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

Transcription

1 High Speed Rail London to the West Midlands and Beyond HS2 Demand Model Analysis February 2010

2 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, 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 , or by 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 ISBN: Unless specified, all maps, tables, diagrams and graphs in this report are a product of HS2 and its consultants.

3 HS2 Demand Model Analysis: Contents Contents Chapter 1: Introduction and Background Data Introduction to this report Acknowledgements The Demand for Transport and Context for HS Demand and Appraisal Helping with Scheme Design...14 Chapter 2: Demand Model Structure and Development Introduction PLANET Long Distance PLANET South and Midlands Heathrow Access Model International Rail Model Additional Modelling Evidence...27 Chapter 3: Our Assumptions and Approach Introduction Demand Growth Assumptions Passenger Preferences and the Treatment of HSR in the Demand Model Measuring Reliability Impacts of HSR Premium Fares Model Applying the HS2 Service Specification Economic Appraisal...38 Chapter 4: Central London Station Location Introduction Choosing a London Station Location Analysis of Euston...44 Chapter 5: London Interchange Station Location Introduction Options for a London Interchange Station The Potential Markets for a London Interchange Station Comparison of Heathrow and Old Oak Common Interchange Stations...55 Chapter 6: Intermediate Station Location Introduction Determining a Location...61 Chapter 7: Central Birmingham Station Location Introduction The Impact of HS Chapter 8: Birmingham Interchange Station Location Introduction Option Sifting An Interchange near Birmingham International Demand and Benefits of Birmingham Interchange

4 HS2 Demand Model Analysis Chapter 9: Connections to High Speed One Introduction Our Approach to Modelling International Demand Analysis of Different International Connections and Services Appraisal of Journey Time Benefits International Connections as Part of a Wider Network...87 Chapter 10: The Overall Business Case for HS Introduction Passenger Demand for HS HS2 Appraisal Costs Appraisal of Benefits from HS Wider Economic Impacts of HS Impact of HS2 on Carbon Emissions HS2 Value for Money Chapter 11: Appendix 1: Appendix 2: Appendix 3: Appendix 4: A Long Term Strategy for High Speed Rail Introduction Long Distance travel in Great Britain Long Term High Speed Networks Wider Networks - Modelling Approach Demand and Benefits of a Long Term Strategy Benefit Cost Ratios of a Wider Network Limitations of Our Analysis and Further Work Extensions to Manchester and Leeds Transport Economic Efficiency Tables A1.1 Introduction Sensitivity Tests on HS2 Central Case A2.1 Testing Our Assumptions A2.2 Changing the forecast level of demand for long distance rail trips A2.3 Changing Background Demand Growth and Prices on Non-Rail Modes A2.4 Premium Fares A2.5 Comparison with a classic line A2.6 Reliability High Speed Rail and Spatial Patterns and Strategies in Cities and Regions A3.1 Introduction A3.2 Why Does Spatial Impact (Land Use Change) Matter? A3.3 How Might HS2 Affect Land Use? A3.4 The Evidence of Land Use Change and HSR A3.5 Conclusions A3.6 References Service Specification for Wider Network Tests

5 Chapter 1: Introduction and Background Data

6 HS2 Demand Model Analysis 1.1 Introduction to this report 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 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 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 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. 6

7 Chapter 1: Introduction and Background Data 1.2 Acknowledgements 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 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 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 Dr Dan Graham & Patricia Melo Reg Harman Demand Modelling and Appraisal - (subcontracted by WS Atkins) Advice on the assessment of wider economic impacts Advice on the spatial impacts of high speed rail 7

8 HS2 Demand Model Analysis 1.3 The Demand for Transport and Context for HS 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) Billion Passenger km Year Total Passenger Travel Cars, Vans and Taxis Other Source: Department for Transport (DfT) Transport Trends 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. 8

9 Chapter 1: Introduction and Background Data Figure 1.3b - Total Passenger Travel by Rail Billion Passenger km Year Source: Office of the Rail Regulator (ORR) National Rail Trends Similarly, long distance trips of over 50 miles have been growing in line with recent trends, with an increase of more than 30% since 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. 9

10 HS2 Demand Model Analysis Table 1.3 Forecast growth in long distance travel Passenger Demand per weekday to and from Central London (two way flow) 2008 Demand 2033 Demand % Growth Birmingham 5,700 13, % Manchester 5,300 14, % Leeds 4,400 12, % Glasgow 800 2, % Liverpool 2,100 5, % Newcastle 2,400 6, % Edinburgh 1,700 5, % Source: Atkins Baseline forecasting report (PLD) 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 The following maps show the number of long distance trips on the WCML in 2008 and the increase in those volumes by 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 HS 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. 10

11 Chapter 1: Introduction and Background Data Figure 1.3c - WCML Long Distance Daily Rail Trips in 2008 Long distance daily trips on the WCML in 2008 Source: Atkins PLANET Long Distance 11

12 HS2 Demand Model Analysis Figure 1.3d - WCML Long Distance Daily Rail Trips in 2033 Glasgow G a w Edinburgh i n u Forecast of long distance daily trips on the WCML in ,867, Newcastle N w c s York o k e d s ul l Liverpool L i p ooll 0 n ch e t r Sheffield e fii 2 21,688 1, Stoke-on-TrentS ok e on -Trer n t Nottingham tin gh , ei c t r P Peterborough t r b o o u g h rm in g h m , 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 Bristol B i s o l Oxford d ReadingR d ng nd on Source: Atkins PLANET Long Distance 12

13 Chapter 1: Introduction and Background Data Figure 1.3e - WCML Long Distance Load Factors in 2033 lasg o w 55 Edinburgh i u g h 62 2 Forecast of laverage daily load factors on long distance WCML services in Newcastle t e 7 York k 86 LeedsL eed s HullH u Manchester M s Liverpool L i ol l 60 Sheffield f d Stoke-on-TrentS ok e on -Trer n t NottinghamN ot n gh am ei c t r Peterborough P b orouo u g h Birmingham r i Do Minimum Crowding Legend (Volume over seats) More than 80% 60% to 80% 40% to 60% 20% to 40% 0 to 20% diffd f Bristol i l xf o r d Reading R a d n g 80 0 London o n Source: Atkins PLANET Long Distance 13

14 HS2 Demand Model Analysis 1.4 Demand and Appraisal Helping with Scheme Design 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 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. 14

15 Chapter 1: Introduction and Background Data 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. 15

16 HS2 Demand Model Analysis 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 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 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). The potential areas to consider in the Longer Term Strategy network designs. 16

17 Chapter 1: Introduction and Background Data Stage Two: Early Modelling 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 m (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 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) The economic results of these tests are described later in this report with the full results provided in the Appendices. 17

18 Chapter 2: Demand Model Structure and Development

19 Chapter 2: Demand Model Structure and Development 2.1 Introduction 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 (interlining). 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 HS 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. 19

20 HS2 Demand Model Analysis 2.2 PLANET Long Distance 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 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

21 Chapter 2: Demand Model Structure and Development 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 of the people put on being able to save time, a concept known as their value of time. 21

22 HS2 Demand Model Analysis 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 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 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

23 Chapter 2: Demand Model Structure and Development 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 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 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. 23

24 HS2 Demand Model Analysis 2.3 PLANET South and Midlands 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) 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) 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. 24

25 Chapter 2: Demand Model Structure and Development 2.4 Heathrow Access Model 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 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 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). 25

26 HS2 Demand Model Analysis Figure HS2 Route and Areas Likely to be Affected by HS 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. 26

27 Chapter 2: Demand Model Structure and Development 2.5 International Rail Model 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 Cities within the International Rail Model UK Manchester Birmingham Edinburgh Glasgow East Midlands Liverpool Europe Paris Frankfurt Amsterdam Brussels Cologne Lyon Newcastle 2.6 Additional Modelling Evidence 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 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. 27

28 Chapter 3: Our Assumptions and Approach

29 Chapter 3: Our Assumptions and Approach 3.1 Introduction 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 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 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

30 HS2 Demand Model Analysis Figure 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 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 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 Forecasts 2 and the air forecasts are based on the 2009 Air Passenger Demand and CO2 forecasts. 3 2 DfT 2008 Road Transport Forecasts DfT 2009 Air Passenger Demand and CO2 forecasts 30

31 Chapter 3: Our Assumptions and Approach 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 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 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 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) These elasticities are applied to estimates of population and employment that have been supplied by DfT s TEMPRO 4 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

32 HS2 Demand Model Analysis 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 Passenger Preferences and the Treatment of HSR in the Demand Model 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 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 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 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. 32

33 Chapter 3: Our Assumptions and Approach 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 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 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. 33