model development report: a report for hs2

High Speed 2 Support

Model Development Report: A Report for HS2

February 2010 Notice

This report was produced by Atkins limited for High Speed Two Limited for the specific purpose of High Speed Two Modelling Framework Development. This report may not be used by any person other than High Speed Two Limited without High Speed Two Limited’s express permission. In any event, Atkins accepts no liability for any costs, liabilities or losses arising as a result of the use of or reliance upon the contents of this report by any person other than High Speed Two Limited.

Document History

JOB NUMBER: 5082342 DOCUMENT REF: 5082342 Model Development Report (26-02-2010).doc

3 Final Steve Miller

Jonathan Foster-Clark

Michael Hayes

Michael Hayes

26/02/10

2 Draft 2 Steve Miller

Steve Miller Michael Hayes

Michael Hayes

16/02/10

1 Draft 1 Matt

Carlson Steve Miller Steve Miller

Michael Hayes

01/02/10

Revision Purpose Description Originated Checked Reviewed Authorised Date

5082342/5082342 Model Development Report (26-02-2010).doc

Model Development Report

Contents Section Page 1. Introduction 5

1.1 Background 5 1.2 Purpose of Report 5 1.3 Organisation of Report 5

2. Overview 6

2.1 Project Scope and Requirements 6 2.2 HS2 Forecasting Framework Specification 9

3. HS2 Forecasting Framework 12

3.1 Framework Description 12 3.2 Framework Operation 14

4. PLANET Long Distance Model 16

4.1 Introduction to the PLANET Strategic Model 16 4.2 Key Features of PLANET Strategic Model 16 4.3 Key Elements of Existing PSM 17 4.4 PSM Update to PLD 21

5. PLANET Long Distance Rail Model 24

5.1 Overview 24 5.2 Demand Matrices 24 5.3 Rail Fare Matrices 33 5.4 Rail Network 34 5.5 Assignment Process 38 5.6 Station Choice 43 5.7 Interfaces 48

6. PLANET Long Distance Highway Model 51

6.1 Overview 51 6.2 Supply Update - Highway Schemes 51 6.3 Demand Data Update 51 6.4 Highway Assignment Process and Parameters 52 6.5 Interfaces 53

7. PLANET Long Distance Air Model 54

7.1 Overview 54 7.2 Demand Data Update 54 7.3 Network Update 56 7.4 Air Assignment Process and Parameters 57

8. PLANET Long Distance Mode Choice Model 58

8.1 Overview 58 8.2 Summary of PSM Mode Choice Model 58 8.3 Review of Existing Mode Choice Model 60 8.4 Recalibrated Mode Choice Model 62 8.5 Distributed Value of Time (DVoT) 64 8.6 Summary of Approach 71

5082342/5082342 Model Development Report (26-02-2010).doc 2


9. PLANET South Model 73

9.1 Overview 73 9.2 Updates for use in HS2 Model Framework 75 9.3 Demand Data and Modifications 76 9.4 Supply Modifications 77 9.5 Assignment Process and Parameters 78 9.6 Demand Responses 79 9.7 Interfaces of PS with other Models 79

10. PLANET Midlands Model 81

10.1 Overview 81 10.2 Updates for use in HS2 Model Framework 84 10.3 Demand Data and Modifications 84 10.4 Supply Modifications 86 10.5 Assignment Process and Parameters 86 10.6 Demand Responses 87 10.7 Interaction of PM with other models 87

11. Heathrow Model 88

11.1 Overview 88 11.2 Interaction with PLD 94 11.3 Example Outputs of Heathrow Model 95 List of Tables

Table 2.1 - Modelling Options 10 Table 4.1 - Treatment of Premium Fares 22 Table 5.1 - Postal Sectors in Solihull 28 Table 5.2 - Treatment of Ticket Types in LENNON 31 Table 5.3 - Journey purpose splits by ticket category 31 Table 5.4 - Approaches to Rail Fare Representation 34 Table 5.5 - PLD Network Bottlenecks 38 Table 5.6 - Assignment Types 39 Table 5.7 - Assignment Parameters 40 Table 5.8 - Stations in PLD Station Choice Model 44 Table 5.9 - Greater London Station Choice Zones 47 Table 5.10 - West Midlands Station Choice Zones 47 Table 5.11 - Example Station Choice Output 47 Table 5.12 - Demand Interface between PLD and PS 49 Table 6.1 - Highway Demand Totals 52 Table 6.2 - Highway Volume Delay Functions 53 Table 7.1 - Annual Domestic End-to-End Air Demand 54 Table 7.2 - PLD 2007 Daily Air Demand 55 Table 7.3 - Percentage of Demand to SE England 55 Table 7.4 - Air Passenger Data & Modelled Flows at Heathrow Airport 56 Table 7.5 - Air Assignment Parameters 57 Table 8.1 - PSM Mode Choice Parameters (2002 Prices and Values) 59 Table 8.2 - Comparison of VoTs (p/min ,2002 prices and values) 65 Table 8.3 - Mu (μ) and Sigma (σ) by purpose 66 Table 8.4 - Business Fares (Average Pounds, one-way) 68 Table 8.5 - Leisure Fares (Average Pounds, one-way) 68 Table 8.6 - Business Demand 69 Table 8.7 - Leisure Demand 70



Table 8.8 - Total Daily Revenue Change (£) 71 Table 9.1 - PS Assignment Parameters 78 Table 9.2 - PS Cordon Points 79 Table 10.1 - PM Assignment Parameters 86 Table 11.1 - Airport Mode Choice Model Parameters 94 Table 11.2 - 2021 “Day1” Business Demand by Mode From Heathrow (produced by Heathrow Model) 95 Table 11.3 - 2021 “Day1” Leisure Demand by Mode From Heathrow (produced by Heathrow Model) 96 List of Figures

Figure 3.1 - PLANET Long Distance Model Framework 12 Figure 3.2 - Base Case Run 15 Figure 3.3 - Test Run Extension 15 Figure 4.1 - PSM Network Coverage 17 Figure 4.2 - PSM Model Elements and Sequence 18 Figure 5.1 - PLD 235 Zone System 25 Figure 5.2 - Postal Sectors in the West Midlands [NRTS leisure trip-rates] 27 Figure 5.3 - Concentration of districts providing journeys to/from particular stations 29 Figure 5.4 - Percentage of car available trips by originating station (NRTS) 29 Figure 5.5 - PLD Matrix 'holes' 33 Figure 5.6 - PLD Rail Network 35 Figure 5.7 - PDFH Adjustment Output 41 Figure 5.8 - Relationship between PLD and LHR / PS / PM 48 Figure 8.1 - Mode Choice Model Structure 58 Figure 8.2 - Fitted High Speed Proportion by Journey Time from EU data on major corridors 64 Figure 8.3 - Chart showing the fit of lognormal distributions of income to actual data 65 Figure 8.4 - Spreadsheet Model 67 Figure 9.1 - PS Matrix 'Island' 77 Figure 10.1 - PM Matrix 'Cordon' 85 Figure 11.1 - Heathrow surface access/egress 2007 (CAA) 89 Figure 11.2 - Heathrow Model Catchment Areas 90 Figure 11.3 - UK Business Mode Choice Hierarchy 91 Figure 11.4 - Foreign Business Mode Choice Hierarchy 91 Figure 11.5 - Leisure Mode Choice Hierarchy 92 Figure 11.6 - Heathrow Demand Model Structure 93

Appendices Appendix A - Transit Line Parameters 97

A.1 Transit Line Validation 97

Appendix B - Highway Parameters 99

B.1 Volume Delay Functions 99

Appendix C - Air Parameters 100

C.1 LHR Model Matrices 100

Appendix D – Model Outputs 101

D.1 Rail Demand Elasticities w.r.t Cost Component Changes, 2021 102 D.2 Air Demand Elasticities w.r.t Cost Component Changes, 2021 103 D.3 Highway Demand Elasticities w.r.t Cost Component Changes, 2021 104 D.4 Rail Demand Elasticities w.r.t Cost Component Changes, 2031 105 D.5 Air Demand Elasticities w.r.t Cost Component Changes, 2031 106 D.6 Highway Demand Elasticities w.r.t Cost Component Changes, 2031 107



1. Introduction 1.1 Background

In April 2009, Atkins was appointed by High Speed Two (HS2) to produce a forecasting framework to support development, testing and appraisal of options for high speed rail between London and Birmingham, as well as a wider high speed rail network across the UK. Atkins was supported by Arup and Sinclair Knight Merz (SKM) in development of the framework and its constituent components.

The forecasting framework was developed in agreement with HS2, Department for Transport (DfT) and an external challenge panel of demand forecasting experts over a period of three months during Summer 2009, with further refinements during Autumn 2009.

1.2 Purpose of Report This report sets out the model development that was undertaken by Atkins and its partners, the reasons why the model structures were chosen, and details of the operational parameters of the models.

The forecasting framework is based on DfT’s existing PLANET Strategic, South and Midlands models. This report does not repeat the information in the separate model development reports underpinning those models, but describes the main features of the models as incorporated into the framework, and the updates and modifications made to the models as part of the frameworks.

For sake of clarity, this report does not include details of the exogenous demand growth assumptions used in HS2 option testing and appraisal work, detailed in the separate Baseline Forecasting Report. It also includes no references to appraisal of individual options, which are described within main HS2 reports and technical annexes.

1.3 Organisation of Report The rest of this report is set out as follows:

Section 2 provides a review of the requirements for the HS2 forecasting framework, and sets out the reasons why this particular forecasting framework was developed;

Subsequently, Section 3 describes the overall forecasting framework and the roles of the different models within the framework;

Section 4 provides more details of the PLANET Long Distance model as a development of the PLANET Strategic Model, with particular details of the rail, highway and air models given in sections 5, 6 and 7 respectively. Details of the mode choice model functionality are included in Section 8;

Sections 9 and 10 provide descriptions of the local PLANET South and Midlands models, respectively, and how they feed into the forecasting framework; and

Section 11 describes the main details of the Heathrow Airport Access model – further details on model development are described in a separate technical report produced by SKM.



2. Overview 2.1 Project Scope and Requirements

2.1.1 Background to High Speed Two Project

In January 2009, the government announced the creation of the company High Speed 2 (HS2) to develop proposals – and case for – a first new line between London and Birmingham. This new line could potentially be extended further north, and serve Heathrow Airport either directly or indirectly.

The work of HS2 built on several previous studies into the case for a north-south high speed rail line in the UK. In 2001, the Strategic Rail Authority commissioned a study into the case for high speed rail between London and Scotland. The principal driver of this work was the need to provide additional capacity on north-south routes. This study reported in 2003 that there was an outline case for several different routes between London, the North of England and Scotland;

In 2005, DfT commissioned further work to look at the case for high speed rail as part of the Eddington Study and development of the subsequent 2007 Rail White Paper. The White Paper noted that there may be a case for high speed rail between London and Birmingham in the future, once small and medium scale capacity enhancements had exhausted available capacity.

After the completion of High Speed One extension to St Pancras in November 2007, political interest in high speed rail increased, with the Conservative Party, engineering consultancy Arup and lobby group Greengauge 21 producing proposals for high speed rail links.

As a result, DfT set out a remit for HS2 to produce proposals for high speed rail between London and Birmingham, and to set out the case for building the line and potentially a wider a high-speed rail network. The case would consist of a number of elements:

A business case, demonstrating that the financial and economic benefits produced by the reduced journey times and reduced congestion across all modes are significant enough to attract public and private sector investment;

An environmental case, setting out the environmental benefits associated with demand shifting to new high speed rail and enhanced conventional rail services away from “carbon-hungry” modes such as road and air; and

A transport case, including how a new high speed line could ameliorate the capacity problems, otherwise expected on the rail network, and which the scheme is able to address.

It is important to note that the work goes beyond the outline business case work undertaken in the 2003 High Speed Line Study and includes the analysis needed to build up more detailed proposals, such as: whether a new line should be completely separate or integrated with the existing network and services; locations of stations and alignments, particularly relating to parkway stations and serving Heathrow Airport; re-use of the capacity released on the existing rail network; operating service patterns and speed of services.

These detailed design questions inevitably involve trade-offs between engineering constraints of construction and operation, and the economic, financial and environmental benefits of the scheme as a whole. Option development requires parallel understanding of all of these aspects.

2.1.2 Analytical Challenges

In modelling terms, the challenges faced by HS2 meant that a wide range of different forecasting and economic questions needed to be addressed, to assist HS2 in making trade-offs between engineering, environmental and economic costs and benefits.



The remit for the framework included being able to address a wide range of analytical challenges, such as:

How many passengers would use a high speed rail link between London and Birmingham, based on different journey times and service patterns? What level of economic benefits would accrue to passengers for each option, including the amount of crowding relief provided on existing West Coast Main Line services? How should capacity released on the existing network be used to maximise benefits from high speed rail?

Should services on the first section of line be integrated with the existing network to provide through services to Scotland? Would the environmental and economic benefits outweigh the costs of providing new high speed rolling stock which would only operate at maximum speed for a small proportion of the total journey?

Do the benefits of removing longer-distance services from the network and allowing more local and regional services to operate outweigh the costs of providing new dedicated alignments and terminal capacity in London and Birmingham for new high speed services?

Can high speed rail be partly financed through premium fares? Is it susceptible to competition from lower cost and speed services offered by open access operators on the existing network, and how could it integrate with existing services on the West Coast Main Line?

What is the most cost-efficient way of serving Heathrow Airport, whether through a high speed station on the airport campus, a hub station close to the airport but located on and connecting with the Great Western Main Line, connecting services from either Old Oak Common or a London terminal station via Crossrail, or other dedicated rail services? Would there be knock-on benefits to passengers in London and the South East in accessing the airport?

Would the development of parkway-style stations on the M25 and M42 reduce or increase the environmental benefits of high speed rail? Would they encourage more people to drive to high speed rail stations, or less people to drive all the way to their destinations?

Where should stations be sited in London and Birmingham, in order to increase accessibility for passengers either by road, public transport or both? What would be the knock-on impacts on local transport networks on accessing the station, such as congestion on underground services in London?

Does high speed rail provide significant mode shift to rail from air and highway? Could it support wider carbon emissions reductions objectives? How much does the case for high speed rail depend on expansion of Heathrow Airport?

How should a wider high speed rail network serving the whole of the UK be developed?

The potential range of analytical questions, and depth of associated analysis, was limited by the timescales available for reporting. Options needed to be narrowed down over a very short timescale, with detailed option testing commencing in June 2009. The preferred option needed to be developed and appraised by the end of October 2009, with final reporting completed by the end of December 2009.

Despite the short timescales, options had to be developed to a level resilient to scrutiny by a wide range of rail industry, private sector and government stakeholders with a similarly wide range of views on which option – if any – should be pursued.

2.1.3 Modelling and Forecasting Challenges

The forecasting approach needs to take into account different behaviours associated with introduction of high speed services on long-distance demand, mode and destination choice and trip-making frequency. These are difficult issues which are still not well understood – indeed they are the subject of ongoing research by DfT. Airport access and international trips display different



behaviours and need to be considered separately, although they interact heavily with other passengers potentially using the same services and stations.

Careful treatment of the trade-off between high speed rail and conventional rail is also needed. This included understanding values of time for different demand segments as well as their valuation of reliability and any “modal constant” associated with passenger perceptions of the comfort or other non-defined benefits of travelling by high speed rail as a mode.

Station location is an important aspect of the development of a preferred option, and the attractiveness of different locations by different access modes. Within London, where short taxi or public transport access are critical in rail competing with highway and air for business trips, the location of any terminal station and its accessibility by all modes from different areas of London and the South East was assessed and fed into the wider assessment of the effectiveness of high speed rail. Again, this issue is magnified when there is the choice between new high speed and conventional rail services operating from different terminal stations.

There is also the critical issue of the interaction between long and short-distance passengers on current long-distance services, especially on the Coventry – Birmingham corridor and the Milton Keynes – London section of the West Coast Main Line. These issues also extend to other areas not under immediate consideration, including Stoke / Crewe to Manchester and the Manchester – Leeds lines.

The issue is magnified on the strategic highway network, where local and long-distance movements (both passenger and freight) share network capacity: reducing the volume of one type of movement may increase the attractiveness of road as an option for other types of movement. Analysis of these issues is made difficult by the lack of up-to-date information on highway movements on the strategic highway networks, and for long-distance trips in general.

2.1.4 Key Requirements for Forecasting Framework

Based on the analytical challenges set out above, the following key functionality requirements were identified for a forecasting framework to support HS2 option development, testing and appraisal:

The ability to develop passenger demand forecasts for a variety of high speed rail options serving different destinations with a range of journey times and service frequencies. The demand impacts should take into account shift from existing rail services as well as mode shift from air and highway modes and impacts on trip frequency and generation;

Specific representation of the interaction between local and long-distance passengers on long-distance services where they serve both markets, particularly on Wolverhampton / Birmingham to London services which also serve the local Coventry to Birmingham and Milton Keynes to London markets;

Understanding the impacts of passengers across the existing rail network re-routeing to take advantage of faster journey times on HS2 services for part of their journey;

Service options for re-using capacity created on the existing network, including enhanced London / Birmingham suburban and inter-regional services;

Demand and economic impacts of different high speed rail station locations, including provision of parkway stations in the Birmingham and London areas and impact on local transport networks through access trips to the high speed rail stations; and

Specific examination of the market for high speed rail access to Heathrow Airport, taking into account the different behaviour of people making airport access trips.

Above and beyond the framework functionality required above, there were more general requirements for the framework to meet HS2’s programme delivery, including:

The need to examine multiple options over a short time period requires fast turn-around times, certainly within an overnight period;



Ensuring the underlying model data is the most accurate and up-to-date available, maximising the forecasting accuracy of the framework;

Transparency of forecasting approach, where forecasting results can be easily analysed and an “audit trail” produced to support model outputs. This is especially important to maintain confidence in forecasting results; and

The ability to provide a wide range of outputs to support option development and appraisal in areas ranging from environmental assessment to operating cost assessment.

Timescales

Inevitably, the level of any framework development is determined by the time available. For the HS2 programme, only three months of forecasting framework development was available from April 2009, to enable option testing to commence in June 2009. Further incremental development was undertaken between July 2009 and October 2009 to support preferred scheme appraisal.

2.2 HS2 Forecasting Framework Specification

2.2.1 Options for HS2 Forecasting Framework

Recognising the needs of the HS2 programme, three broad technical options were identified for developing a forecasting tool that could meet the needs of HS2, as follows:

1. Development of a framework based on the existing PLANET Strategic Model (PSM), which was used for the original 2001-2003 SRA High Speed Rail study. PSM is a strategic, multi modal (air, rail and road) model covering England, with a more limited coverage of Wales and Scotland. The development would involve update and enhancement of the model to address issues about data age and constrained forecasting functionality;

2. Combination of outputs from the existing PSM model with outputs from other models in a spreadsheet-based tool. This approach was similar to that adopted by Network Rail on its 2008/09 New Lines Study, although the spreadsheet model developed by Network Rail was not available for use by HS2; or

3. Either (a) use / adaption of another existing network-based transport demand model, or (b), potentially creation of a new bespoke transport demand model as an alternative to PSM.

It is important to emphasise that the choice of overall modelling approach, and the more detailed methodology, had to relate to HS2’s modelling and forecasting needs rather than the individual functionality or robustness of particular models.

A high level assessment of these three potential approaches was made against the following criteria:

Supporting HS2 Business Case Timescales – a suitably robust modelling tool needed to be available to test High Speed Line options by the end of June 2009 such that the forecasting and evaluation of options could be completed by December 2009;

Model functionality and robustness. As identified above, the modelling tool must be able to: examine the full range of impacts of options for a new line including the impact on the existing rail network and the highway and air networks; be flexible enough to robustly test different routes, station location, service options and fares levels; address the specific issues raised by options for access to Heathrow; and provide the full range of outputs that enable a business case to be constructed; and

Model flexibility for option testing – the modelling tool must be: capable of being run quickly to address a wide range of potential options; transparent, flexible to use with easily interpreted outputs; be capable of being quality assured; and, looking forward, provide a platform for further enhancement to address key new line design issues to a greater level of detail if required as HS2 progresses.



A summary of the assessment of how each of the broad modelling approaches matched HS2’s requirements is shown in the table and discussed further below.

Table 2.1 - Modelling Options

HS2 Modelling and Reporting Requirements

Overall Modelling Approach Options

Supporting HS2 Business Case

Timescales

Model Functionality

and Robustness

Model Flexibility for Option

Testing

1) Update and enhancement of the PLANET Strategic Model (PSM)

2) Combination of outputs of the existing PSM with outputs from other models in a spreadsheet based tool

3a) Use of another existing model or creation of a new bespoke model – non-network based

3b) Use of another existing model or creation of a new bespoke model –network based

In order to meet the technical requirements for a High Speed Rail business case it was imperative that a network-based model was used.

This is because the new line options to be considered will be non-incremental changes to the existing rail network. The options will create many new routeing opportunities and changes in travel times and costs for so many different journeys that the only way to feasibly and robustly calculate routes and associated travel times and costs is using a network model. Network models can do this automatically using algorithms based on travel behaviour, whereas spreadsheet-based models will require pre-coded information or, at best, very simplistic route choice representations. This is vitally important for examining issues associated with high speed rail: for example, choices between travelling on a direct, conventional speed service between, say, London and Stafford or using high speed services from London to Birmingham and connecting regional services to a final station. A network model was also needed where options are looking at yet-to-be-defined corridors other than London-West Midlands, e.g. London-Leeds. Similar issues also arise with station access where a network model enables station choice to be represented explicitly.

A network model is also required to provide times and costs for alternative modes – particularly highway since a network model aids the calculation of highway congestion relief. Network-based models also robustly calculate travel times and costs on an origin-destination basis. This is essential for the economic evaluation of different options.

This over-arching technical requirement has a major bearing on the overall modelling approach and effectively rules out Option 3:

A spreadsheet-only approach does not meet HS2 modelling and appraisal technical requirements. Even though, at first sight, a spreadsheet approach does have some advantages - models can be quicker to develop and run than network-based models and can, potentially, offer greater transparency – they do not meet the flexibility and detail requirements of HS2;

PSM offered the best overall forecasting functionality available of any national network model available. DfT’s Long Distance Model was still under development; DfT’s Network



Modelling Framework only represented travel by rail, and did not have sufficient representation of the interaction between local and long-distance trips; similarly DfT’s National Transport Model lacked sufficient detailed representation of the rail network and had unsuitably long option coding and run times. Other regional models, such as the East of England Regional Transport Model (EERTM) did not provide sufficient coverage for the London to West Midlands corridor, let alone the rest of the UK; and

The HS2 programme required a working modelling framework by June 2009, thus precluding development of a new network-based model to replace PSM – to follow such an approach would be an extremely high risk exercise with no realistic possibility of supporting HS2’s wider programme of delivery by December 2009.

2.2.2 Development of PSM for HS2 Forecasting Framework

While the analysis above set out development of PSM as the only realistic alternative for an HS2 forecasting framework, there are also other positive reasons why PSM was used:

PSM is an accepted, modularised, well understood and recently used model that can form the platform for updating and enhancement. It is a well constructed, documented and tested model that provides the network supply and demand elements that are critical for examining HS2 options;

It has a wide geographic scope, meaning that it can flexibly examine other corridors if required. The networks can be readily updated with new information; and

Though certain elements of the demand model require enhancement the basic building blocks of the model (such as network cost skimming, generalised cost calculations, logit model applications, links to evaluation modules, model output modules) all exist and are understood.

In particular, existing links to evaluation modules allowed use of established robust, audited interfaces and methods to derive economic and environmental impacts from PSM consistent with DfT guidance.

However, in order to make the existing PSM meet HS2’s forecasting requirements more closely, further development was required:

Updating and enhancement of the functionality of the existing PSM, to provide a new PLANET Long Distance (PLD) model. This includes:

New rail, air and highway demand matrices representing 2007/08, reviewing the corresponding model networks for each mode;

Updating the demand model to reflect expert views as to traveller behaviour for long-distance trips; and

Providing new functionality on aspects of station choice, critical for assessing the implications of alternative city centre and parkway station sites;

Linking PLD to the recently updated PLANET South and PLANET Midlands models to enable impacts of new lines on classic rail services to by fully represented. PLANET South and Midlands provide a much better representation of local rail movements, in the London & South East and West Midlands respectively. Interaction of long-distance and local demand on inter-regional services is critical to understanding capacity issues on the rail network; and

Supplementing the PLANET models with an integrated spreadsheet model to address specific issues related to passenger access to Heathrow Airport.

Section 3 describes the integrated framework in more detail, with subsequent sections describing the development of the individual model components.



3. HS2 Forecasting Framework This section describes the overall framework which was developed to test HS2 options, how the framework is operated and how data is passed between each of the models.

Subsequent sections in this report describe how the individual models within the framework are operated.

3.1 Framework Description As set out in the previous section, the forecasting framework consists of three PLANET models, adapted to work together, and a Heathrow spreadsheet-based model combined with overall framework operation batch files which transfer data between the models. The framework is illustrated in Figure 3.1 below.

Figure 3.1 - PLANET Long Distance Model Framework

Each of the three models represents separate markets:

3.1.1 PLANET Long Distance (PLD)

Developed from the existing PLANET Strategic Model, this model represents longer-distance (over 50 miles) domestic travel by road, rail, air and high speed rail. For this framework, the model excludes movements wholly within the West Midlands, wholly within London & South East (and South West), and to/from Heathrow Airport. These excluded travel markets generally exhibit different behaviours than most long-distance trips and are covered by the other three models.

However, as most services between London and the West Midlands also cater for the local West Midlands and South-East to London commuting market, the effect of local passengers on long-distance services are fed into the model from PLANET South and PLANET Midlands by means of “pre-loaded” demand volumes. These volumes represent “local” passengers using particular long-distance services, and who affect the crowding experienced by long-distance passengers. These volumes are not subject to any re-routeing or demand response effects, in contrast to the long-distance passengers.



Similarly, demand associated with access to Heathrow Airport is also imported into PLD from a separate Airport Access spreadsheet model, although this demand is input on a matrix origin-destination basis and allowed to re-route to take advantage of quicker routes or reduced crowding.

While the existing PSM model included a high level station choice model for London and Birmingham (and three other cities), the station choice model did not work at a sufficient level of detail to capture accessibility of different station sites within the Greater London and West Midlands areas. As a result, the station choice procedures were updated to take into account forecast station access times from Railplan (for Greater London) and PRISM (for the Birmingham urban area) models. It is important to note that the inputs on station accessibility are static, i.e. they do not include any feedback or “knock-on” effects of increased London Underground or highway congestion resulting from accessing alternative station locations.

3.1.2 PLANET South (PS)

The existing PLANET South (PS) model has been in use for several years for modelling forecast crowding on the London & South East rail network and associated impacts on London Underground lines. The model represents morning peak period (0700-1000) rail movements within an area which covers the former Network South East area, with less detailed representation beyond those areas.

PS was adapted for this framework to include only trips within London, South East and South West areas, to eliminate the previous overlap with PSM. The model includes both local and long distance services into London. However, long-distance demand – i.e. rail demand between inside and outside London & South East / South West areas – is included in the model by means of “wormholes”.

These “wormholes” are effectively dummy zones that feed origin-destination demand information from PLD into PS into nodes at the edge of the PS network area, having been scaled from all-day to peak-only levels. This demand is then assigned onto long-distance services, with the PS assignment routines allowing the demand to reach their final destinations within the PS area using any other rail or underground services. The model works in a similar way for trips from the London & South East area to the rest of the country.

It is important to note that while long-distance movements are included in PS demand and are assigned in a way that allows them to find quickest and least crowded routes to their final destination, they are not subject to any demand response in PS. By contrast, local trips within the London & South East area are responsive to any changes in crowding as a result of changes to long-distance services and demand – expected to be a significant effect.

3.1.3 PLANET Midlands (PM)

The PLANET Midlands (PM) model was developed during early 2009 for DfT. The model is generally similar to PLANET South but covers the West Midlands and East Midlands rail networks and demand. It also has slightly different functionality from PLANET South in the way that it deals with station choice: while PLANET South uses default EMME/2 assignment processes to assign demand from zones to stations, PLANET Midlands uses detailed accessibility information for each station from each zone.

PLANET Midlands was adapted for the framework by cutting back its area of scope to the immediate greater Birmingham area. Both local and long-distance services are included in the model. However, in contrast to PLANET South, long-distance demand is fed from PLD by a pre-load approach, identifying the level of long-distance passengers on long-distance service groups, and adjusted to reflect the morning peak demand levels.

This altered approach reflects the much higher levels of car access for longer-distance trips to/from Birmingham, and the relatively lower importance of knock-on dispersal effects of crowding on local rail services feeding long-distance rail passengers from the outskirts of Birmingham into central Birmingham to catch long-distance services.



3.1.4 Airport Access Model (LHR)

As explained in the previous section, airport access travellers have different demand response characteristics to most passengers, with relatively little generation, higher values of time and often with access to relatively high quality coach services. Much research has been undertaken by British Airports Authority (BAA), DfT and others on airport access travel models.

The distinct level of detail required for airport trips was met by using a spreadsheet approach developed using experienced gained from the London Airport Surface Access Model (LASAM) by sub-consultants SKM. Demand for trips to and from Heathrow Airport, divided by business and leisure journey purposes is calculated within the spreadsheet using journey time skim information from the PLD model. As described above, resulting demand to Heathrow Airport by mode was then fed back into the PLD model to represent the impact of Heathrow Airport passengers on crowding on long-distance services for long-distance passengers.

It should be emphasised that airport demand is included in PLD if it is domestic (i.e. completely contained within mainland Britain), and in LHR if it is “interlining” with, or completely, international (“interlining” refers to domestic air passengers who are connecting with an international flight). For example, someone flying from Glasgow to Heathrow to access central London is included in PLD. However, someone on the same flight to connect at Heathrow for an onward flight to Sydney is “interlining”, and hence included in LHR.

3.2 Framework Operation The framework can be operated in either “base case” or “test case” modes, and involves running the models sequentially to allow information on passengers on long-distance services to be passed between the models.

As all the models operate incrementally, i.e. reflecting changes in demand / mode share as a result of changes in modal travel costs, the “test case” represents impact of the option test relative to the “base case” network and demand.

3.2.1 Base Case

A base case scenario effectively operates with fixed demand, assigning demand to the networks in a way that reflects journey times and crowding for each mode in each model. This scenario effectively forms the base case around which options can be tested for demand and economic benefit impacts.

In base case operation, each of the framework models are run as shown in Figure 3.2 below, in numerical order from 1.1 PLD, down to 3.1 PLD. Each component of the PLANET models (PLD, PS, PM) is run in their base modes; that is, the mode-choice and generation is switched off in PLD, and elasticity is switched off in PS and PM.

3.2.2 Test Case

A test case is run in numerical order from 1.1 PLD (as above), down to 4.1 PLD, by combining Figure 3.2 with Figure 3.3 below. In the test run, each component of the PLANET models (PLD, PS, PM) are run in their forecasting modes at each step, that is, the mode-choice and generation is switched on in PLD throughout the run, and elasticity is switched on in both PS and PM throughout the run.



1.2 LHR

Surface access costs

1.4 PM

Long distance pre‐loads

1.3 PS

Long distance matrix

Surface access trips

West Midlands pre‐loads

South East Pre‐loads

2.2 LHR


2.4 PM


2.3 PS





2.1 PLD

1.1 PLD

3.1 PLD

Figure 3.2 - Base Case Run

3.2 LHR


3.4 PM


3.3 PS





4.1 PLD

3.1 PLD

Figure 3.3 - Test Run Extension



4. PLANET Long Distance Model This section introduces the previously existing PLANET Strategic Model (PSM) in section 4.1. The key features of that model are discussed in section 4.2, followed by a description of the key model elements in section 4.3. Section 4.4 summarises the development required for the PLANET Long Distance Model (PLD) that replaces PSM in the Model Framework for HS2.

Further details of PLD are to be found in sections 5, 6, 7 and 8 for the Rail model, the Highway model, the Air model and the Mode Choice model respectively.

4.1 Introduction to the PLANET Strategic Model PSM is a strategic passenger transport forecasting tool. It was developed in 2002 for the then Strategic Rail Authority (SRA) to forecast the behaviour of long-distance person trips (of 50km or greater) within Britain at a strategic level.

The geographical coverage of the model is strictly the mainland of Britain. The model specifically does not cover journeys to, from or within any islands or territories in the vicinity of Britain, such as Northern Ireland, the Isle of Man, the Channel Islands, the Hebrides, Orkney or Shetland.

It was intended to be used by the SRA to evaluate the economic and financial impacts of implementing major strategic rail schemes, where the existing PLANET North and South models have been generally considered unsuitable. The SRA intended that the model would be used initially by the separate SRA High Speed Line (HSL) study and to assess potential upgrade schemes for the East Coast Main Line (ECML).

PSM was developed in order to forecast the impact of improvements to the rail network on strategic passenger rail movements within the UK. For the purposes of that project, strategic (passenger rail) flows were defined as passenger rail movements between major traffic generators (including large cities, airports and international rail termini) of over around 100 km in distance.

It is important to note that strategic flows are not necessarily the largest rail flows on the network – for example, commuter lines into London carry far more passengers than the former inter-city network. In addition, it should be recognised that some major flows could be modelled using the regional PLANET South or Midlands models with potentially greater accuracy (e.g. London to Southampton / Bournemouth lies wholly within the PLANET South area and has a significant peak commuting element).

The model covers travel by rail, car (highway) and air. Local buses, long distance coaches and ferries are excluded from the model.

PSM uses demand forecasts for future years which are externally specified and input to the model process, and which incorporate assumptions such as the level of exogenous growth in demand for travel. The demand is geographically divided into 235 zones, corresponding with individual or aggregated local authority districts.

4.2 Key Features of PLANET Strategic Model PSM was designed with the following features:

Full mode choice between car, rail and air for strategic flows across mainland Britain, on the basis of incremental changes in the generalised costs of travel for each option;

Strategic rail route choice where reasonable route choices exist between different strategic routes;

Different demand responses based on travel purposes of commuting, business and other (leisure) markets, each further divided by car availability;



Response to congestion on both highway and rail modes; highway congestion fully interacts with other modes, in that mode shift away from highways results in marginally shorter journey times by highway, which can feed into shorter car access times to stations for car available trips; and

Response to changes in fare levels, although road tolls are not included.

The model provides a range of outputs to inform scheme development and decision-making, including:

Statistics on passenger flows by air, rail and car – such as number of people, travel time and distance travelled;

Passenger flows on the strategic routes;

Levels of road traffic congestion and train passenger crowding anticipated;

Fare revenue by operator group; and

Economic benefits1

4.3 Key Elements of Existing PSM

4.3.1 Constituent Models

PSM includes three models representing travel by rail, air and highway. All modes share the same network scenario, to allow interaction of different modes, such as highway speeds impacting upon air access highway trips and car-available rail access trips.

For all demand data, it should be noted that PLD is independent of the production of demand data, and can be populated by whatever data source is deemed suitable at the time of update.

PSM forecasts strategic trips on the rail, highway and air networks, as shown in Figure 4.1 below.

Rail Highway Air

Figure 4.1 - PSM Network Coverage

1 As a result of the use of PSM, it is possible to assess the economic worth of the proposed intervention, by comparison of travel times and costs between the two scenarios, and allowing for the operating and capital costs involved; this uses conventional Cost Benefit Analysis and Discounted Cash Flow techniques.



4.3.2 Model Operation

PSM comprises a series of individual, although interlinked sub-models. These are designed to operate in a particular sequence which is illustrated in Figure 4.2 below.

Highway Assignment

•Business, Leisure, Commuting

Rail Assignment


Air Assignment

•Business, Leisure

Skims•Base Skims

Highway Assignment


Rail Assignment


Air Assignment

•Business, Leisure

Skims•Test Skims

Mode Choice

•Recalculate Mode Shares

Base Run Test Run

Figure 4.2 - PSM Model Elements and Sequence

The figure shows the operation of the base and test runs of PSM. In both cases, highway, rail and air assignments are run, producing skims respectively. When the mode choice model is run, new modal matrices are produced, and these are re-assigned.

The iterative nature of this sequence is designed to facilitate model convergence. Whilst each mode achieves internal convergence, it should be borne in mind that the overall model may not converge, if there is significant imbalance between demand and supply. It is important that the model is inspected to ensure a satisfactory level of convergence has been achieved at the demand response level in particular.

4.3.3 Rail Model

The rail model represents 'strategic' corridors, such as the main trunk lines across Britain (such as the West Coast Main Line, East Coast Main Line, Midland Main Line, Great Western Main Line and cross-country and trans-Pennine routes). The model does not cover local commuter rail lines, unless they are part of a strategic corridor2. Key features of the rail model include:

Strategic route choice for rail trips across mainland Britain;

Crowding is modelled in terms of a perceived increases in journey time;

Representation of different behaviour by journey purpose and car availability (in terms of response to crowding and willingness to pay fares);

Station access, depending on car availability for that leg of the journey;

The all day service frequency and stopping patterns of trains;

Representations of wait time and interchange time;

Shadow services were incorporated to absorb local trips in the demand matrices;

Fares were applied at the network level (in units of p/km of the passenger’s journey);

Station choice within major cities; and

Demand data by purpose (business, leisure and commuting) and car availability.

2 For instance, the majority of the London, Birmingham, Manchester or Glasgow rail commuter networks are excluded from the model, but those services sharing tracks with the various strategic routes would be included.



Some of these features had been found to not be ideal, and were the subject of revision in the development of the replacement PLD model: see Section 4.4.2 below.

PSM rail demand was originally constructed from the DfT's National Rail Passenger Matrices, built from CAPRI data (from a period prior to the Hatfield disruption in October 2000, to ensure the disruption was not included in the matrices). The demand was segmented into business, leisure and commuting purposes, with a further split car availability.

For the update to PLD, the rail industry's LENNON data for 2007/08 was used, (LENNON replaced CAPRI in 2003).

The LENNON data was collected at station to station level, and distributed to ultimate origins and destination zone using National Rail Travel Survey data. The production of the current PLD rail matrices is discussed in detail in Section 5.2.

4.3.4 Highway Model

The highway model represents the UK strategic road network, with notional access links from model zones to the highway network. Generally, the model includes motorways and the primary route network, with infill in certain areas where the primary route network is sparse. Key features of the highway model include:

Strategic route choice for car trips across mainland Britain;

All day representation of demand, converted to hourly demand to be compatible with hourly speed / flow relationships, based upon COBA (The Cost Benefit Analysis program developed by DfT/HA for road schemes) data;

Strategic demand only, supplemented by local pre-loads;

No representation of junction delays;

Three trip purposes (for strategic trips only), plus a single pre-load for local trips

The highway network is also used for air / rail access trips; and

Demand data from multimodal studies (but updated), as explained below:

PSM highway demand was originally built by converting demand data from the then currently available multimodal models into the PSM zone system, and combining the data in such a way as to remove multiple observations of the same trips. Short distance trips were removed to avoid the strategic-only highway network from being swamped by local traffic. Data was segmented into business, leisure and commuting segments, either by combining more numerous segments (in all but one case) or by estimating a purpose split by trip length.

Vehicle occupancy by purpose was accounted for when converting from vehicles to person trips.

Pre-loads were attached to links to adequately reflect local traffic, to allow the strategic trips to adequately react to background loading levels. These pre-loads are calculated to be the difference between count data and the assigned flows from the demand matrices. The pre-loads solution is a good substitute for a fully national highway model.

It is important to note that local road congestion is not included in the model (as the nodes and links and detailed zone structure do not exist), though this is not an issue as the model is interested in change in journey times on the strategic leg of the highway journeys. Of the included strategic roads, junction delays are not modelled due to the relatively low proportion of delay attributable to junctions for long distance trips.

Since the mode choice model is incremental, the main function of the highway network is to provide robust strategic journey times. In many ways the absence of local networks, with local trips and the associated junction delays, enhances the stability of the model.

4.3.5 Air model

The air model covers most domestic air services in mainland Britain. Key features of the air model are:



Demand data for domestic UK, non-interlining trips.

Two journey purposes (business and leisure)

Representation of fare, service frequency, wait and journey time;

No crowding apart from congestion on road network used for access and egress; and

Car access and egress assumed to and from airports. The choice of airport is partly related to the air service characteristics, such as price and frequency, but also to the access and egress.

Congestion is not modelled on air routes, as numbers are strictly limited to seating capacity. This is not a problem in practice, as air services are more able to respond to demand with pricing mechanisms in the short term, and re-allocation of aircraft and routes in the medium term.

Air passengers have car access at both ends of their air trip. This is consistent with airports outside London (and Heathrow in particular), where public transport access is often poor, and the strategic network represents very few actual airport rail links. In addition, air passengers tend to have a higher value of time and are more likely to use taxi if a car is not available for that leg.

Interliners are not covered by the model, as they add no benefit to the model, and are unlikely to be persuaded to switch to high speed rail due to the inconvenience involved of baggage and transfers.

Trips to Heathrow are handled separately by the Heathrow air access spreadsheet model. This uses the changes in access cost to modify the access mode shares to Heathrow.

Air fares were originally modelled by converting economy and business fares into minutes for leisure and business trip purposes. These were updated to average fare paid in 2008 by route, from CAA (Civil Aviation Authority) data.

4.3.6 Mode Choice Model

PSM uses an incremental mode choice model based on calculations of generalised costs from each of the road, rail and air models.

Within PSM, these overall generalised costs are used to feed the mode choice model which uses the difference in generalised costs (between the do minimum and the option being tested) to produce a new set of mode shares. This process also estimates induced (endogenous) demand growth, driven by the composite cost of travel for all modes. This permits generated trips to occur as a result of a step-change in transport services.

4.3.7 High Speed Rail

The original application of the PSM model (for the HSL Study) included specific representation of high speed rail as an alternative mode to conventional rail. The mode was similar to conventional rail, but also had the following features:

An ability to use high speed rail services coded into the rail model (which are not available to conventional rail passengers);

Premium fares, although these were also linear (i.e. p/km);

A new “sub-mode” choice introduced between conventional rail and high speed rail.

High speed rail was specified as a separate mode to ensure that access to the high speed line is as decided by the mode choice model, rather than by assignment. This allows generalised journey times to be extracted reliably both via and not via the high speed line. In addition, this enhances model stability. However, it does tend to rely on the modal choice mechanism and the mode constant adopted; where there is little material difference between the two rail offers (high speed and classic), other than journey time, it can lead to optimistic levels of demand (the issue of mode choice is further discussed in section 4.4.2 below).



4.4 PSM Update to PLD

4.4.1 Functionality not provided by PSM

PSM was not designed to undertake more detailed analytical tasks, such as:

Specific timetabling tests, as PSM works on the basis of service frequencies. As such, it has no knowledge of 'when' during the day the trains run, but is only concerned with numbers of trains per day. It follows that specific connection times between different trains cannot be tested, as they are not treated explicitly by the model. Instead, the model uses a wait time based upon the frequency of the connecting services, together with a boarding penalty;

Assessment of detailed rail fares by route, as fares are only used as part of the generalised cost of the rail mode as part of the mode choice element; and

Assessing the impact of small highway schemes, as any effect is only likely to be observed by the model if it changes any generalised costs of highway trips significantly.

However, this functionality was not viewed as a priority for assessing high speed rail options at this stage of development – although these may require further development to support future detailed scheme development.

4.4.2 Updates required for PLD Model

Other areas that meant that updates were required to PSM included:

Detailed tests of local or peak-only rail services, as the zone system and network coverage are too sparse for this purpose, while the single time period of PSM cannot distinguish the time of day of services – therefore links were developed with local models;

The station choice model was not designed to look at detailed locations in London or Birmingham; therefore links to Railplan and PRISM were developed. PSM did include basic station choice within major cities (London, Birmingham, Manchester, Bradford, Bristol) – this functionality has been removed and replaced by updated station choice in the London and Birmingham areas only; and

Demand data dated from 2001/02, and thus required updating;

Rail and air services, road networks all required updating to the base year of 2007/08;

The representation of high speed rail within the mode choice model required review for suitability. Subsequent independent audits of PSM have raised questions over whether the behavioural parameters at the time were affected by the Hatfield disaster and subsequent collapse in performance of the rail network, hence over-stating the relative attractiveness of high speed rail. Therefore, alternative mode choice approaches were followed;

Fares in PSM were included on a matrix basis instead of linear (p/km) basis, and were updated to 2007/08 levels. This change was made for several reasons: fares are not linear in Britain (tending to become cheaper per kilometre for longer trips) - this makes linear fares impossible to represent for all stop to stop pairs. Also, yield management of rail fares in recent years means the range of fares paid within and between different Train Operating Companies (TOCs) is growing wider. It was found to be more robust to take matrices of average fares paid for different trip purposes, and not to attempt to model minor changes in fares on competing TOCS. Fares are derived for different journey purposes, by mapping ticket types to journey purposes, see 5.3 in Section 5.

Representation of Heathrow access demand, see Section 11 for details of the LHR spreadsheet model.

These are further discussed below where appropriate.



Highway Demand

For the update to PLD, new demand data was sourced from PRISM (a detailed multi-modal model for the West Midlands) and from NoTAM (the North Thames Highway Assignment Model developed for the Highways Agency) and incorporated. The demand was then rebased to 2008 by balancing the matrices to new totals derived using factors from TEMPRO (the national trip-end model and presentation tool provided by DfT). The pre-loads were updated to represent 2008, and replaced with TRADS (the Traffic and Accidents Database maintained by the Highways Agency) data for the London to Birmingham corridor. The production of the current PLD highway matrices is discussed in detail in Section 6.3.

Mode Choice and Premium Fares

One of the important policy questions for development of high speed rail options was the relative benefits of introducing premium fares for high speed rail services, so that passengers benefitting from faster journeys contribute more towards the construction of the line. While the central case for HS2 assumes no premium fare, some tests are required to understand the impact of premium fares on demand, revenue and economic benefits.

Premium fares for the high speed services are implemented at the matrix level as inputs to the logit mode choice process. This is in preference to modelling fares (and especially premium fares) by assignment, which can run the risk of very 'lumpy' assignments to the high speed (such as 0% or 100%) depending upon the level of fare and difference in journey times. The logit model trades off fares and journey times in much more detail than can be achieved through assignment processes.

Since the mode choice model is incremental, it pivots around the base mode shares. Shares for the high speed mode, as a new mode, have to be forecast by an absolute process. The following options were investigated:

A new mode with mode constants;

A new mode using the Distributed Value of Time (DVoT) approach (which uses different values of time for proportions of the population, and hence has a better representation of people’s responses) requiring no mode constants; or

Modelling high speed as the same mode as conventional rail with an assignment bias.

Each of these approaches has strengths and weaknesses, as summarised in Table 4.1 below.

Table 4.1 - Treatment of Premium Fares

Approach Advantages Disadvantages

New Mode (“h”) with Mode Constants

Already applied in previous PSM – consistent and low risk

Mode constants too high, therefore questions over validity of approach

New Mode (“h”) with DVoT

Well suited to testing premium fares

Still very new approach – more risk in implementation

Less suited where no premium is applied

No New Mode No need for mode constant Analysis of high speed passenger behaviour less transparent

It was concluded that “DVoT” was preferred for premium fares, while “No New Mode” was preferred where no premium fare was to be assumed.

However, to retain a level of flexibility, different versions of the model were developed to cover each of the three approaches and to enable them to be tested and applied.

Section 8 explains these issues in more detail.



Miscellaneous Updates

Finally, the opportunity was taken to improve the overall operation of model by:

Volume-averaging in the assignments;

Updates to Values of Time; and

More general improvements to reduce model run-times.

Also, the development of output macros was undertaken, to automate the production of:

A standard output Excel workbook;

Economics outputs and an analytical spreadsheet; and

Mode choice diagnostics spreadsheets.



5. PLANET Long Distance Rail Model 5.1 Overview

This section explains the PLD rail model, and how it was updated from its origins in the PLANET Strategic Model (PSM). The section will be set out in terms of the updates to the constituent parts of the PSM rail model as follows:

Demand Matrices: the demand by each trip purpose, mode, origin and destination;

Rail Fares: Containing the fares by each trip purpose, origin and destination;

Supply: the highway and rail links, the train and air services;

Assignment: the processes and parameters by which demand is loaded onto the network;

Station Choice: the additional model to improve the split between alternative stations in London and Birmingham;

Interfaces with other models: the ability for PLD to transfer information at an improved level of accuracy with other models.

The overall requirement of this model is to be able to test options for high speed rail services. This requires the supply and demand to be at a level compatible with each other, while also compatible with strategic movements: zones need to be small enough to distinguish trip origins and destination, while not be so small as to cause matrices to become too sparse.

The model is designed to look at strategic trips of greater than 100km, particularly between London and Birmingham. As such, PLD is used to look at the London-Birmingham 'strategic' part of the trip at the all-day level, while PS and PM look at the local ends of the trips during the AM peaks. To combine these models, it was essential not to introduce double counting, so trips are effectively modelled in one model only. For this to occur, the 'best' parts of each model were combined: PS and PM are 'best' in terms of modelling the crowding and route-choice interactions in the south east and West Midlands respectively, while PLD concentrates upon the wider trade-off between different long distance routes and modes at the all day level.

The development of the model has investigated different ways to handle allocation of demand to high speed rail services. This can be done either by assignment (assuming high speed services are the same mode as conventional rail), or by feeding all rail demand into a sub-mode choice model within rail (assuming high speed and conventional services are different modes) thus allowing only those trips who make the trade off between time and cost to use the high speed line. In either case, it is important to point out high speed matrices are not produced at the modal level, but are an output of the PLD model. This will be discussed further in section 8 below.

5.2 Demand Matrices

5.2.1 Overview

This section describes the methodology used to create the 2007/8 base PLD matrices.

To update the rail demand, station-to-station LENNON data was distributed between zones of ultimate origin and destination using the postal sector information reported in the 2004-5 National Rail Travel Survey (NRTS). The latter survey also allowed transformation of the demand data from LENNON ticket types to the journey purpose segmentation used in scheme appraisal. This matrix development is described further in Section 5.2.2 below.

When using a mode-choice model, an important factor in modelling the trade-off between different modes (rail, air or car) is the journey purpose. This is because a passenger's value of time will be significantly higher for a business trip than a leisure or commuting trip, while the amount of crowding a passenger will tolerate is higher for a commuter than a business or leisure traveller. Of



all the purposes, the leisure traveller is likely to be most able to schedule the trip to avoid the most congested times, or to take advantage of cheaper advance purchase tickets.

PLD is concerned with strategic movements greater than 100km between major centres, The zone system is designed such that strategic trips have a reasonable geographical disaggregation, however, many local trips are intrazonal as a result.

PLD is geographically disaggregated into 235 zones, as shown in Figure 5.1 below. These zones are 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.

Figure 5.1 - PLD 235 Zone System



PLD demand is segmented into three journey purposes:

Business;

Commuting (not applicable for Air passengers); or

Leisure / Other.

For rail demand, these are further subdivided by mode and each split into three further classes by car availability:

Car Available - From (CAF);

Car Available - To (CAT);

Non Car Available (NCA);

This makes nine demand and assignment classes in PLD in total, though NCA business demand is combined with CA business demand (this sub class is very small as most business passengers have either a car available, or choose to utilise a taxi).

The car availability split is used to differentiate between different access to rail (Park and Ride or Public Transport), while preventing car use at both ends of a trip.

5.2.2 Rail Demand

This section summarises the processes undertaken to develop the updated rail demand, which involved the following:

Processing LENNON data;

Grouping into ticket categories:

Distributing trips to ultimate origins and destinations (PLD zones);

Deannualisation of LENNON for modelling of typical weekday demand; and

Conversion from ticket category to journey purpose.

Processing LENNON data

PLD’s rail matrices represent origin-to-destination travel on a typical weekday. The production to attraction LENNON/NRTS matrices described below are transposed before input to PLD in order to account for return journey legs. High speed trips are not defined explicitly, but are an output from the modelling process.

The PLD demand matrices built upon the 2007/8 LENNON station-to-station data prepared in early 2009 for PLANET Midlands (PM) and PLANET North.

The latter dataset uses the National Rail Travel Survey (NRTS) to:

(a) Divide travel to/from joint stations (e.g. ‘Birmingham stations’) between individual stations (e.g. New Street, Snow Hill, Moor St) and

(b) Distribute tickets with PTE zonal destinations between individual destination stations.

Given the strategic nature of PLD’s modelling - with matrices based on district to district flows - neither (a) nor (b) is particularly important. However, the LENNON data prepared for PM were already grouped into suitable ticket categories, as described in the following section.

Grouping into ticket categories

The grouping of ticket categories for PLD and PLANET Midlands (PM) reflected work on PLANET South (PS) in 2004-5. As PM and PS are part of the HS2 Framework, it is important that the ticket groupings in the PLD matrices are consistent.

The ticket categories are shown below with mapping to Full-Reduced-Season shown in square brackets.



Long distance Reduced ticketing [= Reduced]

Full fare return [= Full]

Full fare single [= Full]

Cheap Day Return (CDR) [= Reduced]

Point-to-point season [= Season]

Period/season PTE/TfL Travelcard [= Season]

Off-Peak PTE/TfL Travelcard [= Reduced]

All-day TfL Travelcard [= Full]

Advance purchase ticketing is subsumed within the first category.

Later in the processing, Full Fare returns are sub-divided at a flow distance threshold of 80 miles. This reflects differences in NRTS journey purpose (e.g. less commuting at longer distances), and the share of annual travel on a typical weekday. (There is negligible use of Full fare returns on shorter flows at weekends.)

Distributing trips to ultimate origins and destinations (PLD zones)

This section describes how availability of the 2004-5 National Rail Travel Survey (NRTS) has been used to convert LENNON station-to-station data to PLD zone-zone matrices.

During development of PM (see below), DfT agreed that NRTS data could be released with an additional character of postcode data, without violating the Data Protection Act. This allowed ultimate origins and destinations to be identified with granularity at postal sector level.

Figure 5.2 below illustrates how postal sectors differ in size. Typically, an increase in area is associated with lower population density. In the thematic map, the West Midlands postal sectors shaded yellow are those with the highest rail trip-rates for originating leisure travel to the wider south-east (i.e. 25+ trips per weekday in 2004/5 NRTS).

Figure 5.2 - Postal Sectors in the West Midlands [NRTS leisure trip-rates]

PLD zones - which are local authority districts or groups of districts - are considerably larger than postal sectors. GIS methods were used to assign each postal sector to its appropriate



PLD zone. For example, Solihull (PLD zone 181) comprises the following 23 postal sectors (Table 5.1):

Table 5.1 - Postal Sectors in Solihull

B36 0 B90 1 B91 2 B92 9

B36 9 B90 2 B91 3 B93 0

B37 5 B90 3 B91 9 B93 8

B37 6 B90 4 B92 0 B93 9

B37 7 B90 8 B92 7 CV7 7

B40 1 B91 1 B92 8

Within the NRTS data, the top 15 districts producing originating rail trips for each station were identified. For all but the largest stations, this threshold accounted for all originating journeys (see Figure 5.3 below). A lower threshold of 10 was applied to outward egress to the ultimate destination, as distances tend to be shorter with use of the household car precluded.

During the production of the matrices, an issue with NRTS data was brought to HS2’s attention. A small minority of observations to/from London have ultimate origins and destinations transposed, such that an out-and-back rail trip from Manchester Piccadilly to Euston might be shown as produced in Westminster and attracted to Salford. To remove such cases from the analysis of access and egress zonal distribution, a distance cut-off of 80 miles was imposed. This value was chosen as it prevents transposition of districts/zones in the key West Midlands to London market.

With the latter observations removed, Figure 5.3 shows the distribution across stations in the number of districts reported within NRTS as ultimate origins and destinations. Almost all stations appear at least once in the NRTS in each of the 4 market segments, allowing all demand to be channelled to/from the associated district. (Absent stations will tend be the least important in terms of passenger volumes.)

Over 1,500 stations are accessed from least 2 districts by car-owning passengers, compared to a figure of around 750 for non-car available access. At the access threshold, 109 (mostly major) stations are accessed by car owners from at least 15 districts, falling to 31 stations for non-car owning households.

At Euston, the top 15 districts account for 80% and 86% of car available and non-car available access respectively3. At Birmingham New Street the corresponding figures are 97% and 96%, whilst at Manchester Piccadilly there is no residual access, with all demand caught by the top 15 districts. For Euston egress, the top 10 zones account for 92% and 85% of trip attraction amongst car available and non-car available rail travellers respectively. At New Street and Piccadilly, the largest residual is just 2.4% - found amongst non-car owners egressing from the Manchester station.

3 The 15th ranked district is Ealing, with a car available market share of just 1.2%.



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Figure 5.3 - Concentration of districts providing journeys to/from particular stations

Using SPSS software, the car available and non-car available access and egress demand shares, and their associated districts, were merged onto the origin (access) and destination (egress) stations in the 2007/8 LENNON dataset. This allowed distribution of station to station demand between districts of ultimate origin and destination.

The LENNON data were divided between car available and non-car available journeys on the basis of NRTS data aggregated for each origin (i.e. rail trip producing) station. Further disaggregation of car availability (e.g. by journey purpose, ticket type, or destination) was rejected in order to maximise sample sizes.

Figure 5.4 below shows the distribution of car availability rates for outward journeys across NRTS stations. The large spike above 1.0 reflects the fact that at smaller stations with limited NRTS samples, all respondents report ownership of a household car.

Car availablity rates by station (NRTS, 100%=1)

0

50

100

150

200

250

300

350

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Figure 5.4 - Percentage of car available trips by originating station (NRTS)



Station-to-station journeys within a given market segment (i.e. ticket category plus car availability) were then divided between the 150 (15 access * 10 egress) combinations of trip-producing and trip-attracting districts. Finally, conversion of districts to the 235 PLD zones allowed SPSS to be used to aggregate journeys across zone-to-zone pairings4.

An early/pre-validation sense-check of the PLD LENNON data was provided by aggregating districts to regions, and comparing against the 2006/7 regional demand flows shown in ORR’s National Rail Trends (2007/8 edition, Section 7). This revealed a significant shortfall in journeys from the North-West to London. Further investigation revealed that processing of the PM LENNON data had removed southbound trips having ‘Manchester stns’ (joint code) recorded as origin together with ‘London Terminals’ (joint code) as destination. Although this is a trivial issue for a model focussed on commuting into Birmingham (PM), it required an additional stage of processing to correct the PLD matrices.

Deannualisation of LENNON for modelling of typical weekday demand

PLD estimates origin to destination travel on a typical weekday, summed across outward and return journey legs. PM and PS further restrict their modelled travel period to the AM weekday peak, dominated by the outward leg of return journeys (from the point of trip production to the point of attraction).

With modelling undertaken for travel choices/patterns within a single weekday or AM peak, the production of PLANET demand matrices requires the 2007/8 LENNON journeys database to be ‘deannualised’. That is, a methodology is needed to remove travel at weekends, and, in the case of PM and PS, to estimate the proportion of weekday trips occurring in the AM peak. PS deannualisation is undertaken within an SQL server with the factors dependent on flow distance and based on ORCATS (Operating Revenue Computer AllocaTion System, a data source maintained for ATOC as part of the work of Rail Settlement Plan) assumptions for Season tickets, or a bespoke (weeklong) LENNON download undertaken as part of SDG’s update work in 2004/5.

As ORCATS factors are aging (and thought for example to underestimate travel at weekends), and as SDG’s deannualisation factors for the PS area may be ill-suited to PM and PLD, it was decided to deannualise LENNON using up-to-date assumptions for the incidence of weekend travel by ticket category.

For Full-fare returns, deannualisation requires a distance dimension. On the shortest flows, where first class is typically unavailable, no full fare returns will be purchased at the weekend. On longer distance flows, first class ticketing may be purchased at weekends to take advantage of the associated exclusiveness and greater comfort. Moreover, in the case of travel to London, full fares may be bought to avoid evening restrictions on the use of Savers and CDRs (now both ‘off-peak’) affecting the return journey leg. Finally, full-fare returns are valid for a month on longer O-D flows, but only on the day of issue where Savers are unavailable. This means that some long distance returns will be bought at the weekend to allow the return leg to be undertaken during the Monday peak (when Savers are invalid).

4 Analysis proceeded as far as possible using districts rather than PLD zones because of the possibility that PLD zoning might be reviewed for some reason.



The details of the deannualisation used to estimate weekday one-way travel from producer zone to attractor zone are shown below in Table 5.2.

Table 5.2 - Treatment of Ticket Types in LENNON

Ticket Type Processing and Assumption

Long distance Reduced returns (mainly Savers plus Advance Purchase)

Assume 35% of journeys are at weekends / Bank Holidays.

Divide by 2 for outward journeys. (With ‘single leg pricing’ for advance purchase journeys, we assume that half of all journeys are produced at each end of a bilateral flow.)

Full returns – flows under 80 miles Assume 245 working days per annum.

Divide by 2 for outward journeys

Full returns – flows over 80 miles Assume 245 working days per annum.


Singles Assume 30% of journeys at weekends / Bank Holidays.

Seasons Assume 245 working days per annum.


Assume 94% of journeys are during the week.

Cheap Day Returns and PTE day Travelcards (TC)

Assume 35% of CDR journeys and 50% of day TC journeys are at weekends / Bank Holidays.


Conversion from ticket category to journey purpose

A specific analysis of NRTS data was undertaken to quantify the relationship between ticket type and journey purpose. The results of that analysis are summarised in Table 5.3 below. This correspondence was applied to convert from the nine ticket categories to journey purpose. Subsequently, all commuting reported in NRTS on flows of over 80 miles was converted to leisure travel as it is unlikely that respondents/passengers make such journeys on a daily basis, and many may be ‘working away from home’. For the vast majority of these trips, classification as “leisure” rather than “commuting” is far more likely to reflect behavioural attitudes towards travel over such travel distances.

Table 5.3 - Journey purpose splits by ticket category

Ticket grouping Business Leisure Commute

Cheap Day Return 15.9% 46.3% 37.8%

Off-peak Travelcard 20.0% 53.7% 26.3%

Travelcard season 6.2% 12.9% 80.9%

Point-Point season 3.8% 4.8% 91.4%

Other Reduced return 13.8% 40.0% 46.2%

Long distance Reduced return 25.5% 50.2% 24.3%

Full fare return 14.8% 21.7% 63.5%

Long distance Full fare return 48.2% 22.8% 29.0%



Single 15.4% 38.7% 45.8%

The final output of the HS2 LENNON-NRTS analysis was 6 PLD input matrices representing outward travel on a 2007/8 weekday from the zone of trip production to the trip attracting zone. As PLD is an all-day matrix, the P-A matrix was transposed in EMME to produce an A-P matrix for return travel5.

5.2.3 Strengths and weaknesses of Rail Demand Data

Strengths

The purpose splits allow the analysis of a reasonably distinct set of travel behaviours (business, leisure and commuting). This is of paramount importance in order to understand the behaviour of different types of travellers. The three purposes provide a reasonable segmentation at the all-day level; and

The car availability allows a reasonable, if synthetic, split between 'car available from' and 'car available to'. This prevents a passenger, with a car available, from using that car for both access to, and egress from, a rail journey.

Weaknesses

The zoning system can still allow large numbers of local trips, such as Bolton to Manchester. This could potentially swamp the strategic trips in the matrices with local trips, though this is mitigated by the inclusion of local services, which has to be done on an ad-hoc basis. In PLD, this is mitigated by removal of the major commuting demand associated with the West Midlands and South East, where use is made of the PS and PM models instead.

5.2.4 Modification for use in PLD

To enable their use within PLD in conjunction with PS and PM, the PLD matrices have 'holes' cut in them to avoid overlap with PLD, in the PLANET Midlands and South areas, as in Figure 5.5 below:

5 This approach suggests that journeys on full-fare Single tickets should also have been divided by two during deannualisation. However, as PLD is a long distance model where use of Full-Fare singles is relatively rare, this inconsistency is not thought to introduce significant bias to the matrices, and validation tends to confirm this view.



Figure 5.5 - PLD Matrix 'holes'

The outcome of this is internal-internal trips in the West Midlands and South East are removed. Such trips are modelled in the PLANET Midlands and PLANET South models respectively, and are not double-counted in PLD.

5.3 Rail Fare Matrices

5.3.1 Representation of Rail Fares

The representation of rail fares has been amended for PLD.

The original implementation in PSM incorporated fares on a linear basis (p/km travelled), which was included as additional equivalent minutes in the assignment. This was found to cause instability in the assignment process, due to the dominance of fare “minutes” over actual travel time and perceived crowding minutes. In general terms, the fare element of urban trips is very small when converted to minutes per kilometre, potentially having no impact on route choice. Long-distance trips however, are in danger of being dominated to an unrealistic extent by fares converted into minutes per kilometre.

The original fares by assignment was intended to allow a certain amount of trade-off between competing TOCs on competing routes (such as London to Glasgow via the east and west coast routes). In practice however, the presence of fares in the network often resulted in lumpy assignments: where fares and time were approximately equal, the shares were apportioned by headway. Where they differed beyond a reasonable amount, all passengers, for that origin – destination and journey purpose, would switch to the cheaper combined generalised cost route.



The alternative approach is to treat fares on an origin – destination, or matrix, basis.

The strengths and weaknesses of the two approaches are summarised in Table 5.4 below.

Table 5.4 - Approaches to Rail Fare Representation

Approach Advantage Disadvantage

OD Matrix Basis The fares in the matrix are a good overall representation of fares paid by trip purpose and origin destination pair. This is particularly important given the growing range between advanced purchase and walk-on fares due to increased use of yield-management. The prediction of the fares paid would be otherwise very difficult

There is no way of distinguishing routing restrictions, such as single operator tickets, once the fare is attached to the origin destination pair.

Linear (p/km) Basis For extreme fare differentials, route-choice can be influenced (though this becomes very lumpy)

The assumption of linear fares is less appropriate for long distance trips - fares tend to become cheaper per kilometre over longer distances. In many cases, trips such as London to Newcastle may have the same fare as London to Edinburgh.

On balance, the change to handling fares at the matrix level is the more appropriate thing to do, as the purpose of the model is to forecast the trade-off between rail and competing modes, rather than within the rail mode.

As a result of this, fares are now removed from the assignment. Fares are used within the mode-choice model, in section 8 below.

5.3.2 Rail Fare Matrix Production

Rail fares are based on EDGE6 outputs for revenue and journeys, using a simple average yield calculation (revenue / journeys).

It might be noted that for the business segment in particular, an increase in distance may not be associated with an increase in yield. For example, use of Full fares from Glasgow to London is extremely limited, due to the fact that the Standard Class Saver product was, until recently, unrestricted for Anglo-Scottish travel.

A specific exception to this approach of fare production is that of Heathrow. Production of these fares is discussed in section 11 below.

5.4 Rail Network Overview

The PLD rail network represents the strategic rail corridors in mainland Britain, as shown in Figure 5.6 below.

6 EDGE (Exogenous Demand Growth Estimator) is envisaged as the replacement for RIFF (Rail Industry Forecasting Framework). EDGE provides PDFH compliant forecasts of rail journeys and revenue.



Figure 5.6 - PLD Rail Network

The network is populated by two main groups of services: Long distance services on the strategic routes, and local services which share the same links. The local services are in place to prevent the remaining non-intrazonal local trips from swamping the strategic services. This is largely successful, though required some adjustments for particular local “hot spots”, as described in Section 5.4.2 below.

The main focus of the network is to allow reasonable strategic route choice to occur, while being as consistent as possible with the matrices produced for the model, in that strategic trips are possible without being adversely affected by local trips. It is likely that any more detail in the network (such as more local services on non-strategic routes) would be spurious, as the zone system is too sparse to support it. This would result in the appearance of re-assignment, though this would not be backed up by improved representation of origins or destinations. For this reason, the geographical coverage in terms of zones and networks is carried over from PSM.

5.4.1 Rail Network Updates

Nodes in PLD were renumbered from those used in PSM to be consistent with the corresponding numbers in the PS and PM models. This includes copying directionally split nodes such as at Finsbury Park in PS. This was to allow the transfer of data between different models, while retaining compatibility with previously coded PS services.

A check for the currency of the networks was undertaken. New stations and rail links were added where necessary.

Stations

The following two stations were added to PLD, to reflect recent changes in the network and also to refine the network representation:

Kettering; and



Market Harborough.

Rail Links

New links were added to PLD to allow new services to be added to reflect current operations. These were:

Wigan to Salford Crescent;

Stratford to Tottenham Hale;

Kettering to Oakham;

5.4.2 Rail Service Updates

The national rail services were replaced with the December 2007-May 2008 timetable, with Wednesday 13th February 2008 taken as the nominal date. A Network Rail CIF7 file was provided by the DfT to give service definitions, such as journey time and stopping pattern. An accompanying file was provided with stock type, together with the seated and standing capacity, for each train operated.

Methodology

A Perl8 software script was developed to extract data from the CIF file and convert into EMME format in the relevant node numbering system. The approach was as follows:

Extract subset of ‘relevant’ trains from CIF;

For ‘relevant’ trains extract the subset of ‘relevant’ nodes required;

Look up ‘relevant’ nodes dependent on direction and TOC;

Calculate the journey time between ‘relevant’ nodes;

Aggregation of identical lines;

Allocate a PLANET service code to each line;

Allocate an appropriate Vehicle Type;

Export in EMME format; and

Import to EMME and interpolate stop to stop times.

This allowed a consistent and automated approach.

Vehicle Type and Capacities

In general, rail vehicles are given vehicle types of 888, which is a generic vehicle type. The seated and total capacities are held within the ut1 and ut2 fields in each transit line or service, and hence the train capacity is service specific. These values are used in crowding calculations.

Train capacity is fundamentally important when working with crowded assignments. This is to ensure that load factors, as the main building block of a crowding function, are calculated correctly. In principle, seated and total data is available for every rail service (as extracted from the DfT's Network Modelling Framework), though this does not mean every train service is included. This is for several reasons, mainly that as a frequency-based all-day model, PLD does not model every train throughout the day individually, as there is no concept of 'when' during the day the trains run. This is also true of the demand, which represents the entire day, thus ensuring that the network and demand data are consistent. This makes it impossible to differentiate between peak and off-peak trains which may differ in terms of train length (such as 4, 8 or 12 coach local services) and thus capacity.

7 Common Interface File. 8 Practical Extraction and Reporting Language. See http://www.perl.org


http://www.perl.org/


It should be noted that there is a danger of causing assignment instability by modelling mixed length trains, as each will be assigned to in proportion to frequency, and not in proportion to the number of available seats. As such, the most robust way to code services in order to model the all-day level is to aggregate services according to their stopping patterns, and attach the average (service specific) capacity to each set of aggregated trains. This situation is most likely to occur for local services, as the longest distance services from London tend to be fixed formation of 8+ coaches, and not typically divisible.

Since this is less problematic for longer distance services, those services for TOCs in the London to West Midlands corridor, or other long distance routes, are given more explicit treatment of vehicle types in PLD. This allows easier identification of vehicle types for TOCs in the model scope area. These TOCs are as follows:

East Coast (Including Hull Trains, Grand Central)

West Coast

Midland Main Line

Great Western

Cross Country

Chiltern Trains

Wrexham and Shropshire

These TOCs use specific vehicle types.

Network Capacity Bottlenecks

The PLD rail model has large zones with the demand created from a full representation of passenger movements, except for those trips completely within the South East of England or the West Midlands. As a result, it has a large number of intra-zonal trips and shorter trips, often representing travel between stations not explicitly modelled. In addition, it does not include a representation of all local services.

As a result, there are localised situations where the model exhibits extreme overloading, which over estimates the crowding penalties imposed on the longer distance passenger.

This situation can potentially escalate such that capacity is overwhelmed by demand, causing unrealistically high loadings, thus crowding penalties. This can cause major re-assignments between iterations, making convergence more difficult to achieve. This can then feed into the model skim matrices as an unrealistically high origin-destination skim, and can have severe impacts on the demand model, causing significant (and undesired) shifts away from the rail mode. One possible solution would be to have more separate models in those problematic local areas, such as Manchester or Leeds. However, neither area is currently available in PLANET, so a network amendment method was undertaken instead.

As part of the model checking process, localised area of high crowding were identified by plotting the ratio of demand (passengers carried) to supply (seated capacity). These were inspected and network amendments made, usually by inserting additional local services where these had been excluded during the network build process.

As capacity bottlenecks are more marked in the future year networks, where higher demand levels are forecasted, this checking process was mainly undertaken at that stage in the model development and application programme. However, such capacity enhancements were included in all future year tests (Base and Test) to ensure consistency.

Table 5.5 below summarises the amendments made to address such bottlenecks.



Table 5.5 - PLD Network Bottlenecks

Vicinity Amendments

WCML: Preston area Add local services on Wigan - Manchester corridor to reflect current service pattern;

Add connector to serve Liverpool;

Extend modelled services from Wigan to serve Bolton

Swansea - Fishguard Add dummy service between Swansea and Fishguard to reflect those serving Milford Haven and Carmarthen

Leeds - Halifax Add dummy service to represent local services in the corridor

Nottingham Amend Lincoln – Leicester service

Glasgow - Motherwell Add local service to represent local services between Glasgow and Motherwell

5.5 Assignment Process

5.5.1 Overview

“Assignment” is the process of loading the demand on to the available network. It is done using a mathematical algorithm designed to ensure each O-D journey adopts the “shortest” generalised time route, taking into account all the modelled elements of the journey and the perceived relative weights applied to them.

PLD is a frequency-based assignment model. That is to say, it considers all public transport services in the modelled period, with no concept of when they occur during the day. What matters is the relative frequencies for all 'attractive lines' - in other words, those possible journey options for that origin-destination pair which are deemed to be worth considering, once the 'unattractive' options are discarded.

This is in direct contrast to a timetable-based model, which is aware of each train service activity in terms of the time of day it appears at each stop. In this type of model, the journey time is counted from the time the trip is desired (if a trip desires to leave at 11.00 versus 12.00), and all journey aspects depend upon time: the choice of trains to board is limited to those arriving after the passenger arrives at the first station, and will arrive at an interchange station dependent upon the scheduled arrival time. The choice of interchange trains is limited to those arriving after the passenger arrives at the interchange station, and so on.

The advantages and disadvantages of these two approaches are summarised in Table 5.6 below.



Table 5.6 - Assignment Types

Basis Advantages Disadvantages

Frequency A frequency based assignment does not need to know when in time each service is operating - the number of trains per day and their relative journey times is all that is required.

This makes it possible to model broad-brush options quite easily. (This would be problematic in a timetable model, as the dependencies for connecting services need to be known for the future year, which is rarely the case).

The time of day of services is not taken into account, so it is not possible to explicitly model peak versus inter-peak services. This can be handled to some extent with individual local models for specific time periods, but is not ideal as services in geographical areas with no additional models cannot be readily tested.

Timetable Connections can be modelled;

Time of day, Peak versus Inter-Peak, more readily represented

Connections 'have' to be modelled;

Future year data is difficult to obtain;

Matrix data much more difficult

On balance, for testing strategic schemes several years into the future, a frequency-based assignment is more appropriate, and has been retained for PLD.

5.5.2 Generalised Time

PLANET models, in common with many assignment based models, use the concept of generalised time as a measure of the disutility of travel. The model seeks routes that minimise this measure for each passenger then assigns the demand to those routes.

Generalised time comprises the weighted sum of the elements of the journey, as far as these can be determined from the available data. These are then weighted to give an aggregate perceived value in equivalent minutes. The components of generalised time can include:

Access and egress time;

In-vehicle time (derived from the time table data)

Reliability and Ambience;

Crowding penalty;

Waiting time;

Boarding penalty;

The weights used in PLD, which are the same as those developed for PSM, are included in

Table 5.7 below, and further discussed, where appropriate, in the following sections.



Table 5.7 - Assignment Parameters

Parameter Value

Board Penalty (minutes) 30

Wait Time Factor 0.49

Wait Time Weight 2.0

Access/Egress Time Weight 4.0

Board Time Weight 1.0

5.5.3 Access Modes and Weight

For car available trips, access to the rail mode uses data from the highway model. This is either as a car journey, using the highway modelled link times, at the home end of the journey, or an average public transport access speed via the highway network as a proxy for the un-modelled local part of a public transport (typically bus) leg of the journey. The use of the car for the home end permits the choice of an intermediate rail station with a more attractive rail service.

For non-car available trips, access to and egress from the rail mode are both via public transport access, which is heavily biased towards the use of the nearest station.

5.5.4 Reliability and Ambience

Reliability and ambience were included in PSM and were intended to trade off the relative merits of more reliable versus less reliable TOCs, and better versus worse rolling stock.

However, these two parameters were removed in PLD, for the following reasons:

There is lack of agreement on how to implement reliability, i.e. should it be related to journey time, as in PSM, or should it be related to headway;

There is a lack of agreement in the measurement of reliability and ambience; and

The source of future reliability and ambience forecasts are not available for the vast majority of the rail network;

For these reasons, reliability and ambience were removed from model forecasting processes. However, for some test options, assumptions regarding the relative reliability of new high speed services on dedicated infrastructure compared to classic rail services were incorporated in the travel times concerned.

5.5.5 Crowding Penalty

Crowding is particularly important to the realism of the assignment, as ultimately people will re-route to an alternative service, route or mode if the perceived journey time, i.e. including a crowding penalty, is too high. Also, the extent to which crowding is relieved or worsened as part of an option will feed directly into the mode-choice model and the economic appraisal.

This particular type of crowding does not specifically impact upon 'perceived headways', the extent to which a full train is made unattractive to board; this would be more appropriate for an urban model, where journeys are shorter.

It should be borne in mind that the assignment process is not capacity constrained – if there is excess demand, it will incur a high crowding penalty, but will still be present on the minimum generalised time route. Hence it is possible for the model to forecast a passenger load well in excess of seated, or even practical, train capacity.

9 0.4 is used to represent increased timetable knowledge for strategic rail trips.



In PLD, the crowding penalty is applied as a weight on the in-vehicle time for each segment of the train journey:

Perceived Time = Actual Time * (Crowding Penalty)

The level of crowding is converted to a penalty, by reference to the relationships given in the Passenger Demand Forecasting Handbook (PDFH). This quantifies the time penalty factor perceived by passengers for enduring the conditions for various levels of crowding, for each trip purpose10, for both seated and standing passengers. These parameters come from various sources since 1987, and are referenced in full in PDFH (chapter B5).

These factors are multiplied by actual travel time to yield a perceived travel time.

A process is in place to use observed means and standard deviations of train loadings to adjust from the individual train PDFH crowding response to reach a time period adjusted crowding response.

dxexC r

xx

r

prpr 2

2

,2

)(

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dxexBA r

xx

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2

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,

2

)(1

,,2

)(2

1

dxexDC r

xx

prpr

r

2

2

2

)(

1,,

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2

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Where:

x is the average level of vehicle occupancy;

r is the observed standard deviation of vehicle occupancy to TOC group r;

r,p is the lower threshold of crowding for TOC group r and journey purpose p;

Ar,p and Br,p are the lower crowding function parameters for TOC group r and journey purpose p;

Cr,p and Dr,p are the higher crowding function parameters for TOC group r and journey purpose p.

The effect of this adjustment is shown in Figure 5.7 below. In this Figure, “Capacity utilisation” is the ratio of passenger load to seated capacity.

0.80

1.00

1.20

1.40

1.60

1.80

2.00

2.20

2.40

2.60

2.80

0.50

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

1.05

1.10

1.15

1.20

Capacity utilisation

Cro

wd

ing

fac

tor

on

IV

T

Actual

Adjusted

Figure 5.7 - PDFH Adjustment Output

The PDFH ‘per-train’ crowding factor (lower line) can be seen to produce crowding factors greater than 1.0 as capacity utilisation reaches 75%, and increases towards 100%. Above 100% of seating capacity, the crowding factors increases more steeply.

10 Trip purpose is no longer distinguished in PDFH 5



The adjusted crowding factor (upper line) represents the extent to which passenger demand for rail services is unequal throughout the day. For example, a train service with demand of 300 passengers and supply of 500 seats would have a loading factor of 60% across the entire period. This would imply no crowding penalty based upon the table above, However, since the demand is not likely to be equally distributed across the modelled time period (whether an AM peak or all day), but peaked in profile: such peaks needs to be taken account of in the crowding function. For this reason, the above adjustment is undertaken. This implies that the 300 demand and 500 supply will yield a crowding penalty of around 1.1 in the above example.

The adjustment uses different normalised standard deviations for different time periods and service groups. The current model uses the following groups:

Inter-Urban London services;

Sub-Urban London services;

Non London services: and

All Day (07:00 to 22:59)

The PLD model retains the original method of using seated capacity only, when calculating crowding ratios.

5.5.6 Assignment Process

The PLD rail assignment has 3 trip purpose segments (business, leisure and commuting) and 3 car availability segments (non car-available, car-available from, and car available to). This gives 9 assignment classes in a base run, and 18 classes in a high speed test run (as the 9 segments are further subdivided into classic and high speed). Equilibration of supply and demand is by the Method of Successive Averages (MSA), undertaken for 10 iterations. Most of the procedure is carried over from the 2002 Atkins HSL model and the 2004 audited PSM model. The enhancements are explained in this section.

This iterative process is used to achieve convergence in the crowded assignment,

1-nnn ).@voltr-(1+voltr.=@voltr

Where:

flowiterationcurrent __voltrn

MSA_flow=@voltrn

noiteration_n

n1

This ensures that each iteration is combined with all previous iterations in a way which encourages convergence at the link flow level. However, it is important to stress that convergence at the link flow level does not guarantee convergence at the demand model level.

Previous versions of PSM relied upon the final iteration for the resulting model flows (although based upon a crowding penalty which was constructed using an MSA method). While this was mostly adequate, it sometimes caused large changes in flows between two similar scenarios. This was found to be due to the variability of flows resulting from different iterations, no matter how well converged the assignment model. This has been revised for PLD, by replacing the final iteration flow with a suitably converged value, using MSA. This is essential to ensure that model stability at the link level is retained.

There are two variants of the rail assignment model: one for conventional rail only, and another for future year assignments where high speed is treated as a separate mode.



Conventional Rail Approach

This is a 9-class public transport assignment model, as before. It now extends the MSA method to store equilibrated volumes. As discussed above, this guards against large variation in the results in the final assignment, and is a weighted combination of all iterations. Skims (as the O-D based journey time data are referred to), however, are not blended within the MSA method, due to run-time limitations, but calculated based on the final blended network generalised times held on the network. This is a reasonable compromise, given that O-D times are more stable than individual routes between O-D pairs.

The second approach used to model high speed was that of modelling high speed as the same mode as conventional rail. In this case, the sub-mode choice for rail is removed, and all trips can access all rail services. This means 100% of rail trips are in the conventional rail matrix, while none are transferred to the high speed matrices. This means that high speed could be run as a base assignment if necessary.

High Speed Rail – Separate Mode Approach

This is the original method of modelling high speed rail, as carried over from PSM, and is retained when using the DVoT approach to mode choice, when looking at the impact of premium fares.

High speed rail is treated as a sub mode of rail. In general terms, conventional rail passengers can only 'see' the conventional rail network (and do not have access to the high speed services), while those who have chosen to use the high speed mode can 'see' both the conventional and high speed network. It is analogous to a highway toll-choice model, where only those who pay the toll can access the tolled facility.

This format is used to allow access to the high speed network from places not directly connected to it: for instance, a train service from Manchester to London could need to use the conventional rail system for the section between Manchester and Birmingham in a situation where there was no HS2 specific infrastructure provided beyond Birmingham.

Alternatively, in absence of a through service, a person making a trip from Manchester to London who hypothetically chose to use high speed rail would still need to access the high speed rail mode (e.g. at Birmingham) via conventional rail. This could be by conventional rail service.

In either case, both modes are needed, as the high speed mode cannot exist in a vacuum, but has to inter-operate with the wider network in some way.

A macro is run to at the start of the assignment to establish which origin-destination pairs are within scope of the high speed line, and to allow only those trips to be assigned to high speed rail network and to feed them through to the logit model process. The definition of this is to include only those origin-destination pairs which would yield a 15 minute journey time saving by using high speed rail services. This scope definition is partly to stop the well-known side-effect of the logit model, which would otherwise forecast small shares for all O-D pairs, no matter what the difference in journey time.

In contrast to PSM, the within-scope trips macro is now called only once at the beginning of an HSL test run. This is found to enhance stability by calculating in-scope trips once only.

This is similar to the conventional rail assignment process, although it is an 18-class public transport assignment model, with nine classes each for conventional rail and high speed rail. It also now uses an MSA method to store equilibrated volumes in @voltr. This guards against large variation in the results in the final assignment, and is a weighted combination of all iterations.

5.6 Station Choice

5.6.1 Overview

Station choice is of high importance to the HS2 project; the question of whether to locate stations in city centres and/or parkway stations (and which sites) was a central question for the project.

The PSM station choice process was found to work reasonably well in spreading trips between feasible alternatives, such as London to Birmingham via Euston versus Marylebone (to stop



unrealistic domination of a single station), However, this was not sufficient for detailed station choice, such as individual locations within central and west London, as only the main cities of each conurbation were included, and detailed zoning and network data does not exist in PSM. To remedy this for PLD, attention was focused upon Greater London (7 zones and 9 stations) and West Midlands (5 zones and 6 stations). The accessibility of each zone to each possible station was calculated within detailed London and Birmingham models (Railplan and PRISM) to allow the generalised costs of accessing each station to be traded off in a logit model, producing demand weighted shares for the zones in PLD.

As the focus is on London to Birmingham, these locations were expanded to provide more detail. The existing PSM station choice routine was developed for one zone in each of central London, Birmingham, Manchester, Bradford and Bristol, and ignored the other boroughs or districts within those conurbations. The station choice model was originally designed to avoid the problem of “all or nothing” assignments to stations within cities which had two or more viable alternative routes, and to give a smoother assignment between each station. For example, Manchester to Liverpool journeys can be made from either Manchester Victoria or Manchester Piccadilly and Bradford to Leeds journeys can be made from either Bradford Forster Square or Bradford Interchange stations.

It should be noted that the existing PSM includes the option, for car access legs, of using the strategic road network to any rail station. Therefore options such as direct trains from Stafford to London and driving to a Birmingham Parkway station to catch a high speed rail service to London are already available in the model. This additional functionality is only required for areas closer to potential HSR station sites where the large zone sizes and the strategic level of detail of the highway network mean that the model cannot predict station choice with any reasonable degree of accuracy.

The station choice model is updated for PLD in the following ways:

Removing Bradford, Bristol and Manchester;

Concentrating on London and Birmingham;

Using Railplan and PRISM journey time data to inform the station choice.

These changes are explained in this section.

The station choice process now handles the stations listed in Table 5.8 below.

Table 5.8 - Stations in PLD Station Choice Model

London Stations West Midlands Stations

King’s Cross Birmingham New Street

St Pancras Birmingham Moor Street

Euston Birmingham International

Marylebone Solihull

Paddington Potential HS2 Station in central Birmingham

Other existing London Termini Potential HS2 Parkway Station near M42

Potential HS2 Station near Heathrow

Potential HS2 Station in West London

Potential HS2 Station in Central London (not Euston)

The fundamental approach is to use the more detailed Railplan and PRISM zoning systems as subdivisions of the much larger PLD zones. Using databanks created outside the PLD zone system, we use artificial Railplan and PRISM databanks to undertake “park and ride” type



calculations of the accessibility of each of the conventional and high speed rail stations. This allows the demand from the zones representing Greater London (zones 117 to 123 inclusive) and greater Birmingham (zones 5, 47, 176, 181 and 217) to be disaggregated into separate “station specific” dummy zones. Average access times are also imported from the Railplan / PRISM models into the zone connectors to allow the economics and mode choice aspects of the model to operate correctly.

Railplan is the TfL model of public transport assignment in Greater London, and uses 1571 zones with a single journey purpose. A set of public transport generalised journey time data is used to inform station choice. This data was chosen as a good representation of public transport, despite the fact station access is often by car / taxi in London. However, this would have added unnecessary complication to the process, while public transport access is deemed more important in London, particularly in the central area.

PRISM is the West Midlands multi-modal model, using 928 zones. It is segmented into several purposes, but a single set of highway generalised journey time data to each station is used to inform station choice. These highway data were chosen for use, despite the fact that some rail access is via public transport (particularly in Birmingham itself), as there is more possibility of a parkway station in the greater Birmingham area than in greater London.

This process is implemented as part of the existing “separate stations” and “combine stations” processes. Demand from the geographic London and Birmingham zones is distributed to the dummy station specific zones in “separate stations” for assignment and skimming purposes. Once the assignment and skimming is complete, demand-weighted average skims and demand are put back into the original geographic zones before mode choice calculations are undertaken (with demand and skim matrices reset to zero for the station choice zones). Hence, the dummy station specific zones are only altered during the assignment process and are reset to zero after completion of that macro.

Because of the increase in number of dummy zones, the station choice mechanism is removed from Manchester, Bradford and Bristol zones. If necessary, the functionality could be brought back in at a later stage, but effort is focussed on the London to Birmingham corridor.

It should be noted that this mechanism is not designed for assessment of potential demand at an intermediate station (somewhere between Oxford and Northampton). Assessment of demand at these sites will be determined by PSM’s normal highway accessibility mechanisms, although we believe that off-line calculations are likely to be required to give a more detailed understanding of the relative accessibility of any new high speed rail station for commuting to London.

5.6.2 Operation of Station Choice Model

Within a 10-iteration crowded assignment loop, demand is separated out from the geographical zones after the first uncrowded assignment iteration. At the end of the assignment loop, the skims for the station zones are recombined as a weighted average back to the geographic zones. This ensures the demand model operates on the geographical zones. The matrices are stored as geographical zones, with temporary copies for station-choice demand.

The London and West Midlands sections run sequentially, such that London end Station Choice is calculated first. The West Midlands station choice is calculated second, taking into account the London end choices. This prevents 'unlikely' station choices, such as Euston to Snow Hill.

There are two basic aspects to the station choice operation: Separating demand before assignment, and combining weighted skims post assignment. See below for the implementation of Separate Stations and Combine Stations.

Separate Stations

This is a 3-step process to re-allocate demand to the expanded zone system:

Step 1: Export PLD data

Generalised journey times are exported from PLD for the subset of journeys from London to the Non-London zones elsewhere in the model.



Step 2: Calculate Station Shares in Railplan and PRISM

This step is virtually identical between the two areas, and is described in terms of the Railplan data below. It is then repeated with the PRISM data.

Within the Railplan databanks, PLD generalised journey times are imported and weighted to be consistent with the PLD end of the trips. Next, a logit model is used to calculate the share of each London station, for each origin-destination pair, as shown below:

...

StPPadEusKX

KX

gcgcgcgc

gc

KXeeee

eShare

The spread parameter used (lambda) is 0.10.

In addition, average access times are calculated from Railplan data. Once the shares are calculated, the journey direction is transposed and the calculations are repeated for the opposite direction of travel.

Finally, the share and average access times are exported from Railplan.

Step 3: Import and Apply Station Shares

The shares and access times are imported into PLD. From these shares, the actual demand to and from London stations is multiplied by the shares, and new matrices are created.

To ensure consistency, the main city zones are reset to zero, and the intrazonal trips are replaced in the main city zones.

This in effect creates an 'exploded' matrix of 328*328 zones, with a total matching to the 235*235 original,

Combine Stations

At the end of a 10 iteration assignment, the following steps are required:

Add station access times for London / West Midlands; and

Combine demand-weighted skims for city stations.

This gives demand weighted skims to feed the mode-choice model, which then operates on the geographic zones. At all times, copies are kept of the pre- and post- station-choice process.

As the station choice model processes London first then Birmingham, the London end of all trips is given a free choice of station, then the West Midlands end station is calculated. This time, the London choice of each station is already made, so the choice of West Midlands station depends upon the London end. For example, if a proportion of trips chooses Euston at the London end, then those same trips are likely to choose Birmingham New Street or International (rather than Moor Street or Solihull).

5.6.3 Station choice zoning system

The PLD station choice zoning is provided in Table 5.9 for the London stations and in Table 5.10 for the West Midlands stations.

It can be seen that the seven London zones have nine potential station choices, making 63 new zones in total; while the five West Midlands zones have six potential station choices, making 30 new zones. This gives 328 zones in total (235 + 63 + 30). These additional zones are used to facilitate a logit choice of stations from each of the zones.



Table 5.9 - Greater London Station Choice Zones

Zone KGX STP EUS MYB PAD Other HS-LHR

HS-WLon

HS-CLon

Central 117 321 322 323 324 325 326 327 328 329

L-N 118 331 332 333 334 335 336 337 338 339

L-NE 119 341 342 343 344 345 346 347 348 349

L-S 120 351 352 353 354 355 356 357 358 359

L-SE 121 361 362 363 364 365 366 367 368 369

L-SW 122 371 372 373 374 375 376 377 378 379

L-NW 123 381 382 383 384 385 386 387 388 389

Table 5.10 - West Midlands Station Choice Zones

Zone BHM BMO BHI SOL HS-CBirm

HS-PWBirm

BIRM 5 421 422 423 424 425 426

DUDL 47 431 432 433 434 435 436

SAND 176 441 442 443 444 445 446

SOLI 181 451 452 453 454 455 456

WALS 217 461 462 463 464 465 466

5.6.4 Example Outputs from Station Choice Model

Table 5.11 below shows an example output for trips from Central London to the City of Birmingham in the base year (2008).

Table 5.11 - Example Station Choice Output

BHM BMO BHI SOL HS-C Birm

HS-PW Birm

Total

KGX 0% 0% 0% 0% 0% 0% 0%

STP 2% 0% 2% 0% 0% 0% 4%

EUS 49% 0% 37% 0% 0% 0% 87%

MYB 0% 2% 0% 1% 0% 0% 4%

PAD 3% 0% 2% 0% 0% 0% 5%

Other 0% 0% 0% 0% 0% 0% 1%

HS-LHR 0% 0% 0% 0% 0% 0% 0%

HS-W Lon 0% 0% 0% 0% 0% 0% 0%

HS-C Lon 0% 0% 0% 0% 0% 0% 0%

Total 55% 2% 42% 1% 0% 0% 100%



In this example, it can be seen that whilst many station pairs report shares of 0%, 87% of the trips travel from Euston to Birmingham New Street (49%) or International (37%). This minimises the phenomenon of 'unlikely trips'. As usual with a logit model, very small shares go to less attractive options, though these outliers have the option of using a station walk link to find their way on to the best assignment, so are not forced to board at those stations.

5.7 Interfaces

5.7.1 Overview

Section 3 shows the structure of the multiple models. The framework makes use of the best parts of the different models to ensure that strategic trips are handled by PLD, while AM crowding in London and the West Midlands is modelled in PS and PM respectively. To do this, the framework runs the models iteratively, passing the required data from each model to the next. This is shown in detail in Figure 5.8.

LHR


PM


PS



West Midlandspre‐loads


PLD

PLD

Figure 5.8 - Relationship between PLD and LHR / PS / PM

The main interactions are that long distance passengers are exposed to crowding caused by local passengers on stopping services, and vice-versa. This can, in both cases, not only impact on the crowding penalties, but also can also impact on route choice and service loadings, as well as on the generalised journey times and economic benefit calculated.

One option could be to attempt to incorporate both local and long distance demand in a single model, but this would become impracticable. PSM tried to avoid the issue, by providing dummy services to handle the local demand, but this approach was not ideal as it relied on an ad-hoc assessment of where such local services were required.

The preferred approach, as implemented in the HS Modelling Framework, is to differentiate between long distance travel and local travel in the key conurbations, but transfer a representation of those passengers to the other model, on relevant services.

This transfer is, in the main, undertaken by calculating the relevant number of passengers on the services in question, in units of passengers per train per hour, and then transferring that data as a fixed preload on the equivalent services in the other model. The exception to this is the transfer of long distance demand to the PS model, where it was important to be able to identify the detailed route chosen and impacts on commuting services of the change in long distance demand. Accordingly, in this case, the demand using each corridor was transferred, using a series of “select link” processes and assigned alongside the local demand. In each model, the representation of such transferred passengers is included in the calculation of the crowding level and crowding penalty.



The different time periods of the models are handled by using corridor-specific factors to convert select link matrices from 16 hours to 3 hours, or in terms of pre-load passengers per train per hour.

This section describes the interactions between PLD and the other models in the framework. The overall structure is set up to allow model results to be extracted from the most suitable components, such as long distance trips from PLD, intra south east trips from PS. This ensures that these trips are modelled and extracted once and once only from the framework of models. Figure 5.8 above shows the relationships between the models.

5.7.2 Demand transfer to PS

16 hour demand matrices are extracted from PLD at cordon points to the south east. These are converted to 3 hour matrices for import to PS as long distance demand, which was stripped from PS.

Demand transfer from PLD to PS is undertaken by means of a series of “select link” matrices, to enable the pattern of demand to be retained and assigned in PS. This process is undertaken for each direction and for each journey purpose, although the demand by purpose is then aggregated to a single matrix prior to export.

As the zone systems differ between PLD and PS, the demand is exported using zone groups which can then be equivalenced and disaggregated in PS where appropriate. The trip end outside the PS model area is given a unique zone group, as identified in the table below, which is represented as a specific additional zone in PS. The trip end within the PS model area is exported using a zone group “gs” where each PLD zone has a unique definition (gs01 to gs235). In addition factors are applied to convert the daily demand exported from PLD to an equivalent AM Peak period demand, as used in PS.

Details of the select links and zone groups used are provided in Table 5.12 below.

Table 5.12 - Demand Interface between PLD and PS

From Station Station Code

Node No. in PLD

To Station Station Code

Node No. in PLD

@pslnk value

Zone Group

Newport NWP 6870 Cardiff CDF 6868 1 gc01

Newport NWP 6870 Hereford HFD 6818 2 gd01

Gloucester GCR 6856 Cheltenham Spa CNM 6825 3 gf01

Moreton in Marsh MIM 6861 Evesham EVE 6816 4 gg01

Banbury BAN 6008 Leamington Spa LMS 6808 5 gh01

Wolverton WOL 6325 Rugby RUG 6798 6 gj01

Wolverton WOL 6325 Northampton NMP 6043 7 gk01

Bedford BDM 6012 Wellingborough WEL 6805 8 gl01

Peterborough PBO 6740 Leicester LEI 6735 9 gq01

Peterborough PBO 6740 Grantham GRA 6702 10 gr01

Cardiff CDF 6868 Newport NWP 6870 11 gc01

Hereford HFD 6818 Newport NWP 6870 12 gd01

Cheltenham Spa CNM 6825 Gloucester GCR 6856 13 gf01

Evesham EVE 6816 Moreton in Marsh

MIM 6861 14 gg01

Leamington Spa LMS 6808 Banbury BAN 6008 15 gh01



Rugby RUG 6798 Wolverton WOL 6325 16 gj01

Northampton NMP 6043 Wolverton WOL 6325 17 gk01

Wellingborough WEL 6805 Bedford BDM 6012 18 gl01

Leicester LEI 6735 Peterborough PBO 6740 19 gq01

Grantham GRA 6702 Peterborough PBO 6740 20 gr01

HS2 outbound from SE 21 gu01

HS2 inbound to SE 22 gu01

5.7.3 Demand transfer from PS

A set of pre-loads are exported from PS for PLD, as described in section 9.7.3. These represent the local (south east to south east) passengers who choose to travel on longer distance strategic services.

As already discussed, local demand on long distance services within PS is transferred to PLD in units of passengers/train/hour.

5.7.4 Demand transfer to PM

Long distance demand on local services within the PM area is calculated and exported to PM. For the purposes of the transfer, “local services” are those that pass outside the PM cordon, and have at least two stops within that cordon.

Demand is again transferred in units of passengers/train/hour.

5.7.5 Demand transfer from PM

As with PS, local demand on long distance services within PM is calculated and exported for transfer to PLD, again in units of passengers/train/hour.

5.7.6 Cost transfer to LHR

Generalised costs are transferred to the LHR spreadsheet model, as airport access demand is calculated separately there. For further details, please refer to section 11.2.1 below.

5.7.7 Demand transfer from LHR

The final stage of the Heathrow Model is to produce the Heathrow Airport demand for all modes for Heathrow air passenger access and egress. These are then imported into PLD, ready to be assigned to the respective networks.



6. PLANET Long Distance Highway Model 6.1 Overview

This section explains the updates undertaken for the PLD highway model. It covers the following:

Supply updates;

Demand updates;

Highway assignment parameters;

Volume Delay Functions; and

Interfaces with the other models.

6.2 Supply Update - Highway Schemes Using information from DfT, all relevant highway schemes opened up to 2008 were included. The highway schemes applied to the PLD highway network are shown below. These schemes were derived from the Highways Agency list of national schemes, supplied in March 2009.

A1 (M) Wetherby to Walshford (DBFO)

A1(M) Ferrybridge to Hook Moor (DBFO)

M1 J31 to J32 Widening

M1 J6A to J10 Widening

M25 J12-15 Widening

M25 J1b to J3 Widening

M42 J3a to J7 ATM Widening

M6 Carlisle to Guardsmill

M60 J5-8 (formerly M63 J6-9 Widening)

6.3 Demand Data Update

6.3.1 Demand Data

As mentioned in section 4.4.2, the latest matrices were obtained from the PRISM and NoTAM models. These were merged with the existing PLD highway matrix, and rebased to 2008. Growth (to 2008) was calculated by purpose (business, commuting, other). Using a geographical information system (GIS), the PLD zones were matched to the TEMPRO zoning system, and a comparison table prepared, matching each TEMPRO zone to a PLD zone.

Data on the forecast number of trips made to and from each TEMPRO zone, in the PSM base year and the PLD base year, were then extracted from the TEMPRO system by origin and destination, and the comparison table used to transfer these to the PLD zoning system. The growth in number of trips by PLD zone was then calculated.

Note that all trips to and from zone 90 in PSM were allocated to zone 123 in PLD (as zone 90 in PLD has been reserved for Heathrow Airport access and egress).

The calculated growth factors were applied to the trip ends in each zone, and a “furnessing” procedure carried out to ensure the matrix balanced.



Table 6.1 below shows the final matrix totals.

Table 6.1 - Highway Demand Totals

Sector 2007/08

Matrix Totals (cars per day)

Business 1,340,083

Other 2,103,305

Commuter 1,335,254

Total 4,778,642

6.3.2 Link preloads

The PSM demand excluded car trips less than 50km in length, as it is assumed that these will not transfer to a strategic rail network. To compensate for this, the network includes a number of vehicles as preloads on each link, representing the local trips made on that link.

As mentioned in section 4.4.2 above, existing preloads were updated to 2008, using TEMPRO growth factors by region. Links on the London-Birmingham corridor (M1/M6/M40/M45/A45) were separately updated so that assigned flow plus preloads were equal to 2008 TRADS data.

6.3.3 Demand Modification

The matrices as developed from PSM comprise all trips longer than 50 km.

However, within the PLD Rail Model, trips wholly within the notional “islands” (the south of England and the west Midlands) were removed, as this local demand was handled in PLANET South or PLANET Midlands. Those elements in the HS2 model framework utilise a single mode elasticity response, rather than relying on a mode choice model with the additional complexity of a local highway model. Accordingly, it was inappropriate to retain the equivalent (internal to internal) highway trips in PLD.

Such trips were removed from the demand data, and replaced by uplifting the pre-loads to compensate. To derive the additional pre-loads (representing these internal to internal trips), two highway assignments were undertaken: with and without the internal to internal trips. The subtraction of the resulting link flows yielded the additional pre-load data, which was added to the original pre-loads representing short distance demand.

6.3.4 Validation Checks

A selection of journey times were checked for accuracy against publicly-available traffic direction websites. This showed generally acceptable correlation, but most journeys were quicker due to the location of zone connectors and general scale of the network reducing the amount of traffic and hence delay in urban areas. Also, it must be borne in mind that the model relies solely on COBA style speed-flow relationships on links, with no explicit junction delays.

6.4 Highway Assignment Process and Parameters The model uses a multiclass (business, leisure and commuting) generalised cost assignment algorithm. It uses the standard EMME highway equilibrium assignment algorithm to achieve convergence, with up to 50 iterations permitted.



6.4.1 Highway Speed-Flow Functions

COBA speed-flow relationships were coded as volume-delay functions (VDF) for use in EMME/2. New functions were added for a “special case” – a particular suburban dual carriageway, and for motorways with active traffic management/hard shoulder running. The link descriptions and function numbers are listed in Table 6.2 below.

In addition, the model uses a fixed speed (40 kms/hr) on zone connector links.

Table 6.2 - Highway Volume Delay Functions

VDF Road Description

11 D3/4M (Motorway)

12 D2M (2 lane Motorway)

13 D3AP (3 lane dual carriageway)

14 D2AP (2 lane dual carriageway)

15 S10 (Single carriageway, 10m width)

16 D4M-HSR (Motorway with hard shoulder running)

17 D2AP; specific use (Used for the Coventry bypass)

6.5 Interfaces The highway speeds are used to produce Car Available rail access and egress times11 on a subnetwork between zones and stations. This ensures that highway congestion is represented in the choice of railhead for rail passengers who have a car available. The same times are available for air passengers12, who are assumed to have car/taxi available to connect to the relevant airports.

11 At the home end of the trip only. 12 At both ends of the trip.



7. PLANET Long Distance Air Model 7.1 Overview

This section explains the updates to the PLD air model. It covers the following:

Demand updates;

Network (Supply) updates; and

Air assignment process and parameters.

7.2 Demand Data Update

7.2.1 Overview of Approach

New air demand matrices were developed for PLD based on the DfT’s Long Distance Model Phase One Interim model (LDM). The LDM demand data was derived from CAA data, with a base year of 2004. It represents purely domestic travel, i.e. end to end travel only, so domestic transfer flights or ‘interlining passengers’ are not included (such passengers are considered within the LHR model, however). The demand data is broken down into two purposes, business and leisure, as commuting is unlikely to rely on air travel.

The demand data for each purpose type was aggregated to be consistent with the PLD zoning system and factored using CAA data to be representative of 2007/08.

7.2.2 Detailed Methodology

Annual base year (2004) LDM air matrices were provided for both business and leisure passengers. This demand was derived from CAA data and represents purely domestic travel, i.e. end to end travel only, so domestic transfer flights and interlining passengers are not included. The demand data is split into two journey purposes, business and leisure. Commuting air trips are excluded as these are considered to be negligible.

The LDM has 406 zones, which are equivalent to the TEMPRO zoning system. The matrices were converted to the PLD zoning system using GIS. A correspondence table was developed to convert the matrices from 406 zones to 235 zones, permitting demand matrices to be aggregated to the simpler zoning system.

The 2004 PLD matrices were factored to 2007 passenger numbers, based on CAA data. Since 2007 passenger end-to-end movements were only available on services to Heathrow airport, the growth factors to 2007 were developed by comparing all domestic air trips as recorded by CAA data for 2004 and 2007.

As summarised by CAA in “Recent trends in growth of UK air passenger demand, Jan 08”, air passenger numbers in the UK have declined in recent years. On average domestic passenger numbers in the UK fell by approximately 2.6%. The 2004 data was factored to reflect these numbers, as summarised in Table 7.1 below.

Table 7.1 - Annual Domestic End-to-End Air Demand

2004 2007

Business 7,455,111 7,261,279

Leisure 5,867,504 5,714,950

Total 13,322,615 12,976,229

The annual matrices were subsequently converted into daily matrices by dividing by 365, as summarised in Table 7.2 below.



Table 7.2 - PLD 2007 Daily Air Demand

2007

Business 19,893

Leisure 15,657

Total 35,551

PLD allows airport trips to choose between available airports using the strategic highway network, and this functionality is being retained. Associated sense-checks were undertaken, such as:

Checking that the “fare paid” is non-zero for airport available trips, using PLD skims (this ensures that no trips from, say Berkshire to Buckinghamshire are contained within the air matrix); and

Catchment areas by airport for trips to London and South East England.

Analysis indicated that more than 70% of the air demand to South East England is generated from Scotland. Manchester and the North East of England generate the other reasonably significantly demand. This is summarised below in Table 7.3.

Table 7.3 - Percentage of Demand to SE England

Region of UK % of Demand to SE England

Manchester 12%

North East 9%

Scotland 72%

A sense check with the CAA end to end data at Heathrow airport suggests this data to be in the right order with more than half of all end to end trips to Heathrow being from Scotland. Manchester and Newcastle are the other largest generators of demand to Heathrow.

7.2.3 Air demand matrix checks

The following validation checks were undertaken on the updated air demand matrices.

Boarders by airport, checked against CAA data (via DfT’s Long Distance Model validation report); and

Loading figures on all flights, to ensure reasonable levels of boardings per plane.

SKM provided end-to-end domestic passenger movements at Heathrow airport. These were used to check that the air model within PLD was validating appropriately. The results of this validation are summarised in Table 7.4 below.



Table 7.4 - Air Passenger Data & Modelled Flows at Heathrow Airport

Airport CAA Observed Passenger Data

Modelled Passenger Flows

Percentage Difference

Aberdeen 303,536 323,755 +7%

Edinburgh 695,952 719,780 +3%

Glasgow 491,626 516,110 +5%

Inverness 25,511 22,630 -11%

Leeds/ Bradford 84,813 92,345 +9%

Manchester 704,639 644,955 -8%

Newcastle 280,532 269,735 -4%

Teesside 51,533 54,385 +6%

As there is no capacity constraint or crowding on air travel within PLD, the loading figures for each of the transit lines were checked to ensure no flights were carrying unrealistic numbers of passengers.

7.3 Network Update Air services are represented on a simple basis in PSM and PLD, with individual transit lines representing flights operating between different UK airports, with average fares paid by leisure and business users. These were updated to reflect 2007/08 networks and fares.

Average fare data was developed based on CAA survey data, which was sourced from the DfT. The fare data was “average fare paid”, it was not broken down by trip purpose and was available for 1997 - 2008. Where fare data was not available for 2007, fare data was extracted from another year and factored to 2007.

CAA air punctuality statistics were used to update the air services. The punctuality statistics are published for all flights between key airports in the UK. Data was used for October 2007 – on the basis that this is generally regarded as a neutral month. The punctuality statistics data summarises the number of flights operating to/ from the top ten airports in the UK and the flight operator. The monthly flights were converted to typical daily flights and used as the basis of the air services coding.



7.4 Air Assignment Process and Parameters The air assignment is a 2 class assignment (business and leisure), with no crowding process. This is due to the lack of route-choice on the air mode, and reflects the flexibility available to operators both to change the aircraft operated and to manage demand through price or ticket availability. It should also be borne in mind that the air model exists to provide the PLD mode choice model with the generalised costs of the air product, as a competitor to rail. The main parameters used in the assignment are summarised in Table 7.5 below; these were unchanged from the PSM version.

Table 7.5 - Air Assignment Parameters

Parameter Value

Board Penalty (mins.) 163





The air assignment uses the sum of the in-flight travel time and the average fare (converted to equivalent minutes).



8. PLANET Long Distance Mode Choice Model

8.1 Overview The mode choice model’s purpose is to predict estimates of mode share for rail, air, and car, given changes in travel conditions. For the HS2 study, the main requirement is to model the share between high speed and conventional rail as realistically as possible.

The conventional approach to high speed modelling has been to use an absolute logit based on results from stated preference surveys. This approach dates from a time when there was a significant difference between high speed and conventional trains. However, for modern long distance travel, conventional trains are generally new, and offer a degree of comfort that is similar to that offered by high speed services.

Without any significant difference in ride experience, the key question that would need to be addressed for the PLD model would be what factors would influence high speed rail patronage? It would most likely be down to which route had the lowest generalised journey time, together with impacts from improved reliability and any premium fare levels.

The existing PSM model’s mode choice model was taken as the start point for the HS2 study. This approach was reviewed and updated, where necessary, to reflect the latest guidance on the approach to high speed rail modelling.

8.2 Summary of PSM Mode Choice Model The original PSM Mode Choice model uses a hierarchical logit model structure based on the results of stated preference surveys conducted for the HSL study during the late 2001/early 2002. The surveys were focused on longer distance movements appropriate for the strategic nature of PSM. Mode choice parameters were derived using a combination of outputs from the model and other information collected at the same time as the stated preference surveys.

Although the calibration was undertaken at an absolute level, the model is applied incrementally for Car vs PT and Air vs Rail choices. Implicitly, the structure also includes the choice between travelling, not travelling and generation at the top level; a default factor of 1/3 is applied, based on experience in other studies – essentially this means that trip generation is assumed to occur at 1/3 the rate of the highest mode choice effect.

Figure 8.1 - Mode Choice Model Structure

For the HSL Study, the model was applied absolutely where HSL services were introduced. No trip distribution effects were modelled within PSM, although the trip generation factor can be



interpreted as implicitly including some element of trip substitution for shorter distance trips not represented in the PSM trip matrices.

Separate mode choice parameters were derived for business (all), commuting (car available and non car available) and other (car available and non car available). Since only limited information was available for commuting trips, information from the stated preference surveys was extrapolated by comparison with standard PDFH elasticities. The parameters used in the model are shown in Table 8.1

Table 8.1 - PSM Mode Choice Parameters (2002 Prices and Values)

Commuting

CA Commuting

NCA Other CA Other NCA Business

Values of Time (p/min)

Air IVT n/a n/a 18.5 n/a 61.6

Air Headway n/a n/a 2.4 n/a 14.3

Air Access / Egress n/a n/a 18.5 n/a 66.6

Car IVT 12.6 n/a 13.7 n/a 51.2

Car Access / Egress 18.9 n/a 18.4 n/a 66.6

Rail IVT 12.6 12.6 13.7 13.7 51.2

Rail Headway 5.9 5.9 10.3 10.3 27.9

Rail Access / Egress 18.9 18.9 18.4 18.4 66.6

HSL IVT 12.6 12.6 18.5 18.5 61.6

HSL Headway 5.9 5.9 2.4 2.4 27.9

HSL Access / Egress 18.9 18.9 18.5 18.5 66.6

Scaling Parameters (1/p)

PT vs Car 0.000808 n/a 0.00119 n/a 0.000496

Rail vs Air n/a n/a 0.00225 n/a 0.000681

Conv Rail vs HS Rail 0.001166 0.001154 0.00325 0.00503 0.000764

8.2.1 Generalised Cost Equations

Generalised costs for each mode (expressed in pence) are calculated within the mode choice model of PSM according to the following formulations:

GCc = (car journey cost+parking)/occupancy + VoTc*car time (1-way) + VoAcEgc*car access egress time

GCa = (air fare (1-way) + acc/egg cost) + VoTa *air time (1-way) + VoAcEga *air access egress time + VoHeada*air headway

GCr = (rail fare (1-way) + acc/egg cost) + VoTr *rail time (1-way) + VoAcEgr *rail access egress time + VoHeadr*rail headway + VoTr *rail interchange penalty

GChs = (hs rail fare (1-way) + acc/egg cost) + VoThs *hs rail time (1-way) + VoAcEghs*hs rail access egress time + VoHeadhs *hs rail headway + VoThs *hs rail interchange penalty

The two rail related interchange penalties were derived from SRA/PDFH values using the following formula for inter-urban journeys:

rail interchange penalty = (7.16+(0.066*IVTr))*INT0.7



8.3 Review of Existing Mode Choice Model The existing model was reviewed, with the following areas highlighted as particular areas where concerns had been raised in the past:

Generation and trip distribution effects;

The representation of the choice between High Speed and Conventional Rail and the existing High Speed mode constants;

Parameters used within the existing mode choice model structure;

To put the model’s performance in perspective, a review of other modelling was undertaken, including the DfT’s Long Distance Travel Model, to isolate the key areas of refinement required to make the Mode Choice Model suitable for the task.

8.3.1 Generation and Trip Distribution effects

Trip distribution modelling reflects how the relative attractiveness of a location impacts how many people decide to travel there. For example if a fast new rail service connects two cities, more people are likely to travel between them at the expense of other possible destinations. For short distance travel trip distribution has a significant effect, but for longer distance travel the effect is less significant. The key question is whether this effect is noticeable enough to be included within the mode choice model.

An expert review during May 200913 highlighted that it is not standard practice to attempt to model re-distribution for long distance travel. It highlighted that there is also no known evidence, to support the validity of destination choice for forecasting long distance re-distribution in response to new infrastructure. In addition, it also questioned why it should be reasonable to presume re-distribution among long distance trips when they have no interaction with other, shorter journeys, which form the vast majority of all person trips.

Despite this view, there is some research evidence on this effect from the DfT’s Long Distance Travel Model; it would be helpful to form some view as to whether re-distribution might cause significant loss of revenue to other rail services.

The conclusions reached were the following:

The PSM forecast of induced rail travel in the corridor may be assumed in part to be diverted from other corridors and some proportion could be assumed to come from rail. To examine this, the levels of induced travel and the implied elasticities for key movements (e.g London to Birmingham) were reviewed during the Steering Group Meeting (July 2009)

Potentially, the Long Distance Travel Model can be used to provide some information on the potential relative scale of the loss of revenue due to re-distribution arising from HSL services; it seems likely that this could be done through an analysis of the test runs already prepared. However, this would not be possible within HS2 model development timescales.

8.3.2 Representation of the choice between High Speed and Conventional Rail

The choice between conventional and high speed rail has several key fundamental issues:

Whether the existing PSM absolute logit choice structure is applicable, and if so what needs to be updated;

How to deal with premium fares;

David Ashley’s review considered the mode constants to be the least reliable output of stated preference surveys. The mode constant in the original PSM model for high speed rail relative to conventional rail are, approximately 25 -35 minutes for Business and 35-45 minutes for leisure.

13 TN4: Development of HS2 Demand Model, 5th June 2009 (Michael Hayes / David Ashley / John Bates)



The leisure values were insignificant in the original Stated Preference work, but once the value of time was reduced to a more reasonable value, the mode constants increased (to compensate). The other mode choice parameters were deemed suitable (see 8.3.3).

Two possible options to obtain more realistic high speed model parameters were: to obtain values from other models or undertake additional stated preference surveys. The first option was considered inappropriate, and the second would take too much time without any guarantee of providing more reliable values.

A new approach was proposed by the Analytical Challenge Panel (July 2009) as a direct replacement for the absolute choice between high speed and conventional rail. This approach required a distribution of value of time to calculate the proportion of people willing to pay for high speed rail’s journey time savings. The approach intrinsically takes into account a fare premium, and trades this off against the generalised time saving. The advantage of the approach are that it would give better estimates of the effects of fares, but as fare premiums are not the main focus of the study would not necessarily be essential.

The distributed value of time approach (see section 8.5) was initially applied in a spreadsheet model that allowed the process to be debugged and independently reviewed. Once the process had been checked, it was transferred to PLD, and tested for a range of fare premium scenarios. The performance of these tests was reviewed by the HS2 Analytical Challenge Panel (November/December 2009).

As a result of the initial options, the appropriateness of mode constants within the mode choice model was reviewed. The mode constants represent “inherent preferences” such as comfort that may no longer be relevant between modern high speed and conventional trains. . The conclusion reached was that these qualitative differences were of negligible importance compared to the actual time savings that the services offer.

This led to a new approach that would remove the uncertainty of the high speed model parameters, by removing the high speed sub-mode altogether; instead, these high speed services would be considered as conventional services with improved reliability. As the demand using the high speed services would be indistinguishable from the other conventional demand, the share of high speed user would be determined by assignment rather than the matrix approach used within the mode choice model.

Based on the decisions to remove the mode constant, and following the review of the distributed value of time approach, the following decisions were made:

To remove the high speed mode from the central case (where no fare premium would be present)

To undertake any fare premium analysis using the distributed value of time approach.

8.3.3 Model Parameters

The potential issues with the model parameters were the following:

Appropriateness of the values of time;

Segmentation;

Appropriateness of Scaling Parameters.

Comparisons were undertaken between WebTAG, the values of time derived for the distributed value of time approach, and from the existing PSM model. Based on these comparisons (see section 8.5.2), it was concluded that the values of time were adequate to be used within PLD. These were updated to latest estimates for each modelled year (see section 8.4.1).

David Ashley’s review (May 2009) noted that if income segmentation were used, we would need to consider what the parameters of the logit model should be: the scaling parameters and high speed mode constants for each income segment would vary. While this would be technically



feasible, the extra uncertainties associated with the judgements needed to parameterise these procedures would exceed the perceived forecasting benefits.

The use of the distributed value of time approach intrinsically considers value of time segments for the rail market. One of the outstanding challenges is how best to segment this distribution within the appraisal, whilst keeping it consistent with the central case (where only a single central value of time is present).

Typically we might expect urban mode choice parameters to be around -0.03 to -0.06. We expect values to reduce with distance, and the modelling reviewed confirm that for international high speed lines the values are typically around -0.01 and -0.02.

Therefore, PSM values, which are mostly in the range of -0.02 to -0.04, seem credible, recognising the range of distances covered. Commuting is questionable, but as this makes up such a small part of long distance travel, the impact is likely to be small.

The implied elasticities are discussed in detail in section 8.4.2. After the changes to demand, and value of time growth, the output elasticities were deemed adequate.

8.3.4 Other Changes

The mode choice model was rebuilt from the PSM original. The following tasks were undertaken to simplify, debug and improve the performance:

Iterative loops to remove duplicate code and eliminate typing errors;

Additional code for elasticity calculations;

Spreadsheet models used to debug, with feedback to EMME macros;

Distributed Value of Time variant embedded within existing Mode Choice macros;

8.4 Recalibrated Mode Choice Model This section explains how the model parameters were updated to the modelled years, and illustrates how the relative sensitivities of the mode choice model are calculated and how they compare with the expected values.

8.4.1 Future Year Growth in Mode Choice Parameters

The growth assumptions applied in the original PSM model were updated to include the latest guidance. New modelled year values of time were calculated using the latest GDP assumptions. An increase in Value of Time would increase the sensitivity of the model to time changes, which if left unchecked would significantly alter the performance of the mode choice model. To correct for this we used the recommended approach to correct the dispersion parameters (lambdas) to control the increase in sensitivity using the following formula:

ForecastForecastBaseBase VoTVoT ..

8.4.2 Implied Elasticities

Elasticity calculations were undertaken using the additional code integrated into the model. This allowed automated outputs in response to a 10% decrease in each of the following: Rail Fares, Rail Times, Air Fares, Air Times, Highway Fuel Cost and Highway Times respectively. The following general formula was applied:

1*1.0

1

B

BT

B

T

C

CC

D

D

elasticity



Where:

= Test Rail Demand

= Base Rail Demand

st Component

ent

In addition to the stand output of elasticity for the full matrix, a distance banded analysis was carr w sensitivity varied, particularly for the more critical longer distance movement dividual movements was also calculated. The following sep lculated:

s;

ties for key zone pairs (e.g. Birmingham - London).

tputs from the model are shown in Appendix D. The columns left to right ng tested (e.g. Rail Times), and the blocks of rows the mode to which

key points that these elasticities show are:

ort trips (0-50 miles), The elasticity of rail demand to rail fares is in-line

work, but may need addressing at a later stage.

observed m rs within the EU. The elasticities derived from this data

inclu

Compmiles, and se are uniformly higher than those derived from the

n istance range (between 100 and 200 miles) is likely to underestimate

TD

BD

TC = Test Co

BC = Base Cost Compon

ard ied out to determine ho

s. The elasticity of several inarate elasticities were ca

0 – 50 Miles;

50 – 100 Miles;

100 – 150 Miles;

150 – 200 Miles;

200 – 300 Mile

300+ Miles;

Full Matrix; and

10 individual elastici

The implied elasticities ourepresent the elasticity beithe demand refers. The

Excluding shwith guidance; and

The elasticity of highway demand to highway fuel cost is of a lower magnitude than the expected value of -0.3. Since no option tests or sensitivities include changes to highway fuel cost, this is unlikely to be an issue for HS2

The following chart (Figure 8.2) shows a fitted High Speed Share by journey time derived from arket shares on major corrido

are (based on an average speed of 100mph): -0.55 for 200 miles, and -1.55 for 300miles, not ding generation effects.

aring these with PLD, estimated rail journey time elasticities are approximately -1.5 for 200 -3.0 for 300 miles in PLD, The

observed data, which can be attributed, in part, to the generation response. Based on this and other guidance, the elasticities up to 200 miles are at the upper end of the expected range.

However, it should be emphasised though – by contrast – that the level of demand response iPLD at the lower end of the dthe demand impacts of improved rail services.

Inherently, logit based forecasting models give implied elasticities that increase with journey length, contrasting with elasticity based forecasting models which maintain fixed elasticities regardless of journey length. Although the original PSM mode choice calibration specifically investigated a distance relationship, only a weak relationship was isolated. Further investigation and development of parameter models may improve this relationship in the future.



0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 50 100 150 200 250 300 350 400 450 500

Journey Time (Minutes)

HS

Mode S

hare

Figure 8.2 - Fitted High Speed Proportion by Journey Time from EU data on major corridors

8.5 Distributed Value of Time (DVoT) The DVoT approach is used to give a better representation of the change in high speed rail usage as a el,

here the rail vs high speed rail choice is replaced with a new choice mechanism and composite at it fits in with the rest of the incremental tant. To apply this approach a new separate

8.5.1

n – ed Value of Time (DVoT) would view this choice purely as a question of

s

result of premium fares. The approach is implemented as an alternative mode choice modwcost approach. The approach has the advantage thchoice structure and does not require a mode conshigh speed mode (mode h) is required, which is necessary to quantify the trade off between the cost and time savings of the high speed services versus their conventional alternatives.

Background

The appropriateness of the standard absolute logit choice between Classic and High Speed Railhas been questioned due to its central assumption that there are “inherent preferences” betweenthe two sub-modes. These differences manifest themselves as modal constants within the logit model that may distort and exaggerate the predicted market shares. An alternative formulatiousing a distributpassengers’ willingness to pay for time savings on high speed rail over existing classic rail journeytimes. Hence, the model is only useful for testing premium fare scenarios.

The theory behind DVoT is that there is a boundary value of time for each journey, v* that definethe mode share (where all passengers with VoT lower than the threshold choose Classic Rail andthose higher choose High Speed). The proportion choosing High Speed is thus:

1)(])(

*[]/[ vFdvvfCC

vvprRailAllHSRpr SH *)( *TT v

SH

where C and T are the cost and time components of GJT respectively, f(v) is the distribution of VoT, and F(v) its cumulative distribution.

To model this effectively in EMME, a log-normal distribution of passenger Value of Time was reated from NRTS income data that was converted into Value of Time using some additional uidance on the relationship between value of time, income and journey distance. A companion

cg



spreadsheet model was built to independently verify the results produced from the EMME distributed value of time macros.

CDF pax incomes: actual vs log normal: Business/Leisure [to/from LSE > 75 miles]

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.0 12.5 25.0 37.5 50.0 62.5 75.0 87.5 100.0

CA Lei Non-LSE Actual

CA Lei Non-LSE Log norm

CA Lei LSE Actual

CA Lei LSE Log norm

CA Biz Non-LSE Actual

CA Biz Non-LSE Log norm

CA Biz LSE Actual

CA Biz LSE Log norm

Figure 8.3 - Chart showing the fit of lognormal distributions of income to actual data

8.5.2 Derived Values of Time

For DVoT to integrate within the mode choice model a composite cost is required for rail that can pass up to the higher nests of the structure. The theoretical approach that was applied s to calculate e would need the value of time distributions and the corresponding mean value of time for each

in

s each of the high speed and classic sub-modes derived from the original stated

wa the composite cost using an expected maximum utility. For this to be calculated w

purpose.

The values of time derived from the distribution were checked against the existing PSM values to check their compatibility. Any change to the base year value of time would result in a change tomodel sensitivity to time that would need to be compensated for in using the process outlined section 8.4.1. The comparison is difficult to make directly as the original PSM had a set of valueof time for preference work. A comparison of original PSM values of time for classic (C) and high speed (H), the derived NRTS, and the WebTAG values used in the appraisal are shown in Table 8.2.

Table 8.2 - Comparison of VoTs (p/min ,2002 prices and values)

PSM C/H [1] NRTS [2] WebTAG [3] [2] vs [3]

Business CA 51.2 / 61.6 51.40 50.95 1%

Leisure CA 13.7 / 18.5 15.55 7.43 109%

Commuting CA 12.6 / 18.9 23.76 8.4 183%

Leisure NCA 13.7 / 18.5 13.76 7.43 85%

Comm NCA 12.6 / 18.9 17.65 8.4 110%

8.5.3 Variation of the Log-normal distributions

T densit a log-n istribution ishe probability y function of ormal d :

0,2

)(lnexp

2

1),;(x

2

2

x

x

xfX



Where μ and σ are the mean and standard deviation of the variable’s natural logarithm (by definition, the variable’s logarithm is normally distributed).

idual mu and sigma (μ,σ) parameters that define how high speed usage will react to a change in cost. There is a significant amount of variation between purposes that is reflected in the mean/ μ. A higher variation within the distribution is reflected by the size of σ parameter.

purpose

Each distribution derived from the NRTS data had its own indiv

Table 8.3 - Mu (μ) and Sigma (σ) by

μ σ

Business CA 3.85 0.43

Leisure CA 2.54 0.64

Commuting CA 3.04 0.51

Leisure NCA 1 9 2.3 0.7

Comm NCA 2.55 0.8

8.5.4 Differences between the Time and Cost Formulations

The co proach in mode ch dels is to convert all time components to monetary c rnative time formul s used with a conventional logit model and with constant values of time, the results would be ide tical using either. Due to the nature of the integral c T approac would differ. To quantify this difference both ariants were developed in parallel and tested. Generally the time formulation returned a

neral forms of the two variants of

nventional ap oice moosts. If the alte ation i

nh the results alculations for the DVo

vmarginally smaller mode share than the cost formulation. The gethe expected maximum utility calculations are the following:

Tv

TvCv

CU HHC .*)ln(

.*)ln(

*2

Cv

Cv

eT

vTU HHT .

*)ln(.

*)ln(*

22

8.5.5 Implications for Appraisal

With the use of DVoT changes would need to be made to the standard appraisal approach. The main reason for this relates to the central idea behind the approach: that value of time would not be constant, and therefore the average high speed and classic rail value of time would be different for each journey. Hence the standard constant values would no longer be appropriate. The standard appraisal approach of outputting the calculations as times and applying the appraisal

hat the values coming out using DVoT already had the derived

8.5.6

ata nd

arency and allowed the process to be de-bugged and independently checked. When the checking was completed the spreadsheets were used to cross-check the implementation within EMME and to see that what was happening within PLD was sensible.

values of time was modified so tvalues of time applied.

Spreadsheet Models

The DVoT approach has a lot of complicated components. These would be very difficult to check step-by-step in EMME, so initially building and testing was undertaken in spreadsheets using dfrom the PLD model. Sub-matrices of five sample origins (Birmingham, Bristol, London, Leeds aManchester) to all destinations were imported and the necessary calculations undertaken. This approach allowed transp



Figure 8.4 - Spreadsheet Model

8.5.7 Examples of the DVoT Model

To test whether the DVoT Model performed realistically a series of premium fares tests were undertaken. The standard (mode t) model specification was converted as required: the necessary macros were modified, and the high speed lines/services were changed (to mode h) to distinguish them from conventional services. With the mode h present, the high speed sub mode choice would be activated and hence DVoT, as this is where the approach occurs.

everal different premium fare sce

speed services (allowing direct comparison with the central

n-Birmingham, Table 8.4 and Table 8.5 illustrate

ium and 10, 20 and 30 the fixed

S narios were undertaken:

No fare premium for highcase).

In addition to this, we tested three incremental fare changes. Fixed amounts of 10%, 20% and 30% of the London to Birmingham fare were added to all high speed fares uniformly, irrespective of their journey length..

Four key movements were chosen to analyse the effects. These were LondoLondon-Glasgow, London-Liverpool and London-Manchester. the fares for each movement where 0 refers to no fare premadditive percentage of the London-Birmingham fare.



Table 8.4 - Business Fares (Average Pounds, one-way)

0 10 20 30

Premium 0.00 3.84 7.68 11.52

London – Birmingham

38.39 42.23 46.07 49.91

London – Glasgow

44.92 48.76 52.60 56.44

London – Liverpool

51.55 55.39 59.23 63.07

London – Manchester

54.76 58.60 62.44 66.28

Table 8.5 - Leisure Fares (Average Pounds, one-way)

0 10 20 30

0 10 20 30

Premium 0.00 2.24 4.48 6.71

London – Birmingham

22.38 24.62 26.86 29.09

London – Glasgow

35.49 37.73 39.97 42.20

London – Liverpool

30.85 33.09 35.33 37.56

London – Manchester

31.82 34.06 36.30 38.53



The model produced the following results (Table 8.6 and Table 8.7 for Business and Leisure respectively):

Table 8.6 - Business Demand

London to Birmingham

0 10 20 30

Base Classic Rail Demand 785 785 785 785

Base Air Demand 0 0 0 0

Base Highway Demand 469 469 469 469

Base Total Demand 1254 1254 1254 1254

Test Classic Rail Demand 0 0 1 9

Test Air Demand 0 0 0 0

Test Highway Demand 397 412 424 440

Test HS Rail Demand 981 935 898 849

Test Total Demand 1379 1347 1322 1297

London to Glasgow

0 10 20 30










London to Liverpool

0 10 20 30










London to Manchester

0 10 20 30










Table 8.6 shows that business demand does not demonstrate a significant switch to Classic Rail with increase in Fares. The noticeable reduction in High Speed Rail usage relates to a decrease in generated travel.



Table 8.7 - Leisure Demand

London to Birmingham

0 10 20 30










London to Glasgow

0 10 20 30










London to Liverpool

0 10 20 30










London to Manchester

0 10 20 30










As expected, Table 8.7 illustrates that leisure users respond with a greater shift to Classic Rail with fare increase than Business.



Table 8.8 - Total Daily Revenue Change (£)

Business: London-Manchester Business: London-Birmingham

0

10000

20000

30000

40000

50000

0% 10% 20% 30%

0

2000

4000

6000

8000

10000

12000

14000

0% 10% 20% 30%

Leisure: London-Manchester Leisure: London-Birmingham

0

5000

10000

15000

20000

25000

30000

0% 10% 20% 30%

0

2000

4000

6000

8000

10000

12000

14000

0% 10% 20% 30%

Table 8.8 provides an analysis of the revenue changes, and cost/demand levels reveal evidence of the following effects occurring within the DVoT model:

Crowding effects - reductions in travel decreases the level of crowding

Interaction between purposes – with a fare increase purpose with lower value of time will be quicker to transfer to alternatives. This will free up capacity for those with a higher value of time.

Interaction between destinations – two destinations on the same route may impact each other if they have differing levels of attractiveness.

Optimal fare levels – there are noticeable peaks in revenue, though this is different by origin and destination pair.

The complex interaction between these factors makes it difficult to quantify the effects of fare change on a global basis. The situation is made even more complex by the existence of different fare structures based on advanced purchase and time-of-travel ticket restrictions and yield management systems. A greater appreciation these combined effects is required before an optimal fare strategy is reached.

8.6 Summary of Approach For main option testing, high speed services were modelled assuming high speed trains are essentially a route / service choice via assignment in the existing PLD mode choice structure, rather than as a new mode choice. This reflected the three key assumptions of high speed rail in most options being evaluated:

No premium fare assumed, so no need to take into account trade-off behaviour between journey time and fare for potential HS2 travellers;

As a conservative assumption, that high speed rail offers no quality aspects over and above that already delivered by long-distance rail services other than reliability improvements which are explicitly modelled in journey times; and



High speed rail services often effectively substituting for existing long-distance services on the WCML or providing an integrated service provision with residual services on the route. Many passengers making intermediate journeys would either see a like-for-like replacement journey or a mix of connecting high speed and conventional rail services. The assignment model should reflect this situation;

However, in the situation where premium fares are considered, the DVoT approach provides initial views as to the demand and revenue effects. Further work is required to understand appropriate levels of premium fares which could be achieved, and the value for money of implementing such policies.



9. PLANET South Model 9.1 Overview

The PLANET South Model upgrade of 2009 is the subject of a separate report. The main developments to this model are documented here. This section introduces the PLANET South Model (PS) and summarises the development required to incorporate it in the Model Framework for High Speed 2 (HS2).

The PLANET South Model is a single mode (rail passenger) demand forecasting tool designed to forecast passenger flows and travel times for travel on National Rail services during the AM Peak period (07:00 to 10:00) in the south of England. The area covered approximates to that south of a line from the Severn estuary to the Wash, although the model focus is towards the main commuter corridors and services into greater London.

PS was developed to enable the impacts of changes to the National Rail network in the south of England to be assessed, in terms of passenger flows, crowding and journey times.

The model includes travel by public transport only, and represents conditions during the AM Peak Period (07:00 to 10:00) on a typical weekday (Monday – Friday). There is a full representation of National Rail services, together with most London Underground (LUL) and light rail (DLR) services, together with sufficient central London bus services to allow for passenger dispersal to be represented.

PS uses demand growth forecasts for future years which are externally specified and input to the model process, and which incorporate assumptions concerning the level of exogenous growth in demand for travel. This is further discussed in section 9.1.2 below.

9.1.1 Key Features of PLANET South Model

PS offers the following features:

Full representation of NR services in southern England;

Sufficient representation of Transport for London services (LUL, DLR and Bus) to represent competition where relevant and passenger dispersal within greater London;

Rail route choice where reasonable route choices exist;

Different demand responses based on travel purposes of commuting, business and other (leisure) markets;

Response to congestion on the rail modes;

The model provides a range of outputs to inform scheme development and decision-making:

Statistics on passenger flows – such as number of passengers, travel time and distance travelled;

Passenger flows by route;

Levels of train passenger crowding anticipated;

Fare revenue by operator group14; and

Economic benefits

14 Estimated from passenger kilometres



9.1.2 How PS works

Overview

PS essentially “assigns” or loads a pattern of passenger demand onto the available services, allocating passengers to services and routes to ensure their overall generalised journey time (a weighted combination of in-vehicle time, crowding penalty, waiting time, boarding penalty, access and egress times) is kept to a minimum. It then calculates the level of crowding on each service, which changes the crowding penalties, and hence the generalised journey times. The process is repeated iteratively until the model “converges” or reaches a stable balance of supply and demand. This process is further discussed below.

To operate, the model requires two major data elements:

Passenger demand by journey purpose (on an Origin to Destination basis); and

A network of public transport services available to those passengers.

These are further discussed below.

The PLANET South model had been updated to represent 2007/8 patterns of demand and supply (this version of the model is referred to as PSAM v4) just prior to the HS2 model framework development, and hence no major updating to the model’s underlying data was required.

To forecast the impacts of changes to the rail system, the model is usually run for a “forecast year”, or years, to take account of exogenous demand growth, and for a “Base” and a “Test” scenario, to assess the impacts of the changes in services proposed. These aspects are further discussed below.

Passenger Demand Data

Demand is based on a representation of the pattern of demand from 2007/8 ticket sales data, provided from the DfT Rail “LENNON” database, but relates solely to travel during the AM Peak Period (07:00 – 10:00). For travel wholly within greater London, additional demand data was sourced from Transport for London and adjusted to represent the same “base” year, i.e. 2007/8.

Demand is disaggregated into one of three journey purposes, as this is a simple indicator of passenger sensitivity to crowding and fare (as revenue estimation is an output from the model). The three purposes used are:

Commuting;

Business; or

Other (Leisure).

As the demand data is based on all journeys into, out from or within the south of England, it includes passengers that are also represented in PLANET Long Distance model (e.g. journeys from Birmingham to central London).

Rail Network Data

The model represents all National Rail services in southern England, derived from timetable data, for services operating on a typical weekday (Monday – Friday) during the AM Peak Period (07:00 – 10:00). In addition to the representation of National Rail services, there is additional data for the London Underground (LUL) and light rail (DLR) services, based on data provided by Transport for London. This is less detailed than for the National Rail system, but ensures the model is able to adequately represent the role of LUL services as a feeder for longer distance travel, or as a competitor where appropriate. Similarly, but to a lower level of detail, it includes a representation of the feeder system provided by London Bus services within greater London. This additional network representation ensures passenger dispersal within greater London is adequately represented and evaluated.

All services are represented on a frequency basis, rather than on the basis of a service timetable. Stopping patterns, together with any boarding or alighting restrictions, are included.



As PS includes all services operating wholly or partly within southern England, this includes those services represented in the PLANET Long Distance model (e.g. trains from Birmingham to London Euston).

The Assignment Process

The model identifies the route choice for passengers, based on their journey origin and destination, taking into account access and egress time, boarding penalties, waiting time, in-vehicle travel time and crowding, together with penalties where interchange is involved. These elements are combined using weights to give a single measure referred to as a “generalised time”, which the model minimises. The model can, where appropriate, “spread” demand for a particular origin to destination journey between different services or even routes to ensure the generalised time is minimised.

Waiting time is calculated on the basis of the average service frequency, assuming that the passenger arrives randomly, i.e. passengers exhibit no prior knowledge of the service timetable.

The assignment process takes account of passenger crowding on the route chosen by applying a time penalty to the published train journey times, based on research and advice embodied in the Passenger Demand Forecasting Handbook (PDFH). Crowding is calculated based on the number of passengers carried between each station served, compared to the seating and notional standing capacities of the service concerned.

The model does not take into account the fare paid by the passenger. This is excluded as a large proportion of passengers are fare insensitive, as they hold a season ticket or similar, or their route choice is not affected by the fare paid.

Growth in Demand

The model is required to demonstrate two elements of demand growth:

Exogenous growth in demand for rail travel over time (driven by factors such as changes in population and economic activity); and

Endogenous growth in demand, e.g. as a result of changes in rail supply or road congestion.

As already noted, exogenous growth is derived externally to the model from other tools and explicitly defined for PS. This approach ensures an explicit approach to underlying demand growth is applied in PS. It also ensures the assumptions are consistent, both between model tests and with other demand forecasting tools, such as DfT “TEMPRO”. In addition, as different growth assumptions can have a marked effect on the passenger flows and user benefits, it facilitates a flexible approach to testing alternative growth assumptions.

For endogenous growth, the model uses the concept of demand elasticity to forecast the demand response to changes in the services provided, based on the change in generalised time as a result of such changes. There is no capability to identify modal shift or the impact of changes in competing modes, in particular the use of the private car, as there is no comparable representation of the costs of car travel. To enable such changes in generalised time to be calculated, the model is used to compare the change between two scenarios or cases: a “Base” (or “Do Minimum”) and a “Test”.

9.2 Updates for use in HS2 Model Framework The main amendment required was the treatment of longer distance travel demand, which was removed from the three purpose-based tables and replaced by a data interface to the PLANET Long Distance (PLD) model. Similarly, an interface was developed to export to the PLD model the level of passenger loadings forecast on longer distance services, so that those passengers could be taken into account in that model.

The three purpose based demand tables in PS were manipulated to remove demand that crossed a “screenline” between the PS detailed model area and the Midlands, as such trips were already represented in the PLD model, where there was the capability to allow for generation, mode and



route transfer more explicitly. These trips were replaced by transferring equivalent demand data from the associated PLD model run.

This demand data interface took the form of a set of demand tables exported from PLD which contain the pattern of travel on each entry corridor of the rail system into the PS model area. There were 20 corridor points (10 southbound and 10 northbound) plus allowance for a further two points associated with demand on the HS2 corridor. The relevant demand was factored down from the PLD level of daily demand to the AM Peak Period modelled in PS, by means of corridor specific factors. The trip end within the PS area was then disaggregated to the finer zone system used in PS, whilst the corridor concerned was adopted as a dummy location for the remote trip end. This demand table was then assigned as a fourth “user class” alongside the three purpose based demand tables. This approach was adopted to ensure that the route used and impact of changes in such longer distance demand were more accurately reflected, taking account of the more refined route choice and service pattern available in PS.

The passenger loading interface was required to enable PLD to take account of the impact of local passengers on crowding levels on longer distance services. This was calculated in PS in units of passengers per train per hour, for selected (long distance) services only, and exported. This approach enabled this level of demand to be “pre loaded” onto the equivalent services in the PLD model, despite the different time periods and service frequencies used in the two models, and then included in the calculation of crowding within the PLD model assignment process.

9.3 Demand Data and Modifications

9.3.1 PS Demand Data Introduction

The rebuilt PS demand data or matrices are stratified into 3 purposes:

Business;

Leisure; or

Commuting;

They are further split into two further classes per trip purpose:

Production to Attraction; or

Attraction to Production;

The use of productions and attractions is to allow future year matrix growth to be applied at a more reliable level than the OD level, while retaining the flexibility for the elasticity process to apply different elasticities for productions and attractions. In practice the same elasticity is used in PS for productions and attractions within a particular trip purpose.

Although there are six demand classes in total, these are summed to 3 classes in Origin - Destination format for assignment purposes.

9.3.2 PS Matrix Production

PLANET South (PS) is used to model the effects of High Speed rail on rail generalised journey times (GJT) - and particularly the crowding element - within the wider South East. The PS matrices focus on the weekday AM peak, so economic (dis)benefits at other times are estimated using annualisation factors within the HS2 appraisal spreadsheet.

For HS2, the latest version of PS was used: v4, developed during early 2009. PSv4 updates the London South-East AM peak demand matrices (business, commute and leisure) using the 2007/8 LENNON dataset described above, but retains LATS 2001 for distribution of demand to zones15.

Deannualisation in PSv4 is unchanged from PSv3.4, and based on bespoke LENNON downloads for South West Trains, as specified by SDG in 2004/5.

15 The motivation behind the recent National Rail Travel Survey (NRTS) was to provide national data of the type provided by LATS in the LSE area.



In the HS2 Integrated Modelling Framework (IMF), an 'island' is cut in the PS demand matrices, such that only demand internal to the wider South East is retained (i.e. below the blue screenline from South Wales to the Wash in Figure 9.1 below). This is to avoid overlap with origin-destination flows covered by PLD (e.g. Birmingham to London). These flows are replaced, in the HS2 model framework, by the transfer of demand from PLD, using a series of select link analyses, and the combination of these demand elements into a fourth “purpose” (long distance demand), which is assigned in PS alongside the three conventional purpose segments.

Figure 9.1 - PS Matrix 'Island'

9.4 Supply Modifications


A check for the currency of the networks was undertaken. New stations and rail links were added where necessary.

Rail Links

The networks were amended in certain locations, for instance to remove redundant data left from previous network enhancements were undertaken, such as the Redhill Quarry line or the Great Western Main Line.

Stations

The following stations were added to PS, to reflect recent changes in the networks, but also to increase accuracy:

Mitcham Eastfields,

Kettering,

Market Harborough,

Imperial Wharf,

Heathrow Terminal 5

Wood Lane



9.4.2 Rail Service Updates

In all models, the national rail services were replaced with the December 2007-May 2008 timetable, with Wednesday 13th February 2008 taken as the nominal date. This process is documented in the separate report, though is broadly similar to that undertaken to update PLD (See 5.4.2 above).

9.5 Assignment Process and Parameters The rail assignment remains a crowded multiclass assignment, now with 4 trip purpose segments (business, leisure, commuting and long distance). Equilibration of supply and demand is sought by means of the Method of Successive Averages (MSA), using 12 iterations. It should be borne in mind that, whilst MSA is a tried and tested method, problems can occur if there is an excessive imbalance between supply and demand: the model may produce extremely high skims, and therefore model results need to be scrutinised to ensure convergence has been achieved.

The procedure is carried over from the 2009 PLANET South rebuild. The MSA is an iterative process such that it determines the weighted average of the current assigned flow and the result from the previous iteration, i.e.:


Where:


MSA_flow=@voltrn

noiteration_n

n1

This ensures that each iteration is combined with all previous iterations in a way which encourages convergence.

The assignment parameters used are carried forward from PS, and the main parameters are summarised in Table 9.1 below.

Table 9.1 - PS Assignment Parameters

Parameter Value

Board Penalty (minutes) 3.516





The 4th journey “purpose” (long distance demand) is used only for crowding calculations, and is not specifically used for economic evaluation. Additionally, cost skims are also combined with the MSA process.

PS (and PM) uses a modified crowding calculation, where both seated and standing capacities are taken into account when calculating capacity. This is more realistic in the case of AM peak models with significant commuting flows.

16 Rail is generally 3.5, with exceptions on the Metropolitan Line.



9.6 Demand Responses The elasticity process is retained from the rebuilt PS model. This is used to increase or reduce trips at the origin-destination pair level for trips within the south east. It should be borne in mind that this is a single mode model, and there is no associated change in, for example, car demand.

9.7 Interfaces of PS with other Models

9.7.1 Overview

This section describes how the interactions are implemented in PS. The principal interaction, as regards PS, is with PLD.

In each case, the models interact by means of specific text files, which are created automatically as part of the model run.

9.7.2 Demand transfer to PS

As mentioned above, this is undertaken by means of a series of “select link” matrices, to enable the pattern of demand to be retained and assigned in PS.

As the zone systems differ between PLD and PS, the demand is exported using zone groups which can then be equivalenced and disaggregated in PS where appropriate. The trip end outside the PS model area is given a unique zone group, as identified in the table below, which is represented as a specific additional zone in PS. The trip end within the PS model area is exported using a zone group “gs” where each PLD zone has a unique definition (gs01 to gs235), which can then be disaggregated to the corresponding zones in PS. In addition factors are applied to convert the daily demand exported from PLD to an equivalent AM Peak period demand, as used in PS.

Details of the select links, zone groups, dummy zone numbers and AM Peak Factors used are provided in Table 9.2 below. The cordon points create an “island” as already shown in Figure 9.1 above.

The AM Peak Factors are applied as the data is exported from PLD. They were derived by comparison of the individual link flows in PLD with the equivalent link flow in PS prior to the demand modification described above, and ensure the long distance demand is adjusted from the all-day value used in PLD to an appropriate value to represent the AM Peak period modelled in PS. For this reason the process is undertaken for only one direction of travel at a time, this ensures the variation by direction is retained in PS. In two cases (Newport to Hereford and Evesham to Moreton-in-Marsh) this process resulted in intuitively high factors; these locations are ones where there is significant disparity between PLD and PS, although the flows in question are relatively small and of secondary importance to HS2 forecasting. There are other locations listed where the adjustment factor is equally intuitively too small, but, in general, these are at less heavily trafficked locations, and the error is not of major consequence.

Table 9.2 - PS Cordon Points

From Station Station Code

To Station Station Code

Zone Group PS

Dummy Zone

AM Peak Factors

Newport NWP Cardiff CDF gc01 999101 0.2520

Newport NWP Hereford HFD gd01 999102 3.2089

Gloucester GCR Cheltenham Spa CNM gf01 999103 0.0435

Moreton in Marsh MIM Evesham EVE gg01 999104 0.0764

Banbury BAN Leamington Spa LMS gh01 999105 0.1625

Wolverton WOL Rugby RUG gj01 999106 0.1618

Wolverton WOL Northampton NMP gk01 999107 0.0887

Bedford BDM Wellingborough WEL gl01 999108 0.0988

Peterborough PBO Leicester LEI gq01 999109 0.2408



PS AM Peak Station Station

From Station To Station Zone Group Code Code

Dummy Factors Zone

Peterborough PBO Grantham GRA gr01 999110 0.1723

Cardiff CDF Newport NWP gc01 999101 0.1305

Hereford HFD Newport NWP gd01 999102 0.4138

Cheltenham Spa CNM Gloucester GCR gf01 999103 0.0576

Evesham EVE Moreton in Marsh MIM gg01 999104 3.6892

Leamington Spa LMS Banbury BAN gh01 999105 0.3285

Rugby RUG Wolverton WOL gj01 999106 0.2547

Northampton NMP Wolverton WOL gk01 999107 0.6139

Wellingborough WEL Bedford BDM gl01 999108 0.4553

Leicester LEI Peterborough PBO gq01 999109 0.0241

Grantham GRA Peterborough PBO gr01 999110 0.3404

HS2 outbound from SE gu01 999111 0.1250

HS2 inbound to SE gu01 999111 0.4000

The demand data for each cordon point is imported and disaggregated within the PS model area and then accumulated into a single matrix for all the cordon points. The resulting demand matrix can then be assigned as part of the amended PS assignment procedure. This additional long distance demand in PS is not evaluated in the economics, as it is already included in PLD, and is purely there to provide suitable crowding levels in the model, and to enable the patterns of long distance demand dispersal to be better represented and understood.

9.7.3 Demand transfer from PS

The basic process is to identify the local demand in PS which has been assigned to long distance services, and calculate the level of that local demand in units of passenger demand per train per hour. These preloads are then exported from the PS model and imported into PLD.

A service is considered as long distance if it has as least one stop between the London terminus and the “cordon point” and proceeds beyond the “cordon point”. This cordon is shown in Figure 9.1 above. The initial step in the process is to define a series of “dummy services” to represent the corridors of interest, then relevant services in those corridors are “flagged”, and finally the local demand preload values are calculated and exported.



10. PLANET Midlands Model 10.1 Overview

The PLANET Midland Model development of 2009 is the subject of a separate report. The main developments to this model are documented here. This section introduces the PLANET Midlands Model (PM) and summarises the development required to incorporate it in the Model Framework for High Speed 2 (HS2).

The PLANET Midlands Model is a single mode (rail passenger) demand forecasting tool to provide information on passenger flows and travel times for travel on National Rail services during the AM Peak period (07:00 to 10:00). The area covered approximates to the East and West Midlands regions, covering Derby, Nottingham, Leicester, Wolverhampton and Birmingham; although the model focus is towards the main commuter corridors into central Birmingham.

PM was developed to enable the impacts of changes to the National Rail network in the east and west Midlands to be assessed, in terms of passenger flows, crowding and journey times.

The model includes travel by public transport only, and represents conditions during the AM Peak Period (07:00 to 10:00) on a typical weekday (Monday – Friday). There is a full representation of National Rail services, derived from timetable data for services operating on a typical weekday (Monday – Friday).

PM uses demand growth forecasts for future years which are externally specified and input to the model process, and which incorporate assumptions concerning the level of exogenous growth in demand for travel. This is further discussed in section 10.1.2 below.

10.1.1 Key Features of PLANET Midlands Model

PM offers the following features:

Full representation of NR services in east and west Midlands;

Rail route choice where reasonable route choices exist;

Different demand responses based on travel purposes of commuting, business and other (leisure) markets;

Response to congestion on the rail modes; and

Explicit modelling of station choice using detailed accessibility data, see section 10.1.2 below.

The model provides a range of outputs to inform scheme development and decision-making:

Statistics on passenger flows – such as number of passengers, travel time and distance travelled;

Passenger flows by route;

Levels of train passenger crowding anticipated;

Fare revenue by operator group; and

Economic benefits

Whilst PM has a good representation of the National Rail services in the core area, it does not include any representation of bus / tram supply, or even Midlands Metro services; this is not considered to be a significant simplification as far as the use as part of the HS2 model framework, particularly given the accessibility based station choice mechanism.

Demand is based on the patterns of demand from 2007/8 ticket sales data for the same AM Peak Period. Demand is disaggregated into one of three journey purpose, as this is a simple indicator of



passenger sensitivity to crowding and fare (as revenue estimation is an output from the model). The three purposes used are:

Commuting;

Business; or

Other (leisure).

10.1.2 How PM works

Overview

PM essentially “assigns” or loads a pattern of passenger demand onto the available services, allocating passengers to services and routes to ensure their overall generalised journey time (a weighted combination of in-vehicle time, crowding penalty, waiting time, boarding penalty, access and egress times) is kept to a minimum. It then calculates the level of crowding on each service, which changes the crowding penalties, and hence the generalised journey times. The process is repeated iteratively until the model is expected to “converge” or reaches a stable balance of supply and demand. This process is further discussed below.

To operate, the model requires two major data elements:

Passenger demand by journey purpose (on an Origin to Destination basis); and

A network of public transport services available to those passengers.

These are further discussed further in this section.

The PM model was developed to represent 2007/8 patterns of demand and supply just prior to the HS2 model framework development, and hence no major updating to the model’s underlying data was required.

To forecast the impacts of changes to the rail system, the model is usually run for a “forecast year”, or years, to take account of exogenous demand growth, and for a “Base” and a “Test” scenario, to assess the impacts of the changes in services proposed. These aspects are further discussed below.

Passenger Demand Data

Demand is based on a representation of the pattern of demand from 2007/8 ticket sales data, provided from the DfT Rail “LENNON” database, but relates solely to travel during the AM Peak Period (07:00 – 10:00).

Demand is disaggregated into one of three journey purpose, as this is a simple indicator of passenger sensitivity to crowding and fare (as revenue estimation is an output from the model). The three purposes used are:

Commuting;

Business; or

Other (Leisure).

As the demand data is based on all journeys into, out from or within the east or west Midlands, it includes passengers that are also represented in PLANET Long Distance model (e.g. journeys from Birmingham to central London).

PM differs from the other constituent models in the HS2 model framework in that it uses a different approach to station access, station egress and station choice. It utilises recent research, embodied in the national accessibility model, to estimate the accessibility of the nearest five stations to both journey origin and destination, at a very detailed geographical level (known as Census Output Areas). This analysis leads to a station-station choice between 25 possible combinations. This is undertaken separately for Car Available (CA) and (Non Car Available (NCA) passengers, as these experience very different levels of accessibility.



Rail Network Data

The model represents all National Rail services in the east and west Midlands, derived from timetable data, for services operating on a typical weekday (Monday – Friday) during the AM Peak Period (07:00 – 10:00).

All services are represented on a frequency basis, rather than on the basis of a service timetable. Stopping patterns, together with any boarding or alighting restrictions, are included.

As PM includes all services operating wholly or partly within the east and west Midlands, this includes those services represented in the PLANET Long Distance model (e.g. trains from Birmingham New Street to London Euston).

Assignment Process

The model identifies the route choice for passengers, based on their station – station choice, taking into account access and egress time, boarding penalties, waiting time, in-vehicle travel time and crowding, together with penalties where interchange is involved. These elements are combined using weights to give a single measure referred to as a “generalised time”, which the model minimises. The model can, where appropriate, “spread” demand for a particular journey between different services or even routes to ensure the generalised time is minimised.

Waiting time is calculated on the basis of the average service frequency, assuming that the passenger arrives randomly, i.e. passengers exhibit no prior knowledge of the service timetable.

It takes account of passenger crowding on the route chosen by applying a time penalty to the published train journey times, based on research and advice embodied in the Passenger Demand Forecasting Handbook (PDFH). The model identifies the route choice for passengers, based on their journey origin and destination, taking into account access and egress time, boarding, waiting, in-vehicle travel time and crowding, together with penalties where interchange is involved. These elements are combined using weights to give a single measure referred to as a “generalised time”, which the model minimises. The model can, where appropriate, “spread” demand for a particular origin to destination journey, represented as a series of station – station journeys, between different services or even routes to ensure the generalised time is minimised.

Waiting time is calculated on the basis of the average service frequency, assuming that the passenger arrives randomly, i.e. exhibits no prior knowledge of the service timetable.

The assignment process takes account of passenger crowding on the route chosen by applying a time penalty to the published train journey times, based on research and advice embodied in the Passenger Demand Forecasting Handbook (PDFH). Crowding is calculated based on the number of passengers carried between each station served, compared to the seating and notional standing capacities of the service concerned.

The model does not take into account the fare paid by the passenger. This is excluded as a large proportion of passengers are fare insensitive, as they hold a season ticket or similar, or their route choice is not affected by the fare paid.

Growth in Demand

The model is required to demonstrate two elements of demand growth:

Exogenous growth in demand for rail travel over time (driven by factors such as changes in population and economic activity); and

Endogenous growth in demand, e.g. as a result of changes in rail supply or road congestion.

As already noted, exogenous growth is derived externally to the model from other tools and explicitly defined for PM. This approach ensures an explicit approach to underlying demand growth is applied in PM. It also ensures the assumptions are consistent, both between model tests and with other demand forecasting tools, such as DfT “TEMPRO”. In addition, as different growth assumptions can have a marked effect on the passenger flows and user benefits, it facilitates a flexible approach to testing alternative growth assumptions.



For endogenous growth, the model uses the concept of demand elasticity to forecast the demand response to changes in the services provided, based on the change in generalised time as a result of such changes. There is no capability to identify modal shift or the impact of changes in competing modes, in particular the use of the private car, as there is no comparable representation of the costs of car travel. To enable such changes in generalised time to be calculated, the model is used to compare the change between two scenarios or cases: a “Base” (or “Do Minimum”) and a “Test”.

10.2 Updates for use in HS2 Model Framework The PLANET Midlands model was developed to represent 2007/8 patterns of demand and supply (this was a new model developed in part from the previous PLANET North model) just prior to the HS2 model framework development, and hence no major updating to the model’s underlying data was required.

The main amendment required was the treatment of longer distance travel demand, which was removed from the three purpose based tables and replaced by a data interface to the PLANET Long Distance (PLD) model. Similarly, an interface was developed to export to the PLD model the level of passenger loadings forecast on longer distance services, so that those passengers could be taken into account in that model.

The three purpose based demand tables in PM were manipulated to remove demand that crossed a “cordon” around the core West Midlands area centred on Birmingham, as such trips were already represented in the PLD model, where there was the capability to allow for generation, mode and route transfer more explicitly. These trips were replaced by transferring a set of train passenger pre-loads of long distance passengers on relevant services from the associated PLD model run. This approach, which differed from that used in the equivalent process in PS, was an appropriate simplification given the more simple local rail network in Birmingham compared to London.

The passenger loading interface was required to enable PLD to take account of the impact of local passengers on crowding levels on longer distance services.

In both cases, the preload value was calculated in units of passengers per train per hour, for selected (long distance) services only, and exported. This approach enabled this level of demand to be “pre loaded” onto the equivalent services in the other model, despite the different time periods and service frequencies used in the two models, and then included in the calculation of crowding within that model assignment process.

10.3 Demand Data and Modifications PLANET Midlands is used to model the effects of High Speed rail on AM peak crowding within the Midlands. As passengers may react to changes in crowding by switching route, HS2 may also cause significant effects on other GJT components (e.g. in-vehicle times). In common with PS, PM is a weekday AM peak model, so effects are extrapolated to the rest of the week (and year) using annualisation factors. In the case of crowding effects, extrapolation is limited to the afternoon peak, whereas any improvements to local timetables are assumed to apply throughout the day.

Within PM, the demand data relate to LENNON 2007/8. Originating journeys on West Midlands (‘n-network’) travelcards are based on sales recorded in LENNON for each PM station, with distribution to destination stations and destination zones based on the National Rail Travel Survey (NRTS). Station-specific uplifts are applied to reflect the shares of (period/season) Travelcards bought from non-LENNON sales channels (NRTS Q14).

For all tickets (i.e. including point-to-point ticketing), NRTS is used to distribute station-to-station demand between zones of ultimate origin and destination, using the postal sector data (NRTS Q2/Q9). Combined with estimates of car available and non-car available access times, these data underpin PM’s incremental logit modelling of changes in a passenger’s choice of station-to-station



route for a given zone-zone journey, following an intervention affecting timetables and/or crowding17.

PM’s deannualisation applies the same assumptions as PLD to estimate and remove weekend travel. To filter demand further for AM peak trips, it is assumed that weekday travel before 10:00 is dominated by use of Full-fares and Season ticketing. Certain ticket types (e.g. standard class, full-fare day returns) are purchased only because they are valid in the AM weekday peak. This implies that all outward travel is undertaken in this period, such that if LENNON shows 1000 journeys from station A to station B in 2007/8 (including return legs), and with 240 working days a year, around 2 journeys will be produced by ‘A’ and attracted to ‘B’ in each AM peak.

Moreover, for other ticket types - e.g. (former) Cheap Day Returns valid after 09:30 - the NRTS questionnaire asks about time of travel and hence allows the estimation of the shares of (outward) weekday journeys occurring during the AM peak.

PM matrices are split into 2 further classes per trip purpose:

Car Available (CA);

Non-Car Available (NCA);

This makes 6 demand classes in total, though these are summed to 3 classes in Origin - Destination format for assignment. The car availability dimension allows the station choice (logit) model to apply a shorter access time (from demand zone to station zone) when passengers are not limited to public transport or walking.

To avoid overlap with origin-destination flows covered by PLD (e.g. Birmingham – London), the PM demand matrices are truncated for use in HS2 model framework, with trips going beyond a West Midlands “cordon” removed (see Figure 10.1 below). Demand to and from outside the West Midland is transferred from PLD as a pre-load.

Figure 10.1 - PM Matrix 'Cordon'

17 Further information on how PM models the effects of local interventions in the Midlands is available in the DfT’s PLANET Midlands Development Report.



10.4 Supply Modifications


A check for the currency of the networks was undertaken. No new stations or rail links were added.

Rail Service Updates

In all models, the national rail services were replaced with the December 2007-May 2008 timetable, with Wednesday 13th February 2008 taken as the nominal date. This process is documented in the separate report, though is broadly similar to that undertaken to update PLD (See section 5.4.2 above).

10.5 Assignment Process and Parameters The rail assignment is a crowded multiclass assignment with 3 trip purpose segments (business, leisure, and commuting). Equilibration of supply and demand is by the Method of Successive Averages (MSA) for 10 iterations. It should be borne in mind that, whilst MSA is a tried and tested method, problems can occur if there is an excessive imbalance between supply and demand: the model may produce extremely high skims, and therefore model results need to be scrutinised to ensure convergence has been achieved.

The procedure is carried over from the 2009 PM development. The MSA is an iterative process such that it determines the weighted average of the current assigned flow and the result from the previous iteration, i.e.:


Where:


MSA_flow=@voltrn

noiteration_n

n1

This ensures that each iteration is combined with all previous iterations in a way which encourages convergence.

The assignment parameters used are carried forward from PS, and the main parameters are summarised in Table 10.1 below.

Table 10.1 - PM Assignment Parameters

Parameter

Value

Board Penalty (minutes) 20





The assignment in PM remains a 3-class public transport assignment process, but takes account of pre-loads provided from PLD in the calculation of passenger crowding. Additionally, cost skims are also combined with the MSA process.



It is important to point out that the main difference in the HS2 model framework between PM and PS is that PS handles long distance trips as a matrix, while PM handles them as pre-loads.

10.6 Demand Responses The elasticity process is retained from the PM model. This is used to increase or reduce trips at the origin-destination pair level for trips within the west Midlands. It should be borne in mind that this is a single mode model, and there is no associated change in, for example, car demand.

10.7 Interaction of PM with other models

10.7.1 Overview

This section describes how the interactions are implemented in PM. The principal interaction as regards PM is with PLD.

In each case, the models interact by means of specific text files, which are created automatically as part of the model run.

10.7.2 Demand transfer to PM

As mentioned above, the demand is transferred from PLD as a passenger pre-load on selected services. This is done in terms of passengers/train/hour and represents those long distance passengers assigned to use long distance trains which stop at least twice within the PM West Midlands cordon, yet go beyond the cordon, and thus interact with local passengers.

10.7.3 Demand transfer from PM

The basic process is to identify the local demand in PM which has been assigned to long distance services, and calculate the level of that local demand in units of passenger demand per train per hour. These preloads are then exported from the PM model and imported into PLD.

A service is considered as long distance if it has as least two stops within the West Midlands “cordon” and proceeds beyond the “cordon point”.

The initial step in the process is to define a series of “dummy services” to represent the corridors of interest, then relevant services in those corridors are “flagged”, and finally the local demand preload values are calculated and exported.



11. Heathrow Model 11.1 Overview

Passengers travelling to or from airports, particularly for international journeys, have particular characteristics that set them aside from other rail users. For example, they are likely to place greater value upon the reliability of the service, especially when accessing the airport. They may be particularly deterred by interchange, partly because of the added risk of delay, but also due to difficulties associated with changing trains while carrying baggage. Similar reasoning suggests a strong aversion to crowded services.

In recognition of these differences in airport passengers’ generalised costs, mode choice for travel to and from PLD Zone 90 (Heathrow) was modelled by a bespoke spreadsheet developed by SKM, and not by the standard hierarchical logit used by PLD for all other flows.

SKM’s model was based on LASAM18, but included the following additional access modes:

Domestic air interlining - mainly between Heathrow and the airports at Manchester, Newcastle, Edinburgh and Glasgow, and

High speed rail.

Future year demand for travel to/from Heathrow was based on DfT forecasts of growth in air travellers, rather than estimated using the rail industry’s Passenger Demand Forecasting Handbook (as per other flows).

Within the airport model, journeys were divided not only by purpose, but also by UK versus foreign resident, in recognition of the interaction with car availability the in mode choice. The model only included flows that could realistically be abstracted by HS2, based on the London – Birmingham – North West – Scotland corridor.

The spreadsheet interfaced directly with PLD, receiving estimates of generalised cost for each access mode for each PLD zone, and returning estimates of the modal split for each zone’s access/egress journeys to/from Heathrow.

It is important to emphasise, that although interlining international passengers are included in the Heathrow model, other (i.e. purely domestic) air journeys to/from Heathrow are modelled within the standard mode choice structure of PLD.

It should be emphasised that long distance access and egress represent a very small share of the Heathrow total, with London dominant, and much of the longer distance access coming from the M4/GWML corridor where HS2 will not compete. This is evident in Figure 11.1 below.

18 London Airports Surface Access Model, created for BAA. BAA has given permission for the use of LASAM parameters for this project.



Figure 11.1 - Heathrow surface access/egress 2007 (CAA)

For comparison purposes, CAA data suggest that a total of 258k (annual onward air passengers) travelled between Birmingham (PLD Zone 5) and Heathrow in 2008, where there is hardly any air service provision. For Manchester (as defined by PLD Zone 130), the corresponding figure is 176k, of which around 60% are interlining passengers. Central London (PLD Zone 117) accounts for 13.5m, with a further 7m from the remainder of Greater London.

The Heathrow model uses a spreadsheet model developed by SKM drawing upon the knowledge gained during development of LASAM. The Heathrow spreadsheet model is used to estimate the following effects:

diversion to HS2 of current Heathrow surface access trips in the HS2 corridor – excluding trips from London; and

diversion to HS2 of ‘interlining’ air passengers taking a domestic flight to/from Heathrow with a connecting international flight.

The Heathrow model spreadsheet is a standalone spreadsheet that was incorporated into PLD. This section discusses the spreadsheet itself, and section 11.2 will discuss how this spreadsheet was embedded within the framework. Full details of the specification of the model can be found in SKM’s HS2 Airport Demand Model Methodology Report.

11.1.1 Adaption of LASAM

To facilitate the choice of mode from each PLD origin zone, LASAM was adapted to a spreadsheet model with the following key features and simplifications:

Air and High Speed Rail added as modes

Focus on the study corridor: London – West Midlands – North West (excluding London);

Three catchment areas - Manchester, Birmingham and an Intermediate area containing Oxford and Milton Keynes, each containing all PSM zones in those catchment areas;



Remove Heathrow Express, Underground, RailAir Coach and Airport Transfers from this sub model as main mode options, as they are only relevant to trips from London, which are excluded from this implementation. However, the modes are still available as part of longer rail journeys on conventional or high speed rail, the main focus of this study;

Conversion of time periods (AM Peak, Interpeak, PM Peak) to average weekday;

Use of one zone to represent Heathrow. The central terminal area is used as a reference for level-of-service;

Segmentation: PLD Car Available Business and Leisure are the only purposes dealt with by the Heathrow Model with each split into UK/Foreign sub-segments.

11.1.2 Catchment Area

The expected catchment areas for HS2 rail trips are highlighted in Figure 11.2. The catchment areas can be extended to any non-London zones to accommodate a change in HS2 station location.

Figure 11.2 - Heathrow Model Catchment Areas

11.1.3 Mode Choice

The Heathrow mode choice model is used to forecast the change in mode shares from a current situation and can therefore be referred to as an incremental model. To accommodate HS2 being a completely new service, the rail sub-nest uses an absolute model. Where the rail mode share is less than 5% in the base year, forecasts with HS2 are instead incremented off the bus/coach mode share.

The model hierarchy is inherited from LASAM, and is shown below, in Figure 11.3 to Figure 11.5:



Figure 11.3 - UK Business Mode Choice Hierarchy

Figure 11.4 - Foreign Business Mode Choice Hierarchy



Figure 11.5 - Leisure Mode Choice Hierarchy

As most air passengers using Heathrow who originate in the catchment area will be travelling on international rather than domestic flights from Heathrow, international model coefficients and economic assumptions were adopted from LASAM rather than the domestic equivalents.

The final Heathrow model structure, including all inputs, is shown in Figure 11.6 below.



Figure 11.6 - Heathrow Demand Model Structure

To ensure the Airport Demand Model is as compatible with PLD as possible, where available, cost skims from PLD are used in preference to those from LASAM. Key data that was absent from the PLD skims included the following, where the LASAM data was used:

Air Check-in times;

Taxi / Minicab fares;

Airport Parking Charges.

The components of generalised cost for each mode, segment and zone are combined using the following generalised equations:

Rail (L,S,X): .)(*

)(X/L/M/H InterchZ

D

FareAccessWalkWaitTimeR

p

Bus/Coach: .)(*

)(X/L/M/H InterchZ

D

FareAccessWaitTimeB

p

Taxi:

)(

X/L/M/H

DN

FareTimep

Park and Fly:

)(

)(X/L/M/H

DN

VCostPCostTimep



Kiss and Fly: Air:

)(

)(.35.0 2

X/L/M/H

D

TimeN

Time

N

VCostPCostTime d

p

)(D

FareAccessWaitTimep

where D = Highway Distance, = 0.4 and N = Group Size. Ψ= 0 for Business trips, and the Parking Costs PCost are replaced by Hire Costs for Foreign passengers

The generalised cost parameters used in the formulae are given below for both the base year and forecast future year (2031).

Table 11.1 - Airport Mode Choice Model Parameters

Generalised Cost Parameters 2008 2031

UK

B

usin

ess

UK

Le

isur

e

Non

-UK

B

usin

ess

Non

-UK

Le

isur

e

UK

B

usin

ess

UK

Le

isur

e

Non

-UK

B

usin

ess

Non

-UK

Le

isur

e

Value of time (Heathrow) p/min

73.60

27.01

64.77

26.97

110.9

37.52

97.63

37.46

Vehicle operating cost p/km 11.79

5.39

5.39

5.39

10.54

4.81

4.81

4.81

Time coefficient α(p) 0.18

0.20

0.22

0.25

0.18

0.20

0.22

0.25

Wait coefficient β 0.49

0.55

0.47

0.66

0.49

0.55

0.47

0.66

R_Walk coefficient δ 0.17

0.25

0.22

0.30

0.17

0.25

0.22

0.30

Access coefficient φ 0.55

0.96

0.93

1.17

0.55

0.96

0.93

1.17

Rail Interchange coefficient

0.81

0.61

0.44

0.74

0.81

0.61

0.44

0.74

Bus Interchange coefficient

1.63

0.90

0.44

1.09

1.63

0.90

0.44

1.09

K&F time coefficient 2 α(d) 0.13

0.22

0.02

0.10

0.13

0.22

0.02

0.10

K&F time coefficient 3 ψ -

0.001

-

0.002

-

0.001

-

0.002

Distance exponent θ 0.40

0.50

0.40

0.50

0.40

0.50

0.40

0.50

11.2 Interaction with PLD The Heathrow spreadsheet was initially a stand-alone spreadsheet requiring data to be input manually, and its macros to be run interactively. To be part of the PLD framework, the transfer of data to/from the spreadsheet, which occurs several times within a model run, needed to be automated.

This required the following tasks:

All relevant cost and parameter data to be output, and converted to the required format for the Heathrow model before the spreadsheet is run (see Section 11.2.1);

For all Heathrow Model spreadsheets to be run automatically from batch files and close after calculating;



The demand forecasts to be output from the spreadsheet in a suitable text file format for input back into the Framework (see Section 11.2.2).

Matrix calculations undertaken within the Framework to introduce the Heathrow demand forecasts into the assignment and mode choice model where necessary.

11.2.1 Inputs

The Heathrow Model spreadsheet requires all cost components that build the generalised costs for each mode, for all origins/destinations to/from Heathrow. The matrices output from PLD to the Heathrow model are summarised in Appendix Appendix C. The modelled year and directory path are also required so that the correct files and year specific model parameters are used. Each spreadsheet input is created using EMME macros just before the spreadsheet is called. This data is pre-processed by a spreadsheet using Visual Basic, which is then transferred to the Heathrow model spreadsheet.

The Heathrow spreadsheet automatically detects whether the high speed mode=h is present: if the high speed costs are empty, then the high speed mode share is zero. This allows the Heathrow model to work with the standard model or the DVoT approach.

11.2.2 Outputs

Additional Visual Basic code was added to the end of the Heathrow model’s calculations to process the output in text format for EMME. This required a set format to be adopted and specific matrix numbers to be given to the demand by mode so that it would be input back into the databanks successfully. The output process was checked so that the totals in the spreadsheet are preserved throughout the model.

11.2.3 Assignment and Mode Choice interaction

Additional Macros modifications were made to the Framework for the following purposes:

To introduce the Heathrow Model demand into the assignments;

To freeze the Heathrow Demand within the main mode choice model.

Calculations have been inserted into the assignment macros to overwrite demand to/from Heathrow with demand from the spreadsheet model, for all zones. This demand is then assigned to its relevant network, allowing it to interact with other travellers and affect their journeys. Heathrow demand only impacts Planet South and Midlands as a result of its impact on the PLD assignment.

The Heathrow Demand is frozen from the main PLD mode choice model, as it is the best estimate of mode share to/from Heathrow. The Heathrow demand only has indirect impacts within the main mode choice model: this is as a result of the Heathrow demand within the assignment having an impact on the costs that drive the main mode choice model.

11.3 Example Outputs of Heathrow Model The Heathrow model was tested for a variety of model runs. The following analysis shows the impact on Heathrow Demand of the 2021 ‘Day1’ specification. The key details of this test were:

3 high speed trains services per hour (4 in the peak) between Birmingham and London with 41 minute journey times; and

Additional high speed hybrid services from Manchester, Liverpool and Glasgow, with faster journey times than the Do-Minimum.

The following Tables (Table 11.2 and Table 11.3) show the mode shares for all the PLD demand that is derived from the Heathrow Model. This has been aggregated by region, as the numbers of trips for individual zones is small. The tables show demand from Heathrow (the demand to Heathrow is identical).

Table 11.2 - 2021 “Day1” Business Demand by Mode From Heathrow (produced by Heathrow Model)



Do-Min Test

Air Car Rail Air Car Rail % change

Rail

Scotland 264 1 3 264 1 4 34%

North East 119 2 10 119 2 10 -1%

North West 122 11 26 109 10 40 57%

Yorkshire and Humberside 32 50 30 31 49 32 5%

Wales 10 108 64 10 108 65 2%

West Midlands 11 158 87 10 156 90 3%

East Midlands 15 257 61 15 257 61 1%

South West 0 90 2 0 90 2 4%

Total 574 677 282 558 673 303 7%

Table 11.3 - 2021 “Day1” Leisure Demand by Mode From Heathrow (produced by Heathrow Model)

Do-Min Test

Air Car Rail Air Car Rail % change

Rail

Scotland 624 5 25 622 5 27 8%

North East 295 12 52 295 12 52 -1%

North West 351 37 45 337 36 62 38%

Yorkshire and Humberside 104 203 120 104 201 123 2%

Wales 19 364 141 18 363 144 2%

West Midlands 40 468 325 37 467 329 1%

East Midlands 32 525 202 31 525 203 0%

South West 0 104 18 0 104 19 2%

Total 1465 1718 928 1444 1713 956 3%

Table 11.2 and Table 11.3 show consistent trends between Business and Leisure. The results illustrate the regions that benefit most are along the route of the HS2 scheme, with the North West increasing by the largest proportion. The most significant mode shift effect occurring is from Air to Rail which is most likely to relate to Air being the largest market.



Appendix A - Transit Line Parameters A.1 Transit Line Validation

The validation of the services was undertaken in the following way, for all models (PLD, PS and PM). This section explains the checks undertaken when converting the CIF files to PLANET transit line coding. It also explains the data issues encountered with the CIF.

The checks undertaken are broken down into: Issue, Check and Result. The result shown is generally the end result rather than the interim results. This is because the majority of checks were carried out many times over, until the number of errors diminished to zero.

The following checks are carried out each time a modification is made to the specification of the Perl script. The following checks are made until the errors fall to zero.

All TIPLOCs resolve to a node

Issue: To confirm that each TIPLOC mapping to a ‘relevant’ node was given a node number.

Check: ‘Find in Multiple Files’ in TextPad was used with a regular expression with all output files to find any transit segment starting with two spaces followed by a letter:

^ [A-Z]

Result: All segments converted to a node number.

Correct Routing

Issue: To confirm that the correct series of nodes are visited by the transit lines

Check: Sorting transit lines by number of segments in Emme 3 to identify lines with suspiciously high numbers of segments. These often signify a transit line where an incorrect node is specified, causing a large detour to be required.

Result: Transit line routings are sensible.

All lines are named

Issue: All lines are named according to their origin and/or destination. Any line failing to be treated by the process was left with the original CIF identifier of a letter and 5 numbers.

Check: ‘Find in Multiple Files’ in TextPad was used with a regular expression with all output files to find any transit line header starting with a letter followed by a number:

^a ‘[A-Z][0-9]

Result: All transit lines have a name in the correct format of AA111A.

No import errors

Issue: All transit lines should import without error.

Check: The error count in the import report files.



Result: No errors.

Trains Per Hour on Links

Issue: The trains per hour by each TOC on each link needed to match a published source (Railplanner software, paper timetable, PIXC counts at terminus stations)

Check: The number of trains per hour on a link was compared with the existing reference case. These were largely consistent, and areas where matches were less convincing were investigated in more detail.

Result: The transit frequencies matched the published data in spot checks.

Train Aggregation

Issue: Pre- and Post-Aggregation trains per hour should be consistent.

Check: A check was made to confirm that the number of trains was equal.

Result: This was found to be correct, subject to minor rounding differences (such as where a service of 14 individual trains per day is represented as a train every 68.57 minutes).

Trains Per Hour stopping at Nodes

Issue: The number of trains per hour stopping at nodes should be correct.

Check: The number of trains stopping was checked in Emme 3 by displaying proportional circles fed by the following configurable attribute:

ca_nlinesstop_i == isIStop*60/hdw

In particular, locations were checked where ca_nlinesstop_i==0, i.e. no trains stopped there.

Result: This was useful to visually identify areas where a station node had an incorrect number of stopping trains.

Trains Per Hour stopping at London Termini

Issue: The number of trains per hour stopping at London Termini should be correct.

Check: This was compared with Railplanner19 software.

Result: This was found to match to a satisfactory level.

Redundant Services

Issue: Services running ‘perpendicular’ to the modelled axis had to be removed.

Check: The ratio of Route Length / Crowfly Length was used to find those services with very high ratios. The ratios were used to find routes which were deemed not suitable to be included, due to the peripheral nature of the routes.

These services are removed by naming lines with such origins and destinations as ‘XX____’ and automatically deleting them after import.

Result: These are removed.

19 Railplanner Rail Timetable Tool. See http://www.travelinfosystems.com


http://www.travelinfosystems.com/


Appendix B - Highway Parameters B.1 Volume Delay Functions

Function Form

fd10 length / 40 * 60

fd11 (((volau + ul1) / lanes .le. 1200) * ((length / (116 - (.006 * (volau + ul1) / lanes))) * 60)) + (((volau + ul1) / lanes .gt. 1200 .and. (volau + ul1) / lanes .lt. 2520) * (length / (109.5 - (49 * (((volau + ul1) / lanes - 1200) / 1000)))) * 60) + (((volau + ul1) / lanes .ge. 2520) * (length / (45 / (1 + 45 * ((volau + ul1) / lanes - 2520) / (8 * length * 2520))) * 60))

fd12 (((volau + ul1) / lanes .le. 1200) * ((length / (112 - (.006 * (volau + ul1) / lanes))) * 60)) + (((volau + ul1) / lanes .gt. 1200 .and. (volau + ul1) / lanes .lt. 2430) * ((length / (105.5 - (49 * (((volau + ul1) / lanes - 1200) / 1000)))) * 60)) + (((volau + ul1) / lanes .ge. 2430) * (length / (45 / (1 + 45 * (( volau + ul1) / lanes - 2430) / (8 * length * 2430))) * 60))

fd13 (((volau + ul1) / lanes .le. 1080) * ((length / (108.5 - (.006 * (volau + ul1) / lanes))) * 60)) + (((volau + ul1) / lanes .gt. 1080 .and. (volau + ul1) / lanes .lt. 2260) * ((length / (102.5 - (49 * (((volau + ul1) / lanes - 1080) / 1000)))) * 60)) + (((volau + ul1) / lanes .ge. 2260) * (length / (45 / (1 + 45 * (( volau + ul1) / lanes - 2260) / (8 * length * 2260))) * 60))

fd14 (((volau + ul1) / lanes .le. 1080) * ((length / (104.5 - (.006 * (volau + ul1) / lanes))) * 60)) + (((volau + ul1) / lanes .gt. 1080 .and. (volau + ul1) / lanes .lt. 2180) * ((length / (98.5 - (49 * (((volau + ul1) / lanes - 1080) / 1000)))) * 60)) + (((volau + ul1) / lanes .ge. 2180) * (length / (45 / (1 + 45 * (( volau + ul1) / lanes - 2180) / (8 * length * 2180))) * 60))

fd15 (((volau + ul1) / lanes .le. 1100) * ((length / (91 - (.0175 * ( volau + ul1) / lanes))) * 60)) + (((volau + ul1) / lanes .gt. 1100 .and. (volau + ul1) / lanes .lt. 1860) * (length / (71.5 - (.035 * ((volau + ul1) / lanes - 1100)))) * 60) + (((volau + ul1) / lanes .ge. 1860) * (length / (45 / (1 + 45 * ((volau + ul1) / lanes - 1860) / (8 * length * 1860))) * 60))

fd16 (((volau + ul1) / lanes .le. 1200) * ((length / (96 - (.006 * ( volau + ul1) / lanes))) * 60)) + (((volau + ul1) / lanes .gt. 1200 .and. (volau + ul1) / lanes .lt. 2520) * (length / (88 - ( 49 * (((volau + ul1) / lanes - 1200) / 1000)))) * 60) + (((volau + ul1) / lanes .ge. 2520) * (length / (45 / (1 + 45 * ((volau + ul1) / lanes - 2520) / (8 * length * 2520))) * 60))

fd17 (((volau + ul1) / lanes .le. 1050) * ((length / (61 - (.006 * ( volau + ul1) / lanes))) * 60)) + (((volau + ul1) / lanes .gt. 1050 .and. (volau + ul1) / lanes .lt. 1631) * (length / (45 - ( 45 * (((volau + ul1) / lanes - 1200) / 1000)))) * 60) + (((volau + ul1) / lanes .ge. 1631) * (length / (45 / (1 + 45 * ((volau + ul1) / lanes - 1631) / (8 * length * 2520))) * 60))



Appendix C - Air Parameters C.1 LHR Model Matrices

Business Leisure Leisure

Car mf180 mf181 In-vehicle times

Car mf182 mf183 Auxiliary transit times

Car mf184 mf185 Total wait times

Car mf188 mf189 Fare

Car mf178 mf179 Total vehicle operating costs per person

Car mf32 mf33 In-vehicle times

Car mf34 mf34 Distances

High Speed mf225 mf228 From home In-vehicle times (Conventional Component)

High Speed mf226 mf229 To home In-vehicle times (Conventional Component)

High Speed mf235 mf238 From Home Total wait times

High Speed mf236 mf239 To Home Total wait times

High Speed mf245 mf248 From home EDGE Fare

High Speed mf246 mf249 To home EDGE Fare

High Speed mf255 mf258 From home In-vehicle times (High Speed Component)

High Speed mf256 mf259 To home In-vehicle times (High Speed Component)

High Speed mf265 mf268 From home Auxiliary transit times

High Speed mf266 mf269 To home Auxiliary transit times

High Speed mf275 mf278 From home Additional crowded time (Conventional Component)

High Speed mf276 mf279 To home Additional crowded time (Conventional Component)

High Speed mf285 mf288 From home Additional crowded time (High Speed Component)

High Speed mf286 mf289 To home Additional crowded time (High Speed Component)

High Speed mf295 mf298 From Home No. of rail only boardings

High Speed mf296 mf299 To Home No. of rail only boardings

Conventional mf125 mf128 From home Additional crowded time

Conventional mf126 mf129 To home Additional crowded time

Conventional mf145 mf148 From home EDGE Fare

Conventional mf146 mf149 To home EDGE Fare

Conventional mf55 mf58 From home In-vehicle times

Conventional mf56 mf59 To home In-vehicle times

Conventional mf65 mf68 From home Auxiliary transit times

Conventional mf66 mf69 To home Auxiliary transit times

Conventional mf75 mf78 From Home Total wait times

Conventional mf76 mf79 To Home Total wait times

Conventional mf95 mf98 From Home No. of rail only boardings

Conventional mf96 mf99 To Home No. of rail only boardings




Appendix D – Model Outputs


D.1 Rail Demand Elasticities w.r.t Cost Component Changes, 2021

Rail Times Rail Fares Highway Times Highway Fuel Cost Air Times

Bus Other Comm Other Comm Bus Other Comm Other Comm Bus Other Comm Other Comm Bus Other Comm Other Comm Bus Other Comm Other Comm

B'ham - Shef ‐1.22 ‐0.69 ‐0.46 ‐0.31 ‐0.30 ‐0.35 ‐0.60 ‐0.32 ‐0.27 ‐0.21 0.71 0.39 0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Shef ‐ B'ham ‐1.13 ‐0.52 ‐0.51 ‐0.28 ‐0.28 ‐0.39 ‐0.54 ‐0.34 ‐0.29 ‐0.18 0.74 0.28 0.27 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond - Leeds ‐0.76 ‐0.47 0.00 ‐0.42 0.00 ‐0.47 ‐0.70 0.00 ‐0.63 0.00 0.20 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.01 0.00 0.00 0.00

Leeds ‐ Lond ‐0.74 ‐0.45 0.00 ‐0.41 0.00 ‐0.47 ‐0.75 0.00 ‐0.69 0.00 0.19 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00

Manc - Lond ‐1.43 ‐0.56 0.00 ‐0.44 0.00 ‐0.79 ‐0.74 0.00 ‐0.59 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.43 0.06 0.00 0.00 0.00

Lond ‐ Manc ‐1.67 ‐0.58 0.00 ‐0.44 0.00 ‐0.93 ‐0.76 0.00 ‐0.58 0.00 0.45 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.41 0.08 0.00 0.00 0.00

P'boro - Lond 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ P'boro 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Glas - Brig ‐11.41 ‐9.31 0.00 ‐1.28 0.00 ‐1.02 ‐3.94 0.00 ‐0.63 0.00 3.10 1.89 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.65 0.97 0.00 0.00 0.00

Brig ‐ Glas ‐10.78 ‐8.88 0.00 ‐1.25 0.00 ‐1.03 ‐3.98 0.00 ‐0.63 0.00 0.02 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.29 1.37 0.00 0.00 0.00

NYork - LonSW ‐2.11 ‐1.22 0.00 ‐0.50 0.00 ‐0.82 ‐1.42 0.00 ‐0.58 0.00 1.63 1.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.02 0.00 0.00 0.00

LonSW ‐ NYork ‐1.71 ‐0.93 0.00 ‐0.47 0.00 ‐0.72 ‐1.22 0.00 ‐0.62 0.00 1.30 0.77 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.13 0.02 0.00 0.00 0.00

B'ham - Lond ‐0.76 ‐0.36 ‐0.31 ‐0.39 ‐0.44 0.00 ‐0.37 0.00 0.41 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ B'ham ‐0.83 ‐0.34 ‐0.30 ‐0.49 ‐0.48 0.00 ‐0.42 0.00 0.52 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Wolv - Lond ‐1.14 ‐0.49 0.00 ‐0.39 0.00 ‐0.54 ‐0.54 0.00 ‐0.43 0.00 0.62 0.13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ Wolv ‐1.47 ‐0.50 0.00 ‐0.38 0.00 ‐0.56 ‐0.47 0.00 ‐0.36 0.00 0.97 0.15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Cov - Lond ‐0.55 ‐0.27 0.00 ‐0.23 0.00 ‐0.35 ‐0.45 0.00 ‐0.38 0.00 0.33 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ Cov ‐0.46 ‐0.31 0.00 ‐0.23 0.00 ‐0.34 ‐0.56 0.00 ‐0.41 0.00 0.23 0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

B'ham - LondNE ‐1.39 ‐0.86 ‐0.29 ‐0.73 ‐1.15 0.00 ‐0.38 0.00 1.36 0.88 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

LondNE ‐ B'ham ‐1.37 ‐0.72 ‐0.28 ‐0.73 ‐1.06 0.00 ‐0.41 0.00 1.39 0.73 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0-50 miles ‐0.31 ‐0.32 ‐0.15 ‐0.08 ‐0.07 ‐0.09 ‐0.23 ‐0.06 ‐0.06 ‐0.03 0.19 0.02 0.11 0.00 0.00 ‐0.05 ‐0.13 0.00 0.00 0.00 ‐0.05 ‐0.13 0.00 0.00 0.00

50-100 miles ‐1.67 ‐1.23 ‐0.55 ‐0.37 ‐0.35 ‐0.71 ‐1.03 ‐0.26 ‐0.27 ‐0.16 0.25 ‐0.24 0.25 0.00 0.00 ‐0.45 ‐0.57 0.00 0.00 0.00 ‐0.45 ‐0.57 0.00 0.00 0.00

100-150 miles ‐1.63 ‐0.91 ‐0.76 ‐0.47 ‐0.49 ‐0.59 ‐0.76 ‐0.66 ‐0.42 ‐0.32 0.62 0.27 0.24 0.00 0.00 ‐0.15 ‐0.10 0.00 0.00 0.00 ‐0.14 ‐0.10 0.00 0.00 0.00

150-200 miles ‐1.66 ‐0.90 ‐1.86 ‐0.52 ‐0.79 ‐0.55 ‐0.88 ‐2.50 ‐0.58 ‐1.08 0.63 0.28 0.93 0.00 0.00 0.09 0.00 0.00 0.00 0.00 0.28 0.05 0.00 0.00 0.00

200-300 miles ‐3.07 ‐1.85 ‐1.05 ‐0.73 ‐0.93 ‐0.78 ‐1.34 ‐1.06 ‐0.61 ‐0.98 0.43 0.36 0.10 0.00 0.00 ‐0.12 ‐0.07 0.00 0.00 0.00 0.20 0.13 0.00 0.00 0.00

300+ miles ‐6.62 ‐3.98 ‐4.76 ‐0.98 ‐1.34 ‐0.87 ‐2.13 ‐3.16 ‐0.60 ‐0.98 0.43 0.26 2.65 0.00 0.00 0.11 0.04 0.00 0.00 0.00 1.10 0.79 0.00 0.00 0.00

Full matrix -0.85 -0.66 -0.17 -0.25 -0.09 -0.28 -0.52 -0.08 -0.21 -0.04 0.29 0.06 0.12 0.00 0.00 -0.09 -0.17 0.00 0.00 0.00 -0.05 -0.14 0.00 0.00 0.00

Rai

l

CA NCA CA NCACA NCA CA NCA

2021

CA NCA



D.2 Air Demand Elasticities w.r.t Cost Component Changes, 2021



B'ham - Shef 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Shef ‐ B'ham 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond - Leeds 1.62 1.72 0.00 0.00 0.00 1.05 2.42 0.00 0.00 0.00 0.20 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐1.80 ‐1.80 0.00 0.00 0.00

Leeds ‐ Lond 1.62 1.70 0.00 0.00 0.00 1.07 2.62 0.00 0.00 0.00 0.19 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐1.80 ‐1.80 0.00 0.00 0.00

Manc - Lond 1.32 1.72 0.00 0.00 0.00 0.75 2.24 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐1.19 ‐1.49 0.00 0.00 0.00

Lond ‐ Manc 1.14 1.71 0.00 0.00 0.00 0.65 2.18 0.00 0.00 0.00 0.45 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐1.22 ‐1.58 0.00 0.00 0.00

P'boro - Lond 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ P'boro 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Glas - Brig 0.15 0.29 0.00 0.00 0.00 0.01 0.12 0.00 0.00 0.00 3.10 1.89 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐1.28 ‐0.87 0.00 0.00 0.00

Brig ‐ Glas 0.21 0.30 0.00 0.00 0.00 0.02 0.14 0.00 0.00 0.00 0.02 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐0.50 ‐0.40 0.00 0.00 0.00

NYork - LonSW 1.13 1.49 0.00 0.00 0.00 0.46 1.70 0.00 0.00 0.00 1.63 1.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐1.66 ‐1.76 0.00 0.00 0.00

LonSW ‐ NYork 1.28 1.57 0.00 0.00 0.00 0.56 2.01 0.00 0.00 0.00 1.30 0.77 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐1.64 ‐1.75 0.00 0.00 0.00

B'ham - Lond 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ B'ham 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Wolv - Lond 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ Wolv 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Cov - Lond 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ Cov 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

B'ham - LondNE 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

LondNE ‐ B'ham 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0-50 miles 0.01 0.00 0.00 0.00 0.00 0.08 0.04 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

50-100 miles 1.01 0.35 0.00 0.00 0.00 0.87 0.19 0.00 0.00 0.00 1.07 0.19 0.00 0.00 0.00 0.84 ‐0.01 0.00 0.00 0.00 0.84 ‐0.01 0.00 0.00 0.00

100-150 miles 0.58 0.61 0.00 0.00 0.00 0.22 0.49 0.00 0.00 0.00 0.64 0.40 0.00 0.00 0.00 0.05 0.20 0.00 0.00 0.00 ‐0.68 ‐0.21 0.00 0.00 0.00

150-200 miles 0.64 1.04 0.00 0.00 0.00 0.27 1.01 0.00 0.00 0.00 0.60 0.39 0.00 0.00 0.00 0.03 0.06 0.00 0.00 0.00 ‐0.77 ‐0.77 0.00 0.00 0.00

200-300 miles 0.41 0.70 0.00 0.00 0.00 0.10 0.48 0.00 0.00 0.00 0.58 0.36 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐0.70 ‐0.63 0.00 0.00 0.00

300+ miles 0.33 0.67 0.00 0.00 0.00 0.05 0.37 0.00 0.00 0.00 0.30 0.38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐0.60 ‐0.60 0.00 0.00 0.00

Full matrix 0.42 0.72 0.00 0.00 0.00 0.11 0.48 0.00 0.00 0.00 0.44 0.37 0.00 0.00 0.00 0.01 0.01 0.00 0.00 0.00 -0.66 -0.62 0.00 0.00 0.00

NCACA NCA CA NCA CA

Air

2021

CA NCA CA NCA



D.3 Highway Demand Elasticities w.r.t Cost Component Changes, 2021



B'ham - Shef 0.26 0.24 0.44 0.00 0.00 0.07 0.21 0.30 0.00 0.00 ‐0.77 ‐0.57 ‐0.81 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Shef ‐ B'ham 0.21 0.31 0.32 0.00 0.00 0.07 0.32 0.21 0.00 0.00 ‐0.75 ‐0.70 ‐0.70 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond - Leeds 1.04 0.75 0.00 0.00 0.00 0.67 1.09 0.00 0.00 0.00 ‐3.33 ‐2.10 ‐0.75 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00

Leeds ‐ Lond 1.04 0.75 0.00 0.00 0.00 0.68 1.19 0.00 0.00 0.00 ‐3.40 ‐2.14 ‐0.76 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00

Manc - Lond 0.86 0.77 0.00 0.00 0.00 0.48 1.01 0.00 0.00 0.00 ‐3.71 ‐2.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.28 0.03 0.00 0.00 0.00

Lond ‐ Manc 0.69 0.76 0.00 0.00 0.00 0.39 0.99 0.00 0.00 0.00 ‐3.18 ‐2.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.24 0.03 0.00 0.00 0.00

P'boro - Lond 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ P'boro 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Glas - Brig 0.04 0.05 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 ‐3.28 ‐2.35 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.18 0.19 0.00 0.00 0.00

Brig ‐ Glas 0.14 0.13 0.00 0.00 0.00 0.01 0.06 0.00 0.00 0.00 ‐9.25 ‐5.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.85 0.61 0.00 0.00 0.00

NYork - LonSW 0.45 0.32 0.00 0.00 0.00 0.18 0.37 0.00 0.00 0.00 ‐2.16 ‐1.36 ‐0.93 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.00 0.00

LonSW ‐ NYork 0.64 0.50 0.00 0.00 0.00 0.27 0.65 0.00 0.00 0.00 ‐2.78 ‐1.82 ‐0.96 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.01 0.00 0.00 0.00

B'ham - Lond 0.65 0.53 0.00 0.00 0.00 0.34 0.63 0.00 0.00 0.00 ‐1.68 ‐1.25 ‐0.45 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ B'ham 0.58 0.54 0.00 0.00 0.00 0.35 0.75 0.00 0.00 0.00 ‐1.60 ‐1.31 ‐0.46 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Wolv - Lond 0.68 0.64 0.00 0.00 0.00 0.32 0.70 0.00 0.00 0.00 ‐1.57 ‐1.29 ‐0.48 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ Wolv 0.38 0.62 0.00 0.00 0.00 0.15 0.59 0.00 0.00 0.00 ‐1.20 ‐1.31 ‐0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Cov - Lond 0.51 0.41 0.00 0.00 0.00 0.34 0.66 0.00 0.00 0.00 ‐1.60 ‐1.16 ‐0.41 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ Cov 0.57 0.36 0.00 0.00 0.00 0.42 0.64 0.00 0.00 0.00 ‐1.92 ‐1.17 ‐0.44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

B'ham - LondNE 0.02 0.03 0.00 0.00 0.00 0.01 0.04 0.00 0.00 0.00 ‐0.81 ‐0.53 ‐0.51 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

LondNE ‐ B'ham 0.01 0.13 0.00 0.00 0.00 0.01 0.20 0.00 0.00 0.00 ‐0.81 ‐0.73 ‐0.52 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0-50 miles ‐0.16 ‐0.08 0.03 0.00 0.00 ‐0.17 ‐0.08 0.01 0.00 0.00 ‐0.57 ‐0.35 ‐0.25 0.00 0.00 ‐0.17 ‐0.09 0.00 0.00 0.00 ‐0.17 ‐0.09 0.00 0.00 0.00

50-100 miles ‐0.11 ‐0.13 0.05 0.00 0.00 ‐0.14 ‐0.14 0.03 0.00 0.00 ‐0.86 ‐0.65 ‐0.47 0.00 0.00 ‐0.15 ‐0.17 0.00 0.00 0.00 ‐0.15 ‐0.17 0.00 0.00 0.00

100-150 miles 0.21 0.25 0.00 0.00 0.00 0.15 0.22 0.00 0.00 0.00 ‐1.01 ‐0.62 ‐0.66 0.00 0.00 0.12 0.15 0.00 0.00 0.00 0.12 0.15 0.00 0.00 0.00

150-200 miles 0.25 0.27 0.01 0.00 0.00 0.17 0.23 0.01 0.00 0.00 ‐1.41 ‐0.86 ‐0.85 0.00 0.00 0.14 0.14 0.00 0.00 0.00 0.17 0.15 0.00 0.00 0.00

200-300 miles 0.08 0.27 0.00 0.00 0.00 0.01 0.23 0.00 0.00 0.00 ‐1.94 ‐1.10 ‐1.05 0.00 0.00 0.00 0.16 0.00 0.00 0.00 0.05 0.19 0.00 0.00 0.00

300+ miles 0.29 0.26 0.01 0.00 0.00 0.17 0.18 0.00 0.00 0.00 ‐4.29 ‐2.54 ‐1.78 0.00 0.00 0.15 0.12 0.00 0.00 0.00 0.43 0.31 0.00 0.00 0.00

Full matrix -0.11 -0.05 0.03 0.00 0.00 -0.13 -0.06 0.01 0.00 0.00 -0.70 -0.43 -0.28 0.00 0.00 -0.13 -0.07 0.00 0.00 0.00 -0.13 -0.07 0.00 0.00 0.00

2021

CA NCA CA NCA CA NCA CA NCA CA NCA

Hig

hway



D.4 Rail Demand Elasticities w.r.t Cost Component Changes, 2031



B'ham - Shef ‐1.20 ‐0.68 ‐0.45 ‐0.31 ‐0.30 ‐0.31 ‐0.55 ‐0.29 ‐0.25 ‐0.20 0.73 0.40 0.16 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Shef ‐ B'ham ‐1.11 ‐0.51 ‐0.49 ‐0.28 ‐0.28 ‐0.34 ‐0.49 ‐0.31 ‐0.27 ‐0.17 0.76 0.28 0.27 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond - Leeds ‐0.72 ‐0.46 0.00 ‐0.42 0.00 ‐0.40 ‐0.63 0.00 ‐0.58 0.00 0.16 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00

Leeds ‐ Lond ‐0.71 ‐0.45 0.00 ‐0.42 0.00 ‐0.40 ‐0.69 0.00 ‐0.64 0.00 0.14 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00

Manc - Lond ‐1.30 ‐0.53 0.00 ‐0.44 0.00 ‐0.64 ‐0.67 0.00 ‐0.55 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.35 0.05 0.00 0.00 0.00

Lond ‐ Manc ‐1.50 ‐0.55 0.00 ‐0.44 0.00 ‐0.76 ‐0.67 0.00 ‐0.54 0.00 0.41 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.34 0.07 0.00 0.00 0.00

P'boro - Lond 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ P'boro 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Glas - Brig ‐11.18 ‐9.15 0.00 ‐1.30 0.00 ‐0.91 ‐3.65 0.00 ‐0.58 0.00 3.18 1.93 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.66 0.98 0.00 0.00 0.00

Brig ‐ Glas ‐10.95 ‐9.03 0.00 ‐1.26 0.00 ‐0.92 ‐3.69 0.00 ‐0.59 0.00 0.02 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.29 1.37 0.00 0.00 0.00

NYork - LonSW ‐1.89 ‐1.08 0.00 ‐0.49 0.00 ‐0.66 ‐1.19 0.00 ‐0.54 0.00 1.44 0.93 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.08 0.01 0.00 0.00 0.00

LonSW ‐ NYork ‐1.35 ‐0.84 0.00 ‐0.48 0.00 ‐0.56 ‐1.00 0.00 ‐0.57 0.00 1.08 0.62 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.02 0.00 0.00 0.00

B'ham - Lond ‐0.72 ‐0.35 ‐0.31 ‐0.33 ‐0.40 0.00 ‐0.34 0.00 0.36 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ B'ham ‐0.79 ‐0.34 ‐0.30 ‐0.42 ‐0.44 0.00 ‐0.40 0.00 0.47 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Wolv - Lond ‐1.02 ‐0.46 0.00 ‐0.39 0.00 ‐0.43 ‐0.48 0.00 ‐0.40 0.00 0.51 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ Wolv ‐1.40 ‐0.48 0.00 ‐0.39 0.00 ‐0.47 ‐0.42 0.00 ‐0.34 0.00 0.91 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Cov - Lond ‐0.51 ‐0.26 0.00 ‐0.23 0.00 ‐0.30 ‐0.41 0.00 ‐0.36 0.00 0.29 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ Cov ‐0.44 ‐0.29 0.00 ‐0.23 0.00 ‐0.29 ‐0.50 0.00 ‐0.38 0.00 0.20 0.15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

B'ham - LondNE ‐1.38 ‐0.85 ‐0.29 ‐0.65 ‐1.06 0.00 ‐0.36 0.00 1.38 0.89 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

LondNE ‐ B'ham ‐1.38 ‐0.70 ‐0.28 ‐0.65 ‐0.95 0.00 ‐0.38 0.00 1.44 0.72 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0-50 miles ‐0.36 ‐0.38 ‐0.16 ‐0.09 ‐0.08 ‐0.12 ‐0.29 ‐0.06 ‐0.05 ‐0.03 0.18 ‐0.03 0.13 0.00 0.00 ‐0.08 ‐0.19 0.00 0.00 0.00 ‐0.08 ‐0.19 0.00 0.00 0.00

50-100 miles ‐1.70 ‐1.31 ‐0.54 ‐0.36 ‐0.35 ‐0.77 ‐1.11 ‐0.24 ‐0.25 ‐0.15 0.14 ‐0.39 0.25 0.00 0.00 ‐0.53 ‐0.70 0.00 0.00 0.00 ‐0.53 ‐0.70 0.00 0.00 0.00

100-150 miles ‐1.51 ‐0.88 ‐0.71 ‐0.46 ‐0.47 ‐0.55 ‐0.73 ‐0.52 ‐0.40 ‐0.25 0.49 0.18 0.21 0.00 0.00 ‐0.17 ‐0.14 0.00 0.00 0.00 ‐0.16 ‐0.13 0.00 0.00 0.00

150-200 miles ‐1.52 ‐0.85 ‐1.99 ‐0.51 ‐0.79 ‐0.51 ‐0.81 ‐2.41 ‐0.55 ‐0.97 0.48 0.20 1.04 0.00 0.00 0.03 ‐0.03 0.00 0.00 0.00 0.19 0.01 0.00 0.00 0.00

200-300 miles ‐2.84 ‐1.74 ‐1.07 ‐0.72 ‐0.93 ‐0.71 ‐1.22 ‐0.97 ‐0.58 ‐0.89 0.33 0.28 0.13 0.00 0.00 ‐0.15 ‐0.10 0.00 0.00 0.00 0.14 0.07 0.00 0.00 0.00

300+ miles ‐6.30 ‐3.61 ‐4.82 ‐0.96 ‐1.34 ‐0.79 ‐1.84 ‐2.86 ‐0.56 ‐0.89 0.32 0.20 2.74 0.00 0.00 0.06 0.02 0.00 0.00 0.00 1.03 0.71 0.00 0.00 0.00

Full matrix -0.93 -0.73 -0.19 -0.27 -0.10 -0.32 -0.58 -0.07 -0.22 -0.04 0.25 -0.01 0.13 0.00 0.00 -0.13 -0.22 0.00 0.00 0.00 -0.09 -0.20 0.00 0.00 0.00

NCA

Rai

l

CA NCA CA NCACA NCA CA NCA

2031

CA



D.5 Air Demand Elasticities w.r.t Cost Component Changes, 2031



B'ham - Shef 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Shef ‐ B'ham 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond - Leeds 1.65 1.73 0.00 0.00 0.00 0.97 2.30 0.00 0.00 0.00 0.16 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐1.81 ‐1.80 0.00 0.00 0.00

Leeds ‐ Lond 1.66 1.73 0.00 0.00 0.00 0.99 2.49 0.00 0.00 0.00 0.14 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐1.81 ‐1.81 0.00 0.00 0.00

Manc - Lond 1.43 1.75 0.00 0.00 0.00 0.73 2.13 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐1.28 ‐1.51 0.00 0.00 0.00

Lond ‐ Manc 1.26 1.73 0.00 0.00 0.00 0.65 2.08 0.00 0.00 0.00 0.41 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐1.29 ‐1.60 0.00 0.00 0.00

P'boro - Lond 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ P'boro 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Glas - Brig 0.14 0.27 0.00 0.00 0.00 0.01 0.11 0.00 0.00 0.00 3.18 1.93 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐1.27 ‐0.86 0.00 0.00 0.00

Brig ‐ Glas 0.21 0.29 0.00 0.00 0.00 0.02 0.12 0.00 0.00 0.00 0.02 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐0.50 ‐0.39 0.00 0.00 0.00

NYork - LonSW 1.33 1.56 0.00 0.00 0.00 0.49 1.70 0.00 0.00 0.00 1.44 0.93 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐1.69 ‐1.76 0.00 0.00 0.00

LonSW ‐ NYork 1.34 1.70 0.00 0.00 0.00 0.58 1.99 0.00 0.00 0.00 1.08 0.62 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐1.68 ‐1.76 0.00 0.00 0.00

B'ham - Lond 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ B'ham 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Wolv - Lond 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ Wolv 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Cov - Lond 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ Cov 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

B'ham - LondNE 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

LondNE ‐ B'ham 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0-50 miles 0.01 0.00 0.00 0.00 0.00 0.08 0.04 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

50-100 miles 0.96 0.35 0.00 0.00 0.00 0.86 0.18 0.00 0.00 0.00 0.99 0.19 0.00 0.00 0.00 0.84 0.00 0.00 0.00 0.00 0.84 0.00 0.00 0.00 0.00

100-150 miles 0.59 0.58 0.00 0.00 0.00 0.20 0.45 0.00 0.00 0.00 0.63 0.37 0.00 0.00 0.00 0.04 0.18 0.00 0.00 0.00 ‐0.70 ‐0.22 0.00 0.00 0.00

150-200 miles 0.69 1.06 0.00 0.00 0.00 0.27 0.97 0.00 0.00 0.00 0.60 0.38 0.00 0.00 0.00 0.03 0.06 0.00 0.00 0.00 ‐0.79 ‐0.78 0.00 0.00 0.00

200-300 miles 0.43 0.70 0.00 0.00 0.00 0.09 0.45 0.00 0.00 0.00 0.57 0.35 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐0.70 ‐0.62 0.00 0.00 0.00

300+ miles 0.38 0.69 0.00 0.00 0.00 0.05 0.36 0.00 0.00 0.00 0.29 0.36 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐0.61 ‐0.61 0.00 0.00 0.00

Full matrix 0.46 0.74 0.00 0.00 0.00 0.11 0.46 0.00 0.00 0.00 0.43 0.36 0.00 0.00 0.00 0.01 0.01 0.00 0.00 0.00 -0.67 -0.63 0.00 0.00 0.00

NCA CA NCACA NCA CA NCA CA

2031

CA NCA

Air


Model D opment Report

evel


D.6 Highway Demand Elasticities w.r.t Cost Component Changes, 2031



B'ham - Shef 0.27 0.25 0.45 0.00 0.00 0.07 0.20 0.29 0.00 0.00 ‐0.82 ‐0.61 ‐0.86 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Shef ‐ B'ham 0.23 0.32 0.33 0.00 0.00 0.07 0.31 0.21 0.00 0.00 ‐0.81 ‐0.75 ‐0.76 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond - Leeds 1.07 0.76 0.00 0.00 0.00 0.62 1.04 0.00 0.00 0.00 ‐3.62 ‐2.25 ‐0.79 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00

Leeds ‐ Lond 1.08 0.77 0.00 0.00 0.00 0.63 1.13 0.00 0.00 0.00 ‐3.59 ‐2.22 ‐0.78 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00

Manc - Lond 0.94 0.78 0.00 0.00 0.00 0.47 0.96 0.00 0.00 0.00 ‐3.81 ‐2.27 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.23 0.02 0.00 0.00 0.00

Lond ‐ Manc 0.78 0.77 0.00 0.00 0.00 0.39 0.94 0.00 0.00 0.00 ‐3.45 ‐2.38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.21 0.03 0.00 0.00 0.00

P'boro - Lond 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ P'boro 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Glas - Brig 0.04 0.05 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 ‐3.44 ‐2.47 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.18 0.19 0.00 0.00 0.00

Brig ‐ Glas 0.14 0.13 0.00 0.00 0.00 0.01 0.05 0.00 0.00 0.00 ‐9.68 ‐5.44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.85 0.61 0.00 0.00 0.00

NYork - LonSW 0.62 0.42 0.00 0.00 0.00 0.22 0.46 0.00 0.00 0.00 ‐2.53 ‐1.59 ‐0.95 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.00 0.00

LonSW ‐ NYork 0.74 0.60 0.00 0.00 0.00 0.31 0.72 0.00 0.00 0.00 ‐3.28 ‐2.11 ‐0.99 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.01 0.00 0.00 0.00

B'ham - Lond 0.69 0.54 0.00 0.00 0.00 0.33 0.60 0.00 0.00 0.00 ‐1.79 ‐1.30 ‐0.46 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ B'ham 0.62 0.55 0.00 0.00 0.00 0.34 0.71 0.00 0.00 0.00 ‐1.74 ‐1.37 ‐0.48 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Wolv - Lond 0.77 0.66 0.00 0.00 0.00 0.33 0.68 0.00 0.00 0.00 ‐1.75 ‐1.36 ‐0.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ Wolv 0.48 0.66 0.00 0.00 0.00 0.16 0.58 0.00 0.00 0.00 ‐1.39 ‐1.42 ‐0.52 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Cov - Lond 0.54 0.41 0.00 0.00 0.00 0.32 0.63 0.00 0.00 0.00 ‐1.74 ‐1.23 ‐0.42 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Lond ‐ Cov 0.59 0.37 0.00 0.00 0.00 0.39 0.62 0.00 0.00 0.00 ‐2.07 ‐1.26 ‐0.47 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

B'ham - LondNE 0.03 0.03 0.00 0.00 0.00 0.02 0.04 0.00 0.00 0.00 ‐0.85 ‐0.56 ‐0.53 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

LondNE ‐ B'ham 0.02 0.16 0.00 0.00 0.00 0.01 0.22 0.00 0.00 0.00 ‐0.86 ‐0.82 ‐0.55 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0-50 miles ‐0.20 ‐0.10 0.03 0.00 0.00 ‐0.22 ‐0.11 0.01 0.00 0.00 ‐0.63 ‐0.38 ‐0.26 0.00 0.00 ‐0.22 ‐0.12 0.00 0.00 0.00 ‐0.22 ‐0.12 0.00 0.00 0.00

50-100 miles ‐0.15 ‐0.16 0.06 0.00 0.00 ‐0.19 ‐0.18 0.03 0.00 0.00 ‐0.93 ‐0.70 ‐0.49 0.00 0.00 ‐0.20 ‐0.21 0.00 0.00 0.00 ‐0.20 ‐0.21 0.00 0.00 0.00

100-150 miles 0.26 0.31 0.00 0.00 0.00 0.19 0.28 0.00 0.00 0.00 ‐1.02 ‐0.59 ‐0.68 0.00 0.00 0.16 0.21 0.00 0.00 0.00 0.16 0.21 0.00 0.00 0.00

150-200 miles 0.31 0.32 0.00 0.00 0.00 0.21 0.29 0.01 0.00 0.00 ‐1.44 ‐0.85 ‐0.87 0.00 0.00 0.18 0.19 0.00 0.00 0.00 0.21 0.20 0.00 0.00 0.00

200-300 miles 0.08 0.33 0.00 0.00 0.00 0.01 0.27 0.00 0.00 0.00 ‐2.02 ‐1.09 ‐1.07 0.00 0.00 0.00 0.21 0.00 0.00 0.00 0.05 0.23 0.00 0.00 0.00

300+ miles 0.35 0.30 0.00 0.00 0.00 0.21 0.21 0.00 0.00 0.00 ‐4.47 ‐2.62 ‐1.83 0.00 0.00 0.20 0.16 0.00 0.00 0.00 0.49 0.36 0.00 0.00 0.00

Full matrix -0.14 -0.07 0.03 0.00 0.00 -0.16 -0.08 0.01 0.00 0.00 -0.76 -0.46 -0.29 0.00 0.00 -0.17 -0.09 0.00 0.00 0.00 -0.17 -0.09 0.00 0.00 0.00

CA NCANCA CA NCA CA NCA

2031

CA NCA CA

Hig

hway


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5082342/5082342 Model Development Report (26-02-2010).doc

model development report: a report for hs2

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