High Speed Rail London to the West Midlands and Beyond. HS2 Demand Model Analysis

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High Speed Rail

London to the West Midlands and Beyond

HS2 Demand Model Analysis

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While High Speed Two (HS2) Limited has made every effort to ensure the information in this document is accurate, HS2 Ltd does not guarantee the accuracy, completeness or usefulness of the information contained in this document and it cannot accept liability for any loss or damages of any kind resulting from reliance on the information or guidance this document contains. © Copyright, High Speed Two (HS2) Limited, 2009.

Copyright in the typographical arrangements rests with HS2 Limited.

This publication, excluding logos, may be reproduced free of charge in any format or medium for non-commercial research, private study or for internal circulation within an organisation. This is subject to it being reproduced accurately and not used in a misleading context. The title must be acknowledged as copyright and the title of the publication specified.

For any other use of this material please contact HS2 Limited on 020 7944 4908, or by email at HS2Enquiries@hs2.gsi.gov.uk, or by writing to HS2, 3rd Floor, 55 Victoria Street, London, SW1H 0EU.

Further copies of this report can be obtained from www.hs2.org.uk. ISBN: 978-1-84864-075-7

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HS2 Demand Model Analysis: Contents

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Chapter 9: Connections to High Speed One ... 77

9.2 Our Approach to Modelling International Demand ...78

9.3 Analysis of Different International Connections and Services ...82

9.4 Appraisal of Journey Time Benefits ...85

9.5 International Connections as Part of a Wider Network ...87

Chapter 10: The Overall Business Case for HS2 ... 88

10.2 Passenger Demand for HS2 ...89

10.3 HS2 Appraisal Costs ...93

10.4 Appraisal of Benefits from HS2 ...97

10.5 Wider Economic Impacts of HS2 ...100

10.6 Impact of HS2 on Carbon Emissions ...105

10.7 HS2 Value for Money ...109

Chapter 11: A Long Term Strategy for High Speed Rail ...111

11.2 Long Distance travel in Great Britain ...113

11.3 Long Term High Speed Networks ...116

11.4 Wider Networks - Modelling Approach ...119

11.5 Demand and Benefits of a Long Term Strategy ...121

11.6 Benefit Cost Ratios of a Wider Network ...126

11.7 Limitations of Our Analysis and Further Work ...127

11.8 Extensions to Manchester and Leeds ...127

Appendix 1: Transport Economic Efficiency Tables...135

A1.1 Introduction ...136

Appendix 2: Sensitivity Tests on HS2 Central Case ...148

A2.1 Testing Our Assumptions ...149

A2.2 Changing the forecast level of demand for long distance rail trips ...149

A2.3 Changing Background Demand Growth and Prices on Non-Rail Modes ...153

A2.4 Premium Fares ...154

A2.5 Comparison with a classic line ...156

A2.6 Reliability ...157

Appendix 3: High Speed Rail and Spatial Patterns and Strategies in Cities and Regions ...158

A3.1 Introduction ...159

A3.2 Why Does Spatial Impact (Land Use Change) Matter? ...160

A3.3 How Might HS2 Affect Land Use? ...161

A3.4 The Evidence of Land Use Change and HSR ...162

A3.5 Conclusions ...164

A3.6 References ...165

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Chapter 1:

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6 1.1 Introduction to this report

1.1.1 High Speed Two Ltd was established in January 2009 to develop proposals for a new high speed

railway line between London and the West Midlands and to consider the case for high speed rail services linking London, northern England and Scotland.

1.1.2 This report provides further detail on the approach and forecasts of demand used to support ‘High

Speed Rail: London to the West Midlands and Beyond’ (henceforth ‘HS2’s report’). It explains how demand forecasting and appraisal has been used to inform and support the design of the preferred scheme, and provides detail on the expected demand and economic impact of a new high speed rail line.

1.1.3 Chapter 1 provides the context for HS2 by showing the underlying demand for transport and explains

how we used analysis and appraisal to inform the design of HS2. Chapters 2 and 3 consider the modelling approach and the key assumptions that underpin our results, as well as the implications of some of these for the overall results. Chapters 4 to 9 then set out the results of our analysis and the implications for the component parts of the scheme:

• A Central London station.

• A Heathrow/Crossrail Interchange station.

• A Central Birmingham station.

• An interchange or parkway station in the West Midlands.

• The case for an intermediate station between London and the West Midlands.

• The case for a link between HS1 and HS2 and the likely passenger market for international rail

connections.

1.1.4 Chapter 10 draws this together into an overall assessment of the preferred scheme that is set out in

HS2’s Business Case. The report concludes with a strategic level analysis of the possible long term strategy and the implications of this for extensions to Leeds and Manchester.

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Chapter 1: Introduction and Background Data

1.2 Acknowledgements

1.2.1 Throughout the year we have worked closely with a number of organisations whose specialist and

local knowledge has helped to inform our investigation. We have sought to ensure a proper process of quality assurance is in place to validate our approach and results. This has been particularly important given the UK’s relative inexperience in appraising and delivering domestic high speed rail projects – although we have been able to draw on the UK’s growing experience in the delivery of other major projects.

1.2.2 We are grateful to Prof. Robert Cochrane, Prof. Stephen Glaister CBE, Prof. Peter Mackie, Prof

Henry Overman, Dr David Simmonds and Prof. Roger Vickerman who as members of our Analytical Challenge Panel provided independent expert scrutiny on the modelling and appraisal of HS2. Their advice has been invaluable as we formulated our approach and findings. However, our findings are ours alone. There is no intention that the Challenge Panel should be seen as accountable for the conclusions that, ultimately, we alone have reached.

1.2.3 We have also commissioned specialist consultancy advice on a range of topics. Those who have

advised us are listed below and their reports make up several of the supporting documents published alongside the report.

WS Atkins plc

Sinclair Knight Merz Pty Ltd Arup Group Ltd

John Bates

Demand Modelling and Appraisal - (subcontracted by WS Atkins)

Dr Dan Graham & Patricia Melo Advice on the assessment of wider economic impacts

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8 1.3 The Demand for Transport and Context for HS2

1.3.1 The demand for transport has grown substantially over time. As people become richer they tend to

travel further and more often and, as the transport network has grown, so it has become easier to travel. The total distance travelled by passengers on all modes has grown by 36% in the last 20 years as shown in figure 1.3a.

Figure 1.3a - Passenger Travel (All Modes) 1980 - 2007

900 800 700 600 500 400 300 200 100 0 1980 1982 1984 1986 1988 1990 1992 1994 Year 1996 1998 2000 2002 2004 2006 B ill io n P as senger km

Total Passenger Travel Cars, Vans and Taxis Other

Source: Department for Transport (DfT) – Transport Trends

1.3.2 This growth has been driven primarily by increasing car traffic – which accounts for almost 85% of

the overall distance travelled. However in the last 15 years or so, there has also been rapid growth in the number of passengers, and distance travelled, on the UK’s railways. The number of passenger km on rail has increased by just over 70% during this period, compared to less than 15% for cars as shown in Figure 1.3b.

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Figure 1.3b - Total Passenger Travel by Rail 1980 - 2008

60.0 50.0 40.0 30.0 20.0 10.0 0.0 1980 1985 1990 1995 Year 2000 2005 B ill io n P as senger km

Source: Office of the Rail Regulator (ORR) – National Rail Trends

1.3.3 Similarly, long distance trips of over 50 miles have been growing in line with recent trends, with

an increase of more than 30% since 2002. This rapid growth is forecast to continue, with trips into and out of London being particularly important. Table 1.3 summarises the growth forecast using standard industry tools (Passenger Demand Forecasting Handbook (PDFH), version 4.1). These form the basis of the forecasts we have used in our modelling and the assumptions underpinning this are outlined in Chapter 3.

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Table 1.3 – Forecast growth in long distance travel Passenger Demand per weekday

to and from Central London (two way flow)

2008

Demand Demand2033 2008-2033% Growth

Birmingham 5,700 13,300 130% Manchester 5,300 14,200 170% Leeds 4,400 12,200 180% Glasgow 800 2,200 180% Liverpool 2,100 5,400 160% Newcastle 2,400 6,600 180% Edinburgh 1,700 5,500 220%

Source: Atkins Baseline forecasting report (PLD)

1.3.4 Such substantial increases in demand will increase passenger flows and crowding on the West

Coast Main line (WCML). In 2008 there were approximately 45,000 long distance passengers per day using inter-city trains on the southern section of the WCML, with an average train loading across the whole day of 51%. By 2033 long distance demand on the WCML is expected to more than double. Although the Pendolino trains currently running on the WCML would have been lengthened to 11 cars, the average train loading would have increased to around 80%. This is an average figure, with trains during the peak times likely to have even higher loadings.

1.3.5 The following maps show the number of long distance trips on the WCML in 2008 and the increase in

those volumes by 2033. Figure 1.3e shows the load factor on long distance journeys on the WCML by 2033 based on our reference case assumptions about what would happen in the future without HS2.

1.3.6 HS2 offers the opportunity not only to speed up journeys for passengers along the line of the WCML,

but also to provide substantial additional capacity to Birmingham and on long distance trains north of Birmingham. And the capacity released by HS2 can be reused to reduce crowding on short distance services into London.

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Figure 1.3c - WCML Long Distance Daily Rail Trips in 2008

Long distance daily trips on the WCML in 2008

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Figure 1.3d - WCML Long Distance Daily Rail Trips in 2033

88 4441,84341,84311,8 3,8443 6 5 6565,95865,9585,9988 5 4 7 44,857,8,87 4,857 7733 88,7 08,730,70 8,730 11 11 ,11106,111106,111006 116 11 11 ,44105,490105 4900055 9090 66 2 8 21,688 2 8 221,68811, 8,88 21,688 4446,21346,21366,221133 1 6 112,16222,1,1 262 12,162 8 7 8 7 8, 7 8 86788 68,8678,867886 h m B BBirminghamBirminghamrr ii ghnghm i o i e ov o L p l L v p ol Liverpool Liverpool h h M MManchesternnc ec e tt r Manchester O OxfordOxfordOxfo dxfo d

Oxford N t N tiNNottinghamNo ng aNottinghamo nghh ma d d g R dn Reading Re d n Reading L d LLeedseedss Leeds E EdEdinburghEdinburghEdinburghiin u gnn u gh

L L iLeicestere ce ce t rt Leicester d d Sh fiShSheffielde i ll Sheffield n L LLondonoon onddo London w w N cstl Newcastle N c tl Newcastle o o aa GGlasgowG gg ww Glasgow i to i os BBristolB s ll Bristol t oo P h PePeterboroughPeterboroughtt r o o grbbo ouugh H l H lHHullHu lHullu k o k Yo York Yo York n kk -T n S oStoke-on-TrentSto e o -Tre teo r t

Stoke-on-Trent

DM Flow Volume Legend

More Than 30,000 20,000 to 30,000 10,000 to 20,000 3,000 to 10,000 Less than 3,000

Forecast of long distance daily trips on the WCML in 2033

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Figure 1.3e - WCML Long Distance Load Factors in 2033

81 81 81 81 664464 64 88 787878 78 76 76 76 76 99 779 7 79 00 880 8 80 88 6668 68 885855 85 660060 60 88668886 86 77777777 555555 55 6266262 56 56 56 56 h h m n o a N t g Notti g a Nottingham Nottingham cc n MM nch teManchesterch s Manchester d d LL e sLeedse s Leeds O f O o dOxfordx rx ro d Oxford n n RR d gReadingad g Reading g g in h B r a Birmingham Birm n h m Birmingham l o o ii ev po L l L v p ol Liverpool Liverpool Ed h EdEdinburghiin u gnbu gh Edinburgh f i lee d Sh f Sh d Sheffield Sheffield L L iLeicesterLeicestere ce c t rt d d o on oo L n London L n n London N NNNewcastleNe ca tNewcastlewc tee Newcastle G w GGlasgowGlasgowll s oaggow l B B BristolBristolBristoli toi to f diff d ffiff b booouggh Pe r u P te rou h Peterborough Peterborough u u H ll H ll Hull Hull Yo kYorkYo k York kk n-T n So eo r t Stoke-on-Trent Sto e o -Tre t Stoke-on-Trent

Do Minimum Crowding Legend

(Volume over seats)

More than 80% 60% to 80% 40% to 60% 20% to 40% 0 to 20%

Forecast of laverage daily load factors on long distance WCML services in 20033

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14 1.4 Demand and Appraisal – Helping with Scheme Design

1.4.1 The appraisal of transport impacts is a powerful tool not only to aid the justification of a scheme

but also to support its design to ensure it maximises value for money. There are many trade-offs in designing a scheme, with individual components impacting on each other, and impacting on the overall experience of transport users. HS2 is no exception. For example, whether to include an intermediate station involves trading off the benefits to local passengers against costs of building the station (and re-routing the line), delays for other passengers and impacts on the capacity of the line. Appraisal techniques can help to weigh these costs and benefits. Box 1 provides a brief background to appraisal and what it attempts to capture.

1.4.2 We have sought throughout the process to ensure our assessment of the benefits of HS2

appropriately reflects the evidence available, and to weigh these against costs and environmental impacts to ensure that the resulting scheme maximises value for money. This has had implications in a number of areas, including:

• Location of stations. The location of a station will affect its accessibility, and so the attractiveness of a station for long distance passengers. This in turn has implications for demand, revenues and benefits which need to be weighed against the cost and constructability of these stations.

• Inclusion of non-core stations. We were asked to consider the potential for intermediate stations and interchange stations along the line of route, where these added to the business case.

Appraisal helps to weigh the benefits and costs of such stations, and whether they can improve the business case for HS2.

• Line of route and service patterns. By considering the impact on demand and benefits of different journey times we ensured an appropriate trade-off between the cost of the overall HS2 route against the journey time offered by different elements of the scheme. This also helped to inform the service patterns and line of route, with fast links to London being identified as a key factor.

• The use of released capacity. We examined how line capacity on the existing West Coast Main Line might be re-used to provide further benefits.

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Box 1 - What is Appraisal?

Appraisal is an important tool to help Government assess the input of policies and investment. It helps support policy development and ensures the proper use of public resources.

The guidance for appraisal across Government is set out in ‘The Green Book: Appraisal and Evaluation in Central Government’ (HMT, 2003). Transport has a long history in appraisal, and DfT sets out its own guidance on appraising transport schemes which applies Green Book principles to transport investment. This guidance is set out in WebTAG (available on the DfT website).

WebTAG provides a methodology to assess all of the impacts of a transport scheme – both positive and negative. These range from impacts on transport users through to impacts on local communities (e.g. noise or air quality changes), environmental effects on landscape and the global effects - Greenhouse Gas emissions and climate change.

The focus of this technical annex is on the assessment of impacts on transport users, most of which can be modelled, quantified and valued. Our Appraisal of Sustainability addresses the wider impacts covered by WebTAG.

WebTAG’s assessment of impacts on transport users considers the whole journey experience. It includes:

• how long a journey takes, including the impact of congestion and delays on the road network • the financial costs (fares, fuel)

• the other costs imposed during a journey e.g. having to stand on public transport

Each of these impacts has a value to transport users – they would prefer to have faster journeys or less crowding on trains. Transport appraisal attempts to quantify the impacts of a transport scheme and so the value of these benefits to transport users. In the appraisal of HS2 we consider the potential for:

• faster, more reliable and less crowded journeys using HS2

• less crowding from use of released capacity on the classic rail network

• the reduction in congestion (and so faster car trips) for all car users as a result of mode shift Throughout the analysis in this technical annex we consider the trade-offs for different users under different options. For example we consider the time penalty (cost of a slower journey) of stopping at a station compared to the benefits (e.g. faster journeys) of those who would have access to HS2 as a result of the station. In identifying and quantifying these trade-offs we can design a scheme which maximises the benefits of HS2 across all transport users and society in general.

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1.4.3 We took an incremental approach to analysis and appraisal, building on the evidence base and

modelling capabilities as they were developed to enable early decisions to be made and effective prioritisation of work. There were three clear stages to this work.

Stage One: High Level Demand Analysis

1.4.4 During the first few months of the project, we used existing data and evidence sources to help

provide an initial high level or strategic understanding of the key issues. Known as a ‘Ready

Reckoner’, this simple analysis allowed us to quickly gain a high level understanding of current and future demand. It allowed us to conduct:

• A high level assessment of the patterns of demand for long distance trips. This included analysis of:

i. The overall pattern of long distance trips, particularly between London and other major cities in the north of the UK.

ii. Where trips started or ended within London and the West Midlands.

iii. The most suitable locations for intermediate stations between Birmingham and London, and their potential to capture demand.

• Simple tests on the likely level of demand between different locations that might be generated

by HS2. This analysis was undertaken using simple elasticities with respect to journey times and other variables.

• Simple analysis of what the key drivers of the HS2 business case were most likely to be.

1.4.5 This high level analysis provided early support for decisions taken around:

• The need for a city centre terminus at either end of HS2.

• The locations to consider in further detail for an intermediate station.

• The need to utilise capacity on the line (a few trains per hour would not be sufficient to generate

a business case).

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Stage Two: Early Modelling

1.4.6 Early model runs using development or prototype versions of the demand model also allowed high

level analysis of the potential case for HS2, and gave an early indication of the key drivers of benefits. This assessment provided robust evidence to support comparative analysis of different options, and to provide useful information on emerging patterns of demand and benefits. Particular analysis included: • Analysis of the impact of journey time benefits. Early tests suggested that reducing journey

times by one minute would provide benefits of around £300-600m (present value discounted over 60 years in 2009 prices) on a fully utilised high speed line. Box 1 highlights that people value faster journeys, and using initial projections of demand we were able to estimate this overall figure for the likely value HS2 passengers as a whole would place on slight changes to journey times. This supported decisions taken on the line of route demonstrating the importance of journey times for comparison against costs (e.g. tunnelling) and environmental constraints. It also showed the potential disbenefits of stopping trains at intermediate and interchange stations.

• Analysis of the service pattern of HS2 trains. Simple extrapolation (based on wait times) tested the strategic implications of increasing/reducing frequencies to different stations.

• Analysis of the pattern of benefits from different stations. The analysis helped to identify the importance of stations served by HS2, with Birmingham, Manchester, Liverpool and Glasgow representing the bulk of demand and benefits on the west coast main line.

• Comparison of different options for the design of HS2. This provided comparisons of the relative benefits of different locations for interchange stations in London and Birmingham, and an intermediate station. This analysis allowed us to focus our design efforts in advance of final model tests.

Stage Three: Detailed Modelling

1.4.7 The final version of the model was then used to provide an overall assessment of the preferred

scheme, as well as to confirm the incremental impacts of the key components of the scheme. Several model runs were undertaken, including:

• HS2 central case (‘Day One’ scenario, with all preferred components included).

• No interchange station at Old Oak Common.

• Alternative options for a London Interchange Station in the vicinity of Heathrow.

• No Interchange station at Birmingham International.

• A test of the impact of running at classic line speeds (125mph).

1.4.8 The economic results of these tests are described later in this report with the full results provided

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Chapter 2:

Demand Model Structure

and Development

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Chapter 2: Demand Model Structure and Development

2.1 Introduction

2.1.1 This chapter sets out the development of our modelling capability to allow us to understand and

test the costs and benefits of the components of the scheme as well as the overall results for the preferred package. To understand the business case for high speed rail, we employed Atkins Group Ltd to help develop a modelling and forecasting framework consisting of the following elements: • PLANET Long Distance. An update of an existing model (PLANET Strategic) which was developed

by the Strategic Rail Authority (SRA) for their 2001 high speed line study, and has been used for several other rail studies since. PLANET Long Distance considers the multi-modal demand (including domestic aviation) for long distance (greater than 50 miles) trips and is used to forecast HS2 demand.

• PLANET South. Based on an existing model, this focuses on demand for rail trips across London and the South East and has been used for analysis of schemes such as Thameslink. PLANET South allows the impact of short distance trips using long distance services and commuter services on the WCML to be understood, and is particularly important for understanding how best to use capacity on the WCML released by HS2.

• PLANET Midlands. A model similar to PLANET South, but focussed on rail demand in the West Midlands.

• Heathrow Access Model. This model, based on BAA’s London Airport Surface Access Model (LASAM), forecasts the demand for passengers accessing Heathrow Airport for international flights and the potential of high speed rail (HSR) to capture this demand. As well as modelling surface access to Heathrow, this model also forecasts air transfers in which passengers fly to or from Heathrow on domestic flights in order to change onto onward international flights (inter-lining). Domestic (point to point) air trips are not included within this part of the model as they are captured within PLANET Long Distance.

• International Rail Model. A separate model has been developed to forecast the demand for international rail journeys in order to examine the case for connecting HS2 to HS1.

2.1.2 With the exception of the international model, all these models were combined into a single

framework to allow integrated analysis of high speed rail and appraisal of HS2 and its components. The rest of this chapter provides more detail about the structure and design of this modelling

framework and the analytical approach that was used. A more complete description of the modelling approach including reports on the methodology, forecasting approach and validation of the model is published in separate documentation produced by Atkins.

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20 2.2 PLANET Long Distance

2.2.1 PLANET Long Distance forms the heart of the forecasting approach used to model high speed rail

and has a number of characteristics, including:

• Long Distance. The model has a specific focus on long distance travel (over 50 miles)

• National. The model covers the whole of mainland Great Britain. However the model has been primarily designed for analysing a high speed line from London to the North and so the South West, West, Wales and Northern Scotland are less well represented.

• Multi-modal. The model covers domestic trips by air, rail and car. Apart from airport access (see section 2.4) coach is not included as a separate mode as it is not considered that much coach demand would want to transfer to high speed rail. This is demonstrated by coach

passengers already preferring to trade slower journey times compared to rail for cheaper tickets. • Segmented. The model segments people by the type of journey they are making and whether

they have access to a car or not.

• Network based. There is a representation of the national car, rail and air networks. Transport users can therefore choose not only which mode they take, but which route best fits their preferences.

• All day model. Long distance demand and capacity is averaged across the whole day. • Incremental model. Forecasts are based on changes to externally generated trip matrices.

2.2.2 PLANET Long Distance models the number of trips made by each mode and for each journey

purpose between different areas of the country. The model divides the country into 235 geographic areas or zones, with each zone equivalent to districts or aggregations of districts. For example, the 32 boroughs of Greater London are aggregated into 7 geographical sector zones (plus Heathrow as an explicit zone). Rural Cumbria on the other hand retains its constituent districts. This is done to group zones into patterns of similar access and egress (such as Camden and Islington London Boroughs in north London), while acknowledging that east and west Cumbria may have very different access and egress, despite the far smaller population and trip activity in each district. This is shown in Figure 2.2.

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Figure 2.2 – PLANET Long Distance Zones

Box 2 - What is Generalised Journey Time?

The generalised journey time or cost of travelling represents the total inconvenience of travelling between any two places expressed in common units of time or money. In the case of rail, this includes the total time taken for the journey including any time getting to and from stations or time spent waiting for a train. It also includes additional penalties if an interchange is required (in PLANET Long Distance this is equivalent to half an hour journey time penalty), for having to wait for a train, or if the train is crowded. This represents the fact that people would prefer not to change trains, and dislike waiting for a train and having to spend time on crowded trains. In addition the generalised journey time includes the financial cost of a making a journey, expressed in units of time. The conversion between time and money requires an understanding

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2.2.3 To model how journeys are routed across the network, PLANET Long Distance calculates what

is known as the generalised time of travelling between each pair of zones by any mode or route. Trips are then assigned to trains or routes on the network in a way which tries to minimise the generalised journey time of a trip.

2.2.4 The model can predict not only the level of demand between zones but the flows between stations

and the level of demand on specific sections of the rail, air and road network. To forecast the impact of a new high speed rail line, the model calculates the change in generalised costs between pairs of zones as a result of the faster journeys offered by high speed rail. It then applies this change in costs to forecast the change in demand for each mode. This works in three concurrent stages:

• The change in generalised cost of travel across all modes (the ‘composite cost’) is used to

forecast the change in the total number of trips undertaken. Any new or additional trips are generally referred to as trip generation, and represent journeys that would not have been undertaken by any mode in the absence of high speed rail.

• The model forecasts people’s preferred mode of travel using a probability based approach

known as a hierarchical logit function. The logit function determines the probability of someone choosing one mode or another based on the differences in generalised costs.

• These mode shares are then allocated to the rail, air and road network by choosing a route with

the lowest generalised journey time. This allows a degree of re-routing to occur in the light of different journey times and levels of crowding.

2.2.5 The work we commissioned delivers a significant update to the previous version of the model used

by the SRA in their 2001 High Speed Rail Study. This work has included:

• Updating the base year trip matrices to 2008, by incorporating more recent data on rail and air

demand, as well as some limited new data on roads.

• Updating our assumptions about the future shape of the road, rail and air networks to represent

the most recent DfT assumptions on the provision of network capacity.

• Providing a station choice model for the London and Birmingham areas, designed to address how

the accessibility of different station locations has an impact on demand. The station accessibility data is provided by Transport for London’s (TfL) model of public transport access, RailPlan, and

West Midlands Policy Response Integrated Strategy Model (PRISM 1_{). It should be noted that this}

means that the London station choice model is entirely based on public transport access costs, while the Birmingham station choice model is entirely based on car access costs. These choices reflect the data available. This means the results of these elements of the modelling require careful interpretation.

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2.2.6 In addition to updating the functionality and data in the model as described above, we also

significantly changed the way the model was used compared with previous studies. We took the decision that high speed rail has essentially the same characteristics as classic rail (apart from being quicker and more reliable), and hence in our approach high speed rail is treated in exactly the same way as classic rail services. A further discussion on the reasons for this decision can be found in Chapter 3.

2.2.7 The changes have focused on updating demand and network data and improving model functionality.

We have not sought to re-calibrate the model and the parameters which determine forecasts of mode choice and trip generation are largely taken from the previous version of the model originally developed for the 2001 SRA study. In the time available for model development, it was not possible for us conduct any new research or survey work on how travellers might be expected to respond to high speed rail.

2.2.8 With this in mind we have deliberately followed a cautious approach in our analysis and have

wherever possible made assumptions that would be unlikely to overstate the demand for high speed rail. However, we do recognise that further research into some of our assumptions, and in particular the trip generation parameters, could help refine the detail of some areas of our analysis. Overall, the performance of the model has been compared against other forecasting approaches, and found to be broadly similar.

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24 2.3 PLANET South and Midlands

2.3.1 Unlike PLANET Long Distance, PLANET South and Midlands are rail only models, with relatively

simple capabilities for analysing the potential for mode shift. Like the Long Distance model they include a representation of the rail network and train services, but focus on short distance trips and rail services within the South and Midlands respectively. Both of these models have recently been updated as part of work for the DfT and we have not sought to further update the models (other than to integrate them within the wider modelling framework).

2.3.2 There are three critical differences between the structure of these models and PLANET Long Distance:

• The models estimates demand in the am peak only.

• The models calculate mode shift by an elasticity response, so there is a fixed relationship

between changes in generalised journey time and demand on the railway. The models do not have an explicit mode choice model as in PLANET Long Distance.

• The models have a more detailed understanding of where people are travelling to and from

(due to much smaller zones).

2.3.3 The integration of PLANET South and Midlands in the PLANET Long Distance framework was

important as it allows the interaction of long distance and short distance journeys to be understood. In particular, PLANET South allows better modelling of any WCML released capacity used for short distance commuting. PLANET South was also used to understand the impact of HS2 on the London Underground Network.

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2.4 Heathrow Access Model

2.4.1 The final element integrated into the HS2 demand model is based on BAA’s London Airport Surface

Access Model (LASAM) and models the potential for HS2 to deliver improved access to Heathrow. Although based on the hierarchy, parameters and data of the LASAM model, the revised model has been developed independently and is significantly simplified to allow it to run in a spreadsheet modelling air transfers and HSR as access modes.

2.4.2 This delivers a separate mode choice model for analysing and forecasting the number of passengers

accessing Heathrow airport for international flights. The model covers all surface modes, including some such as coach that are not in PLANET. These extra modes have been included because they play a more important role in accessing Heathrow than they do for other types of long distance journey. The Heathrow model also forecasts air transfers in which passengers fly to or from Heathrow in order to change onto onward international flights. Domestic (point to point) air trips are not included within this model as they are captured within PLANET Long Distance.

2.4.3 The Heathrow model is focussed on the areas of the country that are most likely to be most affected

by the high speed rail networks we have considered. These areas are shown in Figure 2.4 below and include cities in the North West, North East and Scotland. It is important to note that the model does not analyse the potential market to Heathrow from areas to the west. This means for instance, that the model does not forecast the demand to Heathrow from (for example) Reading using a London Interchange Station connected to the Great Western Mainline (GWML).

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Figure 2.4 - HS2 Route and Areas Likely to be Affected by HS2

2.4.4 The PLANET Long Distance model automatically feeds the Heathrow spreadsheet model with

information on costs of travelling by each mode to allow it to calculate revised mode shares. The demand information from the Heathrow access model (based on the costs from PLANET) is fed back into the PLANET models to allow an assessment of overall rail loadings and calculation of crowding effects as a result of all demand on HS2.

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2.5 International Rail Model

2.5.1 A model for forecasting the potential demand for international rail trips was separately developed

by our consultants, SKM. Data was collected from a number of existing routes in which rail and air compete in order to understand rail’s mode share in relation to journey time. The model used this data to forecast the likely rail share between a number of UK and continental cities as shown in Table 2.5. This model has not been integrated into the wider HS2 framework, but is used in separate analysis. More detail on this model is provided in Chapter 9.

Table 2.5 - Cities within the International Rail Model

UK Europe

Manchester Paris

Birmingham Frankfurt

Edinburgh Amsterdam

Glasgow Brussels

East Midlands Cologne

Liverpool Lyon

Newcastle

2.6 Additional Modelling Evidence

2.6.1 Our modelling suite provides the level of granularity necessary to support the design of the overall

scheme and provide a business case. It is designed to model the key issues, including station locations, released capacity, mode shift and the overall demand for long distance travel from Birmingham and beyond.

2.6.2 Our modelling suite is not particularly well suited to modelling localised impacts, particularly

around station access and egress. Such impacts are likely to have complex interactions with short distance traffic and demand, and be affected by localised differences in the transport network. We therefore asked Transport for London (TfL) and the West Midlands transport agencies to support our work using their own more detailed models. Some early work has been undertaken using TfL’s Railplan model in London and PRISM in the West Midlands, and although this work is ongoing, it has helped inform our proposals.

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Chapter 3:

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Chapter 3: Our Assumptions and Approach

3.1 Introduction

3.1.1 The HS2 demand model provides a framework for analysing the potential impacts of HS2. However the

assumptions used in the modelling are key to the overall conclusions and the strength of the overall business case. There are two critical issues which will affect both forecasts and appraisal of HS2: • Demand Growth – The PLANET Long Distance model forecasts on an incremental basis. This

means it takes forecasts of demand which are calculated outside of the model and estimates the changes to demand as a result of introducing HS2. This means the demand for HS2 is heavily dependent on the assumptions underpinning the forecasts that are input into the model (the reference case). If there is high demand for travel (across all modes) in the reference case then there is a greater potential market for high speed rail to capture, conversely if there is low demand for travel then there is a smaller market.

• Passenger perceptions of HSR – The way in which passengers view HSR, whether they have an inherent preference for high speed trains and the way they trade off time, money and other characteristics will affect both the number of passengers and potentially the benefits they receive.

3.1.2 We have generally tried to be conservative in our treatment of these issues and have followed advice

on best practice, but there is also a significant degree of uncertainty. This chapter sets out the assumptions on which we have based our analysis, and the basis for these assumptions. More detail on some of these assumptions is provided in the supporting technical documentation produced by Atkins.

3.2 Demand Growth Assumptions

3.2.1 PLANET Long Distance forecasts the future demand for high speed rail in three stages, as

summarised in the diagram below. The model is an incremental demand model which means that the model first requires demand in the future year reference case to be determined. These forecasts are largely exogenous (i.e. they are calculated outside PLANET Long Distance) and can be thought of as the growth in demand that will happen independently of HS2. This growth is driven by changes in population and employment, and in particular, people’s propensity to make more frequent and longer trips as they get richer. The model then calculates how these forecasts change given a change in journey times or cost. This process is summarised in Figure 3.2.

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Figure 3.2 - PLANET Long Distance Forecasting Approach

2008 data on the number of long distance road,

rail and air trips

Future year exogenous or background growth forecasts for road,

air and rail demand provided by DfT

The demand for HS2 calculated as a change to future year

background growth

Base Year demand data

3.2.2 The starting point for forecasting future demand is observations on how long distance trips are

currently made and distributed in the 2008 base year. Data has been collected showing the number of trips over 50 miles made between each pair of zones in the model split by mode and journey purpose (leisure, commuting and business), with some additional breakdowns of whether a car is available to undertake the trip. The data has been collated from a number of different sources:

• Data on domestic air trips has been extracted from Civil Aviation Authority survey data. This data

is considered robust and is consistent with data used by the Department for Transport’s aviation model.

• Rail trips have been derived from a combination of the LENNON ticket sales database and the

National Rail Travel Survey (NRTS). NRTS has been used to understand how people access and egress stations, and to infill areas where the ticket sales database is known to be weak.

• Unfortunately there is no robust national dataset providing the origins and destinations of long

distance car trips. Data on car trip matrices has therefore been derived from regional Multi-Modal Studies, with some updates using the Highway’s Agency North of Thames Highway Model and the West Midlands PRISM model. While this remains one of the best available sources of car trip data on a national basis, the accuracy of the data still remains much weaker than that for rail or air, and considerable uncertainty remains about the accuracy of the demand matrix for this mode.

Demand Growth on Air and Road Networks

3.2.3 The base year demand matrices for road and air are then uplifted to represent the expected growth

that will occur in each of these modes. This exogenous growth is independent of any subsequent changes that are made to the rail network such as high speed rail. Growth in road and air traffic has been based on the Department for Transport’s most recently published forecasts; the road forecasts

are based on the 2008 Road Transport Forecasts2_{and the air forecasts are based on the 2009 Air}

Passenger Demand and CO2 forecasts.3

2 DfT 2008 Road Transport Forecasts - http://www.dft.gov.uk/pgr/economics/ntm/roadtransportforcasts08/ 3 DfT 2009 Air Passenger Demand and CO2 forecasts – http://www.dft.gov.uk/pgr/aviation/atf/co2forecasts09/

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3.2.4 These air and road traffic forecasts have been produced by the National Transport Model (NTM) and

air traffic model (SPASM), and the underlying assumptions used by these models and PLANET are generally consistent with one another. For instance all the models are based on the same TEMPRO planning data. The forecasts also include assumptions about the future road and air networks. The NTM assumes that investment in the road network continues at current trends, while the air traffic model assumes that Heathrow has a third runway. These assumptions are described in the supporting documents produced by Atkins.

Demand Growth on Rail Network

3.2.5 The exogenous rail growth has been calculated using the standard industry and Government

recommended approach in WebTAG known as PDFH (Passenger Demand Forecasting Handbook). This methodology uses a set of fixed relationships between demand and various drivers such as fares, employment, GDP/GVA and population which have been derived from econometric analysis of past rail growth. In keeping with WebTAG the relationship between rail growth and fare is taken from PDFH edition 4.0, whilst all other relationships come from edition 4.1.

3.2.6 There are, however, a number of issues with this approach:

• Unlike the road or air forecasts, PDFH uses fixed income elasticities. This means that for every

1% growth in GDP there is a constant percentage increase in rail demand. Using this approach implies rail demand will grow indefinitely (in line with GDP). There is no slowing of growth, market maturity or saturation effect. Over an appraisal period of 60 years this can have a very strong influence on the appraisal results. To proxy for market maturity and the long term lack of certainty in the forecasting methodology, the Department recommends capping demand growth at a future date after scheme opening. The cap is usually applied at 2026, reflecting 10 or more years of demand growth after scheme opening. However as HS2 will not be opening until the end of 2025 we have decided to cap demand at 2033.

• PDFH v4.1 income elasticities have a distance term and hence can therefore become very high

for very long distance trips. Following advice from the Department PDFH elasticities have been capped so that even for the very longest distance flows they are limited. For example PDFH v4.1 suggests rail demand between Aberdeen to London will grow by 3.7% for every 1% increase in GDP (elasticity of 3.7). Following DfT advice this has been capped at 2.8% growth for every 1% increase in GDP (an elasticity of 2.8).

3.2.7 These elasticities are applied to estimates of population and employment that have been supplied

by DfT’s TEMPRO4_{model. All rail fares are assumed to grow at RPI+1 until 2033, while GDP}

forecasts are in line with the latest Treasury forecasts, but not the current DfT WebTAG guidance. We anticipate DfT will update its forecasts and guidance in due course, but for now it leads to a slight inconsistency, with DfT projections (including those used by HS2 to project air and car demand) using higher GDP projections.

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3.2.8 The demand model uses these exogenous forecasts as the basis for projecting the incremental

impact of HS2. It is also clear that these assumptions are important in generating the strength of the business case. For this reason further sensitivity tests have been undertaken on the impact of different levels of demand. These results are reported in Appendix 2.

3.3 Passenger Preferences and the Treatment of HSR in the

Demand Model

3.3.1 Demand models are an attempt to forecast the preferences and behaviour of the travelling public,

given a change in the transport network. The choices people make are based on a wide variety of factors, and individuals’ preferences will vary compared to the average transport user. Indeed their preference may also vary depending on circumstance – the importance of time and reliability when travelling to catch a flight will be very different to a day trip to the seaside.

3.3.2 Most transport models which explicitly forecast mode choice (e.g. PLANET Long Distance) are

probability based models.5_{They calculate the probability someone will use a particular mode to}

undertake a trip. So even if rail is faster, cheaper or more reliable for a particular trip, it does not mean everyone will use rail. Put another way the model captures both the things we can identify and understand in choices, but also allows for variations that we cannot directly observe. The model is therefore calculating two different things:

• The generalised cost of a mode given average preferences.

• The variation in individuals’ tastes and how this affects the distribution of choices.

3.3.3 This works well for determining people’s choices between existing modes – car, rail, air. In this case

we can observe people’s existing behaviour – how they trade off time or the inherent attractiveness of one mode over another – on the basis of the actual choices they are seen to make, as well as by asking people directly through surveys. However, we cannot observe their behaviour for high speed rail, since it does not currently exist in this country (or has not been in place for long enough) so we can only ask people what they think they might do.

3.3.4 Past surveys have suggested the way people might behave on high speed rail is different to the way

they treat classic rail. The surveys used to develop the original PLANET model, used by the SRA in 2001 for example suggested passengers placed a value on high speed rail over and above the time savings compared to classic rail.

5 These are models with techniques such as the hierarchical logit model used in PLANET Long Distance, rather than elasticity based models such as PLANET South and Midlands.

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3.3.5 There is a variety of reasons why people may prefer travelling by high speed rail as opposed to using

classic rail or another mode. These include:

• High speed rail offers a faster journey.

• High speed trains are more reliable.

• High speed trains are more luxurious.

• The experience of travel on high speed rail is somehow more enjoyable, with for instance better

ticketing, pre-booked seats, better stations etc.

• There are fundamental differences between the characteristics of high speed rail users and

classic rail users, in particular the way in which they value time savings.

3.3.6 A review of the evidence from the SRA survey work suggested that passengers’ preferences could have

been overstated, with the value of reliability a particular issue as the study took place shortly after the Hatfield incident when rail reliability was exceptionally low. We have also taken a conservative view that the quality of rolling stock and stations should not differentiate high speed rail, as in the future the differences between the perceived quality of high speed and classic rail stations and trains could be very small. Neither is there reason to believe that in the absence of premium fares (see section 3.5) the average high speed rail passenger would be somehow different from the average long distance passenger on the classic rail network. In particular there is no reason to believe that the average high speed rail passenger will value time savings more highly on high speed rail.

3.3.7 We therefore concluded that in our analysis of high speed rail we would only consider the impact

of time savings and reliability on demand. As a result high speed and classic rail passengers are treated in exactly the same way within the PLANET Long Distance model. Whilst reliability benefits could have been modelled through adjusting mode choice parameters in PLANET Long Distance, it was felt more appropriate and transparent to model this by adjusting journey times (see section 3.4). This means HSR is not treated as a new mode and there is no mode choice hierarchy between classic rail and HSR. Similarly there is no difference in peoples’ perceived preferences for HSR compared to classic rail and users’ value of time remains the same whether they travel on classic or high speed rail.

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34 3.4 Measuring Reliability Impacts of HSR

3.4.1 Capturing reliability impacts in our modelling creates a particular challenge. Other schemes (at a

detailed level of design) might use explicit models of rail performance – which vary according the precise detail of the scheme, the capacity and demand on the local railway network. Such modelling is not appropriate for the stage of design of HS2.

3.4.2 However it is clear from international experience that high speed lines can offer very high levels of

reliability compared to a mixed use railway such as the WCML, and that this can have a significant impact on demand. The design of the line has also built in costs to ensure the reliability of the line – for example timetabling has been undertaken assuming trains run slower than the maximum line speed to allow for trains to catch up most small delays. As we measure the disbenefits of these increased journey times, it is therefore important that the off-setting benefits of reliability are also understood.

3.4.3 Our approach to modelling reliability is fairly simplistic and involves making adjustments to

the journey times as a proxy for changes in reliability. Our approach considers the potential improvement in reliability that HS2 can deliver by examining one measure of reliability – average minutes lateness (AML). Improvements in AML as a result of HS2 are then converted into an equivalent journey time saving based on evidence in PDFH and WebTAG, which suggests that passengers value 1 minute average lateness as equivalent to 3 minutes of journey time. This ‘artificial’ reduction in journey time is then input into the model to forecast the change in demand due to reliability improvements.

Day One Central Case

3.4.4 We have used DfT’s Network Modelling Framework6_{to forecast the average delay expected on the}

WCML in 2020 without high speed rail. This forecasts that performance on the WCML will mean 91% of trains arriving within 10mins of their scheduled time with an AML of 2-5mins. Consistent with international practice, HS2 is expected to operate at much higher levels of reliability than can be achieved on the classic network. For dedicated services on the high speed line the AML is expected to be less than half a minute.

3.4.5 HS2 hybrid services running on both the high speed and classic network will have some benefit

from running partly on a more reliable network, but will still experience delay when running on the classic line. There is greater uncertainty about the impact on reliability of these journeys since:

• We have not currently used evidence on where trains are delayed – whether this is on the section

to Lichfield which HS2 bypasses, or sections further to the north.

• There are likely to be compound effects – a short delay on a train leaving London may mean it

misses its path further along the line, resulting in further delays. Hence small improvements in reliability on one section of a line could have larger improvements in overall reliability.

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• The impact of HS2 may be complex. The approach to timetabling on HS2 may allow for fast

running and further catch-up time. However capacity constraints may mean that a train which misses its slot on HS2 has a greater impact on the reliability of the line.

3.4.6 We have assumed that the reliability of HS2 hybrid services is proportional to the distance run on

HS2. Table 3.4a below shows the expected performance and equivalent journey time reduction for services on HS2. This means that the modelled journey time between London and Birmingham is reduced from 49 minutes to 41 minutes, with the demand forecasts based on this reduced journey time. A sensitivity test on the impact of these assumptions is included in Appendix 2.

Table 3.4a – Reliability Benefits of HS2

HS2 Service Group AML Classic Rail Forecast AML with _HS2 Change in AML Equivalent Journey Time Reduction (i.e. 3 times AML)

London - Birmingham 2.6 0.1 2.5 8

London - Preston 4.8 2.8 2.0 6

London– Manchester 2.9 1.9 2.0 6

London– Liverpool 3.0 1.0 2.0 6

London - Scotland 4.4 2.4 2.0 6

Day One Extension to Leeds and Manchester

3.4.7 If the Day One high speed network were extended from the West Midlands to serve Manchester

and/or the East Midlands, Sheffield and Leeds then journeys to these destinations would also benefit from improved reliability. The impact this has on the journey time of each link of the network is shown in Table 3.4b. It is assumed that the time savings are distributed across the network in a manner roughly proportional to distance.

Table 3.4b – Reliability Benefits of HS2 extended to Leeds and Manchester

HS2 Service Group AML Classic Rail AML HS2 Change in AML Equivalent Journey _{Time Reduction} London - East Midlands/

Sheffield 1.8 0.1 1.7 5 London - Leeds 2.0 0.1 1.9 6 London - Birmingham 2.6 0.1 2.5 8 London - Manchester 2.9 0.1 2.8 8 London - Liverpool 3.0 0.2 2.8 8 London - Scotland 4.4 1.6 2.8 8

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36 3.5 Premium Fares Model

3.5.1 Section 3.4 of this chapter outlines our approach to modelling the preference of HSR passengers

compared with classic rail. This approach considers the way the average passenger trades-off time, money and other costs of travel. It acknowledges that there will be a variation in these preferences across individuals by working out the probability of any journey being undertaken on a particular mode. This works well so long as the variation in preferences is essentially random, which is likely as long as the characteristics of high speed rail and classic rail are the same. However, if the scheme results in the selection of a group of people with a particular set of characteristics (for example if the people who would tend to use a scheme are of above or below average income) this assumption may not be valid.

3.5.2 Premium fares are an example of this. If a premium fare were charged on HS2, passengers would

have to trade-off time and money – whether to pay more money and get a time saving or stay on classic rail and pay less money but incur a slower journey. Those with a low value of time will tend to use classic rail since they do not value the time saving sufficiently to warrant paying the extra fare. This is not a random variation, rather it reflects the different characteristics of passengers. With a premium fare, by definition, the average high speed rail user will value time savings more highly than the average classic rail user.

3.5.3 Ideally we would capture and model this variation, but the design of our base model does not allow

this, since the value of time of both high speed and classic rail users is assumed to be equal. In addition to this, our decision to treat HSR the same as classic rail meant that the existing model structure prevented analysis of fare premiums over and above those charged on classic rail – the two fares are the same for the purposes of the model.

3.5.4 For these reasons we have developed an alternative modelling approach for modelling premium

fares on high speed rail using PLANET Long Distance. It still assumes an individual sees high speed rail as the same type of mode as classic rail, but rather than assuming that the value of time of rail users is a single average, the approach now assumes there is a range of individuals each with different values of time. In this approach the introduction of a premium fare leads to those with low values of time choosing classic rail and those with high values of time choosing high speed rail.

3.5.5 The distribution of value of time is drawn from evidence from the National Rail Travel Survey,7_with

income used as a proxy for value of time. The resulting model has allowed us to model the impact of premium fares. However it has not been applied to analysis of our central case since this does not include premium fares. The Distributed Value of Time model is not appropriate in this case since it will simply assign all rail users undertaking journeys served by HS2 onto HS2 trains – overstating the likely shift from rail. It was therefore only used for tests of premium fares.

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3.5.6 We report the results of sensitivity tests on premium fares in Appendix 2. Our investigations have so

far identified that, while there may be some scope for premium fares on HS2 to improve affordability, these fares structures are likely to be complex and need more assessment than is possible in the time available. For this reason our central case does not include premium fares, and so does not apply the Distributed Value of Time model. However this is an issue that could be investigated in more detail in the future.

3.6 Applying the HS2 Service Specification

3.6.1 The service pattern that we have modelled is included in the HS2 report and summarised in Figure

3.6. It represents an indicative outline of the possible service specification for the purposes of the demand model.

Figure 3.6 – HS2 Service Specification Running on HS2

Additional peak hour service Running on classic line HS2 station

Existing classic rail station

3.6.2 The development of the service specification is indicative to allow the development of the business

case. It is a credible service plan tested against the capacity of HS2 and the WCML on which some classic compatible trains would run on. It also includes an assessment of the potential for released capacity. However it has not been subject to any degree of timetable validation, and there is the potential for further iterations as the project develops. Any such changes may change both the costs

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3.6.3 The application of this specification is limited slightly by the nature of PLANET Long Distance. In

particular PLANET Long Distance is an all day model, working on average capacity and demand across the whole day. However the crowding function takes account of the variation of demand across the day. For example, an average load factor of 60% across the day would imply crowding during the peaks. The model therefore applies some crowding penalty even though on average trains are not crowded.

3.6.4 The service specification assumes 400m trains will run between Birmingham and London during the

peak hours. However simply adding this additional capacity would be assumed to be spread across the day, and would potentially over-state the level of crowding. For this reason we have assumed (for the purposes of modelling only) that all trains to Birmingham are 400m trains throughout the day.

3.6.5 This will mean that average load factors on Birmingham trains will be slightly understated, but

this is likely to provide a more representative picture of the crowding impacts on HS2, with capacity targeted over the most crowded times. Yield management would also help to spread demand and reduce crowding.

3.7 Economic Appraisal

3.7.1 We have broadly followed DfT’s transport appraisal guidance (WebTAG). The results presented in this

report are all present values, discounted over 60 years (unless otherwise stated). We have deviated from this guidance in three areas.

• Firstly our reference year is 2009, and not 2002. This is a presentational change, and all present

value costs and benefits can be converted to 2002 using a constant factor to allow comparison with other schemes. These adjustments do not change the BCRs.

• Secondly we have considered the evidence on Wider Economic Benefits. We have applied DfT’s

draft guidance on WEBs, however we have also looked at whether there are further impacts that HS2 may generate that are not captured in this methodology. This is discussed in more detail in Chapter 10.

• Finally, when considering the benefits to transport users under scenarios with premium fares we

have applied different values of time to those outlined in WebTAG. In this circumstance the equity weighted values that are used by WebTAG can cause misleading results.

3.7.2 If premium fares were charged on HS2, users would have to trade-off the value of the time saving

against the additional cost of the fare. Users with a high value of time will be better off paying the premium and gaining the time saving. However applying an equity weighted value of time could give the opposite impression (Box 3 provides an example of this).

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Box 3: How to Appraise Time Savings in the Presence

of Premium Fares

The introduction of HSR could represent an additional route choice over the existing WCML services. In this case, those that shift will value the time saving by enough to outweigh any additional fare costs. In other words their welfare will improve. However using a standard value of time may suggest the opposite.

Take the following example:

• A high speed line saves 30 minutes and the fare is £25 higher

• Person A is travelling for business purposes by rail and has a value of time of £70 per hour, so will switch to the high speed line – and be £10 better off

• However using the WebTAG standard value of £36.96 per hour, it appears that this person is £6.52 worse off

So in this case a WebTAG consistent appraisal would suggest HSR has a disbenefit to transport users (when in fact users have benefitted).

3.7.3 All of these changes are necessary for analysis of specific issues relating to HS2 and the questions

we have addressed. However the core analysis and conclusions are all based on appraisal results that are consistent with existing Government guidance.

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