December - The Northern Way: Transport Compact

Transcription

NORTHERN WAY
Model Development and Results for
Northern Way using the South & West
Yorkshire Dynamic Model
Report
December 2006
Prepared for:
Prepared by:
The Northern Way
Steer Davies Gleave
28-32 Upper Ground
London
SE1 9PD
+44 (0)20 7919 8500
www.steerdaviesgleave.com
Model Development and Results for Northern Way using the South & West Yorkshire Dynamic Model
Contents
1.
2.
3.
4.
5.
6.
Page
INTRODUCTION
1
Background
1
This Report
1
THE DYNAMIC URBAN MODEL
2
Introduction
2
Scope of the South and West Yorkshire Model
2
RUNNING THE SWYDM & VALIDATION
6
Validation of the base year (2001)
6
Validation of simulated years 2001 to 2004
9
Conclusions
13
TRANSPORT SCENARIOS TESTED
14
Model Results
18
PRODUCTIVITY IMPACTS
29
Agglomeration effects from the tested interventions
29
Relocation of jobs to more productive locations
31
CONCLUSIONS
33
Results
33
Land
33
Productivity impacts
33
FIGURES
Figure 2.1
South and West Case Study Model Zoning System
4
Figure 3.1
Goodness of fit for Observed vs Modelled TTW Trips (District to
District)
7
Figure 3.2
Jobs, Workforce and Population for Leeds
12
Figure 3.3
Jobs, Workforce and Population for Sheffield
12
Figure 4.1
Test 1 Generalised Time Changes
15
Figure 4.2
Test 2 Generalised Time Changes
17
Figure 4.3
Generalised Time vs Probability of Travel
18
Figure 4.4
Test 1 Change in Travel to Work to Leeds by Origin District
21
Figure 4.5
Test 1 in Travel to Work to Sheffield by Origin District
22
\\Douglas\Work\projects\6700s\6745\Work\Model Development\01 SWYDM\02 Memo\M02 - SWYDM for Phase 1 Model Results V2 6.doc
1
Figure 4.6
Test 2 Change in Travel to Work to Leeds by Origin District
24
Figure 4.7
Test 2 Change in Travel to Work to Sheffield by Origin District
25
Figure 4.8
Test 1&2 Change in Travel to Work to Leeds by Origin District
27
Figure 4.9
Test 1&2 Change in Travel to Work to Sheffield by Origin District
28
TABLES
Table 3.1
Total Travel to Work Trips into Leeds and Sheffield by Mode for
2001
7
Table 3.2
Observed & modelled jobs by District for 2001
8
Table 3.3
Observed and modelled Jobs numbers for 2001 to 2004 (000’s)
9
Table 3.4
Observed and modelled population 2001 to 2004 (000’s)
10
Table 3.5
Observed & modelled workforce 2001 to 2004 (000’s)
11
Table 4.1
Results For Test 1 versus Do Nothing
20
Table 4.2
Results For: Test 2 versus Do Nothing
23
Table 4.3
Results: Test 1&2 Versus Do Nothing
26
Table 5.1
Agglomeration effects from Test 1 (for 2010, in 2003 £pa)
30
Table 5.2
Agglomeration effects from Test 2 (for 2010, in 2003 £pa)
31
Table 5.3
Productivity gain from job relocations (£mpa)
32
APPENDICES
A
FURTHER INFORMATION ON VALIDATION
2
1.
INTRODUCTION
Background
1.1
A model, the South and West Yorkshire Dynamic Model (SWYDM) has been developed
for the Northern Way to aid the understanding of the way transport can improve
connectivity within and across the regions of the North of England, and translate this into
effects on businesses, jobs and patterns of travel to work.
1.2
The model is based on an application of the Dynamic Urban Model for South and West
Yorkshire previously developed and applied as part of research carried out for DfT on
Transport and Business Locations1. The coverage of the model is not as great as the
entire area encompassed by the Northern Way; it extends to South and West Yorkshire,
with Manchester, Humberside and Merseyside as buffer areas. It does not include Tyne
and Wear or Teesside at present, but could be further extended to cover this area as part
of a second phase of work.
This Report
1.3
The purpose of this report is to summarise the work undertaken using this model thus far
and present the results of some strategic transport scenarios.
1.4
The rest of this report is structured as follows:
•
•
•
•
•
1
Chapter 2 provides a more detailed overview of the structure of the Dynamic Urban
Model and the scope of the current South and West Yorkshire Case Study model.
Chapter 3 provides further detail relating to the model structure and the underlying
assumptions within it.
Chapter 4 provides the results of some strategic transport tests undertaken using the
model to look, at a high level, at the impact of changes in transport costs brought
about by investment, particularly how this could impact on the labour market and
businesses.
Chapter 5 extends the analysis to estimate the agglomeration and productivity
benefits that derive from these changes.
Chapter 6 summarises the key findings from the work undertaken thus far.
The Impact of Transport on Business Location Decisions,2006
1
2.
THE DYNAMIC URBAN MODEL
Introduction
2.1
The Dynamic Urban Model has been developed over several years and used in a number
of applications across the UK including the DfT’s South and West Yorkshire Case Study
Model and the Merseyside Strategic Model developed to support the Merseyside Local
Transport Plan in 2005/2006.
2.2
The model combines conventional transport modelling techniques with models of
business formation, population migration and land-use, to provide a view on how all of
these factors work together and interact over time. Starting from a base year of 20012, it
moves through simulated time in small steps, calculating how events are likely to change
in each step. The model provides the means to model processes that are much harder to
handle in traditional equilibrium models, particularly where feedback effects are
concerned, such as the effect of travel conditions on the attractiveness of a place to live.
2.3
The model is built using a general simulation package called Vensim, produced by
Ventana Systems in the US3. The model is zonal, dividing the study area into zones of
varying size. In each zone it keeps track of the population, the businesses, the
infrastructure and the land.
2.4
The main dynamics in the model are created via the idea of the attractiveness of each
zone for different types of activity. The model monitors each zone through time, and
considers how attractive it is from four points of view:
•
•
•
•
From the point of view of businesses and employers;
From the point of view of households;
From the point of view of developers, who build offices and other business premises;
From the point of view of house builders, who provide the housing.
2.5
In general, the more attractive a zone is for an activity, the more that activity will take
place, until new constraints, such as land, or the workforce, limit further growth.
2.6
A technical note is available that describes in further detail the mechanics and structures
within the model4.
Scope of the South and West Yorkshire Model
2.7
The model developed for the earlier DfT research project was not immediately suitable
for the Northern Way study, and several extensions and enhancements had to be made.
These included:
2
2001 tends to be used as the base year in most applications because of the wealth of information available from the
Census to populate the model.
3
see www.vensim.com
4
The Dynamic Urban Model V4.doc
2
•
•
2.8
The re-incorporation of household migration dynamics, which had been excluded
from the original application;
Re-basing the model to 2001 and validating the travel to work matrices for that year.
The original version of the model had a base year of 1991 to meet the requirements
of the DfT study.
A total of 104 zones is included in the new model across seventeen administrative
districts: Barnsley, Doncaster, Rotherham, Sheffield, Bradford, Calderdale, Kirklees,
Leeds, Wakefield, York, Bolsover, Chesterfield, High Peak, North East Derbyshire,
Harrogate, Selby and Bassetlaw. Eight buffer zones have been identified giving a total of
112 modelled zones. Counties adjacent to the modelled area were selected as buffers: the
rest of Derbyshire, rest of North Yorkshire, rest of Nottinghamshire, Humberside,
Manchester, rest of the North West, Lincolnshire and the West Midlands. The map
overleaf illustrates the modelled area and the zone structure (i.e. the 104 internal zones).
3
FIGURE 2.1
SOUTH AND WEST CASE STUDY MODEL ZONING SYSTEM
4
2.9
The SWYDM reads data describing current conditions in the South and West Yorkshire
area from a variety of Excel spreadsheets and text files, while information about the
transport networks have, as part of the DfT research project, been extracted from the
South and West Yorkshire Multi-Modal Study (SWYMMS) model in the form of
generalised time matrices.
2.10
Three modes are represented in the model: highways, public transport and slow modes.
Highway and public transport generalised cost matrices were taken from the SWYMMS
model. No distinction is made between public transport modes; the generalised costs are
composites of all available public transport modes.
2.11
The public transport costs comprised walk time, in vehicle time, average wait,
interchanges and fares. Highway costs were made up of in-vehicle time and operating
costs, where operating cost was a function of distance and pence per kilometre. The
generalised times for slow modes were inferred using Census Travel to Work (TTW) data
and the highway and public transport generalised costs5.
2.12
The SWYDM provides many outputs at the zone level describing jobs, workforce,
businesses, households, population etc. However the main outputs that can be used to
feed into the wider benefit calculations are:
•
•
•
5
Relocation of jobs between zones, including the impact on individual industrial
sectors.
Estimates of how many more people get into work. At present the workforce
(people in work or available for work) is a fixed proportion of the population, but a
varying fraction of the workforce is in work at any time.
Estimates of how the mix of job types on offer and the numbers of people occupying
those jobs changes. It tries to match workforce skills, by category, with job skills
required by employers. If the mix of job types changes, these jobs can only be filled
if the workforce skills are available, and these may come via bigger catchments,
inward migration or training of the existing workforce to convert them from one
skills group to another.
Although the SWYMMS model has over 500 zones, the granularity of the zoning is quite different from the modelled
area we focussed on. Some SWYMMS zones fell into several of our modelled areas whereas others spanned more
than one. We therefore undertook a process of infilling the generalised cost matrices so that each OD pair had an
associated cost. A piece of commercial software, Drivetime, was used to produce car times for all zone pairs in the
model. These times were then substituted where there was no SWYMMS information. We used the observed
mode shares from the Travel to Work Data (TTW) in the 2001 Census together with the known highway travel
costs to infer public transport costs for those flows where we did not have information from the SWYMMS model.
5
3.
RUNNING THE SWYDM & VALIDATION
3.1
Although the original model had been tested and validated, the extensions to it needed for
this work meant that these tests had to be repeated, in the following way.
•
•
•
3.2
To begin, it was necessary to run simulations with data from the 2001 Census to
establish a stable base year position.
We then ran the model for the period 2001 to 2005 to confirm it was replicating
observed trends sufficiently well.
Finally we ran the model into the future to test the transport interventions against a
‘do-nothing’ base case.
The section that follows gives the results of the first two model runs and the level of
validation obtained. Two validation tasks have been carried out after aggregating the zone
figures to Districts:
•
•
The first was checking how well the model replicated known Travel to Work (TTW)
patterns in aggregate and by mode for 2001;
The second was how well the model reproduced observed numbers of businesses,
jobs, workforce and population in 2001.
Validation of the base year (2001)
Travel to Work Patterns and Mode Shares for 2001
3.3
This section describes the validation tests undertaken for TTW forecasts for 2001. Figure
3.1 shows the goodness-of-fit for observed versus modelled TTW trips. Detailed
matrices for total TTW trips are given in Appendix Figures A1.1 and A1.2. Each point on
the graph represents a District.
6
FIGURE 3.1
GOODNESS OF FIT FOR OBSERVED VS MODELLED TTW TRIPS (DISTRICT
TO DISTRICT)
250
y = 0.9788x + 0.2435
R2 = 0.9877
Observed 000s
200
150
100
50
0
0
50
100
150
200
250
Modelled 000s
3.4
The fit is good, with the points clustered around a diagonal line with a slope of 1 – as it
should be – and a high R2.
3.5
Table 3.1 below shows observed and modelled mode shares for travel to work into Leeds
and Sheffield districts: a good fit is observed across all modes.
TABLE 3.1
TOTAL TRAVEL TO WORK TRIPS INTO LEEDS AND SHEFFIELD BY MODE
FOR 2001
Zone category
Car
Public Transport
Slow
TOTAL
Leeds observed
69% (223,395)
20% (65,506)
11% (33,761)
100% (322,662)
Leeds modelled
70% (228,468)
19% (61,265)
11% (35,791)
100% (325,524)
Sheffield observed
67% (140,855)
22% (47,237)
11% (22,173)
100% (210,265)
Sheffield modelled
69% (148,598)
20% (44,318
11% (22,967)
100% (215,883)
Jobs in 2001
3.6
Modelled jobs have been validated against Labour Market Statistics (LMS) accessed via
Nomis. Table 3.2 shows the results.
7
TABLE 3.2
Zone-category
Observed Jobs
Modelled Jobs
% discrepancy
Leeds
385,992
337,944
-12.45%
Wakefield
122,199
108,809
-10.96%
Calderdale
78,793
58,934
-25.20%
Bradford
193,447
158,383
-18.13%
Kirklees
152,984
114,512
-25.15%
Sheffield
231,568
197,111
-14.88%
Rotherham
89,026
77,267
-13.21%
Doncaster
69,498
81,243
16.90%
Barnsley
69,498
58,419
-15.94%
1,393,005
1,192,622
-14.38%
York
101,904
65,948
-35.28%
Selby
27,553
20,383
-26.02%
NE Derbyshire
27,803
17,876
-35.70%
Chesterfield
49,009
38,450
-21.55%
Bolsover
18,827
13,946
-25.93%
Harrogate
65,065
54,309
-16.53%
Bassetlaw
43,952
30,789
-29.95%
1,727,118
1,434,323
-16.95%
SWY Total
TOTAL
3.7
OBSERVED & MODELLED JOBS BY DISTRICT FOR 2001
The model under-predicts the number of jobs across all areas with the exception of
Doncaster. This has occurred as a result of model stabilisation when run with full
dynamics: the model has found a position of stability with fewer jobs than actually was
the case. Time did not allow us the opportunity to correct this, but for the purposes of
this exercise, which was to provide a preliminary assessment of the effects of broad
strategic initiatives, the near-equilibrium position of the model provides an adequate
starting point. While suitable for the scenario testing undertaken as part of this work,
further refinement of this base may be desirable if the model were to be used for scheme
assessment.
8
Validation of simulated years 2001 to 2004
3.8
This section shows the validation tests undertaken for forecasts of jobs, population and
workforce for the period 2001-2004. Actual time series data has been obtained from the
Labour Market Survey for the purpose of comparison. Tables A1.3-A1.5 in the appendix
tabulate the observed and modelled numbers of jobs, workforce and population for the
years 2001 to 2004. Table 3.3, Table 3.4 and Table 3.5 show the modelled and observed
values and changes between 2001 and 2004. In general the fit is reasonable, with the
model under predicting growth in jobs a little in the metropolitan areas of South and West
Yorkshire, but being correct in the wider areas of the city regions, while population
numbers changed only very slightly, with the model replicating this stability. Again,
while suitable for the scenario testing undertaken in this work, further model refinement
may be desirable for detailed scheme assessments.
TABLE 3.3
OBSERVED AND MODELLED JOBS NUMBERS FOR 2001 TO 2004 (000’S)
Obs Jobs
2001
Obs Jobs
2004
% change
Modelled
Jobs 2001
Modelled
Jobs 2004
% change
Leeds
386
420
8.8
338
360
6.6
Wakefield
122
138
13.0
109
108
-0.4
District
Calderdale
79
83
5.9
59
57
-3.9
Bradford
193
197
1.7
158
161
1.9
Kirklees
153
161
5.2
115
110
-3.6
Sheffield
232
246
6.3
197
195
-0.9
Rotherham
89
105
17.7
77
74
-3.8
Doncaster
69
75
8.3
81
85
4.5
Barnsley
69
75
8.3
58
56
-4.7
1,392
1,500
7.8
1,192
1,206
1.2
York
102
101
-1.2
66
65
-1.1
Selby
28
27
-0.4
20
19
-6.1
NE Derbyshire
28
25
-10.9
18
16
-9.7
Chesterfield
49
49
-0.8
38
38
-2.1
Bolsover
19
20
7.4
14
13
-5.3
Harrogate
65
65
0.4
54
54
-1.4
SWY Total
Bassetlaw
44
41
-7.7
31
29
-6.4
Sub-total
335
328
-2.1
241
234
-2.9
1,727
1,828
5.8
1,433
1,440
0.0
TOTAL
Note: Numbers may not sum due to rounding.
9
TABLE 3.4
OBSERVED AND MODELLED POPULATION 2001 TO 2004 (000’S)
Obs Pop 2001
Obs Pop
2004
% change
Mod Pop 2001
Mod Pop 2004
% change
Leeds
716
720
0.6
675
676
0.1
Wakefield
315
320
1.4
285
286
0.4
Calderdale
192
194
1.0
147
143
-2.5
Bradford
471
481
2.2
381
385
1.0
Kirklees
389
393
1.1
323
321
-0.6
Sheffield
513
516
0.6
473
473
-0.1
Rotherham
248
252
1.6
216
216
-0.2
District
Doncaster
287
289
0.7
222
227
2.3
Barnsley
218
221
1.3
186
185
-0.5
3,349
3,386
1.1
2,908
2,912
0.1
York
181
185
2.0
155
155
0.0
Selby
77
77
0.8
47
44
-5.5
NE Derbyshire
97
97
0.5
57
51
-9.6
Chesterfield
99
100
0.9
93
94
0.1
Bolsover
72
73
1.9
47
44
-7.3
Harrogate
152
154
1.7
124
123
-0.7
Bassetlaw
108
110
2.3
72
67
-6.6
Sub-total
786
796
1.2
652
578
-11.4
4,135
4,182
1.1
3,503
3,490
-0.4
SWY Total
TOTAL
10
TABLE 3.5
District
OBSERVED & MODELLED WORKFORCE 2001 TO 2004 (000’S)
Obs WF 2001
Obs WF 2004
% change
Mod. WF 2001
Mod. WF 2004
% change
Leeds
357
356
-0.4
278
278
0.1
Wakefield
147
155
5.5
117
117
0.4
Calderdale
95
92
-3.6
61
60
-2.6
Bradford
205
209
1.9
155
157
1.0
Kirklees
180
188
4.2
135
134
-0.6
Sheffield
237
235
-0.8
184
184
-0.1
Rotherham
117
118
0.8
87
87
-0.2
Doncaster
95
102
7.1
86
88
2.5
Barnsley
95
102
7.1
69
69
-0.6
1,528
1,557
1.9
1,172
1,174
0.2
York
94
92
-1.9
70
70
0.0
Selby
40
38
-5.0
21
20
-5.6
NE Derbyshire
45
46
2.9
24
22
-8.8
Chesterfield
45
46
1.8
38
38
0.1
Bolsover
32
31
-2.8
18
16
-7.5
Harrogate
76
77
1.7
55
55
-0.7
SWY Total
Bassetlaw
48
52
8.1
31
28
-6.8
Outer Area
T t l
TOTAL
380
382
0.5
257
249
-3.2
1,908
1,938
1.6
1,429
1,423
-0.4
3.9
Figures 3.2 and 3.3, below, plot the observed and modelled trends between 2001 and
2004 for Leeds and Sheffield. Modelled behaviour is always below the actual, but the
graphs show that the model is stable and producing gradual changes in jobs and
population that are consistent with actual events.
11
FIGURE 3.2
JOBS, WORKFORCE AND POPULATION FOR LEEDS
Jobs, Workforce and Population for Leeds: Observed Vs. Modelled
800
Observed values in 000s
700
600
Modelled Jobs
500
Observed Jobs
Modelled workforce
400
Observed Workforce
Observed Population
300
Modelled population
200
100
0
2001
FIGURE 3.3
2002
2003
2004
JOBS, WORKFORCE AND POPULATION FOR SHEFFIELDF
Jobs, Workforce and Population for Sheffield:
Observed Vs. Modelled
600
Modelled Jobs
500
Observed Jobs
000s
400
Modelled workforce
Observed Workforce
300
Observed Population
Modelled population
200
100
0
2001
2002
2003
2004
12
Conclusions
3.10
The model allows the population, workforce, jobs and travel to work patterns all to vary
through simulated time as conditions change. The tests show that it replicates travel to
work patterns well, and that although it under-estimates the actual numbers of jobs
somewhat, it generates stable behaviour, with trends in jobs and population over 20012004 that closely match actual events. We conclude that it does provide a sound basis
for preliminary testing of strategic policy directions.
13
4.
TRANSPORT SCENARIOS TESTED
4.1
Two transport scenarios have been tested in the SWYDM with a focus on the area
extending from Leeds in the North to Sheffield in the South, including Rotherham, Leeds,
Bradford, Wakefield and Sheffield.
•
•
The first was to simulate interventions that reduced public transport and highway
generalised times for trips within each of the Leeds and Sheffield City Regions;
The other looked at reducing highway and public transport generalised times for
trips between the two City Regions.
Test 1: Urban Intervention
4.2
The first test reduces transport costs within each urban area. Generalised time reductions
of 10% have been made uniformly between zones which form a contiguous urban area,
around either Leeds or Sheffield. The coverage of this change is shown in the figure
below; zones affected are shaded in green. This means that the generalised time of all
travel between and within zones in each of the contiguous areas is reduced by 10%, but
there is no change between the two separate areas.
14
FIGURE 4.1
TEST 1 GENERALISED TIME CHANGES
15
Test 2: Inter Urban Intervention
4.3
The second test is an inter-urban scheme with three levels of generalised time reduction.
•
•
•
4.4
The biggest improvement is city centre to city centre, with a reduction of 15%
between the zones shaded dark green in Figure 4.2. The cut is applied to road and
rail travel between the two city centres.
The next level is urban area to city centre and vice versa, with a 10% reduction. This
is applied to movements between zones shaded light green and zones shaded dark
green in Figure 4.2, except where they are in the same City Region; in other words, it
applies to the Leeds urban area to Sheffield city centre or the Sheffield urban area to
Leeds city centre. It is assumed that such trips benefit from generalised cost
reductions on the highway network and city centre to city centre rail travel, but the
total reduction is rather less than for city centre to centre trips because of the travel
in the urban area.
The third level of improvement is a 5% reduction for urban area to urban area,
applied to trips between the two areas shaded light green. Such trips benefit from
reduced inter-urban highway and public transport costs but experience either
access/egress costs on the local highway network or PT feeder trips.
In this test there are no changes to intra-urban area generalised times.
16
FIGURE 4.2
TEST 2 GENERALISED TIME CHANGES
17
Model Results
4.5
Results of these model runs now follow. In both cases the test is compared to the do
nothing position with no changes to generalised time. The impacts on travel-to-work
patterns and changes in employment are each reported. The results correspond to the
point ten years after the change in generalised time is introduced.
4.6
Some assumptions have to be made about land availability. If jobs and/or the population
are to grow, they need to be accommodated in business premises and housings units and
these require land. The model is given estimates of the land occupied by buildings in the
base year, but to generate forecasts we have to say how much additional land will be
available for new developments. For this work we have assumed a 10% margin in each
zone. Despite this, the availability of housing in some zones is constrained in future
simulated years and some oscillatory behaviour occurs as demand exceeds supply and
then falls back as the model compensates by reducing inward migration rates. To develop
more accurate forecasts, additional work would be required to determine how much land
might be available in each zone in future. This could form part of the second phase of
model development. However, to indicate the likely form of the consequences of strategic
changes to transport, which is the aim of this work, the current assumptions are a
reasonable starting point.
4.7
Many of the responses to changes in generalised times are modelled using deterrence
curves that plot how people’s willingness to accept travel costs falls as those costs rise.
These curves are used for travel-to-work trips and business-to-business attractiveness.
The travel-to-work deterrence function contained in the model has been calibrated for the
local area and looks like the figure below where the x-axis indicates generalised time
(GT) and the y-axis is the proportion of people willing to accept that GT for a commute
trip. It can be seen that the impact of a given change in the generalised time depends on
whether this occurs on a steep or a flatter part of the curve.
4.8
By and large we might expect Test 1 to focus more on shorter trips, of which a proportion
will lie on the steeper part of the curve, where the marginal impact is greatest. Test 2, by
contrast, focuses on trips more to the right hand end of the curve where the impact is less.
FIGURE 4.3
GENERALISED TIME VS PROBABILITY OF TRAVEL
18
Test 1: Urban Highway and PT Interventions
4.9
Changes in travel to work movements are greater for Leeds than for Sheffield, as Leeds is
a stronger economic centre to begin with. The headline changes are:
•
•
•
•
•
•
•
4.10
6
An increase in inter-district movements, in particular for Bradford and Wakefield,
and corresponding decreases in intra-district movements with the largest changes for
Wakefield, Kirklees and Leeds.
Trips starting in Bradford and ending in Leeds increase by 3,055.
Trips starting in Wakefield and ending in Leeds increase by 2,265.
The net increase for trips into Leeds is close to 5,500 (at the bottom of the table)
whereas for Sheffield trips fall by 750.
Jobs are attracted into Leeds, which sees a growth of nearly 6,000, but many of these
are transferred from the surrounding areas as activity becomes focussed in the city
centre. The net increase in South and West Yorkshire jobs is more modest, at around
1,167.
On the other hand the number of South and West Yorkshire residents in work rises
by 1,923 as access to jobs improves. This has been achieved by reducing the pool of
job vacancies.
There is some growth in households across the region as it becomes a more attractive
place to live, with increases in Bradford, Sheffield and Wakefield.
Table 4.1 below shows the results of the urban intervention test. The last three columns
indicate the change in jobs, businesses and households as a result of the intervention. The
other six columns show the change in travel to work movements; for example the change
in trips starting in Leeds and staying within Leeds is -1,325. The top half of the table
shows the results for the core area, where the full model dynamics were allowed to
operate, while the bottom half shows the results for the outer and buffer zones where jobs
and population were held constant but travel to work patterns were allowed to vary6.
The reason for this is to avoid model boundary effects. Zones close to the edge of the model have only part of their
catchments represented, so if employers there find it harder to recruit from the core area, say, they will suffer more
in the model than they might in reality because in practice they could seek to recruit from zones excluded from the
model.
19
TABLE 4.1
RESULTS FOR TEST 1 VERSUS DO NOTHING
Intra
District
Trips
Origin
InterDistrict
Trips
Buffer
Trips
TOTAL
Trips
TTW
Trips to
Sheffield
TTW
trips to
Leeds
Jobs
Busines
s
H
Holds
Barnsley
48
-34
-6
7
-171
53
-183
-15
8
Doncaster
70
-56
0
14
-51
16
-25
-35
6
Rotherham
-346
305
0
-41
174
6
-132
-8
4
Sheffield
-659
808
-9
140
-659
9
-975
-6
114
Bradford
-977
2,108
0
1,130
-1
3,055
1,916
66
495
Calderdale
-220
296
0
76
-1
69
-1,546
-111
16
Kirklees
-1,361
1,429
0
67
-17
788
-2,463
-138
-4
Leeds
-1,325
1,601
-2
273
-11
-1,325
5,983
391
-223
Wakefield
-1,851
2,148
-42
256
-40
2,265
-1,408
-88
96
SWY Sub
Total
-6,623
8,606
-60
1,923
-778
4,936
1,167
55
513
Bassetlaw
18
-56
1
-37
-42
1
0
0
0
Bolsover
13
-48
1
-34
-53
0
0
0
0
Chesterfield
67
-98
2
-30
-127
0
0
0
0
High Peak
5
-5
0
0
-8
1
0
0
0
NE
Derbyshire
-238
283
-1
45
345
0
0
0
0
Harrogate
-258
295
-4
32
0
276
0
0
0
Selby
18
-5
0
13
0
72
0
0
0
York
-70
76
0
6
0
57
0
0
0
Gr.
Manchester
-131
239
0
108
-27
57
0
0
0
Buffer Zones
-6
-11
-6
-23
-64
41
0
0
0
-583
670
-8
79
25
506
0
0
0
-7,206
9,276
-68
2,002
-753
5,442
1,167
55
513
Other Sub
Total
TOTAL
4.11
The maps below show the changes in travel to work movements into Leeds and Sheffield.
The net change in trips into each district is shown in the text box in the top left hand
corner of each diagram.
20
FIGURE 4.4
TEST 1 CHANGE IN TRAVEL TO WORK TO LEEDS BY ORIGIN DISTRICT
21
FIGURE 4.5
TEST 1 IN TRAVEL TO WORK TO SHEFFIELD BY ORIGIN DISTRICT
22
Test 2: Inter Urban Highway and PT Interventions between Leeds and Sheffield
Regions
4.12
Table 4.2 the results of the inter-urban intervention test. As before, the last three columns
indicate the changes in jobs, businesses and households while the other six columns show
the change in travel to work movements. For example, the change in trips starting in
Leeds and staying within Leeds is -28.
4.13
The scale of change is smaller than for test 1, but we see:
•
•
•
A modest increase in trips between districts and a slight reduction in trips within
them;
A modest increase in the number of people in work; and
A small net increase in the number of jobs, with some displacement towards
Rotherham and Sheffield, which gain, from the other Districts, such as Leeds and
Kirless, which see a small reduction.
TABLE 4.2
Origin
RESULTS FOR: TEST 2 VERSUS DO NOTHING
Intra
District
Trips
InterDistrict
Trips
Buffer
Trips
TOTAL
Trips
TTW trips
to
Sheffield
TTW
trips
to
Leeds
Jobs
H.
Holds
Business
6
19
-2
24
34
-6
-10
0
8
Doncaster
-33
39
0
6
10
0
-116
-6
1
Rotherham
82
-8
0
74
66
14
277
28
11
Sheffield
-133
291
-7
152
-133
161
413
29
-16
Bradford
-23
12
0
-11
8
9
-83
-5
-2
Calderdale
-8
3
0
-4
0
3
-42
-4
-1
Kirklees
-39
38
0
-1
9
7
-138
-7
0
Leeds
-28
24
0
-4
51
-28
-199
-4
-2
Wakefield
-75
82
-2
5
105
-21
27
8
1
SWY Sub Total
-250
501
-11
240
150
140
130
38
1
Bassetlaw
1
26
-1
26
23
0
0
0
0
Bolsover
-2
26
-1
23
22
0
0
0
0
Chesterfield
2
17
-1
18
55
0
0
0
0
High Peak
0
5
0
5
5
0
0
0
0
-73
106
0
32
87
3
0
0
0
Harrogate
0
-1
0
0
0
0
0
0
0
Selby
1
-1
0
0
0
0
0
0
0
York
0
0
0
0
0
0
0
0
0
Gr. Manchester
-2
12
0
11
16
1
0
0
0
Buffer Zones
18
66
18
101
26
0
0
0
0
Other Sub Total
-55
257
15
216
234
4
0
0
0
TOTAL
-306
758
4
456
384
143
130
38
1
Barnsley
NE Derbyshire
23
FIGURE 4.6
TEST 2 CHANGE IN TRAVEL TO WORK TO LEEDS BY ORIGIN DISTRICT
24
FIGURE 4.7
TEST 2 CHANGE IN TRAVEL TO WORK TO SHEFFIELD BY ORIGIN
DISTRICT
25
Test 1+Test2: Inter Urban and Within Region Improvements
4.14
As a final test we have combined both interventions. The result of this test is shown
below.
•
•
•
The biggest gain in jobs is again in Leeds, although slightly less than for test 1, at
5,700.
The net increase in jobs across SWY is slightly more than for test 1, while 2,500
more people are in work across the whole area.
Outside the core SWY area we have again held the jobs and businesses fixed, as in
the other tests, in order to avoid overstating model boundary effects.
TABLE 4.3
Origin
RESULTS: TEST 1&2 VERSUS DO NOTHING
Intra
District
Trips
InterDistrict
Trips
Buffer
Trips
TOTAL
Trips
TTW trips
to
Sheffield
TTW
trips
to
Leeds
Jobs
H.
Holds
Business
Barnsley
53
-12
-8
33
-133
44
-199
-16
17
Doncaster
31
-10
0
21
-40
16
-150
-43
7
Rotherham
-240
286
0
45
250
19
251
28
18
Sheffield
-663
962
-16
283
-663
168
-473
28
90
Bradford
-987
2,115
0
1,128
5
3,059
1,841
63
493
Calderdale
-225
297
0
72
0
70
-1,585
-115
16
Kirklees
-1,391
1,456
0
64
-8
788
-2,591
-146
-4
Leeds
-1,366
1,633
-2
264
29
-1,366
5,742
383
-224
Wakefield
-1,903
2,206
-43
260
48
2,232
-1,477
-92
96
SWY Sub Total
-6,691
8,932
-70
2,171
-513
5,029
1,360
90
507
York
18
-26
0
-8
-16
1
0
0
0
Bolsover
11
-17
0
-6
-30
0
0
0
0
Chesterfield
45
-51
0
-7
-79
0
0
0
0
High Peak
4
2
0
6
-3
1
0
0
0
NE Derbyshire
-238
331
-1
91
390
3
0
0
0
Harrogate
-256
293
-4
32
0
273
0
0
0
Selby
19
-7
0
13
0
71
0
0
0
Bassetlaw
-69
76
0
6
0
57
0
0
0
Gr. Manchester
-134
253
0
119
-9
56
0
0
0
2
101
2
104
-35
41
0
0
0
-599
954
-5
350
218
503
0
0
0
-7,290
9,886
-75
2,521
-295
5,532
1,360
90
507
Buffer Zones
Other Sub Total
TOTAL
26
FIGURE 4.8
TEST 1&2 CHANGE IN TRAVEL TO WORK TO LEEDS BY ORIGIN DISTRICT
27
FIGURE 4.9
TEST 1&2 CHANGE IN TRAVEL TO WORK TO SHEFFIELD BY ORIGIN
DISTRICT
28
5.
PRODUCTIVITY IMPACTS
5.1
The analysis so far has described how changes in accessibility and attractiveness affect
the location of jobs. But these changes have other important impacts on the economy.
•
Agglomeration economies. It is well documented that firms in denser locations
are more productive. By locating in proximity, firms are more likely to interact,
which aids the spreading of knowledge and technology. By locating where access
to labour is better, firms are able to fill vacancies more quickly and they are more
likely to find workers with a better skills match to what they are seeking. The
effect density has on productivity is often referred to as agglomeration
economies.
Transport affects agglomeration through two routes. Transport improvements
mean firms and workers are effectively located closer together, and the findings
presented earlier in this report illustrate that jobs will relocate as a consequence of
changes to transport – which will also affect the job density across locations.
Both effects lead to productivity gains that are not recognised in standard
transport appraisal.
•
Relocation of jobs to more productive locations. For various reasons, the
productivity of similar types of activities differs across space (one of these being
agglomeration economies). Typically jobs located in city centres tend to yield a
higher output per worker than jobs elsewhere, even after correcting for
differences in firms functions or workers’ skills. If commuting was not costly,
workers would compete for jobs in these highly productive (and therefore highly
paid) locations.
However, commuting costs act as a constraint on the supply of labour to city
centres. Where transport improvements enable the growth of employment in
such highly productive locations, there are therefore additional productivity gains.
5.2
Guidance from the Department for Transport now allows us to quantify these
productivity gains. We apply this guidance here to investigate how the two simulated
transport interventions may affect the productivity of firms in the South and West
Yorkshire area. Further detail about the agglomeration methodology can be found in a
paper issued alongside this report.7
Agglomeration effects from the tested interventions
5.3
7
The interventions modelled reduce the cost of movement from and to selected zones
within the modelled area. This, in itself, results in positive agglomeration effects for
these locations. Secondly, the transport improvement causes jobs to relocate. This
can cause further agglomeration effects, but can be either positive or negative for
individual locations, depending on whether they are net gainers or losers of jobs.
“Agglomeration Simulations of Northern Way Update”, SDG, December 2006
29
Test 1
5.4
Table 5.1 summarises the impacts from Test 1 on the two City Regions. The figures
are the net differences between the test case and the do-nothing.
TABLE 5.1
AGGLOMERATION EFFECTS FROM TEST 1 (FOR 2010, IN 2003 £PA)
Sheffield CR
£pa
Leeds CR
£pa
Barnsley
10,516
Harrogate
8,960
Doncaster
-43,075
Selby
-7,719
Rotherham
161,948
Leeds
8,494,366
Sheffield
1,694,806
Bradford
3,371,871
Bassetlaw
-22,658
Calderdale
-3,218
Bolsover
-7,665
Kirklees
500,943
Chesterfield
-15,380
Wakefield
1,819,598
NE Derbyshire
99,146
York
-2,655
Barnsley
10,516
Total Productivity
1,877,638
Total Productivity
14,192,662
Per worker
2.9
Per worker
11.1
5.5
As predicted, the table shows that the areas directly affected by the transport
improvement (primarily Sheffield and Leeds districts and some of the surrounding
areas) see significant positive agglomeration effects. Other areas are negatively
affected because they lose jobs to the central areas. The latter effect is to some extent
exacerbated by the nature of the simulated intervention, for the zones that see transport
cost improvements in this test are strictly delineated, being the green zones in Figure
4.1. In a real intervention we would expect zones outside the green areas also to gain
some reductions in transport costs to the city centres and we might therefore still have
found some positive agglomeration effects in the areas surrounding the two cities.
5.6
The most striking result of Test 1 is the distribution between the two City Regions.
Despite the fact that the improvements affect the cost of movement in Leeds and
Sheffield to a similar extent, only about 10% of the overall productivity gains from
agglomeration fall to Sheffield. The contribution of the employment relocation from
Sheffield to Leeds does not contribute materially to this finding. It appears that
Leeds’s favourable position, both economically and geographically, means it can
derive much larger agglomeration benefits from intra City Region improvements in
transport.
5.7
As can be seen the overall effect is relatively small. Across the two City Regions, the
per-worker increase in productivity is around £3pa for Sheffield and £11pa for Leeds.
30
Test 2
5.8
Table 5.2 presents the results from Test 2. Note that we do not have a benchmark
measure of the relative size of the simulated interventions (such as scheme cost or the
total potential benefits delivered). We can therefore not read anything into the relative
total magnitude of the productivity impacts of the two tests. Our interpretation
therefore focuses on the differences in the distribution of impacts across the city
regions and districts.
TABLE 5.2
AGGLOMERATION EFFECTS FROM TEST 2 (FOR 2010, IN 2003 £PA)
Sheffield CR
£pa
Leeds CR
£pa
Barnsley
-450
Harrogate
-2,244
Doncaster
-2,787
Selby
-2,481
Rotherham
191,458
Leeds
2,031,580
Sheffield
Bassetlaw
2,484,739
907
Bradford
689,683
Calderdale
-4,787
Bolsover
2,232
Kirklees
99,015
Chesterfield
2,279
Wakefield
474,683
NE Derbyshire
85,704
York
Barnsley
Total Productivity
Per worker
2,764,081
4.2
Total Productivity
Per worker
-8,394
-900
3,276,154
2.6
5.9
We see the same general pattern across the districts for Test 2 as we did for Test 1.
The districts directly affected by the simulated changes in transport costs see
significant positive productivity gains, whilst some of the peripheral areas are
marginally worse off due to loss of employment to the centres.
5.10
Again, it is striking to see that the findings mirror those from the employment impacts.
When transport is improved between the City Regions the economic impacts tend to
be more equally distributed, with the net increases now being much more evenly
balanced. Leeds still receives the largest share of the total productivity gains, but the
impact per worker is larger in Sheffield.
Relocation of jobs to more productive locations
5.11
8
The findings from the DUM model presented earlier in this report tell how jobs
relocate within the South and West Yorkshire area following the two interventions.
What we then need to know is how the productivity differs across locations. To be
more specific, we need to know how much more or less productive a given worker
will be in different locations. This is not an easy task. The only evidence that does
exist8 has a relatively crude geographical disaggregation, only distinguishing between
“Estimated Wage Relativities for England”, NERA, February 2002
31
South Yorkshire, West Yorkshire and Rest of Yorkshire. The evidence suggests that
jobs located in West Yorkshire are about 7% more productive than equivalent jobs
located in South Yorkshire and Rest of Yorkshire.
5.12
As a result, interventions that tend to lead to relocation of jobs to West Yorkshire will
tend to lead to increased productivity overall, while attracting jobs away from West
Yorkshire will reduce productivity. Table 5.3 illustrates what this means for the two
interventions tested.
TABLE 5.3
PRODUCTIVITY GAIN FROM JOB RELOCATIONS (£MPA)
Test 1
Test 2
Leeds CR
51.1
-7.9
Sheffield CR
-42.9
8.2
Other modelled areas
-1.3
-0.4
Total
6.9
-0.1
5.13
The intra City Region test causes relocation of jobs from Sheffield and other modelled
areas to Leeds and, since West Yorkshire as a location is more productive than other
modelled locations, this leads to a net gain in productivity.
The net gain is
significant, but smaller than the gains from agglomeration.
5.14
The inter City Region test shows the opposite; a few jobs relocate from Leeds to
Sheffield and other modelled areas, resulting in a net productivity loss, albeit
insignificantly small.
5.15
These results suggest a potential conflict between overall growth in productivity and
distributional impacts. While inter City Region improvements may lead to a more
balanced growth across cities, there is the potential for larger productivity gains
overall by encouraging the growth of cities that are already more productive.
However, we would also repeat the health warning that comes with this calculation.
The productivity per worker data on which they are based is at a scale much more
aggregate than that which the model operates. There will therefore be productivity
differences within West and South Yorkshire that are not adequately addressed in this
calculation.
32
6.
CONCLUSIONS
Results
6.1
For the sub-region of South and West Yorkshire the first two tests produce quite
different patterns of impact. Improving access to the city centres (test 1) has tended to
concentrate activity in Leeds, driven largely by the improvement in commuting and
thus the ability of centrally located employers to recruit. The converse is that it
becomes rather harder for employers located outside central Leeds to recruit, and thus
many of the ‘new’ jobs in Leeds are, in effect, transferred from the neighbouring
areas. Sheffield was not able to capitalise on the changes in the same way, and while
the reasons for this needs further investigation, it seems likely that they are due to the
relative sizes of the two city’s economies in the base year.
6.2
The biggest change is the number of people in work, as indicated by the total number
of TTW trips. This has been absorbed without an equal increase in job numbers by
reducing the pool of job vacancies.
6.3
Test 2 has less impact on commuting because it affects longer trips (see Figure 4.3)
and its consequences are more driven by how it affects business-to-business activities.
Sheffield and Rotherham are now the main gainers, in terms of jobs, although the
changes across the model are quite modest.
6.4
It appears that combining the two tests produces impacts that are largely the sum of
the two9. This follows from the way the two tests have different impacts on
businesses and employment, test 1 primarily affecting commuting, test 2 primarily
affecting business to business activity.
Land
6.5
The model is currently constrained by land availability, both for businesses and
households and revising the assumptions used here may lead to different results.
However we would argue that the types of responses reported here are realistic and
credible and give a good indication of the ways different interventions can shape
outcomes. In the past we have undertaken reviews of local plans and Regional Spatial
Strategies (RSS) in order to model available land as realistically as possible. However
this is a fairly significant task and thus was somewhat outside of the scope of what
could be achieved in the time available for this work. This is a topic that might be
revisited if the work were to be extended later.
Productivity impacts
6.7
9
We have found that the conclusions relevant to the changes in employment largely
hold true for productivity impacts from increased agglomeration. Whilst most areas
are positively affected in both tests, the intra City Region improvements lead to gains
to the stronger of the two economies, Leeds, that far outweighed those to Sheffield.
Inter City Region improvements, however, seem to benefit both economies to a much
Although not reported in Chapter 5, this applies to the productivity gains too.
33
more similar extent.
6.8
On the other hand, jobs located in Leeds tend to be more productive than equivalent
jobs in Sheffield. Since the intra City Region intervention tend to cause jobs to
relocate to Leeds this means an overall productivity gain. The inter City Region test
causes the opposite to happen – a net relocation of jobs from Leeds to Sheffield and a
corresponding reduction of productivity. These finding suggests a possible conflict
between distributional and overall productivity objectives.
6.9
However, the productivity impacts of job relocations is based on much cruder
evidence than the agglomeration analysis and this conclusion should be treated with
some caution.
34
APPENDIX A
FURTHER INFORMATION ON VALIDATION
Appendix
TABLE A1.1
MODELLED 2001 DISTRICT-DISTRICT TRAVEL TO WORK TRIPS (EXCLUDING BUFFER ZONES)
Sheffield
Bradford
Calderdale
Kirklees
Leeds
Wakefield
York
Bolsover
Chesterfield
NE Derbyshire
Harrogate
Selby
Bassetlaw
Doncaster
Rotherham
Barnsley
42,090
3,389
5,443
5,971
362
46
2,782
3,567
9,272
19
36
24
38
6
107
101
1,503
87,377
4,871
1,334
108
6
122
611
2,413
105
43
30
20
12
713
969
Barnsley
Zone Name
Doncaster
Destination
Rotherham
5,251
8,539
44,393
23,440
37
3
162
254
759
5
1,445
413
328
1
38
4,123
Sheffield
3,436
1,259
12,363
157,536
57
7
276
388
635
2
1,629
2,844
4,666
1
11
1,018
Bradford
Calderdale
Kirklees
Leeds
Wakefield
York
Bolsover
Chesterfield
NE Derbyshire
Harrogate
Selby
Bassetlaw
59
15
5
10
110,507
9,008
6,595
33,329
1,034
19
0
0
0
276
32
0
136
8
5
15
14,433
44,897
7,615
4,977
493
5
0
0
0
8
11
0
3,603
120
180
588
13,348
5,377
96,113
16,589
9,857
50
4
3
6
12
46
9
532
197
32
80
23,784
555
5,491
232,936
8,793
1,094
1
0
1
2,923
1,362
6
9,690
2,765
621
598
2,484
172
6,981
18,361
75,555
147
7
5
5
44
1,106
53
6
27
1
1
54
2
19
1,712
113
70,620
0
0
0
511
3,157
1
97
208
3,482
3,884
3
0
12
17
38
0
5,668
5,385
882
0
3
3,077
72
119
965
7,876
2
0
10
14
28
0
3,130
18,088
6,762
0
2
649
159
99
793
11,577
2
0
11
14
28
0
781
7,325
12,929
0
1
249
8
12
1
3
565
9
56
9,852
140
883
0
0
0
43,833
3,851
0
88
769
27
16
112
5
74
2,756
2,082
4,790
1
1
0
110
19,638
12
163
2,619
3,176
2,321
4
0
10
33
123
3
803
225
67
0
20
29,351
Appendix
TABLE A1.2
OBSERVED 2001 DISTRICT-DISTRICT TRAVEL TO WORK TRIPS (EXCLUDING BUFFER ZONES)
Bolsover
Chesterfield
NE Derbyshire
Harrogate
Selby
Bassetlaw
150
434
66
155
131
147
1,236
1,715
88
313
528
565
72
90
2,067
872
150
378
2,822
2,885
54
81
882
20,682
1,459
281
3
0
6
807
138
3
4,295
762
84
3
6
0
63
36
6
102,168
16,865
5,868
206
21
27
21
185
127
27
5,891
230,826
8,840
1,775
21
27
21
3,693
1,658
42
613
5,346
20,644
83,081
382
36
45
30
325
2,659
116
103
149
4,429
466
58,887
3
12
0
1,526
1,618
9
52
33
12
8,631
2,858
1,274
9
6
2,076
102
48
9
1,164
24,856
3,842
9
9
344
88
15
1,361
8,528
11,472
9
15
356
427
1,578
3
3
3
44,696
346
9
2,721
4,045
6
6
0
519
14,995
22
172
27
380
299
147
12
48
27,534
6,803
420
193
1,770
3,083
6,043
6,066
3,697
214
108
229
2,048
2,525
55,796
22,245
143
70
323
1,097
741
9,863
158,541
265
113
543
2,211
91
64
222
126,898
4,496
3,889
42
45
117
6,885
53,297
6,019
1,503
211
289
901
8,560
8,063
580
444
339
763
14,882
2,009
2,489
1,114
518
831
1,763
York
42
210
21
92
253
Bolsover
39
83
500
1,204
12
12
27
Chesterfield
51
135
428
2,789
21
0
12
161
238
1,138
9,283
54
18
24
210
66
63
30
102
1,347
127
249
9,457
Selby
109
424
36
105
280
82
174
6,159
Bassetlaw
157
2,202
1,464
1,768
42
18
27
193
Doncaster
4,843
Barnsley
Wakefield
374
Leeds
54
Kirklees
97
Calderdale
119
Bradford
30
Sheffield
81
Rotherham
York
Destination
50,640
2,159
Doncaster
1,774
77,214
Rotherham
2,922
4,759
Sheffield
3,165
1,928
Bradford
178
96
Zone Name
Barnsley
Calderdale
Kirklees
Leeds
Wakefield
NE Derbyshire
Harrogate
The fit for some districts may appear poor but it should be noted that the total movements are often small e.g. Bassetlaw to Bradford is only 42 trips. The Mean
Square Error of observed versus modelled TTW trips is 73%. This appears high, but the variation in magnitude of TTW trips across destinations is very large. The
model replicates the order of magnitude of TTW trips with reasonable accuracy.
Appendix
TABLE A1.3
OBSERVED AND MODELLED JOBS FOR 2001 TO 2004
Observed jobs
Modelled jobs
Origin Zone
2001
2002
2003
2004
2001
2002
2003
2004
Leeds
386
400
405
420
338
343
351
360
Wakefield
122
125
130
138
109
108
108
108
Calderdale
79
79
81
83
59
58
57
57
Bradford
193
195
196
197
158
161
160
161
Kirklees
153
153
153
161
115
113
112
110
Sheffield
232
234
240
246
197
196
195
195
Rotherham
89
92
97
105
77
76
75
74
Doncaster
69
74
75
75
81
82
83
85
Barnsley
69
74
75
75
58
57
56
56
York
102
100
104
101
66
66
66
65
Selby
28
28
31
27
20
20
19
19
NE Derbyshire
28
26
26
25
18
17
17
16
Chesterfield
49
47
48
49
38
38
38
38
Bolsover
19
18
20
20
14
14
13
13
Harrogate
65
67
68
65
54
54
54
54
Bassetlaw
44
46
43
41
31
30
29
29
Appendix
TABLE A1.4
OBSERVED AND MODELLED POPULATION FOR 2001 TO 2004
Observed population
Modelled population
Origin Zone
2001
2002
2003
2004
2001
2002
2003
2004
Leeds
716
716
715
720
675
675
675
676
Wakefield
315
317
318
320
285
285
286
286
Calderdale
192
193
193
194
147
145
144
143
Bradford
471
474
478
481
381
383
384
385
Kirklees
389
391
391
393
323
322
322
321
Sheffield
513
513
513
516
473
474
474
473
Rotherham
248
250
252
252
216
217
217
216
Doncaster
287
288
288
289
222
224
225
227
Barnsley
218
219
220
221
186
186
186
185
York
181
182
183
185
155
155
155
155
Selby
77
77
77
77
47
46
45
44
NE Derbyshire
97
97
97
97
57
55
53
51
Chesterfield
99
99
100
100
93
94
94
94
Bolsover
72
73
73
73
47
46
45
44
Harrogate
152
152
153
154
124
123
123
123
Bassetlaw
108
109
109
110
72
70
68
67
Appendix
TABLE A1.5
OBSERVED AND MODELLED WORKFORCE FOR 2001 TO 2004
Observed workforce
Modelled workforce
Origin Zone
2001
2002
2003
2004
2001
2002
2003
2004
Leeds
357
348
349
356
278
278
278
278
Wakefield
147
147
156
155
117
117
117
117
Calderdale
95
95
92
92
61
61
60
60
Bradford
205
204
213
209
155
156
156
157
Kirklees
180
187
189
188
135
135
135
134
Sheffield
237
247
248
235
184
184
184
184
Rotherham
117
115
116
118
87
87
87
87
Doncaster
95
94
96
102
86
86
87
88
Barnsley
95
94
96
102
69
69
69
69
York
94
93
93
92
70
69
70
70
Selby
40
39
39
38
21
21
21
20
NE Derbyshire
45
47
51
46
24
23
23
22
Chesterfield
45
50
51
46
38
38
38
38
Bolsover
32
32
33
31
18
17
17
16
Harrogate
76
76
77
77
55
55
55
55
Bassetlaw
48
51
47
52
31
30
29
28
Appendix
Appendix
CONTROL SHEET
Project/Proposal Name:
NORTHERN WAY
Document Title:
Model Development and Results for Northern Way
using the South & West Yorkshire Dynamic Model
Client Contract/Project Number:
SDG Project/Proposal Number:
206745
ISSUE HISTORY
Issue No.
Date
Details
1
18/12/2006
Final version of issue
REVIEW
Originator:
Andrew Davies
Other Contributors:
Lars Rognlien, John Swanson, Neil Chadwick
Review By:
Print:
John Swanson
Sign:
(electronic approval)
DISTRIBUTION
Clients:
Steer Davies Gleave:
Control Sheet

December - The Northern Way: Transport Compact

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