Jernbaneverket

Norwegian High Speed Railway

Assessment Project

Contract 5: Market Analysis

Subjects 2 and 3: Expected Revenue and Passenger Choices

Final Report 18/02/2011

In association with

/Subjects 2 and 3 Surveys Final Report_180211.docx

Contract 5, Subjects 2 and 3 Expected Revenue and Passenger Choices 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 Railway 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 DOCUMENT REF: Subjects 2 and 3 Surveys Final JOB NUMBER: 5096833 Report_180211.docx Revision Purpose Description Originated Checked Reviewed Authorised Date

3 Final JA/PB JM MH/CR WL 18/02/11 2 Draft Final PB JM MH JD 02/02/11 1 Skeleton of final report MH LMG PB MH 29/10/10

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Contract 5: Market Analysis

Subjects 2 and 3: Expected Revenue and Passenger Choices

Final Report

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Table of contents

Executive Summary 6 Background 6 Collecting new evidence to support the market analysis 6 Emerging findings 7 1 Introduction 16 1.1 Background 16 1.2 Overall Context of the Market Analysis Contract 17 1.3 Purpose of specific report 17 1.4 Organisation of report 18 2 Background and Study Objectives 19 2.1 Introduction 19 2.2 Demand and Revenue Forecasting (Subjects 2 and 3) 21 3 Survey Approach and Development 23 3.1 Introduction 23 3.2 Overall Approach 23 3.3 Development of Stated Preference Exercises 24 3.4 Pilot Survey 34 4 Survey Outputs and Analysis 36 4.1 Introduction 36 4.2 Results of Full Survey 36 4.3 Modelling of Mode Choice and Class Choice 38 4.4 Qualitative Aspects of High Speed Rail 54 4.5 Comparison with Other High Speed Rail Mode Choice Models 63 4.6 Trip generation effects 64 5 Conclusions 66 5.1 Introduction 66 5.2 Recommended mode choice models 66 5.3 Demonstration of mode choice effects 69 5.4 Recommendations for further analysis 74 Appendix A – Pilot Survey Report Appendix B – Survey Questions

List of Tables Table 3.1: Attribute levels for pilot mode choice experiment (air users) 28 Table 3.2: Attribute levels for pilot mode choice experiment (train users) 29 Table 3.3: Attribute levels for pilot mode choice experiment (bus users) 30 Table 3.4: Attribute levels for pilot mode choice experiment (car users) 31 Table 3.5: Attribute levels for in-train service experiment 32 Table 4.1: Survey response rates 36

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Table 4.2: Sample distribution by mode and purpose 37 Table 4.3: Sample distribution by origin and destination for long distance trips 37 Table 4.4: Sample distribution by trip length 38 Table 4.5: Interpretation of model fit statistics 42 Table 4.6: Choice model jointly estimated on data from SP1 and SP2 (work purposes) 43 Table 4.7: Choice model jointly estimated on data from SP1 and SP2 (non-work purposes) 46 Table 4.8: Values of time per hour for long-distance private travel in Norway, NOK (2009) 50 Table 4.9: Average values of time per hour from current study, NOK (2010) 50 Table 5.1: Mode choice model for implementation (work purposes) 68 Table 5.2: Mode choice model for implementation (non-work purposes) 69 Table 5.3: Mode Choice Effects (Oslo-Bergen) 70 Table 5.4: Mode Choice % Effects (Oslo-Bergen) 70

List of Figures Figure 2.1: Corridors and Scenarios 20 Figure 3.1: Introduction to HSR alternative presented prior to SP mode choice experiment 26 Figure 3.2: Seat size and spacing descriptions used in the in-train service experiment 32 Figure 4.1: Demographics of sample 38 Figure 4.2: Structure for pooling of SP data in model estimation 40 Figure 4.3: Values of time (NOK per hour) by income group and according to whether the employer paid for the trip (work purposes) 49 Figure 4.4: Values of time (NOK per hour) by income group (non-work purposes) 49 Figure 4.5: Willingness to pay (NOK per return ticket) for improved in-train services (work purposes) 52 Figure 4.6: Willingness to pay (NOK per return ticket) for improved in-train services (non-work purposes) 53 Figure 4.7: Impact of factors on attractiveness of current mode (existing car users) 54 Figure 4.8: Impact of factors on attractiveness of current mode (existing air users) 55 Figure 4.9: Impact of factors on attractiveness of current mode (existing train users) 56 Figure 4.10: Impact of factors on attractiveness of current mode (existing bus users) 57 Figure 4.11: Impact of factors on likelihood of using HSR (existing car users) 58 Figure 4.12: Impact of factors on likelihood of using HSR (existing air users) 59 Figure 4.13: Impact of factors on likelihood of using HSR (existing train users) 60 Figure 4.14: Impact of factors on likelihood of using HSR (existing bus users) 61 Figure 4.15: Impact of tunnels on likelihood of using HSR 62 Figure 4.16: Concerns regarding tunnels 63 Figure 5.1: Implied tree structure for work model 67 Figure 5.2: Oslo-Bergen: HSR Fare against Revenue (2024) 71 Figure 5.3: Rail-Air Market Share (Nelldal-Troche, 2001) 72 Figure 5.4: Rail-Air Market Share (Steer Davies Gleave, 2006) 73

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Executive Summary 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”. The objective of the Phase 2 study 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. Within this overall remit, the objectives of the two Subjects of the Market Analysis Contract covered by this report were: • to determine the willingness to pay (WTP) of travellers by high-speed rail services, given that this mode represents a new product on the Norwegian transport market; and • to gain a quantitative understanding of the aspects of the service that motivate travellers’ choice of mode between high-speed rail and other modes of transport: air, car, bus and ferry. A bespoke survey has therefore been designed, undertaken and analysed for this study which has provided parameters for the demand model system developed in Subject 1 (Demand potential for high speed rail in Norway), which is then used to forecast fare revenues for HSR. Collecting new evidence to support the market analysis The introduction of HSR in Norway would bring a completely new travel alternative for travellers and as such it would not be reasonable to predict demand for this new alternative using existing models. Not only would HSR bring levels of service – journey time, comfort, fares, access characteristics – that are unknown in Norway at present, it would represent an entirely new concept in the market. For this reason evidence on likely demand, and willingness to pay, needs to be collected from stated preference (SP) experiments, which allow the collection of evidence on how travellers consider an alternative which does not exist in the travel market with which they are familiar. For this study, respondents who were making journeys that were plausible candidates for transfer to HSR were interviewed and asked to participate in a stated preference choice experiment. Within the survey the respondent was asked to focus on a recent long distance trip. They were then asked to participate in two SP experiments. The first experiment offered the choice between HSR and the mode that the respondent was observed to use, while the second offered choices between different configurations of the HSR service, posed as a class choice experiment, giving more detailed insight into preferences for aspects of the HSR service. The mode choice experiment was specified with three choice alternatives. The respondent could decide to remain with their existing mode, switch to high speed rail, or if neither of these were acceptable could indicate that they would choose not to make the trip under the scenario described. The attributes which were varied in the mode choice experiment included: travel time (described by access time, waiting time, in-vehicle time and egress time), service frequency, the number of interchanges and the cost of the journey. Respondents were also offered HSR options in which the amount of time with views and amount of time in tunnels differed. The second stated preference experiment was designed to provide willingness-to-pay estimates for the in-train service components, and as such was set up in the context of a choice of travel class. The respondent could choose between a standard carriage and an improved carriage, with

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higher specification on some of the service components but at a higher ticket price. The attributes varied within this experiment included the seat size and spacing, availability of power points and wifi, mobile phone coverage, security of luggage, availability of food and refreshments, and the additional ticket cost to achieve the higher level of services offered. The survey included two sets of ratings questions that sought to provide a more qualitative assessment of the factors that could influence the likelihood of choosing to travel by high speed rail. The first set of questions asked for an initial assessment of how attractive respondents perceived an HSR service to be across a number of dimensions when compared to their current mode for their journey. The second set of questions probed the impact that different service factors, both on the trains and relating to station facilities, would have on the likelihood of using high speed rail. Respondents were also asked about their attitudes to tunnels. The survey was piloted between 16 th and 22 nd December 2010: respondents were recruited through the TNS Norwegian online panel. The analysis of the pilot survey suggested that questionnaire was working as intended and was producing a dataset that was capable of supporting the sort of analysis necessary to feed in to the demand models. Some improvements were identified for the main wave of interviews, and the subsequent phase of surveys were undertaken between 6th and 14 th January 2011. In total 3108 interviews were completed for this study. The data collected from the stated preference experiments were used to support the development of a series of discrete choice models. These provide insight into the drivers of choices between existing modes and HSR, and between different HSR services. A number of steps were required to translate the models estimated from the stated preference experiments to models appropriate for implementation within the other Subjects within this Work Package. The report provides detail on both the development of the choice models and the translation of these models to fit within the wider forecasting framework. Emerging findings Subject 2 - Analysis of expected amount of ticket revenues The models developed from the choice experiments provide an understanding of the impact of different factors on the likelihood of different groups of travellers choosing to switch to high-speed rail. These models capture both the time and cost sensitivity of those making long distance trips, and from these it is possible to infer the value that people place on journey time savings. The models suggest that cost sensitivity is non-linear, with the marginal response to cost decreasing as the journey cost increases. This means that the value of time varies with the cost of the journey under consideration, specifically that values of time increase with increasing journey costs. This finding is consistent with findings elsewhere 1. The models also suggest that the cost sensitivity of respondents decreases as their household income increases. It is possible to make some comparisons with established values by looking at the mean and median VOTs that would be implied from the observed distribution of journey costs within the sample (once the income distribution has been appropriately weighted by mode and purpose to reflect the income distribution for long distance trips within the NTS). Values of time per hour for long-distance private travel in Norway, NOK (2009) Air Car Rail Bus Handbook 140 303 172 94 80

1 Full details of the literature which has been used for assessing the comparability of the model findings are provided in the full report.

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Average values of time per hour from current study, NOK (2010) Value of time (NOK/hour) Air Car Rail Bus HSR Work Mean 372 105 373 197 331 Median 374 127 356 163 337 Non -work Mean 109 21 144 88 97 Median 112 22 146 91 100

When comparing with the values of time in Handbook 140, we see that the recommended values for air and car are closer to those that we find for trips made for work purposes, whereas those for rail and bus are more in line with the values that this study finds for trips made for non-work purposes. Differences in values are to be expected between studies, and it should be remembered that the values from this study relate to the context of a mode choice, specifically a choice between currently available modes and HSR. Moving forward, more complex model specifications could be investigated, for example distributed parameter values, with a view to seeing how these may impact on the implied cost and time sensitivity across the modes. We would also propose a joint estimation that would utilise the data used in the estimation of the NTM5 models alongside the new SP survey data to create a single model that will be stronger across the breadth of all possible scenarios and better integrate with shorter-distance journeys where car dominates as a mode. The models also provide a quantification of the willingness to pay for a range of different in-train service factors. The charts that follow show the value placed on a range of different services and can be used to assess whether a financial case may be made for different rolling stock configurations. In each case of each factor the levels are valued relative to the base situation (which was used to describe the standard carriage configuration in the choice experiment); these have values of zero in the following charts. The error bars presented in the charts are the 95% confidence intervals on the willingness to pay estimates. It can be observed that there are some cases where the willingness to pay for marginal improvements is not significantly different to zero (where the error bars cross the axis).

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Willingness to pay (NOK per return ticket) for improved in-train services (work purposes)

Normal spacing

Wide spacing - male

Wide spacing - female

No power points or Wifi

Power points but no Wifi

Power points + free Wifi which works over half of route

Power points + free Wifi which works through entire route, employer paid for a substantial part of journey Power points + free Wifi which works through entire route, employer did not pay for a substantial part of journey

Unreliable mobile phone coverage on journey

Reliable mobile phone coverage over half of the route

Reliable mobile phone coverage through entire route

Quiet carriage with no mobile phone calls permitted, work involves making regular business trips Quiet carriage with no mobile phone calls permitted, work does not involve making regular business trips

Luggage stored in racks above seat

Racks above seat + option to lock luggage in secure area, use rail to travel in Norway less than a couple of times a year Racks above seat + option to lock luggage in secure area, use rail to travel in Norway a couple of times a year or more

No food and drinks available for purchase on train

Food and drinks available for purchase from separate carriage

Food and drink available for purchase and served at seat

Food and drink included in price of ticket and served at seat, use rail to travel in Norway a couple of times a year or more Food and drink included in price of ticket and served at seat, use rail to travel in Norway less than a couple of times a year -20 0 -10 0 0 100 200 300 400 500 600

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Willingness to pay (NOK per return ticket) for improved in-train services (non-work purposes)

Normal spacing

Wide spacing

No power points or Wifi

Power points but no Wifi

Power points + free Wifi which works over half of route

Power points + free Wifi which works through entire route, not travelling with children

Power points + free Wifi which works through entire route, travelling with children

Unreliable mobile phone coverage on journey

Reliable mobile phone coverage over half of the route

Reliable mobile phone coverage through entire route, aged 16 - 40 years old

Reliable mobile phone coverage through entire route, aged 41 years or older

Quiet carriage with no mobile phone calls permitted

Luggage stored in racks above seat

Racks above seat + option to lock luggage in secure area, travelling on their own

Racks above seat + option to lock luggage in secure area, travelling with others

No food and drinks available for purchase on train

Food and drinks available for purchase from separate carriage

Food and drink available for purchase and served at seat

Food and drink included in price of ticket and served at seat, aged 16 - 40 years old

Food and drink included in price of ticket and served at seat, aged 41 years or older -10 0 0 100 200 300 400 500 600

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The choice models have been implemented within a forecasting framework to provide forecasts of the market shares and ticket revenues under different high-speed rail scenarios (different in- vehicle times, fares and service frequencies). For the purposes of this report some tests are reported for the Oslo-Bergen corridor. The following table shows the results of some of these tests where a base scenario of an HSR service is tested in which HSR has an in-vehicle time of 2 hours 30 mins, a headway of 60 mins (i.e. one train per hour), and a fare equal to the current air fare. Additional scenarios then show the impact of varying the in-vehicle time, the fare, and the headway. Mode Choice Effects (Oslo-Bergen)

Test HSR Service Specification Annual Passengers, (k) Annual Revenue, NOK (m) % Fare IVT Headway Non- Non- Generation (%Air Total Work Total Work Work Work (mins) (mins) Fare)

140 60 100% 1468 929 539 1076 766 310 34.0%

150 60 100% 1363 862 501 999 711 288 33.3% IVT IVT

160 60 100% 1282 803 479 938 662 276 32.6%

150 60 80% 1547 954 593 902 629 273 34.2%

150 60 90% 1452 907 545 955 673 282 33.7%

150 60 100% 1363 862 501 999 711 288 33.3% Fare Fare 150 60 110% 1279 818 461 1034 742 292 32.8%

150 60 120% 1200 776 424 1061 768 293 32.4%

150 60 100% 1363 862 501 999 711 288 33.3%

150 120 100% 1321 833 488 968 688 280 33.0%

Headway Headway 150 240 100% 1241 779 462 908 642 266 32.6%

These results demonstrate the revenues that may be obtained within this corridor under a number of different scenarios. The table also shows that levels of trip generation that the model suggests may occur. The results can also be used to calculate demand elasticities, which can be compared against other studies. The change to HSR in-vehicle times above produce average implied travel time elasticities of -1.01 for all passengers (-1.09 for work and -0.88 for non-work trips). These are within the range reported in the literature. 10% changes in either direction on HSR fares produce average implied elasticites of -0.63 for all passengers (-0.51 for work trips and -0.84 for non-work trips). Again these elasticities correspond well to those reported in the literature. These tests suggest that the model return reasonable rail elasticities when compared to other existing evidence. The following figure presents the forecast changes to high speed rail revenue on the Oslo- Bergen corridor against changes in fares (measured as a % of air fares).

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Oslo-Bergen: HSR Fare against Revenue (2024)

The figure above suggests that: • Total High Speed rail revenues would peak with a fare of approximately 160% of the current air fare, although the total revenue varies by less than 2% between an average fare of 130% and 180%. • Revenues from non-work passengers would peak at a fare equivalent to 120% of the current air fare. • Revenues from work passengers would be forecast to peak at a fare equivalent to 170% of the current air fare. The forecasts undertaken on the Oslo-Bergen corridor suggest that HSR is forecast to obtain: • 65% of the HSR-Air market share with a journey time of 2 hours 30 minutes (Scenario D); and • 45% of the HSR-Air market share with a journey time of 4 hours 30 minutes (Scenario C) This is in line with findings from comparable international evidence. The levels of the forecasts of generated traffic on the Oslo-Bergen corridor also compare well with the international evidence, although for this corridor the forecasts are towards the higher end of the range expected from the literature with induced journeys typically accounting for between 32% and 34% of total high speed rail demand. It is however worth noting that the proportion of generated trips will vary for different high speed corridors as the changes to the total accessibility brought about by the introduction of high speed rail are a function of both the new service provided and the existing alternative services for making a given journey. We therefore conclude that the models estimated from the choice experiments are providing intuitive findings and provide a forecasting framework that can now be used to assess a wide range of different HSR scenarios across the full range of corridors.

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Subject 3 - Passengers choice: preferences for travel and means of transport The report gives a full breakdown of how the survey respondents rated a wide range of factors. Given the focus of the previous section on the market demand and revenues for HSR when considering it as a competing mode to air it is informative to review the factors that the air travellers in the sample stated would influence the attractiveness of HSR compared to air. Impact of factors on attractiveness of current mode (existing air users)

Time required for your journey

Ability to work during your journey

Comfort of your journey

Ease of making your journey AIR much less attractive AIR less attractive Travelling with your group no effect AIR more attractive Luggage requirements AIR much more attractive

Cost of your journey

Security on your journey

Reliability of making your journey

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

For the air travellers the initial impressions of respondents (before completing the choice experiments) were that HSR is more attractive than air across all of the attributes considered. Some respondents see air as being better in some dimensions, but on average, more are positive about the benefits of HSR. It is noteworthy that having been given an indication of the likely HSR travel times, HSR is perceived to offer time advantages over air. Clearly in most cases the flight time will be lower than the HSR in-vehicle time (given the speed advantage of air), so this suggests that without direct prompting the respondents are considering the journey time in its totality, i.e. including the time required to reach the airport and then to check in, pass security, and wait for boarding. This is supported by the high rating also placed on the ease of making an HSR journey compared to air. It is also interesting to note that the attribute which the air respondents are least positive about is the potential reliability of HSR services (although this is generally still viewed as better than air). This suggests that there may be advantage to emphasising this dimension of the new service when promoting HSR schemes with air travellers, particularly for cases where new dedicated alignments are used which could allow higher levels of reliability than shared track. Respondents were also asked about the impact of a range of factors on their likelihood of using HSR. From these ratings questions it can be observed that the factors that respondents report would most influence them to use HSR are the provision of connecting bus and train services that would give an integrated public transport journey and significant savings in journey travel time. It can also be observed that across the users of all modes the highest weight is placed on

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the “fundamentals”, i.e. journey time, accessibility and security. The comfort related factors still have an influence, but generally come lower down the list of traveller priorities. Impact of factors on likelihood of using HSR (existing air users)

1-No effect 2 3 4 5- Much more likely to use HSR Significant savings in your journey travel time

Connecting bus and train services at the train stations Wifi available on trains and in tunnels

Having rest rooms at the end of each carriage

Food and drink available on the trains

Good parking provision at the train stations

Having electrical power points at seats

Well defined and easy walking routes for the connection between the HSR platforms and… Good security at stations

Having plenty of leg room between seats

Direct services to more destinations

Having mobile phone signal in tunnels

Litter removed and restrooms checked during the journey Having wider seats

Having quiet zones on the train

Locked luggage areas available for storing baggage on trains Having well lit carriages

High quality waiting areas offering refreshments

Food and drink served at seats

Staff walking through the train

CCTV coverage of all carriages and contact with the driver or guard

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%

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When asked about their attitude to having a significant proportion of the journey in tunnels, a significant majority (79%) stated that travelling in tunnels would not affect their choice to use high-speed rail. Impact of tunnels on likelihood of using HSR

I would rather use rail if a substantial portion of the journey was in tunnels

Travelling in tunnels would not affect my choice to use high-speed rail

I would probably not travel by rail if a substantial portion of the journey was in tunnels

I would definitely not travel by rail if a substantial portion of the journey was in tunnels

0 10 20 30 40 50 60 70 80 90100 % of respondents

The 16.5% of respondents that indicated that they would either “definitely” or “probably” not travel by rail if a substantial portion of the journey was in tunnels were then asked about their specific concerns. The most significant concern related to the loss of the view of the scenery on the trip, with the next most important concern being the implications of the train having an accident whilst in a tunnel. These findings suggest that there are a wide range of potential factors other than fares that could influence passenger’s decisions of which mode to travel on, and that these can inform the strategies that may be developed to better inform public perception about what a given HSR service may or not be able to offer.

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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 will use and enhance existing information, models and data. New tools will be 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 shown in the National Transport Plan from 2010-2019. This forms the ‘do minimum’ scenario to which the other scenarios will be compared; • Scenario B – upgrade. A more offensive development of the current infrastructure, looking beyond 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

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• Oslo – Gothenburg (to Halden in Norway). The scenarios will be considered in relation to the long distance travel market, specifically 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 Subjects 2 and 3. 1.3 Purpose of specific report The objectives of the two Subjects covered by this report were to: • determine the willingness to pay (WTP) of travellers by high-speed rail services, given that this mode represents a new product on the Norwegian transport market; and • gain a quantitative understanding of the aspects of the service that motivate travellers’ choice of mode between high-speed rail and other modes of transport: air, car, bus and ferry. To achieve these a bespoke survey has been designed, undertaken and analysed for this study. This report documents the content of that survey, the analysis undertaken, and the key findings from this analysis. It also provides the parameters for the demand model system developed in

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Subject 1 (Demand potential for high speed rail in Norway), which can then also be used to forecast fare revenues for HSR. The work to be reported within these Subjects is central to assessing the viability of high-speed rail services in Norway as it provides bespoke evidence from an up-to-date survey on the factors that influence travellers mode choice decisions when considering high speed rail. 1.4 Organisation of report This report is comprised of a number of sections: • Chapter 2 covers the background and study objectives • Chapter 3 sets out the overall approach and the development of the survey • Chapter 4 describes the survey outputs, the development of the models, and the evidence collected on attitudes towards HSR • Chapter 5 concludes by detailing how the models developed from the stated choice data are then adapted for incorporation within the wider demand modelling framework being developed within Subject 1, and illustrates some of the implications of the models once applied to forecasting the demand for HSR.

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2 Background and Study Objectives 2.1 Introduction This Chapter provides an overview of the context of the Market Analysis Contract, the passenger demand forecasting, and how Subjects 2 and 3 contribute to the demand forecasting model. Current long distance rail services in Norway are relatively slow and do not compete with air in terms of journey times and quality. The Ministry of Transport and Communications wishes to determine whether providing a step change in rail provision would be beneficial and contribute to a socio-economically efficient and sustainable solution for a future transport system. To date, a variety of studies have been undertaken looking into the benefits and costs of a high speed rail network in Norway, by various bodies including Jernbaneverket (JBV) and various pressure groups. These studies have resulted in a range of conflicting views regarding how beneficial such improvements in rail would be. The aim of this study is to provide a consistent and robust evidence base showing the likely market for a range of potential journey time enhancements. A national transport model – “NTM5” – already exists for examining the long-distance travel market in Norway. However, NTM5 is not viewed as appropriate for looking at more ambitious high speed rail options for several reasons: • The forecasting parameters used in the model are based around marginal changes in journey times. However, for high-speed rail reductions in journey times from six or seven hours to three hours or less are included as potential options: this level of change is beyond the applicability of the existing parameters meaning that potential mode-shift is poorly estimated; • For many potential users of high speed rail, the introduction of high speed rail represents a distinct, new alternative to existing options of air and rail. This is a combined effect of passenger perception of high speed rail as distinct from existing rail services, new HSR station locations in areas currently inaccessible by air, and potentially different fare structures. Combined with the effect of reduced journey times, the decision-making process – and mode choice model structure – can be very different, requiring consideration of HSR as a “new mode”; and • The existing NTM5 model does not allow a consideration of a wide enough range of quality effects associated with different HSR alternatives, including aspects such as reliability / punctuality, comfort, station accessibility or the perceptions of long sections of tunnel on travelling enjoyment. This can have an effect on both the overall case for HSR and recommendations for design options. As a result, the demand and revenue forecasting of high speed rail options requires a more detailed approach than purely testing options in the NTM5 model; a bespoke model is required development taking these factors into account. More details of the model development, and the main drivers of the model specification are given in a separate report 2; however as part of this model development, it is vital to obtain new information to support appropriate mode choice model structures, so that the decision-making process of potential high speed rail users can be reflected in the most appropriate way possible.

2 Contract 5 Subject 1 “Demand Forecasting” Report (Model Development Report Annex)

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2.1.1 Scope of forecasting Six corridors are being considered by this study as described in Section 1.1 above. On each of these corridors, a range of scenarios is to be considered ranging from minor upgrades of the existing network to new dedicated high speed lines, also described in Section 1.1 above. Figure 2.1 shows a map of the potential corridors being considered for each scenario defined at the start of the study, although minor modifications have been made since. In Phase 3 of the study, detailed corridor studies will be undertaken for the four wholly Norwegian corridors to design the detailed routes for each scenario and determine the journey times and precise locations of stations. These detailed routes and journey times will be tested in the demand forecasting tool which has been developed in the Market Analysis Phase 2 Contract, using the outputs presented in this report. Figure 2.1: Corridors and Scenarios 3

It should be noted that the options presented mean that potential users may switch from a wide range of modes depending on destination: particularly air for the longest-distance trips, but also including coach for shorter movements. In order to focus the analysis further, the following assumptions on the scope of modelling were made, in line with the remit from JBV: • For the purposes of developing the high speed rail model, only longer-distance journeys (above 100km) were included. This is driven by the availability of data from the NTM5 model and recognition that, for the options being considered (Options C and D), relatively little of

3 (Source: Jernbaneverket November 2010)

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the demand and revenue would come from short distance flows. This enables the model parameters to be focussed on accurately representing the more important movements; • The 100km limit particularly applies to flows into Oslo from its hinterland area. This restriction also applies to the NTM5 model, and reflects the separation of scope of the high speed rail study from the parallel Inter-City Study currently being undertaken by JBV which considers this area in more detail. This restriction, again, means that mode choice modelling can more accurately reflect the longer-distance movements; and • Effects on the flows in question are limited to issues of mode choice and trip generation. It is not possible within the timescales available to look at more detailed issues of destination choice for longer-distance trips as part of an HSR forecasting exercise. As a result, the modelling and forecasting exercise was designed to focus on these markets – further work may be required to investigate overlaps or synergies with the parallel Intercity study should particular options prove worthy of more detailed investigation. 2.1.2 Outputs of Forecasting Work The fundamental purpose of the demand and revenue forecasting exercise is to develop tools that can forecast the market potential for HSR services and to generate inputs into the financial and economic analysis of the proposed infrastructure and services. The financial and economic analysis provides decision makers with information to use to develop and agree investment strategies. The financial analysis uses revenue provided by combining the potential fares with the demand forecasts to show the revenue generated from people switching from other modes to HSR as well as newly generated demand (as well as considering other sources of revenue). The revenue is compared to the costs for the infrastructure and the ongoing costs associated with running and maintaining the infrastructure and services. It is important to understand the impact on other modes so that the impact of the scheme on society as a whole can be understood rather than just the impact on the new HSR services. The demand forecasting work needs to be sufficiently focussed to meet this need and inform this decision making, The economic analysis adds monetised benefits to the financial impacts, using outputs from the demand forecasting model, such as journey time benefits and mode shift. Mode shift is used to understand the external benefits of the scheme that arise, such as environmental and accident benefits where passengers shift from a more (or less) environmentally friendly mode or a mode with different accident rates. The full set of monetised and non-monetised benefits considered in the economic analysis is discussed in the Contract 6 (Financial and Economic Analysis), Subject 4 report – Economic Analysis. Again, this emphasises the importance of understanding the nature of the market forecasts – i.e. the extent to which high speed rail patronage is driven by mode shift or generation will inform the extent of economic benefits for the same number of forecast patronage. Beyond the financial and economic appraisal, the forecasting work will also inform option development during Phase 3 of the wider HSR study. 2.2 Demand and Revenue Forecasting (Subjects 2 and 3) This report covers the demand and revenue forecasting aspects of the Market Analysis – Subjects 2 and 3 of the remit given by Jernbaneverket. • Subject 2 (Analysis of expected amount of ticket revenues) focuses on the amount of revenue that HSR would generate from passenger fares. A forecasting framework is developed by ascertaining the amount that passengers would potentially be willing to pay for the new services and therefore forecasting how many passengers will use the new services. This is determined by how much travellers’ are willing to pay to save journey time (the passenger’s value of time) and the utility they get from the HSR services compared to alternative modes. A survey has been undertaken to determine travellers’ willingness to pay,

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and more general values of time by alternative modes, which are then used in the passenger demand forecasting tool, developed in response to Subject 1 of this Contract – Demand Forecasting. • Subject 3 (Passengers choice: preferences for travel and means of transport) identifies potential factors other than fares that could influence passenger’s decisions of which mode to travel on. This was also ascertained through background questions in the survey used to provide the inputs to Subject 2. Outputs from the analysis undertaken as part of Subjects 2 and 3 feed into the wider passenger demand forecasting work, described separately in the Subject 1 (demand forecasting) and Subject 4 (locations and services of stations) reports (freight is considered separately in Subject 5). We emphasise the importance of developing robust parameters for the forecasting work. Although an alternative approach would be to import model parameters, or an entire model structure, from elsewhere, this is not considered to be suitable for a scheme of this magnitude as model parameters, and model structure, are specific to a given location being dependent for instance on local economic attributes and the existing alternatives for travel. Additionally the introduction of a ‘new mode’ (high speed rail) on existing corridors in Norway implies a need for stated preference (SP) analysis, since data for this mode is by definition unavailable from other contexts. Consequently stated preference analysis has been used within this study to estimate statistically reliable behavioural parameters reflecting the passenger preference for each of the alternative travel options and for particular aspects of high speed rail which should be included as part of any subsequent design process. However, it should also be noted that the bespoke models in this study have been limited in their development by the timescales available, with the SP survey design, piloting, data collection, model development and implementation being undertaken within a three month period. There are therefore areas which we believe are worthy of review in more detail in Phase 3 (set out in section 5.4 of this report).

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3 Survey Approach and Development 3.1 Introduction This Chapter gives an overview of the approach to modelling demand for HSR services. The following section describes the approach, based on Stated Preference (SP) data, in general terms. This is followed by descriptions of the design of the SP experiments, the conduct of the pilot survey and the final design of the experiments. 3.2 Overall Approach The introduction of HSR in Norway would bring a completely new travel alternative for travellers and it is not reasonable to predict demand for this new alternative using existing models. Not only would HSR bring levels of service – journey time, comfort, fares, access characteristics – that are unknown in Norway at present, it would represent an entirely new concept in the market. For this reason evidence on likely demand, and willingness to pay, needs to be collected from stated preference (SP) experiments, which allow the collection of evidence on how travellers consider an alternative which does not exist in the travel market with which they are familiar. The functions of the SP experiments are to examine and quantify: • the willingness of travellers to switch from their current alternatives to HSR; and • the importance of the various characteristics of HSR in influencing their choices. In predicting HSR demand, SP has been used to focus on those areas where conventional methods would face the greatest difficulty. In particular, the issue of mode shift is the main focus. Two further forecasting exercises will be used, together with the SP model, to obtain overall demand. First, the overall growth in travel will be predicted, using conventional transport planning methods and forecasting on the basis that HSR will not be introduced (reported in more detail in the report for Subject 1). Relative to that base, the impact of HSR on mode split will be forecast. Finally, the impact of the improvement in accessibility given by HSR on the overall volume of travel will be assessed. To maximise the efficiency of the SP experiment, respondents were selected making journeys that were plausible candidates for transfer to HSR and these respondents were asked to consider a range of scenarios involving HSR to determine their willingness to switch. It would also have been possible to examine their propensity to switch between the existing modes of transport, but to maximise the information concerning HSR demand other choices were not considered. More conventional models, such as the Norwegian National Transport Model, which has a long-distance component, can be used to look at the switching between the existing modes. Respondents were contacted who had made journeys that could plausibly have been made by HSR, had the system been in existence. Travellers by car, rail, air and bus were considered and asked to take part in the SP survey. Each respondent in the SP was asked to participate in two experiments. The first experiment offered the choice between HSR and the mode that the respondent was observed to use, while the second offered choices between different configurations of the HSR service, posed as a class choice experiment, giving more detailed insight into preferences for aspects of the HSR service. The sample was recruited from the TNS online panel. In each experiment, respondents were asked to make a series of choices. Using multiple responses in this way increases the volume of data without a proportional increase in cost and allows the experiment to be designed to maximise the information that can be gained from it, by exploring a range of possibilities with each respondent. However, the responses from an individual cannot be treated as independent and this means that approaches have to be adopted

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in the analysis of the data so that a correct understanding is obtained of the significance of the results. The practical context of the work imposed some limitations on the information that has been collected. The surveys had to be conducted in a limited period, which was outside of the core tourist season, and were restricted to residents of Norway. This means that it was not possible through the surveys to explore the variations in demand that may be the result of seasonal changes or the behaviour of foreign visitors. The practical constraints also influenced the decision regarding how best to sample travellers that make relevant trips, and led to the conclusion that the best coverage of people making long distance trips across a range of different purposes could be achieved by undertaking a web survey utilising a panel of Norwegian residents who could be asked about recent trips they had made. The development of the SP survey was done as two experiments. One of these focussed on the likely mode shift to HSR, while the other dealt with the HSR service aspects. For reality, the first exercise was based on a recent trip made by the respondent, while the second was set up as a class choice. A substantial pilot survey was conducted, following which adjustments were made to the survey, in particular to try to increase the rate of completion. The full survey was then conducted with the improved questionnaire. 3.3 Development of Stated Preference Exercises In order to understand the likely demand for high speed rail services it was decided to develop two stated preference experiments that would be presented to each respondent. The first was a between-mode experiment between their current mode and HSR. Respondents were presented with 9 choice scenarios in this experiment. They were then asked to participate in a second experiment which looked at the willingness to pay for additional services on the train, framed as a class choice experiment. In this experiment respondents were presented with 5 choice scenarios. The first decision in specifying the mode choice experiment was whether the respondent should be asked to make choices amongst all available modes or just their current mode and a new HSR service. There are advantages and disadvantages to each approach, with the consideration of all modes providing richer data for modelling all mode choice decisions, but at the expense of placing a higher cognitive burden on respondents and requiring far more data on the levels of service of all modes to allow sensible choices to be specified. Given the practical constraints of the schedule for the study it was determined that full information on the level of service for all modes could not be complied before the start of the surveys. This therefore steered the team strongly towards a binary mode choice experiment in which respondents were asked to make choices between their existing mode and a new HSR service. It was possible to specify such an experiment with relatively little prior data, instead using self-reported information on journey times and costs from the respondent for their existing mode on the basis of a recent journey (which also built familiarity with the options offered in the choice experiment and made the choices realistic and credible). It was then only necessary to make some assumptions about the possible ranges of journey times and costs for HSR to allow a range of potential alternatives to be presented (these assumptions are described in more detail in section 3.3.1). The approach of using binary choices was determined to be appropriate for this study given that the main policies to be tested are around the specification of the HSR service being offered, and that the impact of changes in journey time and cost on current modes and the related impact that this may have on choices between currently available modes was a second order consideration. The choice experiment was therefore specified with three choice alternatives. The respondent could decide to remain with their existing mode, switch to high speed rail, or if neither of these were acceptable could indicate that they would choose not to make the trip under the scenario described. Having specified the alternatives to be considered, it was necessary to specify the range of attributes which are likely to influence mode choice and which should therefore be included within

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the mode choice experiment. In specifying these experiments RAND Europe was able to draw upon previous experience with designing similar experiments for the market analysis of both the potential UK high speed rail options and the earlier HSL Zuid project in the Netherlands. The attributes which were shortlisted for inclusion within the mode choice experiment were: • Travel time; o Access time (e.g. time to travel from home to station); o Waiting time; o In-vehicle time (including the amount of time with views and amount of time in tunnels); o Egress time (e.g. time to travel from station to final destination); • Service frequency; • Number of interchanges; and • Cost. These attributes are necessary to describe the differences in service that travellers may experience between different modes, and which may influence their choice of which mode to use for a given journey. In addition, these are all aspects of the HSR service which need to be examined when developing the HSR system. It is therefore important to have data that allow the specification of models that can capture the impacts of different policy options. It is acknowledged that this requires respondents to consider a number of attributes in making their choices, but this is also indicative of the amount information that travellers need to take in to account in real life choice situations. However, the task is made somewhat easier for respondents as one of the alternatives was their current mode, which they could relate to relatively easily as they had already provided information on the various components of the level of service for their existing journey using this mode. The experience of the study team is that this level of information of appropriate in mode choice experiments, and in fact some of the previous studies undertaken by the team have been significantly more complex but have still led to datasets capable of supporting the estimation of strong mode choice models 4. It was also acknowledged that in order for respondents to make meaningful and informed choices about their potential use of HSR it would be necessary to explain to them the nature of the services that may be on offer. As a result an introduction was developed that was presented to respondents to introduce the concept of HSR services to them prior to the SP choice experiment. This introduction included both a description of the likely journey times and costs (customised to their existing journey), along with photos of other HSR services to help them visualise what the train rolling stock could potentially look like.

4 See for example, Burge, P., Rohr, C. & Kim, C.W. (2010) Modelling Choices for Long-Distance Travellers in the UK: An SP Analysis of Mode Choice. Proceedings of the European Transport Conference, Glasgow.

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Figure 3.1: Introduction to HSR alternative presented prior to SP mode choice experiment

We would now like to consider how high-speed rail might compare to #MODE# for the journey you made between #ORIGIN# and #DESTINATION# for #PURPOSE# .

Please imagine a service where you could access a high-speed train new #HSROSTATION# which would take you to #HSRDSTATION# .

So for example, for your journey from #ORIGIN# to #DESTINATION# a high-speed rail service might look like:

High-speed rail: Journey time from #ORIGIN # to #HSROSTATION# is #HSRACCESSTIME# Number of interchanges is #HSRNUMTRAININTS# Total rail time, including time for interchanges, is #HSRIVT# Journey time from #HSRDSTATION# to #DESTINATION # is #HSREGRESSTIME# Journey cost for #GROUPTEXT# is #HSRCOST#

The trains would be similar in design to those currently used elsewhere in Europe for high speed services. Some example photos of the different classes of carriage are shown below:

Standard Class First Class

It is unclear at present whether mobile phone coverage or wifi would be available throughout the journey as some options may include the trains travelling through extensive tunnels, although it is likely that power points would be available in the first class carriages to allow travellers to plug in laptops.

The study brief also included a range of service attributes that it was hypothesised could influence the uptake of HSR. These were not included in the main mode choice experiment, but rather to assess their importance through other survey sections, in order to reduce the cognitive burden of the first experiment on respondents. Those factors that related directly to the on-train experience were included within a second stated preference choice experiment, whereas those looking at broader attitudes were covered in a series of rating questions. The second stated preference experiment was set up to provide willingness-to-pay estimates for the in-train service components, and as such was set up in the context of a choice of travel class. The respondent could choose between a standard carriage and an improved carriage, with higher specification on some of the service components but at a higher ticket price. The components included in this experiment were: • Seat spacing; • Availability of power points and wifi; • Mobile phone coverage; • Security of luggage;

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• Availability of food and refreshments; and • Additional ticket cost. The specification of each of the attributes and the levels of these used in the two choice experiments are discussed further in the following sections. During the development of the choice experiments the team from RAND Europe liaised closely with colleagues at Atkins who would be implementing the models developed from the SP elements of the study in the wider demand modelling exercise. This was particularly important to ensure that the outputs from the two Subjects covered by the survey fed seamlessly into the other Subjects within this and other work packages. Prior to the commencement of the piloting of the survey the questionnaire and specification of the choice experiments was submitted to Jernbaneverket for review and formal sign-off. 3.3.1 Mode choice attribute levels In order to ensure that the survey sampled a reasonable numbers of trips of each type, at the start of the survey the respondent was asked to report the number of long distance trips (over 100km) that they had made by purpose and mode in the previous six months. A purpose and mode combination was then selected as the basis of comparison in the SP experiments using the following hierarchy. First the trip was selected on the basis of purpose: 1. Commute 2. Business 3. Leisure, 3 or more nights away 4. Leisure, 1-2 nights away 5. Leisure, day trip Then, if multiple trips for the chosen purpose had been made, one was selected on the basis of mode: 1. Bus 2. Train 3. Air 4. Car 5. Bergen-Stavanger Ferry The respondent was then asked to focus on the most recent long distance trip that they had made for that purpose and mode combination. The survey then asked the respondent for the origin and destination of that trip, which were used to assess whether the journey in question might have a credible HSR alternative (defined as an access and an egress time each less than 2 hours to a potential HSR station location). If not, the respondent was asked about the next trip within the purpose and mode hierarchy. Once an in-scope long distance trip had been established the respondent was asked a series of questions about the trip to establish information on the journey time and cost, and other factors such as the size of the travelling group and whether they had luggage. The times and costs for the existing mode were then used to customise the levels for the stated preference mode choice experiment, with the levels pivoted around the existing journey values. For public transport modes, waiting times, service frequencies, and the number of interchanges were not pivoted from level-of-service data, but rather were specified to cover an appropriate range to cover the scope of likely eventualities.

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The levels for the HSR alternative were specified by looking up information on the average access and egress times from the trip origin and destination zones to the nearest potential HSR stations, along with the average of the minimum and maximum distance for a potential HSR alignment. The distance was then divided by different speed assumptions that would reproduce the range of potential journey times for the four different rail improvement scenarios (A-D) under consideration. An attribute was also included which reported the amount of the in-vehicle HSR time that was spent in tunnels and the amount spent with views. The HSR cost levels were specified differently according to the mode currently used by the respondent. For those currently travelling by air and rail the HSR fare was pivoted off the current air or rail fare. A similar approach was also used for bus users, although this was reviewed for the main survey. For car users it would have been inappropriate to pivot off of the existing car cost as this would typically have led to an underestimate of the HSR costs; instead a matrix of likely HSR fares were compiled using fare data from competing air and rail services that was then referenced during the survey. The following tables show the levels that were tested for each of the attributes in the pilot of the mode choice experiment. Table 3.1: Attribute levels for pilot mode choice experiment (air users) Existing Mode HSR Attribute Level Air if existing = air

Time to get to airport / train 0 0.85 * reported access time 0.75 * base station 1 1.0 * reported access time 1.0 * base 2 1.3 * reported access time 1.5 * base 3 1.5 * reported access time 2.5 * base Time waiting at airport / train 0 30 mins 3 mins station 1 45 mins 5 mins 2 1 hr 10 mins 3 1 hr 30 mins 20 mins Time spent in airplane / train 0 0.75 * reported airtime average distance / 70kph 1 0.85 * reported airtime average distance / 110kph 2 1.0 * reported airtime average distance / 150kph 3 1.3 * reported airtime average distance / 190kph Time spent in tunnels 0 10% of HSR IVT

1 25% of HSR IVT

2 50% of HSR IVT

3 75% of HSR IVT

Time to get from airport / train 0 0.85 * reported egress time 0.75 * base station 1 1.0 * reported egress time 1.0 * base 2 1.3 * reported egress time 1.5 * base 3 1.5 * reported egress time 2.5 * base Service frequency 0 2 per day 2 per day 1 8 per day 5 per day 2 15 per day 8 per day 3 30 per day 10 per day Interchanges 0 No interchanges

1 No interchanges

2 1 interchange

3 2 interchanges

Total travel cost 0 0.75 * reported airfare 0.7 * reported airfare 1 0.85 * reported airfare 0.8 * reported airfare 2 1.0 * reported airfare 1.0 * reported airfare 3 1.3 * reported airfare 1.2 * reported airfare 4 n/a n/a 5 n/a n/a

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Table 3.2: Attribute levels for pilot mode choice experiment (train users) Existing Mode HSR Attribute Level Train if existing = train

Time to get to train station 0 0.85 * reported access time 0.75 * base 1 1.0 * reported access time 1.0 * base 2 1.3 * reported access time 1.5 * base 3 1.5 * reported access time 2.5 * base Time waiting at train station 0 3 mins 3 mins 1 5 mins 5 mins 2 10 mins 10 mins 3 20 mins 20 mins Time spent in train 0 0.75 * reported railtime average distance / 70kph 1 0.85 * reported railtime average distance / 110kph 2 1.0 * reported railtime average distance / 150kph 3 1.2 * reported railtime average distance / 190kph Time spent in tunnels 0 10% of HSR IVT

1 25% of HSR IVT

2 50% of HSR IVT

3 75% of HSR IVT

Time to get from train station 0 0.85 * reported egress time 0.75 * base 1 1.0 * reported egress time 1.0 * base 2 1.3 * reported egress time 1.5 * base 3 1.5 * reported egress time 2.5 * base Service frequency 0 2 per day 2 per day 1 5 per day 5 per day 2 8 per day 8 per day 3 10 per day 10 per day Interchanges 0 No interchanges No interchanges 1 No interchanges No interchanges 2 1 interchange 1 interchange 3 2 interchanges 2 interchanges Total travel cost 0 0.75 * reported railfare 1.0 * reported railfare 1 0.85 * reported railfare 1.2 * reported railfare 2 1.0 * reported railfare 1.3 * reported railfare 3 1.15 * reported railfare 1.4 * reported railfare 4 n/a n/a 5 n/a n/a

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Table 3.3: Attribute levels for pilot mode choice experiment (bus users) Existing Mode HSR Attribute Level Bus if existing = bus

Time to get to bus / train station 0 0.85 * reported access time 0.75 * base 1 1.0 * reported access time 1.0 * base 2 1.3 * reported access time 1.5 * base 3 1.5 * reported access time 2.5 * base Time waiting at bus / train 0 3 mins 3 mins station 1 5 mins 5 mins 2 10 mins 10 mins 3 20 mins 20 mins Time spent in bus / train 0 0.75 * reported bustime average distance / 70kph 1 0.85 * reported bustime average distance / 110kph 2 1.0 * reported bustime average distance / 150kph 3 1.2 * reported bustime average distance / 190kph Time spent in tunnels 0 10% of HSR IVT

1 25% of HSR IVT

2 50% of HSR IVT

3 75% of HSR IVT

Time to get from bus / train 0 0.85 * reported egress time 0.75 * base station 1 1.0 * reported egress time 1.0 * base 2 1.3 * reported egress time 1.5 * base 3 1.5 * reported egress time 2.5 * base Service frequency 0 2 per day 2 per day 1 5 per day 5 per day 2 8 per day 8 per day 3 10 per day 10 per day Interchanges 0 No interchanges No interchanges 1 No interchanges No interchanges 2 1 interchange 1 interchange 3 2 interchanges 2 interchanges Total travel cost 0 0.75 * reported busfare 1.0 * reported busfare 1 0.85 * reported busfare 1.2 * reported busfare 2 1.0 * reported busfare 1.3 * reported busfare 3 1.15 * reported busfare 1.4 * reported busfare 4 n/a n/a 5 n/a n/a

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Table 3.4: Attribute levels for pilot mode choice experiment (car users) Existing Mode HSR Attribute Level Car if existing = car

Time to get to train station 0 0.75 * base

1 1.0 * base

2 1.5 * base

3 2.5 * base

Time waiting at train station 0 3 mins

1 5 mins

2 10 mins

3 20 mins

Time spent in car / train 0 0.85 * reported cartime average distance / 70kph 1 1.0 * reported cartime average distance / 110kph 2 1.15 * reported cartime average distance / 150kph 3 1.3 * reported cartime average distance / 190kph Time spent in tunnels 0 10% of HSR IVT

1 25% of HSR IVT

2 50% of HSR IVT

3 75% of HSR IVT

Time to get from train station 0 0.75 * base

1 1.0 * base

2 1.5 * base

3 2.5 * base

Service frequency 0 2 per day

1 5 per day

2 8 per day

3 10 per day

Interchanges 0 No interchanges

1 No interchanges

2 1 interchange

3 2 interchanges

Total travel cost 0 0.9 * reported carcost 0.7 * basecost 1 1.0 * reported carcost 0.8 * basecost 2 1.2 * reported carcost 0.9 * basecost 3 1.4 * reported carcost 1.0 * basecost 4 n/a 1.2 * basecost 5 n/a 1.4 * basecost

3.3.2 In-train service component attribute levels The second SP choice experiment was presented to all respondents, and had common attribute levels that were not dependent upon the mode used for the existing journey. The following table details the levels used for each of the attributes.

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Table 3.5: Attribute levels for in-train service experiment

Attribute Level Improved carriage Seat spacing 0 Normal spacing 1 Wide spacing Power points and Wifi 0 No power points or Wifi 1 Power points but no Wifi 2 Power points + free Wifi which works over half of route 3 Power points + free Wifi which works through entire route Mobile phones 0 Unreliable mobile phone coverage on journey 1 Reliable mobile phone coverage over half of the route 2 Reliable mobile phone coverage through entire route 3 Quiet carriage with no mobile phone calls permitted Security of luggage 0 Luggage stored in racks above seat 1 Racks above seat + option to lock luggage in secure area Food and refreshments 0 No food and drinks available for purchase on train 1 Food and drinks available for purchase from separate carriage 2 Food and drink available for purchase and served at seat 3 Food and drink included in price of ticket and served at seat Additional ticket cost 0 50 kr more 1 100 kr more 2 250 kr more 3 500 kr more 4 1000 kr more 5 1500 kr more

The seat spacing levels were specified using descriptions of the seat sizes and spacing offered in the standard and first class carriages of Eurostar. Figure 3.2: Seat size and spacing descriptions used in the in-train service experiment

Normal spacing Wide spacing

4 seats across 3 seats across carriage carriage

477 mm wide 665 mm wide 845 mm leg room 945 mm leg room

3.3.3 Qualitative aspects of high speed rail The survey included two sets of questions that sought to provide a more qualitative assessment of the factors that could influence the likelihood of choosing to travel by high speed rail. The first set of questions followed the section that introduced the respondent to high speed rail and asked for their initial assessment of how this would compare to their current mode for the journey that they had made on a scale of “High-speed rail makes my current mode much less attractive” to “High-speed rail makes my current mode much more attractive”. This set of questions was intended to get the respondents to consider a range of the aspects of high speed rail services prior to the mode choice experiment, but also allowed the collection of initial perceptions. The questions asked probed the likely impact of: • the time required for the journey; • the cost of the journey; • the comfort of the journey; • the ability to work during the journey;

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• the security on the journey; • their luggage requirements; • travelling with their group; • the ease of making the journey; and • the reliability of making the journey. Following the two choice experiments the respondents were presented with a second set of questions that probed the impact that different service factors, both on the trains and relating to station facilities, would have on the likelihood of using high speed rail. A five point response field was used with 1 representing “no effect” and 5 representing “much more likely to use high speed rail”. The factors covered were: • significant savings in the journey travel time; • having wider seats; • having plenty of leg room between seats; • having well lit carriages; • having electrical power points at seats; • having wifi available on trains and in tunnels; • having mobile phone signal in tunnels; • having quiet zones on the train; • having food and drink available on the trains; • having food and drink served at seats; • having rest rooms at the end of each carriage; • having litter removed and restrooms checked during the journey; • having staff walking through the train; • having CCTV coverage of all carriages and contact with the driver or guard; • having locked luggage areas available for storing baggage on trains; • having stations with high quality waiting areas offering refreshments; • having good security at stations; • having connecting bus and train services at the train stations; • having well defined and easy walking routes for the connection between the high speed rail platforms and other bus and train services; • having good parking provision at the train stations; and • having trains that continue through on slower track to provide direct services to more destinations without the need to change between trains. Respondents were also asked a couple of questions about their attitudes to tunnels. They were informed that in order to maintain high speeds, a substantial portion of the railway line may have to be built in tunnels and that whilst this would reduce the journey time, it would mean that for a passenger the views of the countryside would be reduced. They were then asked what impact this would have on whether they would choose to travel by high-speed rail.

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Those respondents that indicated that they would either “definitely” or “probably” not travel by rail if a substantial portion of the journey was in tunnels were then asked about their specific concerns. The influence of the proportion of the train journey time spent in tunnels was also explored more fully in the mode choice experiment, as detailed in section 3.3.1. 3.3.4 Zoning system A zoning system is required for the survey exercise to develop plausible costs and choices for trade-offs for survey respondents, as well as to allow potential future analysis of geographic- specific behaviours. For consistency between the model implementation and estimation respondents to the SP surveys have been requested to identify their origins and destinations in the context of the wider forecasting model zoning structure 5. The zoning system was designed to have a higher level of detail in the main Norwegian cities where most of the demand is expected to originate. This allows consideration to be given to the effect on demand of alternative station locations in areas with the highest population densities. Municipalities with low populations were grouped into zones with resident populations of over 25,000 and generally closer to 60,000. In total the model area was divided into 107 zones; this includes 104 area zones within Norway and three ‘point’ zones for Gardermoen Airport, Stockholm and Gothenburg, and provides a reasonable balance between geographic detail and ease-of-use by respondents. 3.4 Pilot Survey 3.4.1 Results of pilot survey The first wave of surveys was undertaken by TNS between Thursday 16th December and Wednesday 22nd December, utilising their Norwegian online panel. Over this period a total of 906 completed surveys were collected. An initial response rate of 61% of contacted respondents was achieved. However, of these, 46% were screened as being out of scope (i.e. no valid long-distance trip made), leaving 54% as in scope. From the in scope respondents, 60% of the surveys were completed, but 40% were not completed. The scale of uncompleted surveys was a cause for some concern (given that respondents were being paid an incentive in the form of credits towards consumer goods or charitable donations to complete the survey). However, some insight in to possible causes of the higher-than-anticipated defection rate was provided from the feedback from respondents and a number of changes to the survey wording and structure were made prior to the second wave of the survey. The analysis of the pilot survey suggested that it was generally working as intended and was producing a dataset that was capable of supporting the sort of analysis necessary to feed in to the demand models. The background questions also provided some useful insights in to a wider range of factors that individuals find important in considering their choice of mode. The models estimated from the data at the pilot stage provide intuitive findings, and suggested that the data would provide a strong basis for developing more detailed models that seek to further explain differences between different groups of travellers. The full pilot survey analysis is included in Appendix A.

5 The development of the forecasting model, and associated zone system structure, is described in the “Model Development Report” annex to the Subject 1 Report and technical note “TN2 Proposed Zoning System”.

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3.4.2 Amendments made following the pilot survey On the basis of the preliminary analysis a number of changes were made to the survey before commencing wave 2 of the data collection: • Clarification to respondents whether costs presented in the first SP experiment relate to a one-way or return journey; • Updating the Car experiment to present return costs if the respondent was making a return journey; • Updating the calculations for the HSR fare in the bus experiments to pivot off the HSR the fare assumptions being used for car respondents, rather than use the existing bus fare for pivoting; • Amending questions about use of the Bergen-Stavanger Ferry to first focus on the use of “High speed boat services”, before then focusing on whether any of the trips used the Bergen-Stavanger service; • Amending questions that include the names of the trip origin and destination to use the wording that respondents provided to identify the name or address of these locations, rather than the formal name of the municipality or Bydeler; • Inclusion on a back button where internal calculations allow so that respondents could amend their responses to earlier questions; and • Addition of a check as to whether the respondent’s reported home postcode deviated substantially from the one registered in the panel data. In such cases, the survey was updated to ask respondents to identify their home zone using the maps within the survey. These changes acted to improve the quality of the data being collected in the second wave of the data collection. The final version of the survey form is provided in Appendix B to this report.

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4 Survey Outputs and Analysis 4.1 Introduction This chapter of the report gives an overview of the survey responses and goes on to describe the models that were developed from the stated preference choice responses. Section 4.4 gives details of the responses to straightforward ‘background’ questions in the survey in simple tabulations. This is followed by a comparison of the model with other mode choice models developed for application to HSR proposals and a discussion of the impacts of HSR on overall travel frequency. 4.2 Results of Full Survey In total 3108 interviews were completed for this study, drawing on the TNS Norwegian online survey panel. Of these, 906 were completed in the first pilot wave of data collection, and 2202 were completed in the second main wave of data collection. As can be seen from Table 4.1, TNS achieved a 37% response rate across the study. However, this also includes the individuals who were screened at an early stage of the survey and found to have not made a valid long distance trip within the previous 6 months which could potentially be substituted by HSR. If these respondents are removed from the calculation then the conversion rate of those that were in scope to completed interviews is 60%. It is also noteworthy that of those respondents that failed to complete the survey, the majority dropped out when in the loop that was searching for a valid in-scope trip, and it is probable that many of these would have been classified as “no valid trip made” if they had followed the loop to the end. Table 4.1: Survey response rates

Pilot Main Total Respondents emailed 5593 10515 16108 Self screened Not in target group 373 553 926 Don't want to participate 101 236 337

Unknown (no answer) 14 34 48

Interviews started 2777 5651 8428 No valid trip made 1284 1893 3177 Incomplete 578 1490 2068 Quota full 0 63 63 Survey error 3 3 6 Completed 906 2202 3108 Survey response rate : completed/( started - quota full) 33 % 39 % 37 % completed/( started - quota full – Conversion : 61 % 60 % 60 % no valid trip made)

As discussed in section 3.3.1, when multiple long-distance trips were made a hierarchy was used to specify the mode and purpose combination which formed the basis of the existing journey. As a result, we would not expect to have a representative sample of trips by purpose and mode, but rather a sample that provides sufficient observations in all segments to allow robust models to be estimated. Table 4.2 shows the breakdown of mode and purpose within the sample. For the purposes of model estimation we have pooled the commute and business respondents to estimate a work model, and the leisure respondents to estimate a non-work model, although in each case we then investigated differences by sub-purpose. This pooling of the data in to work and non-work

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purposes was undertaken to ensure consistency with the demand models being developed in Subject 1 of the study. Table 4.2: Sample distribution by mode and purpose Leisure, 3 % by Leisure, 1-2 Leisure, day Commute Business or more Total mode nights away trip nights away Bus 44 117 72 70 19 322 10% Train 95 254 193 128 34 704 23% Air 87 589 196 155 20 1047 34% Car 86 147 397 300 104 1034 33% Total 312 1107 858 653 177 3107 100% % by purpose 10% 36% 28% 21% 6% 100%

The data is collected by sampling respondents from municipalities that are defined as being within a viable travel distance of one of the potential HSR station locations. Table 4.3 shows the geographic coverage of the sample, and the volumes of data relating to each of the potential HSR station pairs considered in the survey. It can be observed that 29% of the trips surveyed originated in Oslo, and 20% had a destination in Oslo. Again, it should be noted that this is not intended to be representative, as the survey was selecting the trip to be considered on the basis of rules regarding purpose and mode. However, this does suggest that the sample available for model estimation provides good volumes of data for assessing the mode choice behaviour in the key corridors. Table 4.3: Sample distribution by origin and destination for long distance trips HSR Destination Station

Total Total Oslo S S Oslo Bergen Bergen Sandnes Sandnes Drammen Drammen Kristiansa Stavanger Stavanger Sarpsborg Sarpsborg Porsgrunn Stockholm Stockholm % ORIGIN % ORIGIN Trondheim Trondheim Lillehamme Lillehamme Haugesund Gothenburg Gardermoen Sarpsborg 0 28 1 0 26 1 14 15 4 2 36 34 24 67 252 8% Gardermoen 24 0 0 4 10 19 24 7 5 2 31 17 44 25 212 7% Oslo S 1 2 0 0 68 41 91 62 29 25 144 152 154 138 907 29% Drammen 0 8 0 0 15 0 21 10 2 4 26 24 25 33 168 5% Lillehamme 10 8 40 4 0 3 4 5 0 1 6 11 9 7 108 3% Porsgrunn 3 21 34 0 9 0 10 3 2 6 17 17 13 34 169 5% Kristiansa 12 20 89 9 2 13 0 20 4 5 17 7 4 12 214 7% Stavanger 9 14 55 2 1 2 21 0 0 0 20 3 5 4 136 4% Sandnes 7 19 49 10 6 5 19 0 0 0 23 5 3 2 148 5% Haugesund 2 10 30 4 2 4 7 0 2 0 19 3 1 7 91 3% HSR Origin Station Station Origin HSR Bergen 18 26 190 16 9 12 28 35 11 16 0 19 23 22 425 14% Trondheim 14 21 133 10 16 11 9 5 0 1 17 0 13 9 259 8% Stockholm 1 0 10 0 0 1 0 0 0 0 2 0 0 0 14 0% Gothenburg 1 0 2 0 0 0 0 0 1 0 1 0 0 0 5 0% Total 102 177 633 59 164 112 248 162 60 62 359 292 318 360 3108

% DESTINATION 3% 6% 20% 2% 5% 4% 8% 5% 2% 2% 12% 9% 10% 12%

Table 4.4 shows the distribution of the trip lengths of the long distance trips that form the basis of the journeys discussed in the stated preference choices.

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Table 4.4: Sample distribution by trip length Frequency Percent Cumulative Percent < 150 km 125 4.0 4.0 150 to 250 km 509 16.4 20.4 250 to 350 km 497 16.0 36.4 350 to 450 km 279 9.0 45.4 450 to 550 km 1251 40.3 85.6 550 to 650 km 264 8.5 94.1 650 to 750 km 57 1.8 95.9 750 to 850 km 71 2.3 98.2 >= 850 km 55 1.8 100.0 Total 3108 100.0

It should be noted that the rail alignments under consideration mainly have end-to-end distances that fall between 450 and 550km, which accounts for the high proportion of the sample within this trip length band. The alignments also consider intermediate stations at population centres within the 150 to 250km band and 250 to 350km band. The trip length distribution within the sample is therefore intuitive and suggests that the screening criteria for the survey worked as intended. Figure 4.1 shows the composition of the sample in terms of annual household income, employment, and gender. It should be remembered that these are the respondents that have indicated that they have made a long-distance trip within the past 6 months, so it is to be expected that there will be a low number of unemployed people in the sample. It is also interesting to note that the sample contains significantly more men than women. Figure 4.1: Demographics of sample

Don’t want to report More than 1.400.000 NOK 1.200.000 – 1.399.000 NOK 1.000.000 - 1.199.000 NOK 800.000 - 999.999 NOK 600.000 - 799.999 NOK 400.000 - 599.999 NOK 200.000 - 399.999 NOK Below 200.000 NOK Other Household Income Homemaker Employment Student of pupil Gender Welfare Unemployed Retired Self-employed Part -time employee Full-time employee Male Female

0 10 20 30 40 50 60 70

The next section focuses on how the data from this sample was used to estimate models of travellers’ choices, and is followed by an analysis of the how respondents rated the different aspects of the modes that they were asked to consider in the survey. 4.3 Modelling of Mode Choice and Class Choice Discrete choice models can be used to gain insight and quantify the importance of different drivers of decisions between discrete alternatives. These models are constructed by specifying

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the range of alternatives that were available to the decision maker, and describing each of these alternatives with a utility equation which reflects the levels of each of the attributes for each of the alternatives. Each term in the model is multiplied by a coefficient which reflects the size of its impact on the decision making process (Ben-Akiva and Lerman, 1985; Train, 2003). It is the model coefficients that are estimated in the model estimation procedure. The model is based on the assumption that each respondent chooses the alternative that provides him or her with the highest utility. An error term is included on each utility function to reflect unobservable factors in the individual’s utility. The estimation can therefore be conducted within the framework of random utility theory, i.e. accounting for the fact that the analyst has only imperfect insight into the utility functions of the respondents. The most popular and widely available estimation procedure is logit analysis, which assumes that the error terms on the utilities are independently, identically distributed extreme value. The estimation procedure produces estimates of the model coefficients, such that the choices made by the respondents are best represented. The standard statistical criterion of Maximum Likelihood is used to define best fit. The model estimation provides both the values of the coefficients (in utility terms) and information on the statistical significance of the coefficients. Additional terms and non-linear variations in the variables can be added to these utility functions, with the testing of the appropriate forms for the utility functions being an important part of the model estimation process. By examining different functional forms we can investigate whether different groups of respondents place different values on the attributes in the choices, and can also test whether there are certain groups of respondents that are more likely to systematically choose one alternative over another. 4.3.1 Pooling of the SP choice data This study has a number of sources of choice data available to support the development of the choice models. The data for the model development was stratified by both mode and purpose, which gives a dataset in which there is data relating to different contexts. Therefore, in pooling this data it is appropriate to allow for the potential of the utilities from each of these contexts to have differences in variance. A strategy has therefore been pursued where separate models are estimated by purpose (work, non-work), but then data relating to the different mode choice situations (Car-HSR, Air-HSR, Train-HSR, Bus-HSR) are pooled within a given trip purpose but within a model structure that includes different scales to capture the potential differences in error variance. The different scales on the utility variance by mode pair also have an important interpretation in the implementation of the model, as discussed further in section 5.2.2. The data from the second SP experiment which focused on class choice can also be pooled within this modelling structure to provide consistency in the estimation of the cost sensitivity across respondents according to their income. This leads to the structure illustrated in Figure 4.2, which allowed an efficient use of all of the data in the model estimation.

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Figure 4.2: Structure for pooling of SP data in model estimation

SP1 Mode Choice SP 2 Class Choice

Car HSR No Air HSR No Train HSR No Bus HSR No Standard Improved

trip trip trip trip

Scale on Air-HSR choice relative to Car-HSR choice

Scale on Train-HSR choice relative to Car-HSR choice Scale on SP2 data relative to SP1 data

Scale on Bus -HSR choice relative to Car -HSR choice

4.3.2 Approach to developing the choice models Whilst the final models pooled all of the choice data, as shown in Figure 4.2, initially the models from the two stated preference experiments were developed independently to allow tests on the significance of the terms in each of these models in isolation. In each case, an initial model was estimated with coefficients that were applied to all respondents in the sample, providing an indication of the “average” influence of each of the service characteristics. In the stated preference choices the respondents were presented with one way travel times, but with return ticket costs (for their group) if they had purchased a return ticket. This was done to help respondents relate to the information presented in the choices. However, for model estimation it was necessary to ensure that the time and cost units were comparable, so the journey costs were converted to one way costs for each group member. This results in individual rather than group values of time. These models were then developed further to test the extent to which different groups valued different service aspects differently. To identify possible differences we examined cross tables that summarised the in-sample predictive ability of the model. This approach allowed us to approach the problem in a systematic and thorough way. Through such an approach we could satisfy ourselves that the model we developed addressed the key differences within the sample. These tests were conducted on a comprehensive list of background variables, including: • Information on the individual and their household o Respondent’s age o Respondent’s gender o Employment status o Marital status o Level of education o Whether respondent was born in Norway o Personal income o Household size

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o Household tenure o Number of cars in household o Household income • Information about the trip they made o Whether the trip was single or return o Composition of travelling group
Number of children in travelling group
Number of adults in travelling group
Whether all members of the group came from the same household o Number of nights away o Trip purpose o Frequency of making trip o Amount of luggage carried o Who paid (individual, employer or other) o Whether individual worked on the trip • Information about broader travelling habits and attitudes o Whether make regular work trips o How often the individual uses rail o What their perceptions of rail were o Whether they had ever used HSR services o Attitudes towards travelling through tunnels Tests were run to identify whether the time and cost sensitivity varied according to any of these dimensions. In addition tests were also undertaken to identify whether certain groups of respondents were more or less likely to choose a given alternative, i.e. stay with existing mode, switch to HSR, or opt not to make a trip, independent of the information that they were being presented on the levels of service of each alternative on offer. In the analysis of other long distance data sets we have previously found strong evidence of cost damping, which suggests that the marginal sensitivity to cost increases as travel costs increase. This is now recognised as an important response, and members of the study team have advised the UK Department for Transport on the section of their Transport Analysis Guidance 6 that covers this issue regarding model specification. We have therefore undertaken tests on this SP data set to explore whether the model fit improves with a cost damping formulation. These tests demonstrated that for both the work and non-work models the fit to the data improved significantly if a formulation were adopted that included both a linear and a logarithmic cost term. In both cases a common logarithmic cost term was used for all respondents, but separate linear cost terms were estimated for different income groups. 4.3.3 Correcting for the repeated measures nature of the SP data An important advantage of stated preference discrete choice experiments is that several responses can be collected from each individual. This reduces substantially the cost of data

6 http://www.dft.gov.uk/webtag/documents/expert/pdf/unit3.10.2c.pdf , see section 1.11.

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collection and allows for more advanced experimental designs. However, the collection of multiple responses means that each respondent’s basic preferences apply to the series of responses that he or she has given: those responses are therefore interdependent. Naïve analysis methods that assume the independence of observations are therefore, in principle, invalid. While a number of methods can be used to correct for the interdependence of SP observations, experience and a recent review of modelling approaches available to address this issue have shown that a good practical method is to use the “bootstrap” procedure 7. This is a standard statistical method for testing and correcting model misspecifications. This procedure was applied to the models (using 30 subsamples, which led to a good level of convergence) to provide corrected estimates of the standard errors on the coefficient estimates. The following section reports the models and their standard errors after bootstrapping. 4.3.4 Interpreting the model results In reporting the model we present a number of model fit statistics, as described in Table 4.5. Table 4.5: Interpretation of model fit statistics Statistic Definition Observations The number of observations included in the model estimation. Final log (L) This indicates the value of the log-likelihood at convergence. The log-likelihood is defined as the sum of the log of the probabilities of the chosen alternatives, and is the function that is maximised in model estimation. The value of log-likelihood for a single model has no obvious meaning; however, comparing the log-likelihood of two models estimated on the same data allows the statistical significance of new model coefficients to be assessed properly through the Likelihood Ratio test. D.O.F. Degrees of freedom, i.e. the number of coefficients estimated in this model. Note that if a coefficient is fixed to zero then it is not a degree of freedom. Rho 2 (c) If we compare the log-likelihood (LL(final)) value obtained with the log-likelihood of a model with only constants (LL(c)) we get: Rho 2 (c): 1 – LL(final)/LL(c) Again a higher value indicates a better fitting model.

In interpreting the coefficient values the following points should be considered. • A positive coefficient means that the variable level or constant has a positive impact on utility and so reflects a higher probability of choosing the alternatives to which it is applied. • A negative coefficient means that the variable level or constant has a negative impact on utility and so reflects a lower probability of choosing the alternative to which it is applied.

7 See Daly, A. and Hess, S. (2010) Simple Approaches for Random Utility Modelling with Panel Data. Proceedings of the European Transport Conference, Glasgow.

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• Some coefficients are multiplied by continuous variables and therefore reflect the disutility per unit of the variable, e.g. cost, which reflect the relative disutility per additional kroner on the journey cost. • Some service attribute coefficients are applied to categorical variables; these therefore reflect the total utility increase or decrease for that variable, relative to a base situation, e.g. the increase in utility as a result of moving from a situation where there is “unreliable mobile phone coverage on journey” to one where there is “reliable mobile phone coverage through entire route”. • The constants in each model reflect preferences for the alternatives to which they are applied. For example, the constant on air that demonstrates that those travelling with no luggage have an underlying preference for choosing this mode over HSR, over and above any value that is associated with the service attributes. A positive value for a constant indicates that the respondent is more likely to choose that alternative, and a negative value indicates that the respondent is less likely to choose that alternative. The value shown after each coefficient estimate is the t-ratio. This defines the (statistical) significance of the coefficient estimate; regardless of the sign, the larger the t-ratio, the more significant the estimate. A coefficient with a t-ratio greater than +/-1.960 is estimated to be significantly different from zero at the 95% confidence level. A t-ratio of +/-1.645 is significantly different from zero at the 90% confidence interval. The following tables present the model results after bootstrapping, i.e. with corrected estimates of the standard errors on the coefficients, and hence corrected t-ratios. 4.3.5 Model for those travelling on work purpose The model developed for those travelling on work purposes is presented in Table 4.6. Table 4.6: Choice model jointly estimated on data from SP1 and SP2 (work purposes) Term Description Coefficients t-ratio SP1 + SP2 - Jointly estimated terms Cost (kroner) HH income below 800,000 NOK -0.00081 -8.3 HH income 800,000 - 1,199,999 NOK -0.000679 -7.4 HH income more than 1,199,999 NOK -0.000567 -5.6 HH income not known -0.000669 -6.2 Log Cost (kroner) All respondents -0.0592 -3.6 SP1 - Mode Choice In-vehicle time (mins) Car -0.000529 -0.8 Air (door to door travel time) -0.00348 -2.6 Bus -0.00266 -2.8 Train -0.00501 -7.8 HSR -0.00373 -9.0 + Employer pays for travel -0.00209 -5.1 Access and Egress time (mins) Do not apply for air or car -0.00636 -13.0 Waiting time (mins) Do not apply for air or car -0.00518 -2.7 Tunnel perception % of HSR time in tunnels (i.e. 25% = 0.25 * coefficient) -0.152 -3.0 1 / Frequency (services per day) All PT modes -0.749 -10.0 Interchanges All PT modes (number of interchanges) -0.303 -11.0 Return in one day If return journey time < 6 hours 0.26 2.9 Constants Car, if current trip is made in one day 0.556 2.1 Air, if traveller has no luggage 0.194 3.2 Air, if travelling in a group of 3 or more -0.242 -3.1 HSR, if individual perceives rail as "very poor" or does not know -0.338 -4.3 HSR, if traveller is female -0.0928 -1.4 HSR, if indicate would "probably" or "definitely" not use with -0.34 -3.2 tunnels No trip, if respondent pays 0.781 3.8 No trip, if day trip 0.544 2.6 Alternative specific constants HSR, if currently travelling by car 1.01 5.0 HSR, if currently travelling by air 1.18 4.0 HSR, if currently travelling by bus 0.45 1.8 HSR, if currently travelling by train 0.0624 0.4 No trip, if bus user -4.52 -11.0

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No trip, if train user -4.82 -13.0 No trip, if air user -4.58 -11.0 No trip, if car user -4.24 -9.8 Scales on datasets Bus 1 n/a Train 1 n/a Car 1 n/a Air 1.55 9.6 SP2 - Class Choice Seat Spacing Normal spacing 0 n/a Wide spacing - male 0.0866 3.2 Wide spacing - female -0.0283 -0.9 Power points and Wifi No power points or Wifi 0 n/a Power points but no Wifi 0.163 5.8 Power points + free Wifi which works over half of route 0.255 6.1 Power points + free Wifi which works through entire route, 0.378 6.8 employer paid for a substantial part of journey Power points + free Wifi which works through entire route, 0.241 5.2 employer did not pay for a substantial part of journey Mobile phones Unreliable mobile phone coverage on journey 0 n/a Reliable mobile phone coverage over half of the route 0.0371 1.2 Reliable mobile phone coverage through entire route 0.206 5.4 Quiet carriage with no mobile phone calls permitted, 0.304 4.5 work involves making regular business trips Quiet carriage with no mobile phone calls permitted, 0.166 3.3 work does not involve making regular business trips Security of luggage Luggage stored in racks above seat 0 n/a Racks above seat + option to lock luggage in secure area, 0.0674 3.2 use rail to travel in Norway less than a couple of times a year Racks above seat + option to lock luggage in secure area, -0.00755 -0.3 use rail to travel in Norway a couple of times a year or more Food and refreshments No food and drinks available for purchase on train 0 n/a Food and drinks available for purchase from separate carriage 0.149 4.9 Food and drink available for purchase and served at seat 0.261 6.2 Food and drink included in price of ticket and served at seat, 0.327 6.2 use rail to travel in Norway a couple of times a year or more Food and drink included in price of ticket and served at seat, 0.217 4.8 use rail to travel in Norway less than a couple of times a year Constants Standard carriage, if travelling on own 0.122 3.0 Alternative specific constant Standard Carriage 0.119 2.6 Scales from combining data sets Data from different waves Wave 2 data relative to Wave 1 0.93 15.0 Data from different experiments SP2 data relative to SP1 3.11 8.9 Summary statistics Number of observation 21114 D.O.F 54 Final log likelihood -13282.7 Rho²(c) 0.197

From this model we can observe that: • There are strong cost coefficients, which are applied across all modes. The cost sensitivity varies both by the income of the individual making the trip, and by the cost of the trip (as captured in the logarithmic cost term). • The in-vehicle time coefficients by mode are well estimated, and suggest that respondents experience similar levels of disutility for air and HSR travel. Those using train place higher disutility on their travel time, and those using bus place lower disutility on their travel time. It is notable that car travellers place a very low disutility on their in-vehicle time. For air respondents it has not been possible to estimate the sensitivity to separate time components, so the coefficient is estimated on total door-to-door travel time. • The access/egress time coefficient is correctly signed and well estimated, as is the waiting time coefficient. As noted above, these are not applied to air trips. • The proportion of time spent in tunnels is found to have a statistically significant effect on the choices being made by respondents and suggests that respondents have a preference for alignments that require less time to be spent in tunnels. However, the impact of this coefficient can only be assessed when the model is implemented and it is possible to see

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how the preference for shorter travel times (which by definition requires more of the alignment in tunnels) and the preference for less time spent in tunnels act to cancel each other out for different route assumptions. • Service frequency is incorporated in the specification with an inverse term which is applied to all PT modes. This can be translated to a service headway if an assumption is made about how many hours of the day the services are operating. The model coefficient suggests that respondents preferred services with higher frequencies, and that the valuation of frequency is non-linear. • The coefficient on the number of interchanges, defined as within-mode transfers (e.g. train to train) is negative and is strongly estimated for all public transport modes. • Building on our experience from modelling high speed rail in the UK, we observe that there is significant benefit obtained from a mode if it allows a return journey within a day (i.e. return travel time of 6 hours or less). This term is applied to all modes, but shows that HSR can become significantly more attractive to respondents (particularly for commuting and business trips) if it allows the return journey to be made within one day in cases where that would not previously have been viable. A number of different threshold values have been tested and the model provides the best fit to the data when a threshold of 6 hours is used. • We also observe that some groups of respondents show a greater propensity to choose some modes than others. The model for work purposes suggests that car is chosen more often than HSR by those making a long distance trip in one day. Air is chosen more often than HSR for those travelling with no luggage, but less often by those travelling in larger groups (of 3 or more). When looking at the decisions to choose HSR, we see that this is less likely to be chosen by those that currently perceive rail to offer a poor service, and by female respondents. Finally, the “no trip” alternative is more likely to be chosen by those who pay for their own travel, and by those currently making a day trip. • In the class choice section of the model the categorical variables have coefficients estimated relative to a base level, for example, the value of wider spaced seats relative to normal seat spacing. We can see from the model that the wider seat spacing and larger seat size is valued positively and significantly by the male travellers, but plays no significant role in the choices of the female respondents. • The model also shows that having power points in the carriage is valued, and that the benefit of these increases with the availability of wifi services. The choice experiment included some options where the wifi was stated to be available over half the route, and others where it was stated to be available over the whole route. It can be observed that the increased wifi coverage is valued significantly by those respondents having their travel costs paid by their employer, whereas for those respondents paying for their own travel there is no significant difference in the value of the two levels of wifi coverage. • Reliable mobile phone coverage is valued by respondents, but only if it is available for the entire route. Respondents were also offered situations where there was a quiet carriage in which no mobile calls would be permitted. The option of quiet carriages is valued higher by those that make regular business trips. • The option to lock luggage in a secure area on the train was only valued significantly by those that use rail services in Norway infrequently. Those respondents that were more familiar with trains did not place a significant value on having this option in addition to conventional luggage racks above the seats. • Having food and drink available for purchase on the train was valued significantly, and respondents indicated that they would be willing to pay more to have food and drink available for purchase from their seats rather than having to go to a separate carriage to purchase

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their refreshments. Those that use rail services more frequently placed more value on having the food and drink included in the price of their rail tickets.

4.3.6 Model for those travelling on non-work purpose The model developed for those travelling on non-work purposes is presented in Table 4.7.

Table 4.7: Choice model jointly estimated on data from SP1 and SP2 (non-work purposes) Term Description Coefficients t-ratio SP1 + SP2 - Jointly estimated terms Cost (kroner) HH income below 200,000 NOK -0.00163 -6.4 HH income 200,000 NOK or more -0.00137 -11.0 HH income not known -0.00169 -7.1 Log Cost (kroner) All respondents -0.174 -4.8 SP1 - Mode Choice In-vehicle time (mins) Car -0.000753 -4.1 Air (door to door travel time) -0.00316 -5.3 Bus -0.00338 -4.5 Train -0.00481 -9.4 HSR -0.0033 -7.9 Access and Egress time (mins) Do not apply for air or car -0.00762 -10.0 Waiting time (mins) Do not apply for air or car -0.00859 -4.2 Tunnel perception % of HSR time in tunnels (i.e. 25% = 0.25 * coefficient) -0.154 -2.9 1 / Frequency (services per day) All PT modes -0.563 -6.4 Interchanges All PT modes (number of interchanges) -0.265 -9.2 Return in one day If return journey time < 6 hours 0.248 3.2 Constants Car, if current trip is made in one day 0.568 2.5 Car, if current trip is made less frequently than once a year -0.252 -1.6 Car, if individual is retired 0.503 2.7 Car, if purpose is "holiday" 0.359 2.6 HSR, if respondent uses rail less than once per year -0.251 -2.5 HSR, if indicate would "probably" or "definitely" not use with -0.642 -5.7 tunnels No trip, if day trip 0.685 1.9 Alternative specific constants HSR, if currently travelling by car 1.02 6.1 HSR, if currently travelling by air 0.816 4.4 HSR, if currently travelling by bus 0.000161 0.0 HSR, if currently travelling by train 0.04 0.3 No trip, if bus user -5.26 -11.0 No trip, if train user -5.56 -12.0 No trip, if air user -6.1 -12.0 No trip, if car user -4.9 -11.0 Scales on datasets Bus 1 n/a Train 1 n/a Car 1 n/a Air 1 n/a SP2 - Class Choice Seat Spacing Normal spacing 0 n/a Wide spacing 0.03 0.7 Power points and Wifi No power points or Wifi 0 n/a Power points but no Wifi 0.223 3.9 Power points + free Wifi which works over half of route 0.279 4.5 Power points + free Wifi which works through entire route, 0.415 6.4 not travelling with children Power points + free Wifi which works through entire route, 0.667 7.3 travelling with children Mobile phones Unreliable mobile phone coverage on journey 0 n/a Reliable mobile phone coverage over half of the route 0.00193 0.0 Reliable mobile phone coverage through entire route, 0.494 5.8 aged 16 - 40 years old Reliable mobile phone coverage through entire route, 0.31 4.4 aged 41 years or older Quiet carriage with no mobile phone calls permitted 0.364 5.0 Security of luggage Luggage stored in racks above seat 0 n/a Racks above seat + option to lock luggage in secure area, 0.0673 1.4 travelling on their own Racks above seat + option to lock luggage in secure area, 0.278 7.0

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travelling with others Food and refreshments No food and drinks available for purchase on train 0 n/a Food and drinks available for purchase from separate carriage 0.203 3.8 Food and drink available for purchase and served at seat 0.625 8.2 Food and drink included in price of ticket and served at seat, 0.874 9.7 aged 16 - 40 years old Food and drink included in price of ticket and served at seat, 0.471 7.5 aged 41 years or older Constants Improved carriage, if male 0.116 2.7 Improved carriage, if work involves making regular business trips 0.231 3.2 Alternative specific constant Standard Carriage 0.503 6.3 Scales from combining data sets Data from different waves Wave 2 data relative to Wave 1 0.939 14.0 Data from different experiments SP2 data relative to SP1 1.69 10.0 Summary statistics Number of observation 25158 D.O.F 50 Final log likelihood -15672.3 Rho²(c) 0.185

From this model we can observe that: • As with the model for work purposes, there are strong cost coefficients in the model for non- work purposes, and these are applied across all modes. The cost sensitivity varies both by the income of the individual making the trip, and by the cost of the trip (as captured in the logarithmic cost term). • The in-vehicle time coefficients by mode are well estimated, and suggest that respondents experience similar levels of disutility for air, bus and HSR travel. Those using train place higher disutility on their travel time. As with the work model, it is notable that car travellers place a very low disutility on their in-vehicle time. For air respondents the travel time coefficient is estimated on total door-to-door travel time. • The access/egress time coefficient is correctly signed and well estimated, as is the waiting time coefficient. As noted above, these are not applied to air trips. • The proportion of time spent in tunnels is found to have a statistically significant effect on the choices being made by respondents and suggests that respondents have a preference for alignments that require less time to be spent in tunnels. It should be remembered that the impact of this coefficient alongside the travel time coefficients can only be assessed when the model is implemented and different route assumptions can be tested. • Service frequency is incorporated in the specification with an inverse term which is applied to all PT modes. This suggests that respondents preferred services with higher frequencies, and that the valuation of frequency is non-linear. • The coefficient on the number of interchanges is strongly estimated for all public transport modes. • We have a significant constant applied to any mode which allows a return journey within a day (i.e. return travel time of 6 hours or less). This term is applied to all modes, but shows that HSR can become significantly more attractive to respondents if it allows the return journey to be made within one day in cases where that would not previously have been viable. A number of different threshold values have been tested and the model provides the best fit to the data when a threshold of 6 hours is used. • We also observe that some groups of respondents show a greater propensity to choose some modes than others. The model for non-work purposes suggests that car is chosen more often than HSR by those making a long distance trip in one day but less often than HSR for those that only make their long distance trip infrequently. Car is also more attractive than HSR for those that are retired and those travelling for holiday rather than other leisure purposes. When looking at the decisions to choose HSR, we see that this is less likely to be

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chosen by those that currently use rail infrequently and those that state that they are less likely to use an HSR service which would include significant sections in tunnels. Finally, the “no trip” alternative is more likely to be chosen by those currently making a day trip. • In the class choice section of the model we see that the wider seat spacing and larger seat size is not valued positively higher than normal seats for those travelling for non-work purposes. • Having power points in the carriage is valued positively, and the benefit of these increases with the availability of wifi services. The availability of wifi over the whole route is valued highest by those travelling with children. • Reliable mobile phone coverage is valued by respondents, but only if it is available for the entire route, and this is valued higher by those under 40 years of age. The option of quiet carriages is also valued significantly. • The option to lock luggage in a secure area on the train was only valued significantly by those travelling with others, who may as a result have larger volumes of luggage within the travelling group. • Having food and drink available for purchase on the train was valued significantly, and respondents indicated that they would be willing to pay more to have food and drink available for purchase from their seats rather than having to go to a separate carriage to purchase their refreshments. Those under 40 years of age placed more value on having the food and drink included in the price of their rail tickets.

4.3.7 Values of travel time savings The travel time and cost coefficients in the models presented in Table 4.6 and Table 4.7 imply trade-offs between time and cost, i.e. ‘values of time’. These ratios are important for understanding the way in which travellers might respond to a new alternative that offers a different combination of time and cost from the existing modes. Further, values of time are generally known to transport analysts, allowing comparisons to be made with the values implied by the current model, though the long-distance nature of the market analysed in this model reduces the direct comparability with other information. Because the model contains terms with the logarithm of cost, it is not possible to give a single value of time and we can only indicate how the value changes with changing trip cost. Moreover, the models imply different values by mode, travel purpose, income group, and, for work travel, between those whose travel was and was not paid for by their employer. The values are set out in the following charts.

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Figure 4.3: Values of time (NOK per hour) by income group and according to whether the employer paid for the trip (work purposes)

Value of Time vs Cost (HH income below 800K NOK) Value of Time vs Cost (HH income below 800K NOK) & Employer not paid & Employer paid 700 700

600 600

500 Rail 500 Rail 400 HSR 400 HSR Air Air 300 Bus 300 Bus Car Car 200 200 VOT(NOK/hr) VOT(NOK/hr)

100 100

0 0 0 100 200 300 400 500 0 100 200 300 400 500 Cost (NOK) Cost (NOK)

Value of Time vs Cost (HH income 800-1200K NOK) Value of Time vs Cost (HH income 800-1200K NOK) & Employer not paid & Employer paid 700 700

600 600

500 Rail 500 Rail 400 HSR 400 HSR Air Air 300 Bus 300 Bus Car Car 200 200 VOT(NOK/hr) VOT(NOK/hr)

100 100

0 0 0 100 200 300 400 500 0 100 200 300 400 500 Cost (NOK) Cost (NOK)

Value of Time vs Cost (HH income more than 1200K Value of Time vs Cost (HH income more than 1200K NOK) & Employer not paid NOK) & Employer paid 700 500 450 600 400

500 Rail 350 Rail 300 400 HSR HSR Air 250 Air Bus 300 Bus 200 Car Car 200 150 VOT(NOK/hr) VOT(NOK/hr) 100 100 50 0 0 0 100 200 300 400 500 0 100 200 300 400 500 Cost (NOK) Cost (NOK)

Figure 4.4: Values of time (NOK per hour) by income group (non-work purposes)

Value of Time vs Cost (HH income below 200K NOK) Value of Time vs Cost (HH income more than 200K & Non-work NOK) & Non-work 200 200 180 180 160 160 140 Rail 140 Rail 120 Bus 120 Bus 100 HSR 100 HSR Air Air 80 80 Car Car 60 60 VOT(NOK/hr) VOT(NOK/hr) 40 40 20 20 0 0 0 100 200 300 400 500 0 100 200 300 400 500 Cost (NOK) Cost (NOK)

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Although the value of time varies with the cost of the journey under consideration, it is possible to make some comparisons with established values by looking at the mean and median VOTs that would be implied from the observed distribution of journey costs within the sample (once the income distribution has been appropriately weighted by mode and purpose to reflect the income distribution for long distance trips within the NTS). The following tables compare the standard values of time per hour for long-distance private travel in Norway, which are understood to be provided in the ‘Handbook 140’ 8, in NOK of 2009, with the average values from the current study. Table 4.8: Values of time per hour for long-distance private travel in Norway, NOK (2009) Air Car Rail Bus Handbook 140 303 172 94 80

Table 4.9: Average values of time per hour from current study, NOK (2010) Value of time (NOK/hour) Air Car Rail Bus HSR Work Mean 372 105 373 197 331 Median 374 127 356 163 337 Non -work Mean 109 21 144 88 97 Median 112 22 146 91 100

When comparing with the values of time in Handbook 140, we see that the recommended values for air and car are closer to those that we find for trips made for work purposes, whereas those for rail and bus are more in line with the values that this study finds for trips made for non-work purposes. Differences in values are to be expected between studies, and it should be remembered that the values from this study relate to the context of a mode choice, specifically a choice between currently available modes and HSR. Moving forward, more complex model specifications could be investigated, for example distributed parameter values, with a view to seeing how these may impact on the implied cost and time sensitivity across the modes. We would also propose a joint estimation that would utilise the data used in the estimation of the NTM5 models alongside the new SP survey data to create a single model that will be stronger across the breadth of all possible scenarios and better integrate with shorter-distance journeys where car dominates as a mode. This is identified as an area for further investigation in section 5.4. 4.3.8 Implied willingness-to-pay for in-train services The final models pool the data from both choice experiments, and include both linear and logarithmic cost terms, along with segmentation by income. However, it is also informative to review the implications of a simpler model based solely on the data from the second choice experiment that focused on the willingness-to-pay for in-train services in the context of a choice of travelling class. From a simpler model with a single linear cost coefficient estimated across all respondents it is possible to examine the marginal rates of substitution of each service improvement against the additional ticket cost. These ratios provide an estimate of the willingness-to-pay for each of the additional services. The results from these models are shown in Figure 4.5 and Figure 4.6, which show the value placed on each of the service improvements (in NOK) for those travelling on work and non-work purposes respectively. The error bars on these charts show the 95% confidence intervals of the estimates (after bootstrapping). In each case of each factor the levels are valued relative to the base situation (which was used to describe the standard carriage configuration in the choice experiment); these have values of zero in the following charts. It can be observed that there are

8 http://www.vegvesen.no/_attachment/61437/binary/14144 , p. 93, values adjusted from 2005 to 2009.

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some cases where the willingness to pay for marginal improvements is not significantly different to zero (where the error bars cross the axis). It should be noted that the valuations were obtained to assist in the understanding of the willingness to pay for HSR carriage characteristics and how these may impact on issues like class choice, and do not link directly with the impact of these attributes in influencing mode choice. As such, we would advise against using these parameters as increments on the overall high speed rail ASC. However, they do provide valuable insight into issues surrounding HSR product design and the marketing of these services to the population most likely to be considering HSR as a potential substitute for their long distance journey.

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Figure 4.5: Willingness to pay (NOK per return ticket) for improved in-train services (work purposes)

Normal spacing

Wide spacing - male

Wide spacing - female

No power points or Wifi

Power points but no Wifi

Power points + free Wifi which works over half of route

Power points + free Wifi which works through entire route, employer paid for a substantial part of journey Power points + free Wifi which works through entire route, employer did not pay for a substantial part of journey

Unreliable mobile phone coverage on journey

Reliable mobile phone coverage over half of the route

Reliable mobile phone coverage through entire route

Quiet carriage with no mobile phone calls permitted, work involves making regular business trips Quiet carriage with no mobile phone calls permitted, work does not involve making regular business trips

Luggage stored in racks above seat

Racks above seat + option to lock luggage in secure area, use rail to travel in Norway less than a couple of times a year Racks above seat + option to lock luggage in secure area, use rail to travel in Norway a couple of times a year or more

No food and drinks available for purchase on train

Food and drinks available for purchase from separate carriage

Food and drink available for purchase and served at seat

Food and drink included in price of ticket and served at seat, use rail to travel in Norway a couple of times a year or more Food and drink included in price of ticket and served at seat, use rail to travel in Norway less than a couple of times a year -20 0 -10 0 0 100 200 300 400 500 600

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Figure 4.6: Willingness to pay (NOK per return ticket) for improved in-train services (non-work purposes)

Normal spacing

Wide spacing

No power points or Wifi

Power points but no Wifi

Power points + free Wifi which works over half of route

Power points + free Wifi which works through entire route, not travelling with children

Power points + free Wifi which works through entire route, travelling with children

Unreliable mobile phone coverage on journey

Reliable mobile phone coverage over half of the route

Reliable mobile phone coverage through entire route, aged 16 - 40 years old

Reliable mobile phone coverage through entire route, aged 41 years or older

Quiet carriage with no mobile phone calls permitted

Luggage stored in racks above seat

Racks above seat + option to lock luggage in secure area, travelling on their own

Racks above seat + option to lock luggage in secure area, travelling with others

No food and drinks available for purchase on train

Food and drinks available for purchase from separate carriage

Food and drink available for purchase and served at seat

Food and drink included in price of ticket and served at seat, aged 16 - 40 years old

Food and drink included in price of ticket and served at seat, aged 41 years or older -10 0 0 100 200 300 400 500 600

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4.4 Qualitative Aspects of High Speed Rail As described in section 3.3.3, the survey also included two sets of questions that sought to gain a more qualitative assessment of attitudes towards high speed rail outside of the stated choice exercises. This allowed insight in to a broader range of attributes, but did not allow an explicit consideration of the trade-offs that people are willing to make between these. 4.4.1 Initial ratings of HSR compared to current mode The first set of questions followed the section that introduced the respondent to high speed rail and asked respondents to provide an initial assessment of how high speed rail would compare to their current mode for the journey that they had made on a scale of “High-speed rail makes my current mode much less attractive” to “High-speed rail makes my current mode much more attractive”. The following figures (Figure 4.7 to Figure 4.10) show the rankings of these attributes for those currently using each of the modes under consideration. The responses are ordered to show the attributes that the respondents considered made their current mode “less” or “much less” attractive than HSR, i.e. the areas where HSR was perceived to offer the greatest advantage over their current mode. Figure 4.7: Impact of factors on attractiveness of current mode (existing car users)

Security on your journey

Time required for your journey

Comfort of your journey

Ability to work during your journey CAR much less attractive CAR less attractive Ease of making your journey no effect CAR more attractive Travelling with your group CAR much more attractive

Cost of your journey

Reliability of making your journey

Luggage requirements

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

From Figure 4.7 we can observe that current car users were generally positive about HSR and identified the initial proposition of HSR to be more attractive than car on many of the dimensions under consideration; in fact the only aspect where HSR was viewed as inferior to car was on the ability to deal with luggage requirements. These car respondents were particularly positive about their personal security on the journey, the time required to make the trip, the comfort that HSR offered along with the ability to work during their journey and the ease of making the journey. It is also interesting to note that HSR was perceived as being attractive for travelling in a group, although it should be noted that at this stage the respondents were not simultaneously informed

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of the cost implications of this (which is one of the advantages that the choice experiments offer over rating questions that treat each attribute in isolation). Figure 4.8: Impact of factors on attractiveness of current mode (existing air users)

Time required for your journey

Ability to work during your journey

Comfort of your journey

Ease of making your journey AIR much less attractive AIR less attractive Travelling with your group no effect AIR more attractive Luggage requirements AIR much more attractive

Cost of your journey

Security on your journey

Reliability of making your journey

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Figure 4.8 shows the ratings of those currently travelling by air. For this group the initial impressions of respondents are that HSR is more attractive than air across all of the attributes considered. Some respondents see air as being better in some dimensions, but on average, more are positive about the benefits of HSR. It is noteworthy that having been given an indication of the likely HSR travel times, HSR is seen to offer time advantages over air. Clearly in most cases the flight time will be lower than the HSR in-vehicle time (given the speed advantage of air), so this suggests that without direct prompting the respondents are considering the journey time in its totality, i.e. including the time required to reach the airport and then to check in, pass security, and wait for boarding. This is supported by the high rating also placed on the ease of making an HSR journey compared to air. It is also interesting to note that the attribute which the air respondents are least positive about is the potential reliability of HSR services (although this is generally still viewed as better than air). This suggests that there may be advantage to emphasising this dimension of the new service when promoting HSR schemes with air travellers, particularly for cases where new dedicated alignments are used which could allow higher levels of reliability than shared track.

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Figure 4.9: Impact of factors on attractiveness of current mode (existing train users)

Cost of your journey

Time required for your journey

Security on your journey

Ease of making your journey TRAIN much less attractive TRAIN less attractive Comfort of your journey no effect TRAIN more attractive Reliability of making your journey TRAIN much more attractive

Travelling with your group

Ability to work during your journey

Luggage requirements

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

The ratings of HSR by current train users are quite different to those previously seen for car and air users. In Figure 4.9 we can observe that train users are generally more positive about their current train services than the prospect of a new HSR service. In specifying an initial cost for HSR to set some context for these questions the cost suggested was 1.2 times the current train cost (i.e. a 20% premium for the high speed service), it is therefore not surprising that the cost was not viewed as a major deterrent. However, it is interesting to note that the time required for the journey is seen as being a significant deterrent to existing train users. It is likely that those already using train are less time sensitive, but are also making trips that lend themselves well to the existing rail network. There is generally a sense that the current train users have a high level of loyalty to the current train services. This is also reflected in the choice models, where we find that train users do not place a significant value of HSR compared to train over and above any quantifiable time and cost advantages.

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Figure 4.10: Impact of factors on attractiveness of current mode (existing bus users)

Comfort of your journey

Time required for your journey

Ease of making your journey

Ability to work during your journey BUS much less attractive BUS less attractive Security on your journey no effect BUS more attractive Travelling with your group BUS much more attractive

Reliability of making your journey

Cost of your journey

Luggage requirements

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

The attitudes of bus users are far more positive about HSR, and generally quite negative about the bus services that they are currently using for long distance trips. From Figure 4.10 we can see that their first impressions are that HSR is particularly attractive for its comfort, the time savings it offers and the ease it might offer in making the trip. It is not surprising to see that the one area where bus is seen by some as being more attractive is on cost, but on balance more respondents see HSR as being more attractive on this dimension suggesting that there is some consideration of the wider benefits when comparing the two modes. 4.4.2 Impact of service factors on likelihood of using HSR Following the two choice experiments the respondents were presented with a second set of questions that probed the impact that different service factors, both on the trains and relating to station facilities, would have on the likelihood of using high speed rail. A five point response field was used with 1 representing “no effect” and 5 representing “much more likely to use high speed rail”. The following figures (Figure 4.11 to Figure 4.14) show the rankings of these attributes for those currently using each of the modes under consideration. The responses are ordered to show the factors that the respondents considered would have most influence in their decision to use HSR over their current mode (i.e. those with a score or 4 or 5). It is interesting to note that across all modes, the factors that would most influence people to use HSR are the provision of connecting bus and train services that would give an integrated public transport journey and significant savings in journey travel time. It is interesting that car parking provision is also high on the list of priorities, but that the public transport provision is given more weight. It can also be observed that, across all of the figures, the highest weight is placed on the “fundamentals”, i.e. journey time, accessibility and security. The comfort related factors still have an influence, but generally come lower down the list of car users’ priorities.

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Figure 4.11: Impact of factors on likelihood of using HSR (existing car users)

1-No effect 2 3 4 5- Much more likely to use HSR Connecting bus and train services at the train stations Significant savings in your journey travel time

Having rest rooms at the end of each carriage

Good parking provision at the train stations

Well defined and easy walking routes for the connection between the HSR platforms and… Direct services to more destinations

Food and drink available on the trains

Good security at stations

Wifi available on trains and in tunnels

Having plenty of leg room between seats

Litter removed and restrooms checked during the journey Locked luggage areas available for storing baggage on trains Having quiet zones on the train

Having mobile phone signal in tunnels

Having electrical power points at seats

Having wider seats

Food and drink served at seats

High quality waiting areas offering refreshments

Having well lit carriages

Staff walking through the train

CCTV coverage of all carriages and contact with the driver or guard

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%

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Figure 4.12: Impact of factors on likelihood of using HSR (existing air users)

1-No effect 2 3 4 5- Much more likely to use HSR Significant savings in your journey travel time

Connecting bus and train services at the train stations Wifi available on trains and in tunnels

Having rest rooms at the end of each carriage

Food and drink available on the trains

Good parking provision at the train stations

Having electrical power points at seats

Well defined and easy walking routes for the connection between the HSR platforms and… Good security at stations

Having plenty of leg room between seats

Direct services to more destinations

Having mobile phone signal in tunnels

Litter removed and restrooms checked during the journey Having wider seats

Having quiet zones on the train

Locked luggage areas available for storing baggage on trains Having well lit carriages

High quality waiting areas offering refreshments

Food and drink served at seats

Staff walking through the train

CCTV coverage of all carriages and contact with the driver or guard

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%

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Figure 4.13: Impact of factors on likelihood of using HSR (existing train users)

1-No effect 2 3 4 5- Much more likely to use HSR Significant savings in your journey travel time

Connecting bus and train services at the train stations Direct services to more destinations

Having rest rooms at the end of each carriage

Wifi available on trains and in tunnels

Food and drink available on the trains

Well defined and easy walking routes for the connection between the HSR platforms and… Good security at stations

Having electrical power points at seats

Having plenty of leg room between seats

Good parking provision at the train stations

Litter removed and restrooms checked during the journey Having quiet zones on the train

Having mobile phone signal in tunnels

Locked luggage areas available for storing baggage on trains Having wider seats

Having well lit carriages

Staff walking through the train

High quality waiting areas offering refreshments

Food and drink served at seats

CCTV coverage of all carriages and contact with the driver or guard

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%

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Figure 4.14: Impact of factors on likelihood of using HSR (existing bus users)

1-No effect 2 3 4 5- Much more likely to use HSR Significant savings in your journey travel time

Connecting bus and train services at the train stations Having rest rooms at the end of each carriage

Well defined and easy walking routes for the connection between the HSR platforms and… Good security at stations

Direct services to more destinations

Wifi available on trains and in tunnels

Good parking provision at the train stations

Food and drink available on the trains

Having electrical power points at seats

Having plenty of leg room between seats

Locked luggage areas available for storing baggage on trains Having quiet zones on the train

Litter removed and restrooms checked during the journey Food and drink served at seats

Having mobile phone signal in tunnels

High quality waiting areas offering refreshments

Having well lit carriages

Having wider seats

CCTV coverage of all carriages and contact with the driver or guard Staff walking through the train

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%

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4.4.3 Attitudes towards tunnels Respondents were also asked a couple of questions about their attitudes to tunnels. They were informed that in order to maintain high speeds, a substantial portion of the railway line may have to be built in tunnels and that whilst this would reduce the journey time, and that would mean that for a passenger the views of the countryside would be reduced. Figure 4.15 shows the extent to which respondents stated that having a significant proportion of the journey in tunnels would have on whether they would choose to travel by high-speed rail. A significant majority (79%) stated that travelling in tunnels would not affect their choice to use high-speed rail. Figure 4.15: Impact of tunnels on likelihood of using HSR

I would rather use rail if a substantial portion of the journey was in tunnels

Travelling in tunnels would not affect my choice to use high-speed rail

I would probably not travel by rail if a substantial portion of the journey was in tunnels

I would definitely not travel by rail if a substantial portion of the journey was in tunnels

0 10 20 30 40 50 60 70 80 90100 % of respondents

The 16.5% of respondents that indicated that they would either “definitely” or “probably” not travel by rail if a substantial portion of the journey was in tunnels were then asked about their specific concerns. Figure 4.16 shows that the most significant concern related to the loss of the view of the scenery on the trip, with the next most important concern being the implications of the train having an accident whilst in a tunnel. Small numbers of respondents stated additional concerns, which included the potential noise in tunnels, the air pressure that some travellers may fear would affect their ears, and travel sickness. However, each of these were only suggested by 3 respondents each (0.1% of the sample).

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Figure 4.16: Concerns regarding tunnels Percentage of those concerned about tunnels Percentage of sample

I fear the tunnel collapsing

I worry about the implication of the train having an accident whilst in the tunnel

I worry about the train breaking down in the tunnel

Travelling in tunnels makes me feel claustrophobic

I would regret not being able to enjoy the scenery

0 10 20 30 40 50 60 70 80 90100

4.5 Comparison with Other High Speed Rail Mode Choice Models Although the objective of the market analysis exercise is to develop a model which best represents choice of high-speed rail services in Norway, it is worth comparing the proposed model structure against other systems developed for assessing high speed rail options elsewhere in Europe. The demand model for the HSL Zuid system from Amsterdam to Belgium and France represented choice between three modes (air, car and train), with four train alternatives (domestic, Benelux, Thalys (at that time not an HSR system) and HSR) represented as separate modes without any nesting structure. The model did not contain destination choice or travel frequency effects. The model represented a full choice process, i.e. not just a diversion process to HSR, i.e. the models predicted choices over all possible modes including HSR, rather than diversion from the current mode to HSR. Extensive calibration was undertaken to ensure the model met base-year observed flows on the existing modes, after which the alternative-specific constant for HSR was recalibrated. Further corrections were also required to adapt from the binary context of the SP exercises to the multinomial context of the forecasting model. Segmentation in the models was limited, but a ‘day trip’ variable was present (for international leisure trips) giving a bonus to modes that offered less than 6 hours total return travel time. The Long-Distance Model developed for the UK Department for Transport by RAND Europe and Scott Wilson represents choice between four modes (car, rail, air and bus) with HSR appearing as a fifth alternative, nested with rail. Both destination choice and travel frequency are present in the system and it operates as a choice process, partly because the model is aimed at investigating a wide range of long-distance travel policy, not just HSR. The model is based on large amounts of revealed preference data, as well as SP data which addresses demand for HSR, and for this reason avoids many of the calibration issues of purely SP-based models. Segmentation in the model includes income groups, age, sex, car ownership and employment status. A day-trip variable is present. Extensive validation has been carried out, in particular calculating elasticities. The model operates by pivoting from observed or best-estimate base-year matrices.

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Other long-distance models with which RAND Europe has been involved were not explicitly aimed at representing HSR. 4.6 Trip generation effects The provision of a HSR service is likely to increase the total traffic between cities and areas that are served. This increase would arise from an increased frequency of travel by those already travelling between those cities and from the establishment of new work and non-work connections because of the improved accessibility. Whilst some models seek to simultaneously model mode and destination choice, many models of the type we are constructing do not predict explicitly any changes in destination choice that might arise from the provision of HSR. In these models, the frequency change model will indicate an increase in travel between the city pairs served, though it will not predict a corresponding decrease in the alternative destinations that are visited less. For this study the project timetable precluded the option of seeking to simultaneously model destination choice. In the longer term, travel could also be expected to increase because of increased economic activity involving interaction between the cities, e.g. implying changes in the land use. However, these effects are notoriously difficult to predict and it is best to exclude them from a formal forecasting model. Frequency models can be quite simple, predicting an increase in travel as a function of increase in accessibility. An attractive model in this context is the exponential

Tij = T* ij exp ( α.∆A) giving the increased trip-making as a function of the increase ∆A in accessibility. In this context, if accessibility is measured by a logsum from a mode choice model, it can be shown that the model has a number of attractive properties consistent with a utility maximisation framework. 9 Moreover, in that framework the parameter α has a specific interpretation, as the ratio of sensitivity of the frequency model to the next ‘lower’ model in the system, which means that a consistency of values across different studies might be expected. Increased travel frequency can add significantly to the traffic on an improved connection 10 and it is clearly important that this component should be included in a business case for HSR, as the improvement in accessibility can be quite large and the increase in travel correspondingly significant. Evidence from other model systems may be used to ‘import’ a value of α. The best-founded evidence available to us is from the UK Long-Distance Model 11 , which indicates a value of α of 0.273 for non-work travel and values of 0.575 for commuting and 1 for business travel (these parameters are derived from cross-sectional analysis of RP data). This model has a destination choice component and it may be considered that part of the destination choice effect should be included in an increased frequency parameter. However, the next lower model in the UK system is mode choice, so that these α values represent the sensitivity ratio between mode choice and frequency, suggesting that they might be used unamended. Moreover, the value 1 cannot be increased without violating the structural constraints that need to be imposed on these systems.

9 See Daly, A. and Miller, S. (2006) Advances in modelling traffic generation, presented to European Transport Conference, Strasbourg. 10 See Møller, L., Wätjen, W., Pedersen, K.S., Daly, A.J. (1999) Traffiken på Storebælt (Traffic across the Great Belt, published in Danish), Dansk Vejtidsskrift; (1999) Traffic across the Great Belt, English translation presented to International Road Federation regional conference, Lahti, Finland. 11 Rohr, C., Fox, J., Daly, A., Patruni, B., Patil, S., Tsang, F. (2010) Modelling long-distance travel in the UK, presented to European Transport Conference, Glasgow.

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In other systems, such as the HSL-Zuid line from The Netherlands 12 , the model for the Scillies in the UK 13 , or the Storebælt model, the treatment of generated traffic has been limited by the available data. Generally, more approximate methods have been used, based on travellers’ ‘stated intentions’, a rather less reliable for of data than was available for the UK Long Distance Model. For the ‘Greengauge’ project in the UK, a value of 0.3 for α was reported at the UK Transport Statistics User Group meeting in 2010 by John Segal of MVA. They had derived this value by consideration of the literature and professional judgement. For the separate assessment of similar high speed rail proposals in the UK, HS2 applied a scale parameter of 1/3 from the previous level of the hierarchy when considering the choice between travelling, not travelling and generation. This factor has been applied to the Norwegian High Speed Railway Assessment Project; the plausibility of results this produces with relation to other high speed rail corridors is discussed in section 5.3.2.

12 Kroes, E. and Fox, J. (2001) HSL Zuid spreadsheet simulation system, European Transport Conference. 13 Kouwenhoven, M., Rohr, C., Miller, S., Daly, A. and Laird, J. (2006) Evaluating a replacement ferry for the Isles of Scilly using a discrete choice model framework, presented to European Transport Conference, Strasbourg.

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5 Conclusions 5.1 Introduction This concluding chapter of the report first reviews the steps necessary to adapt the models estimated from the stated preference choice experiments to a form appropriate for incorporation within the wider demand modelling framework being developed within Subject 1. This leads to a set of final “simplified” models for taking forward and using as the basis for the parameters in the wider demand forecasts. Finally, we recognise some of the limitations in this Phase of the study and identify areas for further work in the subsequent Phases when specific corridors are researched in greater detail. 5.2 Recommended mode choice models A number of steps are required to translate the models estimated from the stated preference experiments to models appropriate for implementation within the other Subjects within this Work Package. The following sections set out an overview of the structure of the model that has been developed through other Subjects to forecast the demand for HSR, and then explain how the models from the stated preference data have been translated to fit within this forecasting framework. 5.2.1 Overview of the implementation approach In choosing an approach for implementing the model, there were two primary considerations • as far as possible, it is desirable to operate the models in a ‘pivot’ mode, using existing information on base-year flows; • but, of course, for the HSR itself there is no base-year information and a pivot approach will not work. The solution we have adopted for this situation is a hybrid approach, where the HSR is compared on an absolute basis with its nearest competing mode and the remainder of the structure is operated on an incremental basis. From the detailed analysis of the SC data, it was found that the nearest competing mode was air travel. Practically, this means that the split between air and HSR is determined for each travel purpose and each origin-destination pair as a simple binary choice. Then, treating air/HSR as a single mode, the changes in proportions for each mode are calculated in an incremental model and applied to obtain changes in the mode splits observed in the base year. In this calculation, the change in the air/HSR mode are calculated as the increase in the utility of that combination offered by the pair compared to the base-year utility of the air alternative. Changes in other mode utilities can also be taken into account. This approach has the additional advantage that base level-of-service information does not have to be provided for car, bus and existing rail networks, as only changes in those networks need to be known. 5.2.2 Mode choice nesting structure In the SC experiments, survey data was collected from users of each of four existing modes: air, rail, bus and car. In the models, separate scale parameters are estimated for each of these four separate data sources, representing the scale of the error term in each comparison. These scale parameters represent the closeness of the competition between HSR and the existing modes. To move from a model estimated from a series of separate data sources to a mode choice model for application it is necessary to interpret the meaning of the scale parameters. The scale

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parameters, which provide the magnitude of the variance of the error terms from each of the pairs of mode choice data, can be used to define the tree structure. From the work model, we see that the HSR-air utility differences have less variance than the other pair differences. This defines a tree where HSR and air are in the lowest branch, and the other modes are nested together further up the tree. In defining the tree parameters we can work backwards so that the correct values are obtained as we move up each level of the tree. Figure 5.1: Implied tree structure for work model

From Estimation:

Scales Air 1.5545 Car 1.0000 Train 1.0000 Bus 1.0000

For Implementation:

Tree parameter = 1.000 / 1.5545 = 0.6433

All coefficients multiplied by 1.5545 HSR Air Car Train Bus

Check:

HSR-Air - Coeffs * 1.5545 = Coeffs * 1.5545 HSR-Car - Coeffs * 1.5545 * 1.000 / 1.5545 = Coeffs * 1.000 HSR-Train - Coeffs * 1.5545 * 1.000 / 1.5545 = Coeffs * 1.000 HSR-Bus - Coeffs * 1.5545 * 1.000 / 1.5545 = Coeffs * 1.000

The non-work model has a simpler nesting structure as we do not estimated significant differences in the variance of the utility differences between the different data sets. As a result, this leads to an un-nested model structure. 5.2.3 Implementing the segmentation in the models The models that have been estimated contain a number of constants relating to aspects of the travelling party or the journey they are making. In implementing the model, these segmentation constants are not required, because • when the constants apply to car, bus or existing rail modes, then, because of the incremental application of the model they are not required and are simply omitted; and • when the constants apply to air or HSR, they are eliminated by making an additional re- calibration model run. In this run the socio-economic constants are dropped whilst all the parameters relating to other variables are held constant apart from the alternative-specific constant which is re-estimated. This allows the alternative-specific constant for the air-HSR choice to be recalibrated in a simplified model form whilst retaining the time and cost sensitivities revealed through precious models. It should be noted that it is desirable to include the constants in the estimation of the model, as they function to eliminate biases that might otherwise be present. Further segmentations apply to travel time (travel paid by the employer and not paid) and to travel cost (segmented by income). Because the relevant variables differ over origins and destinations, a different approach must be applied. In this case, the average value should be

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calculated over the segments. Because of the nature of the sample, it would be better to use an unbiased external source, e.g. NTS, for the weighting over the segments, but when such a source is not available then the SC sample data can be used. A weighted coefficient value can then be calculated and entered into the application model. For example, the models contain a variable for time for people whose travel is paid by their employer. This is applied in estimation with a value 1 for employer-paid people and a value 0 for other people, multiplied by the relevant time and the relevant parameter. If, say 20% of travellers are paid for, then in application the constant should be applied as a value 0.2 multiplied by the parameter. In forecasting, if it is felt that, say, 25% of travellers might be paid for, then the value could be changed accordingly. Similarly, if income distributions change then the average income parameter could be adjusted. The procedure set out in this section will give a good approximation for forecasting demand. A better procedure would be to segment the forecasting of the mode split, but for the current version of the model this has not been undertaken. 5.2.4 Simplified models Table 5.1 and Table 5.2 show the coefficients of the models that have been simplified for implementation, following the steps set out in 5.2.3. Table 5.1: Mode choice model for implementation (work purposes) Term Description Coefficients Cost (kroner) HH income below 800,000 NOK -0.00126 HH income 800,000 - 1,199,999 NOK -0.00106 HH income more than 1,199,999 NOK -0.000881 Log Cost (kroner) All respondents -0.092 In-vehicle time (mins) Car -0.003113 Air (door to door travel time) -0.00771 Bus -0.00643 Train -0.01008 HSR -0.00808 Access and Egress time (mins) Bus, Train and HSR only (not Air) -0.00988 Waiting time (mins) Bus, Train and HSR only (not Air) -0.00805 Tunnel perception % of HSR time in tunnels (i.e. 25% = 0.25 * coefficient) -0.236 1 / Frequency (services per day) All PT modes -1.16 Interchanges All PT modes (number of interchanges) -0.472 Return in one day If return journey time < 6 hours 0.404 Alternative specific constants HSR (compared to car) n/a HSR (compared to air) 1.59 HSR (compared to bus) n/a HSR (compared to train) n/a Implied structural parameters Bus 1 Train 1 Car 1 Air 0.643

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Table 5.2: Mode choice model for implementation (non-work purposes) Term Description Coefficients Cost (kroner) HH income below 200,000 NOK -0.00163 HH income 200,000 NOK or more -0.00137 Log Cost (kroner) All respondents -0.174 In-vehicle time (mins) Car -0.000753 Air (door to door travel time) -0.00316 Bus -0.00338 Train -0.00481 HSR -0.0033 Access and Egress time (mins) Bus, Train and HSR only (not Air) -0.00762 Waiting time (mins) Bus, Train and HSR only (not Air) -0.00859 Tunnel perception % of HSR time in tunnels (i.e. 25% = 0.25 * coefficient) -0.154 1 / Frequency (services per day) All PT modes -0.563 Interchanges All PT modes (number of interchanges) -0.265 Return in one day If return journey time < 6 hours 0.248 Alternative specific constants HSR (compared to car) n/a HSR (compared to air) 0.622 HSR (compared to bus) n/a HSR (compared to train) n/a Implied structural parameters Bus 1 Train 1 Car 1 Air 1

5.2.5 Model Applicability As explained in the introduction section, the mode choice model has been developed specifically to examine significant improvements in rail journey times, to the extent to which they are competitive with air. The associated stated preference exercise focussed on the trips which corresponded to this scope of analysis. By contrast, more marginal changes to journey times can already be modelled within the NTM5 model, for trips over around 100km. For clarity, we expect the mode choice model developed as part of this work should be used to test the more extensive of the four incremental options for rail development identified within the introduction of this report: • Scenario C – major upgrades achieving significant reductions in end-to-end journey times; and • Scenario D – new HSR. This involves the implementation of newly built, separate HSR lines and/or long sections of line. The NTM5 model should be used to assess the smaller scale upgrades to the classic rail network represented by: • Scenario A – reference case, with relatively minor works undertaken to the existing rail network, as shown in the National Transport Plan from 2010-2019; and • Scenario B – upgrade of existing rail network. There is an area “between” Scenario B and Scenario C where there are cases for either of the models to be used. Subject to the outcome of business case evaluation of particular options, this may be an appropriate area to investigate further, however this is not within the area of scope of the current phase of work. 5.3 Demonstration of mode choice effects 5.3.1 Typical effects of mode choice model Table 5.3 and Table 5.4 below present results from the mode choice model and display the impact on the mode choice and total demand as a result of changes to in-vehicle times, fares and service frequency. Tests are conducted around the base case for the Oslo-Bergen corridor. The

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following table shows the results of some of these tests where a base scenario of an HSR service is tested in which HSR has an in-vehicle time of 2 hours 30 mins, a headway of 60 mins (i.e. one train per hour), and a fare equal to the current air fare. Additional scenarios then show the impact of varying the in-vehicle time, the fare, and the headway. Table 5.3 examines results in absolute terms whilst Table 5.4 shows changes relative to the base case. Table 5.3: Mode Choice Effects (Oslo-Bergen)

Test HSR Service Specification Annual Passengers, (k) Annual Revenue, NOK (m) % Fare IVT Headway Non- Non- Generation (%Air Total Work Total Work Work Work (mins) (mins) Fare)

140 60 100% 1468 929 539 1076 766 310 34.0%

150 60 100% 1363 862 501 999 711 288 33.3% IVT IVT

160 60 100% 1282 803 479 938 662 276 32.6%

150 60 80% 1547 954 593 902 629 273 34.2%

150 60 90% 1452 907 545 955 673 282 33.7%

150 60 100% 1363 862 501 999 711 288 33.3% Fare Fare 150 60 110% 1279 818 461 1034 742 292 32.8%

150 60 120% 1200 776 424 1061 768 293 32.4%

150 60 100% 1363 862 501 999 711 288 33.3%

150 120 100% 1321 833 488 968 688 280 33.0%

Headway Headway 150 240 100% 1241 779 462 908 642 266 32.6%

Table 5.4: Mode Choice % Effects (Oslo-Bergen)

Testing Service Specification Annual Passengers, (k) Annual Revenue, NOK (m)

Fare Non- Non- IVT Hwy (%Air Total Work Total Work Work Work Fare)

140 60 100% 107.7% 107.8% 107.6% 107.7% 107.8% 107.6%

150 60 100% 1363 862 501 999 711 288 IVT IVT

160 60 100% 94.1% 93.2% 95.6% 93.9% 93.2% 95.6%

150 60 80% 113.5% 110.7% 118.3% 90.3% 88.6% 94.7%

150 60 90% 106.5% 105.2% 108.8% 95.6% 94.7% 97.9%

150 60 100% 1363 862 501 999 711 288 Fare Fare 150 60 110% 93.9% 94.9% 92.0% 103.5% 104.4% 101.2%

150 60 120% 88.1% 90.1% 84.6% 106.2% 108.1% 101.6%

150 60 100% 1363 862 501 999 711 288

150 120 100% 97.0% 96.7% 97.3% 96.9% 96.7% 97.3%

Headway Headway 150 240 100% 91.0% 90.4% 92.2% 90.9% 90.4% 92.2%

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From the above tests the model appears to be functioning well when comparing the implied rail elasticities to those presented in the literature: • 10% increased to in-vehicle times from the base case produce implied elasticities of -1.17 for all passengers (-1.33 for work and -0.90 for non-work trips). These are within the range reported in the literature: - RAVE (2003) reported the average travel time elasticity to be between -0.12 and -0.44 from a survey amongst rail travellers in Portugal. - Atkins (2002) report IVT elasticities of -0.92 or -1.31 for work trips and -0.78 or -0.88 for non-work trips. - Román et al. (2010) estimates a demand model for HSR between Madrid-Barcelona. The direct elasticity of demand for train trips -0.38 but is higher, -0.59 for shorter trips. - Rohr et al. (2010) report -0.4 to -0.9 for the UK LDM model. The first 10% increase on HSR fares produce implied elasticites of -0.67 for all passengers (- 0.54 for work trips and -0.88 for non-work trips). Again these elasticities correspond well to those reported in the literature: • RAVE (2003) report an average rail fare elasticity of -0.31 to -0.61 • Atkins (2002) reported fare elasticities of -0.48 or -0.62 for work travel and -0.86 or - 0.72 for non-work travel. • Rohr et al (2010) report cost elasticities of -0.5/-0.6. Figure 5.2 below presents the forecast changes to high speed rail revenue on the Oslo-Bergen corridor against the average high speed rail fare: Figure 5.2: Oslo-Bergen: HSR Fare against Revenue (2024)

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The figure above suggests that: • High Speed rail revenues would peak with a fare of approximately 160% of the current air fare, although the total revenue varies by less than 2% between an average fare of 130% and 180%; • Revenues from non-work passengers would peak at a fare equivalent to 120% of the current air fare; and • Revenues from work passengers would be forecast to peak at a fare equivalent to 170% of the current air fare. 5.3.2 Comparison with observed data Mode Share Previous studies have examined the relationship between travel time by train and the rail-air market share. A strong relationship is observed between the two as the majority of travel time by air is not incurred as in-vehicle time. On the Oslo-Bergen corridor examined above HSR is forecast to obtain: • 65% of the HSR-Air market share with a journey time of 2 hours 30 minutes (Scenario D); and • 45% of the HSR-Air market share with a journey time of 4 hours 30 minutes (Scenario C) Figure 5.3 below shows the international experience of rail-air market share depending on rail travel times according to Nelldal-Troche, (2001) 14 . Figure 5.4 shows the same relationship reproduced from Steer Davies Gleave (2006) 15 , the above results for the Oslo-Bergen corridor are overlaid onto this chart. Figure 5.3: Rail-Air Market Share (Nelldal-Troche, 2001)

14 Reproduced from Bo Lennart Neldall (2010), High Speed Trains in Sweden – a good idea? 15 Steer Davies Gleave (2006), Air and Rail Competition and Complimentarity

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Figure 5.4: Rail-Air Market Share (Steer Davies Gleave, 2006)

From Figure 5.3 and Figure 5.4 it can be seen that the forecast HSR-Air market share from the Bergen-Oslo corridor fits well with the observed results. Although with lower journey times the HSR market share is at the upper levels of the observed results the inferred in-vehicle journey time elasticity from the model has been shown to be similar to those presented in other studies. It is noted that although the above relationship is observed there still exists a significant variation even when rail journey times are similar. On market shares rail/air Steer Davies Gleave (2006) writes, ‘the rail journey time was the single most important factor determining market share, but nonetheless there could be significant variation even where the journey times were similar: for example, routes with rail journey times of about 2 hours 30 minutes had rail shares varying from 44% to 85%. This variation arose because: • Other factors related to the schedule offered or the effective journey time, such as the frequencies offered by each mode and average access times, influence market share; • Other factors not related to the schedule, including price and service quality, also influence market share; and • Definitions of the markets varied between routes, and were sometimes different for air and rail on the same route”. Whilst the above factors are held constant between tests on the Oslo-Bergen corridor, they may vary with the rail journey time on observed corridors.

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Generation Preston (2009 16 ) presents evidence on the amount of traffic generated by new high speed rail services. The levels of induced journeys are typically shown to be between 10-30%. Madrid- Seville is shown to be an exception where generation is cited as 50% of all HSR trips; however it is suggested that some of this may be due to external growth on the line. The levels of generated traffic on the Oslo-Bergen corridor sit on the higher end of the observed range with induced journeys typically accounting for between 32% and 34% of total high speed rail demand. The proportion of generated trips will vary for different high speed corridors as the changes to the total accessibility brought about by the introduction of high speed rail are a function of both the new service provided and the existing alternative services for making a given journey. 5.4 Recommendations for further analysis The models developed within this Phase of the study provide an overview of the main factors influencing mode choice, and provide a quantification of the weight placed on these factors when considering choices between the modes currently available against a potential new HSR service. However, due to the schedule available for this phase of the study, there has been limited time available to develop the mode choice models either using further data collection or analysis of the existing data – greater value and accuracy could be achieved as required at a general level. Depending on the outcome of feasibility analysis of HSR options on individual corridors, the following additional areas of analysis may add further value: • Since the next phase will focus on specific corridors, it could be useful to explore the extent to which the sample may support a more detailed analysis of selected corridors, in which a new high speed line may act as a greater or lesser competitor to existing modes; • It would be informative to undertake a more detailed analysis of the values of time that the current models imply for car users to review why the values may be lower than anticipated and to identify whether there are measures that can be taken in the modelling to bring these closer to the established values; • Depending on the business cases for individual corridors and stations, it may be appropriate to collect more data to analyse the potential for mode shift on flows of around 50km-150km, which are dominated by car journeys and are not adequately captured in NTM5. The degree of compatibility between NTM5 and the current mode choice model should also be examined further; • Specifically, there would also be significant benefits from seeking to estimate a model that combines the SP choice data collected to support the model developed for assessing HSR in this Phase 2 work and the long-distance revealed preference (RP) data that supports NTM5. This joint analysis utilising both RP and SP data would both maximise the return from data collected to date and provide a single forecasting model that could be applied consistently across the assessment of all four rail improvement scenarios; • The timing of the surveys (collected in December and January) also precluded a detailed investigation of the potential demand for high speed rail services from the tourism segment. This could therefore merit further research to review the extent to which preferences of foreign tourists making trips with Norway correspond with those of Norwegians making domestic leisure trips. An initial review of the models previously developed for HSL Zuid and the UK LDM model suggests that either these are segments that have typically not been included within the scope of such models, or in the cases where they have, the non-domestic travellers have not been assigned different parameters in the mode choice models (suggesting that any differences were insignificant). However, if it is believed that the

16 Preston (2009) The Case for High Speed Rail: A review of recent evidence, RAC Foundation

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corridors taken forwards for more detailed examination may benefit from international patronage there would be a case for undertaking a more systematic review of the evidence from a wider range of international mode choice models; and • There is also scope to review the model implementation procedures and develop a framework that allows the segmentation (most notably cost sensitivity by income) to be carried through in to the demand model rather than simplified to an average income response. This would require additional data to support the development of income specific matrices, which at this stage has not been feasible. The priorities for any further analysis will be guided by the emerging needs of the wider Phase 3 business case and option development work.

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Appendix A – Pilot Survey Report

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MEMO

To : Josie Drath, Michael Hayes, Jim Millington (Atkins) From : Peter Burge C.c. : Thomas Karterud (TNS) Subject : Norway HSR – Pilot analysis Date : 22 December, 2010 Reference : Work Package 5: Market Analysis – Subjects 2 & 3

1 Introduction

This note reports on the preliminary analysis of the survey undertaken to support Subjects 2 and 3 of the Market Analysis Work Package of the High Speed Rail Project. The primary objectives of this survey are to: • determine the willingness to pay (WTP) of travellers by high-speed rail services, given that this mode represents a new product on the Norwegian transport market; and • provide a quantitative understanding of the aspects of the service that motivate travellers’ choice of mode between high-speed rail and other modes of transport: air, car, bus and ferry. The survey also includes questions to allow us to identify key segments of the population that may have differences in preferences and choice behaviour. The full list of survey questions is presented in Appendix A1. The models developed from the discrete choice exercises included in the survey will allow generalised cost functions to be developed that will support the requirements of forecasting demand under Subject 1 (Demand potential for high speed rail in Norway). The survey is being conducted in two waves. The first, a survey of 1,000 respondents provides initial insight in to the stated behaviour of respondents across all modes and purposes. On the basis of a preliminary analysis of this data (as reported in this memo), any necessary amendments will be made to the survey prior to the second wave of data collection, in which a further 2,100 responses will be collected.

2 Summary of data collected in wave 1

The first wave of surveys was undertaken by TNS between Thursday 16th December and Wednesday 22nd December, utilising their Norwegian online panel. Over this period a total of 906 completed surveys were collected. It should be noted that this is a considerable achievement given the time within which the surveys were undertaken and some of the issues identified in the scripting of the survey that have led to higher losses of respondents than might usually be expected.

1 The appendices referenced in this memo are available upon request

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Whilst the data available at this stage is just under the 1,000 responses that we had intended to collect, the decision was taken to undertake the pilot analysis on this sample to allow a timely review of the survey prior to the Christmas period and to allow the second wave of data collection to commence promptly in January. The intention is that this second wave will comprise of 2,200 completed surveys to meet the shortfall accepted at this stage.

2.1 Response rates achieved An initial response rate of 61% of contacted respondents was achieved. However, of these, 46% were screened as being out of scope (i.e. no valid long-distance trip made), leaving 54% as in scope. From the in scope respondents, 60% of the surveys were completed, but 40% were not completed. The scale of uncompleted surveys is a cause for some concern (given that respondents were being paid an incentive to complete the survey). However, some insight in to possible causes of the higher than anticipated defection rate is provided from the feedback from respondents discussed in section 7.

2.2 Sample composition Within the survey we asked respondents to report their recent long distance trips, and then used a hierarchy to choose the mode and purpose combination to survey them about. We first selected on the basis of trip purpose: 1. Commute 2. Business 3. Leisure, 3 or more nights away 4. Leisure, 1-2 nights away 5. Leisure, day trip Then, if multiple trips had been undertaken by the chosen purpose, we then selected on the basis of mode: 1. Bus 2. Train 3. Air 4. Car As a result, we do not expect to have a representative sample of trips by purpose and mode, but rather a sample that provides sufficient observations in all segments to allow robust models to be estimated. Figure 1 shows the breakdown of mode and destination within the sample of 906 respondents from wave 1 of the surveys. For the purposes of model estimation we would seek to pool the leisure respondents (but seek differences within the leisure model for those with different durations of stay). The composition of the sample at this stage is encouraging and is leading to an approximately equal split across the key modes, with bus having a lower share as anticipated.

Leisure, 3 Leisure, 1-2 Leisure, day Commute Business or more Total nights away trip nights away Bus 2% 4% 2% 2% 1% 10% Train 5% 10% 7% 4% 1% 28% Air 4% 17% 5% 5% 1% 31% Car 3% 5% 11% 8% 3% 31% Total 14% 36% 25% 20% 6% 100% Figure 1: Sample composition by purpose and mode

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The data is collected by sampling respondents from municipalities that are defined as being within a viable travel distance of one of the potential HSR station locations. Figure 2 shows the geographic coverage of the sample, and the volumes of data relating to each of the potential HSR station pairs considered in the survey. It can be observed that 30% of the trips surveyed originated in Oslo, and 21% had a destination to Oslo. Again, it should be noted that this is not intended to be representative, as we are selecting the trip to be considered on the basis of rules regarding purpose and mode. However, it is encouraging to see that there are good volumes of data available using this approach for assessing the mode choice behaviour in the key corridors.

HSRDSTATION

Total Total Oslo S Oslo S Bergen Sandnes Sandnes Drammen Drammen Kristiansa Kristiansa Stavanger Sarpsborg Sarpsborg Porsgrunn Stockholm Stockholm Trondheim Trondheim Haugesund Haugesund Lillehamme Lillehamme Gothenburg Gothenburg Gardermoen Gardermoen

Sarpsborg 1% 0% 1% 0% 0% 0% 0% 1% 1% 1% 1% 8% Gardermoen 1% 0% 0% 1% 1% 0% 0% 1% 0% 2% 1% 7% Oslo S 0% 3% 1% 3% 2% 1% 1% 6% 5% 5% 3% 30% Drammen 0% 1% 1% 0% 1% 1% 0% 1% 6% Lillehamme 0% 0% 1% 0% 0% 0% 0% 0% 3% Porsgrunn 0% 0% 1% 0% 0% 0% 0% 1% 1% 0% 1% 5% Kristiansa 1% 2% 1% 0% 1% 0% 0% 1% 0% 0% 6% Stavanger 0% 0% 2% 1% 1% 0% 4% Sandnes 1% 2% 0% 0% 0% 0% 1% 5% HSROSTATION HSROSTATION Haugesund 0% 0% 1% 0% 0% 0% 0% 1% 0% 0% 3% Bergen 1% 2% 6% 0% 0% 1% 1% 1% 0% 0% 0% 1% 1% 13% Trondheim 1% 0% 6% 0% 0% 1% 0% 0% 1% 0% 0% 9% Stockholm 0% 0% 0% 1% Total 3% 6% 21% 2% 6% 4% 8% 6% 2% 2% 14% 10% 8% 9% 100% Figure 2: Origin and destination for long distance trip (based on HSR network) In Figure 3 we present the distribution of the trip lengths of the long distance trips that form the basis of the journeys discussed in the stated preference choices. Frequency Percent Cumulative Percent < 150 km 41 4.5% 4.5% 150 to 250 km 138 15.2% 19.8% 250 to 350 km 134 14.8% 34.5% 350 to 450 km 69 7.6% 42.2% 450 to 550 km 402 44.4% 86.5% 550 to 650 km 75 8.3% 94.8% 650 to 750 km 17 1.9% 96.7% 750 to 850 km 23 2.5% 99.2% >= 850 km 7 0.8% 100.0% Total 906 100.0% Figure 3: Distance between origin and destination Figure 4 shows the composition of the sample in terms of personal income, employment, and gender. It should be remembered that these are the respondents that have indicated that they have made a long- distance trip within the past 6 months, so it is to be expected that there will be a low number of unemployed people in the sample. It is also interesting to note that the sample contains significantly more men than women. It is suggested that in subsequent analysis it would be useful to obtain a breakdown of the contacted respondents by such demographic factors from TNS. This would allow a comparison of the distribution of respondents contacted, the distribution of those opting to participate, and the distribution of those that remain in scope for a long-distance trip. This would provide insight in to whether there is a

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sampling bias, or whether the distribution observed is a result of the characteristics of those making long- distance trips.

More than 800,000 NOK 600,000 – 799,999 NOK 400,000 – 599,999 NOK 300,000 – 399,999 NOK 200,000 – 299,999 NOK less than 200,000 NOK Other Homemaker Personal Income Student of pupil Employment Welfare Gender Unemployed Retired Self-employed Part-time employee Full-time employee Male Female

0 10203040506070

Figure 4: Sample demographics

3 Choices made in choice experiments

In the survey respondents participated in two SP experiments. The first was a between-mode experiment between their current mode and HSR. Respondents were presented with 9 choice scenarios in this experiment. They were then asked to participate in a second experiment which looked at the willingness to pay for additional services on the train, framed as a class choice experiment. In this experiment respondents were presented with 5 choice scenarios.

3.1 Structure of the choice experiments In the first experiment the respondents were asked to consider the following factors in making a decision as to whether they would travel by their current mode of a new HSR alternative: • Access time • Waiting time • In-vehicle time o Including amount of time with views and amount of time in tunnels • Egress time

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• Service frequency • Number of interchanges • Cost

An example of a choice for a respondent travelling by air is shown in Figure 5 below (it should be noted that these were translated to Norwegian for the survey).

If the following options were available, which would you choose for your journey?

Air High speed rail

Expected travel times: Time to get to airport / train station 15 mins 15 mins Time waiting at airport / train station 1 hour 10 mins Time spent in airplane / train 1 hour 1 hour 10 mins (Views for 50 mins, Tunnels for 20 mins) Time to get from airport / train station 30 mins 10 mins Total Travel time 2 hours 45 mins 1 hour 45 mins

Service frequency One flight every 2 hours One train every 30 mins

Interchanges Need to make 2 interchanges

Total travel cost 1130 kr return 1300 kr return

Which would you use for your journey?

Figure 5: Example choice screen for Experiment 1 The second experiment, examining the willingness to pay for additional services, considered the following attributes: • Seat spacing • Availability of power points and Wifi • Mobile phone coverage • Security of luggage • Availability of food and refreshments • Additional ticket cost. An example of a choice for a respondent travelling by air is shown in Figure 5 below (again, it should be noted that these were translated to Norwegian for the survey).

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If the following options were available, would you choose the additional fare for the improved carriage?

Standard Carriage Improved Carriage

Seat spacing Normal spacing Wide spacing

4 seats across 3 seats across carriage carriage

477 mm wide 665 mm wide 845 mm leg room 945 mm leg room

Power points and Wifi No power points or Wifi Power points + free Wifi which works over half of route

Mobile phones Unreliable mobile phone coverage on journey Reliable mobile phone coverage on journey through entire route

Security of luggage Luggage stored in racks above seat Racks above seat + option to lock luggage in secure area

Food and refreshments No food and drinks available for purchase on Food and drink available for purchase and train served at seat

Additional ticket cost 500 kr more

Would you pay for the improved carriage? No Yes

Figure 6: Example choice screen for Experiment 2 The levels presented within these choice experiments are detailed in Appendix B.

3.2 Levels of trading observed It is useful at this stage of the survey to review the trading behaviour of respondents within the choice experiments. For the first experiment, by looking across all of the choices made by a given individual we can explore whether they switch between their current mode and the HSR alternative on the basis of the scenarios presented to them. This is important as we have sought to define the travel times and costs to cover a broad enough range to stimulate trading between modes, thereby allowing the estimation of a mode- choice model. The Venn diagrams in Figure 7 show the choices that respondents have exercised across the nine scenarios that they were offered. The white areas include those respondents that did not trade, i.e. consistently chose the same mode, the grey areas include those that switched between one mode and the “no trip” alternative, and the black areas include those where the respondents traded between their existing mode and the HSR service on offer. It is the black areas that give us most data for the estimation of our choice models, although it should also be acknowledged that there are always likely to be some respondents that never switch as they will always consider one mode superior regardless of the journey times and costs.

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Figure 7: Trading behaviour in Experiment 1 The patterns of trading are broadly encouraging. We see that 57% of those currently travelling by car trade between car and HSR, 82% of those currently travelling by air, 75% of those currently travelling by train, and 84% of those currently travelling by bus. In the second SP experiment, which focuses on class choice, we observe good levels of trading. We observe that 84% of the respondents trade between carriage class on the basis of the choices offered, with only 10% always choosing the standard carriage option and 6% always choosing the improved carriage option.

3.3 Influence of cost levels on choices Within the data from the choice experiments we can also examine whether respondents appear to be sensitive to the cost changes being presented. This can help assess whether it may be necessary to explore a broader cost range in some cases. It should be noted that in the scenarios there are also a number of other attributes being varied, and ultimately a full understanding of the sensitivity to each can only be obtained through estimating choice models, however, this analysis provides some useful insight into the patterns within the data. % Choose Multiplier on current car cost % Choose Car Multiplier on estimated HSR cost HSR 0.9 68.3 0.7 38.1 1 66.1 0.8 49.2 1.2 57.8 0.9 31.8 1.4 54.6 1 28.3 1.2 19.1 1.4 17.4 Figure 8: Choices exercised in car experiments on basis of cost levels

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From Figure 8 it can be observed that the cost attributes appear to be working as intended. As the car costs increase the percentage of respondents choosing the car option decrease, similarly as the HSR costs increase the percentage of respondents choosing the HSR option generally decreases. However, a review of the survey coding has revealed that the choices had been specified to consistently present respondents with one-way journey costs (but described as return costs). This was not the intention, so this aspect will be updated for the main phase so that return costs are presented when appropriate, and the wording explaining whether the cost in the choice is a one-way or a return cost will be clarified. % Choose Multiplier on current air cost % Choose Air Multiplier on estimated HSR cost HSR 0.75 57.7 0.7 50.2 0.85 67.2 0.8 53.7 1 59.3 1 45.7 1.3 34.1 1.2 21.4 Figure 9: Choices exercised in air experiments on basis of cost levels The picture is similar for the air respondents, with the trends currently working in the directions anticipated. Multiplier on current train % Choose cost % Choose Train Multiplier on estimated HSR cost HSR 0.75 29.5 1 62.3 1 33.6 1.2 57.8 1.5 37.6 1.3 54.7 2.5 34.5 1.4 42.1 Figure 10: Choices exercised in train experiments on basis of cost levels For those currently travelling by train we observe that the proportion choosing the train alternative appears to be broadly insensitive to the cost presented. It should be noted that for this experiment it has been necessary to employ a constraint within the design where the HSR costs are pivoted off the train cost being presented in the given choice, i.e. HSR is never allowed to be cheaper than the existing train. This constraint is not necessary for the earlier modes as it is possible to have situations where for example the air cost is greater than the HSR cost. However, it is not credible to present a high speed train that is cheaper than a conventional train for the same journey. It is therefore not surprising to see that the train cost does not play as large a role and that the switch between HSR and train is mainly influenced by the premium charged over and above the conventional train fare. % Choose Multiplier on current bus cost % Choose Bus Multiplier on estimated HSR cost HSR 0.75 39.1 1 76.6 1 29.8 1.2 71.5 1.5 14.4 1.3 71.5 2.5 7.2 1.4 73.4 Figure 11: Choices exercised in bus experiments on basis of cost levels For those currently travelling by bus we see a much higher uptake of HSR than on other modes. This is in part because the HSR costs are pivoted off the base bus cost (which will be low in many cases). It is therefore suggested that moving forwards it would be better to use the assumed HSR fares developed for the car respondents as the starting point for the HSR fares. This is likely to produce more realistic HSR fare levels, and will bring a greater level of realism to the bus experiment.

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4 Understanding of the choice experiments

After the choice experiments we asked respondents a series of diagnostic questions to explore their understanding of the choice experiments. When asked whether they were able to compare the different choices, we see that 87% of the respondents felt able to make the comparisons in the first SP experiment and 96% of the respondents in the second SP experiment. Respondents were then asked whether they were able to understand each of the factors presented in the choices. We see that 93% of respondents felt able to understand the factors in the first experiment, and 98% of respondents felt able to understand the factors in the second experiment. These findings are encouraging and suggest that people were able to undertake the choices. In subsequent analysis we will investigate the impact of excluding those that did not understand the exercises from the models.

5 Models estimated from choice data

The data from the stated choice experiments have been used to estimate some preliminary models of mode-choice. It should be noted that the models presented at this stage are relatively simple and do not include a consideration of how preferences and choices may differ between groups of respondents. It should also be noted that the standard errors of the coefficients in these models will be underestimated at present as no correction has been made at this stage for the repeated measures nature of the stated preference responses. However, these models do provide an indication as to the extent to which the choices are working as intended. The initial models look very promising. In the first choice experiment we see that the significantly estimated coefficients all have the anticipated signs. The full pilot model results are shown in Appendix C, in which we present models for all respondents together, and for respondents separated by purpose. From these preliminary models we can conclude that: • We see a strong cost coefficient, which is applied across all modes. • The in-vehicle time coefficients by mode are well estimated, and suggest that respondents experience similar levels of disutility for car and air travel, followed by higher disutility for train and HSR, and highest disutility for each minute of bus in-vehicle time. • The access/egress time coefficients (currently estimated by main mode being accessed) are correctly signed and generally well estimated. They also have sensible magnitudes relative to the in-vehicle time terms. In future models we will also examine whether the access/egress time varies significantly by the mode used for access (rather than the mode being accessed). • The waiting time coefficients are correctly signed, although the values for bus and train waiting time look a little large at this stage. This will be explored further in ongoing analysis.

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• The proportion of time spent in tunnels is not found to have a statistically significant effect on the choices being made by respondents at this stage. This applies equally across all modes, including leisure travel.

• At this stage, service frequency is only found to be statistically significant for choices including train or HSR. The bus and air frequency terms have a lower level of significance, although these may increase with more data, and an analysis of subgroups may reveal some segments that place more value on this aspect than others. • The coefficient on the number of interchanges is strongly estimated across all purposes. • Building on our experience from modelling high speed rail in the UK, we observe that there is significant benefit obtained from a mode if it allows a return journey within a day (i.e. return travel time of 6 hours or less). This term is applied to all modes, but shows that HSR can become significantly more attractive to respondents (particularly for commuting and business trips) if it allows the return journey to be made within one day in cases where that would not previously have been viable. The model from the second choice experiment is also well estimated. From this model we observe that: • We are unable at this stage to estimate a statistically significant value on wider seat spacing and larger seats. • Power points and wifi are viewed as valuable benefits, and are valued more highly by commute and business travellers. • Mobile phone coverage is only valued if it is reliable across the entire route, but some travellers also place high value on having a quiet carriage in which no mobile calls are permitted. Further analysis will explore which segments of the potential market are particularly attracted by this option. • Secure areas for locking luggage are only significantly valued by leisure travellers, and it is highly likely that this is correlated with the amount of luggage that the individual has with them (which we can test from the background questions). • Passengers for all trip purposes place value on having food available for purchase at their seats. It is interesting at this stage to note that business and leisure travellers would also value having food available in a separate carriage, although commuters do not value this. There is generally a preference for the food and drink to not be included within the ticket price, although the leisure travellers prefer this to be all inclusive. • Finally, we observe that the cost coefficient is strongly estimated across all these models, which will allow the calculation of willingness to pay in subsequent models. In summary, the model estimated at this stage suggests that the experiments are working as intended, and provide relatively strong results given the sample size available. Clearly a stronger model would be expected with more data, and far more complex analysis can be undertaken by sub-groups of travellers once the second wave of the data has been collected.

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6 Attitudes to wider range of factors

Before the choice experiments the respondents were asked some questions about which factors they felt made their current mode more attractive, and which less attractive than HSR. The responses to these questions are shown in Figure 12, sorted in order of those that make the current mode “less attractive” or “much less attractive” (i.e. HSR more attractive).

Time required for your journey

Ease of making your journey

Comfort of your journey

Ability to work during your journey

Current mode much less attractive Current mode less attractive Security on your journey no effect Current mode more attractive Travelling with your group Current mode much more attractive

Cost of your journey

Reliability of making your journey

Luggage requirements

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Figure 12: Factors making current mode more or less attractive than HSR It can be seen that HSR is generally perceived to be as good as, or more attractive than, the current mode across many factors. In the next phase of the analysis we will present these ratings separately according to the mode currently used to assist in assessing which factors are seen as most beneficial in each market. Following the choice experiments the respondents were asked a further set of questions where they were asked to rate the role that different factors would play in their decision to use high-speed rail. The responses to these questions are shown in Figure 13, sorted in order of those that make the respondents most likely to use HSR (ratings of 4 or 5).

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Significant savings in your journey travel time

Connecting bus and train services at the train stations

Rest rooms at the end of each carriage

Wifi available on trains and in tunnels

Well defined and easy walking routes for connections with other bus and train services

Good parking provision at the train stations

Trains that continue through on slower track to provide direct services

Food and drink available on the trains 1-No effect Good security at stations 2 Plenty of leg room between seats 3 Electrical power points at seats

Mobile phone signal in tunnels 4

Locked luggage areas available for storing baggage on trains 5- Much more likely Litter removed and restrooms checked during the journey to use HSR

Quiet zones on the train

Well lit carriages

Wider seats

Stations with high quality waiting areas offering refreshments

Food and drink served at seats

Staff walking through the train

CCTV coverage of all carriages and contact with the driver or guard

0% 20% 40% 60% 80% 100%

Figure 13: How likely different factors would be in the decision to use high-speed rail A series of questions were also asked regarding people’s attitudes to a substantial portion of the railway line being built in tunnels. They were reminded that this would reduce the journey times, but would mean that as a passenger their views of the countryside would be reduced. As can be seen from Figure 14, 81% of respondents were indifferent to large proportions of an HSR line being in tunnels, which reinforces the findings from the stated choice experiment where this factor has not been found, to date, to have a significant impact on mode choice decisions.

Frequency Percent I would definitely not travel by rail if a substantial portion of the journey 24 2.6% was in tunnels I would probably not travel by rail if a substantial portion of the journey 113 12.5% was in tunnels Travelling in tunnels would not affect my choice to use high-speed rail 732 80.8% I would rather use rail if a substantial portion of the journey was in tunnels 37 4.1% Total 906 100.0% Figure 14: Stated impact of a substantial proportion of the HSR line being in tunnels

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7 Feedback from the TNS survey team

At the end of the survey we gave respondents an opportunity to provide some feedback on the survey. This is particularly useful for identifying areas where we may seek to make minor amendments to improve comprehension of the questions.

7.1 The Bergen-Stavanger Ferry In collecting information about current use of modes for long distance journeys we included an option for respondents to state that they had used the Bergen-Stavanger ferry. However, this has caused some confusion, and it is not clear to respondents why they are only asked about this fast boat service when there are also others that they might have used. Moving forwards, TNS strongly recommend that the questions are amended so that the respondent is given the option in the questions reporting their trips by mode (Q202, Q204, Q207, Q209, and Q211 ) to report that they have used a “High speed boat service”, with this then followed by an additional question for those reporting the use of “High speed boat service” to establish for how many of these trips they used the Bergen-Stavanger service. It is hoped that this change will reduce confusion and lead to less respondents dropping out of the survey.

7.2 Naming of origins and destinations At present, the questions are framed so that respondent are prompted with the names of the municipality or urban district when asked questions involving the origin or destination of the trip. This has caused some confusion as the name of the municipality or urban district does not always provide an accurate description of the location, and can give the respondent an impression that there is something wrong with the questionnaire. For instance you will be presented information stating that you were travelling to Nittedal despite earlier in the questionnaire indicating that you were travelling to Skedsmo etc. These destinations are close, but clearly geographically different for the respondents. It is therefore suggested that the survey is amended to use the string that respondents provide in the open ended questions to identify the name or address of their trip origins and destinations throughout the whole survey rather than the municipality name. This is simply a case of describing the locations using an alternative (and more readily identifiable) description that is provided by the respondent.

7.3 Introduction to HSR In the questions introducing the concept of an HSR service respondents are told the most likely stations for their journey, so that they have a feel for how far they may have to travel at each end to access the services. This is achieved by identifying the closest potential HSR station to the centroids of the relevant zones within the zoning system. In the majority of cases this will produce sensible results, but there will clearly be some cases where the respondent is asked to consider a station that would not seem the most obvious. For example, an individual travelling from Askim to Trondheim would be asked to consider an HSR service from Sarpsborg to Trondheim (on the basis that the Sarpsborg station is the closest to the zone centroid).

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However, it may seem more logical for someone making this journey to travel north to Oslo and take the HSR service from there to Trondheim. The issue here is that the survey works by selecting the closest stations to origin and destination zones, rather than by seeking to minimise the door to door journey time by adding the access, in-vehicle, and egress time for all possible zone combinations to identify a O-D specific selection of the HSR stations. This would be a significantly larger task given that it would be necessary to identify the access and egress times from every zone to every potential HSR station location and then run a minimisation routine across all possible combinations of HSR stations to identify the one with the shortest door to door journey time. Whilst this is feasible in principle, given the timing constraints on the study it is not feasible in practice. It is therefore suggested that this aspect of the survey remains unchanged with an acceptance that some respondents may be presented a less obvious option for their journey.

7.4 Omission of a back button At present it has not been feasible for TNS to implement a back button within the survey, so respondents cannot go back and change their answers if they subsequently realised that they gave an incorrect response. This would usually be a function available within a web survey, but had to be omitted in the first wave of the data collection as it was causing errors in some of the background calculations. It is recognised that this missing back button is a problem for the survey, and possibly an important source for defection. TNS are now looking at options that could allow the inclusion of the back button without damaging the registry of the data and the script. Whilst they cannot promise that this will be solved entirely, they have undertaken to investigate this further to seek solutions to allow it to be included.

7.5 Respondents that have relocated or moved HZONE At present the survey reads in the respondent’s home location from the panel data that is held on them. However, it is recognised that people may have moved since joining the panel and may not have updated their address details. This can cause problems for this survey as we allow them to state that they “started their journey at home” to save on recollecting this information. For the next wave of the survey a check will be put in place to examine whether their reported home postcode deviates substantially from the one registered in the panel data. In such cases, the respondent would then be asked to identify their home zone using the maps within the survey.

8 Review of survey instrument and suggested changes

Following this preliminary analysis, the coded survey instrument has been reviewed to see if there are areas where it could be refined prior to the main survey phase. The suggested changes are listed below.

8.1 Clarify whether costs presented in SP relate to a one-way or return journey As noted earlier, there is some ambiguity at present whether the costs presented in the SP choices relate to return or one-way costs. This should be clarified by making the following coding changes.

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• For Car respondents o describe as “Return travel cost for your group” if Q401 = 1 or 2 o describe as “One way travel cost for your group” if Q401 = 3

• For Air respondents o describe as “Return travel cost for your group” if Q490 = 2 or 3 o describe as “One way travel cost for your group” if Q490 = 1

• For Train respondents o describe as “Return travel cost for your group” if Q519 = 2 or 5 o describe as “One way travel cost for your group” if Q519 = 1

o describe as “Period travel cost for your group” if Q519 = 3 or 4 • For Bus respondents o describe as “Return travel cost for your group” if Q619 = 2 or 3 o describe as “One way travel cost for your group” if Q619 = 1

8.2 Update Car experiment to present return costs if return journey As noted earlier, the car costs also need adjustment to reflect return costs where appropriate. This should be clarified by making the following coding changes. • Level on car cost in SP1 (car) o If Q401 = 1 or 2, level is CARCOST * CARCOST_MULT * 2 o if Q401 = 3, level is CARCOST * CARCOST_MULT • Level on HSR cost in SP1 (car) o If Q401 = 1 or 2, level is HSRCARFARE * GROUPSIZE * HSRCARFARECOST_DIV * 2 o if Q401 = 3, level is HSRCARFARE * GROUPSIZE * HSRCARFARECOST_DIV

8.3 Update the calculations for the HSR fare in the bus experiments Following our analysis of the trading within the experiments we also conclude that it would be better to pivot the HSR costs for bus respondents off the HSRCARFARE assumptions, rather than use the existing bus fare for pivoting. This should be implemented by making the following coding changes. • Update HSR cost levels

o If Q619 = 2 or 3, level is HSRCARFARE * GROUPSIZE * AIRCOST_DIVB * 2 o if Q619 = 1, level is HSRCARFARE * GROUPSIZE * AIRCOST_DIVB • Use levels of AIRCOST_DIVB of 0.7, 0.9, 1.1, 1.3

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8.4 The Bergen-Stavanger Ferry Amend questions so that the respondent is given the option in the questions reporting their trips by mode (Q202, Q204, Q207, Q209, and Q211 ) to report that they have used a “High speed boat service”, with this then followed by an additional question for those reporting the use of “High speed boat service” to establish for how many of these trips they used the Bergen-Stavanger service.

8.5 Naming of origins and destinations Amend questions to use the string that respondents provide in the open ended questions to identify the name or address of their trip origins and destinations throughout the whole survey.

8.6 Back button TNS will continue to seek a solution to allow a back button to be included.

8.7 Respondents that have relocated or moved HZONE Put in place a check to examine whether their reported home postcode deviates substantially from the one registered in the panel data. In such cases, the respondent will then be asked to identify their home zone using the maps within the survey.

9 Conclusions

The survey is generally working as intended and is producing a dataset that is capable of supporting the sort of analysis necessary to feed in to the demand models. The background questions also provide some useful insights in to a wider range of factors that individuals find important in considering their choice of mode. The models estimated from the data at this stage provide intuitive findings, and the data provides a strong basis for developing more detailed models that seek to further explain differences between different groups of travellers. Further models will now be estimated from the available choice data, which will then be enriched with the 2,200 responses that will be collected in wave 2 of the data collection. On the basis of this preliminary analysis we recommend a number of changes to the survey before commencing wave 2 of the data collection, as set out in section 8. We believe that making these changes should act to improve the quality of the data being collected and strengthen the models that we will estimate following the second wave of the data collection. The data from the two waves can then be combined in the data analysis, and models will be specified that take account of the potential for differences in model scale between the two waves of the survey.

16

Appendix B – Survey Questions

5096833/Subjects 2 and 3 Surveys Final Report_180211.docx

HSR Survey Questions [FINAL VERSION]

Survey Format: 0 – Data read in about respondent from sample 1 – Introduction to survey 2 – Collect data on long-distance travel in the last 6 months 3 – Collect Origin and Destination of selected trip (using zone maps provided) 4 – Collect journey time and cost information for selected trip 5 - Introduction to HSR 6 – HSR Choice Experiment 7 – Train Attributes Experiment 8 – Attitudes towards HSR 9 – Qualitative Questions / Attitudes towards Tunnels 10 – Factors that would make HSR more attractive than existing mode 11 – Socio-economic Questions

SECTION 0 DATA READ IN FROM SAMPLE

Respondent’s age

Home province

Gender • Female • Male

Primary occupation • Full time employee • Part time employee • Self-employed • Retired • Unemployed • On welfare • Student or pupil • Homemaker • Other

Home post code

Home municipality number

Personal income before taxes • Less than 200.000 NOK • 200.000 - 299.999 NOK • 300.000-399.000 NOK • 400.000 - 599.999 NOK • 600.000 - 799.999 NOK • More than 800.000 NOK

Marital status • Married or registered partner • Co-habiting • Unwed / single • Divorced / separated

Last completed education • Primary and lower secondary school / Compulsory education (7-10 years of education) • Upper secondary school (vocational education program, program for general studies, other) • Tertiary vocational education • Higher education, bachelors degree or equivalent

1 • Higher education, masters degree or higher

Number of people living in household, children and adults in total

Combined income of the household, before taxes • Below 200.000 NOK • 200.000 - 399.999 NOK • 400.000 - 599.999 NOK • 600.000 - 799.999 NOK • 800.000 - 999.999 NOK • 1.000.000 - 1.199.000 NOK • 1.200.000 - 1.399.000 NOK • More than 1.400.000 NOK

Number of children in household under 15 years of age • 1 person • 2 persons • 3 persons • 4 persons • 5 persons or more

Number of cars in the household • 0 • 1 • 2 • 3 or more

Does respondent have a drivers license (Class B) • Yes • No

Does respondent have an executive or managerial position • Yes • No

What kind of managerial position does respondent have • CEO or top management • Director or assistant director • Manager • Self employed • Other kind of executive position

Does respondent own or rent their current residence • Own • Rent

Was respondent born in Norway • Yes • No

START OF SURVEY

2 SECTION 1 RECRUITMENT QUESTIONS

(infotext - start) Q100 INTRODUCTION

Thank you for participating in this survey to investigate passenger demand for high-speed rail services.

Jernbaneverket is currently considering the potential options for future developments of the Norwegian rail network, which could include new high speed lines between some of the major cities. At present no decisions have been made, but this study is seeking to provide some insight in to who might decide to use a high speed train if one were available, and under which circumstances.

We will start by asking you some questions regarding the long distances trips that you are currently making, before then asking you to consider how your choices of transport mode may (or may not) change if a high speed rail line were available.

This survey should take about 20 minutes to complete.

Q101 HPOSTCODE What is your home post code? (open numeric – range 0001 - 9999)

PLEASE NOTE: maps for identifying home are omitted

SECTION 2: COLLECT DATA ON LONG-DISTANCE TRAVEL IN LAST 6 MONTHS

Q200 LONGDISTINTRO Firstly, we would like you to think about all of the long-distance trips that you have made in the last 12 months.

We define long-distance trips as being between two locations that are more than 100km away from each other. We are interested only in long distance trips that you have made within Norway, or to Stockholm or Gothenburg. This can include long distance journeys to Gardermoen airport where you transferred to international flights

Q201 LDBUS Have you made any long-distance journeys in the last 6 months for business purposes? (Instruction text) [Business, defined as trips made in the course of business, for business purposes (not including commuting)]

(single)

DP: Response required.

Yes No Don’t know

DP: Response required.

Yes/Ja No/Nei Don’t know/Vet ikke

FILTER 202: IF Q201 = “Yes”

Q202 LDBUSMANY If yes, then how many by car, train, air, bus and the Bergen-Stavanger ferry?

(Instruction text) [If multiple modes of travel have been used, the main mode is defined as the mode used for the longest part of the journey.]

3 (open numeric, min 0, max 9999)

DP: Response required.

Q202_1 Bus Q202_2 Train Q202_3 Air Q202_4 Car Q202_5 High speed boat

FILTER 202_5: IF Q202_5 > 0

Q202_5_X The distance between Bergen and Stavanger can be considered for HSR. How may of these high speed boat trips were on the Bergen-Stavanger ferry?

(open numeric, min 0, max 9999)

DP: Response required.

STOP FILTER 202_5

STOP FILTER 202: TO ALL

Q203 LDCOM Have you made any long-distance commuting journeys in the last 6 months?

(Instruction text) [Commute, defined as trips made to work]

(single)

Yes/Ja No/Nei Don’t know/Vet ikke

FILTER 204: IF Q203 = “Yes”

Q204 LDCOMMANY If yes, then how many by car, train, air, bus and the Bergen-Stavanger ferry?

(Instruction text) [If multiple modes of travel have been used, the main mode is defined as the mode used for the longest part of the journey.]

(open numeric, min 0, max 9999)

DP: Response required.

Q204_1 Bus Q204_2 Train Q204_3 Air Q204_4 Car Q204_5 High speed boat

FILTER 204_5: IF Q204_5 > 0

Q204_5_X The distance between Bergen and Stavanger can be considered for HSR. How may of these high speed boat trips were on the Bergen-Stavanger ferry?

(open numeric, min 0, max 9999)

DP: Response required.

STOP FILTER 204_5

STOP FILTER 204: TO ALL

4

Q205 LDLEIS Have you made any long-distance journeys in the last 6 months for leisure? (single)

DP: Response required.

Yes No Don’t know

FILTER 206: IF Q205 = “Yes”

Q206 LDLEISDAY If yes, then have you made any long-distance leisure day trips?

(Instruction text) [Leisure day trip, journey made for leisure, with no overnight stay]

(single)

DP: Response required.

Yes No Don’t know

FILTER 207: IF Q206 = “Yes”

Q207 LDLEISMANY If yes, then how many by car, train, air, bus and the Bergen-Stavanger ferry?

(Instruction text) [If multiple modes of travel have been used, the main mode is defined as the mode used for the longest part of the journey.]

(open numeric, min 0, max 9999)

DP: Response required.

Q207_1 Bus Q207_2 Train Q207_3 Air Q207_4 Car Q207_5 High speed boat

FILTER 207_5: IF Q207_5 > 0

Q207_5_X The distance between Bergen and Stavanger can be considered for HSR. How may of these high speed boat trips were on the Bergen-Stavanger ferry?

(open numeric, min 0, max 9999)

DP: Response required.

STOP FILTER 207_5

STOP FILTER 207

Q208 LD12NGHT If yes, then have you made any long-distance leisure trips where you have stayed 1 or 2 nights away?

(Instruction text) [Leisure, 2-3 days away, journey made for leisure, staying 1 to 2 nights away]

(single)

DP: Response required.

5

Yes No Don’t know

FILTER 209: IF Q208 = “Yes”

Q209 LD12NGHTMANY If yes, then how many by car, train, air, bus and the Bergen-Stavanger ferry?

(Instruction text) [If multiple modes of travel have been used, the main mode is defined as the mode used for the longest part of the journey.]

(open numeric, min 0, max 9999)

DP: Response required.

Q209_1 Bus Q209_2 Train Q209_3 Air Q209_4 Car Q209_5 High speed boat

FILTER 209_5: IF Q209_5 > 0

Q209_5_X The distance between Bergen and Stavanger can be considered for HSR. How may of these high speed boat trips were on the Bergen-Stavanger ferry?

(open numeric, min 0, max 9999)

DP: Response required.

STOP FILTER 209_5

STOP FILTER 209

Q210 LD3NGHT If yes, then have you made any long-distance leisure trips where you have stayed 3 or more nights away?

(Instruction text) [Leisure, more than 4 days, journey made for leisure, staying 3 or more nights away]

DP: Response required.

Yes No Don’t know

FILTER 211: IF Q210 = “Yes”

Q211 LD3NGHTMANY If yes, then how many by car, train, air, bus and the Bergen-Stavanger ferry?

(Instruction text) [If multiple modes of travel have been used, the main mode is defined as the mode used for the longest part of the journey.]

(open numeric, min 0, max 9999)

DP: Response required.

Q211_1 Bus Q211_2 Train Q211_3 Air Q211_4 Car

6 Q211_5 High speed boat

FILTER 211_5: IF Q211_5 > 0

Q211_5_X The distance between Bergen and Stavanger can be considered for HSR. How may of these high speed boat trips were on the Bergen-Stavanger ferry?

(open numeric, min 0, max 9999)

DP: Response required.

STOP FILTER 211_5

STOP FILTER 211 STOP FILER 206 – TO ALL

7 DP: Focus on one trip using the following hierarchy.

First select on basis of purpose: 1. Commute 2. Business 3. Leisure, 3 or more nights away 4. Leisure, 1-2 nights away 5. Leisure, day trip

If multiple trips by chosen purpose, then select on basis of mode: 1. Bus 2. Train 3. Air 4. Car

DP: RANKING ..

Bus Train Air Car Commute 1 2 3 4 Business 5 6 7 8 Leisure, 3 or more nights away 9 10 11 12 Leisure, 1-2 nights away 13 14 15 16 Leisure, day trip 17 18 19 20

Q_TRIP_RANK – Ranking of trips made by respondent

(multi)

DP: hidden in LIVE

COMPUTE: IF Q204_1 > 0 Q_TRIP_RANK_1 = 1 ”pendlereisen med buss” IF Q204_2 > 0 Q_TRIP_RANK_2 = 1 ”pendlereisen med tog” IF Q204_3 > 0 Q_TRIP_RANK_3 = 1 ”pendlereisen med fly” IF Q204_4 > 0 Q_TRIP_RANK_4 = 1 ”pendlereisen med bil” IF Q202_1 > 0 Q_TRIP_RANK_5 = 1 ”forretningsreisen med buss” IF Q202_2 > 0 Q_TRIP_RANK_6 = 1 ”forretningsreisen med tog” IF Q202_3 > 0 Q_TRIP_RANK_7 = 1 ” forretningsreisen med fly” IF Q202_4 > 0 Q_TRIP_RANK_8 = 1 ” forretningsreisen med bil” IF Q211_1 > 0 Q_TRIP_RANK_9 = 1 ”fritidsreisen med 3 overnattinger eller mer med buss” IF Q211_2 > 0 Q_TRIP_RANK_10 = 1 ”fritidsreisen med 3 overnattinger eller mer med tog” IF Q211_3 > 0 Q_TRIP_RANK_11 = 1 ”fritidsreisen med 3 overnattinger eller mer med fly” IF Q211_4 > 0 Q_TRIP_RANK_12 = 1 ”fritidsreisen med 3 overnattinger eller mer med bil” IF Q209_1 > 0 Q_TRIP_RANK_13 = 1 ”fritidsreisen med 1-2 overnattinger med buss” IF Q209_2 > 0 Q_TRIP_RANK_14 = 1 ”fritidsreisen med 1-2 overnattinger med tog” IF Q209_3 > 0 Q_TRIP_RANK_15 = 1 ”fritidsreisen med 1-2 overnattinger med fly” IF Q209_4 > 0 Q_TRIP_RANK_16 = 1 ”fritidsreisen med 1-2 overnattinger med bil” IF Q207_1 > 0 Q_TRIP_RANK_17 = 1 ”dagsturen i fritiden med buss” IF Q207_2 > 0 Q_TRIP_RANK_18 = 1 ”dagsturen i fritiden med tog” IF Q207_3 > 0 Q_TRIP_RANK_19 = 1 ”dagsturen i fritiden med fly” IF Q207_4 > 0 Q_TRIP_RANK_20 = 1 ”dagsturen i fritiden med bil”

DP: NEED TO IDENTIFY LONG-DISTANCE TRIP TO CONCENTRATE ON FOR SP SURVEY

8 SECTION 3 – ORIGIN AND DESTINATION FOR SELECTED TRIP

LOOP 01 # = 1 TO 20

(infotext) We would now like you to think about the last long distance trip that you made for “PIPE INN Q_TRIP_RANK_#”

Q301 RECALLTIME How long ago did you make this journey?

(single)

DP: Response required.

Less than 1 month ago Between 1 and 2 months ago Between 2 and 3 months ago Between 3 and 4 months ago Between 4 and 5 months ago Between 5 and 6 months ago

Q302 DAYOFTRIP On what day of the week did you make this journey? (single)

DP: Response required.

Monday Tuesday Wednesday Thursday Friday Saturday Sunday

Q303_X ORIGIN_OPEN

Where didi your journey start?

Q303 ORIGIN Was this your home, work or elsewhere? (single)

DP: Response required.

Home Work Somewhere else, please specify (open text)

FILTER OZONE: IF Q303 = ”Work” or ”Somewhere else” or (Q101 (postcode) not equal to postcode in sample)

REGION: Where do you find your place of departure? (single)

DP: Response required. DP: Show map NORWAY (to the left)

Østlandet Sørlandet Vestlandet

9 Midt-Norge Nord-Norge Stockholm eller Gøteborg

FILTER PROV_1: IF REGION = Østlandet

PROV_1: In which of the listed provinces do you find your place of departure? (single)

DP: Response required. DP: Show map Østlandet (to the left)

(infotext) If you can’t find the location you can always go back and look for it inside other areas. (PROVINCE)

Østfold (Halden, Fredrikstad, Sarpsborg, Moss, Askim mfl.) 01 Akershus (Asker, Bærum, Ski, Lillestrøm, Drøbak, Ullensaker og Gardermoen mfl.) 02 Oslo 03 Hedmark (Kongsvinger, Hamar, Elverum, Tynset mfl.) 04 Oppland (Gjøvik, Lillehammer, Fagernes, Otta, Dombås mfl.) 05 Buskerud (Drammen, Røyken, Lier, Hønefoss, Kongsberg, Gol, Geilo, Hemsedal mfl.) 06 Vestfold (Horten, Tønsberg, Sandefjord, Larvik mfl.) 07

STOP FILTER PROV_1

FILTER PROV_2: IF REGION = Sørlandet

PROV_2: In which of the listed provinces do you find your place of departure? (single)

DP: Response required. DP: Show map Sørlandet (to the left)

(infotext) If you can’t find the location you can always go back and look for it inside other areas. (PROVINCE)

Telemark (Porsgrunn, Skien, Kragerø, Notodden, Rjukan mfl.) 08 Aust-Agder (Risør, Tvedestrand, Arendal, Grimstad, Lillesand, Setesdal mfl.) 09 Vest-Agder (Kristiansand, Mandal, Farsund, Flekkefjord) 10

STOP FILTER PROV_2

FILTER PROV_3: IF REGION = Vestlandet

PROV_3: In which of the listed provinces or cities do you find your place of departure? (single)

DP: Response required. DP: Show map Vestlandet (to the left)

(infotext) If you can’t find your destination inside your chosen province or city you can always go back and look for it inside other regions and provinces

(PROVINCE)

Rogaland except Stavanger (Haugesund, Egersund, Sandnes, Bryne, Sauda mfl.) 11 Stavanger 111 Hordaland except Bergen (Voss, Arna, Odda, Stord/Leirvik mfl.) 12 Bergen 121 Sogn- og Fjordane (Førde, Florø, Årdal, Høyanger mfl.) 14 Møre- og Romsdal (Ålesund, Molde, Åndalsnes, Kristiansund mfl.) 15

STOP FILTER PROV_3

FILTER PROV_4: IF REGION = Midt-Norge

10

PROV_4: In which of the listed provinces or cities do you find your place of departure? (single)

DP: Response required. DP: Show map Midt-Norge (to the left)

(infotext) If you can’t find your destination inside your chosen province or city you can always go back and look for it inside other regions and provinces

Sør-Trøndelag except Trondheim (Orkdal, Oppdal, Røros mfl.) 16 Trondheim 161 Nord-Trøndelag (Stjørdal, Steinkjer, Levanger, Verdal mfl.) 17

STOP FILTER PROV_4

FILTER PROV_5: IF REGION = Nord-Norge

PROV_5: In which of the listed provinces do you find your place of departure? (single)

DP: Response required. DP: Show map North-Norway (to the left)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

Nordland (Bodø, Mo i Rana, Narvik, Svolvær, Lofoten og Vesterålen mfl.) 18 Troms (Tromsø, Harstad m.fl) 19 Finnmark (Kirkenes, Alta, Kautokeino, Karajsok, Vadsø, Vardø m.fl) 20

STOP FILTER PROV_5

FILTER PROV_6: IF REGION = Stocholm eller Gøteborg

PROV_6: In which of the listed cities do you find your place of departure? (single)

DP: Response required.

Stockholm 21 Gøteborg 22

+ Don’t know

See variable TettCom in excel file MunComZone.xls (PROV_NO = 21 and 22)

RECORD ZONE (number from file in correct format)

STOP FILTER PROV_6.

FILTER ZONE_01: IF PROV_1 = 01

ZONE_01 Please find the municipality or community for your place of departure in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your place of departure.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Østfold (to the left)

11

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 1) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_01

FILTER ZONE_02: IF PROV_1 = 02

ZONE_02 Please find the municipality or community for your place of departure in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your place of departure.

(single)

(infotext) Please select Gardermoen flyplass if you were flying from that airport to an international destination. (infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Akershus (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 2) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_02

FILTER ZONE_03: IF PROV_1 = 03

ZONE_03 Please find the name of the city area (bydel) for your place of departure in the list below. If you can’t find the name you are looking for, please chose the city area that is nearest to your place of departure

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Oslo (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 3) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_03

FILTER ZONE_04: IF PROV_1 = 04

12 ZONE_04 Please find the municipality or community for your place of departure in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your place of departure.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Hedmark (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 4) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_04

FILTER ZONE_05: IF PROV_1 = 05

ZONE_05 Please find the municipality or community for your place of departure in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your place of departure.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Oppland (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 5) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_05

FILTER ZONE_06: IF PROV_1 = 06

ZONE_06 Please find the municipality or community for your place of departure in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your place of departure.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Buskerud (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

13

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 6) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_06

FILTER ZONE_07: IF PROV_1 = 07

ZONE_07 Please find the municipality or community for your place of departure in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your place of departure.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Vestfold (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 7) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_07

FILTER ZONE_08: IF PROV_2 = 08

ZONE_08 Please find the municipality or community for your place of departure in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your place of departure.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Telemark (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 8) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_08

FILTER ZONE_09: IF PROV_2 = 09

ZONE_09 Please find the municipality or community for your place of departure in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your place of departure.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

14

DP: Response required. DP: Show map Aust Agder (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 9) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_09

FILTER ZONE_10: IF PROV_2 = 10

ZONE_10 Please find the municipality or community for your place of departure in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your place of departure.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Vest Agder (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 10) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_10

FILTER ZONE_11: IF PROV_3 = 11

ZONE_11 Please find the municipality or community for your place of departure in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your place of departure.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Rogaland (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 11) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_11

FILTER ZONE_111: IF PROV_3 = 111

15

ZONE_111 Please find the name of the city area (bydel) for your place of departure in the list below. If you can’t find the name you are looking for, please chose the city area that is nearest to your place of departure

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Stavanger (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 111) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_111

FILTER ZONE_12: IF PROV_3 = 12

ZONE_12 Please find the municipality or community for your place of departure in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your place of departure.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Hordaland (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 12) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_12

FILTER ZONE_121: IF PROV_3 = 121

ZONE_121 Please find the name of the city area (bydel) for your place of departure in the list below. If you can’t find the name you are looking for, please chose the city area that is nearest to your place of departure

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Bergen (to the left)

16 DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 121) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_121

FILTER ZONE_14: IF PROV_3 = 14

ZONE_14 Please find the municipality or community for your place of departure in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your place of departure.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Sogn og Fjordane (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 14) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_14

FILTER ZONE_15: IF PROV_3 = 15

ZONE_15 Please find the municipality or community for your place of departure in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your place of departure.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Møre og Romdsdal (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 15) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_15

FILTER ZONE_16: IF PROV_4 = 16

ZONE_16 Please find the municipality or community for your place of departure in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your place of departure.

17 (single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Sør Trøndelag (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 16) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_16

FILTER ZONE_161: IF PROV_4 = 161

ZONE_161 Please find the name of the city area (bydel) for your place of departure in the list below. If you can’t find the name you are looking for, please chose the city area that is nearest to your place of departure

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Trondheim (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 161) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_161

FILTER ZONE_17: IF PROV_4 = 17

ZONE_17 Please find the municipality or community for your place of departure in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your place of departure.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Nord Trøndelag (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 17) + Don’t know

RECORD ZONE (number from file in correct format)

18 STOP FILTER ZONE_17

FILTER ZONE_18: IF PROV_5 = 18

ZONE_18 Please find the municipality or community for your place of departure in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your place of departure.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Nordland (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 19) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_18

FILTER ZONE_19: IF PROV_5 = 19

ZONE_19 Please find the municipality or community for your place of departure in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your place of departure.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Troms (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 19) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_19

FILTER ZONE_20: IF PROV_5 = 20

ZONE_20 Please find the municipality or community for your place of departure in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your place of departure

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required.

19 DP: Show map Finnmark (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 20) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_20

FILTER ZONE IF DK (Don’t know) in any ZONE_1 to ZONE_20 (including 111, 121 and 161).

ZONE_DK Can you please find the municipality or community for your place of departure in the list of all municipalities of Norway below? If you can’t find the name you are looking for, please chose the municipality or community that is nearest to your place of departure.

(single)

DP: Response required.

DP: Add pop up window to show list of all alternatives (1335 names..) (infotext) Klick here to show alternatives.

Don’t know

STOP FILTER ZONE_DK

DP: COMPUTE ZONE_21 = PROV_6 (Stockholm og Gøteborg) DP: COMPUTE A PRODUCT VARIABLE FOR ZONE (Containing all values of ZONE_1 to ZONE_DK).

COMPUTE OZONE = ZONE_1 TO ZONE_DK (including 111,121, and 161)

STOP FILTER OZONE

Q305 ORIGINZONE

FILTER 310 IF Q303 = “Home”

COMPUTE OZONE = HZONE

STOP FILTER 310

DP: CHECK IS ORIGIN IN SCOPE FOR HSR

DP: LOOKUP WHETHER OZONE IN SCOPE FROM HZONE_INSCOPE.CSV (MOST SHOULD BE FROM SAMPLE). • IF INSCOPE = 0 • TELL RESPONDENT “This journey is not in scope for the survey. We would instead like to focus on a different journey” • GO BACK TO Q210 AND SELECT NEXT TRIP IN THE HIERARCHY (SAME PURPOSE AND NEXT MODE, OR IF NO MORE FOR SAME PURPOSE, NEXT PURPOSE AND FIRST MODE) • THEN RECOLLECT OD ZONE INFORMATION TO CHECK WHETHER LONG DISTANCE TRIP IN SCOPE

IF IN SCOPE = 0 OR ZONE_DK = Don’t know.

(infotext) This journey is not in scope for the survey. We would instead like to focus on a different journey.

20

RETURN TO SECTION 3 – “ORIGIN AND DESTINATION FOR SELECTED TRIP” AND REPEAT SEQUENCE

IF IN SCOPE = 1 – CONTINUE TO Q306

Q306 DESTINATION

Where were you travelling to? (Instruction text) [Please record town/city]

(open text)

REGION: Where do you find your destination? (single)

DP: Response required. DP: Show map NORWAY (to the left)

Østlandet Sørlandet Vestlandet Midt-Norge Nord-Norge Stockholm eller Gøteborg

FILTER PROV_1: IF REGION = Østlandet

PROV_1: In which of the listed provinces do you find your destination? (single)

DP: Response required. DP: Show map Østlandet (to the left)

(infotext) If you can’t find the location you can always go back and look for it inside other areas. (PROVINCE)

Østfold (Halden, Fredrikstad, Sarpsborg, Moss, Askim mfl.) 01 Akershus (Asker, Bærum, Ski, Lillestrøm, Drøbak, Ullensaker og Gardermoen mfl.) 02 Oslo 03 Hedmark (Kongsvinger, Hamar, Elverum, Tynset mfl.) 04 Oppland (Gjøvik, Lillehammer, Fagernes, Otta, Dombås mfl.) 05 Buskerud (Drammen, Røyken, Lier, Hønefoss, Kongsberg, Gol, Geilo, Hemsedal mfl.) 06 Vestfold (Horten, Tønsberg, Sandefjord, Larvik mfl.) 07

STOP FILTER PROV_1

FILTER PROV_2: IF REGION = Sørlandet

PROV_2: In which of the listed provinces do you find your destination? (single)

DP: Response required. DP: Show map Sørlandet (to the left)

(infotext) If you can’t find the location you can always go back and look for it inside other areas. (PROVINCE)

Telemark (Porsgrunn, Skien, Kragerø, Notodden, Rjukan mfl.) 08 Aust-Agder (Risør, Tvedestrand, Arendal, Grimstad, Lillesand, Setesdal mfl.) 09 Vest-Agder (Kristiansand, Mandal, Farsund, Flekkefjord) 10

STOP FILTER PROV_2

FILTER PROV_3: IF REGION = Vestlandet

21

PROV_3: In which of the listed provinces or cities do you find your destination? (single)

DP: Response required. DP: Show map Vestlandet (to the left)

(infotext) If you can’t find your destination inside your chosen province or city you can always go back and look for it inside other regions and provinces

(PROVINCE)

Rogaland except Stavanger (Haugesund, Egersund, Sandnes, Bryne, Sauda mfl.) 11 Stavanger 111 Hordaland except Bergen (Voss, Arna, Odda, Stord/Leirvik mfl.) 12 Bergen 121 Sogn- og Fjordane (Førde, Florø, Årdal, Høyanger mfl.) 14 Møre- og Romsdal (Ålesund, Molde, Åndalsnes, Kristiansund mfl.) 15

STOP FILTER PROV_3

FILTER PROV_4: IF REGION = Midt-Norge

PROV_4: In which of the listed provinces or cities do you find your destination? (single)

DP: Response required. DP: Show map Midt-Norge (to the left)

(infotext) If you can’t find your destination inside your chosen province or city you can always go back and look for it inside other regions and provinces

Sør-Trøndelag except Trondheim (Orkdal, Oppdal, Røros mfl.) 16 Trondheim 161 Nord-Trøndelag (Stjørdal, Steinkjer, Levanger, Verdal mfl.) 17

STOP FILTER PROV_4

FILTER PROV_5: IF REGION = Nord-Norge

PROV_5: In which of the listed provinces do you find your destination? (single)

DP: Response required. DP: Show map North-Norway (to the left)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

Nordland (Bodø, Mo i Rana, Narvik, Svolvær, Lofoten og Vesterålen mfl.) 18 Troms (Tromsø, Harstad m.fl) 19 Finnmark (Kirkenes, Alta, Kautokeino, Karajsok, Vadsø, Vardø m.fl) 20

STOP FILTER PROV_5

FILTER PROV_6: IF REGION = Stocholm eller Gøteborg

PROV_6: In which of the listed cities do you find your destination? (single)

DP: Response required.

Stockholm 21 Gøteborg 22

+ Don’t know

22

See variable TettCom in excel file MunComZone.xls (PROV_NO = 21 and 22)

RECORD ZONE (number from file in correct format)

STOP FILTER PROV_6.

FILTER ZONE_01: IF PROV_1 = 01

ZONE_01 Please find the municipality or community for your destination in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your destination.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Østfold (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 1) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_01

FILTER ZONE_02: IF PROV_1 = 02

ZONE_02 Please find the municipality or community for your destination in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your destination.

(single)

(infotext) Please select Gardermoen flyplass if you were flying from that airport to an international destination. (infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Akershus (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 2) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_02

FILTER ZONE_03: IF PROV_1 = 03

ZONE_03 Please find the name of the city area (bydel) for your destination in the list below. If you can’t find the name you are looking for, please chose the city area that is nearest to your place destination.

23

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Oslo (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 3) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_03

FILTER ZONE_04: IF PROV_1 = 04

ZONE_04 Please find the municipality or community for your destination in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your destination.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Hedmark (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 4) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_04

FILTER ZONE_05: IF PROV_1 = 05

ZONE_05 Please find the municipality or community for your destination in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your destination.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Oppland (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 5) + Don’t know

RECORD ZONE (number from file in correct format)

24

STOP FILTER ZONE_05

FILTER ZONE_06: IF PROV_1 = 06

ZONE_06 Please find the municipality or community for your destination in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your destination.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Buskerud (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 6) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_06

FILTER ZONE_07: IF PROV_1 = 07

ZONE_07 Please find the municipality or community for your destination in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your destination.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Vestfold (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 7) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_07

FILTER ZONE_08: IF PROV_2 = 08

ZONE_08 Please find the municipality or community for your destination in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your destination.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Telemark (to the left)

25

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 8) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_08

FILTER ZONE_09: IF PROV_2 = 09

ZONE_09 Please find the municipality or community for your destination in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your destination.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Aust Agder (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 9) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_09

FILTER ZONE_10: IF PROV_2 = 10

ZONE_10 Please find the municipality or community for your destination in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your destination.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Vest Agder (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 10) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_10

FILTER ZONE_11: IF PROV_3 = 11

26 ZONE_11 Please find the municipality or community for your destination in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your destination.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Rogaland (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 11) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_11

FILTER ZONE_111: IF PROV_3 = 111

ZONE_111 Please find the name of the city area (bydel) for your destination in the list below. If you can’t find the name you are looking for, please chose the city area that is nearest to your place destination.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Stavanger (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 111) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_111

FILTER ZONE_12: IF PROV_3 = 12

ZONE_12 Please find the municipality or community for your destination in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your destination.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Hordaland (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

27 Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 12) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_12

FILTER ZONE_121: IF PROV_3 = 121

ZONE_121 Please find the name of the city area (bydel) for your destination in the list below. If you can’t find the name you are looking for, please chose the city area that is nearest to your place destination.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Bergen (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 121) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_121

FILTER ZONE_14: IF PROV_3 = 14

ZONE_14 Please find the municipality or community for your destination in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your destination.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Sogn og Fjordane (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 14) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_14

FILTER ZONE_15: IF PROV_3 = 15

ZONE_15 Please find the municipality or community for your destination in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your destination.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

28

DP: Response required. DP: Show map Møre og Romdsdal (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 15) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_15

FILTER ZONE_16: IF PROV_4 = 16

ZONE_16 Please find the municipality or community for your destination in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your destination.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Sør Trøndelag (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 16) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_16

FILTER ZONE_161: IF PROV_4 = 161

ZONE_161 Please find the name of the city area (bydel) for your destination in the list below. If you can’t find the name you are looking for, please chose the city area that is nearest to your place destination.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Trondheim (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 161) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_161

29 FILTER ZONE_17: IF PROV_4 = 17

ZONE_17 Please find the municipality or community for your destination in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your destination.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Nord Trøndelag (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 17) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_17

FILTER ZONE_18: IF PROV_5 = 18

ZONE_18 Please find the municipality or community for your destination in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your destination.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Nordland (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 19) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_18

FILTER ZONE_19: IF PROV_5 = 19

ZONE_19 Please find the municipality or community for your destination in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your destination.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Troms (to the left)

30 DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 19) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_19

FILTER ZONE_20: IF PROV_5 = 20

ZONE_20 Please find the municipality or community for your destination in the list below. If you can’t find the name you are looking for, please chose the community that is nearest to your destination.

(single)

(infotext) If you can’t find the location you can always go back and look for it inside other areas.

DP: Response required. DP: Show map Finnmark (to the left)

DP: Add pop up window to show list of all alternatives (only for this province) (infotext) Klick here to show alternatives.

Get relevant names from the variable TettCom in excel file MunComZone.xls (PROV_NO = 20) + Don’t know

RECORD ZONE (number from file in correct format)

STOP FILTER ZONE_20

FILTER ZONE IF DK (Don’t know) in any ZONE_1 to ZONE_20 (including 111, 121 and 161).

ZONE_DK Can you please find the municipality or community for your destination in the list of all municipalities of Norway below? If you can’t find the name you are looking for, please chose the municipality or community that is nearest to your destination..

(single)

DP: Response required.

DP: Add pop up window to show list of all alternatives (1335 names..) (infotext) Klick here to show alternatives.

Don’t know

STOP FILTER ZONE_DK

DP: COMPUTE ZONE_21 = PROV_6 (Stockholm og Gøteborg) DP: COMPUTE A PRODUCT VARIABLE FOR ZONE (Containing all values of ZONE_1 to ZONE_DK).

COMPUTE DESTZONE = ZONE_1 TO ZONE_DK (including 111,121, and 161)

Q308 ODCHECK

DP: CHECK IS ORIGIN-DESTINATION PAIR IN SCOPE FOR HSR

31

LOOKUP WHETHER ORIGINZONE AND DESTZONE IN SCOPE FROM LOS.CSV (MOST SHOULD BE FROM SAMPLE).

IF IN SCOPE = 1 END LOOP

IF IN SCOPE = 0 OR ZONE_DK = Don’t know.

(infotext) This journey is not in scope for the survey. We would instead like to focus on a different journey.

RETURN TO SECTION 3 – “ORIGIN AND DESTINATION FOR SELECTED TRIP” AND REPEAT SEQUENCE

IF LOOP= LOOP25 AND IN SCOPE = 0

TERMINATE SURVEY

DP: LOOKUP OTHER LOS DATA FOR ORIGINZONE AND DESTZONE COMBINATION FROM LOS.CSV FILE

LOSCARDIST HSROSTATION HSRDSTATION HSRACCESSTIME HSRDIST HSREGRESSTIME HSRAEDIST HSRCARFARE

Q210 Purpose Save information for selected journey purpose (Commute, Business, Leisure 3 or more nights away, Leisure 1-2 nights away, Leisure, day trip)

Q211 Mode Save information for selected journey mode (Bus, Train, Air, Car)

32 SECTION 4 – COLLECT JOURNEY TIME AND COST INFORMATION FOR SELECTED TRIP

Q400 ARRIVETIME

What time did you arrive at #Q306#?

(infotext): Please specify in hours and minutes

DP: Response required.

(open numeric – format hh:mm – range 0000 to 2400)

Q401 DIRECTION Was this trip part of a return journey or a one way trip?

(single)

DP: Response required.

Return – outbound leg Return – return leg One way trip

Q402 GROUPSIZE How many people were travelling together on this journey to #306#, including yourself?

DP: Response required.

(open numeric – range 1 to 999)

IF GROUPSIZE = 1, GROUPTEXT = “you” ELSE GROUPTEXT = “your group”

FILTER 403 IF Q402 > 1

Q403 KIDS

How many were children (aged 16 years or less)

STOP FILTER 403

Q404 ADULTS

How many were adults (over 16 years of age)

DP: Response required.

(open numeric – range 1 to 999)

DP: [CHECK THAT KIDS + ADULTS = GROUPSIZE, IF NOT SHOW THE FOLLOWING ERROR MESSAGE

(infotext) The number of adults and kids does not correspond to the total number of persons in your group. Please check this again.

FILTER 405 IF Q402 > 1

Q405 GROUPFROMHOME Is everyone that you travelled with from your household?

(single)

33

DP: Response required. yes no

STOP FILTER 405

FILTER 406 IF PURPOSE NOT EQUAL ”Leisure day trip”

Q406 NIGHTSAWAY

How many nights were you away?

DP: Response required.

(open numeric – range 0 to 183)

STOP FILTER 406

Q407 DETAILEDPURP

What was the main purpose of your journey to #Q306#?

(single)

DP: Response required. commuting business education holiday visiting friends and relatives other, please specify (open text)

Q408 JOURNEYFREQ

How often do you make the journey from #Q303_X# to #306# by #MODE# for the purpose of #PURPOSE#?

(single)

DP: Response required.

More than 3 times/week 1 to 3 times per week 1 to 3 times per month 5 to 11 times per year 1 to 4 times per year less than once per year never before

Q409 LUGGAGE How many large pieces of luggage were you travelling with?

DP: Response required.

(open numeric – range 0 to 99)

Q410 WHOPAID Who paid for all or a substantial part of your journey?

34

(single)

DP: Response required.

I did A family member My employer Other, please specify

FILTER 404 IF PURPOSE NOT EQUAL TO “Leisure”

Q411 DIDWORK Did you do any work on the journey?

(single)

DP: Response required. yes no

STOP FILTER 404

FILTER 412 IF Q211 = Car

Q412 CARAVAIL Could you have made your journey by car?

(single)

DP: Response required. yes no, because I don’t have a licence no, because I don’t have access to a car no, because of other reasons

STOP FILTER 412

35 FILTER 450 IF MODE = CAR

BACKGROUND CAR QUESTIONS

Q450 CARTIME How long did it take you to travel from your #Q303_X# to #Q306#?

Please state the total journey time from when you left your home to when you arrived at your destination, including rest breaks, but excluding any intermediate stops for other leisure activities, and including any delays that you have experienced. infotext: Please specify in hours and minutes

DP: Response required.

(open numeric – format hh:mm – range 00:00 to 96:00)

Q451 CARSTOPS Did you make any stops en route?

(single)

DP: Response required. yes no don’t know /don’t remember

Q452 CARCONGEST Did you experience any unexpected delays on your journey?

(single)

DP: Response required. yes no don’t know /don’t remember

FILTER 453 IF CARCONGEST = yes

Q453 CARCONGTIME How much time did the unexpected delay add to your journey?

DP: Response required.

(open numeric – format hh:mm – range 00:00 to 96:00) / infotext: Please specify in hours and minutes don’t know /don’t remember

STOP FILTER 453

Q454 LOSCARDIST

LOSCARDIST PREVIOUSLY READ IN FROM LOS DATA (IN KMS)

Q455 CARDISTCHECK We estimate that the distance between your #Q303_X# and #Q306# is about # LOSCARDIST # kilometres.

Do you agree with this estimate?

36

(single)

DP: Response required. yes 1 no 2

FILTER 456 IF CARDISTCHECK = no

Q456 CARDISTREVISED What would you estimate the approximate one-way distance by road between your #Q303_X# and #Q306# to be (in kilometres)?

DP: Response required.

(open numeric –range 0001 to 4000)

STOP FILTER 456

Q457 CDISTANCE IF CARDISTCHECK = 1, CDISTANCE = LOSCARDIST IF CARDISTCHECK = 2, CDISTANCE = CARDISTREVISED

Q458 CARTOLLS Did you pay any tolls on your journey?

(single)

DP: Response required. yes no don’t know /don’t remember

FILTER 459 IF CARTOLLS = yes

Q459 CARTOLLSNOK How much did you pay in tolls on your journey?

DP: Response required.

(open numeric –range 0001 to 999) don’t know /don’t remember

STOP FILTER 459

Q460 CARPARKCOSTS Did you have to pay any parking costs at #Q306#? (single)

DP: Response required. yes no don’t know /don’t remember

FILTER 461 IF CARPARKCOSTS = yes

Q461 CARPARKCOSTSNOK How much did you pay in parking costs at #Q306#? (in NOK)

DP: Response required.

(open numeric –range 0001 to 999)

37 don’t know /don’t remember

STOP FILTER 461

Q462 CARCOSTESTIMATE IF PURPOSE = BUSINESS, CARCOSTESTIMATE = (CDISTANCE * 3.65) + CARPARKCOSTSNOK + CARTOLLSNOK

IF PURPOSE <> BUSINESS, CARCOSTESTIMATE = (CDISTANCE * 2.70) + CARPARKCOSTSNOK + CARTOLLSNOK

Q463 CARCOSTCONFIRM We estimate that the total cost of the car journey from #Q303_X# to #Q306#, including tolls and parking costs, is #CARCOSTESTIMATE# (in NOK).

IF PURPOSE = NON-BUSINESS JOURNEYS: (infotext) Note that this cost of driving includes your out-of-pocket costs, including petrol and other running costs such as oil.

IF PURPOSE = BUSINESS JOURNEYS: (infotext) Note that this cost includes costs that you may claim as expenses.

Do you agree with this estimate?

(single)

DP: Response required. yes no don’t know

FILTER 464 IF CARCOSTCONFIRM = no

Q464 CARCOSTREVISED What do you estimate the total driving cost of the car journey from #Q303_X# to #Q306# to be, including tolls, and parking? (in NOK)

DP: Response required.

(open numeric –range 0001 to 9999) don’t know

STOP FILTER 464

Q465 CARCOST

IF CARCOSTCONFIRM = YES CARCOST = CARCOSTESTIMATE IFCARCOSTCONFIRM = NO CARCOST = CARCOSTREVISED IF CARCOSTCONFIRM = NO AND CARCOSTREVISED = DON’T KNOW CARCOST = CARCOSTESTIMATE

Q466 CARNEEDS

Did you need your car at your journey destination for further use?

(single)

DP: Response required.

38 yes no

STOP FILTER 450

FILTER 470 IF MODE = AIR

BACKGROUND AIR QUESTIONS

Q470 AIRACCESS How did you travel from #Q303_X# to the airport? (infotext) If you used more than one mode, please select the main mode used.

(single)

DP: Response required.

Car (parked) Car (dropped off) Car (hired) Taxi Bus Train/Metro Other, please specify (open text)

Q471 OAIRPORT Which airport did you fly from?

(single)

DP: Response required.

(infotext) Primary Norwegian Airports Ålesund Airport, Vigra Alta Airport Bardufoss Airport Bergen Airport, Flesland Bodø Airport Harstad/Narvik Airport, Evenes Haugesund Airport, Karmøy Kirkenes Airport, Høybuktmoen Kristiansand Airport, Kjevik Kristiansund Airport, Kvernberget Molde Airport, Årø Moss Airport, Rygge Oslo Airport, Gardermoen Sandefjord Airport, Torp Stavanger Airport, Sola Svalbard Airport, Longyear Tromsø Airport Trondheim Airport, Værnes

(infotext) Regional Norwegian Airports Andøya Airport Båtsfjord Airport Berlevåg Airport Brønnøysund Airport, Brønnøy Fagernes Airport, Leirin Florø Airport Førde Airport, Bringeland Hammerfest Airport Hasvik Airport Honningsvåg Airport, Valan Lakselv Airport, Banak

39 Leknes Airport Mehamn Airport Mo i Rana Airport, Røssvoll Mosjøen Airport, Kjærstad Namsos Airport Narvik Airport, Framnes Notodden Airport Ny-Ålesund Airport, Hamnerabben Ørland Airport Ørsta-Volda Airport, Hovden Røros Airport Rørvik Airport, Ryum Røst Airport Sandane Airport, Anda Sandnessjøen Airport, Stokka Skien Airport, Geiteryggen Sogndal Airport, Haukåsen Sørkjosen Airport Stokmarknes Airport, Skagen Stord Airport, Sørstokken Svea Airport Svolvær Airport, Helle Vadsø Airport Vardø Airport, Svartnes

(infotext) Swedish airports Stockholm Arlanda Stockholm Bromma Stockholm Skavsta Gothenburg Landvetter Gothenburg City

Q472 AIRACCESSTIME How long did it take you to travel from #Q303_X# to #OAIRPORT# by #AIRACCESS# (in minutes)?

DP: Response required.

(open numeric – format mm – range 000 to 300)

Q473 AIRACCESSCOST How much did it cost for you/your group to travel from #Q303_X# to #OAIRPORT# BY #AIRACCESS#?

DP: Response required. (infotext):Please give us your best estimate/ approximately

(open numeric –range 0000 to 99999)

FILTER 474 IF AIRACCESS = CAR(PARKED)

Q474 AIRACCESSPARKCOST How much did you have to pay to park your car at #OAIRPORT#?

DP: Response required. (infotext):Please give us your best estimate/ approximately (in NOK)

(open numeric –range 0000 to 9999)

STOP FILTER 474

Q475 OEXPECTEDAIRPORTTIME How much time did you allocate to be at #OAIRPORT’ to check-in, go through security and wait for your flight (in minutes)?

40 DP: Response required.

(open numeric – format mm – range 000 to 300)

Q476 OAIRPORTDELAY Was your flight delayed?

(single)

DP: Response required. yes no

FILTER 477 IF OAIRPORTDELAY = yes

Q477 OAIRPORTDELAYTIME How long was your flight delayed?

DP: Response required.

(open numeric – format hh:mm – range 00:00 to 96:00) / infotext: Please specify in hours and minutes

STOP FILTER 477

Q478 DAIRPORT What was your destination airport in Norway or Sweden?

(single)

DP: Response required.

(infotext) Primary Norwegian Airports Ålesund Airport, Vigra Alta Airport Bardufoss Airport Bergen Airport, Flesland Bodø Airport Harstad/Narvik Airport, Evenes Haugesund Airport, Karmøy Kirkenes Airport, Høybuktmoen Kristiansand Airport, Kjevik Kristiansund Airport, Kvernberget Molde Airport, Årø Moss Airport, Rygge Oslo Airport, Gardermoen Sandefjord Airport, Torp Stavanger Airport, Sola Svalbard Airport, Longyear Tromsø Airport Trondheim Airport, Værnes

(infotext) Regional Norwegian Airports Andøya Airport Båtsfjord Airport Berlevåg Airport Brønnøysund Airport, Brønnøy Fagernes Airport, Leirin Florø Airport Førde Airport, Bringeland Hammerfest Airport Hasvik Airport Honningsvåg Airport, Valan

41 Lakselv Airport, Banak Leknes Airport Mehamn Airport Mo i Rana Airport, Røssvoll Mosjøen Airport, Kjærstad Namsos Airport Narvik Airport, Framnes Notodden Airport Ny-Ålesund Airport, Hamnerabben Ørland Airport Ørsta-Volda Airport, Hovden Røros Airport Rørvik Airport, Ryum Røst Airport Sandane Airport, Anda Sandnessjøen Airport, Stokka Skien Airport, Geiteryggen Sogndal Airport, Haukåsen Sørkjosen Airport Stokmarknes Airport, Skagen Stord Airport, Sørstokken Svea Airport Svolvær Airport, Helle Vadsø Airport Vardø Airport, Svartnes

(infotext) Swedish airports Stockholm Arlanda Stockholm Bromma Stockholm Skavsta Gothenburg Landvetter Gothenburg City

Q479 INTERLINE Did you then transfer to another flight to an airport outside of Norway or Sweden?

(single)

DP: Response required.

Yes No

FILTER 480 IF INTERLINE = YES

Q480 FINALAIR Which airport did you ultimately fly to?

[OPEN TEXT]

DP: Response required.

STOP FILTER 480

Q481 AIRFREQ Do you know how many flights there are between #OAIRPORT# and #DAIRPORT# per day?

(single)

DP: Response required.

42 less than 1 flight per day 1 flight per day 2 flights per day between 2 and 6 flights per day more than 6 flights per day I don’t know

Q482 FLIGHTTIME How long was your flight scheduled to take between #OAIRPORT# and #DAIRPORT# (in minutes)?

DP: Response required.

(open numeric – format hh:mm – range 0001 to 2400) / infotext: Please specify in hours and minutes

Q483 DAIRPORT TIME Approximately how long did you spend at #DAIRPORT#, from when you arrived at the gate before departing for your final destination in #Q306#’#Q306#, including the time to disembark the plane and collect your luggage, etc. (in minutes)?

DP: Response required.

(open numeric – format mm – range 000 to 300)

Q484 AIREGRESS How did you travel from #DAIRPORT# to your final destination in #Q306#?#Q306# If you used more than one mode, please select the main mode that you used?

(single)

DP: Response required.

Car (parked) Car (picked up) Car (hired) Taxi Bus Train/Metro Other, please specify (open text)

Q485 AIREGRESSTIME How long did it take you to travel from #DAIRPORT# to #Q306# including wait time (in minutes)?

DP: Response required.

(open numeric – format mm – range 000 to 300)

Q486 AIREGRESSCOST How much did it cost for you/your group to travel from #DAIRPORT# TO #Q306#?(in NOK)

DP: Response required. infotext):Please give us your best estimate/ approximately

(open numeric –range 0000 to 99999)

43 Q487 AIRSUMMARY (infotext) This means that your journey from #Q303_X# to #Q306# was the following:

Journey time from #Q303_X# to #OAIRPORT# is #AIRACCESSTIME# Time at the airport to go through check-in and security is #OEXPECTEDAIRPORTTIME# Flight time is #FLIGHTTIME# Journey time from #DAIRPORT# to #Q306# is #AIREGRESSTIME#

Is this correct?

(single)

DP: Response required. yes no

FILTER IF AIRSUMMARY IS NO, GO BACK TO AIRACCESS

Q488 AIRFAREQUESTIONS (infotext) We would now like to ask you a number of questions about your air tickets.

FILTER 489 IF GROUPSIZE > 1

Q489 AIRGROUPS Were the tickets for your group bought together?

(single)

DP: Response required. yes no don’t know /don’t remember

STOP FILTER 489

Q490 AIRFARERETURN Was your ticket for a single or return journey? (single)

DP: Response required. single return Other, please specify (open text)

Q491 AIRTICKETWHENBOOKED How long before your trip did you book your tickets? (single)

DP: Response required. more than 6 months before the trip a couple of months before the trip a couple of weeks before the trip a week before the trip on the day of the trip Other, please specify (open text)

Q492 AIRCLASS What class of ticket do you have for your flight? (single)

DP: Response required.

44 Economy Class Business Class

Q493 AIRPACKAGE Was your ticket purchased as part of a package holiday, for example including accommodation? (single)

DP: Response required. yes no

FILTER 494 IF AIRPACKAGE = NO

Q494 AIRTICKETCOST How much did your #AIRFARERETURN# ticket between #OAIRPORT# and #DAIRPORT# cost?

DP: IF AIRFARERETURN = “RETURN” THEN ADD THE TEXT: (infotext) Please provide total ticket costs for both the outward and return leg (in NOK) infotext):Please give us your best estimate/ approximately

DP: IF AIRGROUPS = YES (infotext)Please include the cost for everyone travelling in your group.

DP: Response required.

(open numeric –range 0000 to 99999)

STOP FILTER 494

FILTER 495 IF AIRPACKAGE = YES

Q495 AIRPACKCOST Please estimate how much your #AIRFARERETURN# ticket would have cost, if you were paying for the air travel only? (in NOK)

DP: IF AIRGROUPS = YES (infotext) Please include the cost for everyone travelling in your group.

DP: Response required.

(open numeric –range 0000 to 99999)

STOP FILTER 495

Q496 AIRCOST

IF AIRPACKAGE = NO AIRCOST = AIRTICKETCOST + AIRACCESSCOST + AIRACCESSPARKCOST + AIREGRESSCOST

IF AIRPACKAGE = YES AIRCOST = AIRPCKCOST + AIRACCESSCOST + AIRACCESSPARKCOST + AIREGRESSCOST

Q497 AIRMILES Are you a member of an air mile program? (single)

DP: Response required. yes no

FILTER 498 IF AIRMILES = yes

45

Q498 GETAIRMILES Did you obtain any frequent traveller benefits when buying your air ticket?

(single)

DP: Response required.

no yes, air miles yes, other, please specify don’t know

Q499 USEDAIRMILES Did you use any frequent flyer benefits when buying your air ticket?

(single)

DP: Response required. no yes, please specify (open text)

STOP FILTER 498 STOP FILTER 470

46 FILTER 500 IF MODE = RAIL

BACKGROUND RAIL QUESTIONS

Q500 TRAINACCESS How did you travel from #Q303_X# to the train station?

(infotext) If you used more than one mode, please select the main mode used. single)

DP: Response required.

Walk Bicycle Scooter or motorbike Car (parked) Car (dropped off) Car (hired) Taxi Bus, metro, underground or tram Other, please specify (open text)

Q501 OSTATION At which station did you board the train for your rail journey? (infotext) Please specify

(open text)

Q502 TRAINACCESSTIME How long did it take you to travel from your origin in #Q303_X# to #OSTATION# by #TRAINACCESS# (in minutes)?

DP: Response required.

(open numeric – format mm – range 000 to 300)

FILTER 503 IF TRAINACCESS NOT EQUAL TO WALK, BICYCLE OR SCOOTER/MOTORBIKE

Q503 TRAINACCESSCOST How much did it cost for #GROUPTEXT# to travel from #Q303_X# TO #OSTATION# BY #TRAINACCESS# (in NOK)?

DP: Response required. infotext):Please give us your best estimate/ approximately

(open numeric –range 0000 to 99999)

STOP FILTER 503

FILTER 504 IF TRAINACCESS = CAR(PARKED)

Q504 TRAINACCESSPARKCOST How much did you have to pay to park your car at #OSTATION# (in NOK)?

DP: Response required.

(open numeric –range 0000 to 99999)

STOP FILTER 504

Q505 TRAINWAITTIME

47 How long did you wait at #OSTATION# station for your train to depart (in minutes)?

DP: Response required.

(open numeric – format mm – range 000 to 300)

Q506 TRAINDELAY Was your train delayed?

(single)

DP: Response required. yes no don’t know /don’t remember

FILTER 507 IF TRAINDELAY = yes

Q507 TRAINDELAYTIME How long was your train delayed (in minutes)?

DP: Response required. don’t know /don’t remember

(open numeric – format mm – range 000 to 300)

STOP FILTER 507

Q508 DSTATION What was the final rail station on your journey?

Q509 TRAINFREQ Approximately, how frequent are the trains between #OSTATION# and #DSTATION#?

(single)

DP: Response required. less than 1 train service per day 1 train service per day 1 train service every 6 hours 1 train service every 4 hours 1 train service every 2 hours 1 train service every hour 2 train services every hour 4 train services every hour more than 4 train services every hour I don’t know

Q510 TRAININTS Did you have to make any interchanges between trains on your journey?

(single)

DP: Response required. yes no

FILTER 511 IF TRAININTS = YES

Q511 NUMTRAININTS How many interchanges between train services did you make?

48 DP: Response required.

(open numeric– range 00 to 10)

STOP FILTER 511

Q512 TRAINIVT How long was your train journey from #OSTATION# TO #DSTATION#, including any time spent making interchanges, if relevant?

DP: Response required.

(open numeric – format hh:mm – range 0000 to 2400) / infotext: Please specify in hours and minutes

Q513 TRAINEGRESS How did you travel from #DSTATION# to your final destination in #Q306#?

If you used more than one mode, please select the main mode used.

(single)

DP: Response required.

Walk Car (parked) Car (picked up) Car (hired) Taxi Bus, Metro, underground or tram Other, please specify (open text)

Q514 TRAINEGRESSTIME How long did it take for you to travel from #DSTATION# to your final destination in #Q306#?

DP: Response required.

(open numeric – format mm – range 000 to 300)

Q515 TRAINEGRESSCOST How much it cost for #GROUPTEXT# to travel from #DSTATION# to your final destination in #Q306#?

DP: Response required. (infotext):Please give us your best estimate/ approximately

(open numeric –– range 0000 to 99999)

Q516 RAILSUMMARY This means that your journey from #Q303_X# to #Q306# was:

Journey time from #Q303_X# to #OSTATION# is #TRAINACCESSTIME# Time at the station is #TRAINWAITTIME# Number of interchanges is #NUMTRAININTS# Total rail time, including time for interchanges, is #TRAINIVT# Journey time from #DSTATION# to #Q306# is #TRAINEGRESSTIME#

Is this correct?

(single)

DP: Response required.

49 yes no

IF RAILSUMMARY IS NO, GO BACK TO TRAINACCESS

Q517 RAILFAREQUESTIONS (infotext) We would now like to ask you a number of questions about your train ticket.

FILTER 518 IF GROUPSIZE > 1

Q518 RAILGROUPS Were the tickets for your group bought together?

(single)

DP: Response required. yes no don’t know /don’t remember

STOP FILTER 518

Q519 TRAINFARERETURN Was your ticket for a single or return journey?

(single)

DP: Response required.

Single Return Periodebillet 7 day Periodebillet 30 day Other, please specify (open text)

Q520 TRAINTICKETWHENBOOKED How long before your trip did you book your tickets?

(single)

DP: Response required.

More than 6 months before the trip A couple of months before the trip A couple of weeks before the trip A week before the trip On the day of the trip Other, please specify

Q521 TRAINCLASS Which class of ticket did you purchase?

(single)

DP: Response required.

Standard Komfort

Q522 TRAINTICKETCOST How much did your #TRAINFARERETURN# ticket between #OSTATION# and #DSTATION# cost (in NOK)?

(infotext):Please give us your best estimate/ approximately

50 DP: IF RAILGROUPS = YES (infotext) Please include the cost for everyone travelling in your group

DP: Response required.

(open numeric –range 0000 to 99999)

Q523 DISCOUNT Did you receive a discount for having a Kundekort or for being a student or over 67 years old?

(single)

DP: Response required.

Yes No

Q524 TRAINCOST TRAINCOST = TRAINTICKETCOST + TRAINACCESSCOST + TRAINACCESSPARKCOST + TRAINEGRESSCOST

STOP FILTER 500

51

FILTER 600 IF MODE = BUS

BACKGROUND BUS QUESTIONS

Q600 BUSACCESS How did you travel from #Q303_X# to the coach station? (infotext) If you used more than one mode, please select the main mode used.

(single)

DP: Response required.

Walk Bicycle Scooter or motorbike Car (parked) Car (dropped off) Car (hired) Taxi Local bus Bus/Metro Other, please specify (open text)

Q601 BUSOSTATION At which station did you board the coach for your bus journey? (infotext) Please specify

(open text)

Q602 BUSACCESSTIME How long did it take you to travel from your origin in #Q303_X# to #BUSOSTATION# by #BUSACCESS# (in minutes)?

DP: Response required.

(open numeric – format mm – range 000 to 300)

FILTER 603 IF BUSACCESS NOT EQUAL WALK, BICYCLE OR SCOOTER/MOTORBIKE

Q603 BUSACCESSCOST How much did it cost for #GROUPTEXT# to travel from #Q303_X# TO #BUSOSTATION# BY #BUSACCESS# (in NOK)?

DP: Response required.

(open numeric –range 000 to 99999)

STOP FILTER 603

FILTER 604 IF BUSACCESS = CAR(PARKED)

Q604 BUSACCESSPARKCOST How much did you have to pay to park your car at #BUSOSTATION# (in NOK)?

DP: Response required.

(open numeric –range 000 to 99999)

STOP FILTER 604

Q605 BUSWAITTIME How long did you wait at #BUSOSTATION# station for your bus to depart?

DP: Response required.

52

(open numeric – format mm – range 000 to 300)

Q606 BUSDELAY Was your bus delayed?

(single)

DP: Response required. yes no don’t know /don’t remember

FILTER 607 IF BUSDELAY = YES

Q607 BUSDELAYTIME How long was your bus delayed (in minutes)?

(open numeric – format mm – range 000 to 300)

don’t know /don’t remember

STOP FILTER 607

Q608 BUSDSTATION What was the final bus station on your journey? (infotext) Please specify

(open text)

Q609 BUSFREQ Approximately, how frequent are the buses between #BUSOSTATION# and #BUSDSTATION#?

(single)

DP: Response required. less than 1 bus service per day 1 bus service per day 1 bus service every 6 hours 1 bus service every 4 hours 1 bus service every 2 hours 1 bus service every hour 2 bus services every hour 4 bus services every hour More than 4 bus services every hour I don’t know

Q610 BUSINTS Did you have to make any interchanges between buses on your journey?

(single)

DP: Response required. yes no

53 FILTER 611 IF BUSINTS = YES

Q611 NUMBUSINTS How many interchanges between bus services did you make?

DP: Response required.

(open numeric – range 00 to 10)

STOP FILTER 611

Q612 BUSIVT How long was your bus journey from #BUSOSTATION# TO #BUSDSTATION#, including any time spent making interchanges, if relevant?

DP: Response required.

(open numeric – format hh:mm – range 0001 to 2400) / infotext: Please specify in hours and minutes

Q613 BUSEGRESS How did you travel from #DSTATION# to your final destination in #Q306#?

If you used more than one mode, please select the main mode used.

Did you have to make any interchanges between buses on your journey?

(single)

DP: Response required.

Walk Bicycle Scooter or motorbike Car (parked) Car (dropped off) Car (hired) Taxi Local bus Bus/Metro Other, please specify (open text)

Q614 BUSEGRESSTIME How long did it take for you to travel from #DSTATION# to your final destination in #Q306# (in minutes)?

DP: Response required.

(open numeric – format mm – range 000 to 300)

FILTER 615 IF BUSEGRESS NOT EQUAL WALK, BICYCLE OR SCOOTER/MOTORBIKE

Q615 BUSEGRESSCOST How much it cost for #GROUPTEXT# to travel from #DSTATION# to your final destination in #Q306#? (infotext):Please give us your best estimate/ approximately

DP: Response required.

(open numeric – range 0000 to 99999)

STOP FILTER 615

54 Q616 BUSSUMMARY This means that your journey from #Q303_X# to #Q306# was:

Journey time from #Q303_X# to #OSTATION# is #BUSACCESSTIME# Time at the station is #BUSWAITTIME# Number of interchanges is #NUMBUSINTS# Total bus time, including time for interchanges, is #BUSIVT# Journey time from #DSTATION# to #Q306# is #BUSEGRESSTIME#

Is this correct?

(single)

DP: Response required. yes no

IF BUSSUMMARY IS NO, GO BACK TO BUSACCESS

Q617 BUSFAREQUESTIONS (infotext) We would now like to ask you a number of questions about your bus ticket.

FILTER 618 IF GROUPSIZE > 1

Q618 BUSGROUPS Were the tickets for your group bought together?

(single)

DP: Response required. yes no don’t know /don’t remember

STOP FILTER 618

Q619 BUSFARERETURN Was your ticket for a single or return journey?

(single)

DP: Response required. single return other, please specify (open text)

Q620 BUSTICKETWHENBOOKED How long before your trip did you book your tickets? (single)

DP: Response required. more than 6 months before the trip a couple of months before the trip a couple of weeks before the trip a week before the trip on the day of the trip Other, please specify (open text)

55 Q621 BUSTICKETCOST How much did your #BUSFARERETURN# ticket between #BUSOSTATION# and #BUSDSTATION# cost (in NOK)? (infotext):Please give us your best estimate/ approximately

DP: FILTER IF BUSGROUPS = YES (infotext) Please include the cost for everyone travelling in your group

DP: Response required.

(open numeric – range 0000 to 99999)

Q622 BUSCOST

BUSCOST = BUSTICKETCOST + BUSACCESSCOST + BUSACCESSPARKCOST + BUSEGRESSCOST

STOP FILTER 600

56 SECTION 5 - INTRODUCTION TO HSR AND ATTITUDES

Q700 INTRODUCTION

We would now like to consider how high-speed rail might compare to #MODE# for the journey you made between #Q303_X# AND #Q306# for #PURPOSE#.

Please imagine a service where you could access a high-speed train new #HSROSTATION# which would take you to #HSRDSTATION#.

So for example, for your journey from #Q303_X# to #Q306# a high-speed rail service might look like:

High-speed rail: Journey time from #Q303_X# to #HSROSTATION# is #HSRACCESSTIME# Number of interchanges is #HSRNUMTRAININTS# Total rail time, including time for interchanges, is #HSRIVT# Journey time from #HSRDSTATION# to #Q306# is #HSREGRESSTIME# Journey cost for #GROUPTEXT# is #HSRCOST#

The trains would be similar in design to those currently used elsewhere in Europe for high speed services. Some example photos of the different classes of carriage are shown below:

Standard Class First Class

It is unclear at present whether mobile phone coverage or wifi would be available throughout the journey as some options may include the trains travelling through extensive tunnels, although it is likely that power points would be available in the first class carriages to allow travellers to plug in laptops.

[Q701 – Q709 PRESENTED AS A MATRIX WITH THE RESPONSE CODES ACROSS THE TOP]

Q701 TIMEATTITUDE When thinking about the time required for your journey, how would you compare your existing #MODE# journey to high-speed rail? A High-speed rail makes #MODE# MUCH LESS attractive A High-speed rail makes #MODE# LESS attractive A No effect A High-speed rail makes #MODE# MORE attractive A High-speed rail makes #MODE# MUCH MORE attractive

Q702 COSTATTITUDE When thinking about the cost of your journey, how would you compare your existing #MODE# journey to high-speed rail? A High-speed rail makes #MODE# MUCH LESS attractive A High-speed rail makes #MODE# LESS attractive A No effect A High-speed rail makes #MODE# MORE attractive A High-speed rail makes #MODE# MUCH MORE attractive

57 Q703 COMFORTATTITUDE When thinking about the comfort of your journey, how would you compare your existing #MODE# journey to high-speed rail? A High-speed rail makes #MODE# MUCH LESS attractive A High-speed rail makes #MODE# LESS attractive A No effect A High-speed rail makes #MODE# MORE attractive A High-speed rail makes #MODE# MUCH MORE attractive

Q704 WORKATTITUDE When thinking about the ability to work of your journey, how would you compare your existing #MODE# journey to high-speed rail? A High-speed rail makes #MODE# MUCH LESS attractive A High-speed rail makes #MODE# LESS attractive A No effect A High-speed rail makes #MODE# MORE attractive A High-speed rail makes #MODE# MUCH MORE attractive

Q705 SECURITYATTITUDE When thinking about security on your journey, how would you compare your existing #MODE# journey to high-speed rail? A High-speed rail makes #MODE# MUCH LESS attractive A High-speed rail makes #MODE# LESS attractive A No effect A High-speed rail makes #MODE# MORE attractive A High-speed rail makes #MODE# MUCH MORE attractive

Q706 LUGGAGEATTITUDE When thinking about your luggage requirements, how would you compare your existing #MODE# journey to high-speed rail? A High-speed rail makes #MODE# MUCH LESS attractive A High-speed rail makes #MODE# LESS attractive A No effect A High-speed rail makes #MODE# MORE attractive A High-speed rail makes #MODE# MUCH MORE attractive

[ONLY ASK TO RESPONDENTS TRAVELLING IN GROUPS] Q707 GROUPATTITUDE When thinking about travelling with your group, how would you compare your existing #MODE# journey to high-speed rail? A High-speed rail makes #MODE# MUCH LESS attractive A High-speed rail makes #MODE# LESS attractive A No effect A High-speed rail makes #MODE# MORE attractive A High-speed rail makes #MODE# MUCH MORE attractive

Q708 EASEATTITUDE When thinking about the ease of making your journey, how would you compare your existing #MODE# journey to high-speed rail? A High-speed rail makes #MODE# MUCH LESS attractive A High-speed rail makes #MODE# LESS attractive A No effect A High-speed rail makes #MODE# MORE attractive A High-speed rail makes #MODE# MUCH MORE attractive

Q709 RELIABILITYATTITUDE When thinking about the reliability of making your journey, how would you compare your existing #MODE# journey to high-speed rail? A High-speed rail makes #MODE# MUCH LESS attractive A High-speed rail makes #MODE# LESS attractive A No effect A High-speed rail makes #MODE# MORE attractive A High-speed rail makes #MODE# MUCH MORE attractive

58 SECTION 6 – HSR EXPERIMENT

Q710 SPEXPERIMENT1INTRO

We would now like you to consider the journey you made between #Q303_X# and #Q306# and to consider what choice you would have made if a high-speed rail service had been available. We will explore a number of different situations where the high-speed rail train service may have different journey times or costs (which would be affected by where the track is built). We would also like to understand how these choices might change in the future, so will also ask you to consider situations where your existing journey may take longer or cost more than it does at present.

In some of the scenarios presented the high speed rail option may be quicker than your current mode, but in others it may take longer, but be better in other respects.

We will present you with nine hypothetical choice scenarios, where the characteristics of the mode that you used and the characteristics of a high-speed rail alternative are presented. We would like you to carefully consider each of the choices for the journey that you made. If you decide that with the service characteristics presented to you that you would have chosen not to make the journey you can indicate this, but this implies that you would no longer make the trip.

We would like to emphasise that there is not right or wrong answer, so please consider the information for each option carefully and select the option that you would have likely chosen.

[WILL PRESENT GROUP COSTS IN SP CHOICES FOR RETURN JOURNEY]

Q711 BLOCK_SP1 Draw random number between 1-4 and record Use to lookup the nine choice scenarios to present to respondent

SEE SP_Choice_Pilot.xlsx FOR DETAILS OF CHOICES TO PRESENT

MULTIPLCATION FACTOS FOR LEVELS DIFFER BY EXISTING MODE. CAN BE LOOKED UP FROM: CURRENTLY USING CAR = SP1_Car.CSV CURRENTLY USING AIR = SP1_Air.CSV CURRENTLY USING RAIL = SP1_Rail.CSV CURRENTLY USING BUS = SP1_Bus.CSV

EXAMPLE CHOICE FOR AIR-V-HSR

If the following options were available, which would you choose for your journey?

Air High speed rail

Expected travel times: Time to get to airport / train station 15 mins 15 mins Time waiting at airport / train station 1 hour 10 mins Time spent in airplane / train 1 hour 1 hour 10 mins (Views for 50 mins, Tunnels for 20 mins) Time to get from airport / train station 30 mins 10 mins Total Travel time 2 hours 45 mins 1 hour 45 mins

Service frequency One flight every 2 hours One train every 30 mins

Interchanges Need to make 2 interchanges

Total travel cost 1130 kr return 1300 kr return

Which would you use for your journey?

Q711A SP1 – Choice 1 A Existing mode A High-speed rail A Would not make trip

59 Q711B SP1 – Choice 2 A Existing mode A High-speed rail A Would not make trip

Q711C SP1 – Choice 3 A Existing mode A High-speed rail A Would not make trip

Q711D SP1 – Choice 4 A Existing mode A High-speed rail A Would not make trip

Q711E SP1 – Choice 5 A Existing mode A High-speed rail A Would not make trip

Q711F SP1 – Choice 6 A Existing mode A High-speed rail A Would not make trip

Q711G SP1 – Choice 7 A Existing mode A High-speed rail A Would not make trip

Q711H SP1 – Choice 8 A Existing mode A High-speed rail A Would not make trip

Q711I SP1 – Choice 9 A Existing mode A High-speed rail A Would not make trip

Q712 SP1DIAGNOSTIC1 (infotext) We would now like to ask you a few questions about the choice exercises that you have just done.

Were you able to compare the different choices that were presented to you? (single)

DP: Response required. yes no

FILTER 713 IF SP1DIAGNOSTIC1 = NO

Q713 SP1D1WHYNOT Why weren’t you able to compare the different choices that were presented to you? (open text)

STOP FILTER 713

60

Q714 SP1DIAGNOSTIC2 In the choices, were you able to understand each of the different factors we described? (single)

DP: Response required. yes no

FILTER 715 IF SP1DIAGNOSTIC2 = NO

Q715 SP1D2WHYNOT Which factors weren’t clear to you? (single)

DP: Response required. travel times time in tunnels frequency of services costs

STOP FILTER 715

Q716 SP1DIAGNOSTIC3 Did you feel that the options in the choices were realistic? single)

DP: Response required. yes no

FILTER 717 IF SP2DIAGNOSTIC3 = NO

Q717 SP1D3WHYNOT Why didn’t you think that the options were realistic? (open text)

STOP FILTER 717

Q718 SP1DIAGNOSTIC4 Were there any factors that we have not included that you think would be a major consideration for you in choosing whether to switch to using a new high speed rail service? (open text)

61 SECTION 7 – TRAIN CHOICE EXPERIMENT

Q800 SPEXPERIMENT2INTRO

We are also interested in understanding the sorts of train carriages in which you would want to travel for this journey. It would be possible to have a range of additional services included, however, these each have a cost associated with them so we would like to gain a better understanding of which services are most important to you.

In the choices that follow we will present you with an option of travelling in a standard carriage or one with different seating or additional services available. We would like you to weigh up the information provided and indicate whether you would pay the additional amount to upgrade to the improved carriage. Again, there are no right or wrong answers, we are just interested in understanding the choices that you would make.

EXAMPLE CHOICE

If the following options were available, would you choose the additional fare for the improved carriage?

Standard Carriage Improved Carriage

Seat spacing Normal spacing Wide spacing

4 seats across 3 seats across carriage carriage

477 mm wide 665 mm wide 845 mm leg room 945 mm leg room

Power points and Wifi No power points or Wifi Power points + free Wifi which works over half of route

Mobile phones Unreliable mobile phone coverage on journey Reliable mobile phone coverage on journey through entire route

Security of luggage Luggage stored in racks above seat Racks above seat + option to lock luggage in secure area

Food and refreshments No food and drinks available for purchase on Food and drink available for purchase and train served at seat

Additional ticket cost 500 kr more

Would you pay for the improved carriage? No Yes

62 ATTRIBUTE LEVELS

Attribute Level Improved carriage Seat spacing 0 Normal spacing 1 Wide spacing Power points and Wifi 0 No power points or Wifi 1 Power points but no Wifi 2 Power points + free Wifi which works over half of route 3 Power points + free Wifi which works through entire route Mobile phones 0 Unreliable mobile phone coverage on journey 1 Reliable mobile phone coverage over half of the route 2 Reliable mobile phone coverage through entire route 3 Quiet carriage with no mobile phone calls permitted Security of luggage 0 Luggage stored in racks above seat 1 Racks above seat + option to lock luggage in secure area Food and refreshments 0 No food and drinks available for purchase on train 1 Food and drinks available for purchase from separate carriage 2 Food and drink available for purchase and served at seat 3 Food and drink included in price of ticket and served at seat

Q801 BLOCK_SP2 Draw random number between 1-4 and record Use to lookup the six choice scenarios to present to respondent

SEE SP_Choice_Pilot.xlsx FOR DETAILS OF CHOICES TO PRESENT

TEXT FOR LEVELS CAN BE LOOKED UP FROM SP2.CSV

Q801A SP2 Q1 – Choice 1 A Standard carriage A Improved carriage

Q801B SP2 Q1 – Choice 2 A Standard carriage A Improved carriage

Q801C SP2 Q1 – Choice 3 A Standard carriage A Improved carriage

Q801D SP2 Q1 – Choice 4 A Standard carriage A Improved carriage

Q801E SP2 Q1 – Choice 5 A Standard carriage A Improved carriage

Q801F SP2 Q1 – Choice 6 A Standard carriage A Improved carriage

Q802 SP2DIAGNOSTIC1 (infotext) We would now like to ask you a few questions about the choice exercises that you have just done.

63

Were you able to compare the different choices that were presented to you?

(single)

DP: Response required. yes no

FILTER 803 IF SP2DIAGNOSTIC1 = NO

Q803 SP2D1WHYNOT Why weren’t you able to compare the different choices that were presented to you?

(open text)

STOP FILTER 803

Q804 SP2DIAGNOSTIC2 In the choices, were you able to understand each of the different factors we described?

(single)

DP: Response required. yes no

FILTER 805 IF SP2DIAGNOSTIC2 = NO

Q805 SP2D2WHYNOT Which factors weren’t clear to you?

(single)

DP: Response required. seat spacing provision of power points and wifi security of luggage food and refreshments additional ticket cost

STOP FILTER 805

Q806 SP2DIAGNOSTIC3 Did you feel that the options in the choices were realistic?

(single)

DP: Response required. yes no

FILTER 807 IF SP2DIAGNOSTIC3 = NO Q807 SP2D3WHYNOT Why didn’t you think that the options were realistic? (open text)

STOP FILTER 807

64 SECTION 8 – ATTITUDES TO SPECIFIC FACTORS AND RAIL USE

We would also like to investigate how likely different factors would be in your decision to use high-speed rail.

[Q900 – Q920 PRESENTED AS A MATRIX WITH THE 1-5 RATINGS ACROSS THE TOP]

Q900 TRAVELTIMEFACTOR How important would significant savings in your journey travel time be in your decision to use high- speed rail? Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q901 COMFORT1FACTOR How important is having wider seats in your decision to use high-speed rail? Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q902 COMFORT2FACTOR How important is having plenty of leg room between seats in your decision to use high-speed rail? Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q903 COMFORT3FACTOR How important is having well lit carriages in your decision to use high-speed rail? Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q904 WORK1FACTOR How important is it to have electrical power points at seats in your decision to use high-speed rail? Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q905 WORK2FACTOR How important is it to have wifi available on trains and in tunnels in your decision to use high-speed rail? Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q906 WORK4FACTOR How important is it to have mobile phone signal in tunnels in your decision to use high-speed rail? Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q907 SERVICE1FACTOR How important is it to have quiet zones on the train in your decision to use high-speed rail? Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q908 SERVICE2FACTOR How important is it to have food and drink available on the trains in your decision to use high-speed rail? Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q909 SERVICE3FACTOR How important is it to have food and drink served at seats in your decision to use high-speed rail? Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q910 SERVICE4FACTOR How important is it to have rest rooms at the end of each carriage in your decision to use high-speed rail? Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q911 SERVICE5FACTOR How important is it to have litter removed and restrooms checked during the journey in your decision to use high-speed rail? Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q912 SECURITY1FACTOR How important is it to have staff walking through the train in your decision to use high-speed rail? Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q913 SECURITY2FACTOR How important is it to have CCTV coverage of all carriages and contact with the driver or guard in your decision to use high-speed rail?

65 Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q914 SERVICE3FACTOR How important is it to have locked luggage areas available for storing baggage on trains in your decision to use high-speed rail? Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q915 STATION1FACTOR How important is it to have stations with high quality waiting areas offering refreshments in your decision to use high-speed rail? Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q916 STATION2FACTOR How important is it to have good security at stations in your decision to use high-speed rail? Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q917 STATION3FACTOR How important is it to have connecting bus and train services at the train stations in your decision to use high-speed rail? Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q918 STATION4FACTOR How important is it to have well defined and easy walking routes for the connection between the high speed rail platforms and other bus and train services in your decision to use high-speed rail? Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q919 STATION5FACTOR How important is it to have good parking provision at the train stations in your decision to use high- speed rail? Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q920 EXTENDEDSERVICESFACTOR How important is it to have trains that continue through on slower track to provide direct services to more destinations without the need to change between trains in your decision to use high-speed rail? Rating 1 to 5, where 1 = “No effect” to 5 = “Much more likely to use high-speed rail”

Q921 RAILFREQOFTRAVEL Now we would like to ask you some questions about your experience of using rail services.

How often do you use rail to travel in the Norway? (single)

DP: Response required.

More than once a week A couple of times a month Every couple of months A couple of times a year less than once a year I never use rail services in Norway

Q922 RAILPERCEPTION What is your perception of current rail services in the Norway? (single)

DP: Response required. very good good satisfactory poor very poor don’t know

FILTER 923 IF RAILFREQOFTRAVEL NOT EQUAL TO NEVER

66 Q923 HSREXPERIENCE Have you ever been on a high-speed rail train, either in Norway (e.g. Gardermobanen) or in another country? (single)

DP: Response required.

no yes

STOP FILTER 923

SECTION 9 – ATTITUDES TO TUNNELS

Q941 TUNNELS1 In order to maintain high-speeds, a substantial portion of the railway line may have to be built in tunnels. This will reduce the journey time, but would mean that as a passenger your views of the countryside would be reduced. What impact would this have on whether you would choose to travel by high-speed rail? (single)

DP: Response required.

I would definitely not travel by rail if a substantial portion of the journey was in tunnels (1) I would probably not travel by rail if a substantial portion of the journey was in tunnels (2) Travelling in tunnels would not affect my choice to use high-speed rail (3) I would rather use rail if a substantial portion of the journey was in tunnels (4)

FILTER 942 IF Q941 = 1 or 2

Q942 TUNNELS2 What are your specific concerns about travelling in tunnels? [allow multiple choice] (multi)

DP: Response required.

I would regret not being able to enjoy the scenery Travelling in tunnels makes me feel clausterphobic I worry about the train breaking down in the tunnel I worry about the implication of the train having an accident whilst in the tunnel I fear the tunnel collapsing Other, please specify (open text)

STOP FILTER 942

SECTION 10 – SOCIO-ECONOMICS

[NOTE – WE CAN APPEND A LOT OF INFORMATION ABOUT THE RESPONDENT FROM THE TNS PANEL INFORMATION, BUT SHOULD COLLECT THE KEY VARIABLES FOR CROSS-CHECKING AND REDUNDENCY]

Q950 SOCIOINTRO Finally, we would like to ask you a few questions about yourself.

Please note that these questions provide us with important information that will be used for classification purposes only.

All information will be treated confidentially.

Q951 AGE Which of the following age bands are you in? (single)

DP: Response required.

67

16-20 years 21-30 years 31-40 years 41-50 years 51-60 years 61-70 years more than 70 years

Q952 GENDER (single)

DP: Response required.

Female Male

Q953 EMPLOYMENTSTATUS What is your work status? (single)

DP: Response required.

Full-time employee Part-time employee Self-employed Retired Unemployed Welfare Student of pupil Homemaker Other, please specify (opent text)

FILTER 954 IF EMPLOYMENTSTATUS = FULL-TIME EMPLOYEE OR PART-TIME EMPLOYEE OR SELF-EMPLOYED

Q954 EMPLOYEDTRAVEL Does your work involve making regular business trips? (single)

DP: Response required. yes no

STOP FILTER 954

Q955 PERSONALINCOME What is your personal income before taxes? (single)

DP: Response required.

less than 200,000 NOK 200,000 – 299,999 NOK 300,000 – 399,999 NOK 400,000 – 599,999 NOK 600,000 – 799,999 NOK More than 800,000 NOK

Q956 CONCLUSION (infotext) That was the final question. I would again like to thank you for the time you have taken to answer these questions and to reassure you that the information will be used to better understand Norway’s future transport needs.

68

Contact names Michael Hayes Address: Eust on tower, 286 Euston Road, London, NW1 3AT, UK Email: [email protected] Telephone: +44 207 121 2388

Peter Burge Address: RAND Europe, Westbrook Centre , Milton Road, Cambridge, CB4 1YG , UK Email: [email protected] Telephone: +44 1223 353 329

© Atkins Ltd except where stated otherwise.

5096833/The AtkinsSubjects logo, ‘Carbon 2 and 3Critical Surveys Design’ Final andReport_180211.docx the strapline ‘Plan Design Enable’ are trademarks of Atkins Ltd .