH MH JD MH 04/03/11
Contract 5, Subject 1: Demand Forecasting 3 Contract 5: Market Analysis Subject: Demand Forecasting Final Report
Contract 5, Subject 1: Demand Forecasting 4 Table of contents Executive Summary 10 1 Introduction 16 1.1 Background 16 1.2 Overall Context of the Market Analysis Contract 17 1.3 Purpose of Subject 1: Demand Potential for HSR in Norway 17 1.4 This Report 18 2 Current Travel Markets 19 2.1 Introduction 19 2.2 Size of Existing Travel Markets 19 2.3 Comparison of Services 40 2.4 International Benchmarking 52 2.5 Key Overall Conclusions 57 3 Future Do Minimum Travel Market 58 3.1 Introduction 58 3.2 Overview of Future Year Forecasting Approach NTM5 matrices 58 3.3 Future Year Do Minimum Demand Growth 62 3.4 Comparison of Medium Term Forecasts: NTM5 versus Transport Operators 70 3.5 Conclusions 72 4 HSR Demand and Revenue Forecasts 73 4.1 Introduction 73 4.2 Approach to demand and revenue forecasting 73 4.3 Traffic forecasts on national corridors 77 4.4 Oslo Bergen corridor 78 4.5 Bergen/Stavanger Oslo corridor (Haukeli route Y shaped route) 89 4.6 Stavanger Bergen corridor (Haugesund route) 94 4.7 Stavanger Kristiansand Oslo corridor 98 4.8 Trondheim Oslo corridor 109 4.9 Oslo Stockholm corridor 121 4.10 Oslo Gothenburg corridor 130 5 Conclusions 136 5.1 Introduction 136 5.2 Summary of Current Travel Markets 136 5.3 Future (Do Minimum) Travel Market 136 5.4 Commentary on demand and revenue forecasts 137 5.5 Recommendations for Phase 3 141
Contract 5, Subject 1: Demand Forecasting 5 List of Tables Table 2.1 Total annual demand for key corridors in Norway for main transport modes (2010) 23 Table 2.2 Annual business demand for key corridors in Norway for main transport modes (2010) 26 Table 2.3 Annual leisure demand for key corridors in Norway for main transport modes (2010) 27 Table 2.4 Top ten ticketed tourist attractions in Norway (2007) 35 Table 2.5 Top ten free tourist attractions in Norway (2006) 35 Table 2.6 Summary of rail level of service in Trondheim corridor (2010) 41 Table 2.7 Summary of rail level of service in Bergen corridor (2010) 41 Table 2.8 Summary of rail level of service in Kristiansand-Stavanger corridor (2010) 42 Table 2.9 Summary of rail level of service in Gothenburg corridor (2010) 42 Table 2.10 Summary of rail level of service in Stockholm corridor (2010) 43 Table 2.11 Summary of local rail services in Norway 43 Table 2.12 Summary of coach level of service in Trondheim corridor (2010) 44 Table 2.13 Summary of coach level of service in Bergen corridor (2010) 45 Table 2.14 Summary of coach level of service in Kristiansand-Stavanger corridor (2010) 45 Table 2.15 Summary of coach level of service in Bergen-Stavanger corridor (2010) 45 Table 2.16 Summary of coach level of service in Gothenburg corridor (2010) 46 Table 2.17 Summary of coach level of service in Stockholm corridor (2010) 46 Table 2.18 Summary of ferry level of service in Bergen-Stavanger corridor (2010) 47 Table 2.19 Summary of air level of service on key routes within Norway and to Sweden (2010) 47 Table 2.20 Access to key airports in Norway and Sweden (2010) 48 Table 2.21 Summary of 2010 service frequency on key corridors 49 Table 2.22 Summary of 2010 fastest journey times on key corridors (hr:min) 49 Table 2.23 Summary of 2010 range of fares on key corridors (NOK) 50 Table 2.24 Size of HSR markets in other European countries (2008) 52 Table 2.25 Market share of HSR and air on key routes in other European countries (2008) 53 Table 2.26 Summary of level of service on HSR in Sweden (2010) 53 Table 2.27 Summary of level of service on HSR in France (2010) 54 Table 2.28 Summary of level of service on HSR in Germany (2010) 54 Table 2.29 Summary of level of service on HSR in Spain (2010) 55 Table 2.30 Summary of level of service on HSR in the UK (2010) 56 Table 3.1 Population projections index Statistics Norway (SSB) 59 Table 3.2 NSB s future rail demand indices: 2009-2017 (selected years) 70 Table 3.3 Comparison of medium-term rail demand growth rates 2010-2018 71 Table 3.4 Comparison of medium-term air growth rates 2010-2018 71 Table 4.1 Representation of Changes in Supply in NTM5B 74 Table 4.2 HSR / Classic Corridor Level of Service by Scenario 76 Table 4.3 Summary of HSR Routes and Scenarios tested 77 Table 4.4 Corridor Demand by Mode and Purpose (Scenarios A and B1) 79 Table 4.5 Rail demand by trip type and exogenous growth (Scenario A 2024 to 2043) 80 Table 4.6 Impact of Scenario B1 (additional rail journeys over Do Minimum 2024, 2043) 81 Table 4.7 Summary of HSR Demand and Revenue: Scenario C Oslo Bergen (Hallingdal route via Hønefoss, Gol and Voss) 2024 81 Table 4.8 HSR Demand by Origin/Destination type: Scenario C Oslo Bergen (Hallingdal route via Hønefoss, Gol and Voss) 2024 82 Table 4.9 Boardings by station Scenario C Oslo Bergen (Hallingdal route via Hønefoss, Gol and Voss) 2024 82 Table 4.10 Summary of HSR Demand and Revenue: Scenario D Oslo Bergen (via Voss) 2024 85 Table 4.11 HSR Demand by Origin/Destination type: Scenario D Oslo Bergen (via Voss) 2024 85
Contract 5, Subject 1: Demand Forecasting 6 Table 4.12 Boardings by station: Scenario D Oslo Bergen (via Voss) 2024 86 Table 4.13 Summary of HSR Demand and Revenue: Scenario D Oslo Bergen/Stavanger (Haukeli route non-stop) 2024 89 Table 4.14 HSR Demand by Origin/Destination type: Scenario D Oslo Bergen/Stavanger (Haukeli route non-stop) 2024 90 Table 4.15 Boardings by station: Scenario D Oslo Bergen/Stavanger (Haukeli route non-stop) 2024 90 Table 4.16 Summary of HSR Demand and Revenue: Scenario D Stavanger Bergen (via Haugesund) 2024 94 Table 4.17 HSR Demand by Origin/Destination type: Scenario D Stavanger Bergen (via Haugesund) 2024 95 Table 4.18 Boardings by station: Scenario D Stavanger Bergen (via Haugesund) 2024 95 Table 4.19 Corridor Demand by Mode and Purpose (Scenarios A and B2) 99 Table 4.20 Rail demand by trip type and exogenous growth (Scenario A 2024 to 2043) 100 Table 4.21 Growth over Scenario A Scenario B2 101 Table 4.22 Summary of HSR Demand and Revenue: Scenario C Oslo Stavanger (via Drammen, Porsgrunn, Arendal, Kristiansand) 2024 101 Table 4.23 HSR Demand by Origin/Destination type: Scenario C Oslo Stavanger (via Drammen, Porsgrunn, Arendal, Kristiansand) 2024 102 Table 4.24 Boardings by station: Scenario C Oslo Stavanger (via Drammen, Porsgrunn, Arendal, Kristiansand) 2024 102 Table 4.25 Summary of HSR Demand and Revenue: Scenario D Oslo Stavanger (via Porsgrunn and Kristiansand) 2024 105 Table 4.26 HSR Demand by Origin/Destination type: Scenario D Oslo Stavanger (via Porsgrunn and Kristiansand) 2024 105 Table 4.27 Boardings by station: Scenario D Oslo Stavanger (via Porsgrunn and Kristiansand) 2024 106 Table 4.28 Corridor Demand by Mode and Purpose (Scenarios A and B3) 110 Table 4.29 Rail Demand by Sector (Scenarios A and B3) 111 Table 4.30 Growth Over Scenario A Scenario B3 112 Table 4.31 Summary of HSR Demand and Revenue: Scenario C Oslo Trondheim (via Gardermoen, Hamar, Lillehammer and Otta) 2024 112 Table 4.32 HSR Demand by Origin/Destination type: Scenario C Oslo Trondheim (via Gardermoen, Hamar, Lillehammer and Otta) 2024 113 Table 4.33 Boardings by station: Scenario C Oslo Trondheim (via Gardermoen, Hamar, Lillehammer and Otta) 2024 113 Table 4.34 Summary of HSR Demand and Revenue: Scenario D Oslo Trondheim (via Gardermoen) 2024 116 Table 4.35 HSR Demand by Origin/Destination type: Scenario D Oslo Trondheim (via Gardermoen) 2024116 Table 4.36 Boardings by station: Scenario D Oslo Trondheim (via Gardermoen) 2024 117 Table 4.37 Corridor Demand by Mode and Purpose 122 Table 4.38 Rail Demand by Sector (Scenarios A and B5) 123 Table 4.39 Growth over Scenario A Scenario B5 124 Table 4.40 Summary of HSR Demand and Revenue: Scenario C Oslo Stockholm (via Lillestrøm and Kongsvinger) 2024 124 Table 4.41 HSR Demand by Origin/Destination type: Scenario D Oslo Stockholm (via Lillestrøm and Kongsvinger) 2024 125 Table 4.42 Boardings by station: Scenario C Oslo Stockholm (via Lillestrøm and Kongsvinger) 2024 125 Table 4.43 Summary of HSR Demand and Revenue: Scenario D Oslo Stockholm (via Lillestrøm) 2024127 Table 4.44 HSR Demand by Origin/Destination type: Scenario D Oslo Stockholm (via Lillestrøm) 2024127 Table 4.45 Boardings by station: Scenario D Oslo Stockholm (via Lillestrøm) 2024 127 Table 4.46 Corridor Demand by Mode and Purpose (Scenarios A and B4) 131 Table 4.47 Rail Demand by Sector 132
Contract 5, Subject 1: Demand Forecasting 7 Table 4.48 Growth Over Scenario A Scenario B4 133 Table 4.49 Summary of HSR Demand and Revenue: Scenario D Oslo Gothenburg (non-stop) 2024 133 Table 4.50 HSR Demand by Origin/Destination type: Scenario D Oslo Gothenburg (non-stop) 2024 134 Table 4.51 Boardings by station: Scenario D Oslo Gothenburg (non-stop) 2024 134 Table 5.1 HSR Demand per Day by Corridor for Each Scenario (2024) 139 Table 5.2 HSR per Year by Corridor for Each Scenario (2024) 139 Table 5.3 Average HSR Demand per Train by Corridor for Scenarios C and D (2024) 140 Table 5.4 HSR Revenue per Year for Scenarios C and D (2024) 140 List of Figures Figure 2.1 Population density in Norway by municipality (2010) 21 Figure 2.2 Average (median) net income in Norway by municipality (2008) 22 Figure 2.3 City areas included in the calculation of demand for city-to-city travel 24 Figure 2.4 Total demand for each mode by corridor 25 Figure 2.5 Business demand for each mode by corridor (2010) 26 Figure 2.6 Mode share for city-to-city business trips by corridor (2010) 27 Figure 2.7 Leisure demand for each mode by corridor (2010) 28 Figure 2.8 Mode share for city-to-city leisure trips by corridor (2010) 28 Figure 2.9 Wider catchment areas for Norwegian airports for the calculation of road and rail demand 30 Figure 2.10 Number of business trips by corridor and mode (2010) 31 Figure 2.11 Mode share by corridor for business trips (2010) 32 Figure 2.12 Number of leisure trips by corridor and mode (2010) 32 Figure 2.13 Mode share by corridor for leisure trips (2010) 33 Figure 2.14 Location of major ski resorts in Norway 34 Figure 2.15 Location of major tourist attractions and fjords in Norway and international guest nights in 200936 Figure 2.16 Location of major tourist attractions and fjords in Norway and domestic guest nights in 2009 37 Figure 2.17 Proportion of international tourists by arrival mode (2009) 38 Figure 2.18 Proportion of Swedish tourists by arrival mode (2009) 38 Figure 3.1 Population growth profiles (2010-2060) 60 Figure 3.2 Projected growth of business air travel to 2060 63 Figure 3.3 Projected growth of leisure air travel to 2060 63 Figure 3.4 Projected growth of business rail travel to 2060 64 Figure 3.5 Projected growth of leisure rail travel to 2060 64 Figure 3.6 Projected growth of business car travel to 2060 65 Figure 3.7 Projected growth of leisure car travel to 2060 65 Figure 3.8 Projected growth of business coach travel to 2060 66 Figure 3.9 Projected growth of leisure coach travel to 2060 66 Figure 3.10 Compound Annual Growth Rates (CAGR) Business 2010-2060 67 Figure 3.11 Absolute growth in annual journeys Business 2010-2060 67 Figure 3.12 Compound Annual Growth Rates (CAGR) Leisure 2010-2060 68 Figure 3.13 Absolute growth in annual journeys Leisure 2010-2060 68 Figure 3.14 Comparison between population growth and growth in business travel (2010=100) 69 Figure 3.15 Comparison between population growth and leisure travel (2010=100) 69 Figure 3.16 Medium-term rail demand growth by corridor: NSB vs. NTM5 70 Figure 4.1 Scen A (2024) Bergen Daily Demand Profile 78 Figure 4.2 Scen B (2024) Bergen Daily Demand Profile 79 Figure 4.3 Mode Share: Scenario C Oslo Bergen via Hønefoss, Gol and Voss 2024 (Oslo/Akershus- Bergen) 83
Contract 5, Subject 1: Demand Forecasting 8 Figure 4.4 Highest abstraction of journeys: Scenario C Oslo Bergen (Hallingdal route via Hønefoss, Gol and Voss) 2024 83 Figure 4.5 HSR demand by originating zone (annual) and point of boarding (daily) 84 Figure 4.6 Mode Share: Scenario D Oslo Bergen via Voss 2024 (Oslo/Akershus-Bergen) 86 Figure 4.7 Highest abstraction of journeys: Scenario D Oslo Bergen (via Voss) 2024 87 Figure 4.8 HSR demand by originating zone (annual) and point of boarding (daily) 88 Figure 4.9 Mode Share: Scenario D Oslo Bergen non-stop 2024 (Oslo/Akershus-Bergen) 91 Figure 4.10 Mode Share: Scenario D Oslo Stavanger non-stop 2024 (Oslo/Akershus-Stavanger) 91 Figure 4.11 Highest abstraction of journeys: Scenario D Oslo Bergen/Stavanger (Haukeli route non-stop) 2024 92 Figure 4.12 HSR demand by originating zone (annual) and point of boarding (daily) 93 Figure 4.13 Mode Share: Scenario D Stavanger Bergen via Haugesund 2024 (Stavanger-Bergen) 96 Figure 4.14 Highest abstraction of journeys: Scenario D Stavanger Bergen (via Haugesund) 2024 96 Figure 4.15 HSR demand by originating zone (annual) and point of boarding (daily) 97 Figure 4.16 Scen A (2024) Stavanger Daily Demand Profile 98 Figure 4.17 Scen B (2024) Stavanger Daily Demand Profile 99 Figure 4.18 Mode Share: Scenario C Oslo Stavanger via Drammen, Porsgrunn, Arendal, Kristiansand 2024 (Oslo-Stavanger) 103 Figure 4.19 Highest abstraction of journeys: Scenario C Oslo Stavanger (via Drammen, Porsgrunn, Arendal, Kristiansand) 2024 103 Figure 4.20 HSR demand by originating zone (annual) and point of boarding (daily) 104 Figure 4.21 Mode Share: Scenario D Oslo Stavanger via Porsgrunn and Kristiansand 2024 (Oslo- Stavanger) 106 Figure 4.22 Highest abstraction of journeys: Scenario D Oslo Stavanger (via Porsgrunn and Kristiansand) 2024 107 Figure 4.23 HSR demand by originating zone (annual) and point of boarding (daily) 107 Figure 4.24 Scen A (2024) Trondheim Daily Demand Profile 109 Figure 4.25 Scen B (2024) Trondheim Daily Demand Profile 110 Figure 4.26 Mode Share: Scenario C Oslo Trondheim via Gardermoen, Hamar, Lillehammer and Otta 2024 (Oslo/Akershus-Trondheim) 114 Figure 4.27 Highest abstraction of journeys: Scenario C Oslo Trondheim (via Gardermoen, Hamar, Lillehammer and Otta) 2024 114 Figure 4.28 HSR demand by originating zone (annual) and point of boarding (daily) 115 Figure 4.29 Mode Share: Scenario D Oslo Trondheim via Gardermoen 2024 (Oslo/Akershus-Trondheim)117 Figure 4.30 Highest abstraction of journeys: Scenario D Oslo Trondheim (via Gardermoen) 2024 118 Figure 4.31 HSR demand by originating zone (annual) and point of boarding (daily) 119 Figure 4.32 Scen A (2024) Stockholm Daily Demand Profile 121 Figure 4.33 Scen B (2024) Stockholm Daily Demand Profile 122 Figure 4.34 Mode Share: Scenario C Oslo Stockholm via Lillestrøm and Kongsvinger 2024 (Oslo/Akershus-Stockholm) 125 Figure 4.35 Highest abstraction of journeys: Scenario C Oslo Stockholm (via Lillestrøm and Kongsvinger) 2024 126 Figure 4.36 Mode Share: Scenario D Oslo Stockholm via Lillestrøm 2024 (Oslo/Akershus-Stockholm) 128 Figure 4.37 Highest abstraction of journeys: Scenario D Oslo Stockholm (via Lillestrøm) 2024 128 Figure 4.38 Scen A (2024) Gothenburg Daily Demand Profile 130 Figure 4.39 Scen B (2024) Gothenburg Daily Demand Profile 131 Figure 4.40 Mode Share: Scenario D Oslo Gothenburg non-stop 2024 (Oslo/Akershus-Gothenburg) 134 Figure 4.41 Highest abstraction of journeys: Scenario D Oslo Gothenburg (non-stop) 2024 135 Figure 5.1 Incremental HSR journeys by corridor Scenario D over Scenario C (2024 estimates, percentage) 141
Contract 5, Subject 1: Demand Forecasting 9 Appendices A. NSB Zone definitions (station groupings) 143 B. Assumed enhancements in Do Minimum matrices 144 B.1 Do Minimum: road enhancements 144 B.2 Do Minimum: Classic Rail enhancements 144 B.3 Do Minimum: Changes to levels of service on other modes (air, bus and ferry) 144 C. Demand Tables (future year journeys by mode) 145 C.1 Oslo Bergen 146 C.2 Stavanger Bergen 148 C.3 Oslo Stavanger 149 C.4 Oslo Trondheim 151 C.5 Oslo Stockholm 152 C.6 Oslo Gothenburg 153 C.7 County-to-county Matrices 154
Contract 5, Subject 1: Demand Forecasting 10 Executive Summary Introduction Jernbaneverket (JBV) has appointed Atkins to undertake Market Analysis to inform the study into High Speed Rail (HSR) improvements in Norway. This report provides outputs from the demand forecasting (Subject 1) work undertaken to support the market analysis. The existing passenger market has been assessed and a model has been built to forecast the amount of demand that might transfer from other modes to HSR and be generated as a result of the improvements. The demand forecasts are an important input into the socio-economic assessment of the proposed improvements. The forecast use of the improved services and shift from other modes are used to quantify the revenue and monetised benefits of the improvements, which are compared to the costs of building the scheme to understand which improvements are economically worthwhile. A range of potential improvement scenarios are being considered, some are relatively minor upgrades to existing services (Scenarios A and B) and some are more aggressive, providing a fundamental enhancement to the rail services which is akin to a new alternative mode of travel (Scenarios C and D): 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 offensive 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. The Underlying Market The improved HSR services connect five key Norwegian cities: Oslo, Stavanger, Bergen, Trondheim and Kristiansand. Services towards Gothenburg and Stockholm are also being considered. There are smaller urban areas between these key cities that could also be served by the services if there is adequate demand. Our demand analysis has focussed on the longdistance market. Shorter distance flows to and from Oslo are considered by the parallel InterCity Study, and are not included in our analysis. Current long distance travel between these cities (trips of over 100km between the city pairs) is shown in the table below together with mode and journey purpose split. It is noticeable that travel by car is relatively limited as this table present travel between city pairs only. For long-distance travel from areas outside the cities, car travel is much more dominant, particularly for leisure travel. About 65-70% of the trips between Oslo and the major cities of Bergen, Stavanger and Trondheim are leisure trips, although on the Bergen Stavanger corridor the business market is more dominant than on the other routes. Air travel dominates the business market for long distance travel with 74% of business trips between these cities being made by air.
Air Classic rail Car Coach Business Leisure Contract 5, Subject 1: Demand Forecasting 11 Route Rail Distance (km) Total Trips (2010) Oslo Bergen 484 337,000 45% 28% 23% 4% 35% 65% Oslo Stavanger 599 171,000 50% 15% 31% 4% 36% 64% Oslo Kristiansand Bergen Stavanger 365 121,000 23% 22% 39% 16% 29% 70% 192,000 35% 1% 54% 9% 41% 59% Oslo Trondheim 553 277,000 47% 17% 31% 4% 33% 67% The mode share of air is most dominant on the longest distance routes. This is driven by journey times and passengers travelling by air having a higher value of time than those travelling by other modes. Frequency of service will also contribute to higher volumes of business travel choosing air travel given classic rail, for example, only provides four or five services a day between the main centres where as there are up to 28 flights per day. For shorter distances (e.g. Oslo Kristiansand) air is less dominant. By contrast, car dominates the leisure market with 45% of long distance leisure trips made between these city pairs made by car. 25% of the city-city leisure trips are made by air, 22% by rail and just 8% by coach. Classic rail has low mode share for business travel (10-20%) but carries more leisure passengers (20-30%). From the analysis of current service provision, it is clear that air travel provides the best service for inter-urban users on the majority of corridors, with over 20 flights a day between Oslo and Bergen, Trondheim and Stavanger. On the Kristiansand and Gothenburg corridors, the air service is not as frequent, presumably due to the higher quality roads on these routes and the more manageable journey time by car. There are also more frequent coach services on these corridors. The current level of service for rail is presently low for the main long distance routes, with approximately 5 trains a day between the key cities. For more local travel there is a more frequent service. Fares tend to be higher for air compared with rail and coach, although if booked in advance air fares are broadly similar to rail and coach fares booked on the day. In terms of attraction from current services to the new high speed services, HSR in other countries has tended to compete with air services and therefore is dominated by business users, with a strong need for reduced journey times. Leisure passengers on the whole find car travel to be the most convenient mode of transport. For HSR to compete effectively with car travel, provisions will need to be made to ensure strong connectivity to HSR stations and careful consideration of station location must be made. There is a delicate balance between optimising end-to-end journey times for business air users, while at the same time introducing enough intermediate stops to attract leisure users who would currently travel by car. The tourism market may have some impacts on HSR demand, although the most popular attractions tend to be away from cities. The seasonal nature of the tourism leads to variations in demand, and it may be difficult to attract this market to HSR beyond the core city-to-city markets. Given that any HSR scheme is likely to be implemented in several years time, this report analyses expected levels of growth in travel demand even before HSR is implemented. Growth between current levels and future levels has been established using the NTM 5 model. In the medium term, rail demand is assumed to growth at around 2 to 3% per annum depending on the
Contract 5, Subject 1: Demand Forecasting 12 route and air demand is expected to grow at around 2% per annum. This will increase the potential market for HSR services. From an international benchmarking review, we have established that the size of the potential market for HSR in Norway is much smaller than the HSR markets already established in countries such as France and Germany, although the size of the potential market is more similar to that of Sweden. From experience in other European countries where HSR is already well established, there has sometimes been almost total abstraction from air on routes served by HSR as rail journey times have been dramatically reduced and major rail stations are located more conveniently than the airports. HSR Proposed Routes The Demand Potential for High Speed Rail Services Within Phase 2 of the overall High Speed Rail Assessment project, the Market Analysis work has developed a forecasting approach which can be used to test detailed options in Phase 3 of the project. For marginal improvements to the existing services, the NTM 5 long distance demand forecasting model is appropriate for forecasting demand on the improved services. However, for the more aggressive improvements giving a step change in rail services, a more suitable new model was built. The new model used outputs from a Stated Preference (SP) Survey (Subjects 2 and 3 of the Market Analysis work) to determine passenger s likely response to the new services. The scope of the demand forecasting work is the long distance travel market.
Contract 5, Subject 1: Demand Forecasting 13 A number of example scenarios were tested to demonstrate the suitability of the model for forecasting, give initial views on the potential size of HSR markets and allow options to be further developed during Phase 3 of the study. Initial forecasts were developed across the six corridors, assuming levels of service provided by JBV across a range of levels of service improvement from Scenario A (only marginal, planned improvements) to Scenario D (new high speed rail alignments). For the purposes of this report, it was assumed that HSR services would have the same fares as current air services and that there would be no reduction in either air or existing rail services as a result of introduction of HSR. Therefore, the forecasts represent a base on which it is expected that demand may increase as options are developed during Phase 3. In addition, the market for InterCity commuting into Oslo was specifically excluded from consideration, as it is subject to a separate study by JBV. The indicative results from the initial set of modelling tests carried out are set out below. For Scenario D only, a range of demand and revenue has been presented, on the basis of different fare assumptions. It is important to note that when lower fares are assumed, the demand is higher but the overall revenue is reduced. The higher revenue shown corresponds to the option where HSR fares are assumed to equal air fares, as even though there are less passengers, the total yield is forecast to be higher. Further testing of more combinations of alternative frequencies, journey times, stopping patterns and fares will be carried out in Phase 3 to add to this initial set of tests: Between Oslo and Bergen, with the fastest journey times (Scenario D, 2 hours 30 non-stop compared to 6 hours 30 currently), 1.5m to 2.5m trips per year are attracted to the HSR services depending on the fare assumptions used, generating up to 1.3bn NOK per annum. Total rail trips on the corridor would correspond to 2.1m to 3m trips per year. If journey times are reduced to approximately 4 hours 30 minutes (Scenario C), around 1.1 million trips per year are attracted (assuming stops at Hønefoss, Gol and Voss). Compared to Scenario D slightly more trips are abstracted from car and slightly fewer from air. Between Oslo and Stavanger via Kristiansand, Scenario D generates 2.0m to 3.1m trips per annum and up to 1.7bn NOK per annum on HSR services. Services would take 2 hours 30 minutes plus stopping time (compared to 7 hours 40 today) with stops at both Kristiansand and Porsgrunn. Total rail trips in the equivalent corridor would be around 3.3m to 4.4m trips/year. The slower Scenario C service, taking 5 hours 30 minutes in total, stopping at all key locations, generates 1.3m trips and 0.7m NOK per annum. Although the faster Scenario D services are forecast to attract 0.7m to 1.8m more trips per annum, the slower stopping services perform relatively well despite significantly slower end-to-end journey times. The trade off between the revenue generated and the costs of the infrastructure service should be investigated in Phase 3 to see which the optimum service is as the infrastructure for the faster services could cost considerably more than the slower one. Consideration should also be made of the interactions with the inter-city study because of the indirect potential to improve journey times between Oslo and Southern Norway. There is a proposed Y-shaped route from Oslo to both Bergen and Stavanger. This service attracts 2.5m to 4.1m trips per annum (with a total rail market of 4.4m to 6m trips/year); this is less than the combination of the alternative Bergen and Stavanger routes, which together attract about 3.5m to 5.6m demand, as they serve more intermediate markets than the Haukeli route. Up to 2.5bn NOK is generated per year on HSR services with this option. Introducing a service from Bergen to Stavanger attracts 0.7m-0.9m HSR trips per annum stopping at Haugesund, generating up to 0.5bn NOK. The engineering to produce the link is likely to be particularly challenging given the proximity to the coastline and requirement to cross fjords. In Phase 3 the costs associated with this will be understood and, along with all other options, consideration of whether the generated revenue and benefits can justify the costs.
Contract 5, Subject 1: Demand Forecasting 14 Improving journey times between Oslo and Trondheim from 6 hours 40 minutes currently to 2 hours 45 minutes is expected to attract around 1.8m to 2.2m trips per annum on HSR services (2.8m to 3.2m total rail trips per year on the corridor), stopping at Gardermoen Airport, and generates up to 1.3bn NOK per annum. Gardermoen Airport is a key driver of HSR demand; we recommend that options for HSR to Bergen and Stavanger be developed to include direct connections to the airport to increase HSR demand further. On routes towards Stockholm and Gothenburg, current demand forecasting shows around 0.8-1 million trips would be attracted to HSR services (a total of on each of the routes and up to 0.7bn NOK revenue for each corridor. The HSR route to Stockholm takes 3 hours compared to the 6 hour service today and the journey time to Gothenburg is assumed to reduce from about 4 hours to 2 hours and 30 minutes. The route to Gothenburg attracts the most demand, although limitations on available data means that caveats have to be applied to the robustness of this forecast. For the less significant journey time improvements of Scenario B compared to Scenario A (usually saving about an hour from current journey times), about 0.1m rail trips are attracted on the corridors from Oslo to Bergen, Stavanger and Trondheim. Routes for Scenario B towards Sweden were tested but the impact on demand was negligible due to the lack of demand within scope of the forecasting model. Mode Shift For the smaller journey time improvements in Scenario B 90% of the additional rail demand attracted is either newly generated trips or transfer from car. On the route to Bergen, 78% of demand is generated and 14% transfers from car. On routes to Stavanger and Trondheim, almost 60% of the attracted rail trips are generated and around 30% is from car. Less than 5% is transferred from coach or ferry and around 5% is from air. For the faster services in Scenarios C and D tested so far, generally a third of the demand attracted to HSR is newly generated trips, 40% is from air, 15-20% from car and less than 10% from current rail services and coach. The clear exceptions to this are: 60% of the demand attracted to services to Trondheim that stop at Gardermoen is from air; between Bergen and Stavanger less of the HSR demand attracted comes from air (34%), more is transferred from car (24%) and none comes from rail where there currently is no service; and A significant proportion of demand to Gothenburg transfers from car (67%) and almost none transfers from air. For the routes to Stockholm less than 10% is attracted from car and a much more (around 50%) from air. On routes to Stockholm about 40% of HSR trips are generated but fewer on the route to Gothenburg (about a third). These conclusions also should be treated with caution given the current limitations of the Swedish demand data. Journey Purpose For passengers attracted to the new HSR services, generally 60-65% of trips are business trips and 35-40% are leisure trips showing that HSR competes more with air for the market attracted to higher speeds and the service quality offered by HSR. For slower services the percentage of business users is at the bottom end of this range. For example, Oslo to Bergen, for Scenario C services 59% of demand is business but for scenario D services, 63% is business. The key exceptions to this are: Between Oslo and Stavanger business travel is at the higher end of the spectrum at 66% for Scenario D services and 64% for scenario C; From Bergen to Stavanger, business travel is significantly higher at 74% despite there being a lower transfer from air to HSR;
Contract 5, Subject 1: Demand Forecasting 15 The service to Gothenburg has much higher business demand at almost 80% despite almost no transfer from air; and Routes to Stockholm have much less business travel at around 30% of demand, despite a more significant transfer from air. For the smaller improvements in journey times from Scenario A to B, 25-30% of the demand attracted to rail services is business trips, in contrast to the 60-65% business trips with the more significant increases in journey times in Scenarios C and D. Next Steps In Phase 3 further refinements to the modelling and scenario tests will be made in light of a review of the outputs from Contract 1 Technical and Safety Analysis, and Contract 2 Rail Planning and Development. Recommendations from the route alignment and technical feasibility studies will help inform more detailed corridor alignments, stopping patterns and journey times. Further combinations of stops and frequencies can be tested to find the optimum services comparing with the costs to understand the financial viability alongside the demand generated. There will also be interaction with Contract 6 Finance and Economics, in order to incorporate the costs of each option and calculate the relative benefits of each option. There will be an iterative process between the demand and revenue forecasting and socio-economic assessment. It will be important to consider services serving the main airports, particularly Trondheim Værnes and Sandefjord Torp, as the routes serving Gardermoen picked up a significant amount of demand. The trade off of the lost demand from Scenario C type improvements with the lower costs should be a focus to understand whether the incremental benefits of the significantly faster speeds associated with Scenario D are justified given the additional costs. Interaction between the scenarios defined in this study and those being pursued in the Intercity study should be examined to understand if there are additional benefits brought about by connectivity between HSR and improved local rail services, especially in the south-east of Norway, in the counties of Oslo, Østfold and Vestfold. In addition, further refinements to the forecasting approach will be made, to take advantage of any new data available particularly relating to travel to and from Sweden and the long-distance car travel market.
Contract 5, Subject 1: Demand Forecasting 16 1 Introduction 1.1 Background Jernbaneverket 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, improved passability 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 study has been divided into three phases. In Phase 1, 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 2 is 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 2 uses and enhance existing information, models and data. New tools have been created where existing tools are not suitable for assessing high speed rail. In Phase 3 the tools and guiding principles established in Phase 2 will be used to test scenarios and options on the different corridors. This will provide assessments of options and enable recommendations for development and investment strategies in each corridor. This report is a component of the Phase 2 work. The principles established in Phase 2 are to be used to test four scenarios: Scenario A reference case. This is a continuation of the current railway policy and planned improvements, with relatively minor works undertaken, as shown in the National Transport Plan from 2010-2019 This forms the Do Minimum scenario against which the other scenarios will be compared. Scenario B upgrade. A more offensive development of the current infrastructure, with more improvements outside the Intercity area; Scenario C major upgrades achieving high-speed concepts. This is to be based on an aggressive upgrade of the existing network to provide a step change in journey times; and Scenario D new HSR. This involves the implementation of newly built, separate HSR lines. The improvements are being considered on six corridors: Oslo Bergen; Oslo Trondheim; Oslo Kristiansand and Stavanger; Bergen Stavanger; Oslo Stockholm (to Skotterud in Norway); and
Contract 5, Subject 1: Demand Forecasting 17 Oslo Gothenburg (to Halden in Norway). The scenarios will be considered in relation to the long distance travel market, for example for journeys over 100km in distance. Other studies, such as the Intercity Study will look at initiatives for shorter distance travel at a more regional level. Various route alignments, stop patterns, station designs, speed standards and fares will be tested. It will be necessary to assess conditions related to income and costs, environmental concerns, energy consumption, maintenance under winter conditions and the procurement and operational organisation of the services and infrastructure. 1.2 Overall Context of the Market Analysis Contract To achieve Phase 2 of the study, Jernbaneverket has commissioned 6 Contracts: Technical and Safety Analysis; Rail Planning and Development; Environmental Analysis; Commercial and Contract Strategies; Market Analysis; and Financial and Economic Analysis WS Atkins International Ltd (Atkins) is assisting Jernbaneverket in two of the contracts: Market Analysis and Financial and Economic Analysis. This report is part of the Market Analysis Contract. The Market Analysis contract consists of five Subjects: Subject 1: Demand potential for high speed rail services in Norway; Subject 2: Analysis of expected amount of ticket revenues; Subject 3: Passengers choice preferences for travel and means of transport; Subject 4: Location and services of stations / terminals; and Subject 5: Market conditions for fast freight trains. The purpose of the Market Analysis Contract is to establish the size of the potential HSR passenger and freight markets under different HSR scenarios. 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. This report provides the outputs for Subject 1. 1.3 Purpose of Subject 1: Demand Potential for HSR in Norway The objective of Subject 1 is to identify the potential markets that might switch to new HSR services under the different scenarios proposed, on each of the different corridors, and forecast the passenger use of these services. 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 outputs from the demand forecasting are used to predict the revenue and socio-economic impacts of the proposed investments. 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
Contract 5, Subject 1: Demand Forecasting 18 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. At this stage, the outputs from Subject 1 will be outline forecasts for generic options to help shape the priorities for High Speed development and compare the scenarios proposed. In Phase 3 of the study, the forecasts can be updated and refined to provide forecasts against more specific alignments (and therefore more accurate journey times) and station locations, for example. 1.4 This Report This report provides outputs from analysis by mode, by journey purpose and by corridor, of the current (2010) long distance travel market in Norway, as well as forecasts of the future changes in long distance travel expected under each of the improvement scenarios, including the Scenario A reference case ( continuation of current policies ). For improvement Scenarios B, C and D (as defined in Section 1.1), 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. The reporting structure is intended to provide a clear and comprehensive summary of the demand implications for each mode of the potential scenarios for each corridor, allowing robust conclusions to be drawn. Example scenarios have been developed, in agreement with JBV, representing a range of journey time improvements across each of the main corridors. However, we emphasise that these only represent an initial view as to the definition of each potential intervention in terms of HSR journey times, fares, stopping patterns and service frequencies, and make conservative assumptions on the response of airlines and changes to existing rail services. The next phase of work will refine these scenarios to increase demand and revenue forecasts, as well as develop the options in light of cost, economic benefit and environmental assessment information. It should also be noted that detailed reporting of the approach applied to forecasting and modelling is provided in a complementary Model Development Report. The exception is RAND s description of the Stated Preference analysis which is provided in the Final Report for Market Analysis Subjects 2 and 3. The remainder of this report has the following chapters: Chapter 2 - Current travel market; Chapter 3 - Future travel market (Do Minimum Scenario, Scenario A); Chapter 4 - HSR Demand and Revenue Forecasts (Scenarios B, C and D); and Chapter 5 - Conclusions.
Contract 5, Subject 1: Demand Forecasting 19 2 Current Travel Markets 2.1 Introduction This chapter provides a detailed analysis of the current long-distance travel market in Norway, to provide a background to the planning and forecasting of potential HSR lines. The focus is on the six main transport corridors that are being considered in the HSR study. There are three elements to the analysis: Size of existing travel markets analysis of current travel demand for end-to-end journeys and intermediate travel along corridors, as well as consideration of qualitative factors such as seasonal variation of travel; Comparison of product quality reviewing the existing travel opportunities available by air, road, rail and sea, as well as comparing between the different modes; and International benchmarking analysis of corridors in other countries where HSR has already been introduced. The analysis of present travel markets in Norway will help inform the basis for consideration of the potential for HSR development in Norway. This is achieved by determining the size of the markets for business and leisure travellers, the mode of transport used and associated quality of each mode. The size of the markets for each transport mode can then be compared against the current travel opportunities for each mode, as well as HSR markets in other European countries. Limitations on the quality of data available on movements between Norway and Sweden mean that analysis is limited to the relevant internal corridors. 2.2 Size of Existing Travel Markets This subsection presents a review of the current demand for travel along the six principal transport corridors in Norway, each of which may have sufficient demand to justify significant future improvements to rail services. The aim is to facilitate comparison of the relative size of current journey volumes within each corridor with disaggregation by mode (i.e. domestic air, classic rail, car and bus/coach), and by journey purpose (i.e. business versus non-business travel). Within each corridor it is also important to identify travel between Oslo and the other major cities (typically end-to-end trips) to allow the relative importance of journeys to/from other towns to be assessed. This section examines: The distribution of population and income across Norway as a whole; Existing demand for travel between the largest cities immediate urban areas; Existing demand for travel between wider city region catchment areas; and Demand driven by tourism that may to a significant extent be seasonal. The distinction between core urban areas and wider catchments is important, as it reflects the different attractiveness of air, rail and road depending on distance to urban centres and transport gateways. For example, future HSR services with limited stops are liable to have longer access and egress distances than classic rail, with stations displaying regional catchment areas akin to those of the major airports. Meanwhile, the incidence of car use between any pair of urban centres will tend to increase with the diameter of the HSR stations assumed/defined catchment areas.
Contract 5, Subject 1: Demand Forecasting 20 2.2.1 Population and Income Distribution Norway is a relatively sparsely populated country due to the large mountainous regions and fjords which limit development of urban areas in vast areas of the country. The population of Norway is predominantly focused in the south-east of the country, where the topography is far less mountainous, and along the coastline. Figure 2.1 below shows the population density in Norway in 2010 by municipality. 1 This figure illustrates the large sparsely populated areas in the centre of Norway, and the high concentration of population around Oslo. There are also clearly areas of sizable population along the south and west coastline. This demonstrates that the market for travel in Norway is mainly focussed in small areas, particularly in the south-east of the country, while there are large areas where there is relatively little demand. The areas of high population density around the cities and large areas of low density show that travel from major cities is predominantly either local travel within the city and its suburbs, or long-distance travel to other cities. Figure 2.2 illustrates the average income in Norway broken down by municipality. 2 The figure demonstrates that the average income is generally higher in the areas of high population density, but lower within main cities of Norway. This illustrates the point that areas surrounding cities tend to have the highest levels of income: this could potentially affect the willingness to pay fares for HSR services that are higher than existing rail services, and the associated financial viability of HSR. The distribution of population and income shows that the predominant demand for HSR is likely to be for city-to-city travel. Another implication for HSR is that the highest willingness to pay for time savings is generally in cities and surrounding suburban areas, so the positioning of stations and connectivity with local transport will highly influence the implementation of any potential HSR service. 2.2.2 Long-distance Travel Demand: City-to-city This section presents the annual demand for travel between the urban areas of the five major cities, which form the key destinations of the HSR network: Oslo, Bergen, Stavanger, Trondheim and Kristiansand. City-to-city travel is likely to form a large proportion of the total long-distance travel in Norway, due to the distribution of population described above, and that these cities form the main centres of business. Data sources Demand between cities considered here is annual demand for 2010 derived from the NTM5 model. Rail and air demand matrices from this model have been calibrated using Norwegian State Railways (NSB) ticket sales data and Avinor passenger count data, respectively. The NTM5 zones have been aggregated up to form a new HSR zoning system for presentation purposes 3. 1 Population data from Statistics Norway (Jan 2010): http://www.ssb.no/english/subjects/02/01/10/folkber_en/tab-2010-12-16-01-en.htmland Municipality areas from the Norwegian Mapping Authority (2010): http://www.statkart.no/nor/land/fagomrader/arealer_og_tall/ 2 Source: Statistics Norway http://statbank.ssb.no/statistikkbanken/default_fr.asp?pxsid=0&nvl=true&planguage=1&tilside=selecttabl e/hovedtabellhjem.asp&kortnavnweb=inntgeo 3 See separate technical note: TN2 Proposed Zoning System
Contract 5, Subject 1: Demand Forecasting 21 Figure 2.1 Population density in Norway by municipality (2010)
Contract 5, Subject 1: Demand Forecasting 22 Figure 2.2 Average (median) net income in Norway by municipality (2008)
Contract 5, Subject 1: Demand Forecasting 23 It should be noted that the car and coach demand data has been taken directly from NTM5 and has not yet been validated against traffic counts. As the NTM5 model is not calibrated on a detailed level, this means that the data for car and coach demand may be subject to inaccuracies. It is intended that car data in particular will be adjusted for Phase 3 of the work with real traffic counts from the Norwegian Roads Authority (if this can be made available) in order to ensure the accuracy of the data. Figure 2.3 below illustrates the zones included in the calculation of flows from major cities. The cities and flows are defined in terms of travel between municipalities (i.e. kommuner ). For example, Oslo - Bergen includes journeys from the eight urban districts within Bergen municipality to Oslo s 17 urban districts. However, journeys from Bergen s neighbouring municipality, Vaksdal, to Bærum, which neighbours Oslo, are excluded. As described above, the population in Norway is concentrated in the five largest cities. However, Oslo has a large commuter belt, including the municipalities within the county of Akershus. It is hence worth noting when analysing the demand for each route that these suburbs are excluded from the city-to-city journeys. Total Demand Analysis Table 2.1 shows the total travel demand for each city-to-city flow routes, disaggregated by transport mode. Oslo-Bergen is the largest market; these cities constitute the two largest population and employment centres. Table 2.1 Total annual demand for key corridors in Norway for main transport modes (2010) Route Air Classic Rail Car Coach Total Oslo Stavanger 85,000 26,000 53,000 7,000 171,000 Oslo Bergen 150,000 96,000 77,000 13,000 337,000 Oslo Trondheim 130,000 48,000 87,000 12,000 277,000 Oslo Kristiansand 28,000 27,000 47,000 19,000 121,000 Bergen Stavanger 67,000 2,000 104,000 18,000 192,000
Contract 5, Subject 1: Demand Forecasting 24 Figure 2.3 City areas included in the calculation of demand for city-to-city travel
Number of journeys per year Contract 5, Subject 1: Demand Forecasting 25 It is also apparent that air is the dominant mode on the long-distance corridors. This is further illustrated in Figure 2.4, which compares the mode significance by corridor. The key observations are: Demand is highest for air travel on the longer distance routes, namely Oslo to Bergen and Trondheim; Oslo Kristiansand has higher car, rail and coach flows in relation to air travel, probably due to the shorter distance from Oslo negating the overall journey time saving of air travel; There is significantly higher demand for rail travel on the Bergen route, which may be influenced by the popularity of the Oslo-Bergen rail line as a tourist attraction due to the attractive scenery along sections of the route; The Bergen-Stavanger route has significantly higher car flows, influenced by the lack of a direct rail route and limited air services; and Demand for coach travel is significantly lower than rail and air travel on all routes except for the Oslo Kristiansand corridor, where coach travel times and frequencies are significantly more competitive with air due to better road infrastructure. Figure 2.4 Total demand for each mode by corridor 160,000 140,000 120,000 100,000 80,000 60,000 40,000 Oslo - Stavanger Oslo - Bergen Oslo - Trondheim Oslo - Kristiansand Bergen - Stavanger 20,000 0 Air Classic Rail Car Coach Business Demand The demand for travel presented in Table 2.1 can be further disaggregated into business and leisure trips. Table 2.2 presents demand between the major cities in Norway for business travel only, indicating the scale of high value, work-related journeys currently travelling from end-to-end of each corridor.
Number of jouneys per year Contract 5, Subject 1: Demand Forecasting 26 Table 2.2 Annual business demand for key corridors in Norway for main transport modes (2010) Route Air Classic Rail Car Coach Total Oslo Stavanger 51,000 5,000 6,000 <1000 62,000 Oslo Bergen 87,000 22,000 8,000 2,000 118,000 Oslo Trondheim 74,000 8,800 7,000 1,000 91,000 Oslo Kristiansand 19,000 6,200 6,000 4,000 35,000 Bergen Stavanger 52,000 <1000 22,000 4,000 78,000 The table shows that the Oslo-Bergen route has the highest volume of end-to-end business travel, even when Kristiansand and Stavanger are jointly considered as a single corridor. This reflects the high levels of business activity in the Bergen area. The high levels of car and air demand on the Bergen Stavanger corridor reported in the NTM5 model seem unrealistic: further analysis of this market needs to be undertaken to establish the reasonableness of this figure, but no independent data is available to verify this. 90,000 Figure 2.5 Business demand for each mode by corridor (2010) 80,000 70,000 60,000 50,000 40,000 30,000 Oslo - Stavanger Oslo - Bergen Oslo - Trondheim Oslo - Kristiansand Bergen - Stavanger 20,000 10,000 0 Air Classic Rail Car Coach Figure 2.5 compares the significance of modes on each corridor. The overwhelming majority of business travel is conducted by air for all flows except Oslo-Kristiansand. This reflects the higher value of time place on work related journeys, as air is the quickest mode of transport for longdistance journeys. There is significant business rail travel on the Oslo-Bergen route and car travel on the Bergen-Stavanger route. Business coach travel is negligible on all routes.
Contract 5, Subject 1: Demand Forecasting 27 Figure 2.6 presents the mode shares on these corridors and suggests: Air travel dominates between Oslo and all the major cities. Kristiansand is an exception, where the city s relative proximity to the capital allows surface modes to capture almost half of the market; There is higher market share for rail travel (18%) between Oslo and Bergen than between Oslo and Stavanger or Trondheim. The higher share seems to be primarily at the expense of air travel; and There is negligible rail demand between Bergen and Stavanger as there is no direct rail route between the two cities. Indeed, any residual demand is likely to be a result of NTM5 model estimation and should not be noted as significant as in reality there would be no rail travel on this route. The result is a higher proportion of travel on other modes, in particular a shift to travel by car. Figure 2.6 Mode share for city-to-city business trips by corridor (2010) Bergen - Stavanger Oslo - Kristiansand Oslo - Trondheim Oslo - Bergen Air Classic Rail Car Coach Oslo - Stavanger 0% 20% 40% 60% 80% 100% Leisure Demand The leisure segment of the travel market carries a lower value of time and generally larger group sizes, therefore different mode choices may be expected than for business travel. Table 2.3 presents demand for leisure travel between the main urban centres. Table 2.3 Annual leisure demand for key corridors in Norway for main transport modes (2010) Route Air Classic Rail Car Coach Total Oslo Stavanger 35,000 21,000 47,000 6,000 109,000 Oslo Bergen 63,000 75,000 70,000 12,000 219,000 Oslo Trondheim 56,000 39,000 80,000 11,000 186,000 Oslo Kristiansand 8,000 21,000 41,000 16,000 85,000 Bergen Stavanger 16,000 1,000 82,000 14,000 113,000 Bergen is again the largest market, although to a lesser extent than for business; the leisure volume is only 18% higher than that for Trondheim, compared to a figure of 29% for business.
Number of journeys per year Contract 5, Subject 1: Demand Forecasting 28 The share of air travel is much lower for leisure journeys than for business, with a much higher proportion using car and classic rail as shown in Figure 2.7. 90,000 Figure 2.7 Leisure demand for each mode by corridor (2010) 80,000 70,000 60,000 50,000 40,000 30,000 Oslo - Stavanger Oslo - Bergen Oslo - Trondheim Oslo - Kristiansand Bergen - Stavanger 20,000 10,000 0 Air Classic Rail Car Coach Figure 2.8 Mode share for city-to-city leisure trips by corridor (2010) Bergen - Stavanger Oslo - Kristiansand Oslo - Trondheim Oslo - Bergen Air Classic Rail Car Coach Oslo - Stavanger 0% 20% 40% 60% 80% 100% Figure 2.8 presents the mode shares on these corridors and suggests that: Leisure demand is more evenly shared between the different modes of transport, with reduced dominance of air travel. This is to be expected, as leisure journeys are generally more price sensitive, and likely to be made as part of larger groups (particularly families). Both of these factors increase the probability of car use over air and rail;
Contract 5, Subject 1: Demand Forecasting 29 Car demand has the highest mode share, except for Oslo to Bergen, where the leisure market is split almost equally between car, train and air. The scenic nature of the Bergen route may contribute to its higher share for the leisure segment; and Car and coach demand is particularly high on the shorter distance routes between Oslo and Kristiansand, and Bergen and Stavanger. The air demand on these routes is far lower than the longer distances. In summary for city-to-city travel: Oslo-Bergen is the largest market for travel, followed by Oslo-Trondheim and Oslo- Stavanger; For business trips air is the dominant mode, despite the need to travel out of cities to airports. This is because air is much faster than any other mode and business trips attach a high value to time; and For leisure trips the mode share is more balanced. On the Bergen route in particular classic rail takes a much larger market share. 2.2.3 Long-distance Travel: Wider Catchment Area Analysis The previous section studied demand for travel between the primary cities urban areas but existing airports have a much larger catchment area, encompassing entire regions of the country. HSR stations, given adequate intermodal connections (in particular car parking) could perform a similar function to airports in attracting demand from a large catchment area. Hence, this section studies the market for travel between regional corridors. The demand for air travel in this section is based on ticket sales data supplied by Avinor for 2009 and covers all of corridors where HSR is also proposed. In contrast to the data reported above, these figures are airport to airport passenger journeys and do not reflect the distribution of trips between surrounding areas. For this analysis of corresponding car and rail demand from NTM5, regional catchment areas have been drawn around the five key urban areas for the purpose of a direct comparison with the air data. The catchment areas are shown in Figure 2.9. These large catchments reflect the distance travelled to access airports today.
Contract 5, Subject 1: Demand Forecasting 30 Figure 2.9 Wider catchment areas for Norwegian airports for the calculation of road and rail demand
Number of journeys per year Contract 5, Subject 1: Demand Forecasting 31 Business On a wider catchment area basis, business trips from region to region are still dominated by air for the longer distances (Oslo Bergen/Stavanger/Trondheim) but to a lesser extent than on cityto-city trips see Figure 2.10. 500,000 Figure 2.10 Number of business trips by corridor and mode (2010) 450,000 400,000 350,000 300,000 250,000 200,000 150,000 Oslo - Stavanger Oslo - Bergen Oslo - Trondheim Oslo - Kristiansand Bergen - Stavanger 100,000 50,000 0 Air Classic Rail Car The dominance of air travel will tend to increase with distance, reflecting the greater journey time savings over other modes of transport. Volumes of business travellers exceed leisure travellers by at least 20% on each of the air routes. Summing across all of these routes, the business market exceeds the leisure market by over 50%. The interaction of demand and supply and the resulting dominance of air travel can be confirmed by comparing the number of air services per weekday (final column) with corresponding rail data in Table 2.21 below. Figure 2.11 presents the mode split for business demand confirming that car trips dominate the interregional trips for the shorter routes.
Number of journeys per year Contract 5, Subject 1: Demand Forecasting 32 Figure 2.11 Mode share by corridor for business trips (2010) Bergen - Stavanger Oslo - Kristiansand Oslo - Trondheim Oslo - Bergen Car Air Classic Rail Oslo - Stavanger 0% 20% 40% 60% 80% 100% Leisure Figure 2.12 demonstrates that the trend away from air use for leisure journeys is magnified when considering wider catchment areas, with car tending to dominate on all corridors. This is to be expected as the increased access times to airports and rail stations increases when the wider catchment areas are considered. 2,500,000 Figure 2.12 Number of leisure trips by corridor and mode (2010) 2,000,000 1,500,000 1,000,000 Oslo - Stavanger Oslo - Bergen Oslo - Trondheim Oslo - Kristiansand Bergen - Stavanger 500,000 0 Air Classic Rail Car
Contract 5, Subject 1: Demand Forecasting 33 It should be noted that the quality of NTM5 data for long-distance car journeys is of debatable quality, with reports that the modelled data overstates use of car in this segment. Some caution has to be applied to assumptions of potential HSR shift from car until further cross-validation data is available. Figure 2.13 suggests that the Bergen route has the highest proportion of leisure travellers by rail, as was demonstrated for city-to-city travel. Air establishes itself at around 25% of the market on the Oslo Trondheim/Stavanger/Bergen routes. Figure 2.13 Mode share by corridor for leisure trips (2010) Bergen - Stavanger Oslo - Kristiansand Oslo - Trondheim Oslo - Bergen Car Air Classic Rail Oslo - Stavanger 0% 20% 40% 60% 80% 100% Summary Comparing the mode share for region-to-region against those for city-to-city travel documented earlier in this report, it is clear that analysis using the large airport catchment areas significantly raises the mode share of car. This is to be expected, because remote areas some distance from the suburbs, let alone city centres, are unlikely to be well-served by public transport. Thus, the incidence of car use will increase as the focus on cities is diluted. For business trips however, it is clear that air is the dominant mode and is clearly carrying more passengers than the existing classic rail network for passengers with a high value of time. This is driven by journey times, but also by frequency of service. Classic rail only provides four or five services a day between the main centres where as there are up to 28 flights per day (see Section 2.3). 2.2.4 Tourism Market An important aspect of long-distance travel in Norway is the existence of a significant tourism industry. There are two distinct tourism markets in Norway: A winter tourism market based largely on access to ski resorts. This is a more disperse, market largely focussed on travellers from Norway, Sweden and Denmark with a lower proportion of international arrivals by air. Travellers often arrive by car and/or ferry and travel to private ski chalets or resorts across southern Norway; and A summer tourism market, with significant travel to the coast western Norway, particularly Fjordland around Bergen. This has a larger proportion of international travellers arriving by air, with potential to use HSR to access areas of Norway outside Oslo.
Contract 5, Subject 1: Demand Forecasting 34 Figure 2.14 displays the location of the prominent ski resorts in Norway, as well as the connecting transport networks. 4 It can be seen that a large number of such resorts reside on potential HSR corridors, in particular the Oslo-Bergen and Oslo-Trondheim routes. Figure 2.14 Location of major ski resorts in Norway 4 Source: Visit Norway http://www.visitnorway.com/
Contract 5, Subject 1: Demand Forecasting 35 Summer tourism, especially to the fjords in western Norway and in the main cities, also provides demand for leisure travel. Table 2.4 lists the top ticketed attractions in Norway by number of visitors, while Table 2.5 shows the most popular natural attractions. Table 2.4 Top ten ticketed tourist attractions in Norway (2007) 5 Cultural Attraction Location Visitors Fløibanen (Venicular railway) Bergen 1,131,707 Holmenkollen ski jump and Ski Museum Oslo 686,857 Bryggen Bergen 583,510 Kristiansand Zoo and Amusement Park Kristiansand 532,044 Tusenfryd Ås 501,235 Flåm Railway Flåm 457,545 Hadeland Glassverk Jevnaker 431,400 Fredrikstad Fortress, Old Town of Fredrikstad Fredrikstad 372,360 Viking Ship Museum Oslo 314,560 Hunderfossen Theme Park Øyer/Lillehammer 270,500 Table 2.5 Top ten free tourist attractions in Norway (2006) 6 Natural Attraction Location Visitors Vøringsfossen waterfall Eidfjord 655,000 Scenic road Trollstigen Åndalsnes 563,331 Kjosfossen Waterfall Flåm 457,400 World Heritage Site Geirangerfjorden Geiranger 423,643 Låtefossen Waterfall Odda/Hardanger 420,000 Steinsdalsfossen Waterfall Norheimsund/Hardanger 300,000 World Heritage Site Nærøyfjorden Aurland 297,038 Briksdalsbreen Glacier Olden/Stryn 280,000 National Tourist Road Sognefjellsvegen Lom-Luster 253,953 Scenic road Atlanterhavsvegen Averøy/Kristiansund 237,316 The presence of high volume ticketed attractions in Bergen, Oslo and Kristiansand may contribute to the demand for trips between these centres, which could be carried by a HSR network. In contrast, most natural attractions are unlikely to be accommodated by HSR as they lie outside the major centres, as shown in Figure 2.15. Also shown is the number of international tourist guest nights in accommodation establishments for the year 2009 for each county. HSR may offer some possibilities in providing greater access for international travellers arriving by air to gain access to areas outside of Oslo particularly Bergen through reduced travel times. 5 Source: Innovasjon Norge: http://www.innovasjonnorge.no/default.aspx 6 Source: Innovasjon Norge: http://www.innovasjonnorge.no/default.aspx
Contract 5, Subject 1: Demand Forecasting 36 Figure 2.15 Location of major tourist attractions and fjords in Norway and international guest nights in 2009.
Contract 5, Subject 1: Demand Forecasting 37 Figure 2.16 shows the number of domestic guest nights in 2009. 7 Both Figure 2.15 and Figure 2.16 demonstrate that the Bergen and Trondheim corridors attract the most tourist trips. Figure 2.16 Location of major tourist attractions and fjords in Norway and domestic guest nights in 2009 7 Source: Statistics Norway http://www.ssb.no/reiseliv_en/
Contract 5, Subject 1: Demand Forecasting 38 Figure 2.17 shows the proportion of visitors to Norway that arrive by each mode of transport. The figure demonstrates that the majority of visitors access Norway through the international ferry ports and airports, as opposed to by rail and road across the border. Those that do arrive by land based transport tend to originate from Sweden. The high ferry share is owing to the impact of tourist trips to coastal towns and the scenic fjords, with a large number of passengers from Germany and Denmark. Figure 2.17 Proportion of international tourists by arrival mode (2009) 8 Car Train/Coach Ferry Air 0% 20% 40% 60% 80% 100% HSR is unlikely to have much impact on the ferry market share, but the proposed connections to Stockholm and Gothenburg may shift surface transport share to HSR. There is currently only a small proportion of visitors from Sweden arriving by air (see Figure 2.18), suggesting there is limited demand from mode shift to HSR from air in Sweden. Figure 2.18 Proportion of Swedish tourists by arrival mode (2009) 9 Car Train/Coach Ferry Air 0% 20% 40% 60% 80% 100% From this study of the existing tourism related travel market it can be concluded that: Most popular ski resorts and natural attractions are located in remote areas away from the main corridors which drive demand for travel; Some of the most popular ticketed attractions are located in major urban centres and tourism may attract further demand to these centres; 8 Source: Transportøkonomick Institutt, (2009), Gjesteundersøkelsen 9 Source: Transportøkonomick Institutt, (2009), Gjesteundersøkelsen
Contract 5, Subject 1: Demand Forecasting 39 The majority of overnight stays by domestic and foreign visitors are made along the Bergen and Trondheim corridors; and Existing tourists arrive mainly by ferry and air. Swedish tourists, unsurprisingly, use surface transport, with a low air arrival share. 2.2.5 Size of Existing Travel Market: Conclusions This section of the report has examined current patterns of long-distance travel between the major Norwegian cities and travel corridors, using data from the NTM5 model, adjusted with Avinor airport-to-airport passenger data and NSB ticket sales data. Demographic and tourism data has been obtained from Statistics Norway. Key conclusions are: Population tends to concentrated in the south-east of Norway and along the coast. There are large, sparsely populated areas internally resulting in wide spaces between the main areas of population demand. Therefore, development of HSR routes should generally focus on serving end-to-end markets, with the exception of routes to the south-east of Norway; Income levels are highest around the major cities, and in the south-east as a whole. To compete with air travel and provide the best commercial case for HSR accessibility to areas outside city centres may be important; In terms of overall volumes of travel, Oslo-Bergen is significantly larger than other corridors, followed by Oslo-Trondheim and Oslo-Stavanger. International experience suggests that high volumes of travel generally produce the best economic / financial case for HSR routes, suggesting that the Oslo-Bergen and Oslo-Trondheim corridors are most important; Domestic air travel dominates long-distance business trips, due to its speed and frequency compared with other modes. The mode share is highest on the longest distance routes such as Oslo-Trondheim. Any HSR proposal will need to offer an attractive alternative to air to achieve significant modal shift; For leisure trips demand is more evenly spread between car and air compared with business. Leisure passengers are more likely to drive than business passengers, due to costs, lower value of time and the ability to raise private vehicle occupancy. Hence, HSR proposals will need to balance competitiveness with air for business travel with competitiveness with car and price sensitivity for leisure travel; City-to-city travel is more likely to be by air and rail than travel between wider regions. This is because of better road accessibility to urban hinterlands and reduced public transport options. If limited stops are provided on HSR routes, this may restrict its ability to abstract from wider regional flows by car; Classic rail has low mode share for business travel (10-20%) but carries more leisure passengers (20-30%). The Oslo-Bergen route attracts the highest leisure share to classic rail possibly because of the scenic nature of the journey this may inhibit shift to HSR along the same corridor if a similar scenic nature is not achieved on any new alignment; and The tourism market may have some impacts on demand, although the most popular attractions tend to be away from cities. The seasonal nature of the tourism in Norway leads to variations in demand, and it may be difficult to attract this market to HSR beyond the core city-to-city markets.
Contract 5, Subject 1: Demand Forecasting 40 2.3 Comparison of Services 2.3.1 Introduction The following section presents a review of the current level of service along the six transport corridors in Norway, for the purpose of comparing the travel opportunities for each mode of public transport. The parameters in this section have been gathered from a desk study of transport operators websites. The modes considered are rail, air and coach, as well as ferry for the Bergen Stavanger corridor. For each mode the distance, fastest journey time, a range of possible fares (where available) and service frequency is given. 2.3.2 Classic Rail Classic rail services in Norway are operated by NSB, with the exception of the Airport Express Train, operated by Flytoget, which connects Oslo with its main airport, Gardermoen. The NSB network is fairly extensive in southern Norway, although due to the challenging topography, with large mountainous and forested areas, as well as several lakes and fjords, rail travel is relatively slow due to large distances of single track rail with large curvature. The following sections and tables present the level of service from Oslo to key destinations on each corridor. Key destinations are based on potential station locations for the proposed HSR services. Oslo Trondheim The Dovre Line to Trondheim is served by four long-distance trains per day to Trondheim, one of which is a sleeper train. There are also local stopping services at each end of the line, serving the suburbs of Oslo and Trondheim, as well as the airport express services to Gardermoen and regional services on the line from Skien to Lillehammer via Oslo. Gjøvik is served by a regional service operating on a separate line from Oslo. Regional and long-distance services have been accelerated between Oslo Central Station and Eidsvoll, just to the north of Gardermoen Airport, with the opening of the Gardermoen Line in 1998. The line allowed for separation of local and regional services, reducing the possibility of delays. Also the maximum speed of 210 kph is significantly higher than the rest of the network and is the only high speed line in Norway. Table 2.6 provides a summary of the level of service from Oslo to key stations en-route. For Oslo to Oslo Gardermoen, figures for both NSB regional services and airport express services have been shown.
Contract 5, Subject 1: Demand Forecasting 41 Table 2.6 Summary of rail level of service in Trondheim corridor (2010) 10 Route Rail distance (km) 11 Fastest journey time (hours:mins) Cheapest advanced fare (NOK) Walk-up fare (NOK) Services per day Oslo Gardermoen 12 52 00:19 / 00:26-170 / 110 108 / 35 Oslo Gjøvik 124 01:55 199 238 12 Oslo Hamar 126 01:20 199 238 21 Oslo Lillehammer 184 01:57 199 339 21 Oslo Otta 297 03:26 199 536 5 Oslo Trondheim 553 06:36 199 852 4 Oslo Bergen There are five services per day between Oslo and Bergen, including one sleeper train, providing end-to-end travel and linking the villages and ski resorts along the route. On the line between Oslo and Drammen, long-distance services heading towards Bergen share tracks with local, regional and long-distance services heading towards Skien and Kristiansand, as well as airport express trains from Gardermoen. At the opposite end of the Bergen route there are regular commuter services between Voss and Bergen. A new double track line is under construction between Oslo and Asker in order to separate local and regional services on this very busy route, similar to the Gardermoen Line which has already been completed to the east of Oslo. This will reduce delays caused by the high volume of services all using the same tracks. Table 2.7 below gives a summary of the level of service from Oslo to significant stations en-route to Bergen. Table 2.7 Summary of rail level of service in Bergen corridor (2010) Route Rail distance (km) Fastest journey time (hours:mins) Cheapest advanced fare (NOK) Walk-up fare (NOK) Services per day Oslo Drammen 53 00:36-91 63 Oslo Kongsberg 99 01:07-169 25 Oslo Hønefoss 124 01:25-175 5 Oslo Gol 208 02:48 199 373 5 Oslo Geilo 253 03:31 199 459 5 Oslo Voss 385 05:21 199 686 5 Oslo Bergen 484 06:28 199 775 5 10 Sources: http://www.nsb.no/home/, http://www.flytoget.no/eng/ 11 Source: JBV Network Statement 2011 http://www.comitato.com/v2/ 12 Data for airport express train and NSB services
Contract 5, Subject 1: Demand Forecasting 42 Oslo Kristiansand Stavanger There are two regional services that head south west from Oslo; the long-distance service on the Sørland line to Kristiansand and Stavanger, and services from Lillehammer via Oslo that serve the Vestfold line which branches off at Drammen, passing through large towns such as Tønsberg, Sandefjord and Larvik before terminating at Skien. There are five long-distance services per day to Kristiansand, of which four continue on to Stavanger, and there is a branch line serving Arendal. Table 2.8 provides a summary of the level of service to key stations between Oslo and Stavanger served by these routes. Table 2.8 Summary of rail level of service in Kristiansand-Stavanger corridor (2010) Route Rail distance (km) Fastest journey time (hours:mins) Cheapest advanced fare (NOK) Walk-up fare (NOK) Services per day Oslo Drammen 53 00:36-91 63 Oslo Torp Airport 135 01:41 199 224 21 Oslo Porsgrunn 190 02:34 199 300 22* Oslo Arendal 318 04:06 199 501 5* Oslo Kristiansand 365 04:25 199 631 5 Oslo Stavanger 599 07:42 199 886 4 *These routes currently require an interchange Oslo Gothenburg The Østfold Line links Oslo to the Swedish border near Kornsjø. There is an hourly service to Halden, with three trains per day continuing on to Gothenburg. Between Oslo and Moss the regional services share tracks with local Oslo commuter services. A new line is being planned between Oslo and Ski with a line speed of approximately 200 kph, which will segregate local and regional traffic, increasing line capacity and reducing journey times to long-distance destinations. Table 2.9 summarises the level of service from Oslo to key regional destinations on the Østfold Line and onto Gothenburg. Table 2.9 Summary of rail level of service in Gothenburg corridor (2010) Route Rail distance (km) Fastest journey time (hours:mins) Cheapest advanced fare (NOK) Walk-up fare (NOK) Services per day Oslo Ski 24 00:22-60 85 Oslo Moss 60 00:41-123 41 Oslo Fredrikstad 94 01:05-182 21 Oslo Sarpsborg 109 01:19-182 21 Oslo Halden 137 01:43 199 231 21 Oslo Gothenburg 349 03:54 199 484 3
Contract 5, Subject 1: Demand Forecasting 43 Oslo Stockholm NSB provide five long-distance services along the Kongsvinger line to Charlottenberg, which is located in Sweden near to the border with Norway. From here the service is operated by the Swedish operator SJ to Karlstad, with two direct services a day continuing on to Stockholm. At other times of day there are connecting trains from Karlstad. The long-distance services share tracks with local trains to Kongsvinger, as well as Dovre Line services and the Airport Express as far as Lillestrøm. Table 2.10 gives the level of service from Oslo to key stations along the route to Stockholm. Table 2.10 Summary of rail level of service in Stockholm corridor (2010) 13 Route Rail distance (km) Fastest journey time (hours:mins) Cheapest advanced fare (NOK) Walk-up fare (NOK) Services per day Oslo Lillestrøm 21 00:11-60 100 Oslo Kongsvinger 100 01:08-193 11 Oslo Karlstad 327 02:56 199 244 5 Oslo Stockholm 572 06:05 214 494 4 14 Local Services Table 2.11 below provides a summary of the services operating on the suburban routes around the key cities in Norway. It can be seen that these services operate at a far higher frequency than the long distance services. Table 2.11 Summary of local rail services in Norway City Route Termini Frequency Oslo 300 Skøyen Jaren 1 train per hour (tph) to Jaren, 1 train every 2 hours to Gjøvik Oslo 400 Asker Lillestrøm 2tph Oslo 440 Drammen Dal 1tph Oslo 450 Kongsberg Eidsvoll 1tph Oslo 460 Skøyen Kongsvinger 1tph to Ǻrnes, approx. 11 trains per day to Kongsvinger Oslo 500 Skøyen Ski 2tph Oslo 550 Spikkestad Moss 1tph Oslo 560 Skøyen Mysen 1tph Trondheim 26 Lerkendal Steinkjer 1tph, 3 trains per day serving Trondheim to Røros Bergen 45 Bergen Myrdal 2tph to Arna, 1 train every 2 hours to Voss, 4 trains per day to Myrdal Stavanger 59 Stavanger Egersund 4tph to Sandnes, 2tph to Nærbø, 1tph to Egersund 13 Additional source: http://www.sj.se/sj/jsp/polopoly.jsp?d=10&l=en 14 2 services require interchange at Karlstad
Contract 5, Subject 1: Demand Forecasting 44 2.3.3 Coach In conclusion, there is a fairly extensive existing rail network in Norway, serving all of the major transport corridors. For journeys of approximately 100 km or less, there is a high frequency of service, especially in the areas surrounding Oslo, and to some extent, the other main cities. The rail network surrounding Oslo is subject to improvements in the near future, such as doubletracking, to further increase capacity. For long-distance journeys, services are far more infrequent and journey times are long, especially when compared with air travel. Fares for longdistance journeys can be very cheap if booked far in advance. Long-haul coach services in Norway are operated by several private companies, most of which operate under the overarching brand of Nor-way Bussekspress. The services provide a public transport alternative where there is no direct rail service available, such as between Bergen and Stavanger, as well as competing with rail on some routes, particularly in western Norway. As with the rail network, road travel in Norway becomes slower with greater distance from Oslo, due to the challenging topography and weather conditions, hence long-distance journeys are particularly slow. On many routes there is no direct coach service and one or sometimes two interchanges have to be made. In these cases the fastest journey time presented includes interchange time. Oslo Trondheim There is a frequent coach service between Oslo and Gardermoen Airport. There are also some express coaches to regional destinations. Coach travel to Trondheim and further north is less frequent and often requires interchange. Table 2.12 presents a summary of the coach service on the Trondheim corridor. Table 2.12 Summary of coach level of service in Trondheim corridor (2010) 15 Route Road distance (km) 16 Fastest journey time (hours:mins) Minimum fare (NOK) Maximum fare (NOK) Services per day Oslo Oslo Gardermoen 49 00:45 140 200 53 Oslo Hamar 128 02:00 150 150 1 Oslo Lillehammer 184 02:50 150 315 6 Oslo Otta 295 05:05 282 460 5 Oslo Trondheim 496 08:30 199 850 4* *Some require interchange 15 Source: http://www.nor-way.no/?lang=en_gb 16 Source: http://www.vegvesen.no/en/traffic/planning+your+trip/route+planner
Contract 5, Subject 1: Demand Forecasting 45 Oslo Bergen For long-distance travel towards Bergen, interchange is often required to reach the final destination, although there are a limited number of direct coach services each day to intermediate destinations, such as Geilo and Gol, shown in the table below. Table 2.13 gives a summary of the coach service on the Bergen corridor. Table 2.13 Summary of coach level of service in Bergen corridor (2010) Route Road distance (km) Fastest journey time (hours:mins) Minimum fare (NOK) Maximum fare (NOK) Services per day Oslo Gol 191 03:30 355 355 6* Oslo Geilo 243 04:25 420 420 4* Oslo Voss 385 08:00 690 746 4* Oslo Bergen 484 10:25 509 805 4* *Some or all require interchange Oslo Kristiansand Stavanger There is a more comprehensive coach service to towns and cities along the Oslo Stavanger corridor compared with Oslo to Bergen and Trondheim, as shown in Table 2.14 below. Table 2.14 Summary of coach level of service in Kristiansand-Stavanger corridor (2010) Route Road distance (km) Fastest journey time (hours:mins) Minimum fare (NOK) Maximum fare (NOK) Services per day Oslo Torp Airport 118 03:30 465 535 8* Oslo Porsgrunn 141 02:25 310 310 16 Oslo Arendal 261 03:50 350 350 12 Oslo Kristiansand 327 04:30 219 350 18 Oslo Stavanger 540 09:10 219 860 6* *Some require interchange Bergen Stavanger The Bergen Stavanger corridor is the best served by coach, as there is no rail service in this region. Frequent services are provided, although journey times are long, owing to the need to cross several fjords and mountainous areas en-route. The level of service is summarised in Table 2.15. Table 2.15 Summary of coach level of service in Bergen-Stavanger corridor (2010) Route Road distance (km) Fastest journey time (hours:mins) Minimum fare (NOK) Maximum fare (NOK) Services per day Bergen Stord (Leirvik) 83 02:10 220 220 15 Bergen Haugesund 139 03:15 320 320 12 Haugesund Stavanger 89 02:30 240 240 10 Bergen Stavanger 211 05:00 490 490 13
Contract 5, Subject 1: Demand Forecasting 46 Oslo Gothenburg Coaches between Oslo and Gothenburg are operated by the Swedish company Swebus. They offer up to five services a day, with two stopping at Sarpsborg. There is also a regular coach service operated by Nettbuss which runs between Oslo and Halden. Key parameters are shown in Table 2.16 below. Table 2.16 Summary of coach level of service in Gothenburg corridor (2010) 17 Route Road distance (km) 18 Fastest journey time (hours:mins) Minimum fare (NOK) Maximum fare (NOK) Services per day 19 Oslo Moss 58 00:50 125 125 17 Oslo Sarpsborg 89 01:05 57 175 21 Oslo Halden 116 02:10 190 190 7 Oslo Gothenburg 293 03:35 45 243 5 Oslo Stockholm Services between Oslo and Stockholm are also operated by Swebus, with four services per day between Oslo and Karlstad, three of which continue on to Stockholm. There is also a service operated to Stockholm by bus4you. The level of service is shown in Table 2.17. Table 2.17 Summary of coach level of service in Stockholm corridor (2010) Route Road distance (km) Fastest journey time (hours:mins) Minimum fare (NOK) 20 Maximum fare (NOK) Services per day Oslo Karlstad 219 03:05 42 207 4 Oslo Stockholm 523 07:30 77 455 5 2.3.4 Ferry In conclusion, coaches play an important role in Norwegian public transport, in particular serving routes that are not well served by other modes of transport. Coach travel is a cheap alternative for those who cannot afford to run a car. However, on long-distance routes which are well served by air, service frequencies are low and journey times are far slower. For the intercity markets that any potential HSR is likely to serve, coaches are unlikely to offer direct competition, although they will continue to play an important role serving more rural routes and accessing regional airports from city centres. Fast ferries operate along the west coast, across fjords and to islands where it is quicker to follow the waterways than the roads, and some small islands are served by water buses. The only ferry route which is relevant to long-distance travel along the HSR corridors considered is the Flaggruten, operated by Tide. This service connects Bergen and Stavanger, a route which is not served by classic rail. Table 2.18 summarises the level of service to the main destinations served by the Flaggruten. 17 Source: http://www.swebus.se/swebusexpress_com/, http://www.nettbuss.no 18 Source: http://www.rac.co.uk/route-planner/ 19 Number of services varies depending on day of the week 20 Based upon an exchange rate of 1 SEK = 0.88 NOK
Contract 5, Subject 1: Demand Forecasting 47 Table 2.18 Summary of ferry level of service in Bergen-Stavanger corridor (2010) 21 Route Distance (km) Fastest journey time (hours:mins) Walk-up fare (NOK) Services per day Bergen - Stord / Leirvik 60 02:10 310 4 Bergen Haugesund 100 03:10 510 2 Haugesund Stavanger 60 01:20 310 4 Bergen Stavanger 160 04:30 730 2 2.3.5 Air The table shows that ferry travel offers a viable alternative to road travel on this corridor, with journey times similar to those achieved by coach. However, for the city-to-city market between Bergen and Stavanger, air travel is vastly quicker, as described below. Because of the often slow road and rail networks in northern and western Norway there is a high demand for domestic air services for intercity and long-distance travel. The primary airports are served by jets from Scandinavian Airlines and Norwegian, while the smaller regional airports located north of Trondheim are generally served by smaller aircraft operated by Widerøe. Service on Key Routes Table 2.19 below presents the level of service for key air routes from Oslo and between Bergen and Stavanger on a typical weekday. Table 2.19 Summary of air level of service on key routes within Norway and to Sweden (2010) 22 Route Approx. crow flies distance (km) Flight time (hours:mins) Minimum fare (NOK) Maximum fare (NOK) Services per day Oslo Trondheim 390 00:55 299 1990 25 Oslo Bergen 310 00:50 299 1990 28 Oslo Kristiansand 250 00:45 259 1931 8 Oslo Stavanger 300 00:50 299 1990 23 Bergen Stavanger 160 00:35 359 1717 14 Bergen Haugesund 100 00:30 361 1443 2 Oslo Gothenburg 250 00:55 460 2502 8 Oslo Stockholm 420 01:00 299 2227 17 The table shows that for the main long-distance routes, there are a large number of flights per day. For shorter routes, such as Oslo-Kristiansand and Oslo-Gothenburg there are fewer flights, presumably because air has less of an advantage over other modes of travel in terms of journey time savings, and hence there is less demand. There are a large number of flights from Bergen to Stavanger, however. This is possibly due to the combination of a lack of rail service between the two cities, and car travel requiring several transfers to ferry en route to cross fjords. 21 Source: http://eng.tide.no/default.aspx?pageid=1055 22 Sources: http://www.wideroe.no/?language=en, http://www.norwegian.com/en/, http://www.flysas.com/en/uk/?vst=true
Contract 5, Subject 1: Demand Forecasting 48 Access to Airports and Airport Handling Times As is to be expected, air journeys provide the fastest mode of transport for long-distance journeys from Oslo to the other major cities in Norway. It should be noted however that air journeys generally have longer access times from city centres and check-in time should also be considered. As in most countries, rail stations and coach terminals in Norway are usually located in towns and city centres, often adjacent to each other and near stops for local bus routes and light rail systems, allowing for easy interchange between modes. In contrast, the airports in Norway are located some distance from the town / city centres. All Norwegian airports can be reached by car and, in many instances, public transport. Table 2.20 gives an overview of the quality of public transport to / from the major airports serving the relevant corridors, in terms of access time, cost and frequency. Table 2.20 Access to key airports in Norway and Sweden (2010) 23 Airport Distance from city centre (km) Mode Time Cost Frequency (per hr) Oslo Gardermoen 46 Trondheim Værnes 33 Train 00:19 170 8 Coach 00:45 140 53 Train 00:35 64 1 Coach 00:35 100 4 Bergen Flesland 12 Coach 00:25 90 4 Kristiansand Kjevik 16 Coach 00:25 120 1 Stavanger Sola 11 Coach 00:25 90 3 Haugesund (Karmøy) 14 Coach 00:25 70 <1 24 Gothenburg Landvetter 20 Coach 00:30 70 3 Stockholm Arlanda 37 Train 00:20 211 10 Coach 00:35 65 10 The table demonstrates that the airports are all located within 50km of the city centres and access times are all fairly similar, with the larger airports located further from the city centre, namely Oslo and Stockholm, having a faster and more regular service. In addition the check-in time can vary depending on the airport and the type of flight. For the purpose of this work it is assumed that attendance time at the airport will amount to one hour for domestic flights and 1.5 hours for international flights, as recommended by Avinor 25. This will enable a fairer comparison between air travel and other modes. For the comparison between air travel and other modes below, the parameters for air travel have taken into account travel to and from the airport, as well as an assumption for airport check-in time. 23 Sources: http://www.nsb.no/home/, http://www.flybussen.no/, http://www.flygbussarna.se/default.aspx?lang=en, http://www.arlandaexpress.com/start.aspx 24 Coach leaves 20mins after each flight arrives 25 http://www.avinor.no/en/avinor/baggageandcheckin/30_check-in
Contract 5, Subject 1: Demand Forecasting 49 2.3.6 Comparison This section compares the level of service on each corridor offered by each mode. Service Frequency Table 2.21 compares the service frequencies for end-to-end journeys on each corridor. Air journeys are the most frequent with over 20 departures per day for the main domestic routes and Stockholm. Rail and coach journey frequencies are much lower but roughly similar. An exception is Bergen to Stavanger, where there is no rail route, so coach journeys are more frequent to meet the travel demand. Table 2.21 Summary of 2010 service frequency on key corridors Route Air Rail Coach Oslo Trondheim 25 4 2 Oslo Bergen 28 5 4 Oslo Kristiansand 8 5 18 Oslo Stavanger 23 4 6 Bergen Stavanger 14 0 13 Oslo Gothenburg 8 3 11 Oslo Stockholm 17 4 5 Journey Times Air is the fastest mode of transport for long-distance journeys from Oslo to the other major cities in Norway and Sweden, even when access and airport handling times are taken into account. Existing rail is the next fastest mode, followed by coach travel. In two examples (Gothenburg and Kristiansand), the journey time is not significantly different between rail and road; however others such as Trondheim and Bergen show that rail is vastly quicker than coach travel. End-toend journey times between Oslo and Gothenburg by air are not significantly quicker than rail or coach travel. This could explain the dominance of road travel along this corridor, as the advantage of convenience and lower cost of driving, especially for group travel, overrides any small journey time savings by air. Journey times are summarised in Table 2.22 below. Table 2.22 Summary of 2010 fastest journey times on key corridors (hr:min) Route Air Rail Coach Oslo Trondheim 02:49 06:36 08:30 Oslo Bergen 02:34 06:28 10:25 Oslo Kristiansand 02:29 04:25 05:30 Oslo Stavanger 02:34 07:42 09:10 Bergen Stavanger 02:25 N/A 05:00 Oslo Gothenburg 03:14 03:54 03:35 Oslo Stockholm 03:09 06:05 07:30 Fares The cost of air journeys varies greatly, depending on the popularity of the route, the time of day and how far in advance the ticket is booked. Coach and rail fares are set for each route,
Contract 5, Subject 1: Demand Forecasting 50 however there is variation in rail fares as cheaper advance tickets are available, while coach fares vary depending on the route taken. Table 2.23 gives a summary of the cheapest and most expensive fares available for each transport mode. Table 2.23 Summary of 2010 range of fares on key corridors (NOK) Route Air Rail Coach Lower limit Upper limit Lower limit Upper limit Lower limit Upper limit Oslo Trondheim 299 1,990 199 856 495 850 Oslo Bergen 299 1,990 199 788 680 805 Oslo Kristiansand 259 1,931 199 636 350 350 Oslo Stavanger 299 1,990 199 886 860 860 Bergen Stavanger 359 1,717 N/A N/A 320 320 Oslo Gothenburg 460 2,502 199 484 45 243 Oslo Stockholm 299 2,227 214 562 77 455 If purchased on the day, rail fares in Norway are set depending on length of journey. NSB Regional Trains offer a limited number of Minipris (advance) tickets on the majority of their routes, costing NOK 199, 299, and 399. Generous discounts are currently offered on Norwegian trains, including 90% discount for military in uniform, 25-40% student discount and 50% senior citizen discount. There is, however, no discount for group bookings. Coach journeys with Nor-way Bussekspress have a set fare depending on the journey distance, with a 10-40 NOK discount available for online booking (not available on the day of journey). There is also a variation in fares depending on the route taken. Swebus, who operate the Stockholm and Gothenburg coaches, do not have a set price. Fares do not vary greatly around the mean price, but can be higher the closer to the day the journey is booked and the more popular the travel time/date. Flights tend to increase in price the closer to the travel date that the ticket is booked and fares are higher at peak times (weekends, public holidays). Norwegian Air offer cheaper fares for customers booking in advance who do not require a flexible (refundable) ticket, especially at offpeak times. Station / Terminal Facilities A detailed discussion of station and terminal facilities is contained within the report for Subject 4 Location and Services of Stations / Terminals. 2.3.7 Other Factors Weather Conditions The Norwegian transport network is regularly affected by adverse weather due to the harsh climate, particularly in the winter. The long-distance intercity rail and road routes traverse mountain areas which can lengthen journey times when the weather is poor. On the road network in particular, some mountain passes are subject to severe snowstorm problems in the winter, so often they have to be closed, or cars have to drive behind a snowplough in a column formation. The weather contributes greatly towards the passenger choice of transport mode, causing a variation in the seasonal demand on key routes.
Contract 5, Subject 1: Demand Forecasting 51 Ability to Work A key consideration for business travellers is whether they are able to work while travelling. Rail and coach services have the advantage of being able to provide wi-fi and mobile phone reception during the journey. Airlines are often unable to offer this service except in departure lounges. Both rail and air modes offer varying degrees of service quality depending on whether standard or premium (first class) tickets are purchased. NSB do not sell first class tickets but offer a premium service, known as NSB Komfort, which is available for a supplement of 90 NOK on all long-distance routes, regardless of the length of journey. This service offers a separate compartment, complimentary tea and coffee, newspapers and access to a power socket for laptops 26. 2.3.8 Service Comparison Conclusions The classic rail services in Norway are a combination of local, regional and long-distance services, with local and regional services operating at a far greater frequency. On each corridor there are a small number of long-distance rail services linking Oslo with the other main cities in Norway, namely Bergen, Trondheim, Kristiansand and Stavanger, and Stockholm and Gothenburg in Sweden. These services are very infrequent when compared with the number of air services, and the journey times are far slower when compared with air travel. HSR is likely to need to achieve a far higher level of journey frequency as well as improved journey times to abstract from air; Coach services are primarily designed to serve popular routes not well served by rail, for example, Bergen to Stavanger. On other routes well served by air and / or rail, coach services are very infrequent. Journey times are very slow when compared with air and interchange is often required between coach services. However, they provide reasonable competition on the Oslo Kristiansand corridor, where HSR could be expected to abstract some demand; A consideration that must be made with air travel is access to the airport, which results in added time and cost to the overall journey. However, even when these are taken into account, air compares favourably with coach and rail for journeys over 200km. Similar or better accessibility to HSR stations needs to be provided; and Air fares are considerably more expensive than rail and coach if bought on the day or for journeys at peak times, especially if a flexible ticket is required. However, if bought in advance for off-peak journeys, air fares can be in a similar range to comparable rail and coach fares. HSR services need to offer a full range of fares to maximise commercial return and compete effectively with both rail and air on price and journey time, respectively. 26 Source: www.nsb.no
Contract 5, Subject 1: Demand Forecasting 52 2.4 International Benchmarking This section compares the current HSR services in other European countries, namely Sweden, France, Germany, Spain and the UK. Sweden was chosen for this study as it has a similar geography and population density to Norway. France, Germany and Spain were considered as they already have well developed and successful HSR networks; for the UK Atkins was able to draw on extensive local knowledge. 2.4.1 HSR Demand Size of Markets Table 2.24 shows the size of HSR markets in 2008 for selected European countries. Note that demand in the UK is for Eurostar only and does not include domestic high speed travel, which commenced in 2009. The table shows that France currently has the largest demand for HSR, followed by Germany. Spain has the next largest market for HSR, which is likely to growth significantly in the coming decade as it is in the process of expanding its network. The mean distance travelled in Germany is lower than France and Spain due to the very high population density of the country. The UK and Sweden have smaller markets for HSR due to the relatively small size of the networks. Table 2.24 Size of HSR markets in other European countries (2008) 27 Country Operator Passengers per year (thousands) Passenger km per year (millions) Mean distance travelled (km) Germany DB 74,700 23,333 312 Spain RENFE 22,955 10,490 457 France SNCF 116,054 52,564 453 UK Eurostar 9,100 993 109 Sweden SJ 8,764 2,992 341 In comparison with the markets for Germany and France described above, the market for travel in Norway is relatively small. For example, the total passenger journeys made by air, including transfers, in the 6 main corridors based on air ticket count data is just under 6 million per year (see Figure 2.10), compared with 75 million HSR passenger journeys in Germany and 116 million in France. Therefore even if HSR abstracted all air demand on the key corridors in Norway, as well as a proportion of long-distance rail and car journeys, the market would be less than 10% of the size of that in Germany and France. However, the market for HSR would be in the same order of magnitude as that in Sweden, the country with more similar characteristics to Norway, such as population size and distribution. Market Share New HSR routes have proved very successful in abstracting demand from air travel. Table 2.25 summarises the market share on key European routes in 2008. On some shorter routes where HSR from city centre to city centre has a faster overall journey time than air travel, such as Paris to Brussels, air demand has almost diminished completely. On slower HSR routes, such as between Paris and Amsterdam, and Stockholm and Gothenburg, the rail market share is lower. It should be noted that the Madrid-Barcelona line was fully completed in 2008. Therefore it is likely the impact of HSR on the travel markets had not yet taken effect. 27 Source: http://www.uic.org/img/pdf/50-2008_hs_commercialtraffic.pdf
Contract 5, Subject 1: Demand Forecasting 53 Table 2.25 Market share of HSR and air on key routes in other European countries (2008) 28 Route Distance (km) Journey Time (hr:min) Average Speed (kph) Rail Share Air Share Madrid Barcelona 630 02:40 229 50% 50% Madrid Seville 471 02:20 195 83% 17% Paris Amsterdam 450 04:00 113 45% 55% Paris Brussels 310 01:20 219 95% 5% Paris London 444 02:15 197 81% 19% Paris Lyon 430 02:00 215 90% 10% Stockholm Gothenburg 455 03:00 152 62% 38% 2.4.2 Quality of Service The following section describes the level of service provided on HSR routes in the European countries studied. Sweden In Sweden the X2000, Intercity and some regional services, as well as the Arlanda Airport Express, all have a maximum speed of 200 kph, which is reached on certain sections of track. Long-distance destinations served by these trains include Stockholm, Gothenburg and Malmö. The X2000 service is the quickest long-distance service, stopping at the fewest stations on route. Table 2.26 summarises the key routes served by HSR. Table 2.26 Summary of level of service on HSR in Sweden (2010) Route Distance by rail (km) Fastest Journey Time (hrs:mins) Cheapest advanced fare (NOK) 29 Walkup fare (NOK) Max daily freq of fast services Stockholm Copenhagen 661 05:02 282 1,082 6 Stockholm Gothenburg 453 02:52 128 1,092 16 Stockholm Arlanda Airport 39 00:20 N/A 211 79 France The French TGV high speed train has proved very successful since the first high speed line was opened between Paris and Lyon in 1981; patronage has continued to increase as the network has gradually expanded to other areas of France and linked to high speed lines in other countries. The TGV services run on a combination of dedicated HSR lines at speeds of up to 300 kph and at lower speeds on upgraded classic rail lines. Table 2.27 provides a summary of journey times, distances and single fares to major stations served by HSR lines from Paris. 28 Source: De Rus, G., (2008), The Economic Effects of HSR Investment, International Transport Forum 29 Based upon an exchange rate of 1 SEK = 0.88 NOK
Contract 5, Subject 1: Demand Forecasting 54 Table 2.27 Summary of level of service on HSR in France (2010) 30 Route Distance by rail (km) 31 Fastest Journey Time (hrs:mins) Cheapest advanced fare (NOK) 32 Walkup fare (NOK) Max daily freq of fast services Paris Lyon Lyon Part Dieu 429 01:57 202 629 26 Paris Nord Brussels Midi 314 01:20 226 792 30 Paris Lyon Marseille St Charles Paris Nord London St Pancras 750 03:03 202 792 19 495 02:15 374 1,344 16 Paris Est Frankfurt 586 03:49 316 859 5 Paris Est Cologne 473 03:14 235 851 4 Germany Like France, Germany has an extensive HSR network, using a combination of upgraded classic rail, with speeds of up to 250 kph, and sections of new lines, with a line speed of 300 kph. The German high speed network is better integrated with the classic rail network than France, although there is less overall length of segregated high speed lines. This, combined with more station stops due to the higher density of population, results in longer journey times overall on the main routes. High speed services are known as Intercity Express (ICE) and are operated by Deutsch Bahn, the German railway company. Table 2.28 Summary of level of service on HSR in Germany (2010) 33 Route Distance by rail (km) Fastest Journey Time (hrs:mins) Cheapest advanced fare (NOK) Walkup fare (NOK) Max daily freq of fast services Cologne Frankfurt 186 01:02 235 518 29 Cologne Stuttgart 368 02:13 235 794 8 Cologne Berlin 551 04:20 235 883 15 Frankfurt Berlin 593 03:36 235 915 13 Frankfurt Munich 429 03:10 235 737 17 Spain HSR in Spain has grown rapidly in recent years, with large investment into new infrastructure. Journey times between major cities in Spain are now some of the fastest in the world and have an exceptional punctuality record (passengers are given a full refund if the service is over five minutes late on the Madrid Seville route). The high speed lines have been constructed to standard gauge, as opposed to the Spanish wide gauge, in order to link to other European networks, and a line from Barcelona to Perpignan in France is due to be completed in 2012. The 30 Sources: http://www.raileurope.co.uk/, http://www.tgv-europe.com/en/home/ 31 Source: http://www.trainweb.org/tgvpages/jpg/frdistancemap.jpg 32 Based on exchange rate of 1 Euro = 8.1 NOK 33 Source: http://www.bahn.com/i/view/gbr/en/index.shtml
Contract 5, Subject 1: Demand Forecasting 55 high speed service in Spain is known as Alta Velocidad Española (AVE) and is operated by the Spanish state operator RENFE. Table 2.29 Summary of level of service on HSR in Spain (2010) 34 Route Distance by rail (km) Fastest Journey Time (hrs:mins) Cheapest advanced fare (NOK) Walkup fare (NOK) Max daily freq of fast services Madrid Barcelona 651 02:38 437 1,102 18 Madrid Seville 472 02:20 267 656 22 Madrid Malaga 513 02:25 275 697 12 Madrid Valladolid 180 00:56 113 284 13 UK In the UK there is currently one dedicated HSR line, known as High Speed 1, which links London to the Channel Tunnel, enabling services to run to continental Europe at speeds of up to 300 kph. International services to Paris and Brussels on this route are operated by Eurostar. Other operators, such as Deutsche Bahn, have expressed an interest in running services from London to other European destinations such as Amsterdam, Rotterdam and Frankfurt in the future. In December 2009, domestic services began running on High Speed 1 from London St Pancras to Ebbsfleet International or Ashford International before transferring to classic rail to reach destinations in the south-east of England. Domestic services are permitted to run at speeds of up to 225 kph on High Speed 1 and up to 160 kph on classic rail. There are also four other main line routes which operate at speeds of up to 200 kph. Attempts have been made to increase line speeds on classic rail to 225 kph but currently top speeds are limited to 200 kph due to the lack of in-cab signalling, despite the higher potential speed of many of the trains. Table 2.30 provides a summary of journey times, distances and single fares to major stations served by HSR lines from London. 34 Source: http://www.renfe.com/
Contract 5, Subject 1: Demand Forecasting 56 Table 2.30 Summary of level of service on HSR in the UK (2010) 35 Route Distance by rail (km) Fastest Journey Time (hrs:mins) Cheapest advanced fare (NOK) 36 Walkup fare (NOK) Max daily freq of fast services London Euston Birmingham New St London Euston Manchester Piccadilly London Kings Cross Newcastle 182 01:22 67 393 53 304 02:07 106 626 45 432 02:44 130 989 30 London St Pancras Ashford 97 00:36 N/A 255 35 London St Pancras Paris Nord London St Pancras Brussels Midi 495 02:15 374 1344 16 376 01:51 374 1344 10 2.4.3 Station Locations & Quality Generally across Europe high speed trains tend to only stop at major stations en-route. These stations are usually located in or near to large cities, and interchange at these is then possible in order to reach smaller feeder stations. The location of rail stations differs depending on the town/city. Some, such as London St Pancras and Paris Gare de Nord are located right in the heart of the city and thus offer a very competitive service to air travel when considering the reduced access travel time, check-in time and time spent waiting for luggage. In countries such as France, high speed trains use existing tracks and stations which are part of the classic rail network within urban areas. Others stations are located on the outskirts of cities, often allowing for faster interchange with other routes, easy access from the motorway or to serve an airport, with substantial parking facilities. The advantage of these parkway style stations is that they allow for reduced journey times by avoiding urban areas where fast line speeds are not viable. Examples include Ebbsfleet International near London and Gare de Saint-Exupéry near Lyon. These stations often require a further connection to reach the town centre. For this reason these types of station have generally proved far less popular due to a lack of onward public transport connectivity to the nearby towns and cities. Further discussion of HSR station locations and facilities is contained within the report for Subject 4 Location and Services of Stations / Terminals. 2.4.4 International Benchmarking Conclusions Germany and France have well established HSR markets, with tens of millions of travellers per year. On routes where HSR has been introduced, the market share for air has almost completely diminished. The size of the HSR markets in countries such as France and Germany are vastly larger than the likely market in Norway, based on the demand data described in Section 2.2. The likely market for HSR, estimated from abstraction from air travel and long-distance car and rail travel, is more similar to that in Sweden, a country of similar geography and population to Norway. 35 Source: http://www.nationalrail.co.uk/ 36 Based on exchange rate of 1 = 9.6 NOK
Contract 5, Subject 1: Demand Forecasting 57 There is a mixture of approaches across Europe to improving long-distance rail travel, which could be applied to Norway. In some countries, France and Spain in particular, HSR has been rapidly developed with the construction of new lines, with further expansion planned for the next decade. In countries such as the UK and Sweden, the focus of rail development has been on upgrading existing classic rail lines, so the journey times are longer than those in France and Spain. The introduction of HSR in France, Germany and Spain has dramatically reduced rail journey times between major cities. HSR services stop far more infrequently than classic rail, in order to maintain low journey times and compete effectively with air travel. Stations tend to either be located in the city centre or outside of cities in strategic locations, such as adjacent to a motorway or airport, to provide onward connectivity. HSR fares across Europe, when booked in advance, are not much higher than equivalent rail fares in Norway, therefore the premium paid for a better quality of service and faster journey time is very small. When compared with air fares in Norway, HSR fares are far cheaper. 2.5 Key Overall Conclusions The size of the potential market for HSR in Norway is similar to that of Sweden, although much smaller than the equivalent markets already established in countries such as France and Germany. From experience in other European countries where HSR is already well established, there has been almost total abstraction from air on routes served by HSR as rail journey times have been dramatically reduced and major rail stations are located more conveniently than the airports. Business travel in Norway is dominated by air due to the relative speed and frequency of services, and there is a higher value of time associated with these trips. Business travellers are prepared to spend time accessing airports located outside city centres. Conversely, leisure travel is more evenly spread between car, air and rail, and for travel within corridors car travel is dominant, particularly between Oslo and Kristiansand, due to leisure travellers placing a higher value on journey cost and the ability to travel as a group. Therefore, the key market for potential HSR in Norway will be business travel, which is currently served by air, although HSR will look to abstract from the leisure market on long distance routes. Comparison of the level of service for individual modes of public transport indicates that air travel provides the best service for city-to-city travel, both in terms of service frequency and journey time, which explains the high market share. HSR services tend to stop less frequently than classic rail but stations offer good connectivity with other modes of transport. Therefore any potential HSR service in Norway would compete with city-to-city travel currently dominated by air, rather than travel within corridors. In order to compete with air travel, HSR will need to offer a competitive service, in terms of frequency, journey times, fares, accessibility and comfort.
Contract 5, Subject 1: Demand Forecasting 58 3 Future Do Minimum Travel Market 3.1 Introduction This chapter sets out growth forecasts for long distance travel demand in Norway under Scenario A, the Do Minimum scenario. The analysis is based on matrices for car, air, train and coach journeys, as produced by the NTM5 model for 7 years ranging between 2010 (the base year), and 2060. The remaining subsections cover the following areas: Overview of approach to future year forecasting; Discussion of forecasts on key travel markets; Comparison of the core forecasts with other forecasts available; and Conclusions implications for high speed rail development. 3.2 Overview of Future Year Forecasting Approach NTM5 matrices NTM5 defines long distance trips as those exceeding 100 kilometres. Demand is divided between work-related trips and non-work trips, with further disaggregation of the latter into the following 4 sub-categories: Leisure, Visits, Other, and Private. However, in order to avoid overcomplicating the bespoke HSR mode choice modelling, the parameters and matrices used to forecast scenarios C and D have only dual segmentation: that is, work (business) trips versus non-work (all other) trips. NTM5 s future year matrices allow for the effects of rising population and economic activity, as well as trips induced by committed improvements to the transport system. The future year matrices supplied by TØI and used within the bespoke mode choice model, assume no change in domestic air schedules, with 2006 levels of service assumed throughout. Discussions held recently with the airlines suggest this is a reasonable assumption for the Do Minimum level of service on this key mode, from which HSR is expected to abstract much of its demand. With regard to classic rail, the National Transport Plan (NTP) 2010-2019 commits to a programme of rail double-tracking, mainly on congested sections of the Intercity network in the Greater Oslo region. Scenario A assumes that continuation of the current policies - including further works in other regions - will allow long distance rail headways to be cut by 50% by the early 2020s. By contrast, the future year matrices supplied by TØI for the appraisal of Scenarios C and D, and used herein to present Do Minimum growth, assume that levels of service on all modes reflect the networks and timetables expected in 2014. This implies only very modest improvements to long distance classic rail services. However, analysis using NTM5 shows that whilst the improved headways raise classic rail volumes by 9% in Scenario A mainly by abstracting car journeys - total demand volumes and air volumes are unaffected. 3.2.1 Economic and Demographic Demand Forecasts In producing the future year Do Minimum matrices for each of the modes, NTM5 depends heavily on forecasts of Norwegian population at county level. Table 3.1 below shows the population projections for 2018 to 2060 that underlie the NTM5 matrix outputs. These projections are from Statistics Norway s option MMMM (of June 2010) where fertility, life expectancy, domestic mobility and net migration are set at their most likely values. That is, these are the central case projections of population. The rows in the table are ranked according to the overall rate of population growth.
Contract 5, Subject 1: Demand Forecasting 59 Table 3.1 Population projections index Statistics Norway (SSB) County 2010 2018 2020 2024 2030 2043 2060 Rogaland 100 112 115 120 129 146 166 Akershus 100 112 115 120 129 145 165 Oslo 100 113 117 122 130 146 165 Buskerud 100 109 111 115 122 136 153 Vest-Agder 100 108 111 115 122 135 152 Aust-Agder 100 108 110 114 121 134 150 Hordaland 100 109 111 115 122 134 150 Sør-Trøndelag 100 109 111 115 122 134 148 Østfold 100 107 109 113 119 131 146 Vestfold 100 107 109 113 119 130 145 Nord-Trøndelag 100 104 105 108 111 118 127 Hedmark 100 103 103 105 108 114 124 Møre og Ro. 100 104 105 107 110 116 124 Telemark 100 102 103 105 107 113 121 Troms 100 104 105 107 110 115 121 Oppland 100 102 103 104 107 112 120 Finnmark 100 100 100 101 101 104 109 Sogn og Fj. 100 100 100 101 102 104 108 Nordland 100 100 100 101 102 103 106 Total 100 108 110 114 119 131 145 It can be seen that nationally, population is forecast to rise by 45% by the end of the HSR appraisal period in 2060. The lowest growth just 6% is found in Nordland in the far north, whilst the maximum around 65% is forecast for Rogaland (including Stavanger, Sandnes, and Haugesund) and the Oslo area (including the Akershus commuter belt). The rapid population growth expected in Rogaland has positive implications for the potential of the HSR proposals serving Stavanger (the Y-shaped option to Bergen and Stavanger, would also serve Haugesund). Apart from Rogaland (Stavanger), the other county populations associated with the main Norwegian cities are projected to rise as follows: Vest-Agder (Kristiansand) 52%; Hordaland (Bergen) 50%; and Sør-Trøndelag (Trondheim) 48%. Figure 3.1 below shows the temporal profiles of population growth in the counties of most importance to the HSR appraisal (the national profile, and that of Nordland county, are added for reference).
Contract 5, Subject 1: Demand Forecasting 60 Figure 3.1 Population growth profiles (2010-2060) 170 160 150 140 Population growth profiles Rogaland Akershus Oslo Vest-Agder Hordaland Sør-Trøndelag Nordland Total 130 120 110 100 2010 2020 2030 2040 2050 2060 Figure 3.1 shows that in the main HSR markets, rates of population growth actually decline slowly through the HSR appraisal period. National population rises at 1% per annum between 2010 and 2020, falling to 0.6% per annum between 2043 and 2060 (each measured using a compound annual growth rate). The maximum rate of annual growth is 1.8%, predicted for Oslo between 2018 and 2020. 3.2.2 Standard Approach to Forecasting Demand in a Do Minimum Scenario This section describes the method adopted for producing forecasts of future long-distance travel demand, in the Do Minimum (no HSR) scenario. The mode choice model under development requires future year matrices for each of the competing modes from which HSR may abstract, principally air, car and (long-distance) classic rail. Atkins preferred approach to Do Minimum growth is set out in the next subsection, but first the main alternative is described. This would have involved combining a set of forecasts for demand drivers (e.g. GDP, population, rail and air fares, etc.) with corresponding elasticities. An elasticity measures the percentage change in demand (in this case, journeys by a particular mode) to be expected when a particular demand driver changes by 1%. For example, if it is known (or estimated) that the GDP elasticity for non-transfer leisure journeys on domestic Norwegian flights is 2.0, and the long run trend increase in Norwegian GDP is 2.5% per annum, then demand for such journeys will be expected to increase by 5% per annum, holding constant all other demand drivers. Ideally, sub-national socio-economic data would be used, in order to allow the appraisal of each HSR corridor to incorporate spatial disparities in economic growth and population growth. This would tend to benefit the corridors with the brightest market prospects, and penalise the HSR business case elsewhere. For fares effects, the application of an elasticity-based approach would need also to reflect substitution of one mode for another. For example, long-distance rail demand might increase by 2% for every 1% increase in air fares. This requires the estimation of a system of equations, unless fares across all modes change at the same rate. In fact, the NTM5 model assumes that all monetary costs of travel are completely fixed (see below).
Contract 5, Subject 1: Demand Forecasting 61 The final influence on Do Minimum demand which would have required explicit consideration in the alternative approach is planned ( committed ) changes in networks, and associated improvements to levels of service for each mode. In the UK, the PDFH recommends use of a Generalised Journey Time (GJT) approach for estimating the effects on demand of improvements to rail timetables. This combines the effects of changes in station-to-station journey times with allowance for the improvement in convenience when frequencies are increased, or interchanges removed. However, a simple single-mode approach based on GJT elasticities does not model where the additional rail demand is drawn from. That is, there is no distinction between additional rail trips abstracted from other modes, and pure generation (i.e. additional total travel induced by the rail timetable improvement). For Norway HSR, it would have been necessary, for example, to estimate how planned changes in the highways network would impact upon Do Minimum demand for rail and air trips, unless it could be assumed that all such cross-modal effects would be insignificant. By contrast, NTM5 as a multi-modal model is explicitly designed to estimate diversion between modes. At the outset of this study it was anticipated that a new forecasting framework would be constructed to estimate Do Minimum demand by HSR corridor and mode, using the approach set out above. It was anticipated that forecasting parameters (i.e. elasticities), estimated in previous studies, would be made available for forecasting growth in air, classic rail, and road journeys. The parameters for the different modes would then have to be made as consistent as possible, with inclusion of cross-modal effects for changes in relative costs or journey times. However, information on Norwegian demand parameters, even GDP (or income) elasticities and fares elasticities, proved difficult to locate. Avinor informed us that they do not produce separate demand forecasts for each of the domestic air routes, and that growth of 2.1% per annum is assumed between 2011 and 2015. NSB provided forecasts for 2009-2017 based on their transport simulation model. The latter takes into account expected changes in journey times by mode, with allowance for capacity limits, and reliability. Real fares are assumed to be constant. To reflect the positive effects of increased economic activity and real disposable incomes on overall long-distance trip-rates, NSB s model allows for a small positive link from GDP growth to rail demand. However, specific income elasticities are not available. Finally, information on forecasting future growth in Norwegian road journeys was requested from Statens Vegvesen, who provided a full set of base (2010) and future year demand matrices from NTM5, covering all modes. 3.2.3 Preferred Approach to Forecasting Norway HSR Do Minimum Demand In the absence of detailed information on forecasting parameters by mode, it was decided to use the future year matrices from NTM5. The NTM5 matrices were provided for the following years: 2010; 2014; 2018; 2024; 2043; and 2060. With an assumed opening date of 2020, the first forecast year used in the modelling is 2018. Meanwhile, the final year, 2060, allows for demand growth throughout a 40 year appraisal period. Correspondence with TØI revealed that the NTM5 future year matrices are based on national data for economic growth, and regional data for population (for the latter, see Table 3.1 above). In addition, income elasticities are not inputs to NTM5, but can be derived from the model for each mode, with the indirect effect of changes in car ownership exerting a significant effect. As noted elsewhere, the NTM5 Do Minimum future year matrices allow for a number of improvements to the road and rail networks, based mainly on the Norwegian National Transport Plan (2010-2019). For rail, the timetable improvements are predominantly associated with provision of double track, mostly in the intercity network around Oslo. The road and rail
Contract 5, Subject 1: Demand Forecasting 62 enhancements assumed to be delivered in the NTM5 Do Minimum future year matrices are listed in Appendix B. Although the use of NTM5 future year matrices was not envisaged at the outset of work, this approach ensures maximum compatibility of the Do Minimum growth forecasts in the HSR assessment with the appraisal of other Norwegian transport schemes. Finally, it is worth emphasising that the reservations about NTM5 matrices aired by NSB and Statens Vegvesen, primarily concern the scale of long-distance car journeys in the base year (2010), rather than any doubts about the methodology underlying future year growth. 3.3 Future Year Do Minimum Demand Growth This section sets out the growth assumed in the HSR Do Minimum scenario, based on future year matrices from NTM5. Forecasts have been provided for the years 2018, 2024, 2043 and 2060, in addition to the 2010 base. This section first presents the economic and demographic forecasts underlying the demand for travel and then studies the forecast growth rate my mode (air, rail, car, and coach) for business and leisure passengers. These forecasted growth trends are compared with population growth, indicating changes to the propensity to travel as income increases. The growth rates shown are based on city-to-city (i.e. municipality-to-municipality) travel on the potential high speed rail corridors within Norway. As well as charts showing the growth by mode on each corridor, Section 3.3.7 provides a comparison against the population forecasts within NTM5. Growth rates for Sweden have been approximated using the average growth rates for Norway taken from NTM5, in the absence of better quality data.
Contract 5, Subject 1: Demand Forecasting 63 3.3.1 Air Growth (NTM5 2010-2060) Figure 3.2 shows growth indices for business travel by air (2010 =100) projected forward to 2060 by NTM5. The first thing to note is that differences between the corridors are relatively small. This is true of most of the charts in this section. In the case of air business trips, the highest cumulative growth between Oslo and Stavanger exceeds that of the route with the slowest growth Bergen-Stavanger by just 13 percentage points (94% and 81%, respectively). Figure 3.2 shows a tendency for Do Minimum growth to slacken off after around 2020. This is evident across all the corridors, but is most obvious in the case of Bergen-Stavanger. Similar results are found in relation to all modes, and both journey purpose segments. Across all of the domestic HSR corridors, forecast growth in business trips by air falls from 2.0% in 2011, to 1.8% in 2018, to 1.2% in 2028. A slow decline continues thereafter, with growth reaching just 1% per annum by the end of the appraisal period. Figure 3.2 Projected growth of business air travel to 2060 180,000 160,000 140,000 120,000 100,000 80,000 60,000 40,000 20,000 0 Oslo - Stavanger Oslo - Bergen Oslo - Trondheim Oslo - Kristiansand Bergen - Stavanger Figure 3.3 shows the corresponding growth forecasts for air leisure trips. The inter-corridor differences reflect those of business travel. It should be noted, however, that leisure growth rates are generally higher, implying a gradual reduction in the share of air travellers who are on business from 61% in 2010 to 56% in 2060. Figure 3.3 Projected growth of leisure air travel to 2060 180,000 160,000 140,000 120,000 100,000 80,000 60,000 40,000 20,000 0 Oslo - Stavanger Oslo - Bergen Oslo - Trondheim Oslo - Kristiansand Bergen - Stavanger Even though the annual growth rates are relatively low, there is a potential doubling of domestic air passengers over the next fifty years, which will need to be accommodated. Notwithstanding
Contract 5, Subject 1: Demand Forecasting 64 improvements in aircraft efficiency, the projected growth in air demand has clear implications for carbon emissions, even if additional capacity is provided by using larger aircraft. 3.3.2 Classic Rail Growth (NTM5 2010-2060) To reiterate, the Do Minimum growth profiles in this report are based on outputs from NTM5 provided by TØI, and are not strictly compatible with JBV Scenario A. That is, the effects of the anticipated step-change improvement to long distance rail service frequencies are not included. Figure 3.4 shows the growth in rail business trips between 2010 and 2060 projected on this basis and driven almost exclusively by future expected changes in population and income. Once again, NTM5 forecasts that the highest cumulative growth (99%) will be between Oslo and Stavanger. As this exceeds the corresponding figure for air (94%), and as car travel has a small (though rapidly growing) market share, this implies a modest increase in the mode share of rail for business travel. On the other corridors, rail s share of business trips is forecast to decline slightly over time. Figure 3.4 Projected growth of business rail travel to 2060 45,000 40,000 35,000 30,000 25,000 20,000 15,000 10,000 5,000 0 Oslo - Stavanger Oslo - Bergen Oslo - Trondheim Oslo - Kristiansand Figure 3.5 shows the corresponding growth indices for leisure travel by rail, which, in contrast to air, is the dominant journey purpose for rail. Non-work related travel accounts for 79% of all trips in 2010, rising to 83% in 2060. Figure 3.5 Projected growth of leisure rail travel to 2060 180,000 160,000 140,000 120,000 100,000 80,000 60,000 40,000 20,000 0 Oslo - Stavanger Oslo - Bergen Oslo - Trondheim Oslo - Kristiansand The forecast rise in rail leisure trips on the Oslo-Stavanger corridor reaches 176%. The lowest cumulative growth, 131%, is forecast for the largest current rail market: Oslo-Bergen.
Contract 5, Subject 1: Demand Forecasting 65 3.3.3 Car Growth (NTM5 2010-2060) Figure 3.6 shows NTM5 s forecasts of growth in business travel by car between 2010 and 2060. According to the NTM5 matrices, car travel accounts for just 13% of business trips in 2010 and this mode share remains stable throughout the appraisal period. The highest cumulative growth in car business trips is found on the Oslo-Kristiansand and Oslo- Bergen corridors at 116%. Figure 3.6 Projected growth of business car travel to 2060 45,000 40,000 35,000 30,000 25,000 20,000 15,000 10,000 5,000 0 Oslo - Stavanger Oslo - Bergen Oslo - Trondheim Oslo - Kristiansand Bergen - Stavanger Figure 3.7 displays the corresponding growth in car leisure travel. The base 2010 mode share of car is much higher for leisure trips at 44%. However, there are some reservations that NTM5 has a tendency to overestimate long distance car trips. Growth in car leisure travel is forecast to be higher than for other modes, with the growth rate on most routes over 200%, and reaching 250% on Bergen-Stavanger. 300,000 250,000 Figure 3.7 Projected growth of leisure car travel to 2060 200,000 150,000 100,000 50,000 0 Oslo - Stavanger Oslo - Bergen Oslo - Trondheim Oslo - Kristiansand Bergen - Stavanger In both journey purpose segments, the car growth rates are higher than those for rail on all corridors. In the case of leisure trips especially, a significant element of this disparity will be due to the effect of rising incomes on car ownership. However, it is worth reiterating that NTM5 does not allow for capacity constraints on any mode, so worsening road congestion may tend to favour rail in the years ahead.
Contract 5, Subject 1: Demand Forecasting 66 3.3.4 Coach growth (NTM5 2010-2060) Figure 3.8 shows the growth for coach travel forecast in NTM5 for business trips between 2010 and 2060. The base demand for business travel by coach as forecast by NTM5 is already very low for coach travel under 3% market share in 2010. Journey times are long, and passengers tend to switch to faster and more comfortable modes as rising incomes produce greater willingness to pay to reduce travel times. Nevertheless, in the business segment there is relatively rapid growth until around 2020, with the exception of the two shortest routes. The cumulative growth rate to 2060 ranges between 80% Oslo-Kristiansand and 100% Oslo- Trondheim. 220 200 Figure 3.8 Projected growth of business coach travel to 2060 180 160 140 120 100 Oslo - Stavanger Oslo - Bergen Oslo - Trondheim Oslo - Kristiansand Bergen - Stavanger Figure 3.9 displays the corresponding growth for leisure travel by coach, which as with other modes is higher than that for business travel, at between 120% and 160%. The lowest growth is again on the two shortest routes. Coach has a slightly higher 2010 market share in the leisure sector of 8%. Figure 3.9 Projected growth of leisure coach travel to 2060 280 260 240 220 200 180 160 140 120 100 Oslo - Stavanger Oslo - Bergen Oslo - Trondheim Oslo - Kristiansand Bergen - Stavanger
Contract 5, Subject 1: Demand Forecasting 67 3.3.5 Largest and Smallest Growth 2010-2060: Business Trips This section presents the range in the growth of business trips, highlighting the modes and corridors with the fastest and slowest growth. The charts are based on NTM5 outputs for 2010 and 2060, measured at municipality level. Figure 3.10 confirms that for work-related travel, car tends to have the fastest expected growth rates. Figure 3.10 Compound Annual Growth Rates (CAGR) Business 2010-2060 1.8% 1.6% 1.4% 1.2% 1.0% 0.8% 0.6% 0.4% 0.2% 0.0% Business Growth (compound rate p.a.) However, when expressed in absolute terms (Figure 3.11) that is, after allowing for the relative sizes of the various markets in 2010 the largest future changes in journey volumes are found in air demand. The dominance of air over competing modes is particularly striking in this chart, and as HSR is aimed at abstracting air demand, this finding has important implications. Figure 3.11 Absolute growth in annual journeys Business 2010-2060 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0 Business Growth (absolute 2010-2060)
Contract 5, Subject 1: Demand Forecasting 68 3.3.6 Largest and smallest growth 2010-2060: Leisure trips Comparing Figure 3.12 with Figure 3.10 confirms that the leisure market is expected to grow at a faster rate than that for business trips. Within the leisure market, future expected growth in journeys by car between Bergen and Stavanger exhibits a significant differential over the second ranked observation, which is also for a car flow (Oslo-Stavanger). In fact, the top five instances of growth rates in leisure trips are all for car travel. Rail flows are bunched in the middle of the distribution with growth at around 1.75% per annum, whilst air growth rates show the largest variations in ranking between corridors. Figure 3.12 Compound Annual Growth Rates (CAGR) Leisure 2010-2060 3.0% Leisure Growth (compound rate p.a.) 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% Figure 3.13 shows that the largest future increases in Do Minimum leisure trip volumes are again mainly associated with car travel. Figure 3.13 Absolute growth in annual journeys Leisure 2010-2060 250,000 Leisure Growth (absolute 2010-2060) 200,000 150,000 100,000 50,000 0
Contract 5, Subject 1: Demand Forecasting 69 3.3.7 Comparison with Population Growth This section contrasts growth in demand with growth in population. Within NTM5, the disparities in forecasts of demand growth between corridors would be expected to reflect differences in the population projections, which are input to the model at county level (see Table 3.1). Population growth not only implies a direct increase in travel due to more residents, but the fastest increases in GDP and productivity are also likely to be found where population growth is most rapid, as skilled migrants and firms are drawn to regions with buoyant economies. This produces a separate increase in travel demand due to higher incidence of (long distance) travel per person (it is notable that Norway s high per capita income levels already produce some of the highest long distance trip-rates found in the world). Figure 3.14 and Figure 3.15 compare the growth indices for business and leisure travel with the projected growth of population at the non-oslo end of each flow. In the case of Bergen and Stavanger, the population growth is a simple average. Demand growth is shown for (a) the current leading mode, and (b) summed across all existing modes. Population growth 2010-2060 is shown by the black columns. Figure 3.14 Comparison between population growth and growth in business travel (2010=100) 220 200 180 160 140 120 100 Business Growth Comparison (2010-2060) Air All modes Pop 2010-2060 Figure 3.15 Comparison between population growth and leisure travel (2010=100) 350 300 250 200 Leisure Growth Comparison (2010-2060) Car All modes Pop 2010-2060 150 100
Contract 5, Subject 1: Demand Forecasting 70 Figure 3.14 and Figure 3.15 show that growth in long distance travel demand is highest where (non-oslo) population growth is highest; that is, on the Stavanger corridor. Across all corridors, the growth in travel outstrips the growth in population, indicating a rise in long distance trip-rates. In the case of the business segment, demand growth across all modes exceeds population growth by between 28 percentage points (Bergen-Stavanger) and 42 percentage points (Oslo-Trondheim). For leisure, the range is between 92 percentage points (Oslo-Bergen) and 156 percentage points (Bergen-Stavanger). The particularly large difference between leisure growth and business growth on the Bergen-Stavanger corridor may merit further examination. 3.4 Comparison of Medium Term Forecasts: NTM5 versus Transport Operators The previous section presented the growth forecasts derived from our chosen data source NTM5b. In this section we compare medium term forecasts from NTM5 (2010-2018) against those of the rail operator NSB and airport operator Avinor, to check for consistency and to identify any potential weaknesses in the model data. 3.4.1 Medium Term Rail Demand Growth: NTM5 versus NSB Forecasts NSB have provided rail demand indices for the next decade and these are summarised in Table 3.2 below. Table 3.2 NSB s future rail demand indices: 2009-2017 (selected years) 2009 2011 2013 2015 2017 Oslo Bergen 100 99 101 101 102 Oslo Trondheim 100 102 106 106 106 Oslo Kristiansand-Stavanger 100 96 92 92 93 The highest year-on-year growth is 2.7%, forecast for the Trondheim corridor in 2011-2012. This will probably reflect a planned improvement to the classic rail service at that time. Meanwhile the fall in demand on the Kristiansand-Stavanger corridor is presumably due to planned improvements to highways. As far as the effect of rising incomes is concerned, NSB confirm a tendency for its relatively slow long-distance services to lose market share to air travel. Figure 3.16 below provides a comparison of medium term growth forecasts for Norwegian classic rail from the train operator NSB and the NTM5 model. Figure 3.16 Medium-term rail demand growth by corridor: NSB vs. NTM5 30% 25% NTM5 versus NSB growth to 2017/2018 NSB Growth 2009-2017 NTM5 Growth 2010-2018 20% 15% 10% 5% 0% -5% -10% Oslo - Bergen Oslo - Trondheim Oslo - Kristiansand - Stavanger
Contract 5, Subject 1: Demand Forecasting 71 It is clear that NSB s figures are considerably below those forecast by NTM5. The corresponding Compound Annual Growth Rates (CAGR) are presented in Table 3.3 below. Table 3.3 Comparison of medium-term rail demand growth rates 2010-2018 Route NSB CAGR NTM5 CAGR Oslo Bergen 0.3% 2.1% Oslo Trondheim 0.8% 2.3% Oslo Kristiansand Stavanger -0.9% 2.8% By way of contrast, between 2003/4 and 2007/8, long-distance intercity travel in the UK grew by around 5.6% per annum, in spite of regulated fares rising in real terms by 1% per annum 37. However, with long-distance train speeds in Norway currently amongst the slowest in Europe, the current ( classic ) rail service tends to lose passengers to air as incomes rise. Leaving aside the question of whether high speed rail is a closer substitute for classic rail or air, to be answered in the mode choice analysis, the fact that rising incomes tend to transfer Norwegian demand from classic rail to air, suggests that tailoring the service to the requirements of air passengers will become increasingly important. 3.4.2 Air Demand Growth: NTM5 versus Avinor Medium Term Forecasts Table 3.4 below shows that medium-term growth in air demand in the NTM5 matrices (2010 to 2018) closely matches Avinor s assumption of 2.1% per annum. Table 3.4 Comparison of medium-term air growth rates 2010-2018 Route Avinor CAGR NTM5 CAGR Oslo Bergen 2.1% 1.9% Oslo Trondheim 2.1% 2.2% Oslo Kristiansand Stavanger 2.1% 2.1% These comparisons suggest that there is consensus on the likely growth in air passengers, but some questions around the classic rail market. The role of HSR in Norway would be predominantly to capture long distance trips currently served primarily by air. So for this HSR study the consistency of air forecasts in the medium term greatly overshadows the divergence of opinion on classic rail. 37 An element of this rapid British growth is endogenous in nature - driven by improvements in timetables and reliability. Source: National Rail Trends 2009-2010 yearbook (Table 1.1b Passenger kilometres by sector).
Contract 5, Subject 1: Demand Forecasting 72 3.5 Conclusions Future year growth forecasts have been developed based upon demand matrices produced by the NTM5 model. The alternative producing Do Minimum forecasts from first principles was rejected mainly because of the absence of detailed forecasting parameters for each mode. However, an NTM5- based approach has a number of advantages: NTM5 forecasts already account for committed rail and highway schemes and the impact they will have in inducing travel demand; Use of NTM5 ensures maximum compatibility with the growth assumptions applied in the appraisal of other Norwegian schemes; and In the medium term, forecast growth in air demand from NTM5 matches closely the figure of 2.1% per annum assumed for all domestic routes by Avinor, the airport operator. The future year matrices derived from NTM5 have been analysed to understand the growth trends by mode for each corridor. It has been shown that between the largest cities: Business passengers today predominantly use air over long distances and, in the Do Minimum scenario, this is reinforced over the next fifty years with the highest unconstrained growth experienced by this mode; On the Oslo-Bergen corridor, volumes of rail business trips are forecast to grow significantly, although this is due to higher base demand, rather than a faster percentage growth rate; Over the 50 year period leisure experiences higher growth than business; Growth in Do Minimum leisure passengers is focussed on car journeys; Measured in absolute volumes, growth in car journeys is high on all corridors for leisure travel but negligible for business (except on the Bergen-Stavanger corridor where car is competitive with air overall); Growth rates on all modes are higher than the underlying population growth rate, particularly for leisure travel indicating that greater incomes in the future will lead to increased trips; On most corridors, the growth rate of business travel is typically higher up until around 2020 and then slowly reduces over the next 40 years; Classic rail travel will continue to be dominated by leisure users, with an increase from 79% to 83% of trips undertaken by leisure users. Conversely, business users share of air travel will reduce from 61% to 56%; and In order to optimise the HSR business case, it seems appropriate to target business travellers who currently fly, and leisure travellers who currently fly or drive. In the latter case, discounts for group/family travel may be worthwhile.
Contract 5, Subject 1: Demand Forecasting 73 4 HSR Demand and Revenue Forecasts 4.1 Introduction This section of the report presents a summary of the key results for HSR demand and revenue for each of the example HSR route options, based on the current specification of the mode choice model. All the options and hence demand and revenue forecasts will be subject to further refinement in Phase 3, taking into account outputs from the other Phase 2 contracts, the Phase 3 alignment work, and additional data collection and analysis. The structure of this chapter is as follows: Brief description of the approach to the demand and revenue forecasting, including the model assumptions, service specifications for the four scenarios, description of the representation of Scenarios A and B in NTM5; and Presentation of the traffic forecasts for each of the HSR corridors, for Scenarios B, C and D where applicable. For some routes, multiple improvement scenarios are applicable. For example, on the route between Oslo and Bergen via Hallingdal, the improvements could range from a relatively modest upgrade to the existing alignment (Scenario B, with small journey time savings on classic rail) to new HSR infrastructure serving a broadly similar corridor, but with far fewer bends that limit train speeds (Scenario D). For Scenarios C and D, it is assumed that classic rail services operate at current service levels, supplemented by the new (limited stop) HSR services. Section 4.2 outlines briefly the methodology applied in producing the demand and revenue forecasts, including a list of the key assumptions. A detailed description of the approach to demand modelling and forecasting is provided in the Model Development Report (MDR). 4.2 Approach to demand and revenue forecasting Robust demand forecasting is fundamental in ensuring that the assessment of the options for improving long distance rail service in Norway is credible and objective. Not only does the demand forecasting underpin estimates of fare-box income, and hence potential subsidy requirements, but it is also the basis of most of the economic benefits; e.g. time savings and reductions in pollution from cars and aircraft. As this phase of the study is required to consider the possibilities for incremental development of long distance rail services, a dual forecasting approach has been developed. For more modest incremental improvements to classic rail, where abstraction from air is expected to be limited, the NTM5 model is used. This mixed methodology has been adopted because of reservations about the use of NTM5 when modelling large step-change improvements in rail levels of service. However, NTM5 is an established model which has been audited by TØI, accepted as broadly fit-for-purpose, and used for the appraisal of other Norwegian transport schemes. It is therefore retained for the relatively minor timetable improvements under Scenarios A and B. Section 4.2.1 describes the presentation of Scenario B outputs from NTM5. 4.2.1 Representations of Scenarios A and B Atkins was supplied with the NTM5B 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). In terms of target journey times and frequencies, the broad specification for these scenarios is provided in TN6 Scenario Testing. In summary, Scenario A anticipates an increase in train
Contract 5, Subject 1: Demand Forecasting 74 frequency (or reduction in service headway), whilst Scenario B anticipates an improvement in train speed and hence reduction in journey time. Atkins calculated the change, from the Fastest 2010 in the Scenario Testing Note, for each corridor, as shown in Table 4.1. Table 4.1 Representation of Changes in Supply in NTM5B Corridor Scenario A: Headway Factor Scenario B: Journey Time Factor Oslo-Bergen 0.5 0.85 Oslo - Kristiansand - Stavanger 0.5 0.83 Oslo-Trondheim 0.33 0.85 Oslo-Stockholm 0.5 0.92 Oslo-Gothenburg 0.33 0.90 To implement these Scenarios in NTM5B, these corridor specific adjustments were applied to the relevant services. The factors are multiplicative and were applied within the EMME data repository. As a result, the changes modelled in these NTM5B tests do not represent a change in stopping pattern or variation in speed change along the corridor, merely an improvement in headway or journey time at the strategic level. This will be adequate for end to end travel, but may not identify more local demand responses to supply changes at a local level. It should be noted that these changes were applied to both day trains and night trains as both are specified in NTM5B and equally contribute to supply and are available for assignment. The model is based on aggregate daily demand and supply levels. In the case of Scenario A, a single NTM5B test was undertaken, with the train frequencies improved in all five corridors. In the case of Scenario B, a similar initial test was undertaken, with the train journey times improved in all five corridors, mainly to give assurance that the model would respond appropriately to a more marked improvement in the supply side. This was followed by testing one corridor at a time, as proposed in the Scenario Testing Note; the latter results are those reported here. Section 4.2.2 describes the presentation of Scenario C and D outputs from the bespoke demand forecasting model. 4.2.2 Representations of Scenarios C and D For the major interventions envisaged under Scenarios C and D which involve new infrastructure and potentially high specification rolling stock a specially-developed mode choice model is applied. The latter has parameters (essentially values of time by mode) estimated in an associated Stated Preference (SP) exercise using survey responses from a large panel of Norwegian volunteers 38. Assumptions The most important modelling assumptions employed in the bespoke demand model are listed as follows: 38 For more information, see the Final Report for Subjects 2 and 3 (Market Analysis).
Contract 5, Subject 1: Demand Forecasting 75 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. With Stockholm, Gothenburg and Gardermoen airport added as point zones, there is a total of 107 zones. Mode choice structure: The mode choice model is based on the results of SP/ willingness to pay surveys. The model considers the mode choice between air and high speed rail at an absolute level and at increments around the demand from other modes, based on a reduced composite cost of fast modes following the introduction of high speed rail. 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 - Access/egress time penalty weightings: Access/egress time weighting, relative to invehicle 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 based on the levels of service for Scenarios C and D, as presented by JBV in October 2010 (see Table 4.2). Air, coach and classic rail levels of service: are assumed to be the same as the Do Minimum in Scenarios C and D. Air and HSR service frequency penalties: The impact of improvements in air 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 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). As a default, it is assumed that HSR fares are set equal to air fares. However, for scenario D an additional sensitivity test is shown to demonstrate the demand impacts of lower HSR fares, assumed to be around 60% of existing HSR fares broadly comparable to current existing rail fare levels 39. 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 high speed rail 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 high speed rail 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 high speed rail. 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. 39 For sake of clarity, only Scenario D test results are reported for the two fare levels, to demonstrate fare sensitivity. Scenario C results are only reported for HSR fares set to existing fare levels.
Contract 5, Subject 1: Demand Forecasting 76 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. For Scenario C it is assumed that services call at all potential HSR stations (within the most significant towns). For example, on the route between Oslo and Stavanger, intermediate stops are assumed at Drammen, Porsgrunn, Arendal, and Kristiansand. For Scenario D, an intermediate stop is assumed to add 10 minutes to the end-to-end journey time, and fewer stops are assumed. HSR revenue: HSR fares are based on average air fares at 2009 prices. 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, classic rail 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. Further detail of the assumptions applied in modal choice, and future year growth, are provided in the supplementary Model Development Report. We emphasise the assumption that existing air and rail services are assumed to be retained after introduction of HSR services this assumption may be refined in Phase 3 of the overall project. 4.2.3 Rail service specification by Corridor and Scenario In producing the forecasts presented in this report, the rail levels of service (i.e. end-to-end journey times, and service headways) have been set to reflect the journey times and frequencies shown in Table 4.2. For Scenarios C and D, modelled within the bespoke HSR model, these levels of service are applied to HSR, whilst for Scenarios A and B the improvements are made to classic rail timetables, as represented in the NTM5 model. The in-vehicle times between stations for Scenarios C and D in the demand forecasting model have been calculated using the rail distances between the station stops. Table 4.2 HSR / Classic Corridor Level of Service by Scenario Fastest 2010 Classic Rail Scenario A, Classic Rail Scenario B Classic Rail Scenario C High Speed Rail Scenario D High Speed Rail Time Freq Time Freq Time Freq Time Freq Time Freq Oslo- Kristiansand Oslo- Stavanger 04:25 240 04:20 120 03:30 120 03:00 60 02:10 60 07:42 240 07:30 120 06:15 120 05:30 60 02:30 60 Oslo-Bergen 06:28 240 06:30 120 05:30 120 04:30 60 02:30 60 Oslo- Trondheim Oslo- Stockholm Oslo- Gothenburg Bergen- Stavanger 06:38 360 06:30 120 05:30 120 04:30 60 02:45 60 06:07 240 06:00 120 05:30 120 04:00 60 03:00 60 03:55 360 03:30 120 03:10 120 03:00 60 02:30 60 - - - - - - - - 01:35 120
Contract 5, Subject 1: Demand Forecasting 77 4.3 Traffic forecasts on national corridors Initial results for each of the domestic HSR corridors are presented in the remaining subsections of this chapter. Where multiple route options are available on a given corridor, a separate set of results is provided for each. In addition a range of results is shown for Scenario D, based on the testing of fare sensitivities and the provision of a connection to Gardermoen Airport via a connecting service from Oslo Central. Each set of results in Sections 4.4 4.11 for Scenarios A and B includes: Corridor train passenger flows; Demand by mode and purpose for the corridor and mode share; and Spatial pattern of rail demand by journey purpose and growth in demand from Scenario A (Do Minimum) to Scenario B. The corresponding set of results for Scenarios C and D includes: Total HSR revenue and journeys, with separation of work-related (business) and non-work (or leisure) demand; HSR yields (average fares) for business and leisure; Origin-destination HSR journeys by type of flow. That is, journeys are divided as follows: - End-to-end journeys, e.g. Oslo/Akershus Bergen; - Journeys between intermediate stations and an endpoint, e.g. Oslo/Akershus Gol; Gol to Bergen; - Journeys between an endpoint and a zone on another HSR corridor, e.g. Bergen Kristiansand; and - Residual ( Other ) journeys. Percentage mode share on interurban flows (end-to-end journeys) for the corridor; Highest abstraction of journeys based of county, mode of transport and journey purpose (business or leisure); and GIS output showing the spatial pattern of originating journeys (i.e. HSR boardings) by zone, plus daily boardings by HSR station, assuming equal demand on each day of the week. In addition, Appendix C presents detailed annual demand tables showing (a) HSR abstraction by mode and generation, by type of flow, and (b) county-county HSR matrices. The routes to be tested are summarised in Table 4.3 below. Not all combinations of test results are included in this report. Table 4.3 Summary of HSR Routes and Scenarios tested Route Example HSR Stops Test Scenarios Oslo Bergen Hønefoss, Gol, Geilo, Voss A D Oslo Bergen/Stavanger (Haukeli) Drammen, Kongsberg, Bø, Odda D only Bergen Stavanger Haugesund, Leirvik (Stord) D only Oslo Kristiansand Stavanger Drammen, Porsgrunn, Arendal, Kristiansand A D Oslo Trondheim Gardermoen, Hamar, Lillehammer, Otta A D Oslo Stockholm Lillestrøm, Kongsvinger, Karlstad A D Oslo Gothenburg Ski, Moss, Sarpsborg/Fredrikstad, Halden A D
Contract 5, Subject 1: Demand Forecasting 78 4.4 Oslo Bergen corridor This broad corridor can be provided / upgraded according to Scenario B (upgrade) Scenario C (major upgrade, with some new route sections) or Scenario D (High Speed Rail with completely new infrastructure). 4.4.1 Scenario B Applying Scenario B on this corridor would entail improvements (e.g. partial double-tracking) of the existing route to Bergen via Drammen, Hønefoss and Gol to allow for reductions in journey time. Corridor Train Passenger Flows The following graphs show the pattern of train passengers on the main service modelled in this corridor (the day train from Oslo to Bergen and back to Oslo, referred to as service 041a in NTM5B), for the following four key tests: Scenario A for 2024 (Figure 4.1); and Scenario B for 2024 (Figure 4.2). These flows are in terms of passengers per day; over the modelled period of 12 hours the train is assumed to operate every 2 hours. Please note that the scale differs from one figure to another. It can be seen that, in the section nearest to Oslo, the train is relatively lightly loaded, with a substantial increase in passengers boarding at Drammen and a markedly higher level of demand between there and Bergen. The same pattern is apparent in the reverse direction of travel. It can be seen that the peak level of passengers carried in Scenario A in 2024 is of the order of 1,700 per day, rising to around 1,900 with the improved journey time offered in Scenario B. Figure 4.1 Scen A (2024) Bergen Daily Demand Profile
Contract 5, Subject 1: Demand Forecasting 79 Figure 4.2 Scen B (2024) Bergen Daily Demand Profile Pattern of Demand by Mode and Purpose The pattern of total demand within the Oslo Bergen corridor is summarised in Table 4.4 for both the modelled years. The rail element of this demand is further analysed in the following section. It is observed that the modal shift exhibited by NTM5B in response to this improved rail journey time, even at the corridor level, is relatively modest. The change in modal shift from 2024 to 2043 is even more modest, but this is to be anticipated as the NTM5B model does not take account of any road congestion or crowding on trains. It is assumed that the small increase in the air share is driven by assumptions on costs and values of time, as the Level of Service (LoS) inputs are held constant over time. Table 4.4 Corridor Demand by Mode and Purpose (Scenarios A and B1) Scenario A: 2024 Scenario A: 2043 Demand Total Work Propn. Total Work Propn. Car 13375 1399 10% 17888 1660 9% Bus 1226 146 12% 1572 171 11% Boat 237 30 13% 277 33 12% Train 2748 613 22% 3575 733 21% Air 3365 2266 67% 4295 2778 65% Total 20950 4454 21% 27608 5375 19% Mode Share Car 64% 31% 65% 31% Bus 6% 3% 6% 3% Boat 1% 1% 1% 1% Train 13% 14% 13% 14% Air 16% 51% 16% 52% Total 100% 100% 100% 100%
Contract 5, Subject 1: Demand Forecasting 80 Scenario B1: 2024 Scenario B1: 2043 Demand Total Work Propn. Total Work Propn. Car 13332 1394 10% 17832 1654 9% Bus 1219 146 12% 1564 170 11% Boat 235 30 13% 275 33 12% Train 3063 706 23% 3985 845 21% Air 3347 2258 67% 4273 2769 65% Total 21196 4533 21% 27929 5471 20% Mode Share Car 64% 31% 65% 31% Bus 6% 3% 6% 3% Boat 1% 1% 1% 1% Train 15% 16% 14% 16% Air 16% 51% 15% 52% Total 100% 100% 100% 100% Spatial Pattern of Rail Demand The following table (Table 4.5) shows more detailed results for rail passenger demand in the Oslo Bergen corridor, providing a breakdown between end-to-end trips and journeys to/from intermediate areas. Under the Do Minimum (Scenario A), there is a forecast increase in rail demand within the corridor of 30% between the two modelled years, although the figure is lower for work related trips. For both work-related trips and total trips, the end to end patronage (Oslo to Bergen or vice versa) is significant, at nearly 50% of all rail trips, and with the fastest growth. Table 4.5 Rail demand by trip type and exogenous growth (Scenario A 2024 to 2043) Scenario A: 2024 Scenario A: 2043 Growth over 2024 From To Total Work Total Work Total Work Oslo Bergen 562 146 746 178 33% 22% Bergen Oslo 562 146 746 178 33% 22% Oslo Corridor 564 96 731 113 30% 18% Corridor Oslo 564 96 731 113 30% 18% Bergen Corridor 149 47 185 54 24% 16% Corridor Bergen 149 47 185 54 24% 16% Corridor Corridor 190 35 240 41 26% 17% Total in Corridor 2741 612 3566 732 30% 20% Oslo-Bergen as share 41% 48% 42% 49% As shown in Table 4.6, the additional rail demand induced by the improved journey time offered in Scenario B is around 12%, with the greatest impact found in end-to-end work-related journeys (19%).
Contract 5, Subject 1: Demand Forecasting 81 Table 4.6 Impact of Scenario B1 (additional rail journeys over Do Minimum 2024, 2043) Scenario B1: 2024 Scenario B1: 2043 From To Total Work Total Work Oslo Bergen 16% 19% 15% 19% Bergen Oslo 16% 19% 15% 19% Oslo Corridor 9% 12% 9% 12% Corridor Oslo 9% 12% 9% 12% Bergen Corridor 9% 11% 9% 12% Corridor Bergen 9% 11% 9% 12% Corridor Corridor 7% 8% 7% 8% Total in Corridor 12% 15% 11% 15% 4.4.2 Scenario C (Stopping at Hønefoss, Gol and Voss) Applying Scenario C on this corridor would entail more significant infrastructure investment than Scenario B, in order to deliver faster and more frequent rail services. Some new route sections are likely, short cutting circuitous sections of the existing route, and resulting in larger end-toend journey time savings. An example would be the building of a new direct line from Oslo to Hønefoss, to replace the current route via Drammen. Table 4.7 below presents a summary of the overall demand results for an example Scenario C option between Bergen and Oslo via Hallingdal, assuming intermediate stops at Hønefoss, Gol and Voss. Table 4.7 Summary of HSR Demand and Revenue: Scenario C Oslo Bergen (Hallingdal route via Hønefoss, Gol and Voss) 2024 Demand Annual [k] Per day [k] Total HSR journeys 1100 3.1 HSR Business journeys 700 1.8 HSR Leisure journeys 500 1.2 HSR Passenger kilometres (million) 400 1.0 Revenue and yield Annual [NOK million] Average yield [NOK] HSR Total revenue 700 600 HSR revenue from Business travel 500 700 HSR revenue from Leisure travel 200 500 It can be seen that annual journeys in 2024 are estimated at 1.1 million, averaging around 3100 per day. This demand breaks down as 59% business journeys, and 41% leisure journeys. Total annual revenue is estimated at approximately 700 NOK million, with an average (one way) yield of 700 NOK per journey for business, and 500 NOK for leisure. Table 4.8 below provides a breakdown of annual journeys by type of flow, and Table 4.9 shows estimated daily boardings by station.
Contract 5, Subject 1: Demand Forecasting 82 Table 4.8 HSR Demand by Origin/Destination type: Scenario C Oslo Bergen (Hallingdal route via Hønefoss, Gol and Voss) 2024 Oslo-Bergen Annual Total (k) Business (k) Leisure (k) Oslo/Akershus - Bergen 400 250 150 Oslo/Akershus - Intermediate area 250 150 150 Bergen - Intermediate area 50 50 50 Oslo/Akershus - Other HSR corridors 100 50 50 Bergen - Other HSR corridors 100 50 50 Other 200 100 50 Total 1100 650 450 Table 4.8 shows that end-to-end journeys account for over a third (37%) of all trips, ranging between 36% of leisure trips and 37% of business trips. A further 30% of trips are between Greater Oslo and intermediate areas, with 17% between Greater Oslo and other HSR corridors. Some within the category are journeys to/from zones in the Trondheim corridor, with access to HSR via the intermediate stations at Hønefoss and Gol. If the Trondheim corridor were provided as well as the Bergen corridor, these trips would fall away significantly. Table 4.9 Boardings by station Scenario C Oslo Bergen (Hallingdal route via Hønefoss, Gol and Voss) 2024 Station Daily boardings (k) % of total Cumulative for route Oslo 1.3 41% 1.3 Hønefoss 0.1 3% 1.4 Gol 0.1 3% 1.5 Voss 0.4 14% 1.9 Bergen 1.2 39% 3.1 Table 4.9 shows that boardings at two of the intermediate stations Hønefoss and Gol represent only a small fraction of the overall market which produces a total of around 3,000 boardings per day. Voss represents a more significant proportion of daily boardings, less than half that of Oslo or Bergen. Figure 4.3 below shows the forecast mode shares for end-to-end trips in 2024 under Scenario C. Air travel accounts for almost a third of the overall market, followed by car and classic rail. HSR accounts for under a fifth of the market for trips between Oslo and Bergen.
Contract 5, Subject 1: Demand Forecasting 83 Figure 4.3 Mode Share: Scenario C Oslo Bergen via Hønefoss, Gol and Voss 2024 (Oslo/Akershus-Bergen) 18% 26% 4% 30% 22% HSR Car Air Bus Classic Rail Figure 4.4 below shows the major sources of demand abstracted by HSR. These journey data are disaggregated by previous mode, journey purpose and originating county. With Bergen located within Hordaland county, it is clear from the chart that end-to-end air trips are the principal source of abstracted demand. Figure 4.4 Highest abstraction of journeys: Scenario C Oslo Bergen (Hallingdal route via Hønefoss, Gol and Voss) 2024 160000 140000 Abstracted originating jnys 120000 100000 80000 60000 40000 20000 0
Contract 5, Subject 1: Demand Forecasting 84 Figure 4.5 provides a GIS presentation of the spatial pattern of annual HSR trip-ends and daily HSR station boardings. HSR trip-ends are concentrated in the bydeler (i.e. urban districts) within the two cities, with 75% of boardings at either Bergen or Oslo. In addition, a significant share of the boardings at Voss is by passengers accessing from areas to the east of Bergen. The relatively weak demand for HSR boardings (and alightings) at Gol and Hønefoss as shown in Table 4.9 is confirmed by the map. Figure 4.5 HSR demand by originating zone (annual) and point of boarding (daily)
Contract 5, Subject 1: Demand Forecasting 85 4.4.3 Scenario D (with stop at Voss) Applying Scenario D on this corridor would involve the construction of completely new HSR infrastructure, following a broadly similar corridor to the existing line but with far fewer bends that limit train speeds. This will allow for a very significant reduction in journey time and an increase in the frequency of rail services. Table 4.10 below presents a summary of the overall demand results for an example Scenario D option between Bergen and Oslo, assuming an intermediate stop at Voss. The range of demand shown is based on the various sensitivities tested. The lowest demand shown is based on HSR fares equalling current air fares. The highest demand is based on the sensitivity where HSR fares are assumed to equal current rail fares (60% of air fares) and there is provision for a connecting service from Oslo Central to Gardermoen Airport. In terms of revenue, the lowest revenue shown is based on HSR fares equalling rail fares and there is no connection to Gardermoen, while the highest revenue corresponds to the option with HSR fares equalling air fares and a connecting service from Oslo Central to Gardermoen Airport. Table 4.10 Summary of HSR Demand and Revenue: Scenario D Oslo Bergen (via Voss) 2024 Demand Annual [k] Per day [k] Total HSR journeys 1500 2500 4.2 6.8 HSR Business journeys 1000 1400 2.6 4.0 HSR Leisure journeys 600 1000 1.6 2.8 HSR Passenger kilometres (million) 600 1000 1.6 2.7 Revenue and yield Annual [NOK million] Average yield [NOK] HSR Total revenue 700 1300 400-700 HSR revenue from Business travel 500 900 500-800 HSR revenue from Leisure travel 300-400 300-500 It can be seen that annual journeys in 2024 are estimated at between 1.5 and 2.5 million, averaging around 4,200 to 6,800 per day. This demand breaks down as 62% business journeys, and 38% leisure journeys. Total annual revenue is estimated at between 0.7 and 1.3 NOK billion, with an average (one way) yield of 500 to 800 NOK per journey for business, and 300 to 500 NOK for leisure. Total rail passengers (including both HSR and on the existing rail services) on the corridor are forecast to be around 2.1m to 3m trips per year in 2024. Table 4.11 below provides a breakdown of annual journeys by type of flow, and Table 4.12 shows estimated daily boardings by station. Table 4.11 HSR Demand by Origin/Destination type: Scenario D Oslo Bergen (via Voss) 2024 Oslo-Bergen via Voss Annual Total (k) Business (k) Leisure (k) Oslo/Akershus - Bergen 600 800 400 500 200 300 Oslo/Akershus - Intermediate area 400 500 200 200 250 Bergen - Intermediate area 75 100 50 75 0 50 Oslo/Akershus - Other HSR corridors 150 200 75 100 50 100 Bergen - Other HSR corridors 100 500 100 300 50 200 Other 200 500 150 300 50 200 Total 1550 2500 950 1400 600 1000
Contract 5, Subject 1: Demand Forecasting 86 Table 4.11 shows that end-to-end journeys account for over a third (40%) of all trips, ranging between 39% of leisure trips and 42% of business trips. A further 30% of trips are between Greater Oslo and intermediate areas, with 17% between Greater Oslo and other HSR corridors. Table 4.12 Boardings by station: Scenario D Oslo Bergen (via Voss) 2024 Station Daily boardings (k) % of total Cumulative for route Oslo S 1.9 2.4 41-46% 1.9 2.4 Voss 0.6 0.8 13-14% 2.5 3.2 Bergen 1.7 2.7 40-46% 4.2 5.9 Table 4.12 shows that boardings at the intermediate station (Voss) represent a sizable proportion of the overall market which produces a total of between 4,200 and 5,900 HSR boardings per day. The daily boardings at Voss are approximately a third the amount of boardings at Bergen. Figure 4.6 below shows the forecast mode shares for end-to-end trips in 2024 under this Scenario. HSR accounts for almost 40% of the market, which is slightly lower than for the nonstop service. Disaggregation by journey purpose would show that air commands a larger share of business trips, and car a larger share of leisure trips. Figure 4.6 Mode Share: Scenario D Oslo Bergen via Voss 2024 (Oslo/Akershus-Bergen) 16% 23% 3% 38% HSR Car Air Bus Classic Rail 19%
Contract 5, Subject 1: Demand Forecasting 87 Figure 4.7 below shows the major sources of demand abstracted by HSR. These journey data are disaggregated by previous mode, journey purpose, and originating county. With Bergen located within Hordaland county, it is clear that end-to-end air trips are, once again, the principal source of demand abstracted by HSR. Figure 4.7 Highest abstraction of journeys: Scenario D Oslo Bergen (via Voss) 2024 250000 200000 Abstracted originating jnys 150000 100000 50000 0 Figure 4.8 provides a GIS presentation of the spatial pattern of annual HSR trip-ends, and daily HSR station boardings. It can be seen that HSR trip-ends are concentrated in the bydeler (i.e. urban districts) within the cities of Bergen and Oslo. There is also a significant number of boardings shown at Voss. This demand is drawn largely from areas to the north and west of the town.
Contract 5, Subject 1: Demand Forecasting 88 Figure 4.8 HSR demand by originating zone (annual) and point of boarding (daily) 4.4.4 Bergen Corridor Summary and Conclusions The existing route via Drammen, Hønefoss, Geilo and Gol, known as the Hallingdal route, can be upgraded using Scenarios B, C or D. A key infrastructure enhancement for Scenarios C and D would be to construct a new direct line between Oslo and Hønefoss, significantly reducing journey times. For Scenario D, the example case presented is for only one intermediate station at Voss, serving area to the north of Bergen. In this instance, the total rail market could be between 2.1m and 3.0m trips per year (4,200 6,800 daily) in 2024, with HSR revenue estimated at between 0.7 and 1.3bn NOK per year. Of the demand, 33% is generated, with 40% abstracted from air and 17% from car.
Contract 5, Subject 1: Demand Forecasting 89 4.5 Bergen/Stavanger Oslo corridor (Haukeli route Y shaped route) The route via Haukeli can only be provided with new HSR infrastructure (Scenario D), including a new line between Drammen and Bergen via Bø and Odda. This route allows for a branch to Stavanger, providing additional revenue and benefits. This Y-shaped example option could either be operated with separate Oslo-Bergen and Oslo- Stavanger services, or alternatively trains from Oslo could divide at an intermediate station. The results below assume the former. 4.5.1 Scenario D (non-stop) Applying Scenario D on this corridor would involve the construction of completely new HSR infrastructure, following a broadly similar corridor to the existing line, with far fewer bends that limit train speeds. This will allow for a very significant reduction in journey time, and an increase in the frequency of rail services. Table 4.13 below presents a summary of the overall demand results for an example Scenario D option between Bergen/Stavanger and Oslo via Haukeli, assuming that there are no intermediate stops. The range of demand shown is based on the various sensitivities tested. The lowest demand shown is based on HSR fares equalling current air fares. The highest demand is based on the sensitivity where HSR fares are assumed to equal current rail fares (60% of air fares) and there is provision for a connecting service from Oslo Central to Gardermoen Airport. In terms of revenue, the lowest revenue shown is based on HSR fares equalling rail fares and there is no connection to Gardermoen, while the highest revenue corresponds to the option with HSR fares equalling air fares and a connecting service from Oslo Central to Gardermoen Airport. Table 4.13 Summary of HSR Demand and Revenue: Scenario D Oslo Bergen/Stavanger (Haukeli route non-stop) 2024 Demand Annual [k] Per day [k] Total HSR journeys 2500 4100 6.8 11.3 HSR Business journeys 1600 2500 4.3 6.8 HSR Leisure journeys 900 1700 2.5 4.6 HSR Passenger kilometres (million) 1100 1800 3.0 5.1 Revenue and yield Annual [NOK million] Average yield [NOK] HSR Total revenue 1500-2500 500-800 HSR revenue from Business travel 1100-1800 500-900 HSR revenue from Leisure travel 500-700 400-600 It can be seen that annual journeys in 2024 are estimated at between 2.5 and 4.1 million, averaging between 6,800 and 11,300 per day. This demand breaks down as 63% business journeys, and 37% leisure journeys. Total annual revenue is estimated at between 1.5 and 2.5 NOK billion, with an average (one way) yield of 500 to 900 NOK per journey for business, and 400 to 600 NOK for leisure. Total rail trips on the corridor (including both existing rail corridors and the new HSR service) are expected to be around 4.4m to 6.0m per year in 2024. Table 4.14 below provides a breakdown of annual journeys by type of flow for the Oslo-Bergen and Oslo-Stavanger corridors, and Table 4.15 shows estimated daily boardings by station for the Bergen and Stavanger services.
Contract 5, Subject 1: Demand Forecasting 90 Table 4.14 HSR Demand by Origin/Destination type: Scenario D Oslo Bergen/Stavanger (Haukeli route non-stop) 2024 Oslo-Bergen/Stavanger Annual Total (k) Business (k) Leisure (k) Oslo/Akershus Bergen 700 850 450 500 250 350 Oslo/Akershus Stavanger 450 600 300 350 150 200 Oslo/Akershus intermediate Bergen corridor Oslo/Akershus intermediate Stavanger corridor 300 350 150 175 100 150 350 450 200 250 150 200 Bergen Intermediate area 50 100 50 75 0 50 Stavanger Intermediate area 75 100 50 75 0 550 Bergen Other HSR corridors 550-1000 350 650 400 200 Stavanger Other HSR corridors 350 1150 200 700 150 450 Total Demand 4150 2500 2450 1600 1700 900 Table 4.14 shows that end-to-end journeys account for almost half (46%) of all trips, ranging between 44% of leisure trips and 47% of business trips. Of these end-to-end journeys, 59% were made between Oslo and Bergen, and 41% between Oslo and Stavanger. A further 25% of trips are between Greater Oslo and intermediate areas, with 43% in the Bergen corridor and 57% in the Stavanger corridor. It should be noted that with no intermediate stations assumed, these journeys require passengers to double-back after arrival at Bergen, Stavanger or Oslo. Table 4.15 Boardings by station: Scenario D Oslo Bergen/Stavanger (Haukeli route non-stop) 2024 Station Daily boardings (k) % of total Cumulative for route Oslo 3.3 4.0 41-49% 3.3 4.0 Bergen 1.8 3.0 27-30% 5.1 7.0 Stavanger 1.7 2.8 25-29% 6.8 9.8 Table 4.15 shows there are slightly more boardings at Bergen than there are at Stavanger. The approximate HSR boardings per day on Bergen services between 1,800 and 3,000, compared to 1,700 to 2,800 for Stavanger services. Figure 4.9 below shows the forecast mode shares for end-to-end trips in 2024 between Oslo and Bergen under Scenario D. HSR achieves a market share of just over 40%, with air, car and the slow public transport modes (i.e. classic rail plus bus) each accounting for approximately a fifth of the market. Disaggregation by journey purpose would show that air commands a larger share of business trips, and car a larger share of leisure trips.
Contract 5, Subject 1: Demand Forecasting 91 Figure 4.9 Mode Share: Scenario D Oslo Bergen non-stop 2024 (Oslo/Akershus-Bergen) 15% 22% 3% 41% HSR Car Air Bus Classic Rail 19% Figure 4.10 below shows the corresponding mode shares for end-to-end trips between Oslo and Stavanger in 2024 under Scenario D. HSR has a share of nearly half of the overall market, with air having a quarter share. As with Oslo Bergen, car captures nearly 20% of the market and disaggregation by journey purpose would show that air commands a larger share of business trips, and car a larger share of leisure trips. Classic rail has a smaller market share than between Oslo and Bergen. Figure 4.10 Mode Share: Scenario D Oslo Stavanger non-stop 2024 (Oslo/Akershus-Stavanger) 3% 7% 25% 46% HSR Car Air Bus Classic Rail 19% Figure 4.11 below shows the major sources of demand abstracted by HSR. These journey data are disaggregated by previous mode, journey purpose, and originating county. With Bergen located within Hordaland county and Stavanger in Rogaland, it is clear from the chart that end-toend business air trips are the principal source of abstracted demand.
Contract 5, Subject 1: Demand Forecasting 92 Figure 4.11 Highest abstraction of journeys: Scenario D Oslo Bergen/Stavanger (Haukeli route non-stop) 2024 300000 250000 Abstracted originating jnys 200000 150000 100000 50000 0 Figure 4.12 provides a GIS presentation of the spatial pattern of annual HSR trip-ends and daily HSR station boardings. It can be seen that HSR trip-ends are concentrated in the bydeler (i.e. urban districts) within the cities of Bergen, Stavanger and Oslo, with significant demand to the north of Bergen and immediately south of Stavanger.
Contract 5, Subject 1: Demand Forecasting 93 Figure 4.12 HSR demand by originating zone (annual) and point of boarding (daily) 4.5.2 Bergen Corridor (Haukeli Route) Summary and Conclusions The Haukeli route follows a completely new alignment between Bø and Bergen and can be constructed in conjunction with a branch to Stavanger. Analysis of this route with a combination of Oslo Bergen and Oslo Stavanger services suggests that it will induce far higher demand and produce more revenue, albeit probably at a greater construction cost, than a single route to Bergen. For the Haukeli route tested, the total HSR market could be around 1.5 to 2.5m trips per year (6,800 11,300 daily) in 2024, with revenue of between 1.5 and 2.5bn NOK per year. Total rail trips on the corridor (both HSR and existing rail services) are forecast to be around 4.4m to 6m per year in 2024. The sources of demand are similar to the other routes to Bergen with 33% demand generated, 16% abstracted from car and 42% abstracted from air. In reality it is likely that the strongest case for this route would include a stop at Drammen to serve the west of Oslo and provide connectivity with other routes. The impact of the stop at Drammen is currently underestimated in the model as it excludes base trips of 100km or less. Further work will be required in Phase 3 to determine the exact level of service on this route; whether to alternate trains between Bergen and Stavanger or to split trains en-route. There will need to be investigation into the potential of a HSR stop at Haugesund on the branch to Stavanger. Refinement to the model will be required to estimate passenger movements around the suburbs of Oslo and interaction with the improved regional services enabled by the InterCity Study.
Contract 5, Subject 1: Demand Forecasting 94 4.6 Stavanger Bergen corridor (Haugesund route) The route via Haugesund can only be provided with new HSR infrastructure (Scenario D), as it would be an entirely new construction, serving a corridor where there is currently no rail infrastructure. This corridor would likely be constructed in parallel with another corridor either Oslo Bergen, Oslo Stavanger or Oslo Bergen and Stavanger. However, for the purpose of this report the corridor has been tested in isolation. 4.6.1 Scenario D (with stop at Haugesund) Applying Scenario D on this corridor would involve the construction of completely new HSR infrastructure, following a broadly similar corridor to the existing line, with far fewer bends that limit train speeds. This will allow for a very significant reduction in journey time, and an increase in the frequency of rail services. Table 4.16 below presents a summary of the overall demand results for an example Scenario D option between Stavanger and Bergen, assuming a stop at Haugesund. The range of demand shown is based on the various sensitivities tested. The lowest demand shown is based on HSR fares equalling current air fares. The highest demand is based on the sensitivity where HSR fares are assumed to equal current rail fares (60% of air fares). In terms of revenue, the lowest revenue shown is based on HSR fares equalling rail fares, while the highest revenue corresponds to the option with HSR fares equalling air fares. Table 4.16 Summary of HSR Demand and Revenue: Scenario D Stavanger Bergen (via Haugesund) 2024 Demand Annual [k] Per day [k] Total HSR journeys 700 900 2.0 2.5 HSR Business journeys 500 700 1.5 1.8 HSR Leisure journeys 200 300 0.5 0.7 HSR Passenger kilometres (million) 100 200 0.3 0.4 Revenue and yield Annual [NOK, millions] Average yield [NOK] HSR Total revenue 400-500 400-700 HSR revenue from Business travel 300-400 500-800 HSR revenue from Leisure travel 50-100 200-400 It can be seen that annual journeys in 2024 are estimated at between 0.7 and 0.9 million, averaging between 2,000 and 2,500 per day. This demand breaks down as 73% business journeys, and 27% leisure journeys. Total annual revenue is estimated at between 0.4 and 0.5 NOK billion, with an average (one way) yield of 500 to 800 NOK per journey for business, and 200 to 400 NOK for leisure. Table 4.17 below provides a breakdown of annual journeys by type of flow, and Table 4.18 shows estimated daily boardings by station.
Contract 5, Subject 1: Demand Forecasting 95 Table 4.17 HSR Demand by Origin/Destination type: Scenario D Stavanger Bergen (via Haugesund) 2024 Stavanger-Bergen via Haugesund Annual Total (k) Business (k) Leisure (k) Stavanger-Bergen 250 300 175 200 50 75 Stavanger-Corridor 1 1 0 Bergen-Corridor 100 150 100 125 25 50 Stavanger-Other corridors 75 100 50 75 25 50 Bergen-Other corridors 200 250 100 150 50 75 Other 100 150 75 100 25 50 Total 750 900 550 650 200 250 Table 4.17 shows that end-to-end journeys account for nearly a third (31%) of all trips, ranging between 33% of business trips and 27% of leisure trips. A further 19% of trips are between Bergen and intermediate areas and almost no travel between Stavanger and intermediate areas. 24% of demand is between Bergen and other HSR corridors and 11% is between Stavanger and other HSR corridors. Table 4.18 Boardings by station: Scenario D Stavanger Bergen (via Haugesund) 2024 Station Daily boardings (k) % of total Cumulative for route Stavanger 0.8 1.0 38% 0.8 1.0 Haugesund 0.3 12-13% 1.0 1.3 Bergen 1.0 1.2 49% 2.0 2.5 Table 4.18 shows that boardings at the intermediate station (Haugesund) represent a sizable proportion of the overall market which produces a total of between 2,000 and 2,500 HSR boardings per day. Figure 4.13 below shows the forecast mode shares for end-to-end trips in 2024 under Scenario D. HSR accounts for 34% of the market, with air only 13%. Car travel still dominates in this corridor with nearly half the market share while bus has a 6% share. There are is negligible demand for classic rail as there is no such service in this corridor. Disaggregation by journey purpose would show that air commands a larger share of business trips and car a larger share of leisure trips.
Contract 5, Subject 1: Demand Forecasting 96 Figure 4.13 Mode Share: Scenario D Stavanger Bergen via Haugesund 2024 (Stavanger-Bergen) 13% 6% 1% 34% HSR Car Air Bus Classic Rail 46% Figure 4.14 below shows the major sources of demand abstracted by HSR. These journey data are disaggregated by previous mode, journey purpose, and originating county. With Stavanger located within Rogaland county, it is clear from the chart that end-to-end business air and car trips are the principal source of abstracted demand. Figure 4.14 Highest abstraction of journeys: Scenario D Stavanger Bergen (via Haugesund) 2024 18000 16000 14000 12000 10000 8000 6000 4000 2000 0 Abstracted originating jnys Figure 4.15 provides a GIS presentation of the spatial pattern of annual HSR trip-ends and daily HSR station boardings. The majority of the demand is centred around the cities of Stavanger and Bergen, and also the intermediate station at Haugesund.
Contract 5, Subject 1: Demand Forecasting 97 Figure 4.15 HSR demand by originating zone (annual) and point of boarding (daily) 4.6.2 Stavanger Bergen Corridor Summary and Conclusions The Stavanger Bergen route can only be implemented under Scenario D as it will involve the construction of a new rail line for the entirety of the route, as there is no existing direct rail link between the two cities. The example option shown in this report has an intermediate stop at Haugesund. The total HSR market for this route is currently estimated at around 0.7 to 0.9m trips per year (2,000 2,500 per day) in 2024, with revenue of between 0.4 and 0.6bn NOK per year. Of this demand, 40% is generated, with 24% abstracted from car and 34% from air. As the Stavanger Bergen route is a completely new construction, traversing several fjords and mountainous areas, the construction costs for the route could possibly be prohibitive, especially when considering the lack of a direct connection to Oslo. In Phase 3, there will be scope to examine the construction of the Stavanger Bergen route in combination with the implementation of HSR on either the Oslo Bergen, Oslo Stavanger or Oslo Bergen/Stavanger corridors. This will attract increased demand and generate higher revenue.
Contract 5, Subject 1: Demand Forecasting 98 4.7 Stavanger Kristiansand Oslo corridor This broad corridor can be provided/upgraded according to Scenario B (upgrade) Scenario C (major upgrade, with some new route sections alignments) or Scenario D (High Speed Rail with completely new infrastructure). 4.7.1 Scenario B Applying Scenario B on this corridor would entail improvements (e.g. partial double-tracking) of the existing route to Stavanger via Drammen, Kongsberg and Kristiansand to allow for improvements in journey time. Corridor Train Passenger Flows The following graphs show the pattern of train passengers on the main service modelled in this corridor (the Day Train from Oslo to Stavanger and back to Oslo, referred to as service 051c in NTM5B), for the following four key tests: Scenario A for 2024 (Figure 4.16); and Scenario B for 2024 (Figure 4.17). These flows are in terms of passengers per day; over the modelled period of 12 hours the train is assumed to operate every 2 hours. Please note that the scale differs from one Figure to another. As with the Oslo Bergen corridor, it can be seen that, in the section nearest to Oslo, the train is relatively lightly loaded, with a marked increase in passengers boarding at Drammen and a markedly higher level of demand beyond there, although declining towards Stavanger. The same pattern is apparent in the reverse direction of travel. The peak level of passengers carried in Scenario A in 2024 is of the order of 900 per day, rising to around 1,100 with the improved journey time offered in Scenario B. Figure 4.16 Scen A (2024) Stavanger Daily Demand Profile
Contract 5, Subject 1: Demand Forecasting 99 Figure 4.17 Scen B (2024) Stavanger Daily Demand Profile Pattern of Demand by Mode and Purpose The pattern of total demand within the Oslo Stavanger corridor is summarised in the following Table 4.19 for both the modelled years. The rail element of this demand is further analysed in the following section. It may be observed that the modal shift exhibited by NTM5B in response to this improved rail journey time, even at the corridor level, is relatively modest. The change in modal shift from 2024 to 2043 is even more modest. This is to be anticipated as the NTM5B model does not take account of any congestion that may be experienced on road or rail travel; it is assumed that the small increase in the car share is driven by assumptions on costs and values of time, as the LoS data is held constant over time. Table 4.19 Corridor Demand by Mode and Purpose (Scenarios A and B2) Scen A: 2024 Scen A: 2043 Demand Total Work Propn. Total Work Propn. Car 42163 6229 15% 56983 7517 13% Bus 3473 545 16% 4515 647 14% Boat 42 5 12% 51 6 11% Train 4695 1130 24% 6125 1335 22% Air 3450 2509 73% 4437 3109 70% Total 53823 10418 19% 72109 12614 17% Mode Share Car 78% 60% 79% 60% Bus 6% 5% 6% 5% Boat 0% 0% 0% 0% Train 9% 11% 8% 11% Air 6% 24% 6% 25% Total 100% 100% 100% 100%
Contract 5, Subject 1: Demand Forecasting 100 Scen B2: 2024 Scen B2: 2043 Demand Total Work Propn. Total Work Propn. Car 42067 6216 15% 56853 7502 13% Bus 3461 544 16% 4500 646 14% Boat 41 5 12% 50 6 11% Train 5006 1213 24% 6542 1436 22% Air 3433 2499 73% 4414 3097 70% Total 54008 10477 19% 72359 12686 18% Mode Share Car 78% 60% 79% 59% Bus 6% 5% 6% 5% Boat 0% 0% 0% 0% Train 9% 12% 9% 11% Air 6% 24% 6% 25% Total 100% 101% 100% 101% Spatial Pattern of Rail Demand Table 4.20 shows more detailed results for rail passenger demand in the Oslo Stavanger corridor providing a breakdown between end-to-end trips and journeys to/from intermediate areas. Under the Do Minimum (Scenario A), there is a forecast increase in rail demand within the corridor of 40% between the two modelled years, although the figure is lower for work related trips. For both work-related trips and total trips, the end to end patronage (Oslo to Bergen or vice versa) is not significant, at less than 10% of all rail trips. Table 4.20 Rail demand by trip type and exogenous growth (Scenario A 2024 to 2043) Scen A: 2024 Scen A: 2043 Growth over 2024 From To Total Work Total Work Total Work Oslo Stavanger 166 40 231 50 39% 24% Stavanger Oslo 166 40 231 50 39% 24% Oslo Corridor 1837 470 2385 553 30% 18% Corridor Oslo 1837 470 2385 553 30% 18% Stavanger Corridor 77 14 103 17 33% 20% Corridor Stavanger 77 14 103 17 33% 20% Corridor Corridor 528 81 679 95 29% 18% Total in Corridor 4688 1129 6116 1334 30% 18% Oslo-Stavanger as share 7% 7% 8% 7% As shown in Table 4.21, the additional rail demand induced by the improved journey time offered in Scenario B is around 7%, with the greatest impact found in end-to-end work-related journeys (31%).
Contract 5, Subject 1: Demand Forecasting 101 Table 4.21 Growth over Scenario A Scenario B2 Scen B2: 2024 Scen B2: 2043 From To Total Work Total Work Oslo Stavanger 24% 31% 24% 31% Stavanger Oslo 24% 31% 24% 31% Oslo Corridor 5% 5% 5% 5% Corridor Oslo 5% 5% 5% 5% Stavanger Corridor 11% 13% 11% 13% Corridor Stavanger 11% 13% 11% 13% Corridor Corridor 6% 7% 6% 7% Total in Corridor 7% 7% 7% 8% 4.7.2 Scenario C (Stopping at Drammen, Porsgrunn, Arendal and Kristiansand) Applying Scenario C on this corridor would entail more significant infrastructure investment than Scenario B, in order to deliver faster and more frequent rail services. Some new route sections are likely, short cutting circuitous sections of the existing route, and resulting in larger end-toend journey time savings. Table 4.22 below presents a summary of the overall demand results for a Scenario C option between Stavanger and Oslo via Kristiansand, assuming intermediate stops at Drammen, Porsgrunn, Arendal and Kristiansand. Table 4.22 Summary of HSR Demand and Revenue: Scenario C Oslo Stavanger (via Drammen, Porsgrunn, Arendal, Kristiansand) 2024 Demand Annual [k] Per day [k] Total HSR journeys 1300 3.6 HSR Business journeys 900 2.3 HSR Leisure journeys 500 1.3 HSR Passenger kilometres (million) 500 1.2 Revenue and yield Annual [NOK million] Average yield [NOK] HSR Total revenue 700 600 HSR revenue from Business travel 500 600 HSR revenue from Leisure travel 200 500 It can be seen that annual HSR journeys in 2024 are estimated at 1.3 million, averaging around 3,600 per day. This demand breaks down as 64% business journeys, and 36% leisure journeys. Total annual HSR revenue is estimated at approximately 0.7 NOK billion, with an average (one way) yield of 600 NOK per journey for business, and 500 NOK for leisure.
Contract 5, Subject 1: Demand Forecasting 102 Table 4.23 below provides a breakdown of annual journeys by type of flow, and Table 4.24 shows estimated daily boardings by station. Table 4.23 HSR Demand by Origin/Destination type: Scenario C Oslo Stavanger (via Drammen, Porsgrunn, Arendal, Kristiansand) 2024 Oslo-Stavanger Annual Total (k) Business (k) Leisure (k) Oslo/Akershus - Stavanger 200 100 100 Oslo/Akershus - Intermediate area 350 250 150 Stavanger - Intermediate area 150 100 50 Oslo/Akershus - Other HSR corridors 200 150 100 Stavanger - Other HSR corridors 100 100 50 Other 250 200 100 Total 1300 850 450 Table 4.23 shows that end-to-end journeys account for only 16% of all trips, ranging between 14% of leisure trips and 20% of business trips. A further 39% of trips are between Greater Oslo and intermediate areas, with 25% between Greater Oslo and other HSR corridors. Some within the category are journeys to/from zones in the Bergen corridor, with access to HSR via the intermediate station Drammen. If the Bergen corridor were provided as well as the Bergen corridor, these trips would fall away significantly. Table 4.24 Boardings by station: Scenario C Oslo Stavanger (via Drammen, Porsgrunn, Arendal, Kristiansand) 2024 Station Daily boardings (k) % of total Cumulative for route Oslo S 1.1 31% 1.1 Drammen 0.2 5% 1.3 Porsgrunn 0.1 3% 1.4 Arendal 0.3 8% 1.7 Kristiansand 0.6 15% 2.3 Stavanger 1.3 37% 3.6 Table 4.24 shows that boardings at two of the intermediate stations Drammen and to a greater extent Porsgrunn represent only a small fraction of the overall market which produces a total of around 3,600 boardings per day. Arendal and in particular Kristiansand represent a more significant proportion of daily boardings, with boardings at Kristiansand nearly half the number at Oslo. Figure 4.18 below shows the forecast mode shares for end-to-end trips in 2024 under Scenario C. Air accounts for 40% of the market for trips between Oslo and Stavanger, while HSR and car travel have a lower share, capturing approximately a quarter of the market each.
Contract 5, Subject 1: Demand Forecasting 103 Figure 4.18 Mode Share: Scenario C Oslo Stavanger via Drammen, Porsgrunn, Arendal, Kristiansand 2024 (Oslo-Stavanger) 3% 9% 24% 40% 24% HSR Car Air Bus Classic Rail Figure 4.19 below shows the major sources of demand abstracted by HSR. These journey data are disaggregated by previous mode, journey purpose, and originating county. With Stavanger located within Rogaland county, it is clear from the chart that end-to-end air trips are the principal source of abstracted demand, although there is significant abstraction from trips to Vest-Agder, where Kristiansand is located. Figure 4.19 Highest abstraction of journeys: Scenario C Oslo Stavanger (via Drammen, Porsgrunn, Arendal, Kristiansand) 2024 160000 140000 120000 100000 80000 60000 40000 20000 0 Abstracted originating jnys Figure 4.20 provides a GIS presentation of the spatial pattern of annual HSR trip-ends and daily HSR station boardings. There is a large proportion of demand centred on the cities of Oslo, Kristiansand and Stavanger, and in the remainder of Aust and Vest-Agder, with lower demand around the areas surrounding Oslo. There is relatively low demand originating from the county of Vestfold, interchanging at the stations at Drammen and Porsgrunn.
Contract 5, Subject 1: Demand Forecasting 104 Figure 4.20 HSR demand by originating zone (annual) and point of boarding (daily) 4.7.3 Scenario D (stopping at Porsgrunn and Kristiansand) Applying Scenario D on this corridor would involve the construction of completely new HSR infrastructure, following a broadly similar corridor to the existing line, with far fewer bends that limit train speeds. This will allow for a very significant reduction in journey time, and an increase in the frequency of rail services. Table 4.25 below presents a summary of the overall demand results for an example Scenario D option between Stavanger and Oslo via Kristiansand, assuming intermediate stops at Porsgrunn and Kristiansand. The range of demand shown is based on the various sensitivities tested. The lowest demand shown is based on HSR fares equalling current air fares. The highest demand is based on the sensitivity where HSR fares are assumed to equal current rail fares (60% of air fares) and there is provision for a connecting service from Oslo Central to Gardermoen Airport. In terms of revenue, the lowest revenue shown is based on HSR fares equalling rail fares and there is no connection to Gardermoen, while the highest revenue corresponds to the option with HSR fares equalling air fares and a connecting service from Oslo Central to Gardermoen Airport.
Contract 5, Subject 1: Demand Forecasting 105 Table 4.25 Summary of HSR Demand and Revenue: Scenario D Oslo Stavanger (via Porsgrunn and Kristiansand) 2024 Demand Annual [k] Per day [k] Total HSR journeys 2000 3100 5.4 8.4 HSR Business journeys 1300 1900 3.5 5.2 HSR Leisure journeys 700 1200 1.8 3.2 HSR Passenger kilometres (million) 800 1800 2.1 3.4 Revenue and yield Annual [NOK million] Average yield [NOK] HSR Total revenue 1000 1700 400 700 HSR revenue from Business travel 700 1200 500 800 HSR revenue from Leisure travel 300 400 300 500 It can be seen that annual journeys in 2024 are estimated at between 2.0 and 3.1 million, averaging between 5,400 and 8,400 per day. Total rail trips on the corridor (both HSR and on existing rail services) are forecast to be around 3.3m to 4.4m a year in 2024. This demand breaks down as 66% business journeys, and 34% leisure journeys. Total annual revenue is estimated at between 1.0 and 1.7 NOK billion, with an average (one way) yield of 500 to 800 NOK per journey for business and 300 to 500 NOK for leisure. Table 4.26 below provides a breakdown of annual journeys by type of flow, and Table 4.27 shows estimated daily boardings by station. Table 4.26 HSR Demand by Origin/Destination type: Scenario D Oslo Stavanger (via Porsgrunn and Kristiansand) 2024 Oslo-Stavanger via Porsgrunn, Kristiansand Annual Total (k) Business (k) Leisure (k) Oslo/Akershus - Stavanger 400 500 250 300 150 200 Oslo/Akershus - Intermediate area 400 650 300 400 200 250 Stavanger - Intermediate area 200 250 150 150 50 75 Oslo/Akershus - Other HSR corridors 400 550 250 350 150 200 Stavanger - Other HSR corridors 100 350 50 200 50 150 Other 350 850 250 550 100 300 Total 1950 3100 1300 1900 700 1200 Table 4.26 shows that end-to-end journeys account for approximately a fifth (21%) of all trips, ranging between 21% of business trips and 22% of leisure trips. A further 35% of trips are between Greater Oslo and intermediate areas, with 25% between Greater Oslo and other HSR corridors.
Contract 5, Subject 1: Demand Forecasting 106 Table 4.27 Boardings by station: Scenario D Oslo Stavanger (via Porsgrunn and Kristiansand) 2024 Station Daily boardings (k) % of total Cumulative for route Oslo 2.1 2.5 34-38% 2.1 2.5 Porsgrunn 0.3 0.3 5% 2.3 2.8 Kristiansand 0.9 1.4 17-19% 3.2 4.2 Stavanger 2.1 3.2 39-43% 5.4 7,4 Table 4.27 shows that the station at Porsgrunn attracts approximately a third of the daily boardings as Kristiansand. Daily boardings at Porsgrunn constitute 5% of overall demand on a corridor which produces a total of between 5,400 and 7,400 HSR boardings per day. Figure 4.21 below shows the forecast mode shares for end-to-end trips in 2024 under this Scenario. Figure 4.21 Mode Share: Scenario D Oslo Stavanger via Porsgrunn and Kristiansand 2024 (Oslo- Stavanger) 3% 7% 28% 42% HSR Car Air Bus Classic Rail 20% Figure 4.22 below shows the major sources of demand abstracted by HSR. These journey data are disaggregated by previous mode, journey purpose and originating county. With Stavanger located in Rogaland county, it is clear from the chart that end-to-end air trips are the principal source of abstracted demand. With Kristiansand located in Vest-Agder, it can be seen that there is significant abstraction from business air trips to Kristiansand. There is also abstraction from car leisure trips between Oslo and Stavanger.
Contract 5, Subject 1: Demand Forecasting 107 Figure 4.22 Highest abstraction of journeys: Scenario D Oslo Stavanger (via Porsgrunn and Kristiansand) 2024 250000 200000 Abstracted originating jnys 150000 100000 50000 0 Figure 4.23 provides a GIS presentation of the spatial pattern of annual HSR trip-ends and daily HSR station boardings. The figure shows that there is an increase in demand around the station at Porsgrunn, although this demand is relatively low when compared with that surrounding the main cities of Oslo, Kristiansand and Stavanger. Figure 4.23 HSR demand by originating zone (annual) and point of boarding (daily)
Contract 5, Subject 1: Demand Forecasting 108 4.7.4 Stavanger Corridor Summary and Conclusions The current route between Oslo and Stavanger via Kristiansand can be upgraded using Scenarios B, C and D. A key enhancement in Scenario C is to redirect long distance services along the Vestfold Line, which will be double-tracked as part of the InterCity enhancements. For Scenario D a new alignment will be constructed between Drammen and Porsgrunn, with these stations providing connectivity with the Vestfold Line, which serves several urban areas. The new HSR line constructed as part of Scenario D will also follow the south coast providing direct connections to towns such as Arendal. For Scenario D the example test presented in the model for this corridor is with two intermediate stations at Kristiansand and Porsgrunn. For this option the total HSR market is estimated to be around 2.0 to 3.1m trips per year (5,400 7,400 per day) in 2024, with revenue of between 1.0 and 1.7bn NOK per year. Of the total HSR demand, 32% is generated, with 41% abstracted from air and 19% from car. The total rail market on the corridor (HSR services and existing rail services) is estimated to be around 3.3m to 4.4m trips a year in 2024. In Phase 3, further refinement of the model is required to investigate the interaction between the HSR services with local and regional services on the corridor, and to assess the impact of the proposals in the InterCity Study for improving services to the west of Oslo. The model will need to be enhanced to test the recommendations from the station location analysis in Subject 4, which proposes a parkway station at Sandnes, and the relocation of the station in Kristiansand away from the harbour.
Contract 5, Subject 1: Demand Forecasting 109 4.8 Trondheim Oslo corridor This broad corridor can be provided/upgraded according to Scenario B (upgrade) Scenario C (major upgrade, with some new route sections alignments) or Scenario D (High Speed Rail with completely new infrastructure). 4.8.1 Scenario B Applying Scenario B on this corridor would entail improvements (e.g. partial double-tracking) of the existing route to Trondheim via Gardermoen, Hamar, Lillehammer and Otta to allow for improvements in journey time. Corridor Train Passenger Flows The following graphs show the pattern of train passengers on the main service modelled in this corridor (the Day Train from Oslo to Trondheim and back to Oslo, referred to as service 021a in NTM5B), for the following four key tests: Scenario A for 2024 (Figure 4.24); and Scenario B for 2024 (Figure 4.25). These flows are in terms of passengers per day; over the modelled period of 12 hours the train is assumed to operate every 2 hours. Please note that the scale differs from one Figure to another. It can be seen that, in the section nearest to Oslo, the train is relatively lightly loaded, with a marked increase in passengers boarding at Drammen and a markedly higher level of demand between there and Trondheim, although decaying towards Trondheim. The same pattern is apparent in the reverse direction of travel. It can be seen that the peak level of passengers carried in Scenario A in 2024 is of the order of 1,600 per day, rising to around 1,900 with the improved journey time offered in Scenario B. Figure 4.24 Scen A (2024) Trondheim Daily Demand Profile
Contract 5, Subject 1: Demand Forecasting 110 Figure 4.25 Scen B (2024) Trondheim Daily Demand Profile Pattern of Demand by Mode and Purpose The pattern of total demand within the Oslo Trondheim corridor is summarised in the following Table 4.28 for both the modelled years. The rail element of this demand is further analysed in the following section. It may be observed that the modal shift exhibited by NTM5B in response to this improved rail journey time, even at the corridor level, is relatively modest. The change in modal shift from 2024 to 2043 is even more modest, but this is to be anticipated as the NTM5B model does not take account of any congestion that may be experienced on road or rail travel; it is assumed that the small increase in the car share is driven by assumptions on costs and values of time, as the LoS data is held constant over time. Table 4.28 Corridor Demand by Mode and Purpose (Scenarios A and B3) Scen A: 2024 Scen A: 2043 Demand Total Work Propn. Total Work Propn. Car 32503 3602 11% 43065 4257 10% Bus 2452 281 11% 3097 325 11% Boat 198 21 11% 240 24 10% Train 4034 869 22% 5125 1009 20% Air 2781 1719 62% 3522 2095 59% Total 41968 6492 15% 55048 7709 14% Mode Share Car 77% 55% 78% 55% Bus 6% 4% 6% 4% Boat 0% 0% 0% 0% Train 10% 13% 9% 13% Air 7% 26% 6% 27% Total 100% 100% 100% 100%
Contract 5, Subject 1: Demand Forecasting 111 Scen B3: 2024 Scen B3: 2043 Demand Total Work Propn. Total Work Propn. Car 32408 3592 11% 42939 4245 10% Bus 2441 280 11% 3084 324 11% Boat 196 21 11% 238 23 10% Train 4326 946 22% 5502 1101 20% Air 2764 1711 62% 3500 2085 60% Total 42134 6550 16% 55262 7779 14% Mode Share Car 77% 55% 78% 55% Bus 6% 4% 6% 4% Boat 0% 0% 0% 0% Train 10% 15% 10% 14% Air 7% 26% 6% 27% Total 100% 100% 100% 100% Spatial Pattern of Rail Demand Table 4.29 shows more detailed results for rail passenger demand in the Oslo Trondheim corridor, providing a breakdown between end-to-end trips and journeys to/from intermediate areas. Under the Do Minimum (Scenario A), there is a forecast increase in rail demand within the corridor of 30% between the two modelled years, although the figure is lower for work related trips. For both work-related trips and total trips, the end to end patronage (Oslo to Bergen or vice versa) is not so significant, at about 20% of all rail trips. Table 4.29 Rail Demand by Sector (Scenarios A and B3) Scen A: 2024 Scen A: 2043 Growth Over 2024 From To Total Work Total Work Total Work Oslo Trondheim 357 85 483 104 35% 22% Trondheim Oslo 357 85 483 104 35% 22% Oslo Corridor 1415 318 1782 364 26% 15% Corridor Oslo 1415 318 1782 364 26% 15% Trondheim Corridor 114 18 142 21 25% 15% Corridor Trondheim 114 18 142 21 25% 15% Corridor Corridor 255 26 302 28 18% 11% Total in Corridor 4027 868 5116 1008 27% 16% Oslo-Trondheim as share 18% 20% 19% 21% As shown in Table 4.30, the additional rail demand induced by the improved journey time offered in Scenario B is around 7%, with the greatest impact found in end-to-end work-related journeys (24%).
Contract 5, Subject 1: Demand Forecasting 112 Table 4.30 Growth Over Scenario A Scenario B3 Scen B3: 2024 Scen B3: 2043 From To Total Work Total Work Oslo Trondheim 18% 24% 18% 24% Trondheim Oslo 18% 24% 18% 24% Oslo Corridor 5% 5% 5% 5% Corridor Oslo 5% 5% 5% 5% Trondheim Corridor 5% 5% 5% 5% Corridor Trondheim 5% 5% 5% 5% Corridor Corridor 4% 5% 4% 5% Total in Corridor 7% 9% 7% 9% 4.8.2 Scenario C (stopping at Gardermoen, Hamar, Lillehammer, Otta) Applying Scenario C on this corridor would entail more significant infrastructure investment than Scenario B, in order to deliver faster and more frequent rail services. Some new route sections are likely, short cutting circuitous sections of the existing route, and resulting in larger end-toend journey time savings. Table 4.31 below presents a summary of the overall demand results for the Scenario C option between Trondheim and Oslo, assuming intermediate stops at Gardermoen, Hamar, Lillehammer and Otta. Table 4.31 Summary of HSR Demand and Revenue: Scenario C Oslo Trondheim (via Gardermoen, Hamar, Lillehammer and Otta) 2024 Demand Annual [k] Per day [k] Total HSR journeys 1500 4.0 HSR Business journeys 900 2.4 HSR Leisure journeys 600 1.7 HSR Passenger kilometres (million) 600 1.7 Revenue and yield Annual [NOK million] Average yield [NOK] HSR Total revenue 1000 700 HSR revenue from Business travel 700 800 HSR revenue from Leisure travel 300 500 It can be seen that annual HSR journeys in 2024 are estimated at 1.5 million, averaging around 4,000 per day. This demand breaks down as 59% business journeys, and 41% leisure journeys. Total annual revenue is estimated at 1.0 NOK billion, with an average (one way) yield of 800 NOK per journey for business and 500 NOK for leisure. Table 4.32 below provides a breakdown of annual journeys by type of flow, and Table 4.33 shows estimated daily boardings by station.
Contract 5, Subject 1: Demand Forecasting 113 Table 4.32 HSR Demand by Origin/Destination type: Scenario C Oslo Trondheim (via Gardermoen, Hamar, Lillehammer and Otta) 2024 Oslo-Trondheim Annual Total (k) Business (k) Leisure (k) Oslo/Akershus - Trondheim 450 250 200 Oslo/Akershus - Intermediate area 200 100 75 Trondheim - Intermediate area 75 50 25 Oslo/Akershus - Other HSR corridors 50 25 0 Trondheim - Other HSR corridors 400 250 150 Other 350 200 150 Total 1500 850 600 Table 4.32 shows that end-to-end journey account for almost a third (31%) with similar proportions for leisure and business trips. A further 17% of trips are between Greater Oslo and intermediate areas, with 29% between Greater Oslo and other HSR corridors. Table 4.33 Boardings by station: Scenario C Oslo Trondheim (via Gardermoen, Hamar, Lillehammer and Otta) 2024 Station Daily boardings (k) % of total Cumulative for route Oslo 1.1 28% 1.1 Gardermoen 0.7 18% 1.8 Lillehammer 0.1 1% 1.9 Hamar 0.05 1% 2.0 Otta 0.2 6% 2.2 Trondheim 1.9 46% 4.0 Table 4.33 shows that intermediate stations at Lillehammer and Hamar have relatively low patronage. The stations at Gardermoen (18% of the overall market) and to a lesser extent Otta (6% of the overall market) attract a number of daily boardings. The stations at either end of the corridor attract a combined total of 74% of a market which produces around 4,000 HSR boardings per day. Figure 4.26 below shows the forecast mode shares for end-to-end trips in 2024 under Scenario C. HSR accounts for a third of the modal share, with car and air accounting for a further quarter each.
Contract 5, Subject 1: Demand Forecasting 114 Figure 4.26 Mode Share: Scenario C Oslo Trondheim via Gardermoen, Hamar, Lillehammer and Otta 2024 (Oslo/Akershus-Trondheim) 4% 11% 33% HSR Car 26% Air Bus Classic Rail 26% Figure 4.27 below shows the major sources of demand abstracted by HSR. These journey data are disaggregated by previous mode, journey purpose and originating county. With Trondheim located in Sør-Trondelag county, it is clear from the chart that end-to-end air trips are the principal source of abstracted demand, particularly business trips. Figure 4.27 Highest abstraction of journeys: Scenario C Oslo Trondheim (via Gardermoen, Hamar, Lillehammer and Otta) 2024 250000 200000 Abstracted originating jnys 150000 100000 50000 0 Figure 4.28 provides a GIS presentation of the spatial pattern of annual HSR trip-ends, and daily HSR station boardings. The majority of demand is centred in Trondheim and the areas surrounding the city. There is also large demand from the bydeler of Oslo and from Gardermoen Airport. It can also be seen that the demand for stations at Lillehammer and Hamar is minimal in this option, with a station at Otta proving slightly more popular.
Contract 5, Subject 1: Demand Forecasting 115 Figure 4.28 HSR demand by originating zone (annual) and point of boarding (daily)
Contract 5, Subject 1: Demand Forecasting 116 4.8.3 Scenario D (with stop at Gardermoen) Applying Scenario D on this corridor would involve the construction of completely new HSR infrastructure, following a broadly similar corridor to the existing line, with far fewer bends that limit train speeds. This will allow for a very significant reduction in journey time, and an increase in the frequency of rail services. Table 4.34 below presents a summary of the overall demand results for an example Scenario D option between Trondheim and Oslo, assuming an intermediate stop at Gardermoen. The range of demand shown is based on the various sensitivities tested. The lowest demand shown is based on HSR fares equalling current air fares. The highest demand is based on the sensitivity where HSR fares are assumed to equal current rail fares (60% of air fares). In terms of revenue, the lowest revenue shown is based on HSR fares equalling rail fares, while the highest revenue corresponds to the option with HSR fares equalling air fares. Table 4.34 Summary of HSR Demand and Revenue: Scenario D Oslo Trondheim (via Gardermoen) 2024 Demand Annual [k] Per day [k] Total HSR journeys 1800 2200 4.9 6.1 HSR Business journeys 1100 1300 3.0 3.6 HSR Leisure journeys 700 900 1.9 2.5 HSR Passenger kilometres (million) 800 1000 2.1 2.6 Revenue and yield Annual [NOK million] Average yield [NOK] HSR Total revenue 900-1300 400-700 HSR revenue from Business travel 700-900 500-800 HSR revenue from Leisure travel 300-400 300-500 It can be seen that annual journeys in 2024 are estimated at between 1.8 and 2.2 million, averaging between 4,900 and 6,100 per day. This demand breaks down as 61% business journeys, and 39% leisure journeys. Total annual revenue is estimated at between0.9 and 1.3 NOK billion, with an average (one way) yield of 500 to 800 NOK per journey for business and 300 to 500 NOK for leisure. Total rail demand on the corridor is forecast to be around 2.8m to 3.2m trips per year in 2024. Table 4.35 below provides a breakdown of annual journeys by type of flow, and Table 4.36 shows estimated daily boardings by station. Table 4.35 HSR Demand by Origin/Destination type: Scenario D Oslo Trondheim (via Gardermoen) 2024 Oslo-Trondheim via Gardermoen Annual Total (k) Business (k) Leisure (k) Oslo/Akershus - Trondheim 650 800 400 450 250 350 Oslo/Akershus - Intermediate area 250 300 150 175 100 150 Trondheim - Intermediate area 25 50 25 50 0-25 Oslo/Akershus - Other HSR corridors 0 0 0 Trondheim - Other HSR corridors 550 650 300 350 200 250 Other 350 450 200 250 125 150 Total 1800 2200 1100 1300 700 900
Contract 5, Subject 1: Demand Forecasting 117 Table 4.41 shows that end-to-end journeys account for just over a third (35%) of all trips, ranging with little difference in the proportion for business and leisure trips. A further 13% of trips are between Greater Oslo and intermediate areas, with 30% between Trondheim and other HSR corridors. Table 4.36 Boardings by station: Scenario D Oslo Trondheim (via Gardermoen) 2024 Station Daily boardings (k) % of total Cumulative for route Oslo 1.4 1.8 29-30% 1.4 1.8 Gardermoen 1.0 1.2 20-21% 2.5 3.1 Trondheim 2.5 3.0 50% 4.9 6.1 Table 4.36 shows that intermediate station at Gardermoen attracts 20% of overall demand within a corridor which produces a total of between 4,900 and 6,100 HSR boardings per day. The majority of the trips from Gardermoen are in addition to those made from Oslo, rather than abstracted. Hence the overall demand is far higher than there would be for a non-stop option. Figure 4.29 below shows the forecast mode shares for end-to-end trips in 2024 under Scenario D. HSR makes up almost half of the demand, with car and air attracting approximately as much again, in roughly equal measure. There is still a sizable proportion (10%) of classic rail demand. Figure 4.29 Mode Share: Scenario D Oslo Trondheim via Gardermoen 2024 (Oslo/Akershus- Trondheim) 4% 10% 20% 43% HSR Car Air Bus Classic Rail 23% Figure 4.30 below shows the major sources of demand abstracted by HSR. These journey data are disaggregated by previous mode, journey purpose and originating county. With Trondheim located in Sør-Trondelag county, it is clear from the chart that end-to-end air trips are the principal source of abstracted demand. There is abstraction also from Nord-Trondelag for air trips to Oslo.
Contract 5, Subject 1: Demand Forecasting 118 Figure 4.30 Highest abstraction of journeys: Scenario D Oslo Trondheim (via Gardermoen) 2024 350000 300000 Abstracted originating jnys 250000 200000 150000 100000 50000 0 Figure 4.31 provides a GIS presentation of the spatial pattern of annual HSR trip-ends and daily HSR station boardings. There is slightly higher demand in intermediate areas, particularly in the county of Akershus. There is still significant demand from the regions surrounding Trondheim accessing the HSR station to travel to Oslo.
Contract 5, Subject 1: Demand Forecasting 119 Figure 4.31 HSR demand by originating zone (annual) and point of boarding (daily)
Contract 5, Subject 1: Demand Forecasting 120 4.8.4 Trondheim Corridor Summary and Conclusions The existing route to Trondheim via Gardermoen, Hamar, Lillehammer and Otta can be upgraded using Scenarios B, C or D. For Scenario D, the example shown has one intermediate station at Gardermoen Airport. For this option, the total HSR market is around 1.8 to 2.2m trips per year (4,900 6,100 daily) in 2024, with revenue of between 0.9 and 1.3bn NOK per year. Of this demand, 22% is generated, with 62% abstracted from air and 11% from car. The total rail market, including existing rail services, is forecast to be around 2.8m to 3.2m trips a year in 2024. A key consideration for Phase 3 will be to analyse the benefits or disbenefits of constructing a more direct alignment between Gardermoen for Trondheim, which would not be able to serve Hamar, Lillehammer or Otta for connections to towns such as Ǻlesund and Kristiansand on the north coast, but would reduce end-to-end journey times between Oslo and Trondheim. Another consideration for Phase 3 would be to extend the HSR route from Trondheim Central to Værnes Airport, to improve connectivity with the airport and to better serve towns in Nord-Trondelag.
Contract 5, Subject 1: Demand Forecasting 121 4.9 Oslo Stockholm corridor This broad corridor can be provided/upgraded according to Scenario B (upgrade) Scenario C (major upgrade, with some new route sections alignments) or Scenario D (High Speed Rail with completely new infrastructure). 4.9.1 Scenario B Applying Scenario B on this corridor would entail improvements (e.g. partial double-tracking) of the existing route to Stockholm via Lillestrøm, Kongsvinger, and Karlstad to allow for improvements in journey time. Corridor Train Passenger Flows The following graphs show the pattern of train passengers on the main service modelled in this corridor (the day train from Oslo to Stockholm and back to Oslo, referred to as service 011b in NTM5B), for the following four key tests: Scenario A for 2024 (Figure 4.32); and Scenario B for 2024 (Figure 4.33). These flows are in terms of passengers per day; over the modelled period of 12 hours the train is assumed to operate every 2 hours. Please note that the scale differs from one Figure to another. It can be seen that, in the section nearest to Oslo, the train is very lightly loaded, although with a marked increase in passengers boarding at Lillestrom and a markedly higher level of demand between there and Stockholm. The same pattern is apparent in the reverse direction of travel. It should be borne in mind that there is no representation of cross-border demand in NTM5B, and hence these results significantly understate potential demand. However, the low level of incremental change suggests that overall passenger numbers including cross-border travel would not increase significantly in Scenario B. It can be seen that the peak level of passengers carried in Scenario A in 2024 is of the order of only 25 per day; not rising with the improved journey time offered in Scenario B. Figure 4.32 Scen A (2024) Stockholm Daily Demand Profile
Contract 5, Subject 1: Demand Forecasting 122 Figure 4.33 Scen B (2024) Stockholm Daily Demand Profile Pattern of Demand by Mode and Purpose The pattern of total demand within the Oslo Stockholm corridor is summarised in the following Table 4.37 for both the modelled years. The rail element of this demand is further analysed in the following section. It may be observed that the modal shift exhibited by NTM5B in response to this improved rail journey time, even at the corridor level, is relatively modest. The change in modal shift from 2024 to 2043 is even more modest, but this is to be anticipated as the NTM5B model does not take account of any congestion that may be experienced on road or rail travel; it is assumed that the small increase in the car share is driven by assumptions on costs and values of time, as the LoS data is held constant over time. Table 4.37 Corridor Demand by Mode and Purpose Scen A: 2024 Scen A: 2043 Demand Total Work Propn. Total Work Propn. Car 1503 251 17% 1978 294 15% Bus 136 26 19% 170 30 17% Boat 0 0-0 0 - Train 173 45 26% 211 50 24% Air 0 0-0 0 - Total 1812 322 18% 2359 373 16% Mode Share Car 83% 78% 84% 79% Bus 8% 8% 7% 8% Boat 0% 0% 0% 0% Train 10% 14% 9% 13% Air 0% 0% 0% 0% Total 100% 100% 100% 100%
Contract 5, Subject 1: Demand Forecasting 123 Scen B4: 2024 Scen B4: 2043 Demand Total Work Propn. Total Work Propn. Car 1503 251 17% 1978 294 100% Bus 136 26 19% 170 30 100% Boat 0 0-0 0 - Train 173 45 26% 212 50 100% Air 0 0-0 0 - Total 1812 322 18% 2359 373 100% Mode Share Car 83% 78% 84% 79% Bus 8% 8% 7% 8% Boat 0% 0% 0% 0% Train 10% 14% 9% 13% Air 0% 0% 0% 0% Total 100% 100% 100% 100% Pattern of Rail Demand Table 4.38 shows more detailed results for rail passenger demand in the Oslo Stockholm corridor for both the modelled years, although all the numbers are very small. This results from the lack of cross-border demand in NTM5B, as already mentioned. It may be observed that there is a forecast increase on rail demand in the corridor of up to 21% between the two modelled years, although less for work purpose trips, implying a greater relative growth in other purposes (e.g. leisure). There is no evidence of growth in rail demand, as a result of the improved journey time offered in Scenario B. This is caused by the very low levels of demand in this corridor. Table 4.38 Rail Demand by Sector (Scenarios A and B5) Scen A: 2024 Scen A: 2043 Growth Over 2024 From To Total Work Total Work Total Work Oslo Stockholm 0 0 0 0 - - Stockholm Oslo 0 0 0 0 - - Oslo Corridor 83 22 101 24 21% 10% Corridor Oslo 83 22 101 24 21% 10% Stockholm Corridor 0 0 0 0 - - Corridor Stockholm 0 0 0 0 - - Corridor Corridor 0 0 0 0 - - Total in Corridor 166 44 202 49 21% 10% Oslo-Stockholm as share 0% 0% 0% 0%
Contract 5, Subject 1: Demand Forecasting 124 Table 4.39 Growth over Scenario A Scenario B5 Scen B5: 2024 Scen B5: 2043 From To Total Work Total Work Oslo Stockholm - - - - Stockholm Oslo - - - - Oslo Corridor 0% 0% 0% 0% Corridor Oslo 0% 0% 0% 0% Stockholm Corridor - - - - Corridor Stockholm - - - - Corridor Corridor - - - - Total in Corridor 0% 0% 0% 0% 4.9.2 Scenario C (with stops at Lillestrøm and Kongsvinger) Applying Scenario C on this corridor would entail more significant infrastructure investment than Scenario B, in order to deliver faster and more frequent rail services. Some new route sections are likely, short cutting circuitous sections of the existing route and resulting in larger end-to-end journey time savings. Table 4.40 below presents a summary of the overall demand results for the Scenario C option between Stockholm and Oslo, assuming intermediate stops at Lillestrøm and Kongsvinger. Table 4.40 Summary of HSR Demand and Revenue: Scenario C Oslo Stockholm (via Lillestrøm and Kongsvinger) 2024 Demand Annual [k] Per day [k] Total HSR journeys 700 2.0 HSR Business journeys 200 0.6 HSR Leisure journeys 500 1.4 HSR Passenger kilometres (million) 200 0.5 Revenue and yield Annual [NOK million] Average yield [NOK] HSR Total revenue 500 700 HSR revenue from Business travel 200 800 HSR revenue from Leisure travel 300 600 It can be seen that annual journeys in 2024 are estimated at 0.7 million, averaging around 2,000 per day. This demand breaks down as 30% business journeys and 70% leisure journeys. Total annual revenue is estimated at 0.5 NOK billion, with an average (one way) yield of 800 NOK per journey for business and 600 NOK for leisure. Table 4.41 below provides a breakdown of annual journeys by type of flow, and Table 4.42 shows estimated daily boardings by station.
Contract 5, Subject 1: Demand Forecasting 125 Table 4.41 HSR Demand by Origin/Destination type: Scenario D Oslo Stockholm (via Lillestrøm and Kongsvinger) 2024 Oslo-Stockholm Annual Total (k) Business (k) Leisure (k) Oslo/Akershus - Stockholm 700 200 500 Oslo/Akershus - Corridor 0 0 0 Stockholm - Corridor 2 0 0 Oslo/Akershus - Other corridors 25 25 0 Stockholm- Other corridors 0 0 0 Other 0 0 0 Total 750 200 500 Table 4.41 shows that end-to-end journey account for almost all (93%) trips, ranging between 87% of business trips and 96% of leisure trips. A further 4% of trips are between Greater Oslo and other HSR corridors. Table 4.42 Boardings by station: Scenario C Oslo Stockholm (via Lillestrøm and Kongsvinger) 2024 Station Daily boardings (k) % of total Cumulative for route Oslo S 0.5 25% 0.5 Lillestrøm 0.5 26% 1.0 Kongsvinger 0.05 2% 1.1 Stockholm 1.0 47% 2.0 Table 4.42 shows that the intermediate station at Lillestrøm attracts roughly one quarter (26%) of overall demand for a corridor which produces a total of around 2,000 HSR boardings per day. A stop at Kongsvinger attracts a mere 2% of demand. Figure 4.34 below shows the forecast mode shares for end-to-end trips in 2024 under Scenario D. HSR accounts for nearly half (46%) of overall demand, with car taking about a fifth (21%) and air about a third (31%). Figure 4.34 Mode Share: Scenario C Oslo Stockholm via Lillestrøm and Kongsvinger 2024 (Oslo/Akershus-Stockholm) 1% 31% 46% HSR Car Air Bus Classic Rail 21%
Contract 5, Subject 1: Demand Forecasting 126 Figure 4.35 below shows the major sources of demand abstracted by HSR. These journey data are disaggregated by previous mode, journey purpose and originating county. It is clear from the chart that end-to-end air trips are the principal source of abstracted demand. Figure 4.35 Highest abstraction of journeys: Scenario C Oslo Stockholm (via Lillestrøm and Kongsvinger) 2024 90000 80000 70000 60000 50000 40000 30000 20000 10000 0 Abstracted originating jnys 4.9.3 Scenario D (stopping at Lillestrøm) Applying Scenario D on this corridor would involve the construction of completely new HSR infrastructure, following a broadly similar corridor to the existing line, with far fewer bends that limit train speeds. This will allow for a very significant reduction in journey time and an increase in the frequency of rail services. Table 4.43 below presents a summary of the overall demand results for an example Scenario D option between Stockholm and Oslo, with an intermediate stop at Lillestrøm. The range of demand shown is based on the various sensitivities tested. The lowest demand shown is based on HSR fares equalling current air fares. The highest demand is based on the sensitivity where HSR fares are assumed to equal current rail fares (60% of air fares. In terms of revenue, the lowest revenue shown is based on HSR fares equalling rail fares, while the highest revenue corresponds to the option with HSR fares equalling air fares.
Contract 5, Subject 1: Demand Forecasting 127 Table 4.43 Summary of HSR Demand and Revenue: Scenario D Oslo Stockholm (via Lillestrøm) 2024 Demand Annual [k] Per day [k] Total HSR journeys 800 1000 2.3 2.8 HSR Business journeys 200 300 0.7 0.8 HSR Leisure journeys 600 800 1.6 2.1 HSR Passenger kilometres (million) 200 300 0.6 0.7 Revenue and yield Annual [NOK, millions] Average yield [NOK] HSR Total revenue 400-600 400-700 HSR revenue from Business travel 150-200 500-900 HSR revenue from Leisure travel 300-400 400-600 It can be seen that annual HSR journeys in 2024 are estimated at between 0.8 and 1.0 million, averaging between 2,300 and 2,800 per day. This demand breaks down as 30% business journeys, and 70% leisure journeys. Total annual revenue is estimated at between 0.4 and 0.6 NOK billion, with an average (one way) yield of 500 to 900 NOK per journey for business and 400 to 600 NOK for leisure. Table 4.44 below provides a breakdown of annual journeys by type of flow, and Table 4.45 shows estimated daily boardings by station. Table 4.44 HSR Demand by Origin/Destination type: Scenario D Oslo Stockholm (via Lillestrøm) 2024 Oslo-Stockholm Annual Total (k) Business (k) Leisure (k) Oslo/Akershus - Stockholm 800 1000 200 250 550 7250 Oslo/Akershus - Corridor 0 0 0 Stockholm - Corridor 0 0 0 Oslo/Akershus - Other corridors 25 25 0 Stockholm- Other corridors 0 25 0 0 Other 0 0 0 Total 800 1050 250 300 550 750 Table 4.44 shows that end-to-end journey account for almost all (95%) trips. Table 4.45 Boardings by station: Scenario D Oslo Stockholm (via Lillestrøm) 2024 Station Daily boardings (k) % of total Cumulative for route Oslo S 0.6 0.7 25% 0.6 0.7 Lillestrøm 0.6 0.8 27-28% 1.2 1.5 Stockholm 1.1 1.4 48% 2.3 2.8 Table 4.45 shows that demand is split roughly equally between the Oslo and the stop at Lillestrøm, which serves the east of Oslo. The overall HSR demand is approximately 2,300 to 2,800 boardings per day. Figure 4.36 below shows the forecast mode shares for end-to-end trips in 2024 under Scenario D. HSR attracts half of the demand, with air attracting 28% and car 20%.
Contract 5, Subject 1: Demand Forecasting 128 Figure 4.36 Mode Share: Scenario D Oslo Stockholm via Lillestrøm 2024 (Oslo/Akershus- Stockholm) 1% 28% HSR Car 51% Air Bus Classic Rail 20% Figure 4.37 below shows the major sources of demand abstracted by HSR. These journey data are disaggregated by previous mode, journey purpose and originating county. The chart shows that demand is principally abstracted from air travel, particularly leisure. Figure 4.37 Highest abstraction of journeys: Scenario D Oslo Stockholm (via Lillestrøm) 2024 100000 90000 80000 70000 60000 50000 40000 30000 20000 10000 0 Abstracted originating jnys
Contract 5, Subject 1: Demand Forecasting 129 4.9.4 Stockholm Corridor Summary and Conclusions The existing route to Stockholm via Lillestrøm, Kongsvinger and Karlstad can be upgraded using Scenarios B, C or D. A key infrastructure enhancement for Scenario D would be to construct a new, more direct line between Lillestrøm and Karlstad, significantly reducing journey times. For Scenario D the example option presented has an intermediate stop at Lillestrøm only, to provide connectivity with the east of Oslo and Gardermoen Airport. For this option, the total HSR market is between 0.8 and 1.0m trips per year (2,300 2,800 daily) in 2024 with revenue of around 0.4 to 0.6bn NOK per year. Of the demand for HSR, 40% is generated, with 51% abstracted from air and 8% from car. For Phase 3 it is suggested that a stop at Gardermoen Airport is tested instead of Lillestrøm as there is currently only a marginal case for an intermediate station at Lillestrøm. The model will also need to be refined with passenger count data for cross-border trips to Sweden, as well as trips between Karlstad and Stockholm, in order to more accurately model the demand potential on this corridor.
Contract 5, Subject 1: Demand Forecasting 130 4.10 Oslo Gothenburg corridor This broad corridor can be provided/upgraded according to Scenario B (upgrade) Scenario C (major upgrade, with some new route sections alignments) or Scenario D (High Speed Rail with completely new infrastructure). 4.10.1 Scenario B Applying Scenario B on this corridor would entail improvements (e.g. partial double-tracking) of the existing route to Gothenburg via Ski, Moss, Sarpsborg and Halden to allow for improvements in journey time. Corridor Train Passenger Flows The following graphs show the pattern of train passengers on the main service modelled in this corridor (the day train from Oslo to Gothenburg and back to Oslo, referred to as service 001b in NTM5B), for the following four key tests: Scenario A for 2024 (Figure 4.38); and Scenario B for 2024 (Figure 4.39). These flows are in terms of passengers per day; over the modelled period of 12 hours the train is assumed to operate every 2 hours. Please note that the scale differs from one Figure to another. It can be seen that, over most of the route, the train is relatively lightly, if evenly, loaded. This is mainly because NTM5B does not represent cross-border journeys hence actual figures are likely to be significantly higher. The same pattern is apparent in the reverse direction of travel. It can be seen that the peak level of passengers carried in Scenario A in 2043 is of the order of only 300 per day, rising insignificantly with the improved journey time offered in Scenario B. Figure 4.38 Scen A (2024) Gothenburg Daily Demand Profile
Contract 5, Subject 1: Demand Forecasting 131 Figure 4.39 Scen B (2024) Gothenburg Daily Demand Profile Pattern of Demand by Mode and Purpose The pattern of total demand within the Oslo Gothenburg corridor is summarised in the following Table 4.46 for both the modelled years. The rail element of this demand is further analysed in the following section. It may be observed that the modal shift exhibited by NTM5B in response to this improved rail journey time, even at the corridor level, is relatively modest. The change in modal shift from 2024 to 2043 is even more modest, but this is to be anticipated as the NTM5B model does not take account of any congestion that may be experienced on road or rail travel; it is assumed that the small increase in the car share is driven by assumptions on costs and values of time, as the LoS data is held constant over time. Table 4.46 Corridor Demand by Mode and Purpose (Scenarios A and B4) Scen A: 2024 Scen A: 2043 Demand Total Work Propn. Total Work Propn. Car 5107 1202 24% 6984 1468 21% Bus 611 99 16% 803 118 15% Boat 0 0-0 0 - Train 900 226 25% 1175 267 23% Air 0 0-0 0 - Total 6618 1528 23% 8962 1854 21% Mode Share Car 77% 79% 78% 79% Bus 9% 6% 9% 6% Boat 0% 0% 0% 0% Train 14% 15% 13% 14% Air 0% 0% 0% 0% Total 100% 100% 100% 100%
Contract 5, Subject 1: Demand Forecasting 132 Scen B4: 2024 Scen B4: 2043 Demand Total Work Propn. Total Work Propn. Car 5105 1202 24% 6982 1468 21% Bus 611 99 16% 803 118 15% Boat 0 0 0 0 Train 906 228 25% 1184 270 23% Air 0 0 0 0 Total 6623 1529 23% 8969 1856 21% Mode Share Car 77% 79% 78% 79% Bus 9% 6% 9% 6% Boat 0% 0% 0% 0% Train 14% 15% 13% 15% Air 0% 0% 0% 0% Total 100% 100% 100% 100% Pattern of Rail Demand Table 4.47 shows more detailed results for rail passenger demand in the Oslo Gothenburg corridor for both the modelled years. It may be observed that there is a forecast increase on rail demand in the corridor of up to 30% between the two modelled years, although less for work purpose trips, implying a greater relative growth in other purposes (e.g. leisure). For both work purpose trips and total trips, the end to end patronage (Oslo to Gothenburg or vice versa) is fairly significant, at nearly 20% of all rail trips. However, many of the elements in this Table are quite small, reflecting the nature of the corridor and lack of cross-border demand in NTM5B The growth in rail demand, as a result of the improved journey time offered in Scenario B, is minimal, however. Any growth in this corridor would have to come from cross-border demand, which is not included in NTM5B. Table 4.47 Rail Demand by Sector Scen A: 2024 Scen A: 2043 Growth Over 2024 From To Total Work Total Work Total Work Oslo Gothenburg 0 0 0 0 - - Gothenburg Oslo 0 0 0 0 - - Oslo Corridor 446 113 583 133 31% 18% Corridor Oslo 446 113 583 133 31% 18% Gothenburg Corridor 0 0 0 0 - - Corridor Gothenburg 0 0 0 0 - - Corridor Corridor 0 0 1 0 23% 0% Total in Corridor 893 225 1166 266 31% 18% Oslo-Gothenburg as share 0% 0% 0% 0%
Contract 5, Subject 1: Demand Forecasting 133 Table 4.48 Growth Over Scenario A Scenario B4 Scen B4: 2024 Scen B4: 2043 From To Total Work Total Work Oslo Gothenburg - - - - Gothenburg Oslo - - - - Oslo Corridor 1% 1% 1% 1% Corridor Oslo 1% 1% 1% 1% Gothenburg Corridor - - - - Corridor Gothenburg - - - - Corridor Corridor 2% 0% 0% 0% Total in Corridor 1% 1% 1% 1% 4.10.2 Scenario C (with stops at Ski, Moss, Sarpsborg, Halden) For Scenario C an example test has not been possible due to the current lack of sensitivity in the model to intermediate stations on this route within Norway. This is due to the absence of trips of less than 100km in the base demand matrices. This issue will be addressed in Phase 3 through the sourcing of specific demand data for this corridor. 4.10.3 Scenario D (non-stop) Applying Scenario D on this corridor would involve the construction of completely new HSR infrastructure, following a broadly similar corridor to the existing line, with far fewer bends that limit train speeds. This will allow for a very significant reduction in journey time, and an increase in the frequency of rail services. Table 4.49 below presents a summary of the overall demand results for an example Scenario D option between Gothenburg and Oslo, assuming no intermediate stops. The range of demand shown is based on the various sensitivities tested. The lowest demand shown is based on HSR fares equalling current air fares. The highest demand is based on the sensitivity where HSR fares are assumed to equal current rail fares (60% of air fares). In terms of revenue, the lowest revenue shown is based on HSR fares equalling rail fares, while the highest revenue corresponds to the option with HSR fares equalling air fares. Table 4.49 Summary of HSR Demand and Revenue: Scenario D Oslo Gothenburg (non-stop) 2024 Demand Annual [k] Per day [k] Total HSR journeys 800 1000 2.3 2.7 HSR Business journeys 700 800 1.8 2.1 HSR Leisure journeys 150 250 0.5 0.7 HSR Passenger kilometres (million) 100-200 0.4 0.5 Revenue and yield Annual [NOK, millions] Average yield [NOK] HSR Total revenue 500-700 500-800 HSR revenue from Business travel 400-600 500-900 HSR revenue from Leisure travel 50-100 300-500 It can be seen that annual HSR journeys in 2024 are estimated at between 0.8 and 1.0 million, averaging between 2,300 and 2,700 per day. This demand breaks down as 79% business journeys, and 21% leisure journeys. Total annual revenue is estimated at between 0.5 and 0.7 NOK billion, with an average (one way) yield of 500 to 900 NOK per journey for business and 300 to 500 NOK for leisure. Total rail trips on the corridor including those still travelling on existing
Contract 5, Subject 1: Demand Forecasting 134 rail services as well as the HSR services are forecast to be around 1.2m to 1.3m trips per year in 2024. Table 4.50 below provides a breakdown of annual journeys by type of flow, and Table 4.51 shows estimated daily boardings by station. Table 4.50 HSR Demand by Origin/Destination type: Scenario D Oslo Gothenburg (non-stop) 2024 Oslo-Gothenburg Annual Total (k) Business (k) Leisure (k) Oslo/Akershus - Gothenburg 850 1000 650 750 150 250 Oslo/Akershus - Corridor 0 0 0 Gothenburg - Corridor 0 0 0 Oslo/Akershus - Other corridors 0 0 0 Gothenburg - Other corridors 0 0 0 Other 0 0 0 Total 850 1000 650 750 150 250 Table 4.50 shows that end-to-end journey account for almost all (99%) trips. Table 4.51 Boardings by station: Scenario D Oslo Gothenburg (non-stop) 2024 Station Daily boardings (k) % of total Cumulative for route Oslo S 1.1 1.4 50% 1.1 1.4 Gothenburg 1.1 1.4 50% 2.3 2.7 Table 4.51 shows that demand is split equally between the two termini stations as part of the total of 2,300 to 2,700 HSR boardings per day. Figure 4.40 below shows the forecast mode shares for end-to-end trips in 2024 under Scenario D. HSR has just over 40% share of the demand, while the other 60% share is taken by car travel. Figure 4.40 Mode Share: Scenario D Oslo Gothenburg non-stop 2024 (Oslo/Akershus- Gothenburg) 0% 58% 42% HSR Car Air Bus Classic Rail Figure 4.41 below shows the major sources of demand abstracted by HSR. These journey data are disaggregated by previous mode, journey purpose and originating county. It is clear that almost all of the HSR demand is abstracted from end-to-end car trips.
Contract 5, Subject 1: Demand Forecasting 135 Figure 4.41 Highest abstraction of journeys: Scenario D Oslo Gothenburg (non-stop) 2024 250000 200000 Abstracted originating jnys 150000 100000 50000 0 4.10.4 Gothenburg Corridor Summary and Conclusions The existing route to Gothenburg via Ski, Moss, Fredrikstad, Sarpsborg and Halden can be upgraded using Scenarios B, C or D. It should be noted that this corridor is a very different market to the other Oslo corridors as it is dominated by car, with up to 3m cross-border trips per year. Currently, the data made available has not been sufficient enough to accurately model the demand between intermediate stations, such as Sarpsborg and Halden, as the majority of trips in the Østfold area are under 100km in length. The option tested for Scenario D therefore runs non-stop from Oslo to Gothenburg. This option is forecast to attract between 0.8 and 1.0m trips per year by HSR (2,300 2,700 daily), generating 0.5 to 0.7bn NOK of revenue per year. Of this demand 32% is generated, with 67% abstracted from car and only 1% from air, indicating the differing trends on this corridor. Around 1.2m to 1.3m total rail trips per year (both HSR and classic rail) are forecast on the corridor in 2024. For Phase 3 it will be necessary to refine the model by including passenger counts for crossborder trips between Norway and Sweden. In addition, a detailed analysis of short distance journeys in the area will be required to determine potential stations in Norway, which are driven by the commuter market to Oslo. There will be a significant overlap between HSR services and improvements from the InterCity study.
Contract 5, Subject 1: Demand Forecasting 136 5 Conclusions 5.1 Introduction This section provides a summary of the key findings from the analysis of the current travel market in Norway, the discussion of the future market growth assumptions, and the demand and revenue forecasting. Recommendations for future work to be carried out in Phase 3 of the HSR study are also presented. We emphasise that the findings of this report only reflect Stage 2 of the overall High Speed Rail Assessment project, and are designed to assist in the more detailed assessment during Phase 3 of the project. In particular, we emphasise that market analysis is only one of many interacting factors affecting the viability of high speed rail in Norway elements of costs, environmental and economic effects are equally important. 5.2 Summary of Current Travel Markets The size of the potential market for HSR in Norway is much smaller than the HSR markets already established in countries such as France and Germany, but similar to that of Sweden. From experience in other European countries where HSR is already well established, there has been almost total abstraction from air on routes served by HSR as rail journey times have been dramatically reduced and major rail stations are located more conveniently than the airports. Business travel in Norway is dominated by air due to the relative speed and frequency of services, and there is a higher value of time associated with these trips. Business travellers are prepared to spend time accessing airports located outside city centres. Conversely, leisure travel is more evenly spread between car, air and rail. For relatively short distance trips within corridors of up to around 300km (e.g. Oslo-Kristiansand), car travel is dominant.. In part, this dominance is due to leisure travellers placing higher importance on minimising monetary costs, combined with the ability to travel as a group. Another factor is the convenience of access to a car when making visits of extended duration - for out-of-town sightseeing, for example. Therefore, the key market for potential HSR in Norway will be business travel, which is currently served by air. However, HSR will look to abstract from the leisure market on long distance routes, in part because the new mode may allow the possibility of out-and-back travel within a day, avoiding the hotel costs associated with car use. Comparison of the level of service for individual modes of public transport indicates that air travel provides the best service for city-to-city travel, both in terms of service frequency and journey time, which explains the high market share. HSR services tend to stop less frequently than classic rail but city stations offer good connectivity with other modes of transport, including classic (regional/local) rail. Therefore any potential HSR service in Norway would compete mainly with city-to-city travel currently dominated by air, rather than travel within corridors. In order to compete with air travel, HSR will need to offer a competitive service, in terms of frequency, journey times, fares, accessibility and comfort. 5.3 Future (Do Minimum) Travel Market Future year growth forecasts have been developed based upon demand matrices produced by the NTM5 model. These future year matrices have been analysed to understand the growth trends by mode for each corridor. It has been shown that between the largest cities: Business passengers today predominantly use air over long distances and, in the Do Minimum scenario, this is reinforced over the next fifty years with the highest unconstrained growth experienced by this mode; On the Oslo-Bergen corridor, volumes of rail business trips are forecast to grow significantly, although this is due to higher base demand, rather than a faster percentage growth rate;
Contract 5, Subject 1: Demand Forecasting 137 Overall, over the 50 year period, leisure experiences higher growth than business; Growth in Do Minimum leisure passengers is focussed on car journeys; Measured in absolute volumes, growth in car journeys is high on all corridors for leisure travel, but negligible for business (except on the Bergen-Stavanger corridor where car is competitive with air overall); Growth rates on all modes are higher than the underlying population growth rate, particularly for leisure travel indicating that greater incomes in the future will lead to increased trips; On most corridors, the growth rate of business travel is typically higher in the period to 2020 than the 40 years (2021-2060) that follow;; Classic rail travel will continue to be dominated by leisure users, with an increase from 79% to 83% in the share of trips undertaken by leisure users. Conversely, business users share of air travel falls from 61% to 56%; In order to optimise the HSR business case, it seems appropriate to target business travellers who currently fly, and leisure travellers who currently fly or drive. In the latter case, discounts for group/family travel may be worthwhile. 5.4 Commentary on demand and revenue forecasts 5.4.1 Context of forecasts There are several general observations that can be applied across all corridors, relating to the trends in HSR demand and the performance of the demand and revenue forecasting model: Demand forecasts are roughly comparable to those in the previous study conducted by VWI. However, we emphasise that the forecasts in this report have been constructed completely independently and represent different scenarios for HSR in terms of stopping patterns, journey times, service frequencies and fares. Another key difference is that we understand the VWI forecasts encompass all rail services, whereas the forecasts in this report separate HSR and classic rail. The demand and revenue forecasts in this work represent outputs from the forecasting models based on example development scenarios. Much more optimisation work will be needed in Phase 3 around the specifications which could increase or decrease demand and revenue figures significantly. This optimisation work will also include taking into account cost figures, economic benefits and environmental effects. The potential for intermediate stops is especially evident on the Oslo Kristiansand Stavanger corridor and the Oslo Gothenburg corridor where there is relatively high population density. There are strong interactions with the parallel JBV Intercity study, particularly in relation to the market for travel into Oslo of less than 100km, which is specifically excluded from this work and the forecasting results. Developing options which could serve both the long-distance and inter-city markets into Oslo could significantly improve demand and revenue case for options. 5.4.2 Common findings A key trend for all corridors with the notable exception of the Oslo Gothenburg corridor is that most of the demand for HSR is abstracted from air. This means that there is a strong demand and revenue case for adopting Scenario D with the most aggressive reductions in endto-end journey times along each of the main corridors. At the other extreme, once journey times are reduced sufficiently, there is a diminishing market return for reducing journey times further, which may improve the case for intermediate stations. This issue is examined in more detail in the separate Subject 4 report.
Contract 5, Subject 1: Demand Forecasting 138 The relatively small increases in rail demand for Scenario B over Scenario A show that there might be a case for some incremental improvements in journey times, but that these interventions do not cause a major shift away from air on those corridors increases in travel by rail are more incremental, as would be expected. Results on the Trondheim corridor demonstrate there is likely to be strong case for linking all corridors directly to Gardermoen Airport, This is particularly relevant for the domestic HSR corridors to Bergen, Stavanger and Kristiansand, where overall HSR demand could be increased significantly. There is also potential for optimising connections with other key airports in Phase 3, such as an extension of HSR from Trondheim Central Station to Værnes Airport. 5.4.3 Corridor Analysis This section presents a summary of the HSR impacts on the corridors tested, across each of the scenarios. This comparison will enable an initial judgement as to what scale of improvement is viable on each corridor. Further refinement of the demand forecasting model will be required in Phase 3 to determine the optimal stopping pattern for Scenarios C and D, in particular for station stops within city areas, which may alter the demand significantly. There will also be greater distinction between the different HSR route alignments, taking into account the varying lengths and speed characteristics of the respective routes. Table 5.1 presents a comparison of the demand per day for HSR on each corridor under each scenario. Universally, Scenario D on each corridor results in the highest levels of demand as measured by passenger trips. It can be seen that for Scenario C across the corridors under consideration, Oslo Trondheim is the corridor with the highest patronage, presumably due in part to demand for Gardermoen Airport. Oslo Kristiansand Stavanger performs relatively poorly when considering the higher density of population along the route; however, this may be due to the higher end-to-end journey time. Figures would be expected to increase significantly if the line also served the Intercity commuting market into Oslo. For Scenario D, the Oslo Bergen/Stavanger route via Haukeli attracts the most HSR trips, which is unsurprising as it connects three major urban centres in Norway. The next most popular route is Oslo Kristiansand Stavanger corridor; again an expected result as it serves the south coast, which has a relatively high population density. The route has higher demand than Trondheim in Scenario D as the journey time saving is higher between the two scenarios (i.e. 3 hours compared with 1 hour 45 minutes). Either of these two routes could be combined with the Stavanger Bergen route to further increase HSR patronage. Scenario B provides a slight increase in rail patronage (compared to Scenario A) on the Oslo Bergen corridor and the Oslo Kristiansand / Stavanger corridors, but nowhere near the new HSR patronage achieved by Scenarios C and D. For the corridors into Sweden, where relatively little travel is in the NTM5 model (only domestic demand of 100km or more from Oslo is included), the demand effects are negligible.
Contract 5, Subject 1: Demand Forecasting 139 Table 5.1 HSR Demand per Day by Corridor for Each Scenario (2024) Route Passengers per day (2024) 000's 40 Scenario A Scenario B Scenario C Scenario D Oslo Bergen 2.8 3.6 3.1 4.2 6.8 Oslo Bergen/Stavanger ( Haukeli ) - - - 6.8 11.3 Stavanger Bergen - - - 2.0 2.5 Oslo Kristiansand Stavanger 4.7 5.0 3.6 5.4 8.4 Oslo Trondheim 4.0 4.3 4.0 4.9 6.1 Oslo Stockholm 0.2 0.2 2.0 2.3 2.8 Oslo Gothenburg 0.9 0.9-2.3 2.7 The corresponding demand per year is shown in Table 5.2. Table 5.2 HSR per Year by Corridor for Each Scenario (2024) Route Passengers per year (2024) 000's Scenario A Scenario B Scenario C Scenario D Oslo Bergen 1000 1300 1100 1500 2500 Oslo Bergen/Stavanger ( Haukeli ) - - - 2500 4100 Stavanger Bergen - - - 700 900 Oslo Kristiansand Stavanger 1700 1800 1300 2000 3100 Oslo Trondheim 1500 1600 1500 1800 2200 Oslo Stockholm 100 100 700 800 1000 Oslo Gothenburg 300 300-800 1000 Table 5.3 shows the average passengers per train for HSR in Scenarios C and D, on a typical day. These figures are based on the levels of service shown earlier in the report in Table 4.2 (i.e. 60 minute headway for all corridors except Stavanger Bergen, which has a 120 minute headway) with the assumption of an 18 hour day. 40 Note that figures for Scenarios A and B represent overall rail demand on improved existing rail networks, whereas Scenarios C and D represent HSR demand only, and exclude rail demand on the existing rail network.
Contract 5, Subject 1: Demand Forecasting 140 Table 5.3 Average HSR Demand per Train by Corridor for Scenarios C and D (2024) Route Average passengers per train Scenario C Scenario D Oslo Bergen 85 117-188 Oslo Bergen/Stavanger ( Haukeli ) - 185-315 Stavanger Bergen - 112-140 Oslo Kristiansand Stavanger 101 149-234 Oslo Trondheim 112 137-169 Oslo Stockholm 56 63-79 Oslo Gothenburg - 63-76 The average loadings figures should be treated with caution, as there is inherent variation in demand across the day. Comparable average load factors of around 40%-50% are achieved in the UK but still encounter significant crowding during peak periods. Again, depending on fare and other scenario assumptions, these figures could be significantly higher as options are developed during Phase 3. Table 5.4 presents the revenue forecasts for Scenarios C and D, under the current fare assumptions. It can be seen that for Scenario C Trondheim has the highest revenue but for Scenario D the highest revenue is for the Haukeli route to Bergen and Stavanger, with the revenue for the Trondheim and Stavanger routes roughly similar. Table 5.4 HSR Revenue per Year for Scenarios C and D (2024) Route Revenue per year (million NOK) Scenario C Scenario D Oslo Bergen 701 744-1303 Oslo Bergen/Stavanger ( Haukeli ) - 1510-2466 Stavanger Bergen - 395-530 Oslo Kristiansand Stavanger 747 999-1667 Oslo Trondheim 976 947-1290 Oslo Stockholm 507 434-574 Oslo Gothenburg - 479-671 Figure 5.1 shows the additional HSR journeys on each corridor under Scenario D, compared against Scenario C. The results suggest that additional infrastructure investment to reduce journey times may be most successful in delivering further HSR demand on the Stavanger and Bergen corridors, with the least effect felt on the Trondheim and Stockholm corridors. A similar picture emerges when this analysis is conducted in terms of revenue.
Contract 5, Subject 1: Demand Forecasting 141 Figure 5.1 Incremental HSR journeys by corridor Scenario D over Scenario C (2024 estimates, percentage) 60% 50% 40% 30% 20% 10% 0% Oslo - Bergen (Hallingdal) Oslo - Kristiansand - Stavanger Oslo - Trondheim Oslo - Stockholm 5.5 Recommendations for Phase 3 Phase 3 of the overall High Speed Rail Assessment project will take the demand and revenue forecasting model and use it to assess and develop options in more detail. This option development process will include interactions with other work from Phase 2, including assessment of Finance and Economics (Contract 6), Technical and Safety Analysis (Contract 1) and Rail Planning and Development (Contract 2), as well as the detailed alignment work being undertaken in Phase 3. This option development process will also take into account further development of the options to maximise demand and revenue, including: Changes to service assumptions including journey times, operating frequencies and stopping patterns; Refinement of overall option development, including potential interactions with the InterCity market into Oslo and connections to Gardermoen Airport a key market from Bergen and Stavanger; Consideration of potential impacts of responses from airlines and existing rail operators. At present, all forecasts assume the same level of services operate on the existing rail and air routes despite large reductions in the numbers of passengers. If air and rail services were reduced to reflect reduced available revenue, this would in turn increase the market size for HSR options. Beyond the development of options, further refinement of the forecasting approach can be undertaken to improve the robustness of forecasts and the quality of the data which was made available for Phase 2 work. This includes highway count data, and improved data on existing travel to and from Sweden by road and rail. This will also allow a much better representation of potential demand on the Gothenburg and Stockholm corridors.
Contract 5, Subject 1: Demand Forecasting 142
Contract 5, Subject 1: Demand Forecasting 143 A. NSB Zone definitions (station groupings) Table A.1 - NSB Zone Definitions Zone Stations Boroughs, Counties Trondheim Berkåk-Vikhammer Trondheim, Agdenes, Rennebu, Meldal, Orkdal, Midtre Gauldal, Melhus, Skaun, Klæbu Stjørdal Hallstad-Åsen Malvik, Selbu, Tydal, Meråker, Stjørdal, Frosta Levanger Ronglan-Verdal Leksvik, Levanger, Verdal, Mosvik Steinkjer Røra-Grong Verran, Namdalseid, Inderøy, Snåsa Mosjøen Majavatn-Bolna Steinkjer, Namsos, Alstahaug, Leirfjord, Vefsn, Grane, Hattfjelldal, Dønna, Nesna, Hemnes, Rana Lillehammer Ringebu-Moelv Lillehammer, deler av Ringsaker, Nordre Land, Ringebu Øyer og Gausdal. Hamar Brumunddal/Rena-Tangen Hamar, Stange, deler av Ringsaker, Løten, Elverum og Åmot Lillestrøm Stasjoner på Romerike og Nittedal Alle kommuner på Romerike Oslo S Stasjoner i Oslo og Oppegård Oslo kommune, Nesodden og Oppegård Lysaker Lysaker - Slependen Bærum kommune Asker Billingstad-Asker-Spikkestadlinjen Asker, Røyken og Hurum Drammen Drammen-Sande/Darbu/Drolsum Drammen, Lier, Sande, Svelvik, Nedre Eiker, Øvre Eiker, Modum Kongsberg Skollenborg, Kongsberg, Notodden - Trykkerud Kongsberg, Flesberg, Notodden Tønsberg Holmestrand-Larvik Holmestrand, Hof, Horten, Re, Tønsberg, Nøtterøy, Tjøme, Sandefjord, Stokke, Larvik, Andebu og Lardal Skien Porsgrunn, Skien Hjuksebø/Neslandsvatn Porsgrunn, Skien, Siljan, Bamble, Kragerø, Drangedal, Nome, Bø, Sauherad Ski Vevelstad-Ski-Vestby/Heia Ski, Vestby (ikke Son), Ås, Frogn, Rømskog, Trøgstad, Spydeberg, Askim, Eidsberg Moss Sonsveien-Rygge Vestby (Son), Moss, Rygge, Hobøl, Skiptvet Fredrikstad Råde-Halden/Rakkestad Råde, Fredrikstad, Halden, Sarpsborg, Hvaler, Aremark, Marker, Rakkestad Stavanger Stavanger-Ganddal Stavanger, Randaberg, Sandnes, Sola Bryne Øksnavadporten Vigrestad. Hå, Klepp, Time, Gjesdal Egersund Brusand-Sira Egersund, Sokndal, Lund, Bjerkreim Mandal Audnedal Mandal, Farsund, Lindesnes og Lyngdal Kristiansand Marnardal Hynnekleiv Lillesand, Birkenes, Iveland, Kristiansand, Vennesla, Songdalen, Søgne, Marnardal Arendal Gjerstad-Nelaug-Arendal Risør, Grimstad, Arendal, Gjerstad, Vegårshei, Froland, Tvedestrand Bergen Bergen Bergen (- 3 bydeler), Os, Sund, Fjell, Askøy, Arna Arna Stanghelle Indre Arna, Ytre Arna, Espeland bydeler i Bergen, Meland, Radøy, Austrheim, Osterøy og Lindås. Voss Dale Vieren Ulvik, Granvin, Voss
Contract 5, Subject 1: Demand Forecasting 144 B. Assumed enhancements in Do Minimum matrices B.1 Do Minimum: road enhancements The table below shows the highways projects which were considered by TØI in producing the NTM5 future year Do Minimum matrices. Most of these are drawn from the Norwegian National Transport Plan (NTP) 2010-2019. NTP Corridor 2 Oslo Ørje/Magnor E18 Sydhavna Corridor 3 Oslo Grenland Kristiansand E39 Tjensvollkrysset Bussterminal Oslo E18 Vinterkjærkrysset E18 Ny Varoddbru - ny trprg NTP Corridor 4 Stavanger Bergen Ålesund E39 Jektevik - Sandvikvåg E39 Nyborgkrysset inkl. refusjon Lavik Fergekai Langeland - Moskog Utbedring Lotetunnelen - Eid Rv 13 Bugjelet - Brimnes inkl refusjon NTP Corridor 5 Oslo Bergen/Haugesund Loftesnes bru [bridge] NTP Corridor 6 Oslo Trondheim E6 Nordre avlastningsveg i Nidelv bru - Grilstad Nidelv bru refusjon Rv150 Ulvensplitten - Sinsen ny Alnabruterminalen NTP Corridor 6 (continued) E6 Sluppen - Stavne E6 Nidelv bru-grilstad E6 Mjøen - Oppdal S Rv 4 Lygna sør Rv4 Fossumdiagonalen NY Rv 3 Søndre Bjørå bru - Atna Langevatnet - Ospeli bru E136 Flatmark - Monge E136 Monge - Marstein Rv. 70 Freifjordtunnelen Rv. 70 Opdølstranda NTP Corridor 7 Trondheim Bodø Storforshei-Bolna NTP Corridor 8 Bodø Narvik Tromsø Kirkenes Narvik sentrum Indre Nordnes - Skardalen Tana bru Riksgrensen - Skibotn Skaidi - Hammerfest Hesseng - Riksgrense Russland B.2 Do Minimum: Classic Rail enhancements With regard to classic rail services, the following double-tracking projects are included in the NTP. The four schemes shown in italics are included in the NTM5 Do Minimum coding for the each of the future years, with timetables provided by Jernbaneverket. However, inspection of the NTM5 code revealed no effect on future long distance levels of service for classic rail within the HSR corridors. Double tracking: Barkåker-Tønsberg Trønderbanen (including some extensions beyond NTP) Gevingåsen Fjernstyring Mosjøen-Bodø Lysaker-Sandvika Eidsvoll-Hamar (not in NTP 2010-2020) Sandnes-Stavanger Sandbukta-Fredrikstad (not in NTP 2010-2020) Oslo-Ski Drammen-Tønsberg; (not in NTP 2010-2020) Holm-Holmestrand-Nykirke Fjernstyring Mosjøen-Bodø Farriseidet-Porsgrunn B.3 Do Minimum: Changes to levels of service on other modes (air, bus and ferry) For all domestic aviation routes, the level of service is assumed to be unchanged from 2006. For longdistance buses, an increase in frequency of 25% has been included for bus services with a frequency less than once every hour. For boat travel, a 1% per annum increase in frequency is assumed for routes serving Oslo, Bergen and Stavanger. On other routes, 0.5% per year is applied.
C. Demand Tables (future year journeys by mode)
C.1 Oslo Bergen C.1.1 Oslo-Bergen (route Hallingdal) calling at Hønefoss, Gol and Voss 2024: Scenario C Oslo-Bergen (Hallingdal) Hønefoss Gol Voss HSR Car Air Bus Oslo/Akershus - Bergen Oslo/Akershus - intermediate areas Bergen - intermediate areas Oslo/Akershus - other HSR corridors Bergen - other HSR corridors Other Total Classic Rail Scenario C 408,000 320,000 443,000 58,000 267,000 Mode Share 26% 22% 30% 4% 18% Generated Increment 408,000-50,000-180,000-9,000-45,000 124,000 Scenario C 272,000 3,748,000 181,000 287,000 299,000 Mode Share 6% 78% 4% 6% 6% Increment 272,000-64,000-89,000-12,000-15,000 92,000 Scenario C 65,000 845,000 50,000 79,000 67,000 Mode Share 6% 76% 5% 7% 6% Increment 65,000-13,000-27,000-1,000-5,000 19,000 Scenario C 104,000 21,293,000 2,995,000 1,788,000 2,420,000 Mode Share 0% 79% 7% 7% 7% Increment 104,000-23,000-44,000-2,000-3,000 32,000 Scenario C 83,000 1,829,000 879,000 213,000 112,000 Mode Share 0% 75% 11% 6% 8% Increment 83,000-65,000-37,000-2,000-3,000-24,000 Scenario C 182,000 2,600,009,000 9,186,000 2,475,000 40,202,000 Mode Share 0% 98% 0% 0% 2% Increment 182,000-3,000-63,000-10,000-2,000 104,000 Scenario C 1,114,000 2,628,044,000 13,734,000 4,900,000 43,367,000 Mode Share 0% 98% 1% 0% 2% Increment 1,114,000-218,000-440,000-36,000-73,000 347,000 C.1.2 Oslo-Bergen (route Numedal) calling at Voss 2024 Scenario D Oslo-Bergen (Numedal) Voss HSR Car Air Bus Oslo/Akershus - Bergen Oslo/Akershus - intermediate areas Bergen - intermediate areas Oslo/Akershus - other HSR corridors Bergen - other HSR corridors Other Total Classic Rail Scenario D 620,000 300,000 359,000 54,000 247,000 Mode Share 38% 19% 23% 3% 16% Generated Increment 620,000-70,000-264,000-13,000-65,000 208,000 Scenario D 378,000 3,727,000 156,000 284,000 293,000 Mode Share 8% 77% 3% 6% 6% Increment 378,000-85,000-114,000-15,000-21,000 143,000 Scenario D 75,000 845,000 45,000 78,000 66,000 Mode Share 7% 76% 4% 7% 6% Increment 75,000-13,000-32,000-2,000-6,000 22,000 Scenario D 144,000 21,288,000 2,972,000 1,787,000 2,420,000 Mode Share 0% 79% 7% 7% 7% Increment 144,000-28,000-67,000-3,000-3,000 43,000 Scenario D 111,000 1,828,000 863,000 213,000 110,000 Mode Share 0% 75% 11% 6% 8% Increment 111,000-86,000-53,000-2,000-5,000-35,000 Scenario D 203,000 2,600,009,000 9,172,000 2,475,000 40,201,000 Mode Share 0% 98% 0% 0% 2% Increment 203,000 17,000-77,000-10,000-3,000 130,000 Scenario D 1,531,000 2,627,997,000 13,567,000 4,891,000 43,337,000 Mode Share 0% 98% 1% 0% 2% Increment 1,531,000-265,000-607,000-45,000-103,000 511,000
C.1.3 Oslo-Bergen/Stavanger (route Haukeli) non-stop 2024: Scenario D Oslo-Bergen (Haukeli) non-stop HSR Car Air Bus Oslo/Akershus - Bergen Oslo/Akershus - intermediate areas Bergen - intermediate areas Oslo/Akershus - other HSR corridors Bergen - other HSR corridors Other Total Classic Rail Scenario D 686,000 295,000 338,000 53,000 239,000 Mode Share 41% 19% 22% 3% 15% Generated Increment 686,000-75,000-285,000-14,000-73,000 239,000 Scenario D 270,000 3,757,000 174,000 290,000 297,000 Mode Share 6% 79% 4% 6% 6% Increment 270,000-55,000-96,000-9,000-17,000 93,000 Scenario D 67,000 847,000 50,000 79,000 66,000 Mode Share 6% 76% 5% 7% 6% Increment 67,000-11,000-27,000-1,000-6,000 22,000 Scenario D 1,017,000 21,159,000 2,592,000 1,769,000 2,378,000 Mode Share 0% 79% 7% 7% 7% Increment 1,017,000-157,000-447,000-21,000-45,000 347,000 Scenario D 136,000 1,822,000 851,000 212,000 110,000 Mode Share 0% 75% 11% 6% 8% Increment 136,000-97,000-65,000-3,000-5,000-34,000 Scenario D 319,000 2,599,992,000 9,114,000 2,474,000 40,195,000 Mode Share 0% 98% 0% 0% 2% Increment 319,000 5,000-135,000-11,000-9,000 169,000 Scenario D 2,495,000 2,627,872,000 13,119,000 4,877,000 43,285,000 Mode Share 0% 98% 0% 0% 2% Increment 2,495,000-390,000-1,055,000-59,000-155,000 836,000 Oslo-Stavanger (Haukeli) non-stop HSR Car Air Bus Oslo/Akershus - Stavanger Oslo/Akershus - intermediate areas Stavanger - intermediate areas Oslo/Akershus - other HSR corridors Stavanger - other HSR corridors Other Total Classic Rail Scenario D 467,000 195,000 254,000 27,000 71,000 Mode Share 46% 19% 25% 3% 7% Generated Increment 467,000-56,000-217,000-7,000-24,000 163,000 Scenario D 355,000 10,276,000 322,000 915,000 1,138,000 Mode Share 3% 79% 2% 7% 9% Increment 355,000-61,000-147,000-8,000-21,000 118,000 Scenario D 72,000 799,000 124,000 56,000 106,000 Mode Share 6% 69% 11% 5% 9% Increment 72,000-12,000-34,000-1,000-3,000 22,000 Scenario D 1,151,000 14,739,000 2,528,000 1,170,000 1,704,000 Mode Share 0% 71% 14% 5% 10% Increment 1,151,000-171,000-464,000-29,000-91,000 396,000 Scenario D 67,000 1,259,000 433,000 120,000 104,000 Mode Share 0% 71% 14% 6% 9% Increment 67,000-70,000-31,000-2,000-1,000-37,000 Scenario D 383,000 2,600,604,000 9,458,000 2,589,000 40,162,000 Mode Share 0% 98% 0% 0% 2% Increment 383,000-20,000-162,000-12,000-15,000 174,000 Scenario D 2,495,000 2,627,872,000 13,119,000 4,877,000 43,285,000 Mode Share 0% 98% 0% 0% 2% Increment 2,495,000-390,000-1,055,000-59,000-155,000 836,000
C.2 Stavanger Bergen C.2.1 Stavanger-Bergen (route Haugesund) calling at Haugesund 2024: Scenario D Stavanger-Bergen (Haugesund) Haugesund HSR Car Air Bus Stavanger-Bergen Stavanger-Corridor Bergen-Corridor Stavanger-Other corridors Bergen-Other corridors Other Total Classic Rail Scenario D 231,000 310,000 87,000 40,000 4,000 Mode Share 34% 46% 13% 6% 1% Generated Increment 231,000-41,000-85,000-6,000 0 99,000 Scenario D 1,000 222,000 2,000 15,000 0 Mode Share 0% 92% 1% 6% 0% Increment 1,000 0-1,000 0 0 0 Scenario D 137,000 470,000 37,000 43,000 0 Mode Share 20% 68% 5% 6% 0% Increment 137,000-44,000-30,000-4,000 0 59,000 Scenario D 79,000 1,742,000 891,000 150,000 304,000 Mode Share 0% 82% 11% 7% 0% Increment 79,000-20,000-27,000-2,000-1,000 29,000 Scenario D 176,000 2,172,000 1,302,000 266,000 493,000 Mode Share 0% 56% 29% 5% 10% Increment 176,000-76,000-70,000-3,000-2,000 25,000 Scenario D 111,000 2,623,172,000 10,217,000 4,403,000 42,637,000 Mode Share 0% 98% 0% 0% 2% Increment 111,000 7,000-34,000-4,000 1,000 81,000 Scenario D 735,000 2,628,088,000 12,536,000 4,917,000 43,438,000 Mode Share 0% 98% 0% 0% 2% Increment 735,000-174,000-247,000-19,000-2,000 293,000
C.3 Oslo Stavanger C.3.1 Oslo-Stavanger (route Kristiansand) calling at all stations 2024: Scenario C Oslo-Stavanger (Kristiansand) All stations HSR Car Air Bus Oslo/Akershus - Stavanger Oslo/Akershus - intermediate areas Stavanger - intermediate areas Oslo/Akershus - other HSR corridors Stavanger - other HSR corridors Other Total Classic Rail Scenario C 216,000 220,000 368,000 30,000 83,000 Mode Share 24% 24% 40% 3% 9% Generated Increment 216,000-31,000-103,000-4,000-12,000 66,000 Scenario C 371,000 10,235,000 355,000 907,000 1,139,000 Mode Share 3% 79% 3% 7% 9% Increment 371,000-102,000-114,000-16,000-20,000 119,000 Scenario C 144,000 776,000 106,000 54,000 100,000 Mode Share 12% 66% 9% 5% 8% Increment 144,000-35,000-52,000-3,000-9,000 45,000 Scenario C 214,000 14,868,000 2,907,000 1,188,000 1,782,000 Mode Share 0% 71% 14% 5% 10% Increment 214,000-42,000-85,000-11,000-13,000 63,000 Scenario C 114,000 1,249,000 431,000 119,000 90,000 Mode Share 0% 71% 14% 6% 9% Increment 114,000-55,000-33,000-3,000-15,000 8,000 Scenario C 263,000 2,600,616,000 9,524,000 2,593,000 40,161,000 Mode Share 0% 98% 0% 0% 2% Increment 263,000-33,000-96,000-8,000-16,000 110,000 Scenario C 1,322,000 2,627,964,000 13,691,000 4,891,000 43,355,000 Mode Share 0% 98% 1% 0% 2% Increment 1,322,000-298,000-483,000-45,000-85,000 411,000
C.3.2 Oslo-Stavanger (route Kristiansand) calling at Porsgrunn and Kristiansand 2024: Scenario D Oslo-Stavanger (Kristiansand) Porsgrunn HSR Car Air Bus Oslo-Stavanger Oslo-Corridor Stavanger-Corridor Oslo-Other corridors Stavanger-Other corridors Other Total Classic Rail Scenario D 414,000 200,000 274,000 27,000 74,000 Mode Share 42% 20% 28% 3% 7% Generated Increment 414,000-51,000-197,000-7,000-21,000 138,000 Scenario D 497,000 10,231,000 291,000 905,000 1,130,000 Mode Share 4% 78% 2% 7% 9% Increment 497,000-106,000-178,000-18,000-29,000 166,000 Scenario D 196,000 770,000 79,000 54,000 100,000 Mode Share 16% 64% 7% 4% 8% Increment 196,000-41,000-79,000-3,000-9,000 64,000 Scenario D 401,000 14,839,000 2,837,000 1,180,000 1,772,000 Mode Share 0% 71% 14% 5% 10% Increment 401,000-71,000-155,000-19,000-23,000 133,000 Scenario D 95,000 1,255,000 414,000 120,000 100,000 Mode Share 0% 71% 14% 6% 9% Increment 95,000-69,000-50,000-2,000-5,000-31,000 Scenario D 354,000 2,600,601,000 9,473,000 2,591,000 40,164,000 Mode Share 0% 98% 0% 0% 2% Increment 354,000-28,000-147,000-10,000-13,000 156,000 Scenario D 1,957,000 2,627,896,000 13,368,000 4,877,000 43,340,000 Mode Share 0% 98% 0% 0% 2% 1,957,000-366,000-806,000-59,000-100,000 626,000
C.4 Oslo Trondheim C.4.1 Oslo-Trondheim (route Hamar) calling at all stations 2024: Scenario C Oslo-Trondheim (Hamar) All stations HSR Car Air Bus Oslo/Akershus - Trondheim Oslo/Akershus - intermediate areas Trondheim - intermediate areas Oslo/Akershus - other HSR corridors Trondheim - other HSR corridors Other Total Classic Rail Scenario C 453,000 347,000 347,000 54,000 154,000 Mode Share 33% 26% 26% 4% 11% Generated Increment 453,000-67,000-197,000-10,000-33,000 146,000 Scenario C 179,000 7,538,000 310,000 586,000 733,000 Mode Share 2% 81% 3% 6% 8% Increment 179,000-44,000-65,000-5,000-11,000 54,000 Scenario C 74,000 1,585,000 33,000 103,000 87,000 Mode Share 4% 84% 2% 5% 5% Increment 74,000-51,000-17,000-3,000-5,000-2,000 Scenario C 30,000 17,496,000 3,000,000 1,500,000 2,117,000 Mode Share 0% 87% 3% 6% 5% Increment 30,000-6,000-13,000-1,000-1,000 9,000 Scenario C 402,000 523,000 804,000 59,000 54,000 Mode Share 0% 73% 12% 6% 9% Increment 402,000-89,000-341,000-2,000-2,000-32,000 Scenario C 338,000 2,600,568,000 8,808,000 2,606,000 40,240,000 Mode Share 0% 98% 0% 0% 2% Increment 338,000 52,000-239,000-7,000-3,000 141,000 Scenario C 1,476,000 2,628,057,000 13,302,000 4,908,000 43,385,000 Mode Share 0% 98% 0% 0% 2% Increment 1,476,000-205,000-872,000-28,000-55,000 316,000 C.4.2 Oslo-Trondheim (route Hamar) calling at Gardermoen 2024: Scenario D Oslo-Stavanger (Hamar) Gardermoen HSR Car Air Bus Oslo-Trondheim Oslo-Corridor Trondheim-Corridor Oslo-Other corridors Trondheim-Other corridors Other Total Classic Rail Scenario D 627,000 327,000 284,000 51,000 142,000 Mode Share 44% 23% 20% 4% 10% Generated Increment 627,000-87,000-260,000-13,000-45,000 222,000 Scenario D 239,000 7,530,000 284,000 585,000 730,000 Mode Share 3% 80% 3% 6% 8% Increment 239,000-52,000-91,000-6,000-14,000 76,000 Scenario D 34,000 1,600,000 36,000 105,000 90,000 Mode Share 2% 86% 2% 6% 5% Increment 34,000-36,000-14,000-1,000-2,000-19,000 Scenario D 4,000 17,501,000 3,011,000 1,502,000 2,118,000 Mode Share 0% 87% 3% 6% 5% Increment 4,000-1,000-2,000 1,000 0 2,000 Scenario D 543,000 517,000 686,000 59,000 53,000 Mode Share 0% 73% 12% 6% 9% Increment 543,000-115,000-459,000-2,000-3,000-36,000 Scenario D 350,000 2,600,586,000 8,759,000 2,607,000 40,242,000 Mode Share 0% 98% 0% 0% 2% Increment 350,000 90,000-288,000-6,000-1,000 145,000 Scenario D 1,797,000 2,628,061,000 13,060,000 4,909,000 43,375,000 Mode Share 0% 98% 0% 0% 2% Increment 1,797,000-201,000-1,114,000-27,000-65,000 390,000
C.5 Oslo Stockholm C.5.1 Oslo-Stockholm (route Karlstad) calling at all stations 2024: Scenario C Oslo-Stockholm (Karlstad) All stations HSR Car Air Bus Classic Rail Generated Scenario D 689,000 314,000 465,000 0 20,000 Oslo-Stockholm Oslo-Corridor Stockholm-Corridor Oslo-Other corridors Stockholm-Other corridors Other Total Mode Share 46% 21% 31% 0% 1% Increment 689,000-46,000-366,000 0-5,000 272,000 Scenario D 0 532,000 0 47,000 0 Mode Share 0% 92% 0% 8% 0% Increment 0 0 0 0 0 0 Scenario D 2,000 4,000 1,000 0 0 Mode Share 26% 60% 14% 0% 0% Increment 2,000 0 0 0 0 2,000 Scenario D 30,000 24,598,000 3,090,000 2,108,000 3,022,000 Mode Share 0% 80% 20% 0% 0% Increment 30,000-8,000-11,000-1,000-2,000 8,000 Scenario D 9,000 2,777,888,000 1,670,000 0 53,394,000 Mode Share 0% 75% 9% 6% 9% Increment 9,000-51,000-3,000 0 0-45,000 Scenario D 8,000-175,134,000 8,565,000 2,779,000-13,003,000 Mode Share 0% 99% -5% -2% 7% Increment 8,000 45,000-3,000-1,000 0 49,000 Scenario D 738,000 2,628,202,000 13,791,000 4,934,000 43,433,000 Mode Share 0% 98% 1% 0% 2% Increment 738,000-60,000-383,000-2,000-7,000 286,000 C.5.2 Oslo-Stockholm (route Karlstad) calling at Lillestrøm 2024: Scenario D Oslo-Stockholm (Karlstad) Lillestrøm HSR Car Air Bus Classic Rail Generated Scenario D 779,000 308,000 428,000 0 20,000 Oslo-Stockholm Oslo-Corridor Stockholm-Corridor Oslo-Other corridors Stockholm-Other corridors Other Total Mode Share 51% 20% 28% 0% 1% Increment 779,000-52,000-403,000 0-5,000 319,000 Scenario D 0 532,000 0 47,000 0 Mode Share 0% 92% 0% 8% 0% Increment 0 0 0 0 0 0 Scenario D 1,000 4,000 1,000 0 0 Mode Share 17% 67% 16% 0% 0% Increment 1,000 0 0 0 0 1,000 Scenario D 25,000 24,600,000 3,091,000 2,108,000 3,022,000 Mode Share 0% 80% 20% 0% 0% Increment 25,000-6,000-10,000-1,000-2,000 6,000 Scenario D 10,000 2,777,888,000 1,669,000 0 53,394,000 Mode Share 0% 75% 9% 6% 9% Increment 10,000-57,000-4,000 0 0-51,000 Scenario D 7,000-175,134,000 8,567,000 2,779,000-13,003,000 Mode Share 0% 99% -5% -2% 7% Increment 7,000 51,000-1,000-1,000 0 56,000 Scenario D 822,000 2,628,198,000 13,756,000 4,934,000 43,433,000 Mode Share 0% 98% 1% 0% 2% Increment 822,000-64,000-418,000-2,000-7,000 331,000
C.6 Oslo Gothenburg C.6.1 Oslo-Gothenburg (route Halden) non-stop 2024: Scenario D Oslo-Gothenburg (Halden) non-stop HSR Car Air Bus Oslo-Stockholm Oslo-Corridor Stockholm-Corridor Oslo-Other corridors Stockholm-Other corridors Other Total Classic Rail Scenario D 827,000 1,134,000 0 0 6,000 Mode Share 42% 58% 0% 0% 0% Generated Increment 827,000-554,000-12,000 0-1,000 260,000 Scenario D 0 1,863,000 0 220,000 315,000 Mode Share 0% 78% 0% 9% 13% Increment 0 0 0 0 0 0 Scenario D 0 11,000 1,000 0 10,000 Mode Share 1% 50% 3% 0% 45% Increment 0 0 0 0 0 0 Scenario D 0 20,817,000 3,912,000 1,936,000 2,722,000 Mode Share 0% 50% 5% 0% 45% Increment 0 0 0 0 0 0 Scenario D 0 1,300,000 5,000 0 9,000 Mode Share 0% 71% 13% 7% 9% Increment 0-554,000 0 0 0-554,000 Scenario D 1,000 32,242,000 8,687,000 2,780,000 1,674,000 Mode Share 0% 71% 19% 6% 4% Increment 1,000 554,000 0 0 0 555,000 Scenario D 828,000 57,367,000 12,605,000 4,936,000 4,736,000 Mode Share 1% 71% 16% 6% 6% Increment 828,000-554,000-12,000 0-1,000 261,000
Østfold Akershus Oslo Hedmark Oppland Buskerud Vestfold Telemark Aust-Agder Vest-Agder Rogaland Hordaland Sogn og Fjordane Møre og Romsdal Sør-Trøndelag Nord-Trøndelag Nordland Troms Romsa Finnmark Finnmàrku C.7 County-to-county Matrices C.7.1 HSR journeys (over 100 km) county-to-county matrix: Oslo-Bergen (Hallingdal) calling at Hønefoss, Gol and Voss: Scenario C 2024 County to/from Østfold 0 0 0 139 22 18 0 0 5 1 1265 17457 1938 63 290 16 0 0 0 Akershus 0 0 0 455 193 167 0 2 115 22 5841 72213 9685 1208 1058 60 0 0 0 Oslo 0 0 0 2475 548 686 0 10 624 104 19270 234941 32135 4655 4374 279 1 0 0 Hedmark 142 461 2509 47 53 315 60 6 51 41 587 7872 1149 99 77 5 0 0 0 Oppland 23 193 548 53 0 27 10 1 190 153 1832 17457 2494 292 125 7 0 0 0 Buskerud 18 167 686 310 26 0 0 0 182 158 3444 37427 4594 320 639 36 0 0 0 Vestfold 0 0 0 59 10 0 0 0 0 0 788 15229 1264 6 219 14 0 0 0 Telemark 0 2 9 6 1 0 0 0 0 0 172 3556 282 1 39 2 0 0 0 Aust-Agder 5 112 614 51 190 182 0 0 0 0 64 2071 46 0 0 0 0 0 0 Vest-Agder 1 21 101 41 153 159 0 0 0 0 5 283 103 0 0 0 0 0 0 Rogaland 1286 5825 19223 586 1829 3469 806 178 64 5 0 7082 4293 14 1 0 0 0 0 Hordaland 17399 75542 252451 8110 18130 37599 15068 3514 2123 295 7077 525 11794 4246 970 60 0 0 0 Sogn og Fjordane 1934 9922 32860 1181 2566 4626 1254 279 48 106 4292 11701 0 41 189 11 0 0 0 Møre og Romsdal 65 1280 4801 102 303 331 6 1 0 0 14 4244 40 0 6 0 0 0 0 Sør-Trøndelag 296 1085 4448 78 129 659 223 40 0 0 1 970 189 6 0 0 0 0 0 Nord-Trøndelag 16 59 273 5 7 36 14 2 0 0 0 60 11 0 0 0 0 0 0 Nordland 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Troms Romsa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Finnmark Finnmàrku 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Østfold Akershus Oslo Hedmark Oppland Buskerud Vestfold Telemark Aust-Agder Vest-Agder Rogaland Hordaland Sogn og Fjordane Møre og Romsdal Sør-Trøndelag Nord-Trøndelag Nordland Troms Romsa Finnmark Finnmàrku C.7.2 HSR journeys (over 100 km) county-to-county matrix: Oslo-Bergen (Numedal) calling at Voss only: Scenario D 2024 County to/from Østfold 0 0 0 0 2 1 0 0 10 2 2345 26435 2599 86 28 0 0 0 0 Akershus 0 0 0 0 19 15 0 0 202 42 10774 108433 12974 1537 104 0 0 0 0 Oslo 0 0 0 0 52 53 0 0 1135 211 35156 352784 42086 6118 408 0 0 0 0 Hedmark 0 0 0 0 1 1 0 0 15 2 784 9164 1188 97 5 0 0 0 0 Oppland 2 18 49 1 0 2 1 0 15 9 1079 11197 916 71 5 0 0 0 0 Buskerud 1 14 51 1 2 0 0 0 17 8 4495 43556 4339 112 50 0 0 0 0 Vestfold 0 0 0 0 1 0 0 0 0 0 1488 26165 1783 8 21 0 0 0 0 Telemark 0 0 0 0 0 0 0 0 0 0 300 6008 397 1 4 0 0 0 0 Aust-Agder 9 198 1120 15 15 17 0 0 0 0 78 2524 83 0 0 0 0 0 0 Vest-Agder 2 41 207 2 9 8 0 0 0 0 9 506 149 0 0 0 0 0 0 Rogaland 2388 10746 35071 782 1076 4547 1526 312 78 9 0 7896 4674 16 1 0 0 0 0 Hordaland 26347 112602 374334 9440 11722 43649 25923 5942 2588 529 7891 663 13083 4946 496 12 0 0 0 Sogn og Fjordane 2594 13265 42969 1221 944 4373 1774 393 85 154 4674 12985 0 0 10 2 0 0 0 Møre og Romsdal 89 1663 6382 100 73 116 8 1 0 0 16 4946 0 0 0 0 0 0 0 Sør-Trøndelag 28 107 414 5 5 51 21 4 0 0 1 496 10 0 0 0 0 0 0 Nord-Trøndelag 0 0 0 0 0 0 0 0 0 0 0 12 2 0 0 0 0 0 0 Nordland 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Troms Romsa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Finnmark Finnmàrku 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Østfold Akershus Oslo Hedmark Oppland Buskerud Vestfold Telemark Aust-Agder Vest-Agder Rogaland Hordaland Sogn og Fjordane Møre og Romsdal Sør-Trøndelag Nord-Trøndelag Nordland Troms Romsa Finnmark Finnmàrku C.7.3 HSR journeys (over 100 km) county-to-county matrix: Stavanger-Bergen (Haugesund) calling at Haugesund: Scenario D 2024 County to/from Østfold 0 0 0 0 0 0 0 0 0 0 164 266 6 0 0 0 0 0 0 Akershus 0 0 0 0 0 0 0 0 1 2 546 509 15 2 0 0 0 0 0 Oslo 0 0 0 0 0 0 0 0 2 3 1467 1561 54 6 0 0 0 0 0 Hedmark 0 0 0 0 0 0 0 0 1 1 159 12 0 0 0 0 0 0 0 Oppland 0 0 0 0 0 0 0 0 7 14 1123 96 0 0 0 0 0 0 0 Buskerud 0 0 0 0 0 0 0 0 7 12 1429 673 17 0 0 0 0 0 0 Vestfold 0 0 0 0 0 0 0 0 0 0 354 690 13 0 0 0 0 0 0 Telemark 0 0 0 0 0 0 0 0 0 0 697 1680 27 0 0 0 0 0 0 Aust-Agder 0 1 2 1 7 7 0 0 0 0 51 4384 134 0 0 0 0 0 0 Vest-Agder 0 2 4 1 14 12 0 0 0 0 151 7710 259 1 0 0 0 0 0 Rogaland 160 541 1447 160 1121 1432 345 686 50 149 0 332531 7125 54 3 0 0 0 0 Hordaland 265 543 1719 11 93 672 690 1662 4485 8056 332036 5844 511 3 0 0 0 0 0 Sogn og Fjordane 6 16 57 0 0 17 13 26 138 267 7125 518 0 0 0 0 0 0 0 Møre og Romsdal 0 2 6 0 0 0 0 0 0 1 54 3 0 0 0 0 0 0 0 Sør-Trøndelag 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 Nord-Trøndelag 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Nordland 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Troms Romsa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Finnmark Finnmàrku 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Østfold Akershus Oslo Hedmark Oppland Buskerud Vestfold Telemark Aust-Agder Vest-Agder Rogaland Hordaland Sogn og Fjordane Møre og Romsdal Sør-Trøndelag Nord-Trøndelag Nordland Troms Romsa Finnmark Finnmàrku C.7.4 HSR journeys (over 100 km) county-to-county matrix: Oslo-Stavanger (Kristiansand) calling at all stations: Scenario C 2024 County to/from Østfold 0 0 0 29 1 6 0 0 708 1048 14756 993 25 7 25 2 0 0 0 Akershus 0 0 0 41 4 63 249 68 15314 20563 55840 3452 107 133 47 3 0 0 0 Oslo 0 0 0 0 0 237 0 15 60545 64708 166893 11430 392 545 0 0 0 0 0 Hedmark 30 43 0 0 0 312 139 21 1209 1526 4262 206 6 8 0 0 0 0 0 Oppland 1 4 0 0 0 8 6 1 1145 1409 3335 164 5 6 0 0 0 0 0 Buskerud 6 63 237 305 8 0 0 0 1998 2758 29986 1897 56 15 300 23 0 0 0 Vestfold 0 249 0 138 6 0 0 0 120 863 28517 1829 37 2 172 15 0 0 0 Telemark 0 68 15 21 1 0 0 0 0 19 18629 1296 23 1 62 5 0 0 0 Aust-Agder 707 15314 60545 1209 1145 1998 120 0 0 0 26876 2623 88 1 1 0 0 0 0 Vest-Agder 1048 20563 64708 1526 1409 2758 863 19 0 0 99445 9142 389 1 1 0 0 0 0 Rogaland 15293 55712 166525 4251 3326 30388 29232 19354 26876 99319 0 0 0 2 2 0 0 0 0 Hordaland 990 3613 12252 211 173 1889 1811 1282 2706 9553 0 0 0 2 2 0 0 0 0 Sogn og Fjordane 25 111 407 6 5 56 36 22 91 400 0 0 0 0 0 0 0 0 0 Møre og Romsdal 8 147 570 8 7 15 2 1 1 1 2 2 0 0 0 0 0 0 0 Sør-Trøndelag 26 50 0 0 0 309 175 63 1 1 2 2 0 0 0 0 0 0 0 Nord-Trøndelag 2 4 0 0 0 24 15 5 0 0 0 0 0 0 0 0 0 0 0 Nordland 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Troms Romsa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Finnmark Finnmàrku 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Østfold Akershus Oslo Hedmark Oppland Buskerud Vestfold Telemark Aust-Agder Vest-Agder Rogaland Hordaland Sogn og Fjordane Møre og Romsdal Sør-Trøndelag Nord-Trøndelag Nordland Troms Romsa Finnmark Finnmàrku C.7.5 HSR journeys (over 100 km) county-to-county matrix: Oslo-Stavanger (Kristiansand) calling at Kristiansand and Porsgrunn: Scenario D 2024 County to/from Østfold 0 0 0 16 1 0 0 0 434 1521 26928 2223 61 0 14 1 0 0 0 Akershus 0 0 0 0 0 0 400 87 8529 34541 105781 7812 255 0 0 0 0 0 0 Oslo 0 0 0 0 0 0 0 18 40123 110304 319591 26597 985 0 0 0 0 0 0 Hedmark 17 0 0 0 0 0 144 27 695 2474 9417 502 15 0 0 0 0 0 0 Oppland 1 0 0 0 0 0 6 2 651 2307 7706 411 12 0 0 0 0 0 0 Buskerud 0 0 0 0 0 0 0 0 830 3171 38772 2885 88 0 0 0 0 0 0 Vestfold 0 396 0 142 6 0 0 0 63 1705 53610 4289 87 2 138 12 0 0 0 Telemark 0 86 18 27 2 0 0 0 0 26 31100 2752 48 1 80 7 0 0 0 Aust-Agder 434 8529 40123 695 651 830 62 0 0 0 37554 4167 147 0 0 0 0 0 0 Vest-Agder 1521 34541 110304 2474 2307 3171 1705 26 0 0 69513 13524 575 2 2 0 0 0 0 Rogaland 27710 105589 319042 9395 7687 39210 54608 32027 37501 69351 0 0 0 3 3 0 0 0 0 Hordaland 2216 8242 28806 517 439 2877 4244 2721 4299 14092 0 0 0 3 5 0 0 0 0 Sogn og Fjordane 61 266 1028 15 13 88 86 47 151 591 0 0 0 0 0 0 0 0 0 Møre og Romsdal 0 0 0 0 0 0 2 1 0 2 3 3 0 0 0 0 0 0 0 Sør-Trøndelag 14 0 0 0 0 0 143 82 0 2 3 5 0 0 0 0 0 0 0 Nord-Trøndelag 1 0 0 0 0 0 12 7 0 0 0 0 0 0 0 0 0 0 0 Nordland 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Troms Romsa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Finnmark Finnmàrku 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Østfold Akershus Oslo Hedmark Oppland Buskerud Vestfold Telemark Aust-Agder Vest-Agder Rogaland Hordaland Sogn og Fjordane Møre og Romsdal Sør-Trøndelag Nord-Trøndelag Nordland Troms Romsa Finnmark Finnmàrku C.7.6 HSR journeys (over 100 km) county-to-county matrix: Oslo-Trondheim (Hamar) calling at all stations: Scenario C 2024 County to/from Østfold 0 0 0 377 37 3 0 0 0 0 0 0 0 0 377 37 3 0 0 Akershus 0 0 0 1452 356 29 126 18 359 225 578 0 0 0 1452 356 29 126 18 Oslo 0 0 0 6384 875 91 0 0 0 0 0 0 0 0 6384 875 91 0 0 Hedmark 381 1469 6458 371 183 696 206 22 362 258 602 381 1469 6458 371 183 696 206 22 Oppland 37 352 875 178 0 42 15 1 269 215 411 37 352 875 178 0 42 15 1 Buskerud 2 29 88 683 41 0 0 0 9 6 9 2 29 88 683 41 0 0 0 Vestfold 0 128 0 205 15 0 0 0 0 0 0 0 128 0 205 15 0 0 0 Telemark 0 19 0 22 1 0 0 0 0 0 0 0 19 0 22 1 0 0 0 Aust-Agder 0 365 0 364 269 9 0 0 0 0 0 0 365 0 364 269 9 0 0 Vest-Agder 0 229 0 259 215 6 0 0 0 0 0 0 229 0 259 215 6 0 0 Rogaland 0 599 0 609 409 10 0 0 0 0 0 0 599 0 609 409 10 0 0 Hordaland 286 1476 4483 686 513 432 207 37 6 3 4 286 1476 4483 686 513 432 207 37 Sogn og Fjordane 376 2468 8500 961 678 685 186 31 8 5 4 376 2468 8500 961 678 685 186 31 Møre og Romsdal 276 5883 19055 2383 2185 481 28 3 1 1 1 276 5883 19055 2383 2185 481 28 3 Sør-Trøndelag 15132 64360 212377 18717 29146 26282 13398 2640 7 7 9 15132 64360 212377 18717 29146 26282 13398 2640 Nord-Trøndelag 1526 6207 25249 2223 3557 2902 1395 233 0 0 0 1526 6207 25249 2223 3557 2902 1395 233 Nordland 1 14 51 5 11 1 0 0 0 0 0 1 14 51 5 11 1 0 0 Troms Romsa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Finnmark Finnmàrku 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Østfold Akershus Oslo Hedmark Oppland Buskerud Vestfold Telemark Aust-Agder Vest-Agder Rogaland Hordaland Sogn og Fjordane Møre og Romsdal Sør-Trøndelag Nord-Trøndelag Nordland Troms Romsa Finnmark Finnmàrku C.7.7 HSR journeys (over 100 km) county-to-county matrix: Oslo-Trondheim (Hamar) calling at Gardermoen only: Scenario D 2024 County to/from Østfold 0 0 0 53 7 0 0 0 0 0 0 0 2 161 21181 2452 1 0 0 Akershus 0 0 0 190 50 20 129 19 366 230 589 549 240 3648 89340 9788 22 0 0 Oslo 0 0 0 941 186 0 0 0 0 0 0 0 179 11866 291571 39505 79 0 0 Hedmark 53 190 956 30 17 104 70 9 251 167 390 313 114 488 13473 1540 3 0 0 Oppland 7 51 187 17 0 9 5 1 197 127 273 232 86 393 11015 1250 3 0 0 Buskerud 0 21 0 103 8 0 0 0 0 0 0 0 3 241 35171 4466 1 0 0 Vestfold 0 130 0 70 5 0 0 0 0 0 0 0 0 18 20301 2357 0 0 0 Telemark 0 19 0 9 1 0 0 0 0 0 0 0 0 2 4334 413 0 0 0 Aust-Agder 0 373 0 252 197 0 0 0 0 0 0 0 0 0 12 0 0 0 0 Vest-Agder 0 234 0 168 127 0 0 0 0 0 0 0 0 0 12 0 0 0 0 Rogaland 0 611 0 395 272 0 0 0 0 0 0 0 0 1 16 0 0 0 0 Hordaland 0 596 0 331 249 0 0 0 0 0 0 0 0 14 523 50 0 0 0 Sogn og Fjordane 2 254 180 120 90 3 0 0 0 0 0 0 0 4 196 16 0 0 0 Møre og Romsdal 166 3693 11980 492 399 242 18 2 0 0 1 14 4 0 0 0 0 0 0 Sør-Trøndelag 21439 90742 294137 13617 11217 35441 20505 4387 12 12 16 523 196 0 0 0 0 0 0 Nord-Trøndelag 2432 9710 38916 1562 1276 4421 2323 408 0 0 0 48 16 0 0 0 0 0 0 Nordland 1 20 74 3 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 Troms Romsa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Finnmark Finnmàrku 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Østfold Akershus Oslo Hedmark Oppland Buskerud Vestfold Telemark Aust-Agder Vest-Agder Rogaland Hordaland Sogn og Fjordane Møre og Romsdal Sør-Trøndelag Nord-Trøndelag Nordland Troms Romsa Finnmark Finnmàrku C.7.8 HSR journeys (over 100 km) county-to-county matrix: Oslo-Stockholm (Karlstad) calling at Lillestrøm and Kongsvinger: Scenario C 2024 County to/from Østfold 0 0 0 214 1 1 0 0 97 77 178 156 55 19 202 21 0 0 0 Akershus 0 0 0 755 36 51 140 21 1013 671 1523 1515 581 185 784 82 0 0 0 Oslo 0 0 0 3742 5 6 0 0 161 99 236 246 88 33 3012 355 1 0 0 Hedmark 218 764 3789 0 39 401 121 11 256 178 424 354 128 43 0 0 0 0 0 Oppland 1 37 5 38 0 0 1 0 45 31 71 54 21 7 36 4 0 0 0 Buskerud 1 52 7 396 0 0 0 0 0 0 0 0 0 0 369 39 0 0 0 Vestfold 0 142 0 121 1 0 0 0 0 0 0 0 0 0 148 18 0 0 0 Telemark 0 22 0 11 0 0 0 0 0 0 0 0 0 0 24 3 0 0 0 Aust-Agder 97 1021 165 258 45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Vest-Agder 77 676 101 179 31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rogaland 178 1544 246 432 70 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Hordaland 162 1617 272 376 57 0 0 0 0 0 0 0 0 0 3 0 0 0 0 Sogn og Fjordane 57 612 94 135 22 0 0 0 0 0 0 0 0 0 1 0 0 0 0 Møre og Romsdal 19 196 35 45 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Sør-Trøndelag 205 800 3035 0 37 372 149 24 0 0 0 3 1 0 0 0 0 0 0 Nord-Trøndelag 21 82 349 0 4 39 17 3 0 0 0 0 0 0 0 0 0 0 0 Nordland 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Troms Romsa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Finnmark Finnmàrku 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Østfold Akershus Oslo Hedmark Oppland Buskerud Vestfold Telemark Aust-Agder Vest-Agder Rogaland Hordaland Sogn og Fjordane Møre og Romsdal Sør-Trøndelag Nord-Trøndelag Nordland Troms Romsa Finnmark Finnmàrku C.7.9 HSR journeys (over 100 km) county-to-county matrix: Oslo-Stockholm (Karlstad) calling at Lillestrøm only: Scenario D 2024 County to/from Østfold 0 0 0 139 1 1 0 0 96 76 177 155 54 19 135 11 0 0 0 Akershus 0 0 0 286 36 51 139 21 1008 667 1515 1508 578 184 337 26 0 0 0 Oslo 0 0 0 2996 5 6 0 0 160 98 235 245 88 33 2431 222 0 0 0 Hedmark 142 293 3039 0 25 329 91 8 171 112 265 221 82 27 0 0 0 0 0 Oppland 1 36 5 24 0 0 1 0 45 31 70 54 21 7 19 2 0 0 0 Buskerud 1 52 7 324 0 0 0 0 0 0 0 0 0 0 307 25 0 0 0 Vestfold 0 141 0 90 1 0 0 0 0 0 0 0 0 0 122 11 0 0 0 Telemark 0 22 0 8 0 0 0 0 0 0 0 0 0 0 20 2 0 0 0 Aust-Agder 96 1016 164 172 45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Vest-Agder 76 672 101 113 31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rogaland 177 1536 245 270 70 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Hordaland 161 1609 271 234 56 0 0 0 0 0 0 0 0 0 2 0 0 0 0 Sogn og Fjordane 56 609 94 87 22 0 0 0 0 0 0 0 0 0 1 0 0 0 0 Møre og Romsdal 19 195 35 28 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Sør-Trøndelag 137 344 2456 0 20 312 123 20 0 0 0 2 1 0 0 0 0 0 0 Nord-Trøndelag 11 26 219 0 2 25 11 2 0 0 0 0 0 0 0 0 0 0 0 Nordland 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Troms Romsa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Finnmark Finnmàrku 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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