File Note
Project:
Metro Framework
Job No:
Subject:
Leeds NGT - TUBA Application
Date:
60274233 M011
23 January 2014
1.
INTRODUCTION
The Department for Transport’s standard economic appraisal software, TUBA, has been used for the
economic appraisal of the NGT scheme. This note explains the assumptions and data that have been
used as inputs to, and control of, the running of the TUBA software.
2.
GENERAL BACKGROUND INFORMATION
The NGT scheme has been modelled using the Leeds Transport Model (LTM). LTM is a multi-modal suite
of models incorporating highway (using the SATURN software) and public transport (using the CUBE
software) supply models and an overarching demand model. TUBA has been used for the highway and
public transport models separately using relevant information from each as well as demand information
from the demand model.
This note goes on to cover the key assumptions and processes used in each of the separate TUBA runs.
3.
PUBLIC TRANSPORT
Demand Segmentation
The segmentation of demand for TUBA has been derived by combining the segmentation within the
public transport model (concessionary/non-concessionary passengers) with the segmentation in the
Demand Model (which includes combinations of journey purpose, income and car availability). The
combined segmentation is shown in the table below.
Consumer User Classes
Commuting HB
Commuting HB
Commuting HB
Commuting HB
Commuting HB
Commuting HB
Education HB
Education HB
NW Other HB
NW Other HB
NW Other NHB
NW Other NHB
NW Other HB
NW Other HB
NW Other NHB
NW Other NHB
NW Other HB
NW Other HB
NW Other NHB
Low Income P
Low Income P
Medium Income
Medium Income
High Income P
High Income P
All Incomes P
All Incomes P
Low Income P
Low Income P
Low Income P
Low Income P
Medium Income
Medium Income
Medium Income
Medium Income
High Income P
High Income P
High Income P
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
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Public Transport
Car Available
No Car Available
Car Available
No Car Available
Car Available
No Car Available
Car Available
No Car Available
Car Available
No Car Available
Car Available
No Car Available
Car Available
No Car Available
Car Available
No Car Available
Car Available
No Car Available
Car Available
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NW Other NHB
High Income P
Business User Classes
20
No Car Available
Public Transport
Commuting HB
Commuting HB
Education HB
Education HB
NW Other HB
NW Other HB
NW Other NHB
NW Other NHB
Business HB
Business HB
Business NHB
Business NHB
21
22
23
24
25
26
27
28
29
30
31
32
Car Available
No Car Available
Car Available
No Car Available
Car Available
No Car Available
Car Available
No Car Available
Car Available
No Car Available
Car Available
No Car Available
Concessionary
Concessionary
Concessionary
Concessionary
Concessionary
Concessionary
Concessionary
Concessionary
All Incomes P
All Incomes P
All Incomes P
All Incomes P
Appropriate demand matrices have been extracted directly from the demand model in accordance with
the demand segmentation specified above and converted to “TUBA3” format.
Cost Skims
Cost skims were extracted from the CUBE model. The skimming process is carried out at the end of an
assignment. The PT model contains a logit sub mode choice model which determines whether or not
passengers use NGT based on the composite cost.
The cost skims were extracted separately for the four PT model assignment user classes:
 NGT Non-concessionary passengers;
 Non NGT non-concessionary passengers;
 NGT Concessionary passengers; and
 Non NGT Concessionary passengers
The matrices used for input to TUBA are as follows:
 Demand
 Composite generalised cost excluding demand-weighted average fare (in time units)
 Demand-weighted average fare (monetised)
The composite generalised cost is used because the choice of route in CUBE public transport assignment
includes logit-choice models. It includes the following components:
 wait times, perceived
 travel time, perceived, including access and egress times, and including perception of crowding on
rail services.
 boarding penalty
 transfer penalty for transit modes
 fares
Bus stop quality factors were applied to the model network as additional time incurred when accessing a
stop are included as part of the travel time.
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TUBA guidance sets out a method for introducing a new mode (in this case NGT) which relies on
additional model runs at intermediate generalised cost points. Introducing additional model runs and
outputs were not practical with a model of the scale and runtime of LTM. Therefore Composite Costs,
representing the change in the combined public transport network, was used which provides a reflection
of perceived costs and value of choice within the model and is fully consistent with the demand model.
TUBA guidance suggests that the PT time costs used for TUBA should be unweighted for business
classes. For consumer and other classes, waiting time should be factored by 2.5 and interchanging time
(walking and cycling) by 2.0. The reweighting of Composite Cost matrices for TUBA would introduce
unacceptable uncertainty about the consistency between the demand model and the appraisal. It was
therefore agreed with DfT that an appraisal approach based on behavioural model weightings would be
implemented.
The weightings used in the PT model for assignment, and subsequently applied in TUBA, were as
follows:


Walk time = 1.3
Wait factors = 2.85, 2.48 & 2.90 for the AM, IP & PM respectively across all modes
Waiting time for transferring passengers is modelled as half the service headway. Initial waiting times are
also half the headway for short trips, but are capped at 15 minutes.
Time Slices and Annualisation
The matrices described above are extracted for the following time periods, in common with the modelled
periods in the Public Transport model.



AM (average hour 0700-1000)
Interpeak (average hour (1000-1600)
PM (average hour 1600-1800)
Each of these as an annualisation factor associated with it, for the purposes of expanding modelled
results from a typical day to annual totals. The annualisation factors used in the TUBA run are derived in
the note “Annualisation of Economic Benefits”. These are summarised on the table below:
TUBA Time Period
Annualisation Factor
AM
IP
PM
772
1543
514
The annualisation factors have been calculated based on 12 hour weekday only flows, and have been
applied to the corresponding demand, costs and fares from each modelled period. In order to account for
weekend and off peak benefits as series of factors are applied to the inter peak values. These are
applied, external to the TUBA run, at a journey purpose level so that the different proportions of journey
purposes between these times can be accounted for. The factors and how they are calculated is set out
in the Annualisation of Economic Benefits report.
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HIGHWAY
Demand Segmentation
The segmentation of demand for TUBA has been based on the segmentation in the Demand Model and
shown in the table below.
Consumer User Classes
Commuting HB
Low Income
Commuting HB
Low Income
Commuting HB
Medium Income
Commuting HB
Medium Income
Commuting HB
High Income
Commuting HB
High Income
Education HB
All Incomes
Education HB
All Incomes
NW Other HB
Low Income
NW Other HB
Low Income
NW Other NHB
Low Income
NW Other NHB
Low Income
NW Other HB
Medium Income
NW Other HB
Medium Income
NW Other NHB
Medium Income
NW Other NHB
Medium Income
NW Other HB
High Income
NW Other HB
High Income
NW Other NHB
High Income
NW Other NHB
High Income
Business User Classes
Business HB
All Incomes
Business HB
All Incomes
Business NHB
All Incomes
Business NHB
All Incomes
LGV Business
All Incomes
OGV1 (% of OGV)
All Incomes
OGV2 (% of OGV)
All Incomes
Highway
Driver
Passenger
Driver
Passenger
Driver
Passenger
Driver
Passenger
Driver
Passenger
Driver
Passenger
Driver
Passenger
Driver
Passenger
Driver
Passenger
Driver
Passenger
Highway
Driver
Passenger
Driver
Passenger
Driver
Driver
Driver
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
Appropriate demand matrices have been extracted directly from the demand model in accordance with
the demand segmentation specified above and converted to “TUBA3” format.
Cost Skims
Relevant time and distance skims were extracted from the SATURN model. In this model, just 6 user
classes are assigned as follows:


Cars – Low Income
Cars – Med Income
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



Cars – High Income
Cars – Employers Business
LGV
OGV
This means that each of the demand segments identified above needs to be linked to one of the
cost/distance skims extracted from the model. For all non-business segments this is done on the basis of
income with the exception of classes 7 and 8 relating to education, all incomes. For these segments
skims relating to medium incomes are used. For business segments the relationship is clear with demand
segments being linked to the relevant car, LGV or HGV skim. The relationship used in the appraisal
mirrors that between the highway and demand model.
The “SKIM_ALL” process within SATURN has been used providing appropriate path-averaged matrices
of time (seconds) and distance (metres) subsequently converted to the required TUBA input of hours and
kilometres respectively.
The costs skims have been appropriately duplicated for each demand segment and converted to
“TUBA3” format for input to TUBA.
Time Slices and Annualisation
The matrices described above are extracted for the following time periods, in common with the modelled
periods in the Highway model.







AM1 (0700-0800)
AM2 (0800-0900)
AM3 (0900-1000)
Interpeak (average hour 1000-1600)
PM1 (1600-1700)
PM2 (1700-1800)
PM3 (1800-1900)
Each of these as an annualisation factor associated with it, for the purposes of expanding modelled
results from a typical day to annual totals. The annualisation factors used in the TUBA run are derived in
the note “Annualisation of Economic Benefits”. These are summarised on the table below:
TUBA Time Period
AM1
AM2
AM3
IP
PM1
PM2
PM3
Annualisation Factor
246
245
236
1483
240
237
233
Period Factor
727
1483
709
As with the public transport TUBA application, the highway one only calculates weekday benefits. A
series of factors are applied to the inter peak benefits in order to calculate the weekend and off peak
values. These are applied, external to the TUBA run, at a journey purpose level so that the different
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proportions of journey purposes between these times can be accounted for. The factors and how they are
calculated is set out in the Annualisation of Economic Benefits report.
4.
INFORMATION COMMON TO PUBLIC TRANSPORT AND HIGHWAY ASSESSMENTS
Modelled Years and Assessment Period
LTM has been run for two forecast years; 2016 and 2031. An assessment period of 60 years beyond the
opening year of the scheme has been used. The economic appraisal interpolates/extrapolates this
assessment period to represent 60 years of NGT operations.
TUBA Control Files
TUBA requires two control files: an economics file, containing values of time, vehicle operating cost
parameters, forecast changes to these etc. and a scheme file, containing information specific to the
scheme being assessed.
The economics file has been appropriately modified from the standard default version such that the
values and costs for the demand segmentation assumed are correctly reflected.
The scheme file have been developed containing specific information as described in this note. This
includes:




Modelled years and assessment period
Time slices
User classes
Matrix inputs
The economics and scheme files used are appended to this note.
5.
PARKING
To estimate benefit due to the parking model; total person demand, average parking search time,
average parking charge and average walk-time to destination have been extracted from the parking
model by parking site, time period, trip purpose, and modelled scenario. Average search times, charges
and walk-times have then been calculated across all parking sites and person demand summed across
parking sites, retaining the time period and trip purpose separation.
The rule-of-half has then been applied, calculating
(domin demand + test demand)/2 * (test cost – domin cost)
for each time period and purpose
This has been converted to appropriate units (using person values of time where appropriate), summed
over time period and purpose where necessary, and reported as the parking model benefit.
6.
DISCUSSION OF ERRORS AND WARNINGS
TUBA provides a list of standard warning messages in its output file. These are now discussed.
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Highway Model
Ratio of DM to DS Travel Time
This warning is triggered when there are large changes in the travel times from the DM to DS matrices.
The following extract from the TUBA Guidance Note shows the ratios at which warnings are triggered.
The following two tables summarise the number of origin-destination pairs for which this warning is
triggered and the number of DS trips affected. It can be seen that the number of trips affected by serious
warnings (i.e. ratio < 0.33 or >3) is small with 81 trips below the limit, no serious warnings occur for above
the limit scenario.
Below Limit
Year
Time Slice
2016
1
2
3
4
5
6
7
2031
1
2
3
4
5
6
7
Total
SERIOUS
Total OD Pairs
Total Trips
83
157
93
92
88
91
98
93
179
118
78
133
146
93
1542
WARNING
Total OD Pairs
Total Trips
1
6
3
4
5
4
2
2
5
5
5
23
6
9
81
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1656
2187
1449
4018
1620
1541
1787
1389
1831
1609
2079
1609
1437
1445
25657
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105
233
197
251
170
238
132
105
245
261
235
215
158
248
2794
File Note
Above Limit
Year
2016
Time Slice
1
2
3
4
5
6
7
2031
1
2
3
4
5
6
7
Total
WARNING
Total OD Pairs
Total Trips
408
8
1091
43
406
9
392
4
435
27
357
24
361
11
639
25
1160
34
642
24
339
6
365
13
801
76
284
6
7680
309
DM and DS Speeds
The table below summarises the number of origin-destination pairs across the various combinations of
time-slices and journey purposes where a warning concerned with low journey speeds (below 5kph) is
triggered. The table presents data for the Do-Minimum and Do-Something scenario, it also indicates the
number of trips affected by this error. It is evident from the table that a number of differences exist.
Following investigation into this error and analysis undertaken on selected Origin Destination movements,
it can be concluded that majority of these errors are located within the urban area of Leeds where slower
speeds might be expected.
The following chart shows the frequency distribution of trips across the range of speeds (0-5KPH) for the
DM and DS scenarios. It can be seen that there is no immediately apparent systematic difference
between the distributions for the different scenarios. In general, the overall numbers of trips affected is
relatively small and there is no significant change in either the numbers affected or the distribution
between the scenarios so no significant impact on the economic assessment is likely.
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DM
Year
Time Slice
2016
1
2
3
4
5
6
7
1
2
3
4
5
6
7
2031
Total
Total OD
Pairs
217
360
90
60
300
354
218
551
834
199
137
525
852
369
5066
DS
Total OD
Pairs
225
314
98
105
250
355
167
536
770
269
150
585
839
307
4970
Total Trips
24
37
25
27
74
73
42
81
179
63
47
214
235
100
1221
Total Trips
23
35
32
29
60
68
36
81
170
86
43
228
250
86
1228
Further analysis of the distribution of distances within these error messages indicates that over 80% of
the trips associated with these slow speeds have trip lengths less than 0.3km. These errors generally
occur when two zones are close together and have centroid connectors that either share a node or only
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need to travel a short distance on the highway network. Where this travel involves going through a
congested node the delay at that node has a disproportionate impact on the average speed.
Possible Introduction of New Mode
TUBA produces a warning when, for a given origin-destination pair one of either the DM time or DS time
is 0. This might be indicative of a new mode being introduced which can lead to problems calculating the
economic impacts of a scheme.
The table below summarises the origin-destination pairs where this problem is identified and the number
of occurrences across the various time slice and purpose combinations.
These have been investigated in more detail in the Highway Model where it was found that all the
identified OD pairs are pairs of adjacent zones connected directly to one another via centroid connectors
crossing a node and not making any part of the journey on the network “proper”. This means that they are
affected by very small changes in delay at this one node and the small difference in the skims is therefore
due to rounding error in the skimming/conversion process (TUBA being limited to 3 decimal places with
time input in hours). All time differences are in accordance with this explanation being 0.000 against
0.001 hrs in the DM and DS respectively. This, combined with the small number of OD pairs affected
suggests that this will be having a negligible impact on the result of the economic assessment. It is
evident from table below, that this error only occurs in the 2016 model scenario, this error does not occur
in the 2031 model scenario.
Introduction of New Mode Information
Year
Origin
130
222
2016
132
133
25
21
223
Destination
206
247
Total
27
46
27
27
27
248
54
54
520
2031
Total
527
22
22
22
176
No Errors
25
21
54
54
Long Journey Times
In both the DM and DS scenarios there were 798 instances of long journey times flagged up as warnings
(i.e. >10hrs). These are legitimate long journey times between distant zones in the model external to the
main modelled area.
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Public Transport Model
Ratio of DM to DS Travel Time
Ratio of DM to DS travel time lower than limit
34 DM to DS travel time lower than limit for the following were produced indicating an increase in Journey
Times. None of the warnings were serious (ratio <0.33). Only 20 trips are associated with the
movements where these warnings occur therefore the scale of these impacts will be negligible.
Ratio of DM to DS travel time higher than limit
A large number of warnings for the ratio being over the time limit (implying a drop in travel times) were
produced. There were 11,861 warnings but no serious errors. This result is justifiable since considerable
perceived journey time improvements are a result of the NGT scheme (through changes to quality factors
etc). An analysis was undertaken of all records where the DS-DM reduction in travel times was greater
than 40 minutes. O/D pairs with a reduction in journey time only occurred between long distance
destinations from the north of the corridor to the south or vice versa. This seems to be sensible as NGT is
likely to provide a direct service where an interchange would have been required on the Do Minimum.
Long Journey Times
In both the DM and DS scenarios there were 1344 instances of long journey times flagged up as
warnings (i.e. >10hrs). These are legitimate long journey times between distant zones in the model
external to the main modelled area.
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