Demand forecasting technical note

Demand forecasting technical note

Planning and Regulation

Route Planning

New Lines

Contents

Calculating Trunk Costs 7

Calculating Trunk Access Times 8

Summing Trunk and Access GJTs 9

Mode Specific Assumptions and Methods 10

Values of Time and Modal Preference 10

Crowding and Network Topology 13

Mode Split Calculations 17

Running the Model 21

Model Outputs 22

Illustrative Model Results 23

Model Discussion 30

Potential Model Refinements 32

3 COMMUTER MODEL 33

Introduction 33

Scope of Model 33

Methodology Overview 35

Data Inputs 37

Detailed Review of Methodology 38

Model Outputs 44

4 REGIONAL MODEL 47

Introduction 47

Scope of the Model 47

Methodology Overview 48

Data Inputs 48

Detailed Review of Methodology 48

Model Outputs 53

Introduction 57

Model Development and Calibration 57

Model Forecasting 58

Model Outputs 60

6 NEAR EUROPE MODEL 61

Introduction 61

Scope of the Model 61

Methodology Overview 62

Model Outputs 1

FIGURES

Figure 1.1 New Lines Modelling Suite 2

Figure 2.1 Rail Trunk With Feeder Catchment Zones 6

Figure 2.2 Distribution of Virgin West Coast Load Factors (London End) 17

Figure 2.3 Hierachical Logit Model Structure 18

Figure 2.4 Option MB1.4.1 24

Figure 2.5 London Do-Minimum Catchment 25

Figure 2.6 Manchester Do-Minimum (Classic Rail) Catchment 25 Figure 2.7 Manchester Do-Something (New Lines) Catchment 26

Figure 2.8 Do Something GJT breakdown 29

Figure 3.1 Commuter Model Methodology Flow Diagram 35

Figure 4.1 Example of Station Groupings 49

Figure 4.2 Example of Outputs produced by the Regional Model 53 Figure 4.3 Distribution of Demand Increases Greater Than 10,000 Pax Per Year 54 Figure 4.4 Distribution of Demand And Revenue Increases By Station 54

TABLES

Table 2.1 Intercity VoT (2007 prices / 2030 values) 11 Table 2.2 Illustrative Perceived Journey Time Benefit for a 240 Minute Classic Rail

Journey Switching to New Lines 12

Table 2.3 Illustrative Perceived Journey Time Benefit for a 120 Minute Classic Rail

Journey Switching to New Lines 13

Table 2.4 Implied Journey Time Equivalent Crowding Penalties 15 Table 2.5 Proposed Time Equivalence Penalties – Individual Train 16

Table 2.6 Scaling Factors 19

Table 2.7 Do-Minimum Crowding Levels 26

Table 2.8 New Line Crowding 27

Table 2.9 London-Manchester Journeys Results Summary 27

Table 2.10 Top 30 Flows London – Manchester 28

Table 2.11 London - Stockport detail 29

Table 2.12 Total Revenue for Option 1.4.1 29

Table 2.13 Relative market size for the North West and Scotland in 2030 31

Table 3.1 NMF Group Station Zones 34

Table 3.2 PDFH Based Elasticity Methodology Example 36

Table 3.3 Time Period Definitions 36

Table 3.4 GJT Overlays 40

Table 3.5 Crowding Example – Watford Junction to Bushey Link 42 Table 3.6 Absolute Annual Demand and Revenue Change by Flow 44 Table 3.7 Do-Minimum Constrained vs Unconstrained Demand 45 Table 4.1 Lookup Table Used To Calculate Fast-Slow Multipliers 50 Table 4.2 Lookup Table Used to Calculate Add-on Generalised Journey Times 50 Table 4.3 Wait Time Penalties by Ticket Type (mins) 51 Table 4.4 Interchange Penalties by Ticket Type (mins) 52 Table 5.1 2007 Base Year Do Minimum % of Demand by Mode for Scotland 60 Table 5.2 GJT Costs (Perceived Mins) by Mode for Scotland 60 Table 6.1 Mode constants and Scaling Parameters for Near Europe model 1

1

Introduction

Overview of Approach

1.1 The aim of the New Lines project is to evaluate the potential benefits of the construction of new, potentially high speed, rail lines in the UK. These benefits would primarily accrue to the demand using the New Lines (transferring from other rail services or other modes, and new trips), but also from demand generated by improved services on the ‘classic’ (existing) rail network facilitated by the transfer of services to the New Line.

1.2 The demand forecasting framework has therefore been designed to consider the impacts on a range of markets that could be affected by New Lines:

I Long distance Intercity markets served by any New Line;

I The impact on commuter and regional markets as the classic network is commensurately recast and improved as the New Line removes the Intercity services;

I The improvement in the attractiveness of using rail to access the near continent (notably Paris and Brussels), through interchange onto HS1; and

I The improvement in the attractiveness of using rail to access Heathrow, especially if direct services are provided.

1.3 The framework comprises a suite of five, spreadsheet based, models (decision support tools), designed to capture the impact of New Lines on each of the aforementioned markets:

I An Intercity model, designed to forecast the demand impacts of New Lines on the demand for inter city travel, considering how demand may switch from other modes (classic rail, air and car) and how the New Line may ‘generate’ new demand on the New Line corridor (either through changes in destination or through changes in trip frequency). This model has been based on the PLANET Strategic model (PSM) developed for the SRA’s High Speed Line Study in 2002, with rebased demand and model parameters.

I A Commuter model, focusing on the London commuter market and how a recast network would benefit travellers through improvements in journey time,

frequency and crowding

I A Regional model, which focuses on the remaining parts of the network not captured in the Intercity and Commuter models and considers how changes in journeys times and frequencies afforded by a classic rail network recast would improve the rail offer for regional flows (including to and from London for flows other than the major cities).

I A Near Europe model, a simple mode choice model considering how the

improved connections to London from the regional cities would affect demand to Paris and Brussels via HS1 through model shift from air.

I A Heathrow Access model, which considers how indirect or direct New Lines services improving accessibility to Heathrow may affect the choices between surface access modes and between air interlining and surface access.

1.4 Although these models all use the same demand and service specification data and output results on a consistent basis, the methodology of each model is tailored to the specific characteristics of the particular market segment for which it is designed. This means that the scope of each model cannot be considered in

isolation, and if the scope of one is changed then the scope of the others have to be updated accordingly to avoid omitting or double counting results.

1.5 There is the potential for overlap between the first three of these models (Intercity, Commuter and Regional), which collectively deal with wholly domestic travel. To avoid double counting results, a methodical approach was adopted to assign each flow to one of the three models. Firstly, a ‘commuter corridor’ was defined, and all flows lying within this corridor were assigned to the Commuter model; it was then decided which of the remaining flows should belong to the Intercity model, and all the leftover flows were then assigned to the Regional model. The Heathrow Access Model and Near Europe models deal with travel beyond the mainland United Kingdom and as such are mutually exclusive markets, with no potential overlap. The scope of the models as applied to an illustrative New Lines option centred on the West Midlands and North West corridor is illustrated in Figure 1.1.

FIGURE 1.1 NEW LINES MODELLING SUITE

Stevenage Huntingdon Birmingham New Street

Birmingham International Coventry Milton Keynes Central Liverpool Preston Manchester Stockport Crewe Glasgow and Edinburgh Rugby Stafford Stoke on Trent Northampton Watford Regional model Warrington Berkhamsted Intercity model Commuter model Near Europe model To Paris/ Brussels

London and SE

1.6 No primary research has been undertaken for demand data, rather full use has been made of datasets of existing and forecast demand. Of note, use has been made of the demand data originally collected for the SRA study into high speed lines undertaken in 2002, CAA air demand data and RIFF/LENNON rail ticket sales data. These have been used to derive estimates of Base 2007 demand by mode.

1.7 Forecasts of (Do-Minimum) demand in 2030 before any New Lines are introduced are based on DfT forecasts of changes in rail, air and road demand and are therefore consistent with national policy. These forecasts reflect expected changes in transport infrastructure, pricing policies by the respective market players and changes in the socio-economic drivers of demand (such as the spatial distribution and level of population and employment, car ownership and GDP growth). 1.8 The decision support tools are exactly that; at this stage, the key requirement is

that the tools enable relative differences between options to be estimated and assessed with confidence. The tools are not designed, at this stage, to provide absolute forecasts with the certainty required to make a robust decision to proceed with the implementation of New Lines per se. Clearly, Steer Davies Gleave has applied best practice techniques to all of the modelling analysis and has used all its experience and judgement to ensure that the forecast estimates produced are as robust as possible. However, the model outputs are designed to support the

Strategic Business Case and inform the development of train service specifications – the tools are not designed, nor would be appropriate at this stage, to provide inputs to a detailed timetabling exercise.

1.9 In addition to the aforementioned models, an additional Model converts service specifications data into a format usable by the demand models.

1.10 The modelling framework has been developed to a level commensurate with the overall study, namely that of establishing if a case for New Lines exists. Further development of the modelling suite, including enhanced data, would be required should the case be considered in more detail.

This Report

1.11 This report sets out at an overview of the respective models, what they do, how they operate and examples of outputs. This report is not a detailed model

development report akin to a Local Model Validation Report (LMVR) or similar used to set out in detail the model development and application.

1.12 Each model is dealt with in a separate Chapter, as follows:

I The Intercity model is dealt with in Chapter 2;

I Chapter 3 deals with the Commuter model;

I Chapter 4 covers the Regional Model;

I The Heathrow Access Model is covered in Chapter 5; and

2

Intercity Model

Introduction

2.1 The Intercity model is essentially a simplified, spreadsheet based version of the Planet Strategic Model (PSM) developed for the Strategic Rail Authority in 2002 as part of a study considering the case for new high speed rail lines in the UK.

2.2 For relevant origin-destination (OD) pairs (based on the same 235 zoning system as in PSM) the model assigns demand data across four modes:

I Classic Rail (CR) – representing the existing rail network;

I New Rail (NR) – the proposed new rail network which will compete with CR;

I Air; and

I Car.

2.3 For CR, NR and air, the model uses a trunk and feeder approach to defining the transport network. For key trunk journeys, the model determines which OD flows are in scope to travel on that trunk and assigns them to that journey.

2.4 The Intercity model is split into three parts, a Generalised Journey Time (GJT) calculator, a mode split model and a crowding process. The GJT calculator

determines the GJT for in-scope zones and the mode split model uses these costs to determine forecast demand. The crowding process then takes the forecast demand and determines the crowding level that this causes. This result is used to vary the GJT, in turn varying the forecast demand. The crowding process controls this iterative process.

GJT Calculator

2.5 Generalised journey time (GJT) is a measure of the overall temporal cost of a journey and is made up of a number of component costs. These are in-vehicle time (IVT), the journey fare and the service interval penalty. Additionally, other

components may be included, for example interchange penalties, parking costs and waiting time. GJT is measured in minutes and therefore the component parts of the GJT must be converted into minutes. For those components not already measured in minutes, this means applying a Value of Time (VoT) to the component. The VoT is based on research and may vary by journey purpose. The values used are discussed in section 2.47

2.6 The GJT calculator module models the rail and air transport networks as a series of trunk links which are accessible via Trunk Access Points (TAPs). These TAPs are accessible from any of the zones which are included in the model. This concept is illustrated in Figure 2.1

FIGURE 2.1 RAIL TRUNK WITH FEEDER CATCHMENT ZONES

2.7 By setting a parameter, the catchment area of each TAP can be adjusted. The results of the catchment scoping require manual checking to ensure that only zones that would realistically travel via that trunk are in scope. This process simplifies the network model approach that PSM takes by quickly removing journeys that are determined not in scope for a given trunk.

2.8 TAP access times can be varied by each TAP, allowing the user to tighten the catchment where multiple travel options exist. The access time is specified in GJT terms since access time inputs are measured in this unit. Note that whilst the catchments can be varied by TAP, the resulting catchment is then applied to all flows to and from that TAP i.e. the catchment for a London TAP is constant, irrespective of the flow and the associated options for access.

Trunk (rail or air) TAP

Input Data

Demand Data

2.9 The demand data input into the Intercity model is based on data used in PSM. The 2000 PSM data is uplifted to 2007 and then DfT national forecasts are used to grow the rail, air and car demand to 2030 levels. To keep the spreadsheet model to a manageable size only zones deemed relevant to the modelling process are included. For purposes of comparison, 2007 demand data is included as a model input.

Network Data

2.10 DfT’s Network Modelling Framework (NMF) model is the source for rail GJTs between various zones and is used to determine the access times to TAPs. It also provides average fare data.

2.11 PSM data is used for car journey times, both for access to TAPs and for zone to zone journeys. Alongside this data is a crow-flies distance value, used to calculate car fuel costs.

2.12 Trunk journey times used to calculate trunk GJT for air and rail is entered by the user. These are based on 2007, 2030 Do-Minimum or 2030 Do-Something data as appropriate.

2.13 Where a TAP allows rail or car access, the minimum of the two access times is chosen.

Parameters

2.14 Mode choice parameters such as scaling factors, Alternative Specific Constant (ASC), and generation factor can be set. A discussion of VoT and ASC parameters can be found in section 2.47. Generation factors have been reduced from those used in PSM for business users.

Trunk Input

2.15 Trunk fares can be specified for trunk journeys in three categories – business, commuter and other (i.e. leisure). For rail these inputs will normally be calculated from NMF fares data, which are held by ticket type (full, reduced and seasons). A conversion process to journey purpose is undertaken, in effect, yield by journey purpose information is entered into the mode choice model.

2.16 The service interval input is in minutes. Note that the feasibility of a trunk journey is determined by the service interval input.

2.17 As well as specifying the journey time for trunk journeys, journey time information must be input for all parts of the journey where the journey passes over multiple “crowding links”. Crowding penalties are applied on a link by link basis according to the crowding on a link and the journey time over that link.

Calculating Trunk Costs

Journey Time

2.18 The journey time, in minutes, makes up a significant portion of the GJT for a trip. It is weighted for each mode to account for a traveller’s preferences for a mode. For example, if the measured IVT value of time is higher for air than for classic rail, this shows that people are willing to pay more to save a minute travelling by air. In other words, they prefer one minute in the air less than on classic rail. Therefore, a

minute by air is actually “longer” (i.e. it is more costly to the traveller) than a minute by classic rail.

2.19 Weightings are calculated by the ratio of the VoT for each mode. Classic rail is defined as the reference mode, meaning that all of the GJTs calculated for the trunk routes are effectively in units of “classic-rail equivalent minutes”. For example:

)

_

(

)

_

(

)

(

IVT

l

ClassicRai

VoT

IVT

car

VoT

car

Weight

=

2.20 Journey times are subsequently multiplied by the weightings to give a weighted trunk cost.

Fare

2.21 The fare for a journey is another significant cost in the GJT calculation. The fare is divided by the VoT to generate the generalised minutes equivalent. This is done for each journey purpose type. Since business users tend to have a higher VoT than leisure users, this means that the fare is a larger portion of a leisure traveller’s GJT than a business traveller’s even if the same fare applies.

Service Interval

2.22 Since waiting at a station is not typically considered useful time, travellers perceive a disbenefit with longer services. The penalty added to the GJT is calculated using the headways and relevant value of time.

2.23 Note that a service Interval penalty is applied to classic rail, high-speed rail and also to air. It is assumed that values for air are the same as for rail.

Crowding

2.24 Crowding penalties are added on to the GJT. For a discussion of calculating crowding penalties, see section 2.63.

Calculating Trunk Access Times

Access Time Threshold

2.25 An access time threshold parameter can be specified to enable the user to filter out certain OD flows in the overall results by saying, for example, that travellers can access the trunk network only if they are within a certain access time from a TAP. 2.26 The type of access allowable at each TAP is designated by the user. This is designed

to prevent unrealistic car access journey times to city centre stations and to allow car access only to any easily accessible stations.

2.27 Using the access time threshold and access type, a list of zones accessible from each TAP is compiled. For rail access, the minimum access time for the three ticket types (F/R/S) is chosen for comparison against the threshold. Where both car and rail access are permitted at a station, the minimum of the car and the rail access time is used.

2.28 For each zone the closest of any accessible New Line TAP is chosen. It is important to bear this in mind when interpreting the results as choosing the closest TAP to a particular zone may mean removing the feasibility of certain OD pairs since the

closest TAP may not have all trunk journeys available. This is not a significant issue given the New Line service patterns under consideration in the New Lines

programme. An alternative TAP choice methodology is employed for Air as described in section 2.38.

2.29 If a zone contains a TAP there is a parameter describing an intra-zonal access time that represents how long it takes to get to the TAP.

2.30 Note that for car access, travellers with a car available will have it available at one end only. In this model, this complexity is ignored and car available is assumed to mean car available at either end. Since OD pairs are bi-directional (that is to say that, for example, OD 45-67 includes all those travelling from 45 to 67 and 67 to 45) this is a necessary assumption.

Rail Access Time Features

2.31 Whilst the access time for car is the point to point journey time most rail journeys are not usually as direct. For this reason, there is an additional parameter for a secondary rail access time. This is the time taken for a user to access their local station, from which they catch a train to access the TAP. This number represents walking or driving to the station, for example.

2.32 Additionally, if using rail to access a TAP, an interchange penalty representing the inconvenience of the arrival and departure times of services not being aligned, is calculated and added to the access time. This occurs at both the start and end TAP. 2.33 Note that there is no interchange penalty for car users transferring onto the trunk

and this is assumed to be zero, even though in reality, there may be a penalty (probably smaller than rail).

2.34 When determining the closest TAP for a zone, the secondary rail access time is included but the interchange penalty is not. When calculating the overall GJT both additional costs are included.

2.35 Note that access times are weighted according to the ratio of the access time VoT compared to classic rail IVT VoT.

Summing Trunk and Access GJTs

Rail

2.36 The total GJT for all in-scope OD zone pairs is calculated by adding the trunk GJT to the relevant access times. In-scope is defined by the O and D zones having

accessible TAPs and there being a feasible trunk journey between the TAPs. 2.37 For any infeasible journey, the GJT is set to the “infeasible journey length”

constant. This should be a large number showing that the journey would not be made in reality (eg 9999). For zone pairs where the O and D are the same, the GJT is set to zero.

Air

2.38 Rather than choosing the closest TAP, in reality, people would choose to access a trunk network not by how long it takes to get to the nearest TAP but by selecting a TAP that gives them the best overall journey. Although, for rail, the nearest TAP assumption is made for simplicity, it is not satisfactory for air since different flights are available from different airports.

2.39 For example, someone living in North London, wishing to travel to Glasgow

Prestwick may be closest to Luton airport. However, there is no service from Luton to Prestwick. There is however a service from Stansted which they may be willing to take, which should be available to the mode choice model.

2.40 For air, therefore, the top 3 closest airports are chosen for each zone. The flow GJT is then calculated for all 9 combinations of access points (3 at the origin, 3 at the destination). The access points giving the lowest GJT are then selected as the travellers preferred.

Mode Specific Assumptions and Methods

Car

2.41 When calculating the GJT for a car journey the monetary cost of the journey is measured in terms of cost of fuel. It is assumed that there is no wear and tear cost. Fuel cost per kilometre is calculated using methodology set out in the DfT’s

WebTAG. This is multiplied by the distance travelled and then divided by a parameter giving the average number of occupants in a car since the mode split model is comparing the cost against public transport options where the cost is per person.

2.42 Distance: The PSM model includes a straight line “crow-flies” distance for each zone to zone OD. This data is imported into the model and multiplied by a parameter used to account for the fact that no journey by car between zones would actually be a straight line.

2.43 Fuel cost: The WebTAG methodology calculates the fuel efficiency of the average car using set assumptions for various years. Fuel cost is estimated and duty and VAT is applied (VAT at 17.5%). It is assumed that the average speed of traffic is 90kph, although this can be varied by the user. The 2007 fuel cost is then used in model calculations (this can also be varied by the user).

Air

2.44 Car parking is set as a parameter. This is set with an assumption on how much and how long people park at the airport park for.

2.45 Note that air users are assumed to always have a car available and that no one commutes by air.

New Rail and Classic Rail

2.46 Rather than using PDFH interchange penalties, which vary by distance, there is a single parameter for interchange penalty. The PDFH penalty is more relevant when comparing direct journeys against journeys with multiple legs. In this model, it is more likely that the interchange penalty will relate to the frequency of local rail services rather than distance of the entire journey. Additionally, the penalty is not specified by ticket type of journey purpose, since the interchange penalty is weighted in the access time calculations.

Values of Time and Modal Preference

2.47 Since trunk GJTs are weighted in the Intercity model, it is necessary to ensure that the correct combination of Values of Time (VoT)s are used; these are shown in Table 2.1. PSM VoTs are taken as a starting point. These VoTs are uprated to 2030 values (the forecasting year) and modified to a 2007 price base (the forecast price base).

Furthermore, there is a modification to the New Line In-vehicle Time (IVT) VoT as described below.

TABLE 2.1 INTERCITY VOT (2007 PRICES / 2030 VALUES)

Business Commuting CA Commuting NCA Other CA Other NCA

Air IVT 158.1 n/a n/a 40.9 n/a

Air Headway 36.7 n/a n/a 5.3 n/a

Air Access 170.9 n/a n/a 40.9 n/a

Car IVT 131.4 27.9 n/a 30.3 n/a

Car Access 170.9 41.8 n/a 40.7 n/a

CR IVT 131.4 27.9 27.9 30.3 30.3 CR Headway 71.6 13.0 13.0 22.8 22.8 CR Access 170.9 41.8 41.8 40.7 40.7 NR IVT 98.6 27.9 27.9 27.9 27.9 NR Headway 71.6 13.0 13.0 5.3 5.3 NR Access 170.9 41.8 41.8 40.9 40.9

2.48 The PSM model used a higher value of time than the Intercity model, but used a fixed time advantage (mode constant) to produce an overall perceived journey time benefit. However, for long journeys this meant that the perceived journey time saving may actually be less than the real journey time saving, whereas for short journeys the opposite occurred where the perceived journey time saving could be well in excess of that actually obtained. For very short journeys this could lead to a negative journey time for new rail trips. As a counter factor passengers on high speed rail were assumed to have a higher value of time than classic rail passengers. It appears that the air value of time was adopted for new rail passengers.

2.49 The Stated Preference (SP) survey undertaken by SDG tested the model form used in the PSM model, of a fixed mode specific constant and a higher value of time for new rail journeys. The SP analysis found that this formulation was not statistically significant. However it did find that a model with a mode specific constant that varied by journey time (applied by reducing the value of time for passengers on high speed rail) was statistically significant.

2.50 Therefore approach adopted in the Intercity model and informed by the Stated Preference work, was to utilise a lower modal value of time for New Lines and dispense with any mode constant; this created a perceived benefit always in excess of the actual journey time saving. And also ensured that the mode specific

2.51 This is illustrated in Figure 2.2 and 2.3.for a classic rail journey of 240 minutes and 120 minutes respectively transferring to New Lines. For the former the PSM model has a lower perceived journey time where the New Line offers journey times more than 120 minutes; conversely, the approach used in the Intercity model has perceived time benefits consistently above the actual time saving. For the latter, classic rail journey of 120 minutes, the PSM model has relatively large perceived time benefits compared to the Intercity model used here.

TABLE 2.2 ILLUSTRATIVE PERCEIVED JOURNEY TIME BENEFIT FOR A 240 MINUTE CLASSIC RAIL JOURNEY SWITCHING TO NEW LINES

0 50 100 150 200 250 90 100 110 120 130 140 150 160 170 180

New Line time

T im e ben ef it

Actual Time saving

PSM Business time saving

Intercity Model Business time saving PSM Lesiure time saving

TABLE 2.3 ILLUSTRATIVE PERCEIVED JOURNEY TIME BENEFIT FOR A 120 MINUTE

CLASSIC RAIL JOURNEY SWITCHING TO NEW LINES

2.52 Therefore on the basis of the SP results and review of the treatment of modal preference, the Intercity model adopted a factor of 75% of classic rail value of New Line value of time for Business travellers. For Leisure travellers, the ratio is 92%. Commuters are assumed to place no preference on classic rail or New Lines.

2.53 Due to two factors the SP results were not used to replace all parameters. Changing the parameters that exist in the present day would have required model

recalibration. Furthermore the SP analysis produced a value of time for leisure passengers that was deemed too high and not appropriate for use.

Crowding and Network Topology

2.54 Crowding can be an important factor (and therefore an addition to the generalised cost) when someone is choosing how to travel. In order to take crowding into account, it is necessary to include a cost for it in the GJT module.

2.55 The crowding process in the Intercity model works on the New Lines mode of the model only. For a discussion on crowding assumptions and how to use the model to calculate classic rail crowding in the Do-Minimum, see paragraphs 2.94 to 2.96. 2.56 Crowding is calculated on a “link” basis. This means that for a given rail link

between two Trunk Access Points the entire demand and the entire capacity over that link is used to determine the level of crowding over the link.

2.57 The network topology is used to relate the network links and the services and associated demand that run over them. This takes the form of a matrix for each link (up to 20 links are allowed). The matrix is made up of 20x20 TAP origins and

destinations. For each link a flag is set if a train for any particular O to D would run over that link. By adding this information for each link the network topology is defined. This information can then be used to determine how many services run over each link. A further input defining the train stopping pattern, frequency and capacity is required. This therefore enables the total capacity on each link to be calculated. 0 20 40 60 80 100 120 140 160 30 40 50 60 70 80 90

New Line time

T

im

e

benefi

t

Actual Time saving

PSM Business time saving

Intercity Model Business time saving PSM Lesiure time saving

2.58 Note that there is only space for one network topology input per spreadsheet. This means that a single spreadsheet must be used for any particular network topology. It is, however, possible to add all possible links to the network topology and refer to only some of the TAPs in any given scenario.

2.59 Demand on each link is determined by totalling the demand for each OD flow that uses a particular link and a crowding cost for each link is calculated. Crowding costs are calculated on an all day basis using crowding penalty curves derived for this purpose. These curves define the crowding penalty as a proportion of the in-vehicle time; the relevant multiplier is thus applied to the journey time for each link to determine a crowding cost for that link. Note that this process infers that all services over each link are available to all of the demand; clearly there will be instances where this is not the case. Careful design of the TAP and link system can be used to mitigate this effect. This issue should not materially affect the result of the current New Line options given the service patterns modelled.

2.60 The crowding penalty is then fed back into the GJT cost for each trunk, which in turn alters the demand. A macro is used to iterate through the process up to ten times, with the resultant fed-back crowding cost being an average of previous runs to ensure that the process converges.

Valuing Crowding

2.61 The incorporation of crowding into the Intercity model required a relatively simple, robust way of estimating the impact of crowding on demand, consistent with best practice. The exact requirement was for a function to convert average load factor on each TAP to TAP link to a crowding time penalty.

2.62 The methodology used is drawn heavily from that used in the NMF and comprises two key steps:

I Establishment of crowding time penalties by load factor for an individual train based on PDFH valuations; and

I Application of distributions representing the variation of demand over an

appropriate time period to give average crowding time penalties over a number of services.

Crowding Time Penalties

2.63 Whilst PDFH 4.1 expresses crowding penalties in terms of pence per minute, the NMF reflects current DfT guidance and uses an approach where the penalties are represented in terms of minutes of penalty per minute of journey time (as does the PSM model). Previous versions of the PDFH presented crowding penalties in both formats, and whilst it now only quotes monetary valuations, the time equivalent values can be derived by going back to the source studies as explained in the documentation. One of the reasons for using this approach is that it is difficult to apply the monetary approach at a link level as fares are not defined at that level. Table 2.4 details the resulting table of crowding penalties.

TABLE 2.4 IMPLIED JOURNEY TIME EQUIVALENT CROWDING PENALTIES

Standard First Class Outer Inner

60% - - - -70% 0.04 0.04 - - - 0.02 0.04 -80% 0.07 0.08 - - - 0.04 0.08 -90% 0.15 0.16 0.23 0.04 - 0.07 0.11 0.11 100% 0.20 0.23 0.47 0.07 0.08 0.09 0.15 0.22 110% 0.27 0.31 0.11 0.16 0.15 0.21 0.32 120% 0.33 0.39 0.14 0.23 0.22 0.27 0.43 130% 0.40 0.46 0.18 0.31 0.28 0.34 0.54 140% 0.45 0.54 0.22 0.39 0.35 0.40 0.65 150% 0.47 160% 0.55

Standard First Class Outer Inner

Below 100% 0 - - - -100% 2.12 1.70 1.28 1.28 2.12 2.86 1.76 110% 2.33 1.87 1.33 1.33 2.33 2.93 1.89 120% 2.54 2.04 1.38 1.38 2.54 3.01 2.03 130% 2.75 2.21 1.44 1.44 2.75 3.08 2.16 140% 2.96 2.38 1.49 1.49 2.96 3.15 2.30 150% 1.54 1.54 2.43 160% 1.60 1.60 2.57 Standing Penalties (time penalties)

Load Factor

London-based Services Non-London Based Services Leisure

Business Commuting

Leisure Business Commuting Load Factor

London-based Services Non-London Based Services Leisure

Business Commuting

Leisure Business Commuting Sitting Penalties (time penalties)

2.64 These were simplified to a set of recommended values for a single train service, which has been reduced to penalties for Commuting (split London and non-London based) and non-Commuting. This simplification reflects uncertainty in the original valuations, the need for internal consistency and consistency with the standard GJT approach.

2.65 The penalties shown in Table 2.5 were adopted for use in NMF, and have been adopted for this work. The penalties are applied to in-vehicle times at the load factors shown and are additional to actual journey time. Values at 300% load factors are derived by extrapolation from the values at 120% and 140%.

TABLE 2.5 PROPOSED TIME EQUIVALENCE PENALTIES – INDIVIDUAL TRAIN

Load Factor Condition London-Based Commuting1 Non- London Commuting2 Non-Commuting3 60% and below Sitting - - - 80% Sitting - 0.07 0.07 90% Sitting 0.02 0.12 0.12 100% Sitting 0.08 0.17 0.17 120% Sitting 0.19 0.30 0.30 140% Sitting 0.30 0.43 0.43 300% Sitting 1.18 1.47 1.47 Below 100% Standing - - - 100% Standing 1.28 1.76 2.20 120% Standing 1.38 2.03 2.53 140% Standing 1.49 2.30 2.86 300% Standing 2.37 4.46 5.50

Using a Distribution to Estimate Average Crowding Costs for a Link

2.66 Load factors are available for each link within the model. In general this means that the resulting load factors will be averages over a number of train services over an average day.

2.67 The consequence of this is that, if there is any variability in load factors across services, then crowding off will start at an average load factor of less than the 80% shown in Table 2.5. This is because there will be some trains with higher load factors than the average, which incur crowding penalties, and this is not offset by those trains at lower load factors than the average which have zero crowding

penalties. In general, crowding penalties for average load factors will be higher than for individual trains with the same load factor because of this asymmetry.

2.68 The construction of crowding curves assumes that distributions of load factors can be modelled using a Gamma distribution with an assumed distribution between the standard deviation and mean load factor. The ratio of the standard deviation to the mean load factor is assumed to remain constant as the mean load factor varies.

1 _{Arithmetic average of London based Commuting Outer and Inner from Table 2.4}

2 _{Standing penalties as per Non-London based Commuting services, sitting penalties constrained to}

non-Commuting values from Table 2.4

3 _{Arithmetic average of London based Leisure and Standard Business and Non-London based Leisure and}

2.69 The Gamma distribution form is used because it approximates to a Normal

distribution for certain parameter values, except that it is (usefully) non-negative. These distributions allowed us to infer a revised table of crowding penalties based on average load factors.

2.70 The NMF employed a process for fitting Gamma distributions for individual TOCs and time periods and this been adopted for application in the New Lines study, using the distribution parameters from the most appropriate TOCs for the whole day, rather than individual time period, derived by returning to the raw data.

2.71 The main source of data that has been used is a database of loadings on Virgin West Coast between 2 April and 30 June 2006. This has been analysed to understand the variation of weekday load factors on the final link into and out of London; this gives a sd/mean ratio of 0.40, which is consistent with the values used in the NMF. Figure 2.2 illustrates the fit of the distribution to the data.

FIGURE 2.2 DISTRIBUTION OF VIRGIN WEST COAST LOAD FACTORS (LONDON END)

2.72 The VWC data is the most up to date and most comprehensive and analysis of the other NMF TOC data showed that the other TOCs’ results appear to be distributed around VWC’s. Given that VWC is comparable in character to a new High Speed Line and serves similar markets, the value of 0.40 has therefore been recommended for the sd/mean parameter in the New Lines crowding approach.

Mode Split Calculations

2.73 The mode split model is based on the same hierarchical structure and parameters as the PSM model. Figure 2.3 shows the model structure with New Rail offered as an alternative mode to classic rail at the bottom of a 3 level hierarchy.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0.000 0.500 1.000 1.500 Load factor % of di s tr ibuti on l e ss tha n l o a d fa ct or

FIGURE 2.3 HIERACHICAL LOGIT MODEL STRUCTURE

2.74 After classic rail, new rail, air and car generalised costs have been calculated, these costs are used to calculate estimates of the future modal share between the four modes. This is achieved using an incremental nested, or hierarchical, logit model, which is a statistical model commonly used to forecast the choices made by people faced with a series of discrete options. The inputs to the model are the

Do-Minimum modal share and both Do-Do-Minimum and Do-Something generalised costs, along with a number of parameters that influence how sensitive the model is to differences in the generalised costs. The output of the model is a number of conditional probabilities, which are interpreted as being the percentage of travellers choosing each mode when faced with the choice.

2.75 At the top of the nest, costs are aggregated to reflect the overall improvement in transport and this is used to obtain an estimate of generated demand.

Parameters

2.76 The PSM mode choice and generation scaling parameters were employed, but modified to take account of the change in the modelled price base. The PSM model has a base year of 2000 and all monetary values are expressed in a price base of that year; the Intercity model has an equivalent (price) base year of 2007. This esults in, all other things being equal, a bigger monetary cost since the price base is higher. On that basis, the scaling parameters are reduced in compensation. RPI data for 2000 and 2007 was used to inflate values of time, with a corresponding reduction in the scaling parameters. The resulting scaling parameters are set out in Table 2.6. All modes ‘PT’ modes Rail modes Classic Rail

TABLE 2.6 SCALING FACTORS

Scaling factor λ Business Commuti ng (CA) Commuti ng (NCA) Other (CA) Other (NCA)

Classic rail versus

new rail -0.000630 -0.000961 -0.000951 -0.002679 -0.004146

Rail versus air -0.000561 -0.001855

Public transport

versus car -0.000409 -0.000666 -0.000981

Generation -0.000061 -0.000222 -0.000222 -0.000327 -0.000327

2.77 The generation scaling parameters are based on a factor (thetas strictly speaking – ratios of scaling parameters) applied to the scaling parameter of the public

transport versus car top nest. The default PSM value is 1/3 for all purposes, but this was reduced for Business to 0.15, based on review and analysis of model results and comparison and benchmarking against modelled and empirical results obtained elsewhere. It must be noted that the value of the generation scaling parameters

(and hence the factor or theta applied) is a critical parameter and has a very large influence on the level of demand on New Lines. The values chosen are considered to give plausible forecasts, consistent with other forecasting methodologies and benchmarking.

Application of the Mode Choice Model

2.78 The model was applied on an O-D basis and comprises five main steps. In all cases, where New Lines is identified as not competing with classic rail on a particular flow, the Do-Minimum mode split and demand is taken as the default result. Since

crowding is modelled, the following process is done on an iterative process, with ten iterations being undertaken and a dampening process implemented to force

convergence.

Step 1: Classic Rail versus New Rail mode choice

2.79 This stage models the choice between classic rail and new rail. At this stage the model operates on an absolute basis for the Do-Something (i.e. with new rail) scenario only, since new rail does not exist in the Do-Minimum scenario and there is therefore no mode choice to be made.

2.80 Generalised costs are fed into the model in units of pence and modal share is calculated using the formulae:

(

)

)

rail

NR

P(

e

e

e

+

=

(

)

)

rail

CR

P(

e

e

e

+

=

where λ is the scaling factor and GCNR and GCCR are the generalised costs of new rail

2.81 Composite costs for the rail mode were calculated using the formula,

)

ln(

1

e

e

λ

GC

=

−

+

2.82 The change in composite cost between the Do-Minimum and Do-Something was calculated as,

CR rail

rail

Δ

GC

=

GC

−

GC

Step 2: Rail versus air mode choice

2.83 At this stage the change in cost calculated in the lower nest is compared with the generalised cost of air travel, using the incremental logit formula,

(

)

(

)

where Pair and Prail are the Do-Minimum percentage mode share of the air and rail

modes respectively .

2.84 Composite costs are again calculated and fed to the public transport vs car mode choice model. The formula used is

) ln( 1 _{air rail} rail air PT λ GC λ GC B B B P e e P λ GC ₌₋ − ⋅Δ ₊ − ⋅Δ Δ

2.85 If no Do-Minimum air demand exists for a flow, rail is assumed to capture 100% of the modal share and the rail composite cost is fed directly to the next nest in the model hierarchy.

Step 3: Public transport versus car mode choice

2.86 The change in cost calculated in the preceding nest is compared with the

generalised cost of car travel. The same incremental logit formulae are used as in the preceding level, with different costs and parameters as appropriate. Composite costs are again calculated and used to calculate generated demand.

Step 4: Generated demand

2.87 With composite costs calculated at the top nest in the model structure, the generated demand is calculated, with Do-Something demand equating to:

all 3 1 DM DS GC λC e D D = − ⋅Δ

Step 5: Do-Something demand

2.88 In the final stage the generated demand is added to the Do-Minimum demand and the total figure is apportioned using the probabilities calculated at each stage in the mode choice model.

2.89 For a number of flows the difference in GJT between NR and CR is fairly large. This is particularly true of flows with large access times to a NR TAP. The NR mode share parameter can be set to ensure that demand from flows with mode share less than

the parameter threshold is not assigned to New Rail. This ensures that material revenue generated from lots of small revenue gains is not claimed.

Running the Model

2.90 The model needs Do-Minimum costs for classic rail, air and car and Do-Something costs for classic rail, new rail, air and car. Usually when running the model, car and air costs are unchanged from Do-Minimum to Do-Something.

2.91 The model is designed to be able to store and run multiple, but broadly similar, Do-Something scenarios (where a scenario is a set of fares, journey times and service intervals). Each scenario can be run simply by switching the scenario selector in the parameter sheet and rerunning the crowding process.

2.92 However, since the mode choice between new rail and classic rail will only be calculated if the relevant new rail flow is also in scope in the Do-Minimum, it is important to ensure that every scenario run is tested against the relevant Do-Minimum costs. This means that the correct set of Do-Do-Minimum costs must be input into the model and that separate versions of the model will be needed when different sets of Do-Minimum costs are needed (for example when testing options that serve different corridors).

2.93 Furthermore, it is important to note that crowding costs must be calculated for both the Do-Minimum classic rail and the Do-Something new rail costs. It is assumed that there is no crowding on classic rail flows where a New Line flow is introduced on the basis that the majority of the flow will transfer to New Rail, with most flows on the classic network not being subject to crowding (since for example travellers will have used the classic network in order to minimise the fare and hence used advanced booking with a seat). To keep the model size operable, the crowding function is only applicable to the New Rail GJTs and therefore to run crowding on CR in the Do-Minimum, a separate model is used and the CR Do-Minimum scenario run through the NR flow, including the crowding process.

Do-Minimum

2.94 The process for running the CR DM is described below:

I Set up Air information (TAPs, access radii, journey time, service interval and fares)

I Set up CR information (TAPs, access radii, journey time, service interval and fares) in the NR inputs

I Set up the network topology – this will included as many major TAPs as needed to ensure a realistic demand on trains to ensure crowding is correctly calculated

I Set up train service spec to determine train capacity for crowding

I Switch “Use GJT weighting” to “NO”. This ensures that the NR JT weighting is switched off (to ensure that CR GJTs are calculated). Also changing the switch forces the given demand results to be the results of the crowding process (rather than the outputs of the mode split model).

I Set up other parameters

I The figures in the sheet “DM Results for DS” can then be copied to the DS version of the model into the “DM Results Inputs” sheet.

2.95 It is important to check that the right zones are in scope for all TAPs. It might be necessary to modify the IVT manual adjustment parameters to allow certain zones to be assigned as in-scope.

2.96 To ensure correct working of the mode split model, the CR DM should include feasible journeys that are expected to be in scope in the NR DS. This is to ensure there is a competitive CR option for the model split mode to perform its calculations on.

Do-Something

2.97 The process for running the DS is described below:

I Ensure correct DM results have been copied into the “DM Results Inputs” sheet

I Set up Air information (typically unchanged from DM)

I Input NR TAPs and access radii into NR TAP definition

I Input fares, service interval and JT into NR inputs

I Set up Classic rail DS into CR inputs. The same TAPs and access radii must be used as in the DM. Fares, service interval and journey times can be modified.

I Set up NR network topology – this would be point–point links for high speed non-stop rail service

I Set up train service specification to determine train capacity for crowding

I Ensure Switch “Use GJT weighting” is “YES”

I Set up other parameters

I Run crowding

I Further NR scenarios can be run by selecting the relevant scenario in the “GJT Parameters” sheet.

2.98 This process is outlined in the “Model Flow” sheet in the model.

Model Outputs

2.99 A number of model outputs are available to the user. These are described below.

Appraisal Results

2.100 As well as the demand and revenue results, the model produces outputs for appraisal related information such as journey time savings per mode and crowding and fares benefits and disbenefits. These benefits are derived from the calculated logsum results for New Rail vs Classic Rail nest in the mode split model and the results for the air and car directly. The detail of these calculations is discussed in the appraisal document.

Top 30 & Summary

2.101 The summary sheet displays the demand, revenue and passenger miles for the relevant link selected (in the Top 30 sheet). The Top 30 results sheet lists the 30 largest individual flows that make up the results in the summary sheet. This list is

generated using a macro. This is a useful way of determining which flows are in scope for a TAP-TAP link.

2.102 Flows with no demand are not shown here but as there may be some zones in scope with no demand, users can use the autofilters on the “Re-map demand” sheet to search for all zones in scope (irrespective of demand).

Single Flow Debug

2.103 A single flow can be studied using the spreadsheet to look at how demand GJT changes, how demand reacts to that and also what components make up the GJT. This can be very useful for debugging the mode choice decisions made by users on the edges of high speed catchments.

Illustrative Model Results

Scenario Setup

2.104 In this section, the results of a model run for an example scenario are presented. Option MB1.4.1 (shown in) is run through version 3.0 of the Intercity model. The following assumptions are made for key parameters:

I VoT inputs are as described in Table 2.1;

I No MSC;

I Scaling parameters and generation factors as set out in Table 2.6;

I Fares from NMF (converted to 2007 price base);

I 30% premium on NR fares; and

FIGURE 2.4 OPTION MB1.4.1 Edinburgh Glasgow Liverpool Birmingham Manchester London Central Preston WM South Junc. GM North Junction WM West Junc. GM South Junc. WM North Junc. Mersey Junction Caledonian Junc Warrington Form ation 10 10 10 10 10 10 10 10 10 10 10 10 10 10 5 5 5 5 Glasg ow Edinburgh Preston Liverpool W arrington M anchester Birm ingham London 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 5 16 17 18 Option M B 1.4.1 (TPH ) Do-Minimum

2.105 A full DM network of 20 TAPs is defined and feasible journey times and costs are input. Additionally, a network of links is defined to enable train crowding calculations.

2.106 Figure 2.5 and Figure 2.6 respectively show the zones in catchment for London and Manchester/Stockport in the Classic Rail Do-Minimum scenario. For London the catchment radius is set to 90 minutes (GJT) and for Manchester it is 60 minutes (GJT). Both stations are allowed rail access only to reflect the difficulty of access by car. Note that the catchments do not change depending on the flow being served. 2.107 For London, the catchment has been manually adjusted to include South Coast

zones since anyone travelling from these zones by rail must access services to the North via a London station. Manchester has a reasonably small catchment compared to London since other nearby TAPs are specified such as Liverpool, Warrington and Leeds.

FIGURE 2.5 LONDON DO-MINIMUM CATCHMENT

2.108 The crowding levels that are generated are shown in Table 2.7. Pendolino WCML trains are assumed to have 577 seats and the ECML IEP Intercity is assumed to have 639. Of note, the load factor on the Birmingham services peak at 59% out of London, with the equivalent load factor for Manchester services peaking at 44%. The highest load factor occurs on the Glasgow service, with high volumes and crowding to Warrington and beyond.

TABLE 2.7 DO-MINIMUM CROWDING LEVELS

Link TAP - TAP journey Demand Capacity Load Factor Journey Time Crowding Cost (Comm) Crowding Cost (Non-Comm)

per day per day mins mins mins A London Central to Coventry 32,721 55,392 59% 63 2.6 6.0 B Coventry to Birmingham Central 28,236 55,392 51% 20 0.3 0.9 C Birmingham Central to Wolverhampton 1,919 18,464 10% 24 0.0 0.0 D London Central to Stafford 9,264 18,464 50% 81 1.1 3.2 E Stafford to Liverpool Central 8,550 18,464 46% 46 0.3 1.1 F London Central to Warrington 16,360 27,696 59% 108 4.4 10.2 G Warrington to Wigan 14,539 18,464 79% 11 1.9 3.6 H Wigan to Preston 12,708 18,464 69% 13 1.2 2.5 I Preston to Lancaster 6,956 18,464 38% 18 0.0 0.1 J Lancaster to Oxenholme 6,028 18,464 33% 15 0.0 0.0 K Oxenholme to Carlisle 4,702 18,464 25% 36 0.0 0.0 L Carlisle to Glasgow 3,938 18,464 21% 71 0.0 0.0 M London Central to Stockport 24,426 55,392 44% 119 0.6 2.2 N Stockport to Manchester 16,216 55,392 29% 7 0.0 0.0 O London Central to Leeds 18,813 40,896 46% 116 0.8 2.9 P London Central to York 25,940 61,344 42% 118 0.4 1.7 Q York to Newcastle 17,073 40,896 42% 50 0.2 0.7 R Manchester to Preston 2,439 14,400 17% 48 0.0 0.0 S Newcastle to Edinburgh 7,447 20,448 36% 84 0.1 0.4 T Wolverhampton to Stockport 2,928 9,600 31% 65 0.0 0.1

Do-Something

2.109 As illustrated in Figure 2.7, the Manchester catchment increases significantly to reflect the fact that people are willing to travel further to access New Lines because of the reduced trunk journey time. The catchment area for London does not change.

2.110 New Line crowding is shown in Table 2.8. The capacities of the trains are assumed to be 650 seats. Note that the London to Glasgow demand is shown on the London to Preston and Preston to Glasgow links. The highest load factors are shown on the Scottish services, with the Birmingham and Manchester all day load factors being lower than the Do-Minimum at 32% and 42% respectively.

TABLE 2.8 NEW LINE CROWDING

Link TAP - TAP journey Demand Capacity Load Factor

Journey Time Crowding Cost (Comm) Crowding Cost (Non-Comm)

per day per day mins mins mins

A London Central to Birmingham Central 26,915 83,200 32% 46.0 0.0 0.1

B London Central to Manchester Central 34,986 83,200 42% 66.0 0.2 0.9

C Birmingham Central to Manchester Central 6,689 41,600 16% 38.0 0.0 0.0

D London Central to Warrington 20,000 41,600 48% 66.0 0.7 2.1

E Liverpool Central to Warrington 12,148 41,600 29% 15.0 0.0 0.0

F London Central to Preston 23,504 41,600 57% 73.0 2.4 5.8

G London Central to Glasgow 0 0 0% 136.0 0.0 0.0

H London Central to Edinburgh 21,528 41,600 52% 129.0 2.4 6.4

I Manchester Central to Preston 7,295 20,800 35% 15.0 0.0 0.1

J Preston to Glasgow 18,565 62,400 30% 59.0 0.0 0.1

K Preston to Edinburgh 2,555 20,800 12% 59.0 0.0 0.0

L Birmingham Central to Preston 2,577 41,600 6% 46.0 0.0 0.0

M 0 0 0% 0.0 0.0 0.0 N 0 0 0% 0.0 0.0 0.0 O 0 0 0% 0.0 0.0 0.0 P 0 0 0% 0.0 0.0 0.0 Q 0 0 0% 0.0 0.0 0.0 R 0 0 0% 0.0 0.0 0.0 S 0 0 0% 0.0 0.0 0.0 T 0 0 0% 0.0 0.0 0.0 Results Summary

2.111 The summary in Table 2.9 shows that demand on the London - Manchester new line trunk is 10.50m journeys per year of which 7.13m of these journeys are abstracted from Classic rail. It is important to understand that the total classic rail journeys figure shown is not the number of journeys made on the London to Manchester classic rail trunk. It is the number of classic rail journeys that are made between the same set of origins and destinations that are in scope for the new line. For example Classic Rail journeys between Leeds and London are included since a small proportion of these passengers may choose to travel to London via the New Line from Manchester due to the high speed of the service offered between London and Manchester.

TABLE 2.9 LONDON-MANCHESTER JOURNEYS RESULTS SUMMARY

CR NR Air Car Total

DM mill jnys /year 12.10 0.00 0.40 3.84 16.34

DS mill jnys /year 4.97 10.50 0.20 3.07 18.74

Difference mill jnys /year -7.13 10.50 -0.20 -0.76 2.40

% Difference -59% -51% -20% 15%

2.112 The breakdown of the Top 30 value (by revenue) zone pairs on the Manchester corridor is shown in Table 2.10

2.113 Looking more closely at a single flow (Table 2.11), in this case London to Stockport Business travellers, the results of the mode choice calculations is visible. For London to Stockport there is a significant GJT benefit from travelling via New rail and 91% of the existing classic rail passengers shift. As well as mode shift from air and car, there is generation of 713 passengers per day on this flow.

TABLE 2.10 TOP 30 FLOWS LONDON – MANCHESTER

2030 Do Minimum 2030 Do Something

CR Air Car CR NR Air Car

Flow Zone 1 Zone 2 Demand Revenue Yield Demand Demand Demand Revenue Yield Demand Revenue Yield Demand Demand

m jnys /

year £m / year £/jny

m jnys / year

m jnys / year

m jnys /

year £m / year £/jny

m jnys /

year £m / year £/jny

m jnys / year

m jnys / year

1 117_130 London Central Manchester 4.0 195.5 49.1 0.0 0.2 0.2 9.5 47.3 5.1 331.8 64.5 0.0 0.1

2 117_187 London Central Stockport 1.9 140.8 74.8 0.0 0.1 0.1 9.4 78.0 2.3 153.1 65.7 0.0 0.0

3 117_125 London Central Macclesfield 0.4 27.6 78.0 0.0 0.0 0.0 0.7 81.2 0.6 38.5 68.5 0.0 0.0

4 117_210 London Central Tameside 0.3 17.7 50.8 0.0 0.0 0.0 0.9 48.9 0.5 30.9 66.1 0.0 0.0

5 105_117 Leeds London Central 4.4 239.1 54.7 0.0 0.2 4.2 228.1 54.5 0.3 20.6 78.5 0.0 0.1

6 100_117 Kirklees London Central 0.2 13.8 56.9 0.0 0.1 0.1 3.2 55.9 0.3 20.4 70.4 0.0 0.0

7 17_130 Brighton & Hove Manchester 0.1 7.3 62.7 0.0 0.1 0.0 0.4 59.7 0.2 14.4 77.6 0.0 0.0

8 118_130 London North Manchester 0.1 4.5 53.8 0.0 0.1 0.0 0.2 52.2 0.2 11.0 70.1 0.0 0.0

9 9_117 Bolton London Central 0.1 3.0 50.6 0.0 0.0 0.0 0.2 49.6 0.1 7.2 67.1 0.0 0.0

10 117_157 London Central Oldham 0.1 3.1 50.7 0.0 0.0 0.0 0.2 49.5 0.1 6.9 67.0 0.0 0.0

11 123_130 London West Manchester 0.0 1.3 53.0 0.0 0.1 0.0 0.1 52.3 0.1 5.9 70.3 0.0 0.0

12 104_130 Kent West Manchester 0.0 0.9 58.9 0.0 0.1 0.0 0.1 58.3 0.1 4.1 76.3 0.0 0.1

13 124_130 Luton Manchester 0.0 0.7 61.0 0.0 0.2 0.0 0.1 59.9 0.0 3.7 78.0 0.0 0.2

14 93_130 Hertfordshire Mml Manchester 0.0 1.5 56.6 0.0 0.0 0.0 0.1 54.4 0.0 3.6 72.3 0.0 0.0

15 122_130 London South West Manchester 0.0 0.6 51.5 0.0 0.1 0.0 0.1 51.7 0.1 3.5 69.8 0.0 0.0

16 92_130 Hertfordshire Ecml Manchester 0.0 0.6 54.2 0.0 0.2 0.0 0.1 54.6 0.0 3.1 72.6 0.0 0.1

17 17_210 Brighton & Hove Tameside 0.0 0.7 63.6 0.0 0.1 0.0 0.1 62.1 0.0 2.8 80.0 0.0 0.0

18 71_130 Essex South Manchester 0.0 0.5 57.1 0.0 0.2 0.0 0.1 57.2 0.0 2.6 75.2 0.0 0.1

19 130_208 Manchester Surrey North West 0.0 0.4 55.8 0.0 0.0 0.0 0.1 55.1 0.0 2.4 73.4 0.0 0.0

20 130_207 Manchester Surrey East 0.0 0.5 55.0 0.0 0.0 0.0 0.1 54.5 0.0 2.4 72.6 0.0 0.0

21 130_209 Manchester Surrey South West 0.0 0.8 56.7 0.0 0.0 0.0 0.1 55.7 0.0 2.3 73.6 0.0 0.0

22 120_130 London South / Croydon Manchester 0.0 0.6 52.0 0.0 0.0 0.0 0.1 51.3 0.0 2.2 69.2 0.0 0.0

23 121_130 London South East Manchester 0.0 0.4 51.9 0.0 0.0 0.0 0.0 52.3 0.0 1.9 70.2 0.0 0.0

24 17_187 Brighton & Hove Stockport 0.0 0.8 87.1 0.0 0.0 0.0 0.1 89.1 0.0 1.7 77.8 0.0 0.0

25 100_118 Kirklees London North 0.0 0.8 59.5 0.0 0.0 0.0 0.2 59.3 0.0 1.6 74.2 0.0 0.0

26 69_130 East Sussex Manchester 0.0 0.3 59.8 0.0 0.0 0.0 0.0 59.2 0.0 1.5 77.2 0.0 0.0

27 117_170 London Central Rochdale 0.0 0.3 50.7 0.0 0.0 0.0 0.0 50.9 0.0 1.4 68.9 0.0 0.0

28 13_130 Bedfordshire North Manchester 0.0 0.2 60.1 0.0 0.0 0.0 0.0 59.4 0.0 1.2 77.4 0.0 0.0

29 130_225 Manchester West Sussex Central 0.0 0.3 58.8 0.0 0.0 0.0 0.0 56.8 0.0 1.0 74.7 0.0 0.0

30 103_130 Kent East Manchester 0.0 0.3 60.6 0.0 0.0 0.0 0.0 59.0 0.0 0.9 76.8 0.0 0.0

TABLE 2.11 LONDON - STOCKPORT DETAIL

117_187 Business CA

per day CR NR Air Car Total

DM GJT mins 246 0 260 210 DS GJT mins 260 207 260 210 % Diff GJT 6% N/A 0% 0% DM Dmd jnys 3395 0 28 146 3569 DS Dmd jnys 316 3908 6 52 4282 Diff Dmd jnys -3079 3908 -22 -94 713 % Diff Dmd -91% N/A -78% -64% 20% DM Rev £ 274,406 0 274,406 DS Rev £ 25,543 278,869 304,413 Diff Rev £ -248,863 278,869 30,006

% Diff Rev -91% N/A 11%

GJT Calculations

2.114 Figure 2.8 shows how GJT is built up for the London to Stockport flow for New Rail for business travellers. The large reduction in journey time is the key part of the NR GJT reduction. This is offset by an increased access time since users must travel to Manchester rather than catching the train from Stockport. The difference in fares is largely immaterial to business users, who value time highly.

FIGURE 2.8 DO SOMETHING GJT BREAKDOWN DS GJT breakdow n 0 50 100 150 200 250 300 CR NR Air Car mi n s Ot her Constant Access Fare Access Time Headway Trunk Fare Trunk Time Totals

2.115 For this example option, the total revenue increment from DM (shown in Table 2.12) to DS is £1.53bn. This number will drive the business case, along with the associated journey time, crowding and fare benefits.

TABLE 2.12 TOTAL REVENUE FOR OPTION 1.4.1

CR NR Total

DM £m / year 1,654 0 1,654

DS £m / year 534 2,653 3,187

Diff £m / year -1,120 2,653 1,533

Model Discussion

Experience of High Speed Rail

2.116 A review has been undertaken of the available literature on international experience of the introduction of high speed rail. This indicates that the introduction of high speed rail can lead to significant increases in rail demand. For example when air passengers transferring to other flights are excluded Eurostar services are estimated to carry 80% of the London to Paris market.

2.117 Journey time elasticities implied by observed changes in behaviour have been utilised to benchmark the New Line forecasts. The best available data existing relates to the initial stage of the Paris to Lyon line; this reduced train journey times by around 30% and had an implied JT elasticity of around -1.6. The second southern section stage reduced JTs by 25% but only saw a JT elasticity of -1.1. This is lower since significant transfer from air had been largely completed in stage one.

2.118 These values have been compared to the journey time elasticities implied by the New Line forecasts of circa -0.4 for Birmingham to London, -0.8 between Manchester and London and -1.54 _{for Edinburgh to London where there is the most}

to be gained from air competition. Of note, the Edinburgh elasticity is very comparable to the Paris-Lyon experience and again provides comfort that the New Lines forecasts are credible and robust.

2.119 Other evidence supports this. In a 1992 stated preference study considering air and rail travel in the UK Wardman et al found a rail journey time elasticity between –1.2 and –1.5 depending upon the size of the journey time change. Bhat (1995) reports a rail in-vehicle time elasticity of -1.56 from a study of air, rail and car competition in Canada.

2.120 There is mixed evidence on the amount of traffic that would be generated by a new high speed rail service. Wilken (2000) reports that surveys of AVE passengers indicated that 15% of the additional rail traffic was newly generated. However Bonnafous (1987) found that 49% of the additional traffic on Paris Lyons in the first four years was generated traffic.

2.121 As part of the New Lines project only a small scale review of the publicly available evidence on the impact of New Lines has been undertaken. At the next stage of the project a more significant workstream investigating the experience of existing high speed rail services should be undertaken.

UK demand flows

2.122 Forecast New Line demand for particular flows have also been compared to that achieved by existing and forecast UK flows. In particular, in option MB1.4.1 the market between Edinburgh and London is forecast to grow to 6.5m passengers per annum. The New Line journey time between Scotland and London is broadly equivalent to that from Manchester and Leeds to London today; service frequencies are comparable at 2tph or 3tph. Their Do-Minimum demand is forecast to reach 6.8

4 _{This is the average with and without any fare premia, the elasticity values being 1.46 and 1.62}

respectively. This reflects the uncertainty around fare changes on the Paris-Lyon route on which the elasticity is being compared.

million and 5.6 million respectively by 2030, so the demand forecasts for Edinburgh are commensurate given the rail journey times.

2.123 This is set against the relative sizes of the population. Manchester is forecast5 _by

2030 to have a population some 75% greater than SE Scotland (the area served by a New Lines service to Edinburgh), with West Yorkshire (the Leeds area conurbation) some 55% greater. However, these areas are much more accessible, with road and rail offering reasonable accessibility. Conversely, only air arguably offers the same for Scotland (where rail is well over 4 hours and car has journey times of 7 hours or more).

2.124 Were accessibility levels comparable, then it can be argued that the demand for travel would be comparable, after accounting for the relative attractiveness of the markets. This is illustrated in Table 5.6 for the South East to North West and South East to Scotland markets. The attractiveness is measured by the relative level of population and employment in the two markets (the South East is common to both and hence does not affect the relative attractiveness); overall, the North West has 40% higher population and employment than Scotland in 2030.

2.125 In 2030, the Do-Minimum demand to the North West is 40 million trips per annum. Whilst the attractiveness of Scotland is some 30% lower, the demand to Scotland is nearly 50% lower, reflecting the relatively poor accessibility to Scotland from the South East. New Lines improves rail accessibility to Scotland to a level equivalent to that currently experienced for the North West; the forecasts indicate that the Scottish market grows to a size commensurate with the North West accounting for the differences in population and employment (i.e. 40/28 = 1.4).

TABLE 2.13 RELATIVE MARKET SIZE FOR THE NORTH WEST AND SCOTLAND IN 2030 South East6 to: 2030 attractiveness

index (population and employment) 2030 Do-Minimum (million journeys p.a.) 2030 Do-Something (million journeys p.a.)

North West 1.4 40 n/a

Scotland 1 21 28

Summary

2.126 The preceding discussion has set out a set of benchmarking analysis to demonstrate the credibility and robustness of the New Lines forecasts. The forecasts are consistent with PDFH, the industry standard rail demand forecasting tool, commensurate with experience elsewhere and accord with comparison of demand on other key rail flows.

2.127 However, it must be emphasised that any forecasting is prone to uncertainty and that should the case for New Lines be considered further, the forecasting process should be developed in more detail, notably with regard the preference for New

5 TEMPRO 5.4.

Lines over other modes and the propensity for New Lines to generate demand. Other refinements for consideration are set out below.

Potential Model Refinements

2.128 The key model refinement would be to update the demand data from the original 2000 base data. Although this was undertaken here to a new 2007 base, a more thorough update could be done using observed data, in particular for the