Manchester Hub Phase 1

Transcription

The Northern Way:
Manchester Hub Phase 1 Transport Modelling and Benefit Assessment
April 2009
Manchester Hub
Phase 1 Study - Transport Modelling and
Benefit Assessment
Final Report
April 2009
Prepared for:
Prepared by:
Northern Way
Steer Davies Gleave
28-32 Upper Ground
London
SE1 9PD
+44 (0)20 7910 5000
www.steerdaviesgleave.com
Contents
Contents
1
2
INTRODUCTION AND BACKGROUND
1
Background
1
The Importance of the Manchester Hub
1
Manchester Hub Phase 1 Study
1
This Report
2
TRANSPORT MODELLING AND BENEFIT ASSESSMENT METHODOLOGY OVERVIEW
3
Required Outputs
3
Modelling Approach
3
The Manchester Hub Rail Corridors
5
Modelling of Passenger Service Level Improvement Benefits
10
Modelling of Rail Freight Improvement Benefits
11
3
USING THE ECONOMIC SCENARIOS
13
4
TIMETABLE AND ROLLING STOCK SCENARIOS
15
Do Minimum Scenario
15
Test Service Level Improvement Scenario
15
5
GEOGRAPHICAL ZONING
19
6
EXOGENOUS DEMAND GROWTH (WITH RIFF MODEL)
23
Approach
23
Base Data
23
Growth Assumptions
24
Outputs
27
7
IMPACT OF SERVICE IMPROVEMENT ON DEMAND (MOIRA)
29
8
AIRPORT FLOWS MODEL
31
Introduction
31
Calibration
32
Application
34
Outputs
34
“NEW FLOWS” MODEL
37
Objective
37
Selection of Flows
37
Inputs to the Model
37
Specification and Calibration of the Model
38
9
Contents
10
11
12
13
14
15
Estimated Parameters
42
Application
43
CROWDING MODEL
45
Inputs
45
Approach
45
Outputs
47
TRANSPORT BENEFITS APPRAISAL
49
Overview
49
Inputs
49
Approach
50
Outputs
51
WIDER ECONOMIC BENEFITS APPRAISAL
53
Transport Data (demand and generalised costs)
53
Economic Data
54
Agglomeration Elasticities
54
Wider Economic Benefits Calculations
54
Forecasting Other Years
56
Aggregation to Corridors
56
Outputs
56
FREIGHT MODEL
59
Introduction
59
Approach
60
Valuation of SLMs
62
MODELLING RESULTS FOR THE TEST TIMETABLE SCENARIO
63
Exogenous Growth
63
Forecast Passenger Demand
68
Metrics by Corridor
70
Appraisal Results
75
Freight Modelling and Appraisal Results
80
OUTPUT STATEMENT METRICS
81
Timetable-driven Benefits
81
Capacity-driven Benefits
87
Freight Benefits
89
Contents
FIGURES
Figure 2.1
Schematic of Modelling Approach
4
Figure 2.2
Manchester Hub Corridors
7
Figure 2.3
Northern City Regions
9
Figure 2.4
Passenger Service Level Benefit Assessment Schematic
11
Figure 4.1
Manchester Hub - Test Scenario Suburban Service Pattern
17
Figure 4.2
Manchester Hub - Test Scenario Long Distance Service Pattern
18
Figure 5.1
Manchester Hub MOIRA OR23 and RIFF-Lite Zoning Schemes
20
Figure 5.2
WEB zoning scheme
21
Figure 6.1
Economic Forecasts Trend vs Tempro (National)
24
Figure 6.2
Economic Forecasts Trend vs. Tempro (Manchester)
25
Figure 6.3
Economic forecasts Trend Plus vs Trend Scenarios (Manchester)
25
Figure 8.1
Airport Model Summary outputs
35
Figure 9.1
Chosen Model All Ticket Types with GVA within 1500m
41
Figure 9.2
Residual Erros plotted against independent variables
41
Figure 9.3
Residual Errors plotted against actual journeys
42
Figure 9.4
Change in Journeys under Do Minimum and TeST Scenarios
44
Figure 11.1
Appraisal structure
49
Figure 13.1
Rail Freight in the North West RUS Area
59
Figure 14.1
Forecast GVA Growth, 2007/8 to 2019/20, for Trend and Trend Plus
Economic Scenarios
63
Forecast employment growth, 2007/8 to 2019/20, for Trend and Trend
Plus economic scenarios
64
Forecast Passenger Journeys Growth, 2007/8 to 2019/20, for Trend and
Trend Plus Economic Scenarios
65
Figure 14.4
Manchester Hub Passenger Journey Forecasts by Corridor
68
Figure 14.5
Manchester Hub Passenger Journey Forecasts by Corridor
69
Figure 14.6
Trend Scenario benefits by corridor and flow geography
78
Figure 14.7
Trend Scenario benefits by corridor and benefit type
79
Figure 14.8
Trend Plus Scenario benefits by corridor and flow geography type
79
Figure 14.9
Trend Plus Scenario benefits by corridor and benefit type
80
Figure 14.2
Figure 14.3
Contents
TABLES
Table 2.1
Manchester Hub Corridors
Table 6.1
Journeys and Revenue from PTE products
24
Table 6.2
Overlay in RIFF-Lite applied to PTE areas
26
Table 6.3
Exogenous growth drivers - sources and assumptions
27
Table 6.4
Exogenous growth impact by corridor
28
Table 8.1
Airport Model Parameter values
33
Table 8.2
Air passenger forecasts
34
Table 8.3
Airport Model Summary outputs
35
Table 9.1
New Flows models estimated
39
Table 9.2
Glossary Of Terms
40
Table 9.3
New Flows Gravity Model Estimated Parameters
42
Table 10.1
Crowding Uplift / Suppression Factors (2019)
48
Table 11.1
Example Appraisal model outputs only for one corridor / Geography
Classification
51
Table 12.1
Wider Economic Benefits – Trend Scenario
57
Table 12.2
Wider Economic Benefits - Trend Plus Scenario
58
Table 13.1
Comparison of container freight transport costs
61
Table 13.2
SLM inputs
62
Table 14.1
Exogenous Passenger Growth by Corridor and flow geography Type Trend Scenario
66
Exogenous Passenger Growth by Corridor and flow Geography Type Trend PLUS Scenario
67
Table 14.3
Impact of Test Timetable and Rolling Stock on Demand (2019/20)
70
Table 14.4
GJT Improvement by corridor
71
Table 14.5
GJT Change Splits
72
Table 14.6
Incremental seats in AM peak by corridor
74
Table 14.7
Incremental seats in HIGH AM peak by corridor
74
Table 14.8
Summary of benefits due to improvement in passenger rail services
75
Table 14.9
Total Benefits by Benefit and Geography type - Trend Scenario
76
Table 14.10
Total Benefits by Benefit and Geography Type - Trend Plus Scenario
77
Table 15.1
Trend Scenario - Timetable-related benefits per GJT minute improvement
by benefit type
81
Trend Scenario - Timetable-related benefits per GJT minute improvement
by flow type
82
Trend Scenario - Timetable-related benefits split by element of GJT
improvement
83
Table 14.2
Table 15.2
Table 15.3
6
Contents
Table 15.4
Trend Plus Scenario - Timetable-related benefits per GJT minute
improvement by benefit type
84
Trend Plus Scenario - Timetable-related benefits per GJT minute
improvement by flow type
85
Trend Plus Scenario - Timetable-related benefits split by element of GJT
improvement
86
Table 15.7
Trend Scenario – crowding benefit per incremental AM peak seat
87
Table 15.8
Trend Plus Scenario – crowding benefit per incremental AM peak seat
88
Table 15.9
Trend Scenario – crowding benefit per incremental HIGH AM peak seat
88
Table 15.10
Trend Plus Scenario – crowding benefit per incremental HIGH AM peak
seat
89
Table 15.5
Table 15.6
APPENDICES
DETAILED APPRAISAL RESULTS
TEST TIMETABLE SCENARIO
Contents
Phase 1 Study - Transport Modelling and Benefit Assessment
1
Introduction and Background
Background
1.1
1.2
The Northern Way Growth Strategy published in 2004 identified three overarching
transport objectives that reflect closely the conclusions on investment focus
contained in the Eddington Transport Study published subsequently in December
2006:
I
Better links within city regions;
I
Better links between city regions; and
I
Better connections to the global gateways – airports and ports.
These objectives were developed by the Northern Way into a Strategic Direction for
Transport that examined which transport interventions, in terms of policies and
schemes, would best serve the aim of improving radically the North’s economic
competitiveness. In that report, the single most important investment needed in the
North’s rail network is identified as being the Manchester Hub, because it critically
affects the operation of both freight and passenger services across the whole of the
North.
The Importance of the Manchester Hub
1.3
Addressing the capacity constraints in and around the Manchester Hub is critical for
supporting growth in rail commuting to Manchester city centre, the improvement of
rail links between the North’s City Regions, rail access to Manchester Airport, the
largest international airport in the North, the continued improvement of fast rail
links to London and for the growth of rail freight traffic, including to and from the
North’s major ports. Resolution of the Manchester Hub problem, which is essentially
the capacity constraint created by the poorly-interconnected Victorian-era rail
networks in Manchester, will require capital investment. The improved
infrastructure that would result will allow the reliable operation of a muchenhanced set of services.
Manchester Hub Phase 1 Study
1.4
On 4th October 2007, the then Department for Transport Minister, Rosie Winterton
announced that a study would be undertaken to develop proposals to enhance the
capacity and functionality of the Manchester Hub. This was is response to the work
of the Northern Way. Subsequently, the structure of the study was agreed to be in
two phases, with the first phase led by the Northern Way and the second by
Network Rail.
1.5
The objective of Phase 1 is to produce a Manchester Hub Output Statement as a
formal input to the subsequent Phase 2. Part of the Output Statement takes the
form of a quantified assessment of the economic value of rail service level
1
improvements in and around the Manchester Hub. These quantified assessments
and the approach to deriving them form the subject of this report.
1.6
The delivery of Phase 1 required the undertaking of an Economics Study to produce
two economic scenarios (named Trend and Trend Plus, respectively) which have
feed into this Transport Study. The Transport Study consists of the modelling of
transport impacts of rail services improvements and an assessment of the resulting
economic benefits for each of the two economic scenarios.
1.7
The results of the Transport Study and the Economic Study, together with preexisting work establishing the evidence base behind the Manchester Hub and
capturing stakeholders’ requirements have been used to create the Output
Statement, which is a separate and stand-alone document.
This Report
1.8
This Final Report sets out the work undertaken and the results from the Transport
Study. The Economics Study has been undertaken by Experian, and is reported
separately. As noted, the Output Statement is a stand-alone document.
1.9
This Report sets out:
1.10
I
An overview of the methodology (Chapter 2);
I
Using the Economic Scenarios (Chapter 3);
I
The Timetable and Rolling Stock Scenarios (Chapter 4);
I
Details of the methodology and sub-models (Chapters 5 to 13);
I
Results of the Benefit Assessment for the Test Scenario (Chapter 14); and
I
Disaggregated Rates of Benefit for the Output Statement (Chapter 15).
Appendix A gives more detailed results and Appendix B gives the train service
specification corresponding to the test timetable scenario.
2
2
Transport Modelling and Benefit Assessment
Methodology Overview
Required Outputs
2.1
The purpose of the transport modelling and benefit assessment described in this
report is to provide a key component of the Manchester Hub Output Statement. It
establishes the economic value of a plausible set of rail service level improvements
in and around the Manchester Hub. In Phase 1, we only consider the benefits of
service improvements, and not the costs, which will be assessed in the light of
particular options developed in the Phase 2 study. The methodology is tailored to
this requirement.
2.2
In assessing benefits, we have used the traditional appraisal tools to assess transport
benefits, as well as the more recently developed Wider Economic Benefits
methodology. We have chosen to present results in terms of:
I
Different types of benefits per unit improvement of service level; and
I
Geographically disaggregated by rail corridor (and geographically by journey
type within corridor).
2.3
These results, which are incorporated within the Output Statement, in themselves
help provide guidance on the type of scheme to be developed in Phase 2. They are
also used to evidence the Conditional Outputs that form an integral part of the
Output Statement.
2.4
The results presented here focus attention on those potential improvements that
offer the highest benefit per unit service improvement, and further, contribute to
understanding the type of improvement (e.g. journey time, frequency, connections
or crowding) likely to drive greatest benefits. In turn, this will facilitate the
optioneering and formal appraisal processes in Phase 2.
2.5
Results have been provided for both the Trend and Trend Plus economic growth
scenarios.
Modelling Approach
2.6
2.7
In common with all appraisal processes, it was necessary to define:
I
A “do-minimum” case, i.e. what would happen if no (uncommitted)
improvements were undertaken; and
I
A “test” timetable scenario for a set of exemplar service level improvements
proposed, with appropriate rolling stock enhancements.
Additionally, because of the particular circumstances of the study, we have also
defined key metrics against which the estimated economic benefits can be
considered, at a suitable level of geographical disaggregation. These are:
3
2.8
I
Changes in average generalised journey times within each geographical
corridor; and
I
Changes in seats available on long-distance and suburban trains in each corridor
during the AM peak.
The diagram below shows a schematic of the overall approach, with the appraisal of
benefits translating, via the changes to metrics arising from the introduction of the
Test Timetable scenario, into the Output Statement rates of disaggregated benefits.
FIGURE 2.1 SCHEMATIC OF MODELLING APPROACH
Economic Model: Assumptions
based on updated TEMPRO / City
Region Development Plans
Assumptions Based on
Stakeholder Consultation /
HLOS
Assumptions Based on
DfT White Paper and
Freight RUS
Economic Scenarios:
Timetable and Rolling Stock Scenarios
Freight Growth Scenario
Trend and Trend Plus
Test and Do Minimum
Freight
Model
Passenger
Models
Benefit
Appraisal
Results
Timetable and rolling
stock metrics
Output Statement – Disaggregated Rates of Benefit
(for Trend and Trend Plus scenarios)
2.9
The modelling approach has also been strongly influenced by an understanding of
stakeholders’ aspirations for the Manchester Hub (and which are set out in other
documents as well as in the Output Statement itself). The improvements sought by
Stakeholders have fed into the design of the “test” timetable scenario, allowing the
marginal economic value of those improvements to be assessed.
2.10
During the consultation process, Stakeholders identified a number of relevant
markets for which improvement were necessary, as follows:
I
The Manchester commuter network:
journeys to work, other trips;
I
Current longer distance markets:
within the North, to London and cross country, business and other purpose
trips;
I
Markets not currently well served by rail:
4
for example, between corridors currently on the rail network but with no, or
very poor connections to/across Manchester, and which could have much
better levels of service;
I
Air passengers to Manchester Airport:
UK and overseas passengers, business and leisure; and
I
Freight:
inter-modal, other commodities.
2.11
2.12
These markets have helped to determine the modelling approach described in more
detail below. In particular we have distinguished between:
I
Trips to and from Manchester Airport;
I
Journeys across Manchester (i.e. not to or from the centre), or not directly
linked to the centre, currently not well-served by rail;
I
Suburban and long-distance services; and
I
Freight.
The approach to modelling the benefits from passenger service level improvements
and, separately, the benefits from improvements allowing additional freight services
to operate, are set out below. The detailed modelling approach for each element is
then set out in subsequent sections.
The Manchester Hub Rail Corridors
2.13
The size and complexity of the Manchester Hub meant that it was necessary to
disaggregate the rail network into well-defined corridors into the Hub, so that the
impact of particular service changes could be assessed and assigned to the relevant
part of the network, as a basis for the subsequent Optioneering of potential
solutions in Phase 2 of the study. We defined a set of 14 corridors, based on a
combination of Network Rail’s corridor definitions (from the North West RUS) and an
analysis of the service patterns currently operating. The map in Figure 2.2 shows
the corridors.
2.14
For convenience, we have labelled the corridors as follows (Table 2.1).
5
TABLE 2.1
MANCHESTER HUB CORRIDORS
1
Southport via Wigan
2
Preston and the North via Bolton
3
Blackburn
4
Bradford via Rochdale
5
Yorkshire and the Humber & the North East via Leeds
5a Leeds and York
5b North of York (towards Tees, Tyne and Scotland)
5c East of Leeds (towards Hull)
6
Glossop / Hadfield
7
Marple / Romiley
8
Yorkshire & the East Midlands via Sheffield
9
Buxton
10
London, Birmingham and the South (via WCML)
11
Manchester Airport
12
Chester via Northwich
13
Liverpool via Irlam
14
Liverpool / Chester via Warrington
2.15
All rail stations used in the modelling (in practice those used in MOIRA OR23, as
described below) were then mapped to the rail corridors. Stations in Central
Manchester were classified as “corridor 0”, i.e. effectively being in all the 14
corridors, while stations not in any corridor were mapped as a residual, though large
category.
2.16
Note that corridor 5 (the North Trans-Pennine route) has, where appropriate, been
sub-divided into three sub-corridors to allow a distinction to be made between
journeys on the core route to Leeds and York (5a), those travelling from further
away, in the direction of the Tees Valley, Tyne and Wear and Scotland (5b) and
those travelling from the Hull direction (5c).
6
FIGURE 2.2 MANCHESTER HUB CORRIDORS
7
2.17
The corridors, and the mapping of stations to each corridor, thus enabled the
benefits estimated in the modelling to be ascribed, firstly, to one of the 14
corridors and, secondly, to one of 10 “flow geography types”, defining the type of
journey, in terms of the origin and destinations, as follows:
1.
Flows to/from Central Manchester from Inner Manchester City Region
2.
Flows to/from Central Manchester from Outer Manchester City Region
3.
Flows to/from Central Manchester from Other City Regions
4.
Flows to/from Central Manchester from Long Distance stations
5.
Other intra corridor flows
6.
Inter corridor flows where one or both ends is in the Manchester City Region
7.
Inter corridor flows where neither end is in the Manchester City Region
8.
Flows to/from Manchester Airport from Manchester City Region
9.
Flows to/from Manchester Airport from outside Manchester City Region
10.
Flows to all other station
2.18
Note that in the above classification, double-counting is avoided in categories 6 and
7 (inter-corridor journeys) by basing the assignment to corridor on journey origins
(on a producer-attractor, or point of sale, rather than direction of travel, basis).
2.19
The flow geography categories have also been used, in a slightly modified form, in
the presentation of the breakdown of exogenous growth.
2.20
The map below shows the definitions of City Regions used.
8
FIGURE 2.3 NORTHERN CITY REGIONS
9
Modelling of Passenger Service Level Improvement Benefits
2.21
2.22
The process for modelling the economic value of passenger service level
improvements in and around the Manchester Hub required the following steps:
I
Development of a suitable interface to receive the outputs from the Economic
Model scenarios;
I
Development of suitable geographical “zoning” schemes, including the
definition of rail corridors into central Manchester;
I
Definition of the “do-minimum” scenario against which the test scenario can be
tested;
I
Development of a “test scenario” rail service specification, which captured
stakeholder aspirations to allow the benefits associated with these
improvements to be assessed;
I
Modelling of the impact of exogenous factors (economic growth, etc.) on rail
demand;
I
Modelling of the impact of the improved rail services on rail demand;
I
Modelling the impact of crowding;
I
Developing tools to undertake the appraisal of transport benefits; and
I
Developing tools to undertake the appraisal of wider economic benefits.
At the core of this process are the models of rail demand. Where appropriate, we
have used the standard industry tools for forecasting exogenous growth (RIFF-Lite)
and service changes (MOIRA). However, there are two special cases where this is
not an appropriate approach, namely for:
I
Journeys to Manchester Airport; and
I
Journeys on “new flows” where demand was historically low, but where the
change to the service specification would result in a significantly changed
generalised journey time, beyond the level appropriate for the use of the
elasticity parameters in MOIRA.
2.23
We have therefore developed a model of rail journeys to the Airport, taking account
of the air passenger forecast for Manchester Airport and the modelled impact of
service level changes on rail’s airport access share. This is described in Chapter 8.
2.24
Similarly, we have developed a model of rail journeys on connecting flows across
Manchester, where significantly improved journey times, frequencies and
connectivity between services means that a “new flows” model, using a gravity
model approach is appropriate. This is described in Chapter 9.
2.25
The schematic diagram below (Figure 2.4) shows the relationships between the
various input data, assumptions and models used to assess the economic value of
enhanced passenger services.
10
FIGURE 2.4 PASSENGER SERVICE LEVEL BENEFIT ASSESSMENT SCHEMATIC
Economic Model
Trend and Trend Plus Scenarios
Rolling stock
assumptions
(do-min =
HLOS)
Timetable
changes by
corridor (do-min
= Dec08 TT)
Disaggregated
economic actuals
and forecasts for
Trend and Trend
Plus scenarios
Exogenous
growth
parameters
based on
PDFH with
HLOS overlay
Base
demand
data:
LENNON
with infill for
GMPTE
RIFF - LITE
Crowding
Model
Base rail
GJTs, car
journey
times
New flows
gravity
model
Do-min MOIRA base
matrix
Test Scenario
MOIRA base matrix
(future year journeys and
revenues for all flows)
(future year journeys
and revenues,
for airport flows and
“new flows” only)
Airport
passenger
survey /
Airport
passenger
forecast
Airport
flows
model
MOIRA
Appraisal – Conventional and Wider Economic Benefits
Modelling of Rail Freight Improvement Benefits
2.26
The approach taken towards the modelling of freight improvement benefits
complements that used for passenger service improvement, but is necessarily much
simpler, due to the lack of available detailed demand forecast information and
accepted modelling tools.
2.27
Rail freight in the Manchester area uses a number of freight terminals, but those at
Trafford Park have particular importance, especially for inter-modal freight. Other
terminals in the Manchester area are important, for example, for handling
aggregates. For this reason, the explicit definition of rail corridors, used for
modelling passenger services, is inappropriate. Instead, we have focused on the
likely demand for freight trains for particular commodities to the Manchester area.
In practice, evidence suggests that major growth is likely to be in the inter-modal
container sector, and we have concentrated our analysis on this.
2.28
There are no currently industry-accepted freight forecasts beyond 2014-15, the end
date of Network Rail’s Freight RUS document (March 2007), although we are aware
that Rail Freight Group have developed forecasts to 2030. (We have not had access
to the detailed results of these however, and they do not, in any case, necessarily
represent an accepted industry view.) We have therefore based our analysis on
assumptions for future growth that we believe are consistent with the levels of
growth assumed within the Freight RUS, and consistent with Government ambition
for a doubling of traffic (Rail White Paper, 2007).
11
2.29
We have validated our forecast by comparing costs of road and rail freight to
demonstrate the economic viability of the rail freight options for freight flows from
key origins (principally the deep sea ports for container freight, in particular
Southampton and Felixstowe).
2.30
Based on our forecast additional demand, we have identified where additional
freight paths into Manchester are required and the number that are required, and
we have estimated the societal benefits for the operation of these paths. These can
also be stated in terms of benefit per additional freight path.
12
3
Using the Economic Scenarios
3.1
In establishing the long-term economic benefits of rail service improvements, it is
essential to define one or more economic scenarios under which those
improvements will be implemented. For this study, it was decided to estimate
benefits for two different economic scenarios:
I
The “Trend” Scenario, essentially conforming to Government economic growth
and planning assumptions (with population and employment conforming,
approximately, to the forecasts in DfT’s TEMPRO models); and
I
The “Trend Plus” Scenario, based on the assumption that economic growth in
the North of England will be accelerated by the implementation of the three
Regional Economic Strategies and the eight City Regions’ Development
Programmes.
3.2
Based on this approach, it is possible to assess the value of the service
improvements in a scenario essentially recognised by the forecasting community
(the Trend Scenario), as well as under a “stretch target” scenario in which the City
Regions’ plans are successful. The comparison of the results under the two scenarios
should provide insights into (the transport component) of the likely benefits to be
achieved should those plans be successful in enhancing economic growth in the
North.
3.3
The Trend and Trend Plus scenarios were developed by economic consultants
Experian, with the outputs feeding into the transport modelling and benefit
assessment described in this Report. The format of the output, described below,
was designed to ensure that the maximum value from the information in the
Economic Study was captured for transport modelling purposes.
3.4
The key outputs from Experian’s analysis were a baseline set of values (2006) and
annual forecasts to 2030 for the following variables:
I
GVA:
total and disaggregated into nine industry groupings, based on SIC codes;
I
Workplace Employment:
total and disaggregated into nine industry groupings, based on SIC codes;
I
Population;
I
Households;
I
Residence-based employment:
total and disaggregated into nine occupation types; and
I
3.5
Unemployment.
These data items were used to drive the impact of exogenous variables on rail
demand (see Chapter 6) and in the “New Flows” model (see Chapter 9). For this to
be successful, it was necessary to map the economic data into the transport model
13
at an appropriate level of geographical disaggregation, capturing the differential
changes in these factors in each geographical area.
3.6
The zoning used in the transport modelling is described in Chapter 5. This zoning
was based, to a large extent, on rail geography, whereas the economic data is
available at various hierarchies of regional, local authority and (Census) “output
area”. Bringing these together successfully required the economic data to be
available at a sufficiently disaggregate level that each geographical unit of the
economic data could unambiguously be mapped to a transport modelling zone. For
this purpose, the lower level “Super Output Area” (LSOA) unit was used by Experian.
There are approximately 30,000 of these covering England and Wales. For Scotland,
Intermediate areas were used (about a further 1000 zones).
14
4
Timetable and Rolling Stock Scenarios
Do Minimum Scenario
4.1
In this study (as in any modelling and appraisal process) it was necessary to define
the base case against which any proposed scheme or “test scenario” could be
tested. In this study, the appraisal does not involve any estimation of costs, nor is a
particular scheme being formally appraised. Nevertheless, in order to provide a
useful estimation of the economic value of service improvements, a similar
approach is required, with the definition of a base or “do minimum” case, as well as
of a “test scenario” (described below).
4.2
It is assumed that any scheme emerging from the Manchester Hub study (in Phase 2)
would not be able to be developed during CP4 (2009-2014) as no funding is currently
available to implement such a scheme in this period . It has therefore been assumed
that any emerging scheme would be developed during CP5 (2014-19).
4.3
The Do Minimum case has, therefore, been assumed to consist of any developments
that are planned and/or funded during the CP4. These include:
I
The December 2008 timetable (no information on any timetable changes
beyond this being available);
I
Fleet strengthening as specified under the Government’s HLOS commitments to
the two relevant TOCs, Northern and TPE;
I
Fleet strengthening as specified under the Transport Innovation Fund (TIF)
scheme for Greater Manchester1;
I
Infrastructure works committed under HLOS (although in practice these do not
materially affect the modelling); and
I
The Manchester Airport air passenger forecast, as set out in the Airport’s
Masterplan.
Test Service Level Improvement Scenario
Background
4.4
1
We have defined a “test scenario” of significant service improvements,
incorporating, for each corridor, changes to:
I
Journey times;
I
Service frequencies;
The do-minimum case included relevant schemes proposed for CP4 (2009/10 – 2013/14), i.e. those
proposed in the North West RUS, the Freight RUS and the rolling-stock enhancements of the
Manchester TIF programme. In the light of the rejection of the congestion charging in the recent
referendum and the subsequent abandonment of the charging proposal, when undertaken any
future appraisals, it will be necessary to revisit these assumptions in respect of the TIF
programme. However, the findings described in this report on the benefits per unit enhancement
on corridors approaching the Hub remain valid.
15
I
Direct through services or convenient interchange at a Central Manchester
station; and
I
Additional vehicles to reduce overcrowding.
4.5
The changes assumed reflect plausible and significant, but incremental, changes to
existing service levels. They differ corridor by corridor reflecting market and
operational realities. The overall impact of the timetable change is to reduce
average (generalised) journey times within the corridors by 8%.
4.6
In reality, it is likely that any actual scheme developed would provide a different
balance of service improvements from those specified in our test timetable
scenario. For this reason, while we present the results of modelling the benefits of
the test scenario (in comparison with the do minimum case) to give a clear and
understandable outline of the likely economic impact if the test scenario were to be
delivered, we have translated these benefits into a per-unit-of-service-improvement
basis for the purposes of developing the Output Statement.
4.7
The benefit assessment for the test timetable scenario is presented in Chapter 14.
The Output Statement metrics are presented in Chapter 15.
Details of the test timetable scenario
Train Services
4.8
The test scenario was based, as noted above, on specifying a level of service which
would be likely to go a significant way to meeting stakeholders’ broad aspirations.
In defining the scenario we were, however, conscious of the need to remain realistic
in terms of the improvements that could, potentially, be delivered.
4.9
For modelling purposes, it was also necessary to specify a detailed timetable (coded
into the MOIRA system). While it was therefore also necessary, given the
requirements of MOIRA, to make assumptions about the exact stations served, in
particular in Central Manchester, the benefit assessment is not dependent on the
exact details of this. The test scenario should, therefore, be interpreted in terms of
the level of service set out for each corridor to and from Central Manchester, with
through services to other corridors. Service frequency objectives were established
for suburban and long-distance services respectively, and target journey times for
key destinations were also established.
4.10
The test timetable scenario is shown schematically by corridor in Figure 4.1
(suburban services) and Figure 4.2 (long-distance services) below. Full details, with
through services between corridors, trains per hour and journey times to key
destinations, are shown in Appendix B.
Rolling Stock
4.11
In the test scenario rolling stock was assumed to be sufficient to match exogenous
demand growth and to ensure a lower level of crowding than in the Do Minimum
scenario, despite the additional demand generated by improved services. For all
groups of services this implied a significant increase in the available number of seats
in the AM peak (in some cases, this was a very substantial increase).
16
FIGURE 4.1 MANCHESTER HUB - TEST SCENARIO SUBURBAN SERVICE PATTERN
17
FIGURE 4.2 MANCHESTER HUB - TEST SCENARIO LONG DISTANCE SERVICE PATTERN
18
5
Geographical Zoning
5.1
In a modelling exercise of this nature, a key preparatory activity is the development
of geographical zoning schemes used in the modelling. It is essential to find a
balance between sufficient detail to capture the essence of the geographical
relationships within the area to be modelled without using excessive detail, which
adds to the complexity, timescales and feasibility of the modelling.
MOIRA OR23 Zones
5.2
In order to implement the modelling, the rail corridors and services had to be
represented by a number of zoning schemes. In each case, the schemes were
established to provide a lot of detail for the area in and around Manchester, with
decreasing disaggregation as the distance away from Manchester increased.
Different schemes were established for different purposes.
5.3
The most detailed zoning scheme was the set of stations and groups of stations used
by the MOIRA model used in the study. MOIRA version OR23, which is DfT’s version
for the north of England, was used, as its zoning system is ideal for focusing on
Manchester. This scheme has approximately 700 zones.
5.4
Conceptually, as MOIRA is a rail-focused model, its “zones” do not represent
geographical areas, but rather, groups of stations. However, it was necessary to
define such geographical areas in order to map the economic data (which as noted
above, was provided on an LSOA basis – see paragraph 3.6) onto the MOIRA zones.
5.5
To achieve this, based on the geographical location of Great Britain’s 2500 stations,
a set of “Voronois” were established, using GIS-based software2. These created
areas around each station, where the boundaries were situated so that all points
within the boundary were nearer to that station than to any other station. Where
stations were grouped within the MOIRA zone, the Voronois were then combined to
form the final geographical definition for each MOIRA zone.
5.6
This enabled each LSOA to be assigned to one MOIRA zone using GIS software (the
LSOAs being significantly smaller in extent than each MOIRA zone).
5.7
The MOIRA OR23 zones were used directly in modelling rail services within the
MOIRA model (Chapter 7 below) and also within the New Flows model (Chapter 9
below). They also formed the foundation for two less disaggregate zoning systems
used for the exogenous demand growth forecasts (using RIFF) and for the estimation
of Wider Economic Benefits (WEBs).
RIFF Macrozones
5.8
2
The RIFF model was used to forecast the growth in rail demand over time due to
exogenous factors (such as GDP, employment, etc.). Although a “Northern Case
A set of “Voronois”, named after their Russian inventor, defines the bounded areas around each of a
set of given points such that all other points within each boundary are nearer to the given point
defining the Voronoi than to any other given point.
19
Study” had previously been developed to cover the region, as part of the suite of
models focusing on each geographical region provided for the PDFC, it was felt
necessary to create a bespoke version for this study, which distinguished different
parts of, in particular, Greater Manchester, based on which rail corridor they were
situated in. The version created had 91 different zones, generally simple
combinations of the MOIRA OR23 zones.
5.9
The MOIRA OR23 and RIFF Macrozoning schemes, as mapped to geographical areas
using the Voronoi approach described above, are illustrated in Figure 5.1 below.
FIGURE 5.1 MANCHESTER HUB MOIRA OR23 AND RIFF-LITE ZONING SCHEMES
20
Wider Economic Benefit Zoning
5.10
For the purpose of assessing Wider Economic Benefits of rail service improvements
(see Chapter 12), it was necessary to develop a third zoning scheme, intermediate
between the MOIRA OR23 and RIFF-Lite schemes described above, as the raw MOIRA
data contains too many zones for practical WEBs calculation. The zones created are
based on the MOIRA system in Greater Manchester and on the RIFF zones outside
Greater Manchester.
5.11
This procedure resulted in approximately 190 zones across Great Britain. The WEBs
zoning scheme is illustrated in Figure 5.2 below.
FIGURE 5.2 WEB ZONING SCHEME
21
MOIRA Service Codes
5.12
In addition to the location- and area-based zoning systems described above, it was
also necessary to categorise the trains operated through the Manchester Hub, so
that they could be tied to the rail corridor through which they operated (trains that
passed through Manchester, emerging into a different corridor, were categorised
separately to the relevant corridor for each part of their journey). This
categorisation enabled the impact of crowding on trains in each corridor to be
identified (see Chapter 10).
5.13
The categorisation of trains into corridors was achieved by adapting the MOIRA
model’s service code system, which classifies trains into groups of services operated
by the Train Operating Companies (TOCs). Because the rail corridors for the study
were originally defined with reference to existing service patterns, it was necessary
only to modify a minority of train codings.
5.14
The effect of this recoding was that, on each corridor, all trains were grouped into
one or two service codes (for long-distance and suburban services respectively), in
all the timetables used in the study, thus allowing comparisons between different
timetables on a service code by service code basis, easily translated into corridor by
corridor terms.
22
6
Exogenous Demand Growth (with RIFF Model)
Approach
6.1
We used the RIFF-Lite forecasting model to model the effect of exogenous factors
and fares on future rail demand. The purpose of the model was to produce future
year growth indices by zone pair (station, or grouped station, origin and destination
pair) that could be applied to the base year matrix of rail demand used in this study
to produce future year base matrices for 2019/20, 2024/25 and 2029/30.
6.2
RIFF-Lite is a modelling application developed by Steer Davies Gleave for the rail
industry. RIFF-Lite provides a framework within which it is possible to forecast how
the demand for rail passenger journeys will change given changes in a number of
“demand drivers” that are known to influence rail demand. It uses an elasticity
based approach to model the growth in rail demand. We have used the PDFH 4.1
parameters for all demand drivers.
6.3
Rail demand is forecast on an origin/destination zone basis and further segmented
by flow category, ticket type, journey purpose and distance band. The factors may
be internal i.e. actions taken by train service operators, e.g. fares, or external, e.g.
economic, demographic or competitive factors.
6.4
We have created a customised Case Study model which has 91 zones covering the
whole of Great Britain. The zoning is such that the geographical focus is on the core
study area i.e. in and around Manchester with larger and more granular zones
moving away from the GMPTE area. This is illustrated in Figure 5.1 above.
Base Data
6.5
RIFF-Lite is underpinned by a base matrix of journeys and revenue data derived
from LENNON and is segmented by five ticket types: Full, Reduced, Seasons,
Reduced Walk-up, Reduced Advance and Miscellaneous. We have used the 2007/08
Lennon data for the base matrix.
6.6
Because non-geographical tickets (i.e. those not specifying a start and an end
station) are not included in the Lennon data used in the base matrix, we also
developed an “infill” matrix to supplement this to account for sales of PTE products
in the GMPTE area. Table 6.1 below shows the rail patronage and revenue
associated with GMPTE products and the percentage uplift of journeys and revenue
in GMPTE area due to these.
23
TABLE 6.1
JOURNEYS AND REVENUE FROM PTE PRODUCTS
In filled
Matrix
Uplift to
Base
demand
Full
Seasons
Reduced
Total
Journeys
(’000s)
178
3,882
256
4,316
Revenue
(£’000s)
260
5,109
316
5,686
Journeys
0.21%
2.96%
0.16%
1.2%
Revenue
0.03%
1.03%
0.02%
0.20%
Growth Assumptions
The main external demand drivers considered were GDP, employment, population,
car ownership, motoring cost, air passenger growth and bus fare and time. The
growth assumptions for the economic factors (GDP, employment and population) are
provided by Economic Study conducted by Experian. As previously described Trend
and Trend Plus economic scenarios were defined for which economic projections
were provided by Experian (as described at paragraph 3.1 above). The Experian
forecasts capture the forward view of economic performance current in Summer
2008. The two figures below (Figure 6.1 and Figure 6.2) illustrate how economic
growth forecasts for GDP per capita, employment and population from Experian
Economic study compare with those from forecasts from Tempro v5.4 at National
level and for Manchester area.
FIGURE 6.1 ECONOMIC FORECASTS TREND VS TEMPRO (NATIONAL)
National: Comparison Trend vs Tempro growth rates
3.00%
2.50%
2.00%
1.50%
1.00%
0.50%
30
29
20
28
20
27
20
26
20
25
20
24
20
23
20
22
20
21
20
20
20
19
Employment Trend
Population Trend
20
18
20
17
20
16
20
15
20
14
20
13
20
12
Employment Tempro
GDP Per capita tempro
20
11
20
10
20
09
20
08
20
-0.50%
20
07
0.00%
20
6.7
Population Tempro
GDP Per capita Trend
24
FIGURE 6.2 ECONOMIC FORECASTS TREND VS. TEMPRO (MANCHESTER)
Manchester: Comparison Trend vs Tempro growth Rates
3.50%
3.00%
2.50%
2.00%
1.50%
1.00%
0.50%
30
28
29
20
20
27
20
26
20
25
20
20
23
22
24
20
20
20
20
19
21
20
20
18
Employment Trend
GDP Tempro
Population Tempro
GDP Trend
The corresponding results for the Trend Plus economic scenario, for Manchester, are
shown in the chart below. The impact of Trend Plus, compared to the Trend
Scenario, is less significant, since the differences between the scenarios only affect
the North of England, with no changes in other regions.
FIGURE 6.3 ECONOMIC FORECASTS TREND PLUS VS TREND SCENARIOS
(MANCHESTER)
Comparison Growth Rates: Trend vs Trend Plus
6.00%
5.00%
4.00%
3.00%
2.00%
1.00%
Employment Trend
Employment Trend Plus
GVA Trend
GVA Trend Plus
Population Trend
Population Trend Plus
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
0.00%
2007
6.8
20
17
Employment Tempro
Population Trend
20
15
16
20
20
13
14
20
20
20
11
10
12
20
20
20
08
09
20
20
20
07
0.00%
25
6.9
In addition to these input growth rates, we have also applied a further demand
driver to account for the more rapid growth in PTE areas that has recently been
experienced, and which is not fully explained by the standard PDFH exogenous
elasticity parameters. This is consistent with Network Modelling Framework and
DfT’s forecasting approach for HLOS. Table 6.2 below provides the details of the
values assigned to this demand driver for the relevant PTEs for each year under
consideration.
TABLE 6.2
6.10
OVERLAY IN RIFF-LITE APPLIED TO PTE AREAS
GMPTE
Metro
Nexus
SYPTE
2007
3.66%
9.84%
10.83%
13.93%
2008
3.04%
7.39%
7.68%
10.40%
2009
2.12%
4.94%
3.91%
6.77%
2010
1.51%
1.83%
-0.20%
1.81%
2011
1.53%
1.84%
-0.18%
1.83%
2012
1.54%
1.85%
-0.16%
1.85%
2013
1.56%
1.86%
-0.14%
1.87%
2014
1.17%
1.39%
-0.11%
1.40%
2015
0.78%
0.93%
-0.07%
0.94%
2016
0.39%
0.46%
-0.04%
0.47%
2017
0.00%
0.00%
0.00%
0.00%
The following table lists the demand drivers included and the source for growth
assumptions for these.
26
TABLE 6.3
EXOGENOUS GROWTH DRIVERS - SOURCES AND ASSUMPTIONS
Demand Driver Name
Source / Assumption
Fare
RPI + 1%
GDP per Capita
Experian Economic Study
Employment
Population
Car Ownership
Tempro v5.4
Fuel Cost
WebTAG assumptions
Car Time
Network Modelling Framework assumptions derived from
DfT’s National Transport Model
Bus Cost
Bus Time
Overlay for PTE areas
DfT NMF and Appraisal for HLOS evidence pack
Outputs
The case study model forecasts from base year 2007/08 through to 2030/31.
6.11
Future year growth indices were produced for year 2019/20 and 2029/30 by ticket
type and Moira zone pair. The impact on demand by corridor to 2019/20 is shown in
Table 6.4 (separately for the Trend and Trend Plus economic scenarios). This is also
shown graphically below in Figure 14.3. Note that the data in this table does not
take account of the impact of any timetable change (i.e. it assumes the Do Minimum
timetable and rolling stock).
27
TABLE 6.4
EXOGENOUS GROWTH IMPACT BY CORRIDOR
Forecast exogenous growth of rail journeys, based on Trend and Trend Plus scenarios
Corr.
No.
Journeys
(m)
2007/08
Journeys
(m)
2019/20
Trend
Growth
Trend
Growth
2007/08
to
2019/20
(%)
Trend
CAGR
2007/08
to
2019/20
(%)
Journeys
(m)
2019/20
Trend
Plus
Growth
Trend
Plus
Growth
2007/08
to
2019/20
(%)
Trend
Plus
CAGR
2007/08
to
2019/20
(%)
3.5
4.8
38%
2.7%
5.5
58%
3.9%
16.0
22.2
39%
2.8%
23.5
47%
3.2%
1
Corridor
Southport via Wigan
2
Preston and the North via
Bolton
3
Blackburn
1.8
2.4
35%
2.5%
2.6
49%
3.4%
4
3.0
4.3
44%
3.1%
5.2
75%
4.8%
5
Yorkshire and the
Humber & the North East
via Leeds
17.1
22.4
31%
2.3%
27.9
64%
4.2%
6
Glossop / Hadfield
1.5
2.2
41%
2.9%
2.5
61%
4.1%
7
Marple / Romiley
1.3
2.0
49%
3.4%
2.2
70%
4.5%
8
Yorkshire & the East
Midlands via Sheffield
8.6
12.4
44%
3.1%
14.1
63%
4.2%
9
Buxton
1.9
2.9
49%
3.4%
3.3
68%
4.4%
39.1
55.3
41%
2.9%
57.7
48%
3.3%
10
London, Birmingham and
the South (via WCML)
11
Manchester Airport
2.3
3.5
49%
3.4%
3.6
58%
3.9%
12
2.1
2.9
38%
2.7%
3.4
63%
4.1%
13
Liverpool via Irlam
1.6
2.1
34%
2.5%
2.4
50%
3.4%
14
Liverpool / Chester via
Warrington
5.5
7.1
30%
2.2%
7.9
44%
3.1%
105.3
146.4
39%
2.8%
161.8
54%
3.6%
Total
28
7
Impact of Service Improvement on Demand (MOIRA)
7.1
Once the exogenous factors impacting rail growth have been estimated, the next
stage in the modelling of demand is to estimate the impact of the improvement in
passenger services represented by the test scenario, compared to the do minimum
scenario.
7.2
The main tool for this analysis is the rail industry’s MOIRA model (although, as
described below, for certain “new” flows and flows to Manchester Airport, a
different approach is used, and the MOIRA results are disregarded). MOIRA is a wellestablished and widely used tool within the industry. The primary purpose of which
is to estimate the impact of timetable changes on demand and revenue, and it is
therefore well suited to the required task.
7.3
The basic methodology within MOIRA is to compare the average “generalised
journey time” (actual journey time, with adjustments to allow for service interval
and connection penalties) for journeys between all origin-destination pairs in the
both do minimum and test scenario timetables. The change in the average GJT is
then converted, by use of pre-established elasticity parameters into uplifts to
demand resulting from introduction of the new timetable. As part of this
methodology, MOIRA also allocates passenger journeys to individual trains, and
hence to “service groups” (groupings of train services). The model produces outputs
both by origin-destination pair and by service group, allowing detailed comparisons
of the effects of timetable changes to be assessed.
7.4
MOIRA uses two main classes of data: rail timetables and a matrix of journeys and
revenues by origin-destination pair (also known as flows). In this study, the relevant
timetables for use in MOIRA were:
I
A historical rail timetable for May 2007 (required for calibration and
baselining);
I
The anticipated “do-minimum” scenario timetable, based on the recently
introduced December 2008 timetable; and
I
A representation of the “test” scenario service specification, coded into
MOIRA.
7.5
While both the May 2007 and December 2008 timetables already existed within
MOIRA, and needed only some recoding of trains into different service groups, the
test scenario needed to be coded manually into the system. This was done on the
basis of the service specification set out in Appendix B, on a “standard hour” basis
(i.e. the standard pattern repeated throughout the day). Since operating costs are
not considered in this study, there was no need to optimise the timetable to fit with
peak, off-peak and night patterns of service. Rolling stock was treated separately,
within the Crowding Model (see Chapter 10).
7.6
All three timetables were coded (or recoded) into a consistent set of service groups,
with one service group for all suburban services and one for all long-distance
services in each corridor, as noted at 5.12 above.
29
7.7
Coding the test scenario into MOIRA required more operational detail than set out in
the service specification (in particular, a routing through particular Manchester
stations), but as noted above, the choices used in the coding within MOIRA were not
intended to be other than a modelling construct, and do not significantly impact the
modelling results.
7.8
The second set of data used by MOIRA is a “base matrix” of annual journeys and
revenues by origin-destination pair (flow). This base matrix may be derived from
historical data (usually from the LENNON ticket data base) or it can be created by
RIFF or other forecasting models, to produce future-year base matrices.
7.9
In this study, base matrices were created for:
I
2007/8 (based on LENNON data supplemented by an “infill” for nongeographical GMPTE tickets); and
I
2019/20, 2024/25 and 2029/30 (based on future year forecasts from RIFF, the
New Flows model and the Airport Flows model).
7.10
The outputs from MOIRA were fed into other models within the modelling suite, in
particular, the Crowding and Appraisal Models. The MOIRA base matrix of journeys
and revenue (and the corresponding output matrix resulting from the timetable
change) were used as the raw material for the subsequent analysis. However, for
the origin-destination pairs covered by the New Flows and Airport Flows, the MOIRA
outputs were disregarded and replaced by the relevant outputs from those models.
7.11
MOIRA also outputs train loading and service group data, which was used by the
Crowding Model.
30
8
Airport Flows Model
Introduction
8.1
It was determined that standard tools for estimating numbers of rail journeys, such
as RIFF-Lite and MOIRA, were not appropriate for access to and from the Airport as
the Airport is not a standard destination, being neither a residential area nor a
place of work for many people travelling there. Therefore Airport traffic was
predicted to increase in line with air passenger forecasts from the Manchester
Airport Masterplan, taking account of changes in surface access mode share.
8.2
The Airport Flows model uses Revealed Preference analysis to estimate parameters
for different modes based on Civil Aviation Authority survey data of passengers using
Manchester Airport. This Revealed Preference analysis uses a logit model approach
which estimates parameters based on the mode shares in each zone.
8.3
These parameters were then applied using an incremental logit approach to
estimate new surface access mode shares after the rail schemes have been
implemented and, in turn, rail demand in future years, taking account of forecast
airport passenger growth.
Inputs
8.4
The following inputs were required for this model:
I
Civil Aviation Authority survey data for passengers using Manchester Airport;
I
Air passenger forecasts from the Manchester Airport Masterplan;
I
Fuel price growth assumptions;
I
Rail fare growth assumptions; and
I
Parking, fuel consumption and taxi tariff assumptions.
8.5
The Civil Aviation Authority survey data contained records of passenger origins in
terms of either the first half of postcodes or districts. In order to use this data it
was first mapped to MOIRA zones, weighted using population data.
8.6
Air passenger forecasts from the Manchester Airport Masterplan were required to
grow this survey data in future years. Forecasts were obtained for 2005, 2015 and
2030. Interpolation was used to find the growth from 2007 to the modelled years of
2019, 2024 and 2029.
8.7
Fuel price, rail fare, parking, fuel consumption and taxi tariff assumptions were all
used to estimate costs required in order to calibrate the mode choice model. Fuel
price growth was taken from RIFF-Lite and rail fares were assumed to grow at 1%
per annum in real terms.
31
Calibration
8.8
The data were then split out into four categories, which it was believed would have
different preferences when it came to mode choice. These categories were:
I
United Kingdom based business travellers;
I
United Kingdom based leisure travellers;
I
Overseas based business travellers; and
I
Overseas based leisure travellers.
8.9
For each of these a logit model was used to estimate parameters for four modes:
‘kiss and fly’, ‘park and fly’, rail and taxi. ‘Kiss and fly’ refers to those passengers
dropped off at the airport by car while ‘park and fly’ refers to passengers driving
themselves to the airport and parking in one of the nearby car parks.
8.10
A number of different formulations were tested for the logit model. These included
estimating the parameters for three of the four residence/journey purpose splits as
increments on the other one or estimating the parameters using separate models.
Although estimating the parameters for different increments is a more efficient use
of the data, in this case it was found to produce less statistically robust results.
However, this was not an issue as there was enough data to estimate separate
models successfully.
8.11
Other tests included estimating a single β parameter for each mode and allowing
the γ parameter to vary across modes. These also produced sensible results but the
log likelihood of these estimations was lower, indicating that they did not fit the
data as well.
8.12
The utility equations, which are measures of the relative attractiveness of each
mode, were as follows:
I
V Kiss and fly = MSC
Kiss and fly
I
V Park and fly = MSC
Park and fly
I
V Rail = β
I
V Taxi = MSC
Rail
GJT
Taxi
Rail
+β
+β
Car
+β
Car
+ γ Cost
Car
GJT
GJT
Taxi
Kiss and fly
GJT
Park and fly
+ γ Cost
Park and fly
Rail
+ γ Cost
Taxi
8.13
In these equations, MSC stands for mode specific constant and GJT stands for
generalised journey time. It is also important to note that the cost parameter for
each mode is estimated based on the group travel cost. This represents the fact that
when a group travels together they will only pay parking or taxi charges once per
group, while when travelling by rail a number of tickets will have to be purchased.
8.14
A mode specific constant cannot be estimated for all modes as these have to be
relative to a fixed point. Therefore it was decided prior to estimation that the mode
specific constant for rail would be fixed to 0 and all other modes would be relative
to this. This assumption does not affect the results the differences in mode specific
constants would remain the same no matter which one was fixed to 0.
32
8.15
In this model, β is the logit parameter on time (with units s-1) and γ is the
parameter on cost (with units £-1). These represent the sensitivity of users to each
of these variables and the larger they are in magnitude the more sensitive travellers
are to their effects.
8.16
By dividing β by γ, one can obtain an estimated value of time. We would expect this
value of time to differ between modes and as we would expect sensitivity to cost to
be the same regardless of the mode travelled this is represented by different values
of β for different modes.
8.17
In the final formulation we have estimated two different β values, one for highway
modes (‘kiss and ride’, ‘park and ride’ and taxi) and one for rail. It would be
expected that the rail value of time would be lower than that for the other modes,
due to reasons such as the ability to work at the same time as travelling by rail and
higher levels of comfort.
8.18
The parameter values estimated using these utility equations were as follows. All of
these values were statistically significant at the 95% confidence level.
TABLE 8.1
8.19
AIRPORT MODEL PARAMETER VALUES
‘Kiss and
fly’
‘Park and
fly’
Rail
Taxi
MSC (UK Leisure)
-1.950
2.170
1.630
MSC (Overseas Leisure)
-0.816
1.940
1.020
MSC (UK Business)
-3.670
2.050
1.500
MSC (Overseas Business)
-2.900
1.680
1.760
Beta (UK Leisure)
-0.023
-0.023
-0.010
-0.023
Beta (Overseas Leisure)
-0.019
-0.019
-0.005
-0.019
Beta (UK Business)
-0.024
-0.024
-0.012
-0.024
Beta (Overseas Business)
-0.026
-0.026
-0.010
-0.026
Gamma (UK Leisure)
-0.014
-0.014
-0.014
Gamma (Overseas Leisure)
-0.017
-0.017
-0.017
Gamma (UK Business)
-0.014
-0.014
-0.014
Gamma (Overseas Business)
-0.019
-0.019
-0.019
The mode specific constants above all have sensible values. One would expect ‘park
and fly’ to have the highest mode specific constant and ‘kiss and fly’ to have the
lowest because ‘park and fly’ is the easiest option without considering time or cost
components and ‘kiss and fly’ is the hardest.
33
8.20
One would also expect the parameters on time and cost (beta and gamma
respectively) to have a negative sign because, as they increase, the attractiveness
of an option decreases. As such the signs and relative magnitudes of all of the
parameter values are sensible.
Application
8.21
The estimated parameters were used to forecast new mode shares in an incremental
logit model where changes were made to the rail journey times based on the test
timetable.
8.22
An incremental logit approach has the advantage that it pivots from the existing
mode splits based on the change in utility so the results more closely match reality.
8.23
With the test timetable these resulted in an increase in the rail mode share from
8.9% to 10.1% of all journeys to and from the airport. Although this does not appear
to be a large change, it does represent a 13% increase in rail journeys and revenues.
8.24
The new mode shares were then applied to grown airport passenger demand figures
to estimate numbers of journeys to each destination in future years. This was
exogenous growth based on airport demand (and not specifically on the Trend or
Trend Plus economic scenarios). The following air passenger forecasts were used.
TABLE 8.2
AIR PASSENGER FORECASTS
Year
Passengers (millions)
2005
20
2015
30
2030
45
8.25
This growth in numbers of passengers was applied uniformly across all of the MOIRA
zones as we currently have no further information on which zones are likely to
increase numbers of air passengers faster or more slowly than others.
8.26
The revenue in future years was calculated by multiplying the demand by the
current fare grown at 1% per annum.
Outputs
8.27
Using the mode shares with the rail scheme and the grown demand, produces the
following totals of journeys and revenue across all MOIRA zones.
34
TABLE 8.3
AIRPORT MODEL SUMMARY OUTPUTS
Journeys
Revenue (£ 000s)
952,742
9,683
2019 (with scheme)
1,668,798
19,036
2024 (with scheme)
1,914,209
22,950
2029 (with scheme)
2,159,621
27.213
Current (no scheme)
The following chart shows the projected numbers of rail journeys with and without
the rail test timetable.
FIGURE 8.1 AIRPORT MODEL SUMMARY OUTPUTS
2500000
2000000
1500000
Journeys
8.28
1000000
500000
0
2005
2010
2015
2020
2025
2030
2035
Year
With scheme
Without scheme
35
9
“New Flows” Model
Objective
9.1
The purpose of developing the “New Flows” model was to estimate the demand in
new markets currently poorly served by rail (typically slow, infrequent services or
where there is an inconvenient interchange required), but which would, under the
proposed test timetable, have a significantly improved service.
9.2
For such flows, the Passenger Demand Forecasting Handbook (PDFH) method of
applying a GJT elasticity using MOIRA may not be appropriate, as there is no existing
market to be grown, and the GJT changes are too high for the elasticity values in
PDFH to be reliable.
9.3
We therefore decided to create a bespoke model to estimate demand for such
flows. The New Flows Model is based upon the principle of a “gravity model”. Such a
gravity model estimates passenger demand between localities based upon the
respective localities’ characteristics such as population, employment and GVA, as
well as the characteristics of the rail and road journeys made between the pair of
localities.
Selection of Flows
9.4
For the purposes of modelling new flows, we have defined them to be cross-corridor
flows which go through Manchester (as well as flows from the Burnley corridor,
which currently has poor connections to the city). The model was calibrated on all
flows meeting these criteria, but was only then applied to such flows meeting the
further criterion that rail generalised journey times increased by over 20% in the
test timetable scenario, compared to the do-minimum scenario.
Inputs to the Model
9.5
Key inputs to the model were:
I
Economic actual data for 2006 (GVA, population, employment);
I
Forecast economic data for 2019, 2024 and 2029;
I
Historical journey and revenue data for each in-scope rail flow (from LENNON,
with GMPTE infill);
I
Forecast rail journey and revenue data for 2019, 2024 and 2029 from the RIFF
model (used for rebasing future year forecasts in the do-minimum case);
I
Rail generalised journey times for the do minimum and test scenarios, from
MOIRA;
I
Car journey times (from drivetime software);
I
Rail yields (revenue per journey) from LENNON); and
I
Value of time parameters from WebTAG.
37
Specification and Calibration of the Model
9.6
The specification of our gravity model in its basic form is as follows:
β
γ
V = α ( X O X D GC δ CJT ε )
9.7
The independent explanatory variable X at the origin and destination represent a tobe-determined economic parameter for each location. The parameters β and γ
represent the elasticity of demand to the economic variable X at the origin and
destination respectively.
9.8
A further independent explanatory variable relating to the time and cost of a
journey between the two localities, captured by the Generalised Cost was also
included in the basic form of the gravity model. The parameter δ refers to the
elasticity of demand to Generalised Cost. The final explanatory variable is car
journey time (CJT), with the parameter ε representing the elasticity to car journey
time.
9.9
The gravity model was estimated by a non-linear least-squares estimation technique
using the statistical software package STATA. This modelling approach is
appropriate, in that it produces maximum-likelihood estimates of the parameters,
on the condition that the residual errors (the part of the variation in the observed
values not explained by the model) are independent and drawn from the same
normal distribution. This assumes that there is no significant heteroscedacity in
the data (variation of the errors dependent on the independent variables) - see
Figure 9.2 below.
9.10
The table below (Table 9.1) summarises the different models which were tested in
terms of the proposed drivers of journeys chosen at the origin and destination, and
the journey purposes we had chosen to model separately by proxy of ticket type.
38
TABLE 9.1
NEW FLOWS MODELS ESTIMATED
Model
Driver
Journey
Purpose
Car Journey Time
Model Specification
Goodness of
Fit
#
Origin
Destination
Ticket Type
Included (Yes/No)
R-squared
1
Total GVA
Total GVA
Full
No
VF = α(GVAO GVAD GC )
2
Total GVA
Total GVA
Full
Yes
VF = α(GVAO GVAD GC CJT )
3
Total GVA
Total GVA
Reduced
No
VR = α(GVAO GVAD GC )
4
Total GVA
Total GVA
Reduced
Yes
VR = α(GVAO GVAD GC CJT )
5
Total Population
Total Work Employment
Season
No
VS = α(POPO WRK_EMPD GC )
6
Total Population
Total Work Employment
Season
Yes
VS = α(POPO WRK_EMPD GC CJT )
7
GVA within 1500m
GVA within 1500m
Full
Yes
VF = α(GVAO1 GVAD1 GC CJT )
8
GVA within 1500m
GVA within 1500m
Reduced
Yes
VR = α(GVAO1 GVAD1 GC CJT )
9
Population within 1500m
Work Employment within
1500m
Season
Yes
VS = α(POPO1 WRK_EMPD1 GC CJT )
10
Residency Employment
within 1500m
1500m
Season
Yes
VS = α(RES_EMPO1 WRK_EMPD1 GC CJT )
11
Weighted Population within
10km
1500m
Season
Yes
VS = α(POPO2 WRK_EMPD1 GC CJT )
12
Total Population
Total Population
All
Yes
VTOT = α(POPO POPD GC CJT )
0.24
13
GVA within 1500m
GVA within 1500m
All
No
VTOT = α(GVAO1 GVAD1 GC)
β
γ
0.71
Chosen Model
GVA within 1500m
GVA within 1500m
All
Yes
VTOT = α(GVAO1 GVAD1 GC CJT )
β
γ
β
γ
δ
β
γ
δ
β
γ
δ
β
γ
δ
0.45
ε
0.52
0.42
ε
0.47
β
γ
δ
β
γ
δ
0.32
ε
0.34
β
γ
δ
ε
0.80
β
γ
δ
ε
0.78
β
γ
δ
β
γ
β
γ
β
ε
γ
δ
δ
δ
0.36
ε
0.37
ε
0.40
ε
δ
ε
0.79
39
9.11
The table below (Table 9.2) provides a glossary of terms which are used in Table
9.1.
TABLE 9.2
GLOSSARY OF TERMS
Term
Formula In
Model
Description
Car Journey Time
CJT
The average time taken in minutes to drive
between a pair of localities
Full Fare Demand
VF
Full Fare journeys between localities
Generalised Cost
GC
The cost in pounds associated with making a rail
journey between two localities
GVA within 1500m
GVA1
Gross Value Added (£m) within 1500m of the
station for a given locality
Reduced Fare
Demand
VR
Reduced Fare journeys between localities
Residency
Employment
within 1500m
RES_EMP1
Number of the employed who live within 1500m
of the station in the given locality
Season Demand
VS
Season ticket journeys between localities
Total Demand
VTOT
Sum of all ticket type-based journeys between
localities
Total GVA
GVA
Gross Value Added (£m) for a given locality
Total Work
Employment
WRK_EMP
Number of people who work at the given locality
Weighted
Population within
10km
POP2
Number of people living within 1500m plus a
third of those living within 1500m to 10km of the
station
Work Employment
within 1500m
WRK_EMP1
Number of people who work within 1500m of the
station in the given locality
9.12
In light of only two of the three ticket types being estimated satisfactorily (Full and
Reduced Fare), we have proposed an aggregated model to estimate the sum of all
journeys between two localities.
9.13
The chart below (Figure 9.1) illustrates the goodness of fit for our aggregated model
which uses GVA within 1500m at the origin and destination, as well as Generalised
Cost and Car Journey Time.
40
FIGURE 9.1 CHOSEN MODEL ALL TICKET TYPES WITH GVA WITHIN 1500M
9.14
The aggregated model appears to be suitable for the purposes of forecasting
demand for new flows given a high R-squared of 0.79.
9.15
The graphs below (Figure 9.2 and Figure 9.3)show the residual values (actual
journeys – estimated journeys) plotted against each of the four independent
variables, and also against actual journeys. These demonstrate a lack of
heteroscedacity in the data, justifying the use of the non-linear least squares
estimation approach. Note that the 45º lines on the residual vs. actual journeys
chart arises simply because neither the estimated nor the actual journeys can be
negative, so the residuals are bounded by the lines y = ±x.
FIGURE 9.2 RESIDUAL ERROS PLOTTED AGAINST INDEPENDENT VARIABLES
41
FIGURE 9.3 RESIDUAL ERRORS PLOTTED AGAINST ACTUAL JOURNEYS
Estimated Parameters
9.16
The table below (Table 9.3) shows the estimated parameters from the selected
gravity model.
TABLE 9.3
NEW FLOWS GRAVITY MODEL ESTIMATED PARAMETERS
Parameter
Coefficient
t stat
p-value
Lower
Confidence
Interval
Upper
Confidence
Interval
α
0.0000211
9.20
0.000
0.0000166
0.0000256
β
1.30
160.25
0.000
1.28
1.32
γ
1.63
155.05
0.000
1.61
1.65
δ
-3.35
-95.46
0.000
-3.42
-3.28
ε
2.20
73.88
0.000
2.14
2.26
9.17
The table shows that all of our parameters are strongly significant since the t-stats
are outside the ±1.96 range (5% significance level).
9.18
The elasticity of demand to the GVA within 1500m at the origin (β) is 1.30. This
value probably reflects the greater propensity to rail travel of the populations of
more economically active and prosperous regions. The elasticity of demand to the
GVA within 1500m at the destination (γ) is 1.63. This reflects the importance of the
level of economic activity at the journey destination in driving rail demand.
42
9.19
The elasticity of demand to generalised rail cost is -3.35, and to car journey time is
+2.20. This can be interpreted as saying that the ratio of rail journey times (the
largest component of generalised rail costs) to car journey times is highly significant
in predicting rail demand – roughly speaking, if the ratio of rail journey time to car
journey time decreases by 10% (with the average journey time remaining
unchanged), demand will rise by 20%. In addition, rail demand rises approximately
linearly with average journey time reductions (i.e. across for both modes
simultaneously). The overall impact of a rail journey time reduction is highly
significant, with elasticity greater than -3, assuming no change to car journey times.
9.20
This result is, of course, very different from the normally accepted values in the rail
industry’s PDFH3, which assumes an elasticity to generalised journey time of about 0.9. However, we should expect a higher value, since we are using generalised cost
rather than generalised journey time (if GJT is 67% of GC, then an elasticity of -0.9
to GJT corresponds to an elasticity of -1.2 to GC). More significantly, the New Flows
model calibration has focused exclusively on cross-Manchester journeys (or from
locations such as Burnley without direct services to the city), where convenient
direct services only benefit a few flows (Leeds-Liverpool being the most obvious
example). In this data set, there is a very high sensitivity to the quality of train
service between the journey origin and destination, which is intuitively plausible.
The high sensitivity to car journey times is also plausible (i.e. for journeys with low
car journey times compared to rail journey times, rail mode share is very low in
comparison to journeys with fast, direct rail links).
Application
9.21
The new flow journeys between localities was estimated for each forecast year
(2019, 2024 and 2029) using the aggregated New Flows Model. The New Flows Model
was only applied to journeys where the timetable improvements led to a significant
change in GJT (> 20% improvement).
9.22
Having analysed the results, we concluded that a number of adjustments to the raw
forecasts were required. Firstly, to avoid anomalous results on particular flows, we
applied the model in incremental form, increasing base year actual journeys by the
ratio of forecast journeys for the forecast year to the model’s predicted journeys
for the base year.
9.23
Secondly, since the model results were affected by changing values of time in future
years, which affected the rail generalised cost parameter, but not car journey
times, we felt it appropriate not to use the model to forecast forwards through time
at an aggregate level, but only to predict the relative changes in demand between
different pairs of origins and destinations, as well as the impact of the improved rail
services.
3
Passenger Demand Forecasting Handbook, version 4.1
43
9.24
Therefore, we calculated a factor to rebase the model’s forecast aggregate demand
across all calibrated flows in the do-minimum scenario to equal the corresponding
aggregate do-minimum forecast from the RIFF model. The resulting adjustment
factor was then applied individually to the forecasts for each flow in both the dominimum and test scenario cases.
9.25
Finally, the forecast new flow journeys between localities (which was estimated
independently of ticket type) was disaggregated by ticket type using the current
distribution of journeys between localities by ticket type.
9.26
The chart below (Figure 9.4) illustrates the change in forecast New Flows journeys
by forecast year under the do minimum and scheme scenarios.
FIGURE 9.4 CHANGE IN JOURNEYS UNDER DO MINIMUM AND TEST SCENARIOS
2.0
1.8
1.6
1.4
Journeys
1.2
1.0
0.8
0.6
0.4
0.2
0.0
2019 DoMin
2019 Scheme
2024 DoMin
Southport via Wigan
Blackburn
Marple / Romiley
Buxton
2024 Scheme
2029 DoMin
2029 Scheme
Glossop / Hadfield
Liverpool via Irlam
44
10
Crowding Model
10.1
Crowding modelling for the study was undertaken using a crowding model suite
specially tailored to produce the required outputs. The large geographic area under
consideration and the period over which the benefits were to be assessed meant
that careful thought had to be given to our modelling approach and assumptions.
Inputs
10.2
10.3
The crowding model required a variety of inputs. The key inputs are listed below:
I
Base, Do Minimum, and Test Scenario timetables;
I
Rolling stock for each timetable;
I
Capacity information for rolling stock;
I
Current demand (Loads input);
I
Growth forecasts by corridor, segregated by Long Distance and Suburban
services; and
I
Crowding penalties.
Note that the reason for the inclusion of the Base (December 2007) timetable in the
crowding model is to enable the calculation of demand uplifts on current demand to
unconstrained demand.
Approach
10.4
With a diverse geographic area and a large range of different types of train services
it was important to ensure that appropriate assumptions were applied to different
passenger segments while keeping modelling complexity at a level commensurate
with the resources available. It was also desirable to maintain a measure of
consistency with current DfT appraisal practice, in particular HLOS appraisal.
10.5
For HLOS appraisal work, Steer Davies Gleave has developed a crowding model
which incorporates the use of DfT crowding penalties. It was decided that this would
be an appropriate model to use and was duly modified to handle the scale of inputs
required.
10.6
Once crowding penalties are calculated using the DfT crowding penalty calculator
the crowding model uses conventional crowding curve methodology4 to un-constrain
demand and then re-constrain (without the need for iteration implicit in some
crowding penalty based approaches).
4
A crowding curve provides a mapping between “crowded” and “uncrowded” load factors
45
Scope of crowding modelling
10.7
For the purposes of the crowding modelling we considered trains arriving into
Manchester in the AM peak and leaving Manchester in the PM peak. The AM peak
period was defined as 07:00-10:00 and the PM peak period as 16:00-19:00. The
rationale was that this would capture the majority of crowding while maintaining a
manageable number of trains in the crowding model. For trains that met the criteria
the whole route of the train was included and therefore crowding dis-benefit was
included for the whole route of the train.
Loads Inputs
10.8
Detailed train leg-by-leg data was obtained from MOIRA for each of the three
timetables (Base, Do Minimum, Test Scenario). This included loads estimates based
on 07/08 demand levels. This provided a demand base which could then be grown
using growth rates applied within the model. The base loadings from MOIRA included
the ORCATS effect of changing the timetable but not timetable induced growth (GJT
effect).
Growth Inputs
10.9
Exogenous demand forecasts were developed on an O-D basis which was not directly
applicable to the crowding model which works on a leg-by-leg basis. However, by
utilising the service code representation of corridors in MOIRA it was possible to
calculate growth rates by corridor and service type (Long Distance or Suburban).
This was done by producing summary journey outputs from MOIRA based on future
year RIFF matrices. Timetable growth was calculated separately using further MOIRA
outputs and combined with the exogenous growth.
Rolling Stock and Seating Assumptions
10.10
For the Test Scenario the allocation of rolling stock was performed on an iterative
basis. Initially, for each train Start – End combination rolling stock was allocated as
in the Do Minimum i.e. the same rolling stock was used in the Test Scenario as in the
Do Minimum for the same or similar services. Once an initial run of the crowding
model had been performed rolling stock formations were lengthened (up to a limit
of 6 carriages in most cases) until all corridor-service type combinations displayed
an absolute crowding benefit over the Do Minimum (or at least trivially negative).
Rolling stock was also added to ensure that the increase in capacity matched any
increase in demand over the appraisal period i.e. in general demand should be
unsuppressed rather than suppressed.
10.11
The DfT crowding penalty calculator employs a methodology which enables the
calculation of different penalties for different rolling stock types. To utilise this
functionality it was necessary to know seat numbers and standing capacity for the
different rolling stock types. To populate this data set we used in-house knowledge
and, particularly for standing capacities, current assumptions in the NMF.
46
Crowding Penalty Parameters
10.12
10.13
As well as seating and capacity assumptions the other key parameters in the DfT
crowding penalty calculator were:
I
Journey purpose split;
I
Average trip length; and
I
London/Non-London penalties.
Journey purpose splits were calculated using London Travelcard area journey
purpose by ticket type splits5. Two splits were calculated, one for Long Distance
services and one for Suburban services. Average trip lengths were also calculated for
these two categories of service using MOIRA outputs. Calculating these two sets of
parameters enabled us to use two sets of crowding penalties, one for Suburban
passengers and the other for Long Distance passengers. This was important as these
two sets of passengers could be expected to have different levels of tolerance to
crowding.
Outputs
10.14
Two main sets of output were produced. Firstly, uplift or suppression factors to
apply to the “semi-unconstrained” demand forecasts and secondly crowding minute
benefits by corridor for the Test Scenario over the Do Minimum. Semi-unconstrained
demand is current constrained demand grown with an unconstrained growth rate. In
the Test Scenario we ensured that capacity grew at least as fast as semiunconstrained demand. In most cases capacity growth outstripped demand growth
and therefore the uplift or suppression factors were mostly positive i.e. we released
constrained demand in addition to demand growth.
10.15
The uplift/suppression factors for the Do Minimum and Test Scenario timetables and
rolling stock in 2019, are shown in the table below, by corridor and type of service,
for both the Trend and Trend Plus economic scenarios. Uplifts are generally greater
(or suppression factors lower) in the Test Timetable scenario due to greater
capacity; uplifts are generally lower (or suppression factors greater) in the Trend
Plus economic scenario, as demand is higher than in the Trend scenario, with the
same levels of capacity assumed.
5
Given the urban nature of the Manchester Hub area it was felt appropriate to use London crowding
penalties, the rationale being that the Manchester area is likely to have similar crowding conditions to
when the PDFH parameters (on which the DfT penalties are based) were calibrated in London.
47
TABLE 10.1 CROWDING UPLIFT / SUPPRESSION FACTORS (2019)
Crowding Uplift/Suppression Factors, 2019
Corridor
Service Type
Southport via Wigan
Trend Scenario
Trend Plus Scenario
Do Minimum
Test Timetable
Do Minimum
Test Timetable
Suburban
-1.2%
4.1%
-2.2%
4.0%
Southport via Wigan
Long Distance
-1.3%
1.1%
-1.9%
0.5%
Bolton
Suburban
1.6%
7.2%
0.8%
6.8%
Blackburn
Suburban
-1.9%
-0.1%
-3.0%
-0.6%
Long Distance
0.3%
0.7%
0.2%
0.5%
Suburban
4.4%
5.5%
3.3%
4.8%
Yorkshire and the Humber
& the North East via Leeds
Long Distance
-1.7%
1.8%
-3.1%
1.0%
Suburban
-1.8%
4.3%
-3.7%
4.0%
Glossop / Hadfield
Suburban
0.1%
0.1%
0.1%
0.1%
Marple / Romiley
Suburban
0.0%
0.2%
-0.2%
0.1%
Long Distance
-1.5%
0.5%
-2.3%
0.4%
Suburban
1.7%
2.0%
1.2%
1.9%
Buxton
Suburban
0.9%
6.3%
-1.1%
5.8%
Long Distance
-0.2%
0.4%
-0.5%
0.3%
Suburban
-0.8%
-0.3%
-1.1%
-0.7%
Manchester Airport
Long Distance
-3.0%
-0.3%
-3.4%
-0.4%
Manchester Airport
Suburban
0.2%
0.9%
0.2%
0.9%
Suburban
3.5%
1.8%
2.0%
1.8%
Liverpool via Irlam
Suburban
-6.2%
3.2%
-7.6%
3.0%
Warrington
Long Distance
0.2%
4.9%
-0.3%
4.7%
Warrington
Suburban
0.9%
1.1%
0.6%
1.1%
10.16
Secondly, the appraisal required the output of “crowding minutes”. Crowding
minutes represent the cost of crowding to passengers as an amount of time. For
example, if a person travels on a train that is 120% loaded for ten minutes that can
also be represented as a 12 minute journey on a 60% loaded (i.e. un-crowded) train.
In this example the cost of crowding to the passenger is equivalent to an additional
two minutes on their journey, hence crowding minutes. Crowding minutes by
corridor were fed into the benefits model where they could be given a monetary
value.
10.17
The resultant monetary crowding benefits, by corridor, are shown in Appendix Table
A1.1 (Trend economic scenario) and Appendix Table A1.2 (Trend Plus).
48
11
Transport Benefits Appraisal
Overview
11.1
As described in paragraph 2.2 the development of the approach for valuing the
transport benefits of the proposals has been undertaken following the Department
for Transport’s approach, as set out in WebTAG. A spreadsheet model was
constructed to process the outputs from the forecasting models described above and
produce discounted cashflows for the transport (and wider economic) benefit
streams.
11.2
The diagram below (Figure 11.1) illustrates the structure of the appraisal model. It
shows the process involved for a single corridor, with the results then collated for
all corridors in the final results table.
FIGURE 11.1 APPRAISAL STRUCTURE
Properties
Control
Info
Parameters
Calculations
Selecting Data
One corridor results
Organising Data
Outputs
Appraisal period
Discounting
Summary
One corridor results
Select Corridor
Result table
Inputs
2019 Moira
2019
2024 Moira
2024
2029 Moira
2029
Distribution
Profiles
Discounting
Totals
Total chart
Within corridor chart
To other corridor chart
To the airport chart
To the rest of Uk chart
Crowding
WEBs
WebTAG
Inputs
11.3
11.4
The model inputs were the outputs of the MOIRA model, the crowding model and
the wider economic benefits model, in each case disaggregated by the 14 corridors.
For the MOIRA and WEBs model outputs, corridor results were further disaggregated
by the type of journey in the corridor, in particular into:
I
Within the corridor and to/from central Manchester;
I
To other corridors;
I
To the airport; and
I
To/from the rest of UK.
The MOIRA outputs provided, for three modelling years, 2019/20, 2024/25 and
2029/30:
I
Journeys, passenger miles and revenues for the do-minimum and test scenario;
I
Average GJT minutes; and
I
Aggregate GJT improvement between the do minimum and test scenarios.
49
11.5
The Crowding Model provided, for the same modelling years, aggregate crowding
benefit minutes for existing and new users, applying the rule of a half.
11.6
The WEBs model provided aggregate benefits for 2029 due to:
I
Imperfect Competition;
I
Agglomeration; and
I
Labour Market Efficiencies.
Approach
11.7
The model outputs were processed by the appraisal model in order to convert single
year forecasts into profiles for the life of the assessment period. These were then
translated into a unifying unit, i.e. monetary value, with price base and value
adjustments made accordingly. Having produced the monetary cashflows for each
benefit stream, they were discounted in order to provide a Present Value in 2002
prices, in accordance with DfT WebTAG requirements.
11.8
The principal assumptions employed for the forecasting of the monetised benefits
were:
11.9
11.10
I
Allocation of benefits to journey purpose based upon correspondence with full,
season and reduced ticketing as determined in the National Rail Travel Survey
Final 2008 Report;
I
Interpolation of model outputs between the forecast years of 2019, 2024 and
2029, taking 2019 as the opening year and assuming no further growth beyond
2029;
I
A 60 year appraisal period from the opening year of 2019;
I
WebTAG values of time (Rail business, commuter and other) and value of time
growth;
I
WebTAG values for non user benefits (North West) due to transfer of trips from
road to rail;
I
HM Treasury discounting at 3.5% to 2037 and 3% from 2038 to 2078; and
I
Values deflated to 2002 prices.
The transport users benefits valued were:
I
Revenue increment;
I
Generalised journey time saving; and
I
Crowding time saving.
The non transport user benefits (based upon the forecast change in highwaykilometres due to transfer from road to rail) measured were:
I
Car externalities (congestion, infrastructure, Accident savings, local air quality,
noise and greenhouse gases); and
I
Indirect tax.
50
11.11
Wider Economic Benefits were calculated directly in the WEBs model (Chapter 12)
for 2029. WEBs for other years were then calculated pro-rata to the other benefits
described above, based on the relationship between WEBs and other benefits in
2029.
Outputs
11.12
Appendix A provides full results for each corridor. As an illustration of the results
Table 11.1 provides the outputs for flows between Central Manchester and the
Outer Manchester City Region (outside the M60) for the Bradford via Rochdale
corridor.
TABLE 11.1 EXAMPLE APPRAISAL MODEL OUTPUTS ONLY FOR ONE CORRIDOR /
GEOGRAPHY CLASSIFICATION
11.13
Benefit Type
£m PV
Revenue increment
£7.38
Journey time savings
£81.53
Crowding
£23.15
Non user benefits
£2.65
Wider economic benefits
£25.01
Total
£139.72
A summary of the benefits for all the corridors, and a discussion on the results is
provided in Chapter 14 of this report.
51
12
Wider Economic Benefits Appraisal
12.1
The calculation of wider economic benefits (WEBs) requires two types of inputs,
transport demand and revenue as well as economic output data. The calculation of
the WEBs for the Northern Way required varying inputs which were combined for the
calculation of the WEBs. These inputs then fed into a standard WEBs analysis.
Transport Data (demand and generalised costs)
12.2
The demand data for the WEBs calculation comes from two sources: MOIRA model
outputs for the do-minimum and do-something scenarios in 2029 and the 2006 travel
to work survey data. MOIRA data by origin-destination pair was extracted including
number of journeys, revenue and generalised journey time, all by ticket type. The
MOIRA data was then aggregated to a zoning system created for the WEBs analysis.
12.3
Once aggregated to the new zoning system, the MOIRA rail data was converted from
ticket type to journey purpose. The conversion was done using data from the
National Rail Travel Survey. Based on data in the NRTS report proportions of each
journey purpose (work, commuting and leisure) were calculated as a percentage of
each ticket type. Demand and weighted averages of revenue and generalised
journey times were then distributed. The final result was demand, revenue and
generalised journey time by origin-destination pair and journey purpose.
12.4
Travel to work survey data (including only the number of journeys) was also
aggregated to our zoning system. Travel to work survey data, however, was only in
the survey year (2006) and needed to be inverted to include return-trips and
annualised based on an assumed number of work days. The growth in the travel to
work journeys was based on growth in employment across all zones between 2006
and 2029. The growth in employment forecast was based on data from Experian.
The travel to work data was assumed not to change between the do-minimum and
do-something scenarios.
12.5
The final demand forecasts are based on a combination of the MOIRA and TTW data.
MOIRA data was used for all rail forecasts and TTW data for all non-rail forecasts.
MOIRA data is used for all rail demand, by purpose and TTW data for all non-rail
commuting demand. National Travel Survey data was then used for estimating nonrail/non-commuting demand. The result of this analysis and combination resulted in
a complete set on inter and intra-zonal demand by mode and journey purpose for all
WEB zones.
12.6
In addition to the demand, generalised costs were also calculated for each mode
and journey purpose. Generalised costs for rail are based on two strands of
information – revenue per passenger and generalised journey time. The generalised
journey time is used in conjunction with the value of time, which varies by trip
purpose, to calculate the monetary cost of the time used to travel. This cost is then
added to the revenue per passenger to result in the full cost to a passenger of a
single journey. This analysis was competed across all origin-destination pairs by
journey purpose in the WEBs analysis.
53
12.7
The generalised cost of car travel is based on DfT guidance. Based on a populationweighted centre for each zone, we calculated the distance and time taken to drive
to each of the other zones from each zone. This data was then used, in conjunction
with DfT guidance, to calculate the cost of driving from each zone to each other
zone as well as intra-zonal costs (fuel and wear and tear on vehicle). We also
assumed DfT standard growth in fuel economy as well and price changes. The time
taken to drive each trip was then used with the value of time (again, by purpose) to
derive a monetary cost of the time for each trip. This cost was then added to the
DfT value for fuel and maintenance costs on the car to result in the full cost of
driving, by purpose, for all origin destination pairs. Trips by other modes were
assumed to have a total cost double that of car trips.
Economic Data
12.8
The economic data was provided by the Experian Economic Model and by the ONS
Nomis system. The Experian data included gross value added and employment by
sector across all LSOA zones. This data was aggregated to the WEB zoning system
for the year 2029. The average GVA across all sectors by zone and total employment
by zone is then calculated.
12.9
Nomis data provided weekly 2008 earnings by NUTS1 zones. This data was then
aggregated to the WEB zoning system and deflated to 2002 prices and annualised
based on the standard DfT discount rate. These figures were then factored up to
estimated earnings in 2029 by looking at GVA growth between 2008 and 2029 from
data provided by Experian. The result is annual earnings in 2029 in 2002 prices by
WEB zone.
Agglomeration Elasticities
12.10
The evidence on the impact that increased accessibility has on productivity takes
the form of agglomeration elasticities that vary by economic sector (but not by
location). For each model zone the appropriate elasticity therefore depends on the
sectoral composition in that model zone. As per DfT advice we therefore calculate
the agglomeration elasticity for each zone as the employment weighted average of
the national elasticities.
Wider Economic Benefits Calculations
12.11
The economic and transport data outlined above feed into the WEBs calculation and
drive the overall benefits calculations for agglomeration and labour market impacts.
Agglomeration
12.12
Agglomeration benefits are driven by the increase in ‘effective density’ between the
do-minimum and do-something scenarios. The effective density of a zone is based
on the generalised cost of travel and the employment in the zone. The resulting
figure, the effective density, is a measure of employment density based on the cost
of transport. In the do-something scenario lower journey costs increase the
effective density.
54
12.13
The change in journey costs between the scenarios represents the change in journey
time and the change in the monetary cost of travel (if any) between the two
scenarios. When combined with the employment this results in the change in
effective density.
12.14
The agglomeration elasticity, or the relationship between the change in effective
density and GVA, is then applied to the effective density resulting in the percentage
change in GVA (change in effective density is raised to the exponent of the
elasticity). The percentage change in GVA is then used to calculate the added GVA
to the zone as a result of agglomeration.
12.15
For the purpose of this analysis the agglomeration benefits have been further
aggregated to a corridor system which is consistent with the traditional appraisal.
Here agglomeration benefits or dis-benefits are not presented by WEB zone, but by
corridor.
Labour Market Impacts
12.16
The driver of the labour market impacts is the change in the effective wages the
workers receive. With a decrease in commuting costs wages effectively increase and
this can bring more workers into the labour force.
12.17
The labour market calculations require that the change in commuting costs is
determined between all of the zones in the system. The impact the change in
commuting costs has is then compared to earnings in each zone. The result is the
percentage change in earnings as a result of the do-something scenario.
12.18
The labour market elasticity is then used to calculate what impact the change in
wages will have on labour market supply (the number of workers). This percentage
is then used to calculate the total number of additional workers that will enter the
labour force as a result of the do-something scenario. The number of workers is
then multiplied by average GVA per worker in each zone resulting in the added GVA
by zone for the labour market.
12.19
As with the agglomeration calculation, the benefits can then be summed across all
zones to result in the full labour market benefits of the scheme. For this analysis,
however, the benefits have been broken down by specific corridor. The
methodology for the aggregation from WEB zones to corridors is explained below.
Imperfect Competition
12.20
The impact that the scheme will have on competition is estimated to be 10% of the
business users’ benefits. We have calculated business users’ benefits as the change
in generalised costs multiplied by the total demand between the two scenarios. The
result is the total value of the benefit received from the do-something scenario.
This benefit is then divided by as per the ‘rule of half’.
55
Forecasting Other Years
12.21
The WEBs analysis was only conducted for the year 2029 for practical and time
constraint reasons. The agglomeration, labour market and imperfect competition
impacts are roughly proportional to the traditional appraisal over time. Therefore
the WEBs benefits assume a constant proportion of the traditional appraisal benefits
for years before and after 2029. This assumption overstates benefits post-2029 and
understates benefits pre-2029, but overall provides a reasonable forecast of the
overall WEBs for the appraisal period.
Aggregation to Corridors
12.22
The outputs for the agglomeration and labour market impacts were then aggregated
to the corridor system. For each of the corridors four different origin-destination
rules were applied. For each zonal origin-destination pair we derived a weighting
factor based on the demand present in the corridor as a proportion of the total
demand in the zoning system. This method was used the share out the benefits
assigned to each of the zones to the corridors according to the origin-destination
rules. The result is benefits by corridor and destination rather then by the model’s
zoning system.
Outputs
12.23
The tables below (Table 12.1 and Table 12.2) show the results of the wider
economic benefit assessment on a 60-year PV basis (2002 prices), by type of benefit.
Agglomeration benefits dominate, representing nearly 90% of the total WEBs.
Overall, WEBs represent 19% of total benefits in both the Trend and Trend Plus
Scenarios, which is within the typical range. WEBs are largest for flows between
different corridors (rather than flows within each corridor or to stations beyond the
corridors), reflecting the greater improvement in “effective density” on those types
of flows.
56
TABLE 12.1 WIDER ECONOMIC BENEFITS – TREND SCENARIO
Trend Scenario
Corridor
Agglomeration
Imperfect
Competition
Labour
Total
Agglomeration
Imperfect
Competition
Labour
87.9
8.9
0.0
96.8
91%
9%
0%
372.3
39.5
2.0
413.8
90%
10%
0%
85.0
6.1
0.0
91.0
93%
7%
0%
1
Southport via Wigan
2
Preston and the
North via Bolton
3
Blackburn
4
Bradford via
Rochdale
215.4
14.7
0.3
230.4
93%
6%
0%
5
Yorkshire and the
Humber & the North
East via Leeds
489.9
52.1
0.1
542.1
90%
10%
0%
6
Glossop / Hadfield
16.5
7.1
0.1
23.7
70%
30%
0%
7
Marple / Romiley
14.3
4.9
0.0
19.2
75%
25%
0%
8
Midlands via
Sheffield
324.1
26.3
0.0
350.4
93%
7%
0%
9
Buxton
31.0
12.5
0.5
44.0
70%
28%
1%
256.0
81.9
0.5
338.4
76%
24%
0%
10
London, Birmingham
and the South (via
WCML)
11
Manchester Airport
30.5
16.2
0.0
46.7
65%
35%
0%
12
Chester via
Northwich
25.7
10.2
0.0
35.9
72%
28%
0%
13
Liverpool via Irlam
2.2
1.6
0.0
3.8
58%
42%
0%
14
Liverpool / Chester
via Warrington
173.0
23.2
0.1
196.4
88%
12%
0%
2,123.7
305.1
3.7
2,432.5
87%
13%
0%
Total
57
TABLE 12.2 WIDER ECONOMIC BENEFITS - TREND PLUS SCENARIO
Trend Plus
Scenario
Corridor
Agglomeration
Imperfect
Competition
Labour
Total
Agglomeration
Imperfect
Competition
Labour
1
Southport via Wigan
115.6
8.8
0.0
124.5
93%
7%
0%
2
Preston and the
North via Bolton
441.9
39.4
2.2
483.5
91%
8%
0%
3
Blackburn
102.1
6.0
0.0
108.0
94%
6%
0%
4
Bradford via
Rochdale
295.7
14.6
0.4
310.8
95%
5%
0%
5
Yorkshire and the
Humber & the North
East via Leeds
718.3
51.9
0.2
770.4
93%
7%
0%
6
Glossop / Hadfield
23.1
7.0
0.2
30.3
76%
23%
1%
7
Marple / Romiley
22.9
4.9
0.0
27.7
82%
18%
0%
8
Midlands via
Sheffield
453.1
26.2
0.0
479.3
95%
5%
0%
9
Buxton
40.1
12.5
0.5
53.1
76%
23%
1%
274.4
81.4
0.5
356.3
77%
23%
0%
10
London, Birmingham
and the South (via
WCML)
11
Manchester Airport
40.4
16.2
0.0
56.6
71%
29%
0%
12
Chester via
Northwich
32.3
10.1
0.0
42.4
76%
24%
0%
13
Liverpool via Irlam
6.8
1.6
0.0
8.4
81%
19%
0%
14
Liverpool / Chester
via Warrington
213.2
23.1
0.1
236.4
90%
10%
0%
2,779.9
303.8
4.2
3,087.8
90%
10%
0%
Total
58
13
Freight Model
Introduction
13.1
The existing levels of freight utilisation by route are illustrated in the figure below.
They show that the main flows are inter-modal container traffic into the terminals
at Trafford Park and aggregates (limestone) from the Peak District. There are also
significant coal movements around Fiddlers Ferry Power station.
FIGURE 13.1 RAIL FREIGHT IN THE NORTH WEST RUS AREA
(Source: Network Rail, Northwest RUS)
13.2
For the purposes of this study the analysis has been focused upon the inter-modal
freight traffic and the potential of delivering additional freight paths to Trafford
Park.
13.3
The total volume of container traffic in the UK is increasing and rail is increasing its
modal share of this market. Existing services link the Channel Tunnel and the ports
of Southampton, Felixstowe, Tilbury, Purfleet, Teesside, Grangemouth and the Isle
of Grain with Trafford Park in Manchester. Trafford Park also receives domestic
inter-modal traffic.
13.4
It is forecast that this trend will continue with the greatest overall level of growth
anticipated to be for deep sea (intercontinental) inter-modal traffic, although
strong growth in domestic inter-modal business is also expected.
59
13.5
The ability to accommodate freight growth through the provision of additional
freight paths is challenging given the capacity constraints in and around the
Manchester Hub.
13.6
Given the forecast market for freight and the constraints on the network it is
reasonable to assume that any additional paths created will result in additional rail
freight tonnage.
Approach
13.7
In order to identify the economic benefit of generated freight paths a comparative
assessment was made for road and rail container freight between the ports of
Southampton and Felixstowe and Manchester. These flows are representative of the
current inter-modal flows to the Manchester area. There are aspirations to develop
trans-Pennine inter-modal traffic, for example from Teesport to the North West.
Previous Northern Way has demonstrated the market potential for this and shown
that the realisation of this market will be dependent upon timely development of
rail and port infrastructure, as well as developments in the supply chain. The
Northern Way continues to work with its partners, including the port industry and
Network Rail to realise these developments.
13.8
The most noticeable feature of distribution by road is its flexibility. One can treat
the pool of labour and vehicles as completely variable, augmenting the supply of
each with agency men and hired vehicles respectively when required. This is not
possible for rail freight.
13.9
The major cost elements can be categorised as follows:
I
Fixed costs (often called standing costs in distribution):
Vehicle depreciation;
Road fund licences;
Vehicle insurance;
Warehouse operating costs;
Head office costs, etc; and
I
Variable costs:
Labour;
Tyres;
Fuel;
Lubricating Oil; and
Maintenance.
60
13.10
Depreciation, licences, insurance and driver labour are time related, whereas fuel,
oil, maintenance and tyres are mileage related. To calculate the hourly charge,
annualised depreciation and other fixed costs are divided by the annual hours
worked by a vehicle (fleet average). To this is added the labour rate, which is the
company’s drivers wage bill, including overtime, premium payments, sickness and
holiday pay, NI contributions, pensions and other payroll costs, all divided by the
total number of driver hours worked over say, a year.
13.11
The model used to cost the operation charges a fixed amount per hour that the
hauliers’ assets are in use. The larger a distribution company becomes, and the
wider spread its activity base, the easier it becomes to accept work which consumes
a small part of the working day. A haulier can redirect drivers and vehicles into
other business. Provided overheads are kept low, this is a great source of
comparative advantage over smaller companies, typically with bigger unproductive
gaps in their schedules. This principle should translate into rail freight businesses.
13.12
The analysis is based on an average utilisation of 80 hours per week, which
compares well with most of the big distribution concerns. Most of the latter share
the same problem, i.e. reduced utilisation at night, which tends to cap overall
capacity utilisation. Some cannot access customers’ premises outwith “normal
working hours” and others are limited because even in the large distribution
centres, intake generally ceases in the late evening and out-loading will not
generally recommence until 0400 hours. Thus, the only activity open to most
businesses is trunking-type work, carried out with the largest vehicles, thereby
under utilising the fleet as a whole.
13.13
Having calculated the basic cost of operation an uplift for overheads and profit has
been applied. The forecast price per container was estimated at around £475 for a
round-trip between Southampton and Trafford Park and £530 for a comparable trip
between Felixstowe and Trafford Park.
13.14
For rail freight the forecast operating cost has been similarly estimated, taking
account of the fixed and variable costs.
13.15
The table below (Table 13.1) presents the comparable costs by mode. This
demonstrates the reasonableness of assuming that additional rail freight paths to
Trafford Park from the ports of Southampton and Felixstowe would be taken up.
TABLE 13.1 COMPARISON OF CONTAINER FREIGHT TRANSPORT COSTS
13.16
Origin
Destination
Cost per RAIL
container
Cost per ROAD
container
Southampton
Trafford Park
£388.16
£475.75
Felixstowe
Trafford Park
£482.86
£530.74
The quantification of the benefit of shifting freight from road to rail is based upon
the valuation of sensitive lorry miles (SLMs). The values are designed to reflect the
costs to society of HGVs on roads and the economic and environmental benefits to
be gained from shifting freight from road to rail (or water).
61
Valuation of SLMs
13.17
The valuation of SLMs has been undertaken on the basis of DfT guidance. The
assumptions employed are show in the following table.
TABLE 13.2 SLM INPUTS
Distance (miles)
SLM (£ per lorry)
Southampton to Trafford Park
224
106.6
Felixstowe to Trafford Park
239
125.2
13.18
The freight trains are assumed to carry 25 TEUs and the SLM values are grown over
time in line with DfT guidance.
13.19
The number of freight paths generated by investment in the Manchester Hub is
based upon the forecasts set out in the Freight Route Utilisation Strategy (March
2007). The document forecasts 30% growth to 2014/15. This would provide around
25 freight paths a day. For the purposes of the assessment it is assumed that the
growth in paths continues to 2030. This would deliver around 37 daily freight paths.
13.20
For the assessment on container freight is considered and only Trafford Park has
been considered as a freight destination. There may be alternative locations, but
for the purposes of this assessment the assumption is deemed to be reasonable and
to enable an indicative freight forecast to be produced.
13.21
The estimated value for SLMs for Manchester Hub is £612.2m PV for the 60 year
assessment period.
13.22
The SLM methodology that has been adopted captures the conventional economic
benefits associated with a transfer of freight from road to rail. It does not include
the wider economic benefits. Recent work supporting a number of rail gauge
enhancement schemes suggests that WEBs can add a significant further percentage
to the benefit stream.
62
14
Modelling Results for the Test Timetable Scenario
Exogenous Growth
Economic Model Results
14.1
The Experian GVA and Employment forecasts (see Figure 6.1, Figure 6.2 and Figure
6.3) can be mapped onto each of the Manchester Hub corridors (and also on Central
Manchester). The following charts show the overall growth forecast between 2007/8
and 2019/20 for GVA and Employment, for the Trend and Trend Plus economic
scenarios (and the corresponding cumulative average growth rates).
FIGURE 14.1 FORECAST GVA GROWTH, 2007/8 TO 2019/20,
FOR TREND AND TREND PLUS ECONOMIC SCENARIOS
Trend and Trend Plus GVA Percentage Growth By Corridor 2007/8 to 2019/20 (with CAGR figures shown)
70%
4.7%
60%
50%
4.2%
4.1%
4.4%
3.5%
40%
3.7%
2.8%
3.1%
2.7%
3.9%
4.0%
4.0%
2.9%
3.2%
2.8%
30%
20%
2.7%
2.2%
3.2%
2.8%
2.5%
2.3%
2.2%
2.7%
2.5%
2.4%
2.2%
2.1%
2.6%
2.2%
1.9%
10%
/C
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Trend
Trend Plus
63
FIGURE 14.2 FORECAST EMPLOYMENT GROWTH, 2007/8 TO 2019/20,
Trend and Trend Plus Employment Percentage Growth by Corridor 2007/8 to 2019/20 (with CAGR figures shown)
Percentage Growth in Employment (%)
30%
25%
1.9%
20%
1.8%
15%
1.4%
1.3%
1.1%
10%
1.0%
0.9%
0.5%
0.3%
W
C
M
L)
an
ch
es
te
rA
C
he
irp
or
st
er
t
vi
a
N
or
th
w
Li
Li
ic
ve
ve
h
rp
r
po
oo
ol
l/
vi
C
a
he
Irl
st
am
er
vi
a
W
ar
rin
gt
on
in
gh
am
an
d
Ea
st
th
e
&
Lo
nd
on
,
Bi
rm
Yo
rk
sh
ir e
0.3%
(v
ia
So
ut
h
M
th
e
id
l
an
ds
M
ar
pl
e
vi
a
/R
Sh
ef
fi
el
d
om
ile
y
ie
ld
/H
ad
f
Le
ed
s
G
lo
ss
op
Ea
st
vi
a
vi
a
Yo
rk
sh
ire
an
d
th
e
H
um
be
r&
Br
ad
fo
rd
0.3%
Bu
xt
on
0.3%
R
oc
hd
al
e
Bl
ac
kb
ur
n
C
en
tra
lM
an
ch
So
es
te
ut
Pr
r
hp
es
or
to
t
n
vi
an
a
d
W
th
ig
e
an
N
or
th
vi
a
Bo
lto
n
0.9%
0.6%
0.5%
0.4%
Trend
14.2
1.3%
0.6%
0.1%
0%
1.0%
0.6%
N
or
th
0.3%
0.3%
1.1%
0.6%
0.8%
M
0.7%
0.4%
0.7%
th
e
5%
0.9%
Trend Plus
The charts show that the Trend Plus scenario forecast has much stronger growth on
most corridors than does the Trend scenario.
Rail Demand growth
14.3
The corresponding demand growth for 2019/20, compared to 2007/8, by corridor,
was as shown in the chart below. Note that this excludes the impact due to the
implementation of the Test Timetable (which is considered below).
64
FIGURE 14.3 FORECAST PASSENGER JOURNEYS GROWTH, 2007/8 TO 2019/20,
Trend and Trend Plus Exogenous Journeys Percentage Growth by Corridor 2007/8 to 2019/20 (with CAGR figures shown)
90%
Percentage Change in Journeys
80%
70%
60%
4.6%
5.2%
4.8%
4.4%
4.2%
4.1%
50%
3.9%
3.3%
3.7%
3.4%
4.1%
3.7%
4.3%
4.2%
3.2%
40%
3.6%
3.5%
3.1%
30%
3.5%
20%
3.1%
2.8%
2.7%
2.6%
2.3%
3.4%
3.1%
3.0%
2.7%
2.3%
2.3%
3.1%
2.9%
2.7%
2.5%
2.8%
2.2%
10%
Trend
vi
a
al
To
t
st
e
rv
ia
W
ar
rin
gt
on
Ir l
am
wi
ch
ol
/C
he
st
e
rv
ia
N
te
r
C
he
Li
ve
rp
o
C
M
an
ch
es
M
or
th
Ai
rp
or
t
L)
n
W
ol
Li
ve
rp
o
Lo
nd
o
n,
Bi
rm
in
g
&
th
e
ha
m
an
d
th
Ea
st
M
e
id
la
n
So
u
ds
th
via
(v
ia
Sh
Bu
xt
o
ef
fie
ld
ile
y
ld
fie
/R
om
e
ar
pl
M
ds
ee
/H
ad
Hu
ll)
( to
wa
rd
s
)
rk
Yo
Sc
ot
la
nd
d
an
G
lo
ss
op
Yo
rk
sh
ire
wa
rd
(to
rk
Yo
N
or
th
of
Ea
st
of
L
s
&
be
r
H
um
e
th
an
d
Yo
rk
sh
ire
s,
Ty
ne
Le
th
Te
e
e
Br
n
es
to
Pr
d
an
ed
s
via
Ea
st
d
No
r th
or
ad
f
e
th
an
d
Le
ed
s
al
e
n
via
Ro
ch
d
lto
Bo
ig
a
via
W
ia
No
r th
tv
po
r
th
So
u
Bl
ac
kb
ur
n
n
0%
Trend Plus
14.4
The two tables below break down the growth by corridor into a number of “flow
geography types”, based on the origin and destination of each journey, for the
Trend and Trend Plus Scenarios, respectively.
14.5
It may be noticed that, for some sets of corridors in the tables below, growths to or
from Manchester Airport are identical. The reason for this is that the airport flows
model used CAA survey data in which respondents identified their home residence
only by the first part of the postcode, making it difficult to attribute precisely
journeys to the true rail station origin, especially where trip makers have
alternative options. Comparing CAA-derived distributions with the base year data,
which is derived from actual rail ticket usage data can give anomalous results. To
address this, we have therefore grouped some neighbouring corridors for the
purpose of calculating forecast journey growth to or from the airport.
65
TABLE 14.1 EXOGENOUS PASSENGER GROWTH BY CORRIDOR AND FLOW GEOGRAPHY TYPE - TREND SCENARIO
Corridor
Flows
to/from
Central
Manchester
from Inner
Manchester
City Region
Flows to/from
Central
Manchester
from Outer
Manchester
City Region
Flows
to/from
Central
Manchester
from Other
City Regions
Flows
to/from
Central
Manchester
from Long
Distance
stations
Other
intra
corridor
flows
Inter corridor
flows where
one or both
ends is in the
Manchester
City Region
Inter corridor
flows where
neither end
is in the
Manchester
City Region
Flows
to/from
Manchester
Airport
Total
47.7%
49.4%
35.7%
0.0%
27.8%
43.4%
29.2%
29.2%
38.1%
38.9%
1
Southport via Wigan
2
0.0%
46.3%
44.7%
38.2%
33.2%
29.8%
54.6%
31.1%
3
Blackburn
54.1%
43.2%
39.0%
0.0%
30.4%
29.3%
28.5%
31.1%
35.7%
4
46.3%
42.5%
40.7%
0.0%
30.8%
20.8%
54.0%
31.1%
44.9%
5
Yorkshire and the Humber & the North
East via Leeds
0.0%
45.1%
28.9%
29.5%
32.2%
25.8%
30.5%
31.1%
31.7%
5a
Leeds and York
0.0%
45.1%
29.3%
0.0%
33.3%
26.0%
26.7%
31.1%
31.1%
5b
North of York (towards Tees, Tyne and
Scotland)
0.0%
0.0%
26.3%
29.5%
30.7%
22.3%
32.5%
31.1%
31.4%
5c
East of Leeds (towards Hull)
0.0%
0.0%
28.7%
0.0%
22.0%
29.5%
40.4%
31.1%
37.5%
6
Glossop / Hadfield
48.4%
44.7%
0.0%
0.0%
27.2%
32.6%
0.0%
45.9%
41.9%
7
Marple / Romiley
47.7%
47.5%
35.0%
0.0%
43.7%
47.5%
58.0%
45.9%
51.4%
8
Yorkshire & the East Midlands via
Sheffield
48.8%
0.0%
40.8%
33.3%
47.9%
39.4%
44.1%
45.9%
43.8%
Buxton
56.8%
51.2%
0.0%
0.0%
31.2%
37.6%
0.0%
45.9%
49.7%
0.0%
46.7%
0.0%
52.2%
40.4%
41.8%
37.5%
-4.5%
41.2%
9
10
London, Birmingham and the South
(via WCML)
11
Manchester Airport
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
44.6%
44.6%
12
0.0%
58.0%
37.8%
0.0%
26.3%
32.8%
24.6%
34.6%
38.1%
13
Liverpool via Irlam
36.1%
35.6%
34.0%
0.0%
31.2%
34.5%
29.4%
19.5%
35.2%
14
46.0%
39.9%
34.7%
29.9%
23.8%
22.4%
31.5%
19.5%
29.2%
Total
52.3%
46.5%
36.3%
48.8%
37.3%
35.8%
37.9%
36.8%
39.0%
66
TABLE 14.2 EXOGENOUS PASSENGER GROWTH BY CORRIDOR AND FLOW GEOGRAPHY TYPE - TREND PLUS SCENARIO
Corridor
Flows
to/from
Central
Manchester
from Inner
Manchester
City Region
Flows to/from
Central
Manchester
from Outer
Manchester
City Region
Flows
to/from
Central
Manchester
from Other
City Regions
Flows
to/from
Central
Manchester
from Long
Distance
stations
Other
intra
corridor
flows
Inter corridor
flows where
one or both
ends is in the
Manchester
City Region
Inter corridor
flows where
neither end
is in the
Manchester
City Region
Flows
to/from
Manchester
Airport
Total
67.1%
69.2%
53.5%
0.0%
42.7%
79.8%
42.0%
31.1%
57.7%
46.6%
1
Southport via Wigan
2
0.0%
65.2%
65.0%
46.6%
36.0%
51.8%
63.7%
33.5%
3
Blackburn
73.0%
60.9%
52.8%
0.0%
42.6%
45.3%
42.9%
33.5%
49.7%
4
66.4%
62.2%
78.2%
0.0%
58.1%
46.5%
97.6%
32.9%
75.4%
5
Yorkshire and the Humber & the
North East via Leeds
0.0%
62.5%
57.2%
51.2%
56.0%
56.4%
75.2%
32.9%
63.8%
5a
Leeds and York
0.0%
62.5%
58.5%
0.0%
60.0%
58.0%
80.6%
32.9%
65.2%
5b
North of York (towards Tees, Tyne
and Scotland)
0.0%
0.0%
44.9%
51.2%
46.5%
39.6%
60.7%
32.9%
54.6%
5c
0.0%
0.0%
59.0%
0.0%
45.7%
64.6%
97.1%
32.9%
84.8%
6
Glossop / Hadfield
70.2%
64.3%
0.0%
0.0%
45.6%
56.2%
0.0%
48.3%
61.5%
7
Marple / Romiley
68.5%
67.9%
57.0%
0.0%
64.7%
71.8%
77.0%
48.3%
72.1%
8
Sheffield
69.4%
0.0%
64.7%
46.6%
62.9%
57.1%
66.4%
48.3%
63.0%
Buxton
75.3%
68.3%
0.0%
0.0%
49.2%
61.7%
0.0%
48.3%
68.4%
0.0%
67.1%
0.0%
81.9%
43.1%
61.2%
38.0%
2.8%
47.4%
9
10
London, Birmingham and the South
(via WCML)
11
Manchester Airport
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
54.6%
54.6%
12
0.0%
93.9%
57.9%
0.0%
44.2%
58.6%
42.2%
41.6%
62.5%
13
Liverpool via Irlam
52.0%
52.3%
52.4%
0.0%
48.0%
57.4%
41.7%
23.2%
51.1%
14
64.9%
58.6%
52.9%
41.4%
32.9%
34.2%
48.0%
23.2%
43.6%
Total
71.4%
66.6%
58.9%
74.7%
44.6%
60.0%
58.9%
43.5%
53.6%
67
Forecast Passenger Demand
14.6
Rail passenger demand in the Manchester Hub corridors is forecast to rise from 105
million journeys in 2007/8 to 156 million in 2019/20, 178 million in 2024/5 and 202
million in 2029 under the Trend Scenario, assuming that the Test Timetable Scenario
is implemented. Under the Trend Plus scenario, the corresponding forecasts are 174
million in 2019/20, 201 million in 2024/5 and 231 million in 2029/30.
14.7
This is illustrated in the chart below (Figure 14.4), where demand is shown for each
of these forecast years (and the base year), separately for the Do Minimum (no
timetable change) and Test scenario timetable, as well as for both the Trend and
Trend Plus economic scenarios.
FIGURE 14.4 MANCHESTER HUB PASSENGER JOURNEY FORECASTS BY CORRIDOR
Trend & Trend Plus Scenario Journey Growth Under DoMin and Test Timetable Changes
250
Passenger Journeys (Millions)
200
150
100
50
0
Base
Trend
Trend
Plus
Trend
DoMin
2007/8
14.8
Trend
Plus
Trend
Test
Trend
Plus
Trend
DoMin
2019/20
Trend
Plus
Test
Trend
Trend
Plus
Trend
DoMin
2024/25
Trend
Plus
Test
2029/30
Central Manchester
Southport via Wigan
Blackburn
Glossop / Hadfield
Marple / Romiley
Buxton
Manchester Airport
Liverpool via Irlam
The following chart shows the changes in journeys in each corridor, in each of the
forecast years, as a result of the introduction of the Test Scenario timetable
change.
68
FIGURE 14.5 MANCHESTER HUB PASSENGER JOURNEY FORECASTS BY CORRIDOR
Trend & Trend Plus Scenario Journeys Changes due to Test Timetable Change
30
25
Passenger Journeys (Millions)
20
15
10
5
Trend
Trend Plus
2019/20
Central Manchester
Blackburn
Glossop / Hadfield
Buxton
14.9
Trend
Trend Plus
2024/25
Southport via Wigan
Marple / Romiley
Liverpool via Irlam
Trend
Trend Plus
2029/30
Manchester Airport
The impact of the Test Timetable (and associated rolling stock assumptions) on
demand, compared with the Do Minimum case, is shown in Table 14.3 below for
2019/20, for both the Trend and Trend Plus Economic Scenarios.
69
TABLE 14.3 IMPACT OF TEST TIMETABLE AND ROLLING STOCK ON DEMAND
(2019/20)
Comparison of demand forecasts for 2019/20 for Do Minimum and Test Timetables, under Trend and Trend Plus
Journeys (m)
Journeys (m)
2019/20
2019/20
Journeys
Journeys
(m) 2019/20
(m) 2019/20
Trend
Trend Plus
Trend
Growth
Trend Plus
Growth
Test
Test
Change
Change
Growth
Growth
Timetable
% Do Minimum
Timetable
%
Corridor
Do Minimum
Southport
via
Wigan
1
4.8
5.3
9.6%
5.5
6.0
10.1%
2
Bolton
3
22.2
24.1
8.2%
23.5
25.6
8.8%
Blackburn
2.4
2.7
13.7%
2.6
3.0
14.0%
4
4.3
5.0
14.9%
5.2
6.0
14.7%
5
22.4
24.4
8.9%
27.9
30.4
8.8%
6
Glossop / Hadfield
2.2
2.6
20.6%
2.5
3.0
20.7%
7
Marple / Romiley
2.0
2.2
12.1%
2.2
2.5
12.3%
8
12.4
13.1
5.7%
14.1
14.9
5.7%
9
Buxton
2.9
3.8
32.0%
3.3
4.3
33.4%
55.3
56.9
3.0%
57.7
59.5
3.2%
10
11
Manchester Airport
3.5
3.9
11.7%
3.6
4.1
11.7%
12
2.9
3.3
14.8%
3.4
3.9
16.2%
13
Liverpool via Irlam
2.1
2.3
10.1%
2.4
2.6
10.8%
14
Warrington
7.1
8.0
12.4%
7.9
9.0
13.3%
146.4
157.5
7.6%
161.8
174.8
8.1%
Total
Metrics by Corridor
14.10
The appraisal in this study is, as already noted, unusual, in that no formal scheme is
actually being appraised (and there are, therefore, no costs associated with any
benefits calculated). We have therefore developed a “Test Scenario” timetable,
which captures the broad aspirations of stakeholder, and hence allowing the value
of such aspirations to be tested. However, the exact form of any scheme brought
forward to improve the Hub is as yet unknown, and is unlikely to match the
improvements in the Test Scenario.
14.11
We have therefore developed a set of metrics for the Output Statement, which form
the formal input to Phase 2 of the Manchester Hub study, being taken forward by
Network Rail. These metrics are based on defining appropriate benefit per unit
improvement measures.
70
14.12
The benefits relating to improved passenger services estimated in this study were:
I
Revenue increment;
I
Journey time improvements;
I
Crowding;
I
Non-user benefits; and
I
Wider Economic Benefits.
14.13
For freight services, the benefits were the monetary value of Sensitive Lorry Miles
saved over the appraisal period.
14.14
All passenger service improvement benefits, with the exception of crowding, were
driven by timetable enhancements (faster journey times, higher service frequency
or reduced interchange), so it makes sense to measure the value of benefits against
a measure of the improvement in generalised journey time (which brings together
actual journey time with additional elements for service interval and interchange
penalties). We have therefore assessed these benefit categories against the change
in GJT between the Do Minimum and Test timetable cases.
14.15
The average GJT in the Do Minimum and Test Timetables, and the corresponding
changes and % changes (for journeys within each corridor, including to or from
Central Manchester) are as shown in Table 14.4 below. The split between the
different elements of GJT causing this change (in-vehicle time, service interval and
interchanges) are shown in percentage terms in Table 14.3.
TABLE 14.4 GJT IMPROVEMENT BY CORRIDOR
Corridor
Average GJT
within corridor
(Do
Minimum)
Average GJT
within corridor
(Test
Timetable)
GJT change
(within-corridor
journeys,
minutes)
GJT % change
(within-corridor
journeys)
1
Southport via Wigan
55.00
48.62
-6.37
-12%
2
Preston and the
North via Bolton
66.25
60.72
-5.52
-8%
3
Blackburn
65.59
56.98
-8.61
-13%
4
Bradford via
Rochdale
45.47
37.58
-7.89
-17%
5
Yorkshire and the
Humber & the North
East via Leeds
55.54
50.01
-5.53
-10%
6
Glossop / Hadfield
47.83
37.91
-9.92
-21%
7
Marple / Romiley
48.22
41.81
-6.41
-13%
8
Midlands via
Sheffield
64.15
58.38
-5.77
-9%
71
Buxton
44.99
34.58
-10.41
-23%
10
London, Birmingham
and the South (via
WCML)
74.30
71.47
-2.83
-4%
11
Manchester Airport
36.09
31.08
-5.01
-14%
12
Chester via
Northwich
71.75
54.58
-17.17
-24%
13
Liverpool via Irlam
50.25
48.92
-1.33
-3%
14
Liverpool / Chester
via Warrington
63.17
54.96
-8.21
-13%
Total
65.36
59.81
-5.55
-8%
9
TABLE 14.5 GJT CHANGE SPLITS
Generalised Journey Time Change
Splits
Corridor
In-vehicle
Service
Interval
Interchange
1
Southport via Wigan
69%
31%
0%
2
Preston and the
North via Bolton
78%
20%
2%
3
Blackburn
101%
-1%
0%
4
Bradford via
Rochdale
90%
10%
0%
5
Yorkshire and the
Humber & the North
East via Leeds
76%
24%
0%
6
Glossop / Hadfield
84%
16%
0%
7
Marple / Romiley
98%
2%
-1%
8
Midlands via
Sheffield
39%
60%
1%
9
Buxton
69%
31%
0%
110%
-11%
2%
10
London, Birmingham
and the South (via
WCML)
11
Manchester Airport
63%
37%
0%
12
Chester via
Northwich
34%
72%
-5%
13
Liverpool via Irlam
227%
45%
-172%
14
Liverpool / Chester
via Warrington
91%
20%
-10%
Total
85%
16%
-1%
72
14.16
In respect of Crowding, we have assumed that, in the Do Minimum case, additional
vehicles are provided as per the HLOS (and TIF) assumptions. For the Test timetable
scenario, we have assumed that additional train services have at least as many
vehicles as corresponding services in the Do Minimum scenario, and, where
significant crowding was identified, we further increased the number of vehicles, so
that there was no significant crowding in the Test scenario (for the Trend Economic
Scenario).
14.17
We have quantified the additional capacity in terms of additional seats provided in
the AM peak, roughly corresponding to a requirement for additional vehicles (though
of course some vehicles may be able to be used more than once during the three
hour peak). The first table below (Table 14.6) shows the incremental AM peak seats
in each corridor, broken down by suburban and long-distance services, while the
second table (Table 14.7) shows incremental seats in the High AM Peak (0800-0900).
73
TABLE 14.6 INCREMENTAL SEATS IN AM PEAK BY CORRIDOR
Incremental AM Peak Seats
Corridor
Suburban
Services
Longdistance
Services
All
Services
1
Southport via Wigan
4,292
0
4,292
2
2,188
992
3,180
3
Blackburn
894
0
894
4
430
1,808
2,238
5
3,736
3,094
6,830
6
Glossop / Hadfield
2,840
0
2,840
7
Marple / Romiley
1,062
0
1,062
8
1,548
498
2,046
9
Buxton
2,100
0
2,100
10
852
3,320
4,172
11
Manchester Airport
3,590
1,306
4,896
12
1,341
0
1,341
13
Liverpool via Irlam
3,367
0
3,367
14
576
2,366
2,942
28,816
13,384
42,200
Longdistance
Services
All
Services
Total
TABLE 14.7 INCREMENTAL SEATS IN HIGH AM PEAK BY CORRIDOR
Incremental AM HIGH Peak Seats
Corridor
1
Southport via Wigan
1,201
0
1,201
2
810
552
1,362
3
Blackburn
149
0
149
4
20
452
472
5
1,937
883
2,820
6
Glossop / Hadfield
0
0
0
7
Marple / Romiley
354
0
354
8
466
359
825
9
Buxton
10
11
Manchester Airport
12
13
Liverpool via Irlam
14
Total
14.18
Suburban
Services
1,200
0
1,200
284
1,156
1,440
-451
878
427
447
0
447
1,097
0
1,097
92
519
611
7,606
4,799
12,405
Freight benefits have been estimated by assuming that additional freight paths will
be identified, and used, by inter-modal freight services to Trafford Park. The
estimated benefits have therefore been compared against the incremental freight
paths we assume will be available, i.e. an additional 15 paths per day.
74
Appraisal Results
14.19
As noted in paragraph 11.8, we have undertaken a standard 60-year appraisal of the
Test scenario train service, compared to the Do Minimum case, assuming that the
Test scenario timetable and associated additional rolling stock are in place from
2019. The total benefits of the test scenario £12,735 million under the Trend
Economic Scenario and £16,168 million under the Trend Plus Economic Scenario
(both numbers are 60 year PV, 2002 prices). The results are shown for each corridor
in Table 14.5 below.
14.20
More detailed results are presented in Appendix A.
TABLE 14.8 SUMMARY OF BENEFITS DUE TO IMPROVEMENT IN PASSENGER RAIL
SERVICES
Scenario Benefits £m PV
Corridor
Trend Plus
Change
431
556
125
%
Change
29%
1
Southport via Wigan
2
1,719
2,011
292
17%
3
Blackburn
288
356
67
23%
4
636
841
206
32%
5
Yorkshire & the North East via Leeds
2,435
3,342
908
37%
5a
Leeds and York
1,566
2,152
586
37%
5b
North of York (towards Tees, Tyne and Scotland)
562
739
177
32%
5c
307
452
144
47%
6
Glossop / Hadfield
190
230
40
21%
7
Marple / Romiley
148
184
36
24%
8
1,374
1,766
392
29%
9
Buxton
395
496
102
26%
2,882
3,550
668
23%
Manchester Airport
362
395
33
9%
12
380
502
122
32%
13
Liverpool via Irlam
274
367
93
34%
14
878
1,117
239
27%
10
11
Other trips
Total
14.21
Trend
329
439
110
33%
12,721
16,152
3,432
27%
The two tables below (Tables 14.6 and 14.7) show the aggregate benefits across all
corridors split by benefit type and the flow geography. Note that Crowding benefits
have been assumed to arise on journeys within corridors only, and Wider Economic
Benefits have not been calculated for flows outside the Manchester Hub corridor
routes.
75
TABLE 14.9 TOTAL BENEFITS BY BENEFIT AND GEOGRAPHY TYPE - TREND SCENARIO
Trend Scenario
Benefit
£ million NPV
Revenue increment
Flows
to/from
Central
Manchester
from Inner
Manchester
City Region
Flows
to/from
Central
Manchester
from Outer
Manchester
City Region
Flows
to/from
Central
Manchester
from Other
City
Regions
Flows
to/from
Central
Manchester
from Long
Distance
stations
Other
intra
corridor
flows
Inter
corridor
flows
where one
or both
ends is in
the
Manchester
City Region
Inter
corridor
flows
where
neither end
is in the
Manchester
City Region
Flows
to/from
Manchester
Airport
from
Manchester
City Region
Flows
to/from
Manchester
Airport
from
outside
Manchester
City Region
Flows to
all other
station
Total
2
85
241
352
335
1
187
8
86
107
1,403
23
930
1,124
661
1,075
254
687
115
438
809
6,116
Crowding
0
2,225
0
0
0
0
0
79
0
0
2,304
Non user Benefits
Wider Economic
Benefits
Total
1
34
119
185
47
8
41
2
5
24
465
1
390
281
105
170
186
1,211
33
55
0
2,433
27
3,664
1,765
1,302
1,627
450
2,125
238
583
940
12,721
76
TABLE 14.10 TOTAL BENEFITS BY BENEFIT AND GEOGRAPHY TYPE - TREND PLUS SCENARIO
Trend Scenario
Benefit
£ million NPV
Revenue increment
Flows
to/from
Central
Manchester
from Inner
Manchester
City Region
Flows
to/from
Central
Manchester
from Outer
Manchester
City Region
Flows
to/from
Central
Manchester
from Other
City
Regions
Flows
to/from
Central
Manchester
from Long
Distance
stations
Other
intra
corridor
flows
Inter
corridor
flows
where one
or both
ends is in
the
Manchester
City Region
Inter
corridor
flows
where
neither end
is in the
Manchester
City Region
Flows
to/from
Manchester
Airport
from
Manchester
City Region
Flows
to/from
Manchester
Airport
from
outside
Manchester
City Region
Flows to
all other
station
Total
3
101
294
460
406
43
181
9
86
138
1,722
28
1,108
1,356
839
1,252
327
1,013
126
441
1,002
7,492
Crowding
0
3,195
0
0
0
0
0
93
0
0
3,288
Non user Benefits
Wider Economic
Benefits
Total
1
40
144
197
50
20
73
3
5
30
563
2
471
350
111
189
248
1,609
41
67
0
3,088
33
4,915
2,145
1,607
1,896
638
2,876
272
599
1,170
16,152
77
14.22
The benefits for the Trend Scenario are illustrated in the following charts (in terms
of a 60-year PV in £m at 2002 prices). Figure 14.6 shows total benefits by corridor,
split by journey type within corridor (i.e. “within corridor”, including journeys to or
from central Manchester, “to the airport”, “to other corridors” and “to or from the
rest of the UK”). Benefits by category are shown in Figure 14.7. These are:
I
Revenue increment;
I
Journey time improvements;
I
Crowding;
I
Non-user benefits; and
I
Wider Economic Benefits.
FIGURE 14.6 TREND SCENARIO BENEFITS BY CORRIDOR AND FLOW GEOGRAPHY
Test Rail Service Benefits by Corridor and Flow-type - Trend Scenario
Flows to/from Manchester
Airport which are to/from
outside Greater
Manchester City Region
Airport which are in
Greater Manchester City
Region
Inter corridor flows where
neither end is in the
Region
one or both ends is in the
Region
Intra corridor flows
£3,000
£2,000
£1,500
£1,000
£500
All Other
Liverpool via Irlam
Manchester Airport
Buxton
London, Birmingham and the South (via
WCML)
Marple / Romiley
Corridor
Sheffield
Glossop / Hadfield
Leeds and York
Scotland)
Blackburn
-£500
£0
Southport via Wigan
£ Millions PV (2002 prices)
£2,500
Flows to/from Central
Manchester to/from Long
Distance stations
Manchester to/from Other
City Regions
Manchester to/from Outer
Manchester to/from Inner
78
FIGURE 14.7 TREND SCENARIO BENEFITS BY CORRIDOR AND BENEFIT TYPE
Test Rail Service Benefits by Corridor and Benefit Type - Trend Scenario
£3,000
£2,500
£2,000
Wider Economic Benefits
Non user Benefits
Crowding
£1,500
Revenue Increment
£1,000
£500
14.23
All Other
Liverpool via Irlam
Manchester Airport
Buxton
Corridor
WCML)
Marple / Romiley
Sheffield
Glossop / Hadfield
Leeds and York
Scotland)
Blackburn
-£500
Southport via Wigan
£0
The corresponding benefits for the Trend Plus Scenario are illustrated in the
following charts (Figure 14.8 and Figure 14.9).
FIGURE 14.8 TREND PLUS SCENARIO BENEFITS BY CORRIDOR AND FLOW
GEOGRAPHY TYPE
Test Rail Service Benefits by Corridor and Flow-type - Trend Plus Scenario
Airport which are to/from
outside Greater
Airport which are in
Region
neither end is in the
Region
one or both ends is in the
Region
Intra corridor flows
£3,500
£2,500
£2,000
£1,500
£1,000
£500
All Other
Liverpool via Irlam
Manchester Airport
Buxton
WCML)
Marple / Romiley
Corridor
Sheffield
Glossop / Hadfield
Leeds and York
Scotland)
Blackburn
-£500
£0
Southport via Wigan
£3,000
Manchester to/from Long
Distance stations
Manchester to/from Other
City Regions
Manchester to/from Outer
Manchester to/from Inner
79
FIGURE 14.9 TREND PLUS SCENARIO BENEFITS BY CORRIDOR AND BENEFIT TYPE
Test Rail Service Benefits by Corridor and Benefit Type - Trend Plus Scenario
£3,500
£3,000
Wider Economic Benefits
£2,500
Non user Benefits
Crowding
£2,000
£1,500
Revenue Increment
£1,000
£500
All Other
Liverpool via Irlam
Manchester Airport
Buxton
WCML)
Marple / Romiley
Sheffield
Glossop / Hadfield
Leeds and York
Scotland)
Blackburn
Southport via Wigan
£0
Corridor
Freight Modelling and Appraisal Results
14.24
The estimated value for SLMs for Manchester Hub is £612.2m PV for the 60 year
assessment period. This equates to around £41m PV for each incremental daily
freight path into the Manchester area (modelled as Trafford Park).
80
15
Output Statement Metrics
15.1
Based on the results for the Test timetable scenario set out in Chapter 14, we have
calculated the metrics for the Output Statement, for both the Trend and Trend Plus
Economic Scenarios. These are:
I
Timetable-driven passenger service benefit per unit GJT improvement;
I
Crowding benefits per incremental AM peak seat; and
I
Freight benefits per incremental freight path to Trafford Park.
Timetable-driven Benefits
15.2
The tables below show each timetable-driven benefit per minute improvement in
GJT (on within-corridor flows). The first table splits this by type of benefit and the
second by the type of flow. The third table splits the per-GJT-improvement benefits
into the portion due to, respectively, faster journey times, reduced service interval
and reduced interchange requirements.
TABLE 15.1 TREND SCENARIO - TIMETABLE-RELATED BENEFITS PER GJT MINUTE
IMPROVEMENT BY BENEFIT TYPE
Trend Scenario
Corridor
1
Southport via Wigan
2
3
4
5
Timetable-related benefits per GJT minute improvement (£
million NPV, 2002 prices)
Revenue
Increment
Journey
Time
Nonuser
benefits
Wider
Economic
Benefits
Total
timetablerelated
benefits
3
31
2
15
50
25
149
11
75
260
Blackburn
2
16
1
11
29
5
42
2
29
78
45
178
13
98
333
5a
Leeds and York
34
130
10
49
222
5b
7
30
2
29
69
5c
4
18
1
20
42
6
Glossop / Hadfield
1
16
0
2
19
7
Marple / Romiley
1
19
0
3
23
8
17
106
6
61
190
9
Buxton
2
25
1
4
33
219
460
70
120
869
10
11
Manchester Airport
6
40
1
9
57
12
1
16
0
2
19
13
Liverpool via Irlam
1
25
3
3
33
14
12
49
5
24
90
253
1102
84
438
1,877
Total
81
TABLE 15.2 TREND SCENARIO - TIMETABLE-RELATED BENEFITS PER GJT MINUTE IMPROVEMENT BY FLOW TYPE
Trend Scenario
Corridor
1
2
3
4
5
5a
5b
5c
6
7
8
9
10
11
12
13
14
Southport via Wigan
Preston and the North
via Bolton
Blackburn
Yorkshire & the North
East via Leeds
Leeds and York
North of York (towards
Tees, Tyne and
Scotland)
East of Leeds (towards
Hull)
Glossop / Hadfield
Marple / Romiley
Buxton
London, Birmingham
and the South (via
WCML)
Manchester Airport
Liverpool via Irlam
Warrington
Total
Other intra
corridor
flows
Inter
corridor
flows
where one
or both
ends is in
the
Manchester
City Region
Inter corridor
flows where
neither end is
in the
Manchester
City Region
Flows
to/from
Manchester
Airport from
Manchester
City Region
Flows
to/from
Manchester
Airport
from
outside
Manchester
City Region
Flows to all
other station
Total
0.0
4.5
11.8
5.6
2.3
0.9
3.6
50.3
61.0
21.9
16.6
14.3
62.0
0.5
20.7
18.1
259.8
0.5
14.8
18.3
11.4
0.0
0.0
-0.9
3.1
1.3
11.4
7.6
20.2
-0.5
2.0
0.9
5.8
1.6
8.5
28.8
77.7
0.0
27.8
103.1
3.3
30.8
10.4
117.6
0.9
17.1
22.0
333.0
0.0
18.6
74.6
0.2
27.0
7.6
68.1
0.8
10.3
14.7
221.8
0.0
5.5
19.6
3.0
2.0
1.9
28.8
0.1
3.3
4.6
68.9
0.0
3.7
9.0
0.1
1.8
0.9
20.7
0.0
3.5
2.8
42.2
1.4
-0.1
14.2
13.1
0.0
1.4
0.0
0.0
0.2
1.4
2.4
3.7
0.0
1.2
0.6
0.7
0.0
0.9
0.4
0.5
19.2
22.8
-0.2
8.7
38.6
13.1
25.3
6.9
60.7
0.4
8.1
28.2
189.7
-2.0
23.5
0.0
0.0
3.3
6.8
0.0
0.2
0.0
0.9
32.7
0.0
30.1
0.0
376.4
393.6
-2.1
51.0
-0.5
10.7
9.7
868.9
0.0
0.0
7.6
0.0
10.3
10.5
0.0
3.6
-10.4
0.0
0.0
0.0
0.0
2.0
-5.0
0.0
-1.6
1.1
0.0
3.4
22.0
17.7
0.1
5.1
38.9
0.1
7.3
0.0
1.3
-5.6
56.6
19.2
32.5
0.5
2.1
37.0
2.7
-1.8
1.3
34.2
1.8
3.1
9.5
90.3
4.9
259.1
318.0
234.7
293.1
81.1
382.8
28.7
105.0
169.5
1,876.9
Flows
to/from
Central
Manchester
from Inner
Manchester
City Region
Flows
to/from
Central
Manchester
from Outer
Manchester
City Region
Flows
to/from
Central
Manchester
from Other
City
Regions
Flows
to/from
Central
Manchester
from Long
Distance
stations
1.0
16.4
4.2
0.0
44.8
0.0
0.4
82
TABLE 15.3 TREND SCENARIO - TIMETABLE-RELATED BENEFITS SPLIT BY ELEMENT
OF GJT IMPROVEMENT
Trend Scenario
Corridor
1
Southport via Wigan
2
3
Blackburn
4
5
6
7
Timetable-related benefits per GJT minute
improvement (£ million NPV, 2002 prices)
In-vehicle
time
Service
Interval
Interchange
Penalty
Total
timetablerelated
benefits
35
16
0
50
202
53
5
260
29
0
0
29
70
8
0
78
252
80
0
333
Glossop / Hadfield
16
3
0
19
Marple / Romiley
22
0
0
23
8
74
114
2
190
9
Buxton
22
10
0
33
952
-98
14
869
36
21
0
57
6
14
-1
19
74
14
-56
33
10
11
Manchester Airport
12
13
Liverpool via Irlam
14
Total
82
18
-9
90
1,587
302
-12
1,877
83
15.3
The tables below show the corresponding results for the Trend Plus Scenario.
TABLE 15.4 TREND PLUS SCENARIO - TIMETABLE-RELATED BENEFITS PER GJT
MINUTE IMPROVEMENT BY BENEFIT TYPE
Trend Plus Scenario
Corridor
1
Southport via Wigan
2
3
Timetable-related benefits per GJT minute improvement
(£ million NPV, 2002 prices)
Revenue
Increment
Journey
Time
Nonuser
benefits
Wider
Economic
Benefits
Total
timetablerelated
benefits
4
36
3
20
63
29
172
13
88
301
Blackburn
3
18
1
13
34
4
7
53
3
39
101
5
50
233
18
139
440
5a
Leeds and York
37
176
13
70
296
5b
8
35
3
42
88
5c
4
23
1
28
56
6
Glossop / Hadfield
1
18
0
3
23
7
Marple / Romiley
2
22
0
4
28
8
21
124
8
83
235
9
Buxton
4
30
1
5
39
282
562
74
126
1,044
Manchester Airport
6
42
1
11
60
1
21
0
2
25
13
Liverpool via Irlam
4
26
4
6
40
14
12
66
6
29
113
310
1350
101
556
2,318
10
11
12
Total
84
TABLE 15.5 TREND PLUS SCENARIO - TIMETABLE-RELATED BENEFITS PER GJT MINUTE IMPROVEMENT BY FLOW TYPE
Trend Plus Scenario
Other intra
corridor
flows
Inter
corridor
flows
where one
or both
ends is in
the
Manchester
City Region
Inter corridor
flows where
neither end is
in the
Manchester
City Region
Flows
to/from
Manchester
Airport from
Manchester
City Region
Flows
to/from
Manchester
Airport
from
outside
Manchester
City Region
Flows to all
other station
Total
0.0
5.3
16.8
7.2
2.5
1.0
4.2
62.6
71.0
23.1
18.9
20.8
74.1
0.6
20.8
20.6
301.3
0.5
17.8
21.3
15.0
0.0
0.0
-1.0
3.9
1.6
15.7
9.5
29.0
-0.5
2.1
0.9
6.1
1.8
10.9
34.2
101.1
0.0
36.5
130.5
4.0
38.2
14.9
171.1
1.0
17.4
26.7
440.3
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
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.6
-0.1
16.8
15.7
0.0
1.7
0.0
0.0
0.3
1.7
3.3
5.2
0.0
1.6
0.6
0.7
0.0
1.0
0.5
0.6
23.1
28.1
-0.2
10.8
46.8
14.9
29.7
10.0
84.3
0.4
8.3
30.0
235.0
Buxton
-2.2
27.2
0.0
0.0
3.8
9.2
0.0
0.2
0.0
1.1
39.3
0.0
31.8
0.0
475.8
452.4
-2.5
56.1
-0.6
10.9
20.1
1,044.0
0.0
0.0
8.9
0.0
13.2
13.0
0.0
4.2
-12.1
0.0
0.0
0.0
0.0
2.4
-5.9
0.0
-2.0
2.7
0.0
5.1
26.3
20.3
0.1
5.5
40.1
0.1
7.7
0.0
1.5
-5.7
60.4
24.6
40.4
0.5
2.4
43.7
3.0
-1.7
1.8
47.1
2.1
3.3
10.8
112.9
6.0
310.0
386.5
289.5
341.7
115.0
518.3
32.3
107.8
210.8
2,318.0
Corridor
1
2
3
4
5
5a
5b
5c
6
7
8
9
10
11
12
13
14
Southport via Wigan
Bolton
Blackburn
Yorkshire & the North East
via Leeds
Leeds and York
Tees, Tyne and Scotland)
East of Leeds (towards
Hull)
Glossop / Hadfield
Marple / Romiley
Manchester Airport
Liverpool via Irlam
Warrington
Total
Flows
to/from
Central
Manchester
from Inner
Manchester
City Region
Flows
to/from
Central
Manchester
from Outer
Manchester
City Region
Flows
to/from
Central
Manchester
from Other
City
Regions
Flows
to/from
Central
Manchester
from Long
Distance
stations
1.2
19.4
5.0
0.0
51.4
0.0
0.5
85
TABLE 15.6 TREND PLUS SCENARIO - TIMETABLE-RELATED BENEFITS SPLIT BY
ELEMENT OF GJT IMPROVEMENT
Trend Plus Scenario
Corridor
Timetable-related benefits per GJT minute
improvement (£ million NPV, 2002 prices)
In-vehicle
time
Service
Interval
Interchange
Penalty
Total
timetablerelated
benefits
43
19
0
63
301
1
Southport via Wigan
2
235
61
5
3
Blackburn
35
0
0
34
4
91
10
0
101
5
334
106
1
440
6
Glossop / Hadfield
19
4
0
23
7
Marple / Romiley
28
1
0
28
8
91
141
3
235
9
Buxton
27
12
0
39
1,144
-117
17
1,044
38
22
0
60
10
11
Manchester Airport
12
13
Liverpool via Irlam
14
Total
8
18
-1
25
92
18
-69
40
102
22
-12
113
1,959
373
-15
2,318
86
Capacity-driven Benefits
15.4
The tables below show the crowding benefit per incremental AM peak seat, that is,
the value of the reduction in “crowded minutes” by existing and new passengers as
a result of additional capacity (rolling stock) supplied in the operation of the Test
timetable, compared with Do Minimum timetable (and rolling stock assumptions).
Note that the crowding benefit is for both AM and PM peak periods, but is shown per
incremental AM peak seat, since the same incremental seats would also be available
in the PM peak.
The tables show the benefits for the Trend and Trend Plus
Scenarios respectively.
TABLE 15.7 TREND SCENARIO – CROWDING BENEFIT PER INCREMENTAL AM PEAK
SEAT
Trend Scenario – Peak
Corridor
Crowding benefit per incremental AM
peak seat, £'000 NPV (2002 prices)
Suburban
Services
Longdistance
Services
All
Services
25.7
1
Southport via Wigan
25.7
0.0
2
17.0
248.3
89.1
3
Blackburn
45.4
0.0
45.4
4
47.2
1.6
10.3
5
20.1
167.0
86.7
6
Glossop / Hadfield
0.0
0.0
0.0
7
Marple / Romiley
2.0
0.0
2.0
8
7.9
537.2
136.7
9
Buxton
26.0
0.0
26.0
10
17.9
122.6
101.2
11
Manchester Airport
0.0
60.3
16.1
12
36.8
0.0
36.8
13
Liverpool via Irlam
68.5
0.0
68.5
14
2.2
57.3
46.5
22.5
123.6
54.6
Total
87
TABLE 15.8 TREND PLUS SCENARIO – CROWDING BENEFIT PER INCREMENTAL AM
PEAK SEAT
Trend Plus Scenario - Peak
Corridor
Suburban
Services
Long-distance
Services
All
Services
1
Southport via Wigan
36.6
0.0
36.6
2
26.4
291.3
109.0
3
Blackburn
68.4
0.0
68.4
4
93.2
2.4
19.9
5
30.7
255.6
132.6
6
Glossop / Hadfield
0.3
0.0
0.3
7
Marple / Romiley
3.6
0.0
3.6
8
15.2
777.9
200.8
9
Buxton
41.5
0.0
41.5
10
22.6
173.3
142.5
11
Manchester Airport
0.0
71.2
19.0
12
59.1
0.0
59.1
13
Liverpool via Irlam
93.0
0.0
93.0
14
5.4
79.1
64.6
33.3
173.9
77.9
Total
15.5
Crowding benefit per incremental AM peak
seat, £'000 NPV (2002 prices)
We have also calculated the corresponding benefits per incremental High AM Peak
seat, that is, the benefit per incremental seat during the period 0800-1000. Note
that the crowding benefit is for both the AM and PM High Peaks (0800-0900 and
1700-1800), but again, is shown per incremental AM high peak seat, since the same
seats would be available for the high PM peak. These are shown, for the Trend and
Trend Plus Scenarios, respectively, in the tables below.
TABLE 15.9 TREND SCENARIO – CROWDING BENEFIT PER INCREMENTAL HIGH AM
PEAK SEAT
Trend Scenario – High Peak
Corridor
Crowding benefit per incremental HIGH AM
peak seat, £'000 NPV (2002 prices)
Suburban
Services
Long-distance
Services
All
Services
1
Southport via Wigan
38.5
0.0
38.5
2
18.3
177.0
82.6
3
Blackburn
193.4
0.0
193.4
4
817.4
-1.2
33.5
5
19.5
214.6
80.6
6
Glossop / Hadfield
0.0
0.0
0.0
7
Marple / Romiley
5.4
0.0
5.4
8
22.4
290.5
139.1
9
Buxton
24.8
0.0
24.8
10
62.4
108.3
99.3
11
Manchester Airport
0.2
39.7
81.4
12
70.1
0.0
70.1
13
Liverpool via Irlam
94.3
0.0
94.3
14
13.5
170.1
146.5
Total
44.7
133.2
78.9
88
TABLE 15.10 TREND PLUS SCENARIO – CROWDING BENEFIT PER INCREMENTAL HIGH
AM PEAK SEAT
Trend Plus Scenario – High Peak
Corridor
Crowding benefit per incremental HIGH
AM peak seat, £'000 NPV (2002 prices)
Suburban
Services
Longdistance
Services
All
Services
1
Southport via Wigan
66.3
0.0
66.3
2
39.7
383.3
179.0
3
Blackburn
188.2
0.0
188.2
4
666.6
-0.9
27.3
5
33.2
365.0
137.1
6
Glossop / Hadfield
0.0
0.0
0.0
7
Marple / Romiley
3.7
0.0
3.7
8
37.0
478.9
229.3
9
Buxton
10
11
Manchester Airport
12
13
Liverpool via Irlam
14
28.8
0.0
28.8
128.4
223.0
204.3
0.4
76.7
157.2
72.7
0.0
72.7
157.6
0.0
157.6
14.8
187.3
161.4
Total
67.5
235.0
132.3
Freight Benefits
15.6
The benefits, in terms of a 60-year PV appraisal, of growth in available freight paths
to Trafford Park, consistent with DfT growth assumptions, are estimated at £41
million per additional freight path.
89
90
APPENDIX
A
DETAILED APPRAISAL RESULTS
Appendix A
Appendix A
A1.
APPRAISAL BENEFITS
A1.1
The tables and charts give fuller details on the results of the modelling of the Test
Timetable Scenario, beyond that presented in Chapter 14, for both the Trend and
Trend Plus economic scenarios.
A1.2
The following two tables show benefits by corridor, flow category and benefit type
for the Trend and Trend Plus Scenarios respectively.
Appendix A
APPENDIX: TABLE A1.1
No
1
Benefit
Flows to/from
Central
Manchester
to/from Inner
Manchester City
Region
Flows to/from
Central
Manchester
to/from Outer
Manchester
City Region
Revenue increment
£0.48
£7.57
£3.14
£0.00
£1.54
£0.32
£0.98
£0.64
£0.57
£1.99
£17.24
£5.03
£76.85
£16.22
£0.00
£22.85
£25.61
£11.55
£12.17
£4.44
£20.47
£195.18
Crowding
£0.00
£110.50
£0.00
£0.00
£0.00
£0.00
£0.00
£110.50
Corridor Name
Southport via Wigan
5
5a
5b
5c
Leeds and York
Leeds and York
Total
(£ millions)
£0.00
£0.00
£6.02
£3.69
£0.00
£0.28
£0.50
£0.09
£0.18
£0.05
£0.44
£13.97
£3.56
£0.00
£3.81
£48.72
£23.25
£1.88
£0.73
Total
£6.48
£214.91
£26.61
£0.00
£28.49
£75.14
£35.87
£14.87
£5.79
£22.90
£431.06
Revenue increment
£0.00
£20.66
£41.63
£22.98
£7.78
£4.06
£16.24
£0.20
£15.36
£11.05
£139.94
£219.93
£86.10
£820.85
£11.35
£96.79
£0.00
£156.72
£67.36
£70.33
£44.04
£84.24
£2.35
£89.80
£0.00
£283.48
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£5.52
£22.63
£21.17
£2.28
£1.27
£3.75
£0.06
£1.33
£2.60
£60.61
£0.00
£64.71
£52.65
£9.69
£11.17
£29.70
£238.04
£0.18
£7.64
£0.00
£413.78
Total
£0.00
£531.10
£336.83
£121.20
£91.55
£79.06
£342.27
£2.78
£114.12
£99.74
£1,718.66
Revenue increment
£0.00
£0.53
£13.63
£0.00
-£0.08
-£0.33
-£2.45
-£0.06
£1.36
£1.30
£13.90
-£0.05
-£4.68
£105.02
£0.00
-£6.01
£7.02
£18.05
-£3.34
£5.31
£12.14
£133.47
£0.00
£40.63
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£40.63
Crowding
Non user benefits
£283.48
£0.00
£0.35
£8.14
£0.00
-£0.06
£0.24
£0.31
-£0.01
£0.03
£0.24
£9.25
-£0.01
£7.71
£30.51
£0.00
-£1.29
£4.24
£49.40
-£0.62
£1.07
£0.00
£91.01
Total
-£0.07
£44.54
£157.30
£0.00
-£7.44
£11.17
£65.31
-£4.03
£7.78
£13.68
£288.25
£0.16
£7.38
£7.23
£0.00
£1.02
£3.00
£4.29
£0.59
£5.93
£6.23
£35.82
Revenue increment
Flows to all
other station
£0.10
4
£0.00
Inter corridor
Flows to/from
Flows to/from
flows where
Manchester Manchester Airport
neither end is Airport which are
which are to/from
in the Greater
in Greater
outside Greater
Manchester Manchester City
Manchester City
City Region
Region
Region
£0.87
Preston and the North Crowding
via Bolton Non user benefits
Blackburn
Inter corridor
flows where one
or both ends is
in the Greater
Intra corridor Manchester City
flows
Region
3
Flows to/from
Central
Flows to/from
Manchester Central Manchester
to/from Other
to/from Long
City Regions Distance stations
Non user benefits
2
TREND SCENARIO BENEFITS
£2.42
£81.53
£51.66
£0.00
£18.84
£49.72
£21.83
£13.09
£34.04
£59.25
£332.38
Crowding
£0.00
£23.15
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£23.15
Non user benefits
£0.06
£2.65
£5.72
£0.00
£0.23
£2.06
£1.38
£0.13
£0.22
£1.49
£13.94
£0.58
£25.01
£25.61
£0.00
£4.50
£35.09
£131.98
£1.84
£5.84
£0.00
£230.44
Total
£3.21
£139.72
£90.22
£0.00
£24.59
£89.88
£159.47
£15.64
£46.02
£66.97
£635.73
Revenue increment
£0.00
£7.42
£96.27
£3.40
£18.62
£5.88
£82.89
£0.32
£14.86
£17.04
£246.70
£0.00
£41.14
£344.58
£11.18
£126.47
£32.94
£245.79
£4.12
£74.63
£101.62
£982.47
Crowding
£0.00
£591.82
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£591.82
Non user benefits
£0.00
£3.46
£40.38
£1.75
£5.06
£0.71
£16.51
£0.08
£0.60
£2.98
£71.52
£0.00
£101.90
£89.57
£1.80
£20.36
£18.04
£305.60
£0.24
£4.58
£0.00
£542.09
Total
£0.00
£745.75
£570.79
£18.13
£170.52
£57.57
£650.78
£4.76
£94.67
£121.63
£2,434.60
Revenue increment
£0.00
£7.42
£72.35
£0.00
£16.90
£6.19
£61.98
£0.26
£7.90
£12.40
£185.40
£0.00
£41.14
£267.27
£0.00
£117.82
£25.46
£148.69
£3.74
£46.70
£66.72
£717.54
Crowding
£0.00
£338.39
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£338.39
Non user benefits
£0.00
£3.46
£28.25
£0.00
£4.71
£1.40
£13.37
£0.07
£0.31
£1.97
£53.55
£0.00
£50.95
£44.78
£0.90
£10.18
£9.02
£152.80
£0.12
£2.29
£0.00
£271.04
Total
£0.00
£441.36
£412.66
£0.90
£149.61
£42.07
£376.84
£4.19
£57.21
£81.08
£1,565.92
Revenue increment
£0.00
£0.00
£18.26
£3.40
£1.08
-£0.51
£11.20
£0.07
£2.75
£3.01
£39.26
£0.00
£0.00
£54.17
£11.18
£3.67
£6.47
£55.62
£0.40
£14.09
£21.60
£167.21
£0.00
£180.03
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£180.03
Crowding
Tees, Tyne and
Scotland) Non user benefits
£0.00
£0.00
£9.24
£1.75
£0.26
-£0.63
£1.08
£0.01
£0.13
£0.61
£12.45
£0.00
£30.57
£26.87
£0.54
£6.11
£5.41
£91.68
£0.07
£1.37
£0.00
£162.63
Total
£0.00
£210.60
£108.54
£16.87
£11.12
£10.75
£159.57
£0.55
£18.35
£25.22
£561.57
Revenue increment
£0.00
£0.00
£5.66
£0.00
£0.64
£0.19
£9.71
£0.00
£4.21
£1.63
£22.04
£0.00
£0.00
£23.14
£0.00
£4.98
£1.01
£41.48
-£0.02
£13.83
£13.30
£97.73
£0.00
£73.41
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£73.41
£0.00
£0.00
£2.89
£0.00
£0.10
-£0.07
£2.05
£0.00
£0.16
£0.40
£5.52
£0.00
£20.38
£17.91
£0.36
£4.07
£3.61
£61.12
£0.05
£0.92
£0.00
£108.42
Total
£0.00
£93.79
£49.59
£0.36
£9.79
£4.75
£114.37
£0.03
£19.11
£15.33
£307.12
East of Leeds Crowding
(towards Hull) Non user benefits
Appendix A
No
Corridor Name
Benefit
Revenue increment
7
8
Glossop / Hadfield
Marple / Romiley
Inter corridor
Flows to/from
Flows to/from
flows where
which are to/from
in the Greater
in Greater
outside Greater
Manchester City
City Region
Region
Region
Flows to all
other station
Total
(£ millions)
£8.47
£0.00
£0.00
£0.40
-£1.34
£0.00
£0.25
£0.00
£0.37
£8.82
£0.00
£0.00
£1.80
£12.17
£0.00
£4.82
£0.00
£3.47
£155.21
Crowding
£0.00
-£0.10
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
-£0.10
Non user benefits
£0.01
£1.89
£0.00
£0.00
£0.08
£0.41
£0.00
£0.04
£0.00
£0.07
£2.50
£0.93
£9.52
£0.00
£0.00
£0.14
£12.55
£0.00
£0.54
£0.00
£0.00
£23.69
Total
£13.44
£140.91
£0.00
£0.00
£2.41
£23.79
£0.00
£5.66
£0.00
£3.91
£190.12
Revenue increment
-£0.06
£5.08
£0.62
£0.00
£0.21
-£2.29
£0.15
£0.23
£1.38
£0.26
£5.57
-£0.69
£70.88
£6.75
£0.00
£8.16
£19.84
£4.06
£3.77
£4.27
£2.96
£120.00
£2.09
Crowding
£0.00
£2.09
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
Non user benefits
-£0.01
£0.90
£0.11
£0.00
£0.02
£0.61
-£0.01
£0.02
-£0.03
£0.04
£1.64
-£0.07
£7.11
£1.58
£0.00
£0.80
£5.42
£3.62
£0.31
£0.40
£0.00
£19.17
£148.46
Total
-£0.83
£86.06
£9.05
£0.00
£9.18
£23.59
£7.82
£4.32
£6.02
£3.26
Revenue increment
-£0.04
£0.00
£29.68
£10.60
£9.74
£1.83
£22.68
£0.14
£9.50
£14.68
£98.81
-£0.76
£0.00
£132.07
£49.55
£113.43
£23.39
£114.70
£1.75
£34.27
£144.11
£612.51
£279.74
£0.00
£279.74
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.05
£0.00
£14.32
£6.28
£2.60
£0.54
£4.86
£0.04
£0.31
£3.64
£32.62
-£0.14
£50.22
£46.34
£8.90
£20.36
£14.16
£207.76
£0.13
£2.67
£0.00
£350.38
Total
-£0.89
£329.96
£222.41
£75.33
£146.13
£39.91
£349.99
£2.05
£46.75
£162.43
£1,374.06
Revenue increment
-£1.18
£23.85
£0.00
£0.00
£2.08
-£4.57
£0.00
£0.18
£0.00
£1.00
£21.36
-£18.87
£197.98
£0.00
£0.00
£29.77
£45.90
£0.00
£1.84
£0.00
£8.05
£264.67
Crowding
£0.00
£54.66
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£54.66
Non user benefits
£0.67
£7.01
£0.00
£0.00
£0.39
£1.65
£0.00
£0.01
£0.00
£0.20
£9.93
-£1.14
£15.33
£0.00
£0.00
£1.81
£27.68
£0.00
£0.30
£0.00
£0.00
£43.96
£394.58
Total
10
Inter corridor
flows where one
or both ends is
in the Greater
flows
Region
£121.14
Yorkshire & the East Crowding
Midlands via Sheffield Non user benefits
Buxton
Flows to/from
Central
Flows to/from
to/from Other
to/from Long
£0.68
9
Flows to/from
Central
Manchester
to/from Outer
Manchester
City Region
£11.82
6
Flows to/from
Central
Manchester
to/from Inner
Manchester City
Region
-£20.52
£298.83
£0.00
£0.00
£34.05
£70.65
£0.00
£2.32
£0.00
£9.25
Revenue increment
£0.00
£1.97
£0.00
£312.04
£292.04
-£0.05
£8.61
£0.19
£6.17
-£0.45
£620.53
£0.00
£7.43
£0.00
£519.20
£677.73
-£4.01
£59.72
-£1.31
£18.72
£25.94
£1,303.42
£0.00
£422.40
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£422.40
£0.00
£7.02
£0.00
£150.98
£35.74
£0.32
£1.11
£0.01
£0.11
£1.83
London, Birmingham
Crowding
and the South (via
WCML) Non user benefits
Total
£197.12
£0.00
£68.87
£0.00
£83.19
£108.60
-£2.19
£74.85
-£0.30
£5.36
£0.00
£338.38
£0.00
£507.70
£0.00
£1,065.41
£1,114.11
-£5.93
£144.29
-£1.41
£30.36
£27.32
£2,881.86
Appendix A
No
11
12
13
Corridor Name
Manchester Airport
Liverpool via Irlam
Benefit
Inter corridor
flows where one
or both ends is
in the Greater
flows
Region
Inter corridor
Flows to/from
Flows to/from
flows where
which are to/from
in the Greater
in Greater
outside Greater
Manchester City
City Region
Region
Region
Flows to all
other station
Total
(£ millions)
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£4.29
£27.28
£0.00
£31.58
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£57.09
£145.16
£0.00
£202.24
£78.67
Crowding
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£78.67
£0.00
£0.00
Non user benefits
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£1.26
£1.60
£0.00
£2.86
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£25.97
£20.75
£0.00
£46.72
£362.08
Total
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£167.28
£194.80
£0.00
Revenue increment
£0.00
-£1.49
£6.14
£0.00
£1.64
-£3.95
£10.34
£0.03
£0.27
£2.04
£15.02
£0.00
£171.87
£51.05
£0.00
£31.46
-£11.78
£14.78
£1.20
£0.95
£20.64
£280.15
Crowding
£0.00
£49.30
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£49.30
Non user benefits
£0.00
-£4.08
£1.74
£0.00
£0.17
-£0.40
£1.57
£0.01
£0.00
£0.28
-£0.70
£0.00
£9.93
£2.93
£0.00
£1.41
-£11.07
£32.01
£0.30
£0.40
£0.00
£35.91
Total
£0.00
£225.53
£61.87
£0.00
£34.68
-£27.20
£58.70
£1.54
£1.61
£22.96
£379.68
Revenue increment
£0.68
£3.02
-£0.06
£0.00
-£0.12
-£3.22
£1.21
£0.38
£0.35
-£0.46
£1.78
£9.32
£7.77
-£15.42
£0.00
-£6.45
£4.36
£27.54
£5.51
£7.62
-£6.93
£33.31
Crowding
£0.00
£230.80
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£230.80
Non user benefits
£0.07
£2.42
£1.69
£0.00
-£0.03
£0.20
£0.12
£0.02
£0.02
-£0.04
£4.48
£0.03
£0.76
-£0.05
£0.00
-£0.02
£0.07
£0.44
£0.91
£1.69
£0.00
£3.83
£274.21
£10.11
£244.76
-£13.84
£0.00
-£6.62
£1.41
£29.31
£6.82
£9.68
-£7.43
Revenue increment
£0.22
£0.11
£42.56
£3.08
-£0.20
£2.02
£41.69
£0.78
£2.65
£9.15
£102.05
£3.14
£1.06
£212.05
£13.34
-£12.89
£5.21
£84.34
£12.23
£18.48
£66.58
£403.55
£136.84
£0.00
£136.84
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.07
£0.54
£20.76
£4.45
-£0.24
£0.39
£11.07
£0.23
£0.36
£1.84
£39.47
£0.35
£15.20
£28.20
£1.47
-£1.42
£3.39
£143.78
£1.82
£3.56
£0.00
£196.35
Total
£3.77
£153.75
£303.57
£22.35
-£14.75
£11.01
£280.87
£15.06
£25.05
£77.56
£878.26
Revenue increment
£1.17
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£42.60
£43.77
£11.61
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£265.02
£276.63
£0.00
Crowding
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
Non user benefits
-£0.02
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£8.62
£8.59
Total
£0.00
£12.76
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£316.23
£0.00
£329.00
Revenue increment
£2.11
£84.57
£240.84
£352.10
£334.65
£1.37
£186.61
£8.17
£85.67
£106.79
£1,402.88
£22.97
£929.69
£1,123.89
£660.64
£1,075.50
£254.40
£686.59
£115.29
£437.68
£809.41
£6,116.05
Crowding
£0.00
£2,225.31
£0.00
£0.00
£0.00
£0.00
£0.00
£78.67
£0.00
£0.00
£2,303.98
Non user benefits
£1.00
£33.71
£119.17
£184.63
£46.53
£8.50
£40.75
£2.08
£4.60
£24.23
£465.19
£1.39
£390.24
£280.90
£105.05
£170.22
£185.80
£1,210.73
£33.48
£54.69
£0.00
£2,432.50
£27.46
£3,663.52
£1,764.81
£1,302.42
£1,626.91
£450.05
£2,124.68
£237.69
£582.65
£940.43
£12,720.60
TOTAL
Flows to/from
Central
Flows to/from
to/from Other
to/from Long
Liverpool / Chester via Crowding
Warrington Non user benefits
All Other
Flows to/from
Central
Manchester
to/from Outer
Manchester
City Region
Revenue increment
Total
14
Flows to/from
Central
Manchester
to/from Inner
Manchester City
Region
Total
Appendix A
APPENDIX: TABLE A1.2
No
1
2
Benefit
Flows to/from
Central Manchester
to/from Inner
Manchester City
Region
Flows to/from
Central
Manchester
to/from Outer
Manchester City
Region
Revenue increment
£0.58
£9.33
£3.94
£0.00
£1.81
£5.89
£89.81
£18.99
£0.00
£26.94
Crowding
£0.00
£156.98
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
Non user benefits
£0.12
£7.32
£4.36
£0.00
£0.33
£2.97
£0.42
£0.18
Corridor Name
Southport via Wigan
5
5a
5b
5c
Leeds and York
Leeds and York
Inter corridor
flows where
neither end is in
the Greater
Manchester City
Region
Flows to/from
Manchester
Airport which
are in Greater
Manchester
City Region
Flows to/from
Manchester Airport
which are to/from
outside Greater
Manchester City
Region
Flows to all
other station
£5.02
£1.87
£0.66
£0.58
£2.31
£26.10
£35.73
£13.59
£12.55
£4.51
£23.76
£231.77
£0.00
£0.00
£156.98
£0.05
£0.52
£16.28
Total
(£ millions)
£1.07
£17.20
£4.38
£0.00
£4.69
£63.50
£30.31
£2.41
£0.94
£7.65
£280.65
£31.68
£0.00
£33.77
£107.22
£46.18
£15.80
£6.08
£26.59
£555.63
£124.49
£158.72
Revenue increment
£0.00
£24.02
£49.08
£25.23
£9.00
£9.81
£13.50
£0.22
£15.35
£12.52
£0.00
£181.48
£258.26
£69.94
£80.10
£64.35
£103.47
£2.69
£89.77
£98.33
£948.40
£0.00
£346.68
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£346.68
£4.99
£7.27
£0.06
£0.00
£72.05
£58.62
£10.79
£12.43
£35.58
£285.14
£0.21
£8.67
£0.00
£483.47
Total
£0.00
£630.79
£392.34
£127.60
£104.15
£114.73
£409.37
£3.18
£115.11
£113.79
£2,011.07
4
Inter corridor
flows where one
or both ends is in
the Greater
Intra corridor
Manchester City
flows
Region
Total
Preston and the North Crowding
via Bolton Non user benefits
Blackburn
Flows to/from
Flows to/from
Central
Central
Manchester
Manchester
to/from Other
to/from Long
Revenue increment
3
TREND SCENARIO PLUS BENEFITS
Crowding
£0.00
£6.55
£26.39
£21.65
£2.62
£1.32
£2.94
£73.80
£0.00
£0.67
£14.75
£0.00
-£0.10
£1.11
£2.42
-£0.05
£1.36
£1.43
£21.60
-£0.06
-£5.60
£123.84
£0.00
-£6.85
£7.32
£19.32
-£3.27
£5.34
£13.39
£153.44
£0.00
£61.11
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.46
£9.27
£0.00
-£0.07
£0.35
£1.04
-£0.01
£0.03
£0.27
£11.35
-£0.01
£9.06
£35.88
£0.00
-£1.52
£5.07
£59.05
-£0.70
£1.21
£0.00
£108.04
Total
£355.53
Non user benefits
£61.11
-£0.07
£65.70
£183.75
£0.00
-£8.53
£13.86
£81.83
-£4.03
£7.94
£15.09
Revenue increment
£0.19
£8.69
£9.43
£0.00
£1.27
£8.24
£9.74
£0.61
£5.93
£8.01
£52.11
£2.85
£95.77
£67.62
£0.00
£23.60
£64.06
£36.74
£13.66
£34.16
£75.80
£414.26
Crowding
£0.00
£44.46
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
Non user benefits
£0.07
£3.27
£7.70
£0.00
£0.29
£3.43
£2.68
£0.14
£0.23
£1.92
£19.73
£0.76
£32.91
£33.70
£0.00
£5.92
£47.70
£179.39
£2.50
£7.95
£0.00
£310.82
Total
£3.88
£185.10
£118.45
£0.00
£31.07
£123.42
£228.55
£16.92
£48.26
£85.74
£841.38
£0.00
£44.46
Revenue increment
£0.00
£9.31
£121.12
£4.14
£21.67
£6.72
£76.36
£0.39
£14.89
£20.82
£275.41
£0.00
£48.47
£427.82
£13.29
£155.52
£46.57
£397.33
£4.86
£74.87
£123.37
£1,292.12
Crowding
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
Non user benefits
£0.00
£4.18
£50.65
£2.06
£6.14
£2.73
£28.33
£0.10
£0.61
£3.70
£98.49
£0.00
£139.78
£905.71
£122.86
£2.47
£27.93
£0.00
£26.26
£0.00
£444.95
£0.30
£5.87
£0.00
£770.42
£905.71
Total
£0.00
£1,107.44
£722.45
£21.97
£211.26
£82.28
£946.97
£5.65
£96.24
£147.89
£3,342.16
Revenue increment
£0.00
£9.31
£91.96
£0.00
£19.59
£5.68
£55.52
£0.31
£7.92
£15.38
£205.67
£0.00
£48.47
£334.86
£0.00
£144.56
£39.77
£273.95
£4.40
£46.90
£81.41
£974.32
£512.72
Crowding
£0.00
£512.72
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
Non user benefits
£0.00
£4.18
£36.33
£0.00
£5.70
£2.38
£22.62
£0.08
£0.32
£2.48
£74.10
£0.00
£69.89
£61.43
£1.23
£13.96
£13.13
£222.48
£0.15
£2.94
£0.00
£385.21
Total
£0.00
£644.56
£524.57
£1.23
£183.82
£60.96
£574.57
£4.95
£58.07
£99.28
£2,152.01
Revenue increment
£0.00
£0.00
£21.88
£4.14
£1.26
£0.82
£11.56
£0.08
£2.76
£3.46
£45.97
£0.00
£0.00
£63.71
£13.29
£4.61
£5.38
£64.22
£0.48
£14.14
£25.35
£191.18
£0.00
Crowding
Tees, Tyne and
Non
user benefits
Scotland)
£253.65
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£10.62
£2.06
£0.31
£0.25
£2.51
£0.01
£0.13
£0.71
£16.60
£0.00
£41.93
£36.86
£0.74
£8.38
£7.88
£133.49
£0.09
£1.76
£0.00
£231.13
Total
£0.00
£295.59
£133.06
£20.24
£14.56
£14.32
£211.78
£0.67
£18.80
£29.52
£738.53
Revenue increment
£0.00
£0.00
£7.28
£0.00
£0.82
£0.22
£9.27
£0.00
£4.21
£1.98
£0.00
£0.00
£29.26
£0.00
£6.35
£1.43
£59.17
-£0.02
£13.83
£16.61
£126.62
£0.00
£139.34
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£139.34
East of Leeds Crowding
(towards Hull) Non user benefits
£253.65
£23.77
£0.00
£0.00
£3.71
£0.00
£0.13
£0.10
£3.19
£0.00
£0.16
£0.51
£7.79
£0.00
£27.96
£24.57
£0.49
£5.59
£5.25
£88.99
£0.06
£1.17
£0.00
£154.08
Total
£0.00
£167.30
£64.82
£0.49
£12.89
£6.99
£160.62
£0.03
£19.37
£19.10
£451.61
Appendix A
No
Corridor Name
Benefit
Revenue increment
6
Glossop / Hadfield
8
Marple / Romiley
Inter corridor
flows where
neither end is in
the Greater
Manchester City
Region
Flows to/from
Manchester
Airport which
are in Greater
Manchester
City Region
Flows to/from
Manchester Airport
which are to/from
outside Greater
Manchester City
Region
Flows to all
other station
Total
(£ millions)
£0.80
£9.94
£0.00
£0.00
£0.47
£1.22
£0.00
£0.26
£0.00
£0.46
£13.14
£14.06
£143.36
£0.00
£0.00
£2.08
£14.03
£0.00
£4.99
£0.00
£4.28
£182.80
£0.00
£0.88
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.88
£2.20
£0.00
£0.00
£0.09
£0.29
£0.00
£0.05
£0.00
£0.09
£2.74
£1.07
£10.98
£0.00
£0.00
£0.16
£17.34
£0.00
£0.73
£0.00
£0.00
£30.28
Total
£15.95
£167.36
£0.00
£0.00
£2.80
£32.88
£0.00
£6.02
£0.00
£4.83
£229.84
Revenue increment
-£0.07
£6.01
£0.74
£0.00
£0.23
£1.83
£0.28
£0.23
£1.38
£0.30
£10.94
-£0.76
£83.93
£7.96
£0.00
£9.48
£22.61
£4.48
£3.91
£4.27
£3.46
£139.33
£0.00
£3.84
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£3.84
Non user benefits
Crowding
-£0.02
£1.07
£0.13
£0.00
£0.02
£0.84
£0.04
£0.02
-£0.03
£0.05
£2.12
-£0.09
£9.79
£2.17
£0.00
£1.10
£8.26
£5.52
£0.43
£0.56
£0.00
£27.74
Total
-£0.94
£104.63
£10.99
£0.00
£10.82
£33.55
£10.33
£4.58
£6.18
£3.82
£183.97
Revenue increment
-£0.04
£0.00
£36.63
£12.36
£11.29
£4.73
£28.52
£0.17
£9.52
£15.81
£118.98
-£0.89
£0.00
£158.71
£55.58
£131.75
£29.48
£147.91
£2.13
£34.49
£153.42
£712.57
£0.00
£410.94
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£410.94
£0.05
£0.00
£17.31
£6.78
£3.03
£2.79
£9.99
£0.04
£0.31
£3.91
£44.21
-£0.17
£62.04
£57.25
£10.99
£25.16
£20.44
£299.84
£0.17
£3.64
£0.00
£479.35
Total
-£1.04
£472.98
£269.89
£85.70
£171.23
£57.43
£486.25
£2.52
£47.96
£173.13
£1,766.05
Revenue increment
-£1.10
£28.49
£0.00
£0.00
£2.45
£6.33
£0.00
£0.18
£0.00
£1.24
£37.59
-£21.77
£229.22
£0.00
£0.00
£35.10
£53.11
£0.00
£1.91
£0.00
£9.83
£307.40
Crowding
£0.00
£87.12
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£87.12
Non user benefits
£0.78
£8.36
£0.00
£0.00
£0.46
£1.41
£0.00
£0.01
£0.00
£0.25
£11.27
-£1.26
£16.93
£0.00
£0.00
£2.00
£35.02
£0.00
£0.39
£0.00
£0.00
£53.07
-£23.36
£370.12
£0.00
£0.00
£40.00
£95.88
£0.00
£2.49
£0.00
£11.33
£496.45
£0.00
£2.14
£0.00
£415.16
£356.30
£1.96
£9.58
£0.24
£6.24
£5.49
£797.10
£0.00
£9.29
£0.00
£685.39
£776.87
-£6.86
£60.16
-£1.76
£18.98
£48.93
£1,591.00
£0.00
£594.52
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£594.52
£0.00
£8.33
£0.00
£161.48
£36.76
£0.44
£1.21
£0.02
£0.11
£2.39
£210.74
Total
Revenue increment
10
Inter corridor
flows where one
or both ends is in
the Greater
Intra corridor
Manchester City
flows
Region
£0.01
Yorkshire & the East Crowding
Midlands via Sheffield Non user benefits
Buxton
Flows to/from
Flows to/from
Central
Central
Manchester
Manchester
to/from Other
to/from Long
Crowding
9
Flows to/from
Central
Manchester
to/from Outer
Manchester City
Region
Non user benefits
7
Flows to/from
Central Manchester
to/from Inner
Manchester City
Region
London, Birmingham
Crowding
and the South (via
WCML) Non user benefits
£0.00
£70.26
£0.00
£84.87
£110.79
-£2.57
£87.73
-£0.31
£5.54
£0.00
£356.32
Total
£0.00
£684.54
£0.00
£1,346.89
£1,280.71
-£7.03
£158.67
-£1.81
£30.88
£56.81
£3,549.67
Appendix A
No
11
12
Benefit
Flows to/from
Central Manchester
to/from Inner
Manchester City
Region
Flows to/from
Central
Manchester
to/from Outer
Manchester City
Region
Revenue increment
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£4.82
£27.40
£0.00
£32.22
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£64.10
£146.39
£0.00
£210.49
Crowding
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£92.92
£0.00
£0.00
£92.92
Non user benefits
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£1.64
£1.63
£0.00
£3.27
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£31.36
£25.24
£0.00
£56.60
£395.49
Corridor Name
Manchester Airport
Liverpool via Irlam
Flows to/from
Manchester Airport
which are to/from
outside Greater
Manchester City
Region
Flows to all
other station
Total
(£ millions)
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£194.84
£200.66
£0.00
£0.00
-£1.49
£7.25
£0.00
£1.92
-£5.01
£10.27
£0.03
£0.27
£2.28
£15.51
£0.00
£221.76
£60.33
£0.00
£37.11
-£14.98
£34.60
£1.24
£0.93
£23.61
£364.60
Crowding
£0.00
£79.23
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£79.23
Non user benefits
£0.00
-£4.82
£2.07
£0.00
£0.20
-£0.57
£3.17
£0.01
£0.00
£0.31
£0.37
£0.00
£11.20
£3.31
£0.00
£1.59
-£13.38
£38.69
£0.42
£0.56
£0.00
£42.39
Total
£0.00
£305.87
£72.96
£0.00
£40.83
-£33.94
£86.72
£1.70
£1.76
£26.21
£502.11
Revenue increment
£0.81
£3.91
£0.15
£0.00
-£0.14
-£0.59
£1.59
£0.39
£0.35
-£0.48
£5.99
£10.88
£9.08
-£18.17
£0.00
-£7.63
£3.53
£30.20
£5.71
£7.66
-£7.02
£34.23
Crowding
£0.00
£313.16
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£313.16
Non user benefits
£0.08
£2.85
£2.01
£0.00
-£0.03
£0.12
£0.22
£0.02
£0.02
-£0.05
£5.24
£0.06
£1.50
-£0.09
£0.00
-£0.04
£0.49
£3.14
£1.17
£2.19
£0.00
£8.43
£11.82
£330.50
-£16.11
£0.00
-£7.84
£3.54
£35.16
£7.30
£10.23
-£7.55
£367.05
Revenue increment
£0.26
£0.15
£51.38
£3.53
-£0.12
£1.69
£26.38
£0.90
£2.75
£10.39
£97.32
£3.65
£1.22
£250.99
£14.75
-£11.98
£8.20
£165.60
£13.73
£19.64
£75.87
£541.66
£0.00
£190.17
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£190.17
£0.09
£0.62
£24.30
£4.62
-£0.22
£0.63
£18.64
£0.28
£0.39
£2.08
£51.42
£0.39
£17.34
£32.18
£1.68
-£1.62
£4.14
£175.60
£2.27
£4.42
£0.00
£236.41
Total
£4.39
£209.51
£358.84
£24.57
-£13.94
£14.66
£386.23
£17.17
£27.20
£88.34
£1,116.98
Revenue increment
£1.38
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£57.61
£58.99
£13.73
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£354.55
£368.27
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
-£0.03
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£11.55
£11.53
Crowding
Non user benefits
Total
Revenue increment
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£15.08
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£0.00
£423.71
£438.79
£2.81
£101.17
£294.45
£460.41
£406.05
£43.06
£180.51
£9.05
£86.01
£138.20
£1,721.73
£27.57
£1,107.79
£1,356.34
£838.94
£1,252.09
£327.15
£1,013.40
£126.47
£441.02
£1,001.58
£7,492.35
Crowding
£0.00
£3,194.79
£0.00
£0.00
£0.00
£0.00
£0.00
£92.92
£0.00
£0.00
£3,287.70
Non user benefits
£1.16
£40.40
£144.19
£196.59
£49.61
£20.42
£73.00
£2.57
£4.69
£29.95
£562.57
£1.81
£471.05
£350.25
£110.80
£188.59
£247.85
£1,609.36
£41.34
£66.79
£0.00
£3,087.83
£33.35
£4,915.18
£2,145.23
£1,606.74
£1,896.34
£638.48
£2,876.27
£272.35
£598.50
£1,169.73
£16,152.18
TOTAL
Flows to/from
Manchester
Airport which
are in Greater
Manchester
City Region
£0.00
Liverpool / Chester via Crowding
Warrington Non user benefits
All Other
Inter corridor
flows where
neither end is in
the Greater
Manchester City
Region
Total
Total
14
Inter corridor
flows where one
or both ends is in
the Greater
Intra corridor
Manchester City
flows
Region
Revenue increment
13
Flows to/from
Flows to/from
Central
Central
Manchester
Manchester
to/from Other
to/from Long
Total
Appendix A
Appendix B
APPENDIX
B
Appendix B
Appendix B
B1.
B1.1
The table below sets out the Test Scenario Service Specification for each of the 14
Manchester Hub corridors. The corresponding services in the Do Minimum case are
those in the December 2008 timetable.
APPENDIX: TABLE B1.1 MANCHESTER HUB TEST SCEARIO SERVICE SPECIFICATION
Corridor 1 – Wigan / Southport via Atherton
Suburban Services
I Southport – Central Manchester ( - Manchester Airport, corridor 11 )
2 tph (1 fast, 1 semi-fast)
I
Kirkby – Wigan – Central Manchester ( - Hebden Bridge, corridor 4)
I
2 tph (all stations)
I
Corridor 2 – Southport / Preston via Bolton
Long-distance Services
I Scotland / Lake District – Manchester ( - Manchester Airport, corridor 11 )
I
I
2 tph (principal stations, 1 from Edinburgh/Glasgow, joining at Carstairs,
1 from Barrow/Oxenholme alternating)
Preston – Manchester: 30 minutes
Blackpool North – Manchester ( - Sheffield, Norwich, corridor 8)
I
I
2 tph
I
Blackpool – Manchester: 45 minutes
Suburban Services
I Blackpool North – Wigan – Central Manchester
I
1 tph (all stations north/west of Bolton)
Wigan – Bolton – Central Manchester
I
I
1 tph (all stations north/west of Bolton)
Corridor 3 – Blackburn
Suburban Services
I Clitheroe / Burnley – Blackburn – Central Manchester
I
2 tph (alternating Clitheroe and Burnley)
I
Blackburn – Manchester: 30 minutes
Corridor 4 – Rochdale / Calder Valley / Bradford
I Bradford – Manchester ( - Manchester Airport, corridor 11 )
I
2 tph (principal stations)
I
Bradford – Manchester: 45 minutes
Suburban Services
I Hebden Bridge / Burnley (via Todmorden) – Central Manchester ( - Kirkby,
corridor 1 )
I 2 tph (all stations, alternating Hebden Bridge / Burnley)
I
Todmorden – Manchester: 30 minutes
Appendix B
Corridor 5 – Leeds (North TransPennine)
I (North East) – York – Leeds – Manchester ( - Manchester Airport, corridor 11
)
2 tph (principal stations plus Ashton under Lyne, alternately from
Middlesbrough, Newcastle)
Leeds – Manchester: 40 minutes
I
I
York – Leeds – Manchester ( - Liverpool via Chat Moss, corridor 14 )
I
I
I
Hull – Leeds – Manchester ( - Chester, corridor 14 )
I
I
I
Suburban Services
I Huddersfield – Central Manchester
I
2 tph (all stations east of Stalybridge)
I
Huddersfield – Manchester: 30 minutes
Corridor 6 – Glossop / Hadfield
Suburban Services
I Glossop – Central Manchester
I
I
Glossop – Manchester: 25 minutes
Hadfield – Central Manchester
I
I
I
Hadfield – Manchester: 25 minutes
Corridor 7 – Marple / Romiley
Suburban Services
I Chinley - Romiley – Central Manchester
I
2 tph (all stations via Brinnington)
I
Chinley – Manchester: 30 minutes
Rose Hill – Central Manchester
I
I
2 tph (all stations via Hyde)
I
Rose Hill – Manchester: 20 minutes
Corridor 8 – Sheffield (South TransPennine)
I Cleethorpes – Sheffield – Manchester ( - Manchester Airport, corridor 11 )
I
2 tph (principal stations, via Brinnington)
I
Sheffield – Manchester: 45 minutes
Norwich – Sheffield – Manchester ( - Blackpool North, corridor 2 )
I
I
2 tph (principal stations, via Brinnington)
Suburban Services
I Sheffield – Stockport – Central Manchester
Appendix B
2 tph (all stations Sheffield – Chinley, plus Stockport)
I
Corridor 9 – Buxton
Suburban Services
I Buxton – Stockport – Central Manchester
I
I
Buxton – Manchester: 35 minutes
Corridor 10 – West Coast Main Line - Stoke / Crewe
I London Euston – Manchester
4 tph (selected principal stations, 1 via Crewe, 3 via Stoke)
I
(South of England) – Birmingham – Manchester
I
2 tph (selected principal stations)
I
(South of England) – Clapham Jn – Manchester
I
2 tph (selected principal stations)
I
Suburban Services
I Macclesfield – Central Manchester
I
I
Macclesfield – Manchester: 25 minutes
Crewe – Stockport – Central Manchester
I
I
Corridor 11 – Manchester Airport
I Manchester Airport – Manchester ( - Scotland/Lake District, corridor 2 )
I
I
2 tph (principal stations, 1 from Edinburgh/Glasgow, joining at Carstairs,
1 from
Barrow/Oxenholme alternating)
Manchester Airport – Manchester: 15 minutes
Crewe – Manchester Airport – Manchester ( - Bradford, corridor 4 )
I
I
2 tph (calling at Alderley Edge and Manchester Airport)
Crewe – Manchester Airport: 15 minutes; Manchester Airport –
Manchester: 15 minutes
Manchester Airport – Manchester ( - Leeds – York – North East, corridor 5 )
I
I
I
2 tph (principal stations, alternating Middlesbrough and Newcastle)
I
Manchester Airport – Manchester ( - Sheffield – Cleethorpes, corridor 8 )
I
I
I
Suburban Services
I Manchester Airport – Central Manchester ( - Southport, corridor 1 )
I
I
Manchester Airport – Central Manchester ( - Warrington – Liverpool,
corridor 13 )
I
Appendix B
Corridor 12 – Chester via Northwich
Suburban Services
I Chester – Altrincham
I
2 tph (all stations, connecting to Metrolink at Altrincham)
Corridor 13 – Liverpool via Irlam (CLC)
Suburban Services
I Liverpool – Warrington – Manchester ( - Manchester Airport, corridor 11 )
I
I
Liverpool Manchester: 30 minutes
Warrington – Manchester (connecting with Liverpool - Warrington service)
I
I
I
Warrington – Manchester: 30 minutes
Corridor 14 – Liverpool / Chester (via Chat Moss)
I Liverpool – Manchester ( - Leeds – York, corridor 5 )
I
I
Liverpool – Manchester: 30 minutes
Chester – Manchester ( - Leeds – Hull, corridor 5 )
I
I
Suburban Services
I Earlston – Central Manchester
I
Appendix B
CONTROL SHEET
Project/Proposal Name
Manchester Hub
Document Title
Phase 1 Study - Transport Modelling and Benefit
Assessment
Client Contract/Project No.
SDG Project/Proposal No.
22054302
ISSUE HISTORY
Issue No. 3
Date 3 April 09
Details: Final
REVIEW
Originator
Peter Wiener
Other Contributors
Review by
Print
Neil Chadwick
Sign
DISTRIBUTION
Clients
John Jarvis
Steer Davies Gleave:
Jim Steer
Neil Chadwick
Control Sheet
The Northern Way Transport Team
Yorkshire Forward, Victoria House,
Victoria Place, Leeds LS11 5AE
www.thenorthernway.co.uk
[email protected]
Tel: 0113 394 9590
© The Northern Way 2008

Manchester Hub Phase 1

Transcription

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