Planning and Regulation
Route Planning
New Lines
Programme
Demand forecasting technical note
Contents
Contents
1
2
3
4
5
2
INTRODUCTION
1
Overview of Approach
1
This Report
3
INTERCITY MODEL
5
Introduction
5
GJT Calculator
5
Input Data
7
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
COMMUTER MODEL
33
Introduction
33
Scope of Model
33
Methodology Overview
35
Data Inputs
37
Detailed Review of Methodology
38
Model Outputs
44
REGIONAL MODEL
47
Introduction
47
Scope of the Model
47
Methodology Overview
48
Data Inputs
48
Detailed Review of Methodology
48
Model Outputs
53
HEATHROW ACCESS MODEL
57
6
Introduction
57
Model Development and Calibration
57
Model Forecasting
58
Model Outputs
60
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
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
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
TABLES
Table 2.3
Contents
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
4
1
New Lines Programme: Demand forecasting technical note
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:
1.3
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.
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
New Lines Programme: Demand forecasting technical note
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
Glasgow and
Edinburgh
Preston
Manchester
Liverpool
Stockport
Intercity model
Warrington
Crewe
Stoke on
Trent
Near Europe
model
Stafford
Birmingham New Street
Birmingham International
To Paris/
Brussels
Rugby
Coventry
Northampton
Regional model
Commuter
model
Milton
Keynes
Central
Huntingdon
Stevenage
Berkhamsted
Watford
Heathrow
Access Model
London and SE
1.6
2
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.
New Lines Programme: Demand forecasting technical note
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
I
Chapter 6 details the Near Europe Model.
3
New Lines Programme: Demand forecasting technical note
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
2.6
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
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
5
New Lines Programme: Demand forecasting technical note
FIGURE 2.1
RAIL TRUNK WITH FEEDER CATCHMENT ZONES
TAP
Trunk (rail or air)
TAP
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.
6
New Lines Programme: Demand forecasting technical note
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
7
New Lines Programme: Demand forecasting technical note
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:
Weight (car ) =
2.20
VoT (car _ IVT )
VoT (ClassicRail _ IVT )
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
8
New Lines Programme: Demand forecasting technical note
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.
9
New Lines Programme: Demand forecasting technical note
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
10
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).
New Lines Programme: Demand forecasting technical note
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
preference would vary with journey length.
11
New Lines Programme: Demand forecasting technical note
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
250
Actual Time saving
PSM Business time saving
Intercity Model Business time saving
PSM Lesiure time saving
Intercity Model Leisure time saving
Time benefit
200
150
100
50
0
90
12
100
110
120
130
140
New Line time
150
160
170
180
New Lines Programme: Demand forecasting technical note
TABLE 2.3
ILLUSTRATIVE PERCEIVED JOURNEY TIME BENEFIT FOR A 120 MINUTE
160
Actual Time saving
PSM Business time saving
Intercity Model Business time saving
PSM Lesiure time saving
Intercity Model Leisure time saving
140
Time benefit
120
100
80
60
40
20
0
30
40
50
60
New Line time
70
80
90
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.
13
New Lines Programme: Demand forecasting technical note
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
14
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.
New Lines Programme: Demand forecasting technical note
TABLE 2.4
IMPLIED JOURNEY TIME EQUIVALENT CROWDING PENALTIES
Sitting Penalties (time penalties)
Load Factor
60%
70%
80%
90%
100%
110%
120%
130%
140%
150%
160%
Leisure
0.04
0.07
0.15
0.20
0.27
0.33
0.40
0.45
London-based Services
Business
Commuting
Standard
First Class
Outer
Inner
0.04
0.08
0.16
0.23
0.04
0.23
0.47
0.07
0.08
0.31
0.11
0.16
0.39
0.14
0.23
0.46
0.18
0.31
0.54
0.22
0.39
0.47
0.55
Non-London Based Services
Leisure
0.02
0.04
0.07
0.09
0.15
0.22
0.28
0.35
Business Commuting
0.04
0.08
0.11
0.11
0.15
0.22
0.21
0.32
0.27
0.43
0.34
0.54
0.40
0.65
Standing Penalties (time penalties)
Load Factor
Below 100%
100%
110%
120%
130%
140%
150%
160%
Leisure
0
2.12
2.33
2.54
2.75
2.96
London-based Services
Business
Commuting
Standard
First Class
Outer
Inner
1.70
1.28
1.28
1.33
1.87
1.33
2.04
1.38
1.38
2.21
1.44
1.44
2.38
1.49
1.49
1.54
1.54
1.60
1.60
Non-London Based Services
Leisure
2.12
2.33
2.54
2.75
2.96
Business Commuting
2.86
1.76
2.93
1.89
3.01
2.03
3.08
2.16
3.15
2.30
2.43
2.57
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%.
15
New Lines Programme: Demand forecasting technical note
TABLE 2.5
PROPOSED TIME EQUIVALENCE PENALTIES – INDIVIDUAL TRAIN
Load Factor
Condition
London-Based
1
Commuting
NonLondon
Commuting2
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
-
-
NonCommuting3
-
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 nonCommuting values from Table 2.4
3
Arithmetic average of London based Leisure and Standard Business and Non-London based Leisure and
Business values from Table 2.4
16
New Lines Programme: Demand forecasting technical note
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)
All weekday flows starting or ending in London
Analytically derived distribution
100%
90%
% of distribution less than load factor
80%
70%
60%
50%
40%
30%
20%
10%
0%
0.000
0.500
1.000
1.500
Load factor
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.
17
New Lines Programme: Demand forecasting technical note
FIGURE 2.3
HIERACHICAL LOGIT MODEL STRUCTURE
All modes
‘PT’ modes
Rail modes
Classic
Rail
New Rail
Air
Car
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 DoMinimum modal share and both 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
18
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.
New Lines Programme: Demand forecasting technical note
TABLE 2.6
2.77
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
Public transport
versus car
-0.000409
-0.000666
Generation
-0.000061
-0.000222
-0.001855
-0.000981
-0.000222
-0.000327
-0.000327
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:
e − λA ⋅GCNR
P( NR rail) = − λA ⋅GC NR
e
+ e − λ⋅ A GCCR
(
)
e − λA⋅GCCR
and P(CR rail) = − λ ⋅GC
e A NR + e − λA⋅GCCR
(
)
where λ is the scaling factor and GCNR and GCCR are the generalised costs of new rail
and classic rail respectively.
19
New Lines Programme: Demand forecasting technical note
2.81
Composite costs for the rail mode were calculated using the formula,
1
ln(e − λA ⋅GCNR + e − λA ⋅GCCR )
λA
GCrail = −
2.82
The change in composite cost between the Do-Minimum and Do-Something was
calculated as,
ΔGC rail = GC rail − GCCR
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,
P(Rail PT) =
− λB ⋅ΔGCrail
rail
− λB ⋅ΔGCrail
− λB ⋅ΔGCair
rail
air
(P
P e
+P e
e
)
and
P(Air PT) =
− λB ⋅ΔGCair
Pair
− λB ⋅ΔGCrail
− λB ⋅ΔGCair
rail
air
(P
e
e
+P e
)
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
ΔGC PT = −
2.85
1
ln(Pair e − λB ⋅ΔGCair + Prail e − λB ⋅ΔGCrail )
λB
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:
DDS = DDM e
1
− λC ⋅ΔGCall
3
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
20
New Lines Programme: Demand forecasting technical note
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, DoSomething 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 DoMinimum costs. This means that the correct set of 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 DoMinimum, 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
Clear then run the crowding macro
21
New Lines Programme: Demand forecasting technical note
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
2.98
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 nonstop 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.
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
22
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
New Lines Programme: Demand forecasting technical note
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
I
66 mins 4tph London to Manchester, 129 mins 2 tph London to Edinburgh.
23
New Lines Programme: Demand forecasting technical note
FIGURE 2.4
OPTION MB1.4.1
Edinburgh
Glasgow
Option M B 1.4.1 (TPH )
Form ation 10 10 10 10 10 10 10 10 10 10 10 10 10 10 5
5
5
5
Glasg ow
Edinb urgh
Caledonian Junc
Preston
Liver pool
W arr ington
M anchester
Birm ingham
Lond on
1
Preston
2
3
4
5
6
7
8
9 10 11 12 13 14 1 5 16 17 18
GM North Junction
Manchester
Liverpool
GM South Junc.
Warrington
Mersey Junction
WM North Junc.
Birmingham
WM South Junc.
WM West Junc.
London Central
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.
24
New Lines Programme: Demand forecasting technical note
FIGURE 2.5
LONDON DO-MINIMUM CATCHMENT
FIGURE 2.6
MANCHESTER DO-MINIMUM (CLASSIC RAIL) CATCHMENT
25
New Lines Programme: Demand forecasting technical note
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
TAP - TAP journey
Link
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
London Central to Coventry
Coventry to Birmingham Central
Birmingham Central to Wolverhampton
London Central to Stafford
Stafford to Liverpool Central
London Central to Warrington
Warrington to Wigan
Wigan to Preston
Preston to Lancaster
Lancaster to Oxenholme
Oxenholme to Carlisle
Carlisle to Glasgow
London Central to Stockport
Stockport to Manchester
London Central to Leeds
London Central to York
York to Newcastle
Manchester to Preston
Newcastle to Edinburgh
Wolverhampton to Stockport
Demand
per day
32,721
28,236
1,919
9,264
8,550
16,360
14,539
12,708
6,956
6,028
4,702
3,938
24,426
16,216
18,813
25,940
17,073
2,439
7,447
2,928
Capacity
per day
55,392
55,392
18,464
18,464
18,464
27,696
18,464
18,464
18,464
18,464
18,464
18,464
55,392
55,392
40,896
61,344
40,896
14,400
20,448
9,600
Load Factor
59%
51%
10%
50%
46%
59%
79%
69%
38%
33%
25%
21%
44%
29%
46%
42%
42%
17%
36%
31%
Journey
Time
mins
63
20
24
81
46
108
11
13
18
15
36
71
119
7
116
118
50
48
84
65
Crowding Cost Crowding Cost
(Comm)
(Non-Comm)
mins
mins
2.6
6.0
0.3
0.9
0.0
0.0
1.1
3.2
0.3
1.1
4.4
10.2
1.9
3.6
1.2
2.5
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.6
2.2
0.0
0.0
0.8
2.9
0.4
1.7
0.2
0.7
0.0
0.0
0.1
0.4
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.
FIGURE 2.7
26
MANCHESTER DO-SOMETHING (NEW LINES) CATCHMENT
New Lines Programme: Demand forecasting technical note
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
TAP - TAP journey
Link
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
NEW LINE CROWDING
London Central to Birmingham Central
London Central to Manchester Central
Birmingham Central to Manchester Central
London Central to Warrington
Liverpool Central to Warrington
London Central to Preston
London Central to Glasgow
London Central to Edinburgh
Manchester Central to Preston
Preston to Glasgow
Preston to Edinburgh
Birmingham Central to Preston
Demand Capacity Load Factor
per day per day
26,915
83,200
32%
34,986
83,200
42%
6,689
41,600
16%
20,000
41,600
48%
12,148
41,600
29%
23,504
41,600
57%
0
0
0%
21,528
41,600
52%
7,295
20,800
35%
18,565
62,400
30%
2,555
20,800
12%
2,577
41,600
6%
0
0
0%
0
0
0%
0
0
0%
0
0
0%
0
0
0%
0
0
0%
0
0
0%
0
0
0%
Journey Crowding Cost
Time
(Comm)
mins
mins
46.0
0.0
66.0
0.2
38.0
0.0
66.0
0.7
15.0
0.0
73.0
2.4
136.0
0.0
129.0
2.4
15.0
0.0
59.0
0.0
59.0
0.0
46.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Crowding Cost
(Non-Comm)
mins
0.1
0.9
0.0
2.1
0.0
5.8
0.0
6.4
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
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
DM
mill jnys /year
DS
mill jnys /year
Difference mill jnys /year
% Difference
CR
12.10
4.97
-7.13
-59%
NR
0.00
10.50
10.50
Air
0.40
0.20
-0.20
-51%
Car
3.84
3.07
-0.76
-20%
Total
16.34
18.74
2.40
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.
27
New Lines Programme: Demand forecasting technical note
TABLE 2.10 TOP 30 FLOWS LONDON – MANCHESTER
2030 Do Minimum
Flow
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Total
28
117_130
117_187
117_125
117_210
105_117
100_117
17_130
118_130
9_117
117_157
123_130
104_130
124_130
93_130
122_130
92_130
17_210
71_130
130_208
130_207
130_209
120_130
121_130
17_187
100_118
69_130
117_170
13_130
130_225
103_130
Zone 1
Zone 2
London Central
London Central
London Central
London Central
Leeds
Kirklees
Brighton & Hove
London North
Bolton
London Central
London West
Kent West
Luton
Hertfordshire Mml
London South West
Hertfordshire Ecml
Brighton & Hove
Essex South
Manchester
Manchester
Manchester
London South / Croydon
London South East
Brighton & Hove
Kirklees
East Sussex
London Central
Bedfordshire North
Manchester
Kent East
Manchester
Stockport
Macclesfield
Tameside
London Central
London Central
Manchester
Manchester
London Central
Oldham
Manchester
Manchester
Manchester
Manchester
Manchester
Manchester
Tameside
Manchester
Surrey North West
Surrey East
Surrey South West
Manchester
Manchester
Stockport
London North
Manchester
Rochdale
Manchester
West Sussex Central
Manchester
CR
Demand Revenue
m jnys /
year £m / year
4.0
195.5
1.9
140.8
0.4
27.6
0.3
17.7
4.4
239.1
0.2
13.8
0.1
7.3
0.1
4.5
0.1
3.0
0.1
3.1
0.0
1.3
0.0
0.9
0.0
0.7
0.0
1.5
0.0
0.6
0.0
0.6
0.0
0.7
0.0
0.5
0.0
0.4
0.0
0.5
0.0
0.8
0.0
0.6
0.0
0.4
0.0
0.8
0.0
0.8
0.0
0.3
0.0
0.3
0.0
0.2
0.0
0.3
0.0
0.3
11.71
665.0
Yield
£/jny
49.1
74.8
78.0
50.8
54.7
56.9
62.7
53.8
50.6
50.7
53.0
58.9
61.0
56.6
51.5
54.2
63.6
57.1
55.8
55.0
56.7
52.0
51.9
87.1
59.5
59.8
50.7
60.1
58.8
60.6
56.8
Air
Car
Demand Demand
m jnys / m jnys /
year
year
0.0
0.2
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.2
0.0
0.1
0.0
0.1
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.1
0.0
0.2
0.0
0.0
0.0
0.1
0.0
0.2
0.0
0.1
0.0
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.27
1.9
2030 Do Something
CR
Demand Revenue
m jnys /
year £m / year
0.2
9.5
0.1
9.4
0.0
0.7
0.0
0.9
4.2
228.1
0.1
3.2
0.0
0.4
0.0
0.2
0.0
0.2
0.0
0.2
0.0
0.1
0.0
0.1
0.0
0.1
0.0
0.1
0.0
0.1
0.0
0.1
0.0
0.1
0.0
0.1
0.0
0.1
0.0
0.1
0.0
0.1
0.0
0.1
0.0
0.0
0.0
0.1
0.0
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
4.63
254.2
Yield
£/jny
47.3
78.0
81.2
48.9
54.5
55.9
59.7
52.2
49.6
49.5
52.3
58.3
59.9
54.4
51.7
54.6
62.1
57.2
55.1
54.5
55.7
51.3
52.3
89.1
59.3
59.2
50.9
59.4
56.8
59.0
54.9
NR
Demand Revenue
m jnys /
year £m / year
5.1
331.8
2.3
153.1
0.6
38.5
0.5
30.9
0.3
20.6
0.3
20.4
0.2
14.4
0.2
11.0
0.1
7.2
0.1
6.9
0.1
5.9
0.1
4.1
0.0
3.7
0.0
3.6
0.1
3.5
0.0
3.1
0.0
2.8
0.0
2.6
0.0
2.4
0.0
2.4
0.0
2.3
0.0
2.2
0.0
1.9
0.0
1.7
0.0
1.6
0.0
1.5
0.0
1.4
0.0
1.2
0.0
1.0
0.0
0.9
10.29
684.6
Yield
£/jny
64.5
65.7
68.5
66.1
78.5
70.4
77.6
70.1
67.1
67.0
70.3
76.3
78.0
72.3
69.8
72.6
80.0
75.2
73.4
72.6
73.6
69.2
70.2
77.8
74.2
77.2
68.9
77.4
74.7
76.8
66.6
Air
Car
Demand Demand
m jnys / m jnys /
year
year
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.2
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.10
1.26
New Lines Programme: Demand forecasting technical note
TABLE 2.11 LONDON - STOCKPORT DETAIL
DM GJT
DS GJT
% Diff GJT
DM Dmd
DS Dmd
Diff Dmd
% Diff Dmd
DM Rev
DS Rev
Diff Rev
% Diff Rev
per day
mins
mins
jnys
jnys
jnys
£
£
£
CR
246
260
6%
3395
316
-3079
-91%
274,406
25,543
-248,863
-91%
117_187
NR
0
207
N/A
0
3908
3908
N/A
0
278,869
278,869
N/A
Business CA
Air
Car
260
210
260
210
0%
0%
28
146
6
52
-22
-94
-78%
-64%
Total
3569
4282
713
20%
274,406
304,413
30,006
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
300
250
Ot her
mins
200
Constant
Access Fare
150
Access Time
Headway
Trunk Fare
100
Trunk Time
50
0
CR
NR
Air
Car
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
DM
DS
Diff
% Diff
£m / year
£m / year
£m / year
CR
1,654
534
-1,120
-68%
NR
0
2,653
2,653
Total
1,654
3,187
1,533
93%
29
New Lines Programme: Demand forecasting technical note
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
4
30
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
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.
New Lines Programme: Demand forecasting technical note
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:
North West
Scotland
2030 attractiveness
index (population
and employment)
2030 Do-Minimum
(million journeys
p.a.)
2030 Do-Something
(million journeys
p.a.)
1.4
40
n/a
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
6
TEMPRO 5.4.
Government Office Regions of London, South East and East of England.
31
New Lines Programme: Demand forecasting technical note
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 air and rail
data where good data sources exist (the CAA passenger survey and ticket sales data
respectively). Updating road data should also be considered, although the source
for such data is not clear and would need to be investigated.
2.129
Associated with any updates to base demand data, forecast 2030 demand could also
be refined by obtaining more detailed data from the DfT forecasts. Use of the NMF
data for rail is robust, but the air and highway forecasts could be done at a more
disaggregate level.
2.130
It would also be helpful to have a greater understanding of the evidence and
analysis underlying the original Planet Strategic model formulation.
2.131
The analysis would also benefit from greater spatial disaggregation and network
based analysis, particularly in London and the South East, where access to the
respective modal networks (classic rail and New Lines stations, and airports) can
have a material influence on mode and route choice.
2.132
The increased catchment of the New Line may lead to an increase in the number /
length of car trips to access rail services. This has not been explicitly taken into
account.
2.133
Model structures and functionality could also be refined, to better capture the
impacts of New Lines on trip patterns and land use impacts. This should also
include updated model calibration to ensure current behavioural patterns are
captured in the modelling process (for example to reflect extended air travel
security arrangements and any impacts environmental concerns have had on
decision making). Finally, the use of incremental models should be reviewed where
the existing market is dominated by a single mode. An option would be to have an
incremental model for those flows where several modes are used, but with absolute
models where rail in particular has no share currently (and hence will continue to
have no share even if New Lines becomes a realistic choice). This might lead to a
greater proportion of the revenue gain being from mode shift and would slightly
suppress the generative effects due to additional crowding.
32
New Lines Programme: Demand forecasting technical note
3
Commuter Model
Introduction
3.1
This Chapter focuses on the model for commuter services on the classic network, a
market segment characterised by highly peaked demand and high levels of
crowding. The construction of new lines could alleviate these problems - and
thereby potentially increase demand and revenue from these services - by
facilitating a recast of the timetable along the commuter routes.
3.2
As with all the demand models, there are three main types of data used in the
commuter model: demand data provided by the NMF (Network Modelling
Framework), service specification data and generalised journey time parameters
provided by PDFH. The model uses the generalised journey time parameters to
estimate the effect of the service specification data on the unconstrained demand
data from the NMF, before introducing the effect of crowding along the line.
3.3
The commuter model does not model changes in fares; this means that fares are
implicitly assumed to remain unchanged between the Do-Minimum and DoSomething scenarios.
Scope of Model
3.4
The suite of demand models have been designed and developed to be transferable
across any route. Any particular route or station configurations can easily be
considered within the models. Initially however they have been populated with West
Coast Main Line route information although alternative route information could be
included to change the geographical scope of the model.
3.5
The potential extent of the commuter market was established by analysis of the
service specifications. For the West Coast Main Line, Northampton was identified as
the logical limit of scope for the commuter model. Whilst there will clearly be a
limited number who commute into London Euston from beyond Northampton, the
majority would be from within this boundary. Also this is the area within which the
majority of crowding is expected to occur. As such, flows from further afield were
deemed out of scope for the Commuter model and would therefore be considered
within either the Regional or Intercity models.
3.6
For the West Coast Main Line, 8 NMF group station zones are considered within the
model; these are listed in Table 3.1.
33
New Lines Programme: Demand forecasting technical note
TABLE 3.1
NMF GROUP STATION ZONES
NMF Zone
Zone Name
NMP
Northampton
MKC
Milton Keynes Central
HML
Hemel Hempstead
WFJ
Watford Junction
BSH
Bushey
HRW
Harrow & Wealdstone
QPW
Queens Park London
XLD
London BR
3.7
Due to the nature of stopping patterns in the Do-Minimum and Do-Something service
specifications, these 8 NMF station group zones had to be disaggregated into smaller
groups to allow appropriate allocation of demand. 18 groups were identified and
flows between each of these O-D pairs considered. This yields 153 individual flows
within the model (if each direction is combined); however since 19 of these
potential flows are between newly formed groups within the original NMF station
group zones (which are therefore intrazonal demand and hence no demand data is
available), only 134 of them are modelled.
3.8
In addition to these, London BR is disaggregated into 5 destination zones termed
Mainline, Central, Inner, Outer and Counties. Journeys into London are allocated to
one of these groupings dependant on the final destination of the trip. This is
necessary for use within generalised journey time (GJT) calculations.
3.9
Whilst only the West Coast Main Line itself is considered within the model, demand
change due to service specification alterations in our Do-Something scenario are
dependant on overall journey times. To account for the variation in additional travel
time incurred by commuters dependant on their final destination, GJT overlays are
adopted for each of these 5 destination zones. This has a dampening effect on
demand increases as the overall journey time percentage change is less than for
journeys simply into London Euston itself.
3.10
Much of the specification of scope occurs within the ‘INPT; Definitions’ tab of the
commuter model. Here the user specifies the stations within scope as well as the
service groups and particular scenarios under consideration. These then feed
through into the model calculations.
34
New Lines Programme: Demand forecasting technical note
Methodology Overview
3.11
Figure 3.1 gives an outline of the basic methodology adopted within the commuter
model.
FIGURE 3.1
COMMUTER MODEL METHODOLOGY FLOW DIAGRAM
NMF constraineddemand
NMF performance /
crowdedGJTs
Timetableinputs
Domin unconstrained
demand
Do something
unconstrained demand
By TOD: Domin
unconstrained demand
By TOD: Dosomething
unconstrained demand
MOIRAtime of day
profiles
Crowdingmethodology
Domin constrained
demand
Do something
constraineddemand
Benefits
3.12
An elasticity based approach using guidance from the Passenger Demand Forecasting
Handbook (PDFH) v4.1 is adopted to quantify the negative impacts of crowding on
passenger demand. Constrained weekday demand extracted from the NMF at
individual flow level is transformed into equivalent unconstrained weekday demand
via NMF performance and crowded GJTs. This provides us with our Do-Minimum
unconstrained weekday demand.
3.13
Outputs from the New Lines timetable model are used to calculate GJTs by flow for
both the Do-Minimum and Do-Something scenarios. Using a PDFH elasticity based
approach, the Do-Something unconstrained weekday demand is forecast from the
Do-Minimum unconstrained weekday demand and changes in the journey time and
frequency changes between the two service specifications.
3.14
Table 3.2overleaf shows how this approach is implemented for a particular flow.
35
New Lines Programme: Demand forecasting technical note
TABLE 3.2
PDFH BASED ELASTICITY METHODOLOGY EXAMPLE
Do Minimum unconstrained demand (DMU)
150
Do Minimum flow GJT (GJTDM)
30
Do Something flow GJT (GJTDS)
25
Elasticity (E)
-0.8
Do Something unconstrained demand (DSU):
174
DSU = DMU * ( GJTDS / GJTDM ) ^ E
This represents a 16% increase in unconstrained demand
3.15
Such methodology is used for all flows to produce changes in demand on a flow by
flow basis. MOIRA time of day profiles are then used to split the demand out into
hourly periods. These vary in profile dependant on the origin and destination
stations’ blueness7 rating as well as the journey time along the route.
3.16
Demand is then aggregated back into five time of day groups specified in Table 3.2
(consistent with the NMF).
TABLE 3.3
TIME PERIOD DEFINITIONS
Period name
Time specification
AM Peak
08:00 – 09:00
AM Shoulder Peak
07:00 – 08:00 and 09:00 – 10:00
Off Peak
10:00 – 16:00 and 19:00 – 07:00
PM Peak
17:00 - 18:00
PM Shoulder peak
16:00 – 17:00 and 18:00 – 19:00
3.17
The key periods of interest for journeys into London are the AM Peak and AM
Shoulder Peaks since these are the periods of most demand. As such, it is only
within these two periods that consideration of crowding is undertaken.
3.18
Unconstrained demand is fed through the model crowding methodology by scenario,
by time of day and by service group; the service groups have been pre-defined
within the Timetable model. Each link along a route is considered individually, thus
7
36
A stations’ blueness is a measure of it’s attractiveness in terms of trip generation (i.e. Milton
Keynes will attract more trips to it than Wolverton)
New Lines Programme: Demand forecasting technical note
allowing people to be crowded off as a train moves along the line. NMF crowding
curves are used to obtain constrained load factors from the equivalent
unconstrained load factors at each station, thus providing constrained demand by
flow. The difference between these two figures provides us with the absolute
number at any particular point along the line who have been crowded off.
3.19
The output from this methodology is both unconstrained and constrained demand
for each scenario by flow, by time of day and by service group. This allows the
calculation of individual journey time and crowding benefits for any particular flow.
Data Inputs
3.20
This section aims to provide further information on the datasets used within the
model, along with any caveats to be aware of when interpreting their meaning.
Network Modelling Framework (NMF) Data
3.21
The NMF is a key source within the Commuter model for a number of different data
inputs – demand data, revenue data, crowded and performance GJTs and crowding
curves. The NMF run used is HLOS run 1584.
3.22
The NMF comprises 566 station group zones which represent geographical
aggregations of all stations within the UK. Zones are referred to by the Primary
station name with all demand from stations within the group being routed via this
station.
3.23
Forecasts of constrained demand and revenue are output for each origin and
destination pair of station group zones in 2030 (revenue is given in 2004/05 prices).
In additional to this, crowded and performance GJTs are output for each flow.
These represent the difference between the perceived generalised journey times of
passengers in crowded or un-crowded conditions.
3.24
Crowding curves derived from NMF are also used within the model. These are precalculated curves which define the relationship between constrained and
unconstrained load factors. The approach assumes that crowding costs can be
defined in time equivalent terms and as such a crowding penalty can be viewed as a
perceived % increase in journey time over a particular demand section. An elasticity
approach based on PDFH can then forecast the decrease in demand due to crowded
conditions. A process of iteration is then required, the equilibrium of which is our
required constrained load factor.
3.25
These pre-calculated curves vary dependant on the level of commuters on a route,
since values of time and therefore implied time penalties vary by journey purpose.
Calculations of the journey purpose splits within our model are based upon data
gleaned from the NRTS.
National Rail Travel Survey (NRTS)
3.26
The NRTS is a survey looking at passenger travel by rail throughout Great Britain. It
is used within the model to provide information on journey purpose by ticket type.
3.27
It is used within the model to provide information on journey purpose by ticket
type. Individual ticket types are assigned as being full, reduced or season tickets
and the corresponding split of commuting to other journey purposes extracted. An
estimate of the overall percentage of commuters on the line split by time of day
which is used to choose the appropriate crowding curve is then calculated.
37
New Lines Programme: Demand forecasting technical note
3.28
The survey looks at Great Britain as a whole so is not disaggregated by specific route
or region. To allow for this, data from the NMF is used to provide expected splits of
tickets types across the whole commuter market under consideration, and the
journey purpose splits adjusted appropriately.
New Lines Timetable Model
3.29
Service specifications have been produced for the Do-Minimum and Do-Something
for both a standard peak and off-peak hour. These are inputs into the New Lines
timetable model. Within this model, these are used to output journey times and
frequencies for each flow as well as the proportion of passengers who would use
each service group. This is achieved by assuming uniform demand throughout the
peak hour period and the assignment of demand to trains according to the shortest
journey time to their individual destinations.
3.30
Specification of these outputs is done so based on flows between primary stations in
each newly defined NMF subgroup zone to ensure a consistent basis to the demand
data. For the commuter model, journeys where an interchange between rail
services is necessary are excluded from scope.
MOIRA Profiles
3.31
The model uses MOIRA time of day profiles to split demand out into hourly periods.
These vary in profile dependant on the journey time along the route, flow type and
on the origin and destinations’ ‘blueness’ rating. A stations’ blueness is a measure
of it’s attractiveness in terms of trip generation.
Office of Rail Regulation (ORR) Station Usage Data
3.32
Data extracted from the NMF is on a station group zone basis and as such cannot
provide demand data disaggregated by individual stations. When considering the
variation in train stopping patterns however, it is necessary to have data at this
level.
3.33
To achieve this, entries and exits from 2006/07 are taken from the ORR website in
order to estimate the proportions using each station in 2030. This is then used to
redistribute demand across newly defined subgroups before importing the data into
the Commuter model. An implicit assumption here is that the distribution of
demand across stations remains constant when comparing 2007 and 2030 entries and
exits.
Detailed Review of Methodology
3.34
38
This section aims to expand on the methodology overview already provided. Broadly
the commuter model methodology can be split into 5 main segments:
I
Calculation of Do-Minimum unconstrained demand;
I
Calculation of Do-Something unconstrained demand;
I
Allocation by time of day and flow grouping;
I
Crowding methodology; and
I
Calculation of benefits.
New Lines Programme: Demand forecasting technical note
Calculation of Do-Minimum Unconstrained Demand
3.35
The Network Modelling Framework (NMF) has been used to generate Do-Minimum
demand based upon 2030 forecasts. Constrained demand for full, reduced and
season tickets from the NMF is extracted by flow; this is done so at both weekday
and annual level. Data is fed through the model at a weekday level with the annual
demand data being used latterly to calculate appropriate annualisation factors.
3.36
The scope of the Commuter model extends as far as Northampton in our West Coast
Main Line test case and as such there are 8 NMF station group zones which are
extracted. Stopping patterns in the Do-Minimum and Do-Something service
specifications are not regular however across an entire NMF station group zone and
therefore these 8 NMF station group zones have to be disaggregated into smaller
groups to allow appropriate allocation of demand.
3.37
This is particularly relevant in our West Coast Main Line test case when considering
the difference between DC Line trains and the rest of the line. The DC Line trains
stop at all intermediate stations and are thus able to serve all passengers;
conversely many of the other trains merely stop at the primary stations along the
route. This clearly leads to a disparity in journey time and as a consequence, a
tendency towards using the faster trains when considering service group allocation
within the New Lines timetable model. In reality however, a proportion of this
demand would have no option but to use the slower DC Lines.
3.38
Given that, the NMF station group zones were disaggregated as discussed previously.
As demand data is not available for these NMF intrazonal flows, changes between
such flows are not modelled. By the nature of the subgroup allocation process, such
flows will be small and as such assuming no change between the Do-Minimum and
Do-Something scenarios will have no major impact on the final outputs.
3.39
Crowded and performance GJTs for each flow are also extracted to enable the
derivation of equivalent unconstrained demand from the NMF data. A standard
elasticity based approach from PDFH v4.1 is adopted to undertake this conversion.
Calculation of Do-Something Unconstrained Demand
3.40
Having calculated the Do-Minimum unconstrained demand for each flow, the same
PDFH based approach to forecast the Do-Something unconstrained demand is used.
This relies on information with regards journey time and frequency extracted from
the New Lines timetable model and based upon Do-Minimum and Do-Something
service specification data. As such, the Do-Something unconstrained demand is the
Do-Minimum unconstrained demand uplifted by the effect of service specification
changes between the Do-Minimum and Do-Something service specifications.
3.41
For each in scope flow, frequencies, average journey times and distributions of
demand in a peak hour period are extracted for both the Do-Minimum and DoSomething service specifications. It should be noted that within the commuter
model, flows whereby an interchange is required are not considered within scope.
For the vast majority of demand along the line however this is not an issue; indeed
only 0.4% of demand in the Do-Minimum scenario is unaccounted for due to this
omission.
3.42
In order to capture the intrinsic disparity in overall journey time experienced by
travellers to London dependant on their actual final destination, overlays of
39
New Lines Programme: Demand forecasting technical note
additional journey time are added to the appropriate flows; these overlays are
shown in Table 3.4.
TABLE 3.4
GJT OVERLAYS
Destination zone
Mainline (London BR & Zone Euston)
Additional GJT (mins)
0
Central (Greater London zones – e.g. Oxford Circus,
Westminster)
25
Inner (London Central PSM zone – e.g. City of London,
Southwark)
44
Outer (London Government Office Region)
55
Counties (Counties – e.g. Sussex, Kent)
102
3.43
Allocation of trip destinations to one of these zones is dependant on the actual final
destination of trips to different London and South East stations. In-vehicle times,
waiting times, interchange and walking times are calculated for a selection of
representative destinations, and average weighted GJTs calculated to produce the
overlays in Table 3.4. These are simply added to each flow dependant on the
appropriate destination.
3.44
The effect of this is to dampen the demand change due to GJT improvements on the
West Coast Main Line segment of trips. These figures have been calculated
specifically for trips passing through London Euston and as such would need to be
calculated separately for tests on alternative lines.
Allocation by Time of Day and Flow Grouping
3.45
The nature of the commuter market means significant crowding issues can arise in
the AM and PM peak periods of an individual day, but train loadings are often very
low throughout the off-peak. As such, it was deemed prudent to just consider the
effects of crowding in these peak periods.
3.46
Times of day profiles extracted from MOIRA are used to disaggregate the data into
15 minute segments throughout the day. Station ‘blueness’ is used for each
individual origin and destination to assign an appropriate flow-type to each DoMinimum flow within the model. Along with journey time information previously
extracted from the New Lines timetable model, the relevant profile is allocated to
each flow to assign demand to different periods throughout the day. Demand is then
aggregated back into 5 time of day groups specified in Table 3.3.
3.47
Calculation up to this stage in our West Coast Main Line test case is done so for all
134 in scope flows but with London BR split into 5 groups dependant on the final
destination of trips. This yields a total of 202 flows under consideration. However,
our crowding considerations are simply for the West Coast Main Line itself, not for
segments of the trip beyond London Euston. As such it is necessary to re-group
London BR into a unique destination.
3.48
Note, it would not have been appropriate to simply start with one unique London
destination since the additional effect of variable GJTs as discussed previously
40
New Lines Programme: Demand forecasting technical note
would be lost. The result of this would have been an over-estimate of the benefits
of any service specification improvements. Having calculated each GJT change and
thus demand change at a disaggregated level however, these can simply be summed
to produce an overall change by our base 134 flows across the route.
3.49
It is also worth noting that at this stage the demand for each flow is halved in order
to obtain the approximate one-way demand; only flows into London are considered
and hence people are crowded off in the AM peak and Am shoulder peak only. This
is then factored back up at the outputs stage to obtain the impact on two-way
demand, assuming similar crowding impacts in the pm peak.
Crowding Methodology
3.50
The National Rail Travel Survey (NRTS) is used to provide information on the journey
purpose by different ticket types. These are then allocated as full, reduced or
season tickets to be comparable with our demand data. The survey looks at Great
Britain as a whole so is not disaggregated by specific route or region. To allow for
this, data from the NMF is used to provide expected splits of tickets types across the
whole commuter market under consideration, and the journey purpose splits
adjusted appropriately.
3.51
The balance of demand across ticket types is then used to calculate the percentage
of commuters on the route in each time of day period. The average percentage of
commuters across the AM peak and AM shoulder peak in our West Coast Main Line
test case is 69%.
3.52
This is then used to extract the appropriate NMF crowding curve to be used within
the crowding methodology.
3.53
When considering crowding along the line, data is fed through by scenario, service
group and time of day. As discussed above, demand is allocated to each service
group within the New Lines timetable model. After the aforementioned appropriate
reallocation of some demand, this is then used to provide demand for any individual
service group on a flow by flow basis.
3.54
The key feature of the methodology is that crowding is considered by individual link.
As such, numbers on a particular service group and the implied load factors at any
point along the line can be calculated. It is therefore possible to produce a profile
of crowding whereby those further down the line are more susceptible to be
crowded off.
3.55
Table 3.5 shows an example of the crowding methodology for a particular link.
41
New Lines Programme: Demand forecasting technical note
TABLE 3.5
CROWDING EXAMPLE – WATFORD JUNCTION TO BUSHEY LINK
Unconstrained arrivals at head station
1,750
De-constrained passengers carried forward
750
Total unconstrained demand on link
2,500
Total service group seating capacity
3,234
Total available seating capacity
2,734
Implied unconstrained load factor
91%
Equivalent constrained load factor
82%
Total constrained demand on link
2250
Total to be crowded off
250
3.56
Having established the demand for a particular scenario, service group and time of
day, this provides us with arrivals at the head station of a particular link. Table 6.3
shows that there are 1,750 unconstrained arrivals at Watford Junction.
3.57
This is then added to the demand carried forward from the previous link, less those
getting off at the head station. Demand carried forward at the start of the route, in
our test case Northampton, is assumed to be zero (in most cases, see further
discussion below) else it is the calculated demand from all previous links. Note that
the carried forward demand is output in constrained terms, and as such must be deconstrained using NMF crowding curves first in order to be input on a consistent
basis as the arrivals. In the example above, this gives us 750 de-constrained
passengers carried forward from further up the line (i.e. having boarded at stations
between Northampton and Hemel Hempstead).
3.58
The total unconstrained demand offered on the link is simply the sum of these two;
2,500 people in this example. The next stage is to consider the total capacity of any
particular service group. The particular rolling stock considered here are 11-car
class 390 which have 577 seats.
3.59
The NMF crowding curves work off proxy seats for any particular rolling stock, which
in this case is 539 seats. This adjustment is to account for the variation across
rolling stock in balance of seating and standing capacity. Where high-density rolling
stock is used, more capacity is set aside for standing as opposed to seating, and as
such simply using the seating capacity in our calculations would over-estimate
crowding issues. Proxy seats are used to uplift this effective seating capacity to take
account of this balance.
3.60
The particular example being considered above would have 6 trains per hour in the
AM peak period, thus yielding a total capacity of 3,234.
3.61
Whilst most service groups are effectively self-contained services from Northampton
or closer to London Euston, some would be used by passengers from further up the
line. The scope of this model does not incorporate such demand as it starts outside
our designated commuter zone; dependant on the exact flow, it would be
42
New Lines Programme: Demand forecasting technical note
considered within one of the other two New Lines demand models. To avoid any
possibility of double-counting, these people are excluded from the commuter
model. However, their effects in terms of train crowding does need to be taken
account of.
3.62
To deal with this, outputs from the New Lines regional model are extracted for all
flows into London for both the Do-Minimum and Do-Something scenarios and input
into the commuter model. In a similar process to the Do-Minimum and Do-Something
commuter service specifications, demand from these stations are allocated to a
particular service group. These flows are then treated in the same way as our
commuter demand (i.e disaggregated by time of day and direction) to give us the
total demand from all of these stations by scenario, by service group and by time of
day. Since these people are arriving first onto the trains, they are then allocated
seats all the way into London by reducing the effective seating capacity on each
train. In this way, their effect in terms of crowding is felt but they are not included
in any demand calculations undertaken. In the example in table 6.3, this pre-loaded
is taken to be 500 and thus reduced the effective capacity by this amount down to
2,734.
3.63
The unconstrained load factor is then simply calculated by dividing the total
unconstrained demand by the effective capacity. This gives us an implied load of
91%. Looking up the appropriate values on our NMF crowding curve, this gives an
equivalent constrained load factor of 82%.
3.64
Applying this constrained load factor to our calculated effective capacity provides
us with the total constrained load for our link; in this case 2,250. The number to be
crowded off is then the difference between the total unconstrained and constrained
demand; in our example this means 250 people must be crowded off this link.
3.65
Having determined the absolute number to be crowded off the link, the model then
determines who should be crowded off and from where. The first people to be
crowded off are those who are forced to stand as they are more adversely affected
by crowded conditions than those who are able to sit. If there are seats available,
everyone on the train is assumed to occupy them and therefore there are no people
standing until the unconstrained load factor exceeds 100%. The nature of the link
based process means that those already on the train are allocated seats first and as
such, new boarders are more likely to have to stand and consequentially more likely
to be crowded off.
3.66
In our example however, the unconstrained load factor is less than 100% so everyone
is able to sit. Therefore the 250 people to be crowded off must be from seated
passengers. Note that crowding is assumed to result in a decrease in demand prior
to 100% load. This is the approach taken in the NMF as perceived journey times
increase after the load factor rises above 60%.
3.67
Once allocation of new arrivals and carried forward demand to be either standing or
sitting has been undertaken, they are treated as being equally likely to be crowded
off. The logic behind this is that all will be experiencing the same conditions and
thus have the same propensity to chose not to travel by rail in crowded
circumstances. Allocation of those to be crowded off is therefore done
proportionally to the absolute number of boarders and carried forward demand. In
our example, this means that 70% (175 passengers) and 30% (75 passengers) of those
crowded off are boarders and carried forward passengers respectively.
43
New Lines Programme: Demand forecasting technical note
3.68
In terms of carried forward demand, consideration is first made with regards
demand from each preceding station and proportionally allocated to each before
demand is similarly proportionally removed from each flow.
3.69
The result of this is that unconstrained and constrained demand for each of the 28
flows can be tracked for each link along the line. The process above is undertaken
for all links to London Euston where the final output is the overall constrained
demand on the line for each flow.
3.70
As demand is fed through the crowding methodology for each scenario, service
group and time of day, this process needs to be repeated 40 times (assuming 10
service groups per scenario) to output the overall unconstrained vs constrained
demand for the entire West Coast Main Line example.
3.71
Clearly this could be onerous in terms of time and memory to run, thus a macro has
been set up to automate this process. This can be run from the ‘Run Model’ tab
where the results of the particular run are also output and stored.
Model Outputs
The model is able to output changes in demand directly by comparing the results of
each flow in the Do-Minimum and Do-Something scenarios. Revenue data extracted
from the NMF is then used to forecasts the equivalent change in revenue – clearly
demand changes for shorter journeys will have less of an impact. Figure 3.5 shows
the most significant changes seen in our West Coast Main Line test case by flow.
12,000
1,000
10,000
0
LBZ-WFJ
HML-XLD
MKC-LBZ
NMP-WFJ
TRI-XLD
NMP-MKC
BSH-HRW
HRW-WIJ
-400
HRW-QPW
BSH-XLD
WFJ-WMB
-200
KBN-XLD
0
MKC-WFJ
2,000
SBP-XLD
200
HRW-XLD
4,000
WMB-XLD
400
LBZ-XLD
6,000
QPW-XLD
600
WIJ-XLD
8,000
WFJ-XLD
800
Annual revenue change (£000's)
ABSOLUTE ANNUAL DEMAND AND REVENUE CHANGE BY FLOW
1,200
MKC-XLD
Annual demand change (000's)
TABLE 3.6
NMP-XLD
3.72
-2,000
-4,000
Flow
Demand
3.73
44
Revenue
Whilst outputs can be viewed on an individual flow level, they can also be viewed in
a cumulative manner along the route. Table 3.7 shows the increasing impact of
crowding in the Do-Minimum scenario up the line from Northampton to London
Euston.
New Lines Programme: Demand forecasting technical note
TABLE 3.7
DO-MINIMUM CONSTRAINED VS UNCONSTRAINED DEMAND
Annual demand (000's)
25,000
20,000
15,000
10,000
KBN-XLD
QPW-KBN
WIJ-QPW
SBP-WIJ
WMB-SBP
KNT-WMB
HRW-KNT
HTE-HRW
CPK-HTE
BSH-CPK
WFH-BSH
WFJ-WFH
HML-WFJ
TRI-HML
LBZ-TRI
MKC-LBZ
0
NMP-MKC
5,000
Flow
Unconstrained demand
Constrained demand
3.74
As you would expect given the crowding methodology, the number crowded off
increases as you move close to London Euston, hence the increased disparity
between the unconstrained and constrained demand.
3.75
Outputs comparing such results between the two scenarios are able to help inform
the users of where the introduced Do-Something service specification is working
well in comparison to the Do-Minimum.
3.76
A fundamental part of the model outputs is to produce information to be exported
into the New Lines appraisal model. There are 6 key outputs for this:
I
Do-Minimum demand
I
Do-Something demand
I
Revenue generation
I
Change in passenger miles
I
Journey time benefits
I
Crowding benefits.
3.77
Demand outputs are extracted directly from the crowding methodology and
annualised using annualisation factors out of the NMF. Average revenue per trip is
extracted again from the NMF to convert this demand into anticipated annual
revenue. Changes in passenger demand are converted into changes in passenger
miles simply by applying distance factors to each individual flow.
3.78
Outputs from the crowding methodology give us unconstrained and constrained
demand in the AM peak and AM shoulder peak by flow. Since crowding is not
considered an issue in the off peak, these two demand figures are the same for the
off peak period and can simply be extracted from our calculated demand by time of
day. In order to calculate journey time and crowding benefits for each flow,
45
New Lines Programme: Demand forecasting technical note
demand is aggregated into peak and off-peak periods to allow the appropriate use
of peak or off-peak un-crowded GJTs.
3.79
It should be noted that all appraisal outputs are subsequently doubled to account
for the fact that the model only looks at one-way demand. Underlying this is the
assumption that all trips made into London by rail invariably need to be made again
from London. This seems reasonable since the choice of mode of travel is made in
the inbound trip, not the outbound in most cases; this is particularly applicable to
the commuter market.
3.80
There is also an implicit assumption that the pm peak period is the reversal of the
am peak period. It should be noted that the am peak is, in general, more peaked
than the pm peak period so this assumption will lead to a slightly higher forecast of
crowding in the pm peak than may occur in reality. However this overestimate will
be of a small magnitude.
3.81
Crowded GJTs are then calculated using a standard elasticity based approach from
PDFH v4.1. This in conjunction with un-crowded GJTs, unconstrained and
constrained demand is used to calculate journey time and crowding benefits for
each flow.
3.82
Existing users experience a journey time benefit equal to the absolute change in DoMinimum to Do-Something un-crowded GJT. Newly generated users are assumed to
experience only half this benefit.
3.83
Crowding benefits are calculated dependant on the ratio of change between the two
scenarios’ crowded and un-crowded GJTs. This ensures that any absolute journey
time benefit is removed before crowding is considered, thus avoiding the possibility
of double-counting benefits. The model outputs both of the benefits by individual
weekday trip, and as such they must be annualised using the same factors as the
demand to obtain total annual benefits for any particular flow.
46
New Lines Programme: Demand forecasting technical note
4
Regional Model
Introduction
4.1
This Chapter focuses on the model for regional services on the classic network, a
market segment often characterised by less frequent trains, higher in-vehicle times
and less direct services. The construction of New Lines could alleviate these issues,
and thereby increase demand for these services, by facilitating a recast of the
timetable on the entire classic network.
4.2
The Regional Model measures the impacts on demand of changes in frequencies, invehicle times and interchanges, but not from changes in levels of crowding; this is
deemed only to be a material issue on intercity and commuter services.
4.3
There are three main types of data used in the regional model: demand data
provided by the NMF (Network Modelling Framework), service specification data and
parameters provided by PDFH. The model uses the parameters from PDFH to
estimate the effect of the service specification data on the demand data from the
NMF.
4.4
It should be noted that as with the commuter model the regional model does not
model changes in fares; this means that fares are implicitly assumed to remain
unchanged between the DM and DS scenarios. Also note that the model applies PDFH
methodologies, which are known to underestimate the impacts on demand of large
changes in generalised journey times.
Scope of the Model
4.5
The Regional model has been populated solely with West Coast Main Line demand
and service specification data. There are 41 stations designated as ‘in-scope’ for
this route; these 41 stations represent those stations on the West Coast Main Line
which are designated as NMF ‘primary stations’.
4.6
As well as flows between these 41 stations, the Regional model also includes flows
that lie partially on the West Coast Main Line but that begin and / or end at stations
on a different line. These flows are handled by defining of six external zones and by
assigning each of these zones with an ‘add-on’ generalised journey time, which is
added to the GJT calculated for the portion of the journey lying on the WCML.
4.7
For the West Coast Main Line, Northampton was identified as the limit of scope for
the Commuter Model. Whilst there will clearly be a limited number who commute
into London Euston from beyond Northampton, the majority of commuting journeys
are from within this boundary so flows from further afield were deemed out of
scope for the Commuter Model.
4.8
It should be made clear that all the demand models were designed and built to be
able to model any route. The models have all been designed in a generic way that
should, in theory, enable them to be easily transferable to other lines simply by
inputting the appropriate demand and service specification data.
47
New Lines Programme: Demand forecasting technical note
Methodology Overview
4.9
As previously outlined, the Regional Model forecasts changes in demand resulting
from changes in frequency, in-vehicle times and interchanges, for services on the
classic network. The service specification data used is for a standard hour and the
model assumes that the service specification at each station is identical in both
directions and remains the same throughout the day.
4.10
The model calculates Do-Minimum and Do-Something generalised journey times from
the service specifications and then uses an elasticity-based approach to forecast
changes in demand (and revenue) as a function of the proportional changes in
generalised journey time.
Data Inputs
Network Modelling Framework (NMF)
4.11
The NMF comprises 566 zones which together represent all stations in the UK; zones
are referred to by their primary station name, and for modelling purposes it is
assumed that all demand from stations within the zone is routed via this station.
Demand and revenue forecasts for 2030 are output for each zone pair (revenue is
given in 2004/05 prices).
4.12
The NMF also provides the x and y coordinates of all primary stations, which were
used to estimate the distances between stations by first calculating the straight-line
distance then adding a 20% uplift. This information was used for the calculation of
interchange penalties and the change in passenger miles between the Do Minimum
and Do Something options (the latter is then output to the Appraisal Model).
4.13
It should be noted that due to the relatively coarse zoning of the demand data the
regional model should not be used to optimise a service specification. It is designed
to estimate the likely value of a defined service specification.
New Lines Timetable Model
4.14
Do-Minimum and Do-Something service specifications are fed as inputs into the
Timetable Model. The Timetable Model processes these inputs and outputs
frequencies, in-vehicle times and the number of interchanges required, for each
primary station to primary station flow.
4.15
These outputs are based on flows between primary stations to ensure consistency
with the demand and revenue data from the NMF. Journeys requiring two or more
interchanges were excluded entirely from the timetable model, and therefore also
from the regional model, because the number of possible journeys becomes to large
to work with when more than one interchange is considered.
Detailed Review of Methodology
4.16
48
There are three key methodological components of the regional model: the first
involves designating the flows that are ‘in-scope’ for the model, the second involves
obtaining a generalised journey time for each of these flows, and the third involves
using these generalised journey times to forecast changes in demand and revenue.
These three components are all described in detail in this section. The second
component is the more difficult of the three to implement as converting service
specification data into generalised journey times is a complex task.
New Lines Programme: Demand forecasting technical note
Designation of In-scope Flows
4.17
The flows that should be modelled in the regional model vary depending on the
option being tested. Any flows that would use the New Line are modelled in the
Inter city model rather than the regional model. For example in the case of an
option that just featured New Line services between London, Birmingham and
Manchester flows between London and Liverpool would fall within the scope of the
regional model. However if the New Line services also served the London to
Liverpool market this flow would then be modelled in the Inter city model and
would have to be excluded from the regional model to avoid double counting.
Therefore for each model run a number of flows are designated as being out of
scope for the regional model.
4.18
For each option tested in the model a set of station groups is defined, with each
group corresponding to one of several predefined locations (note that although the
groupings vary across the different options, the list of locations remains constant).
An example of one of the station groupings used can be seen in Figure 4.1.
FIGURE 4.1 EXAMPLE OF STATION GROUPINGS
Group
London
Birmingham
Manchester
Warrington
Liverpool
Preston
Glasgow
Lancaster
Cumbria
Edinburgh
Trent Valley
Stations
London Euston
Birmingham New Street
Manchester Piccadilly
Warrington Bank Quay
Liverpool Sth Pway Allerto
Preston
Glasgow Central
Lancaster
Carlisle
Edinburgh
Tamworth.
Queens Park
Sandwell and Dudley
Stockport
Wigan North Western
Liverpool Lime Street
Blackpool North
Motherwell
Wembley Central
Wolverhampton
Cheadle Hulme
Chester
Runcorn
Harrow and Wealdstone
Bushey
Oxenholme Lake District
Lichfield Trent Valley
Stafford
4.19
Flows between all stations in one group and all stations in another group are then
designated as either in-scope or out-of-scope, depending on the particular
configuration of the option being tested. Specifically, if two locations in the list are
directly connected on the New Line then flows between the corresponding groups
are deemed out-of-scope.
4.20
Each location therefore represents a proposed New Lines station (with the exception
of Trent Valley), and the groupings represent the catchment areas of these
proposed stations. The reason for allowing these groupings to vary across the
different options is that the catchment area of a particular New Lines station will
depend on the option being tested.
4.21
In practice, the groupings used for each option tested are almost identical to those
given in above. The only differences between the options are in the Warrington and
Preston groups; by default, Wigan North Western was taken to be in the Warrington
group, but if there was no New Lines station at Warrington and one at Preston then
it was moved to the Preston group.
4.22
The Trent Valley group was included in the list so as to be able to designate flows
between Trent Valley stations and Birmingham stations as out-of-scope. Although
these journeys can be done purely or partially on the West Coast Main Line, in
practice they would not be and therefore are excluded from the Regional Model.
4.23
Note that, with the exception of the Trent Valley to Birmingham flows, when
designating which group pairs were in-scope for the Regional Model care was always
taken to designate the corresponding flows as out-of-scope for the Intercity Model.
This ensured that there was no omission or double counting of results.
49
New Lines Programme: Demand forecasting technical note
GJT Calculations
4.24
The first step in setting up the GJT calculations involved defining a methodology for
specifying which services between a given pair of stations would be designated as
‘attractive’. The rationale was that only relatively fast services will be considered
attractive by passengers; slower services are effectively ignored and should
therefore be excluded from the GJT calculations.
4.25
For each flow a ‘cut-off’ in-vehicle time was defined and only services with invehicle times less than or equal to this cut-off were deemed to be attractive. This
cut-off in-vehicle time was obtained by multiplying the minimum in-vehicle time for
the flow with a frequency-dependent ‘fast-slow multiplier’, and, if the minimum invehicle time was less than 30 minutes, adding an extra five minutes. The additional
of five minutes was designed to avoid anomalies such as a 12 minutes journey being
deemed as slow where the faster services was only two minutes quicker.
4.26
Of the numerous approaches tested, this was found to give the most intuitively
plausible division of services into attractive and non-attractive categories. Table 4.1
shows the lookup table used to calculate the fast-slow multipliers.
TABLE 4.1
4.27
Frequency (trains per hour)
Multiplier
2
1.5
3
1.35
4+
1.25
The next step in setting up the GJT calculations involved defining the add-on
generalised journey times for flows not lying entirely on the West Coast Main Line,
by considering typical ‘cross-London’ journeys to from London Euston to each of the
London external zones and calculating the corresponding GJTs. The numbers used in
the model are shown in Table 4.2.
TABLE 4.2
50
LOOKUP TABLE USED TO CALCULATE FAST-SLOW MULTIPLIERS
LOOKUP TABLE USED TO CALCULATE ADD-ON GENERALISED JOURNEY TIMES
Area
Add-on GJT (mins)
Mainline terminus
0
Central London
25
Inner London
44
Outer London
55
Home counties
102
Remote
150
New Lines Programme: Demand forecasting technical note
4.28
The GJT for a given flow, and for a given ticket type, was calculated using the
following standard PDFH formula:
GJT = wait time + journey time + interchange penalty + add-on GJT.
4.29
The wait times were obtained from the PDFH lookup table (shown in Table 4.3).
TABLE 4.3
4.30
WAIT TIME PENALTIES BY TICKET TYPE (MINS)
Service interval
Full
Reduced
Season
5
5
5
5
10
10
10
10
15
15
14
15
20
19
17
19
30
26
21
26
40
31
23
31
60
39
27
39
90
51
33
51
120
63
39
63
180
87
51
87
The PDFH lookup table used to calculate the interchange penalties is shown in Table
4.4. The actual interchange penalty for a given flow was obtained using a straightline interpolation between the nearest two distances explicitly given in this lookup
table, except if the flow’s distance was greater than 325 miles, in which case the
penalties for a flow with a distance of 325 miles were used.
51
New Lines Programme: Demand forecasting technical note
TABLE 4.4
4.31
INTERCHANGE PENALTIES BY TICKET TYPE (MINS)
Journey distance
Full
Reduced
Season
0
10
10
7
15
15
15
10
30
19
19
12
50
25
25
16
70
31
31
20
100
40
40
26
150
55
55
36
200
65
65
36
300
85
85
36
325
90
90
36
Two GJTs were calculated for each flow / ticket type combination, one including
only attractive direct services and the other including attractive direct services and
interchange services. The actual GJT was then taken as the smaller of the two, the
rationale being that if the GJT obtained using the interchange services is larger then
the interchange services will effectively be ignored by passengers.
Forecasting Changes in Demand and Revenue
4.32
The Do-Minimum demand in the Regional Model was based on 2030 constrained
demand forecasts for full, reduced and season tickets, obtained from the NMF. The
model works at the annual level, but weekday demand is also fed through the model
in order to provide ‘pre-loading’ outputs for the Commuter Model.
4.33
There are 41 stations designated as in-scope for the West Coast Main Line, of which
8 lie in the commuter corridor, giving a total of 41*41–8*8 = 1,617 in-scope flows
lying entirely on the WCML. However, due to the inclusion of flows beginning and /
or ending on a different line, the actual number of flows in the model is much larger
than this at 4,016.
4.34
Having calculated the Do Minimum and Do Something GJTs as described above, the
model uses the PDFH elasticity-based approach to forecast Do Something demand.
An elasticity of -0.9 was used for Full, Reduced and Season ticket types, in
accordance with PDFH recommendations.
4.35
Since fares are assumed to remain constant in the Regional Model, changes in
revenue are calculated in exactly the same way as changes in demand are
calculated. Fares are taken from the NMF, expressed as 2030 values to a 2004/5
price base. An uplift factor of 9.8% is applied to convert the revenue into 2007
prices, for consistency with the overall modelling framework.
52
New Lines Programme: Demand forecasting technical note
4.36
Given that the Do Something timetable has not been optimised for interchanges in
the same way the Do Minimum timetable has, only flows with an absolute GJT
change of greater than 10% were included in the calculations to avoid spurious
results.
Model Outputs
4.37
The Regional Model aims to assess the impacts of New Lines on demand and revenue
for regional services on the existing network, and the model therefore outputs key
indicators of these impacts. An example of the outputs produced by the model is
given in Figure 4.2 (these figures are for revenue – similar outputs can be produced
for demand).
FIGURE 4.2
EXAMPLE OF OUTPUTS PRODUCED BY THE REGIONAL MODEL
Total Revenue
DM
DS
m /yr
m /yr
£460.8
Total Revenue Increase
Proportional Absolute
m /yr
£488.5
6.0%
£27.7
Top Flows by Absolute Change
Increase
Orig Name
Dest Name
/yr
£4,129,311
London Euston
Nuneaton
£3,502,133
London Euston
Rugby
£3,152,669
London Euston
Lichfield Trent Valley
£2,533,330
London Euston
Telford Central
£2,398,032
London Euston
Shrewsbury
Bottom Flows by Absolute Change
Increase
Orig Name
Dest Name
/yr
-£1,003,239
London Euston
Stoke-on-trent
-£360,995
Wilmslow
Manchester Piccadilly
-£221,589
Warrington Bank Quay
Glasgow Central
-£159,077
Macclesfield
Manchester Piccadilly
-£149,351
London Euston
Hartford
4.38
In addition, the model provides flow-level graphical diagnostics to help ensure that
the results produced are sensible. An example of one of these diagnostics is given in
Figure 4.3, which shows the distribution of demand increases for all flows where the
demand increases by more than 10,000 passengers per year (this 10,000 is an input
that can be easily changed to produce a different graph).
53
New Lines Programme: Demand forecasting technical note
FIGURE 4.3
DISTRIBUTION OF DEMAND INCREASES GREATER THAN 10,000 PAX PER YEAR
Distribution of Demand Increases
200,000
Demand Increase (pax /yr)
150,000
100,000
50,000
0
0
5
10
15
20
25
30
35
40
-50,000
-100,000
-150,000
4.39
The model also provides station-level graphical diagnostics to help ensure that the
results produced are consistent with the timetable inputs. An example of one of
these diagnostics is given in Figure 7.3, which shows the total absolute changes in
demand and revenue for all flows beginning or ending at each of the 41 NMF primary
stations on the West Coast Main Line.
FIGURE 4.4 DISTRIBUTION OF DEMAND AND REVENUE INCREASES BY STATION
Absolute Increases by NMF Station Group
Edinburgh
Glasgow C
Carlisle
Motherwell
Lancaster
Oxenholme L D
Preston
Blackpool N
Wigan N W
Warrington B Q
Runcorn
Liverpool L S
Chester
Hartford
Stockport
Manchester P
Wilmslow
Cheadle Hulme
Crewe
Macclesfield
Stafford
Stoke-on-trent
Lichfield T V
Nuneaton
Tamworth.
Telford C
Shrewsbury
Sandwell & D
Wolverhampton
Birmingham I
Birmingham N
-£5
Absolute Increase (m / yr)
£0
Rugby
0.0
Coventry
£5
Northampton
0.2
Hemel H
£10
Milton K C
0.4
Bushey
£15
Watford J
0.6
Harrow & W
£20
Queens Park
0.8
-0.2
54
Revenue
£25
London Euston
Absolute Increase (m pax /yr)
Demand
1.0
New Lines Programme: Demand forecasting technical note
4.40
4.41
The Regional Model produces the following outputs, by ticket type, to be fed into
the New Lines Appraisal Model:
I
Do Minimum demand;
I
Do Something demand;
I
revenue generation;
I
change in passenger miles;
I
journey time benefits; and
I
crowding benefits.
Finally, weekday demand is also fed through the model in order to provide ‘preloading’ outputs for the Commuter Model. This demand is treated in exactly the
same way as the annual demand in the model itself, but it is only output for flows
between London Euston and those stations lying outside the commuter corridor.
55
New Lines Programme: Demand forecasting technical note
5
Heathrow Access Model
Introduction
5.1
Construction of a New Line to serve the West Midlands, the North West and Scotland
for journeys into London could potentially encourage mode shift away from road and
air for journeys accessing Heathrow for onward air travel. This effect could be
increased by serving Heathrow directly, either routing a New Lines via Heathrow or
via a diverging main line. This chapter details the analysis undertaken and model
developed to assess the potential scale of such modal shift.
5.2
Under consideration is interlining and surface access demand into Heathrow; wholly
domestic demand is considered in the Intercity model and as such is not included
here.
5.3
The Heathrow Access Model has not been modelled to the same level of detail and
rigour of the domestic New Lines models. As such, all results should be treated as
“indicative estimates” only.
Model Development and Calibration
Model Structure
5.4
The Heathrow Access Model is a simple multinomial mode choice model looking
across all the access (main) modes to Heathrow, namely air (interlining), rail, car
and coach. There is no segmentation, all demand being treated as a single market.
Data Inputs and Scenarios
5.5
Calibration of the mode split model was undertaken using 2007 data before
forecasting demand to 2030 (see discussion in ‘Model Forecasting’ section). Data
was obtained from the CAA Airport flow report with regards air demand into
Heathrow. This provides numbers of business and leisure interlining passengers in
2007 from all UK airports. Given the scope of the proposed New Line, only demand
from Edinburgh, Glasgow and Manchester airports has been considered.
5.6
Surface access demand levels were obtained via the CAA Passenger Survey Report
2007/2008. This is used to provide 2007 levels of demand from car, coach (inc. all
forms of road based public transport) and rail. The two datasets together provide an
overall picture of current demand levels into Heathrow.
5.7
Additional data was purchased from the CAA to allow further disaggregation of this
data at country and district level. In accordance with the aforementioned scope,
the model considers demand for districts within the West Midlands, North West and
Scotland who are deemed to potentially benefit from a new high speed line.
5.8
We have estimated 2007 Generalised Journey Time (GJT) costs at county level for
air, coach, car and rail using existing (publicly available) datasets and information
available on the internet. Consideration was given to real and perceived costs
incurred due to headway, journey time, fare, interchange penalties and access
times at both ends of the journey. Weightings, as employed in the Intercity model,
were assigned to each of these components based on research into the relative
importance of each cost for each mode.
57
New Lines Programme: Demand forecasting technical note
Calibration
5.9
The core model is applied as a multi-modal incremental logit model; this is
consistent with WebTAG guidance and better represents the changes in modal
shares afforded by New Lines where some modes currently have a low share, but
where the journey cost is arguably prohibitive. This was particularly important with
regards Scottish demand allocation to modes other than air. Although such demand
is low, there is a current market which an absolute choice model would effectively
ignore.
5.10
Estimation of the model parameters is undertaken via optimisation of a model error
parameter based on the percentage errors of each individual forecast. Based on
current input parameters, the model calculates the anticipated demand for each
mode by county and compares this to known current demand from the
aforementioned data sources to produce a percentage error. Each error is then
weighted according to user inputs and summed to obtain an overall model error.
5.11
Next the model incrementally changes each parameter that the user defines as
variable to try to find a more optimal solution. For each change, the model error is
calculated in order to ascertain the best option. When a solution which further
reduces the model error is found, the current parameters are replaced with these
new ones and the process is repeated.
5.12
This process is automated within the model via a macro. The macro is stopped after
a maximum of 400 runs, though in practice a solution is often found significantly
before this point is reached.
5.13
A multitude of solutions can be obtained dependant on variations of weighting for
each element of the model error or the limits of acceptable values that parameters
are allowed to take. After significant testing of the model approach, it was decided
most appropriate to assume a simple uniform weighting across all model error
elements. In this way, the square difference between the modelled and observed
demand is the only attribute of the model error.
5.14
Mode constants were fixed at 0 for both coach and car and at -40 for air travel.
Testing of the model has shown these simple parameters to be effective with
regards calibration and forecasting. It also avoids any possibility of calibration
producing constants outside reasonable bounds.
Model Forecasting
5.15
Consistent with the forecasting framework, a forecast year of 2030 has been
assumed. DfT forecasts suggest demand from Heathrow will increase by 100% from
2005 levels by 2030. A high level assumption that our overall market in 2030 would
therefore be twice that of our 2007 demand has been made.
5.16
Assumptions with regards the future 2030 costs of travel for each mode were made
in order to forecast our 2030 Do Minimum mode shares. For the classic rail network,
the assumptions are consistent with the service specification set out in our Do
Minimum scenario used in both the Commuter and Regional models.
5.17
Assumptions with regards fare growth and journey time changes (e.g. due to
increased road congestion) are also made where applicable. This provides us with
the perceived cost of journeys by each mode into London Heathrow, and as such a
basis for forecasting our Do Minimum shares.
58
New Lines Programme: Demand forecasting technical note
5.18
Calculation of the individual market share of each mode for each county takes the
form of an incremental logit model as shown below:
PFi =
[ (
PBi × exp s × C Fi − C Bi
∑ {P
n
j =1
Bj
[ (
)]
× exp s × C F j − C B j
)]}
PFi =
Future year % market share for mode i
PBi =
Base year % market share for mode i
PBj =
Base year % market share for modes j = 1 to n
CFi =
Future year perceived cost (inc. mode constant) for mode i
CBi =
Base year perceived cost (inc. mode constant) for mode i
CFj =
Future year perceived cost (inc. mode constant) for modes j = 1 to n
CBj =
Base year perceived cost (inc. mode constant) for modes j = 1 to n
s=
General scaling parameter
n=
Number of modes under consideration
5.19
This yields the forecast proportion of demand in our Do Minimum scenario for each
mode in each district under consideration.
5.20
To forecast the potential effect of a New Line, equivalent costs are then calculated
for any given option. It is assumed within the model that classic rail passengers
would switch to New Lines whenever this yields a lower GJT. As such, only a generic
rail mode is considered within the model (though it should be noted that weightings
attributed to New Lines as opposed to classic rail do differ when calculating GJTs,
and in this way the perceived cost benefits due to modal preference between classic
and new rail are reflected).
5.21
As with the forecasting of Do Minimum demand, Do Something costs are run through
the model which outputs the anticipated proportion of demand for each mode.
Applying this to the forecast market (discussed above), the expected numbers on
each mode in each scenario is obtained, and as such the magnitude of extracted
demand off air and road and onto rail. An example of this process is given below.
Example
5.22
Table 5.1 gives an example of the forecast Do Minimum demand shares for each
mode in Scotland; Table 5.2 gives the forecast Do Minimum and Do Something GJT
costs. The particular Do Something scenario under consideration envisions a halfhourly service from both Edinburgh and Glasgow (with those from Edinburgh having
to interchange at Preston) with a journey time of just over 3 hours into Heathrow
Airport.
59
New Lines Programme: Demand forecasting technical note
TABLE 5.1
Car
Air
Coach
Rail
2.1%
93.9%
1.6%
2.5%
TABLE 5.2
5.23
2007 BASE YEAR DO MINIMUM % OF DEMAND BY MODE FOR SCOTLAND
GJT COSTS (PERCEIVED MINS) BY MODE FOR SCOTLAND
Car
Air
Coach
Rail
Base year
363
133
698
381
Future year
366
106
746
215
Application of the model yields a forecast mode share of rail from Scotland of
21.7%. The corresponding shares for car, air and coach would be 1.0%, 76.9% and
0.4% respectively. It should be noted that these figures are for all of Scotland. And
as such air would remain the quickest mode for a significant number of O-D pairs.
Model Outputs
5.24
As well as forecasting absolute changes in annual demand as a result of a new high
speed line, the Heathrow Access Model produces other outputs:
I
Do-Minimum and Do-Something Demand;
I
Change in annual revenue by mode;
I
Change in road passenger km’s;
I
Change in actual rail journey time;
I
Change in perceived rail journey time; and
I
Change in rail user charges.
5.25
These are standard outputs from the model whenever an option is tested and can be
used within economic appraisals of new line options.
5.26
As discussed above, the Heathrow access model has not been modelled to the same
level of detail and rigour of the domestic New Lines models. As such, all results
should be treated as “indicative estimates” only.
60
New Lines Programme: Demand forecasting technical note
6
Near Europe Model
Introduction
6.1
The introduction of a New Line from the West Midlands, the North West and
Scotland to London will reduce the overall rail journey time from principal cities in
the United Kingdom to mainland Europe. This Chapter outlines the development and
application of a simple mode choice model to estimate the amount of rail demand
that could be abstracted from air four principal UK cities; Birmingham, Manchester,
Glasgow and Edinburgh to two major European cities; Paris and Brussels (via HS1) as
a result of New Lines interventions in the UK.
6.2
The air demand for each flow in 2030 has been estimated using CAA Airport flow
data and DfT forecasts for short haul growth from UK airports from 2005 to 2030.
6.3
Two scenarios have been considered, both assuming a New London terminal at or
near St.Pancras. The first involves an interchange between New Lines services and
Eurostar trains at St. Pancras; the second scenario involves a direct service to
Europe that calls at St. Pancras but without passengers changing trains.
6.4
For the model development, information about observed rail:air mode shares on
Eurostar services in 2008 has been used to estimate the model parameters. The
Generalised Journey Time (GJT) costs for air and rail have been calculated using
existing datasets and information available on the internet.
6.5
As with the Heathrow Access model. The results from this model should be treated
as “indicative estimates” only as they have not been modelled to the same detail
and rigour as the domestic New Lines models.
Scope of the Model
6.6
The Near Europe model has been populated with journey time, frequency and
average fare information for UK airports (Birmingham, Manchester, Glasgow,
Prestwick and Edinburgh) to Near Europe (Paris CDG, Paris Orly, Brussels and
Charleroi). Similarly for the domestic classic line routes journey time, frequency
and average fare data for from Birmingham, Manchester and Edinburgh to London.
The data for the New Lines interventions is obtained from option MB1.4.1. Eurostar
journeys between London and Paris and London and Brussels. All the journey time,
frequency and fare information is based on current (2008) data.
6.7
The model does not consider any other journey opportunities such as Leeds to Paris
or Manchester to Amsterdam as there would only be a marginal change in demand
on these types of flows as a result of New Lines interventions on a West Coast
alignment. However, the model has been designed in a way that would allow other
journey opportunities to be modelled if required.
6.8
The demand data and forecasts for both air and rail are constant total point-topoint demand; no account is made of changes in catchment area or of generated
demand. Only changes in rail:air mode share are modelled.
61
New Lines Programme: Demand forecasting technical note
Methodology Overview
6.9
The Near Europe model forecasts the change in air-rail mode-share that results from
changes in Generalised Journey Time (GJT) due to New Lines interventions. The
change in mode-share is then applied to the forecast air and rail demand for 2030.
Generalised Journey Time
6.10
The GJT consists of the following elements:
I
Time to access domestic airport from journey start point
I
Check in time
I
Time to journey end point from Europe airport
I
In vehicle time
I
Service frequency interval
I
Fare (2030 values in 2007 price base)
I
Interchange penalty (rail only)
I
Mode Constant
6.11
It is assumed that there is an 18-hour period of operation for flights and trains
service. The service frequency interval (in minutes) is calculated as “60 * 18 / The
Number of Flights per Day”. The number of flights per day is taken as the average
number of flights on Thursdays in September.
6.12
The air fares are taken from 2008 data. It is assumed that there is a RPI - 1% per
annum change in air fares to 2030. The Eurostar fares are also 2008 values, it is
assumed that these change at RPI per annum until 2030. The domestic classic line
fares are from NMF 2004-05, it is assumed that these change at RPI + 1% per annum
until 2025 then at RPI until 2030. The fares for the New Lines are assumed to be at a
premium of 30% greater than the Classic Line fares. All of the fares are converted
into a 2007 price base.
6.13
The same values of time used in the Intercity model have been used to convert fares
from prices to minutes. The High Speed journey time multiplier and access time
multiplier are also the same as in the inter-city model.
6.14
For the purposes of the Near Europe model, it is assumed that the New Lines London
terminal is at St. Pancras and as such represents a best case scenario. This means
that no connection mode is required to link passengers between the New Lines
terminal and the High Speed One terminal. The interchange penalty is calculated
using a PDFH approach based on the journey distance.
6.15
The mode constant represents an extra cost for a particular mode and is used to
represent a number ‘softer factors’ that may influence the choice between
different modes that aren’t already included in the GJT. There is an element of
uncertainty about the value of this mode constant due to the qualitative nature of
this cost. Therefore a range of mode constants have been used ranging from 20
minutes to 40 minutes extra cost to air.
62
New Lines Programme: Demand forecasting technical note
Demand
6.16
6.17
CAA Airport flow data from 2005 is uplifted using the DfT forecasts for short haul
growth from UK airports from 2005 to 2030. Short haul demand is expected to
increase by the following percentages from 2005 to 2030:
I
Birmingham by 114%
I
Manchester by 80%
I
Edinburgh by 100%
I
Glasgow 50%
2030 rail demand from Birmingham, Manchester and Scotland to Paris and Brussels
can be inferred from the Do Minimum rail mode share. For example, if Air Demand
for a particular flow is 90,000 passengers per year and the forecast Do Minimum rail
share of air-rail market is 10% then it can be inferred that the rail demand is 10,000
passengers per year. The total market size for that flow would be 100,000
passengers a year.
Mode Share Calculation and Calibration
6.18
The Near Europe model is an absolute mode share model (in contrast to the Intercity
model which is an incremental mode share model). The formula used to calculate
the mode share of air is:
P( Air Rail) =
(
1+ e
1
− λ ⋅( GC Air − GC Rail )
)
where λ is the scaling factor and GCAir and GCRail are the generalised costs of air and
rail respectively. Similarly the formula to calculate the mode share of rail is:
P( Rail Air) =
6.19
(
1+ e
1
− λ ⋅( GCRail −GCAir )
)
To estimate the value of the scaling factor the model is calibrated using known
mode share values for London to Brussels and London to Paris. Eurostar publishes
the air-rail mode share of their services. The 2008 mode share values for London to
Paris (76%) and London to Brussels (72%) have been used. This is a conservative
estimate as 2008 was the first full year of High Speed 1 operation so the market
may not be fully developed. Mode share will also have been dampened as a result
of the Channel Tunnel fire which caused a large amount of disruption to services.
The scalar is chosen to minimise the difference between the known mode share
and the forecast mode share. The model is calibrated for each mode constant
value selected, as shown in Table 6.1; given there are only two points to calibrate
the model, a range of parameters and forecasts have been estimated.
63
TABLE 6.1
MODE CONSTANTS AND SCALING PARAMETERS FOR NEAR EUROPE MODEL
Mode Constant (in favour of rail)
Scaling Parameter
20
-0.0213
30
-0.0177
40
-0.0151
Model Outputs
6.20
The mode share for the Birmingham, Manchester and Scotland to Paris and Brussels
flows is calculated using the equations above and the calibrated scaling factor. The
rail demand is calculated as the rail mode share multiplied by the total market size
for that flow. The model also calculates the change in passenger miles and revenue
for the New Line services.