CPB

Worthwhile Use of Travel
Time and Applications in
the United Kingdom
Discussion Paper

176
Richard Batley
University of Leeds,
United Kingdom

Thijs Dekker
University of Leeds,
United Kingdom

Iven Stead
Department for Transport,
United Kingdom

Roundtable

Corporate Partnership
Board

176
Richard Batley
University of Leeds,
United Kingdom

Thijs Dekker
University of Leeds,
United Kingdom

Iven Stead
Department for Transport,
United Kingdom

Roundtable

Worthwhile Use of Travel
Time and Applications in
the United Kingdom
Discussion Paper

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Cite this work as: Batley, R., T. Dekker and I. Stead (2020), “Worthwhile Use of Travel Time and Applicationsin the United Kingdom”, International Transport Forum Discussion Papers, No. 2020/04, OECD Publishing,Paris.

WORTHWHILE USE OF TRAVEL TIME AND APPLICATIONS IN THE UNITED KINGDOM | DISCUSSION PAPER NO. 2020/04 | ITF ROUNDTABLE 176

Acknowledgements
This paper is largely underpinned by the study “Provision of market research for value of time savings andreliability” (RM3388 SB-1569) undertaken by the Arup, ITS Leeds and Accent consortium for the UK
Department for Transport (referred to henceforth as the “Department”).
The authors acknowledge the contributions of various parties which helped facilitate that study, as follows:•

Staff from the UK Department for Transport’s Transport Analysis and Strategic Modelling (TASM)
Division, in particular the project officer Adam Spencer, and colleagues Alice Crossley, Margot
Shatz and Marcus Spray, who guided the study on a day-to-day basis.

•

Members of the Department’s wider Project Board, who reviewed the progress and direction of
the study at key milestones.

•

Stakeholders from various public and private sector organisations across the UK transport sector,
who attended workshops at the outset of the study and at the end of the survey design andtesting phase.

•

Operators of railway stations, “other PT” services, motorway service stations, petrol stations andother areas where interviewers worked.

•

Members of the Analytical Challenge Team who reviewed the study, namely Professor Glenn
Lyons (University of West of England, United Kingdom), Professor Jonas Eliasson (Royal Instituteof Technology, Sweden) and Professor Kay Axhausen (Swiss Federal Institute of Technology).

While the study was guided, monitored and reviewed by the Department, the results reported, findings
inferred, and views expressed in the present paper are the responsibility of the authors and not necessarily
a statement of the Department’s policy or guidance.

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Table of Contents
Introduction ........................................................................................................................................ 6
United Kingdom’s 2014-15 value of travel time savings study: The rationale ...................................... 7
Rationale for the investigation into productive time use ................................................................ 8
Estimating business value of travel time savings and the willingness-to-pay approach...................... 10
Key findings from the 2014-15 value of travel time savings study ..................................................... 13
Recommended multipliers ............................................................................................................ 14
Analysis of the Hensher Equation.................................................................................................. 16
Commentary on Hensher parameter analysis ............................................................................... 17
Approach to implementation and lessons for other countries .......................................................... 19
DfT consultation process ............................................................................................................... 20
Appraisal issues surrounding modal variation in values ................................................................ 22
Variations in values over time ....................................................................................................... 24
Modelling, forecasting, appraisal and land use policy implications: DfT case studies ......................... 25
Case study 1: High-speed two ....................................................................................................... 26
Case study 2: Roads Investment Strategy 1 .................................................................................. 27
Summary and synthesis ..................................................................................................................... 28
Notes................................................................................................................................................. 30
References ........................................................................................................................................ 31
Annex A. DfT portfolio testing............................................................................................................ 33

FiguresFigure 1. Activities undertaken by employees during trip by mode ................................................... 9
Figure 2. Business values of time by mode and distance .................................................................. 21
Figure 3. Continuous function versus four bands for employers’ business trips by car .................... 21
Figure 4. Continuous function versus four bands for employers’ business trips by rail .................... 22
Figure 5. Income relationships estimated jointly and separately by mode....................................... 23
Figure A.1. Adjusted benefit to cost ratios for a range of strategic road network schemes ............. 33
Figure A.2. Adjusted benefit to cost ratios for a range of rail schemes ............................................ 33
Figure A.3. Adjusted benefit to cost ratios for a range of local road schemes.................................. 34

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TablesTable 1. Summary of survey design .................................................................................................... 8
Table 2. Business value of travel time savings by method of calculation, mode and distance ......... 12
Table 3. VTTS for a Level 1 appraisal with illustrative distance bands .............................................. 14
Table 4a. VTTS multipliers, with SP2 VTTS taken as base .................................................................. 15
Table 4b. VTTS multipliers, with SP3 VTTS for “A few seats free but had to sit next to someone/could
not sit with people travelling with. Some standing.” taken as base.................................................. 15
Table 4c. VTTS multipliers, with “seated 100% load” taken as base ................................................. 16
Table 4d. VTTS multipliers, with SP1 VTTS taken as base.................................................................. 16
Table 5a. Hensher parameter analysis for employer SP survey ........................................................ 18
Table 5b. Hensher parameter analysis for employee RP survey ....................................................... 18
Table 6. HS2 appraisal impacts ......................................................................................................... 26
Table 7. RIS1 appraisal impacts ......................................................................................................... 27© OECD/ITF 2020

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Introduction
As a leading country in transport modelling and appraisal, the United Kingdom has made a significant
contribution towards the development and application of methods for valuing the non-market effects oftransport infrastructure and policies. Among these effects, the value of travel time savings (VTTS) is usually
the largest single component of the monetised benefits of transport infrastructure projects and policies.
Furthermore, time-related benefits such as the value of travel time reliability (VTTR) and crowding andcongestion relief are conventionally valued as multipliers of the VTTS.
Prior to the most recent study (Arup, ITS Leeds and Accent, 2015a), there had been three waves of national
VTTS studies in Britain. First, a series of research studies during the 1960s, the results of which weresynthesised and adopted by the Department in appraisal guidance. Second, the MVA, ITS Leeds and TSU
Oxford (1987) study, which led to updated guidance. Third, the 1994 study by Accent and Hague Consulting
Group (AHCG, 1999), which was re-analysed by ITS Leeds (Mackie et al., 2003) and adopted in the newly(at that time) constituted WebTAG guidance. WebTAG, an acronym for WEB-based Transport Appraisal
Guidance, was established as a definitive handbook for developing the economic case for transport
investment schemes funded by the UK public purse.
Over the subsequent 15 years, these values were regularly updated by the UK Department for Transport(DfT) 1 for income growth but were not resurveyed. This was despite material changes taking place, notonly to income but also to travel costs, demography and the mix of travel by purpose and trip length.
Possibly more significant was that the world was changing in other ways: the internet revolution, thequality and comfort of travel, working practices and, perhaps most fundamentally, the ways in whichpeople perceived time spent travelling. These phenomena were beyond the scope of simple updating of
historical values in line with income growth.
Against this background, DfT embarked upon a research programme in 2009 to review the theoretical,
methodological and evidential basis of WebTAG guidance on VTTS. The Department commissioned thefollowing: scoping and review studies on the valuation of time savings for non-work travel (ITS Leeds, John
Bates and DTU, 2010) and for business travel (ITS Leeds, John Bates and KTH, 2013); studies to further
understand the uncertainty around non-work values (ITS Leeds and John Bates, 2013); and meta-analysis
of non-work values post-1994 (ITS Leeds, Arup and URS, 2013). DfT documented its conclusions from thesestudies in the DfT (2013) report. Among them was the decision to commission a major research study to
re-survey national values of not only VTTS but also VTTR. The Arup, ITS Leeds and Accent consortium won
the commercial bidding competition for the contract in June 2014.
The study took place in two phases in the challenging timeframe of 11 months. DfT held an inception
meeting with the contracted organisation on 3 June 2014, after which the DfT convened and led aworkshop with stakeholders potentially affected by revisions to VTTS guidance. Phase 1 of the study, which
took place from June to September 2014, involved the development and testing of methods forundertaking the requisite market research. This phase culminated in an extensive pilot survey conductedin two waves, and the estimation of behavioural values on this dataset using discrete choice modelling
methods. Having reviewed the Phase 1 report, and convened a further workshop with stakeholders, theDepartment took the decision to proceed to Phase 2.
Phase 2 took place from October 2014 to April 2015. Using the methods developed in Phase 1, Phase 2
involved a substantial field survey and detailed modelling to complete estimation of the values of travel
time using the collected data. DfT released the final report (Arup, ITS Leeds and Accent, 2015a) after a
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period of assimilation of the results, along with their own interpretation of the findings and proposals for
implementation of the findings in WebTAG (DfT, 2015). DfT then consulted with stakeholders potentiallyaffected by the proposed changes to VTTS. On completion of this consultation (DfT, 2016a), DfT finalised
the new proposals on VTTS and announced new WebTAG guidance in July 2016 (DfT, 2016b). This became
definitive guidance in March 2017 (DfT, 2017a).
This paper highlights insights and conclusions from the UK experience, including the following:•

the rationale for the investigation, particularly around productive time use and the relationshipwith VTTS•

the approaches investigated and the reasons for the selected willingness-to-pay (WTP) approach•

the key findings from the 2014-15 study•

the approach to implementation and lessons for other countries•

modelling, forecasting, appraisal and land use policy implications due to distance-based VTTS andpotentially time-varying VTTS if the level of time use increases with technologies over time.

United Kingdom’s 2014-15 value of travel timesavings study: The rationale
The DfT specified the following aims for the 2014-15 research study:•

to provide recommended, up-to-date national average values of in-vehicle travel time savings forbusiness and non-work travel based on primary research using modern, innovative methods•

to investigate the factors that cause variation in the values (e.g. by mode, purpose, income, trip
distance or duration, productive use of travel time, etc.) and use this to inform recommended
segmentation of the values•

to improve understanding of the uncertainties around the values, including estimating
confidence intervals of the recommended values•

to consistently estimate values for other trip characteristics for which values are derived from thevalues of in-vehicle time savings.

In pursuit of these aims, Arup, ITS Leeds and Accent employed an analysis framework based upon the
primary dimensions of trip purpose and mode of travel (see Table 1). Key features of this framework
included the following:•

The walk and cycle research included in the initial scope encountered significant methodologicalchallenges. It was eventually reported to DfT separately from the mechanised modes, and with
only tentative recommendations. (Arup, ITS Leeds and Accent, 2015b)•

The Department, informed by the scoping studies, directed that VTTS should be valued using WTP
methods for both non-work and business travel.

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The latter directive in respect of business reflected the Department’s interest in replacing thelong-standing Cost Saving Approach (CSA) (e.g. Harrison, 1974) for valuing business travel timesavings with WTP – if the evidence base was adequate to support such a change.

•

While the direction was to implement WTP methods primarily through stated preference (SP)data, the Department encouraged the use of revealed preference (RP) data as a validation device.

•

The Department directed that business travel should be valued from the separate perspectivesof employee and employer. With regards to the latter, the employee is effectively spending thebusiness’s time and money. It is important, therefore, that the employee reports a WTP
representative of his or her employer’s interests. While directed to examine business using WTP,
the Department specifically excluded the Hensher equation (Hensher, 1977) from the scope.

Table 1. Summary of survey design
Travel mode

Trip purpose
CommuteOther NonWork

Employees’
Business

Employers’
Business

Car

SP

SP

SP

SP

Bus

SP

SP

N/A

N/A

Rail*

SP & RP

SP & RP

SP & RP

SP

Other PT**

SP

SP

SP

SP

Walk & Cycle

SP

SP

N/A

N/A

SP
experiments

Covariates

SP1: Time

IncomeSP2: Time &Reliability

Distance/Duration

Productive Time
SP3: Time &Quality(e.g. crowding,congestion andother types oftime)

Trip Typeetc.

Notes: SP = Stated Preference; RP = Revealed Preference; N/A = Deemed inapplicable on the grounds that trip
rates are relatively low.
* “Rail” refers to heavy rail.
** “Other PT” refers to other public transport, namely trams, light rail and the London Underground.

Rationale for the investigation into productive time use
The presence of “Covariates” in Table 1 demonstrates a particular interest in the extent to which featuresof the traveller or trip – such as the traveller’s income or the length of the trip – might influence VTTS
estimates. Arup, ITS Leeds and Accent conducted an extensive search for factors causing variation in thevalues, involving a large number of traveller and trip features collected from the SP and RP surveys.
As with other national studies, significant evidence showed that VTTS increased with income. This wasprevalent in all mode and purpose segments except for bus and “Other PT” commuting. It also found that
VTTS varied with the travel time and cost of the trip. The implication of these results was that VTTS also
increased with trip distance, given that travel time and cost are closely related to distance, with the costsensitivity tending to decline more steeply than the time sensitivity.

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A test of the influence of a wide range of factors on VTTS showed that time use (i.e. the traveller’s ability
to do something else while travelling, to work or surf the net), geography (i.e. area, urban/rural), current
travel conditions (i.e. congestion and crowding) and current road types had little or no impact on VTTS, allelse equal. This could indicate that travellers do not necessarily relate the completion of hypotheticalchoice tasks back to the real-world journey to which these choice tasks relate.
The result relating to time use is of particular interest to the present paper. It is useful, then, to digressslightly and provide additional background concerning the survey approach in this regard. Focussing here
upon employees’ business travel, since this represents a key segment potentially affected by the
productivity of travel time, respondents were reminded of their reported one-way trip time and asked
approximately how much of that time was spent undertaking work and non-work related activities.
Figure 1 illustrates that the main activities undertaken by mode were:•car: listening to music (53 minutes on average) and driving (17 minutes)•

train: using smart phone/eBook/tablet/computer (33 minutes, of which 17 minutes was work
related), work-related use of laptop/tablet (26 minutes), doing nothing/relaxing/looking out thewindow (22 minutes), listening to music (14 minutes) and other job-related work (13 minutes).
Figure 1. Activities undertaken by employees during trip by mode (average minutes)

Fickling et al. (2009) used slightly different categories. They showed the proportion of travellers
undertaking work-related activities as follows: preparing for a meeting (38%), making and receiving calls(43%), talking to colleagues or other (12%), use of a laptop (23%), use of a PDA or Blackberry (25%), otherwork related to employment (36%). By comparison, in the 2014-15 survey, 35% used a laptop, 56% useda Smartphone or Blackberry and 29% did other work related to employment. There has been a notable© OECD/ITF 2020

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increase in the use of electronic devices for work-related activities on-train since 2009. It would seem clearthat a large proportion of rail travel time is spent on non-work activities, though the caveat remains that
more recent research has exposed the propensity for measurement error in self-reporting of time spenton activities (Robles Alejo, 2017).
DfT’s long-standing CSA approach to valuing business travel time savings had attracted growing challenge
post-1994 because of its inability to reflect the increasing opportunities for people to work while travelling.
On the other hand, the WTP approach should in principle reflect how travel time is used, given current
travel conditions and opportunities to use that time. While the results indicated that VTTS did not varywith time use, this is not to say that time use is unimportant. It is possible that the results could have beendifferent if the opportunities to use travel time productively had been substantively different for the trips
in progress when surveyed. Another possibility is that the importance of time use was not fully capturedby the SP exercise, despite the best efforts of the Arup, ITS Leeds and Accent team.
On the deterministic variation of VTTS, Arup/ITS Leeds/Accent found evidence of size and sign effects,
although these varied in their nature and strength across modes, choice tasks and attributes (i.e. time andcost). It is conceivable that such effects were an artefact of the SP exercises. However, even if they werenot, a given reference point could become less relevant as travellers and travel conditions change overtime. Notwithstanding such considerations, appraisal ideally requires reference-free estimates of VTTS.
The Arup, ITS Leeds and Accent modelling work sought to identify the prevalence of size and sign effects,
before eliciting values which neutralised these effects. For the majority of the aforementioned sources ofvariation, multipliers of the VTTS were estimated using one category of a given attribute as the base inwhich case the multiplier was set to a value of one. This is explained later in this paper. This means thatthe base estimate of the VTTR related to an individual and trip at the base values for these covariates.

Estimating business value of travel time savingsand the willingness-to-pay approach
Whereas willingness to pay (WTP) is very much the dominant approach for valuing non-work VTTS, thereis less unanimity on the best approach for valuing business VTTS. The rationale behind the approach
followed in the 2014-15 study was developed in the course of the ITS Leeds, John Bates and KTH (2013)
scoping study – and can be summarised as follows.
Analysing business trip-making is inherently more complex than analysing trips made for non-work
purposes where people are making their own travel decisions involving their own time and money. Models
require a greater degree of interpretation and judgement. The scoping study expressed reservations
regarding the CSA traditionally employed by DfT. It essentially reiterated long-standing and well-rehearsed
concerns that not all travel time is unproductive and not all time savings would be converted into
productive use to the benefit of the company. In particular, the digital revolution has increased thepotential for using travel time productively. Other arguments against the CSA surround difficulties in
estimating the value of the marginal productivity of labour (which underpins the approach), the benefitsof spending more time at the destination (with a client or at a sales pitch), and the benefits of avoiding

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overnight accommodation and travel in unsocial hours. However, these effects should be taken into
account in a WTP-based valuation, eliciting a reliable representation of what the company would pay.
It might be argued that employers should be the focus of a WTP-based valuation since it is they who will
actually be purchasing the time savings. ITS Leeds, John Bates and KTH (2013) suggested surveying
employers as to how much they would be prepared to pay to reduce their employees’ travel time. In
theory, if the CSA is a valid representation of the value of business travel time savings, then the employer
should simply express a WTP in line with the CSA. However, in practice, there are recognised difficulties
and uncertainties surrounding a valuation approach based on surveying employers. For example, the datacollection costs are high, there are challenges involved in identifying the appropriate employer agent, andeven then the agent may not be entirely familiar with specific kinds of business trips. Furthermore, such avaluation would require a representative sample of travel-using employers.
A potentially complementary approach is to undertake employee surveys – using either RP or SP
approaches – but couched within an awareness of company travel policy. Compared to collecting
employers’ surveys, obtaining large samples of employees travelling on company business is relatively
straightforward. The business scoping study indeed demonstrated that SP studies along these lines tend
to be the norm. The concern is whether employees are able to make choices in response to hypotheticalscenarios that accurately represent the company’s willingness-to-pay or, worse still if they simply
represent their own willingness-to-pay. If the employee is to be an acceptable proxy for the employer,then that employee needs to respond in accordance with the company’s interests as opposed to personal
interests. An interesting case here is self-employed business travellers, where one might presume thatcompany and private interests are one-and-the-same and that SP responses would, therefore, reflect what
the company would pay.
In the scoping study, ITS Leeds, John Bates and KTH expressed a preference for WTP-based approaches,using different methods for corroborative and interpretive reasons. They proposed that well designed
quantitative research, when properly conducted, can provide a coherent story as to how business travel
time savings are valued. Better still, it can elicit direct estimates of WTP that lend themselves tocomparison against the CSA.
Against this background, the business travel component of the 2014-15 Arup, ITS Leeds and Accent study
was informed by three sources of survey evidence, namely employer SP, employee SP, and employee RP.
The information collected on income and working hours in the course of the survey also allowed
comparison with the CSA. In order to reconcile these different perspectives on business VTTS, two lines ofenquiry were pursued, investigating: 1) the degree of similarity between SP-based estimates of VTTS andthe CSA; and 2) the degree of consistency between various properties of the VTTS emanating from thedifferent surveys.
Generally speaking, Arup, ITS Leeds and Accent found similar values for the two different SP analyses(employer and employee), and (for some occupational types) similar values for the SP analyses and theCSA. This was particularly so for blue-collar workers, who would be expected to have relatively low
productivity while travelling. For briefcase travellers, who are more likely to be productive, SP-based values
appeared to be lower than the CSA. Moreover, the degree of similarity between the SP-based VTTS andthe CSA was partly dictated by the trip length distribution and did not hold over all distances. The selfemployed values were lower than those for employees. This result would seem plausible if the time saved
is taken as leisure.
Turning to the properties of the VTTS estimates, the theoretically-driven CSA embodies an income
elasticity of one (i.e. implying that VTTS increases in direct proportion to income) and applies a constantunit value to all trips (e.g. irrespective of time, cost, distance, travel conditions, productivity, etc.). By© OECD/ITF 2020

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contrast, the SP-based VTTS exhibited income elasticities within the 0.3 to 0.4 range (and significantly lessthan one) and significant variability by several of the aforementioned dimensions (and notably by
distance). Thus, while there was some correspondence between the actual estimates of VTTS from the
CSA and SP analyses, this correspondence did not extend to key properties of those estimates.
Table 2. Business value of travel time savings by method of calculation, mode and distance(2014 perceived prices and values, GBP/hr)
Source/method

Distance

All modes

Car

Bus

Other PT

Rail

Previous WebTAG (2014 prices
and values)

All distances

25.47

24.43

15.64

24.72

30.07

CSA estimate from National
Travel Survey (NTS) 2010 to2012 data (2014 prices andvalues)All distances

28.27

27.05

13.13

26.33

36.46

Employees’ business SP
reweighted to NTS 2010-2012(2014 prices and values)

All distances

18.23

16.74

N/A

8.33

27.61

<5 miles

5.39

5.27

N/A

8.33

n/a

5-20 miles

8.84

8.79

N/A

8.28

10.19

>=20 miles

21.14

19.51

N/A

N/A

28.99

>=50 miles

24.55

22.53

N/A

N/A

32.56

>=100 miles

28.62

25.74

N/A

N/A

N/A

Notes: All modes: Distance-weighted, income option 1, SP1 Δt=10.
Tool version 1.1: PT cost is imputed for a trip with a zero cost, and employers paying for EB trips.
WebTAG Other PT is Underground passengers.
WebTAG car EB is weighted average of driver and passenger (vehicle occupancy of 1.2).

Having reconciled the various sources of evidence on business VTTS, Arup, ITS Leeds and Accentrecommended that the employee SP survey should be the definitive source of evidence taken forward to
the implementation tool (discussed later in this paper). This was because it generated – with somequalifications – similar values to the employer SP survey, but offered a considerably more substantial
dataset amenable to generating statistically robust values for a range of trip and traveller segments.
Furthermore, the employee dataset was more comparable to the NTS data used as the basis for the sample
enumeration. That is to say, the tool applied the choice model from the employee SP to business trips in
the NTS to derive an average value over specified segmentations, as shown in Table 2.
The average distance-weighted personal income across the NTS sample was GBP 46 615 (2014 prices andvalues). This gives a business VTTS in 2014 perceived prices of GBP 28.27 using the CSA (second row, thirdcolumn). This compares to the CSA-based WebTAG values which have an “All modes” value of GBP 25.47(first row, third column). Comparing these values to the SP-based values re-weighted for NTS (third row),
it can be seen that the VTTS for employees’ business across all modes is GBP 18.23. This is 72% of the
WebTAG value. There is also substantial variation by mode, with Other PT lowest at GBP 8.33 and Railhighest at GBP 27.61 for the “All distances” values. As proportions of the WebTAG values, these range
from 34% (Other PT) to 92% (Rail). Moreover, Table 2 shows that the SP-based values are substantially lessat low distances than the previous WebTAG values, but as trip distances increase the SP-based values
increase to be close to the previous WebTAG values at long distances (>50 miles).

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Key findings from the 2014-15 value of travel timesavings study
Returning to a more general perspective across all modes and purposes, the Arup, ITS Leeds and Accent
based their recommendations concerning VTTS for use in appraisal on several technical considerations:•

In the short-term, the headline estimate of VTTS should be based on SP1 with Δt = 10.2. In themedium term, pending further development, there could be a case for replacing SP1 with SP3.
Arup, ITS Leeds and Accent recognise, however, that each of the three SP experimental tradeoffs, namely time/money (SP1), time/money/reliability (SP2), and time/money/quality (SP3),
could potentially be used to elicit the headline estimate of VTTS.

•

Appraisal values should continue to disaggregate VTTS by trip purpose since there were material
differences between the three trip purposes.

•

VTTS should continue to be distance-weighted but should be disaggregated into distance bands
to reduce the level of approximation between the standard VTTS values and the “real” scheme
level VTTS value. Arup, ITS Leeds and Accent recommended further work to determine
appropriate distance bands for use in appraisal (discussed later in this paper).

•An “All modes” value should be used for non-work trips due to non-work VTTS reflecting some
self-selectivity between modes. Modal values should be used for business, as it was consideredthat observed differences across modes represented real differences.

•

In the case of business, appraisal values should be based on those employees who reported that
their employers would be willing to pay for time savings.

•

Distinction should be drawn between appraisals of small and medium-sized schemes (referred toas Level 1 in the United Kingdom), and appraisals of major schemes and policies (Level 2) andsignificant “user pays” initiatives (Level 3). For Level 1 appraisal, standard national values of time
can be used. For Level 2, the values may be amended to more accurately reflect local conditions.
For Level 3, appraisal values derived from bespoke quality surveys would be appropriate.

For level 1 and 2 appraisals, additional recommendations were made with two different income options.
Income option 1 uses the observed variations in income by person and trip to calculate an average value,
whereas income option 2 treats all non-work trips as having the same average household income.
•Non-work: for Level 1 appraisals with VTTS distance-banded, as recommended above, income
option 2 should be applied. If, however, distance-banding is not implemented – at least in the
short-term – income option 1 should be used. For Level 2 appraisals, income option 1 should beapplied at the appropriate regional level.

•

Business: for Level 1, income option 1 should be applied using national data and applied Level 2
at the appropriate regional level.

Table 3 shows these recommendations for a Level 1 appraisal. This table also presents, for the purpose ofcomparison, the previous WebTAG values converted to a comparable base (2014 perceived prices).

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Table 3. VTTS for a Level 1 appraisal with illustrative distance bands(2014 perceived prices, GBP/hr)
SourceDistance

CommuteOther nonwork

Employees’ businessAll modes

All modes

All
modes

Car

Bus

Other
PT

Rail

24.72

30.07

WebTAG

All

7.62

6.77

25.47

24.43

15.64

Re-surveyed
values

All

11.21

5.12

18.23

16.74

N/A

<20 miles

8.27

3.62

8.31

8.21

N/A

6.49

16.05

15.85

N/A

9.27

28.62

25.74

N/A

20 to 100 miles>= 100 miles

12.15

27.61
8.33

10.11
28.99

Notes: Distance weighted, “All” distance values based on income option 1. For distance-banded values, non-workbased on income option 2 (household income = GBP 49 684) and business on income option 1. VTTS imputed for
PT trips with zero cost, SP1 VTTS, ∆t=10, employers paying for EB trips, tool version 1.1.

Recommended multipliers
In addition to the overall VTTS, Arup, ITS Leeds and Accent also made recommendations for adjustments
to these values for different types of time. This paper presents them as multipliers. In doing this, accountmust be taken of the different VTTS coming from the different SP experimental trade-offs (i.e. SP1-3), as
well as the general approach of using SP1 values for the overall recommendations about VTTS.
With reference to the SP2 results, the valuations for average time presented in the reliability experimentexceeded the SP1 values by a factor of 1.31 for commute, 2.17 for other non-work, and 1.52 for
employees’ business. Now it might be argued that by implying the possibility of unreliability, there is somesuggestion of (greater) congestion. However, the questionnaire stated that the situation was the same as
the reference trip, while the reasons for variation in overall travel time were attributed to “improvements
in traffic control”, and the variation (unreliability) was attributed to “breakdowns, unplanned roadworks,
or general traffic”. It is not obvious that this has to imply that SP2 values exceed SP1 values, particularly
not at the scale seen for other non-work, where the value was well in excess of that for heavy traffic.
This also presented a problem for the reliability ratio (i.e. relative sensitivity to standard deviation of travel
time and mean travel time), as Arup, ITS Leeds and Accent had to decide whether to take the value of thestandard deviation relative to the SP2 VTTS or the SP1 VTTS. The former gave values of 0.33 (commuting),
0.42 (business) and 0.35 (other non-work), the latter gave values of 0.43, 0.64 and 0.77 respectively. The
former values are low by “received wisdom” (though the evidence base for that is not especially strong),
while, at least for other non-work, the SP1-based result is close to the previous WebTAG value of 0.8.
On balance, Arup, ITS Leeds and Accent interpreted these values relative to the SP2 time multiplier, on the
grounds of internal consistency within the SP2 experiment. So, for example, the reliability ratio for car was
taken from the ratio of the value of travel time to the value of average travel time from SP2 and multipliedby the relevant SP1 VTTS to get an absolute valuation of the standard deviation (Table 4a). The same
approach was taken for the early and late multipliers. This is also in line with the way reliability ratios were
derived in other work (e.g. Black and Towriss, 1993). However, this was not a strongly-based
recommendation, and Arup, ITS Leeds and Accent acknowledged the conundrum of explaining the highSP2 time multiplier. The fact that the SP2 VTTS were rather higher than those for SP1 in the case of car
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and rail does mean, of course, that the implied valuations of reliability will be lower. Without a clear
understanding of the reason for the difference between SP1 and SP2 VTTS, this must be considered anarbitrary judgment.
Table 4a. VTTS multipliers, with SP2 VTTS taken as baseMode

Multiplier

CommuteOther non-work

Employees’
business

Car

Reliability ratio

0.33

0.35

0.42

Bus

Value of early

-2.69

-3.20

N/A

Value of late

2.88

2.52

N/A

Value of early

-2.40

-2.98

-1.66

Value of late

1.75

2.24

1.95

Value of early

-1.77

-2.34

-1.55

Value of late

2.86

3.21

2.76

Other PTRail

The remaining multipliers may be considered of lesser importance for reliability, but it is worth notingsome additional complications which arose in the case of public transport crowding (SP3). For the public
transport modes, Arup, ITS Leeds and Accent opted to align the results with the level of crowding closest
to the SP1 VTTS. For bus and other public transport, this corresponds to the level “A few seats free but had
to sit next to someone/could not sit with people travelling with. Some standing” (Table 4b). For rail,
however, it corresponds to a load factor of 100% (i.e. “all seats taken but no standing” (Table 4c).
Table 4b. VTTS multipliers, with SP3 VTTS for “A few seats free but had to sit next to someone/could not sit
with people travelling with. Some standing.” taken as baseMode

Multiplier

CommuteOther nonwork

Employees’
businessBus

Plenty of seats free and did not have to sit next to anyone.

0.85

0.83

N/A

A few seats free but had to sit next to someone/ could not
sit with people travelling with.

0.89

0.84

N/A

A few seats free but had to sit next to someone/ could not
sit with people travelling with. Some standing.

1.00

1.00

N/A

No seats free – a few others standing.

1.24

1.30

N/A

No seats free – densely packed.

2.14

2.32

N/A

Plenty of seats free and did not have to sit next to anyone.

0.95

1.00

1.00

A few seats free but had to sit next to someone/could not
sit with people travelling with.

0.97

1.00

1.00

A few seats free but had to sit next to someone/ could not
sit with people travelling with. Some standing.

1.00

1.00

1.00

No seats free – a few others standing.

1.13

1.10

1.17

No seats free – densely packed.

1.70

1.87

1.78

Other PT

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Table 4c. VTTS multipliers, with “seated 100% load” taken as baseMode

Multiplier

CommuteOther non-work

Employees’
businessRailseated 50% load

0.73

0.72

0.75

seated 75% load

0.79

0.72

0.76

seated 100% load

1.00

1.00

1.00

seated one passenger per m2

1.09

1.14

1.13

seated three passengers per m2

1.31

1.39

1.36

standing 0.5 passengers per m2

1.16

1.21

1.29

standing one passenger per m2

1.19

1.27

1.38

standing two passengers per
m2

1.32

1.57

1.56

standing three passengers per
m2

1.57

1.79

1.61

standing four passengers per
m2

1.86

2.17

2.03

Turning to the congested values for car, in all cases the SP1 VTTS fell inside the range between the lightand heavy traffic values from SP3, though Arup, ITS Leeds and Accent acknowledged the difficulty ofjustifying this apart from the commuting case. Since, in any case, these ratios were considered to be only
indicative of the possible impact of congestion, Arup, ITS Leeds and Accent divided the SP3 values for thethree levels by the SP1 VTTS to get the multipliers in Table 4d.
Table 4d. VTTS multipliers, with SP1 VTTS taken as baseMode

Multiplier

CommuteOther non-work

Employees’
business

Car

Free-flow

0.51

0.47

0.42

Light traffic

0.72

0.83

0.68

Heavy traffic

1.37

1.89

1.26

Value of free-flow

0.99

1.22

N/A

Value of slow down

1.39

1.36

N/A

Value of dwell time

0.68

1.57

N/A

Value of headway

1.68

1.60

N/A

Bus

Analysis of the Hensher Equation
Before the 2014-15 Arup, ITS Leeds and Accent study, UK appraisal value of business travel time savings(VBTTS) in WebTAG was based on the CSA, which remains commonplace across most of the developed
world. Following the conclusions of the research, WebTAG adopted values based on WTP (as in Table 3
above). However, outside of the core remit of the study, a substantial body of evidence was collected on
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the Hensher parameters p, q and r. The results from this work can be found in Appendix H of thePhase 2 report (Arup, ITS Leeds and Accent, 2015a). Unfortunately, due to a routing problem in theemployee questionnaire, the Hensher parameter SP questions were not asked, and the evidence that
follows is therefore restricted to employer SP and employee RP.
The current conventional representation of the Hensher equation was first set out by Fowkes et al. (1986),
and can be expressed as follows:
where:VBTTS = (1-r-pq) MPL+ MPF + (1-r) VW + r VL

r is the proportion of travel time saved that is used for leisurep is the proportion of travel time saved that is at the expense of work done while travellingq is the relative productivity of work done while travelling relative to at the workplace
MPL is the value of the marginal product of labour
MPF is the value of extra output due to reduced (travel) fatigue
VW is the difference between the employee’s valuations of contracted work time and travel time
VL is the difference between the employee’s valuations of leisure time (i.e. the residual time given thework contract) and travel time
Note that p and r also give rise to companion parameters p* and r*, which relate to the proportion of total
travel time used to do work, and the proportion of total travel time at the expense of leisure, respectively.
Drawing on Wardman et al. (2015), there are two noteworthy reduced forms of the Hensher equation
which can be estimated using date from the 2014-15 Arup, ITS Leeds and Accent study. The first of these,so called HE1, was advanced by Fowkes (2001) and assumes that business travellers are, on average,
indifferent between business travel time and working at their normal workplace, so that VW=0.
HE1: VBTTS = (1-r-pq) MPL + MPF + r VL

The second reduced form, so called HE3, applies where there is equilibrium between the allocation of time
to work and leisure, so that VW=VL.
HE3: VBTTS = (1-r-pq) MPL + MPF + VP

Where VP=VW=VL is the benefit to the employee from reduced business travel time and is independentof whether the time saved is spent at work or on leisure. This paper sets both VL in HE1 and VP in HE3
equal to the “other non-work” VTTS from the 2014-15 Arup, ITS Leeds and Accent study, segmented bymode. Strictly speaking, this is an approximation, as the other non-work VTTS refers to the value of reduced
leisure (as opposed to business) travel time. The values presented below, therefore implicitly assume thatbusiness travel time carries the same disutility as leisure travel time.

Commentary on Hensher parameter analysis
The employers’ SP evidence (Table 5a) highlights that the perceived productivity of time (q) spent working
in car- and rail-based journeys is high. However, the amount of time spent working (p*) on such journeys
is much lower for car journeys. Moreover, outward journeys by rail are much more productive than return
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to the firm as denoted by the implied VBTTS. It is noteworthy that for car SP, p* is significantly less than p.
This could arise from respondents interpreting the SP questionnaire as asking whether the travel time
saved would be converted to work (which is effectively measured by 1-r), as opposed to whether work
would have been done in the travel time saved (which is the correct interpretation of p).
Table 5a. Hensher parameter analysis for employer SP survey
Stated Preference(employer survey)

Car

Rail

Out

ReturnOut

Return

p*

0.07

0.07

0.45

0.33

r*

0.08

0.07

0.19

0.21

p

0.40

q

0.92

Implied VBTTS HE1*

GBP 20.88

GBP 21.07

GBP 15.48

GBP 18.45

Implied VBTTS HE3*

GBP 25.39

GBP 25.63

GBP 22.52

GBP 25.31

CSA VBTTS

GBP 23.94

GBP 33.06

WTP VBTTS

GBP 14.05

GBP 21.31

0.45
0.87

The employers’ RP exercise (Table 5b) was based around operator decisions on specific railway stretches
associated with different travel times. Respondents were asked about how they would use the additional
travel time (time saved) when using the alternative operator in order to elicit the p value. Compared to
the SPURT study (Fickling et al., 2009), the p reported here is low (i.e. travel time is less productive thanunder SPURT), hence the HE-implied VBTTS comes out very small, all else equal. In essence, this implies
that when taking the other operator (with an associated change in journey time), respondents would
hardly do more (or less) work. The values for q and p* are similar to those observed in the employers’ SP(rail), and the WTP-based VBTTS estimates are also comparable across SP and RP.
Table 5b. Hensher parameter analysis for employee RP survey
Rail Revealed Preference(employee survey)

Out

Return

p*

0.5

0.35

r*

0.25

0.35

p

0.40

Fast

Slow

SPURT

p

0.16

0.09

0.41

r

0.60

0.56

0.52

q

0.98

1.04

0.97

Implied VBTTS HE1*

GBP 10.18

GBP 11.84

Implied VBTTS HE3*

GBP 16.69

GBP 17.49

Implied VBTTS HE1

GBP 12.70

GBP 15.53

GBP 7.05

Implied VBTTS HE3

GBP 16.17

GBP 19.35

GBP 11.21

CSA VBTTS

GBP 26.98

WTP VBTTS

GBP 20.43

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On the relationship between the average Hensher parameters (p*, r*) and their marginal counterparts (p,r), intuitively r>r* and p<p*. This is on the basis that there is only so much productive work that can bedone on a given trip, so at the margin, time savings are more likely to be converted into leisure time.
Likewise, at the margin it is less likely that marginal time savings are at the expense of work done while
travelling: the time saving does not eat into the productive portion of the journey. In the RP data, theseintuitive relationships are borne out. However, p* from the employee RP is close to p from employer rail
SP (which in turn is quite close to p* from employer SP). This may suggest that while employers believe
the proportions of marginal and average business travel time spent working are similar, this is notnecessarily borne out by employee behaviour in practice.
Finally, comparing VBTTS across the different approaches, the CSA invariably gives the highest values
except for the car employer SP sample, where the high HE values are driven by very low p*. As expected,
the HE3 values are generally higher than the HE1 values, and the HE* values tend to be lower than their
HE counterparts (although this is sensitive to (and non-linear in) r). Based on the employer SP, HE3* values
exceed their WTP counterparts, whereas for employee RP even the HE3 values are lower than WTP. Thisis primarily driven by high r, given that the MPL generally greatly exceeds VP, HE3 VBTTS is strongly
decreasing in r. Intuitively, where lots of the time saving is devoted to leisure, the VBTTS is low because(given the work contract) a relatively high proportion of business travel time takes place outside of working
time and therefore most of the time savings are not reinvested in production.

Approach to implementation andlessons for other countries
The recommended VTTS values for the appraisal of selected aggregations of the travelling population, as
previously described, are obtained from an implementation tool. In short, the implementation tool applies
sample enumeration combining the estimated choice models derived from an imperfectly representative
sample of travellers with the characteristics of a representative sample of travellers for purposes of
establishing recommended values for appraisal.
In the 2003 study (Mackie et al., 2003), the only covariates were distance and income, while separate
models were derived for the commuting and other non-work purposes (given the adherence to the CSA atthat time, the business models were not taken forward to guidance). This meant that representative values
could be calculated by providing a matrix of trips for each cell representing a combination of distance andincome, applying the formula to each cell, and calculating a weighted average. In the 2014-15 study, bycontrast, the scope of the model was much wider in that a) it contained many more covariates, and b)
valuations were generated for a number of quantities in addition to travel time, such that a matrix-based
approach would have been unwieldy. While the principles are essentially the same, it was more convenient
to make use of a sample enumeration approach. This involved the calculation of appropriate valuations(of time, etc.) for each observation in the sample, making use of the relevant covariates, followed by thecalculation of weighted averages over the sample to ensure national representativeness.
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estimation procedure to ensure complete compatibility. In addition, the estimated standard errors weretransferred such that each NTS trip generated information about the statistical reliability of its valuations,obviating the need for a special subsequent step to calculate the confidence intervals associated with therecommended values. It was possible to restrict the calculation of the quantity to summations over the
NTS sample observations with particular characteristics, whether or not these characteristics were within
the set of covariates defining the valuation formula. Separate valuations could then be derived for
geographical breakdowns or for income bands, as well as for mode, purpose, etc. The implementation tool
was developed using the statistical software “R”, which permits the calculation of valuations for different
segments and based on a variety of weighting options to provide the Department with maximum flexibility.
The notion of sample enumeration is transferable to all other countries with an equivalent dataset to the
NTS. A local survey would need to be conducted, however, to obtain a set of nationally representative
choice models and collect average characteristics for non-NTS-based variables with a significant influencein the behavioural models. Notably, the improved SP design approach of the UK VTTS study would need tobe adopted in order to identify all the size and sign effects included in the models.
In this way, the implementation tool provides a straightforward framework to compare the VTTS across avariety of segments, such as travel modes, purpose and other trip characteristics. If and when a uniform
survey and analysis is implemented across Europe and repeated at regular time intervals, this would allow
better identification of differences in the VTTS across countries and over time (ITS Leeds and Arup, 2018).

DfT consultation process
The Department launched a consultation on proposals for updating its WebTAG appraisal and modelling
guidance covering values of time (DfT, 2015) in October 2015, following the conclusion of the primaryresearch project (Arup, ITS Leeds and Accent, 2015a). This covered a wide range of changes, including:•

updated VTTS for use in appraisal across all modes and journey purposes, excluding freight•segmentation of business values of time into three discrete distance bands (0-50km, 50-100km
and 100+km), by mode (Figure 2)•

a revised reliability ratio of 0.4 (down from 0.8), which applies for car travel only•

a revised wait time multiplier of 2.0 (down from 2.5)•

a revised late time multiplier for non-rail public transport of 2.4 (down from 3.0), in line with therevised wait time multiplier.

These changes were generally well-received by stakeholders (DfT, 2016a), with the exception of the
distance banding for business values. Stakeholders felt that the large differences in values between bands
could generate undesirable bias in the appraisal of schemes near a band threshold, potentially creating
unpredictable “cliff-edge” effects were the values to be included in transport modelling. DfT responded tothese criticisms by further developing the distance-based business values. The result was the following
three-tier hierarchy of approaches in its appraisal guidance, which also met with certain scheme
promoters’ preference for simple and proportionate appraisal tools:
1. The development of a continuous valuation function, which was implemented in the DfT’s official
cost-benefit analysis software, TUBA (see Figure 3 for car and Figure 4 for rail).
2. Adjustment of the three distance bands developed for the consultation into four distance bands:
0-50km, 50-100km, 100-200km, 200km+.
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3. In the event that the above two approaches were infeasible or disproportionate to the need, theapplication of simple distance-weighted average VTTS by mode.

Figure 2. Business values of time by mode and distance

Figure 3. Continuous function versus four bands for employers’ business trips by car© OECD/ITF 2020

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Figure 4. Continuous function versus four bands for employers’ business trips by rail

The findings on productive time use were another important dimension of the interpretation of the study’s
findings in the context of a practical appraisal. A key critique of the Department's previous business valuesof travel time savings and the assumptions that supported them was that they failed to account for the
productive use of travel time, and the increased opportunities to use travel time more productively due todevelopments in mobile technologies. There are two key attributes of the adopted WTP methods that
potentially address this critique. The first is to ask respondents directly on how travel time is used, given
current technologies and the ability to use travel time productively. The second is to use such responses
to investigate how the use of travel time affects the value of travel time saved.
DfT remained confident, despite Arup, ITS Leeds and Accent’s “neutral” finding concerning the effect of
productive time use on valuations, that the inclusion of control variables for time use in the reference trips
meant that the values estimated accurately reflected the WTP for changes in travel time given current
opportunities to use travel time productively. However, the Department also recognised that
improvements in technology and changes in business travel behaviour could potentially change thesevalues in the future.

Appraisal issues surrounding modal variation in values
Another striking finding from the primary research was the variation in values across modes. Arup, ITS
Leeds and Accent reported an ordering of mode values such that VTTS rail > VTTS car > VTTS bus, which isessentially the reverse of what one would expect based on comfort differentials alone. This is, however, inline with previous empirical studies of VTTS in a UK context (e.g. Mackie et al., 2003). Mackie et al. notedin Sections 7.3 and 8.3 a number of reasons why values might vary by mode:•

22

The income and socio-economic characteristics of travellers might vary systematically by mode.
Low-income users with low average VTTS might gravitate to mode A, while high-income users
with high average VTTS might tend to choose mode B.

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The composition of trips and purposes might vary systematically by mode. Mode A might have astrong market share in short distance trips, while mode B might be stronger at longer distances.

•

A cross section of people with given income and socio-economic characteristics making a trip will
have a distribution of values of time (and individual values may vary according to the constraints
faced). People with low VTTS for that trip will self-select into relatively low cost/high time modes
and vice versa.

•

For any individual, VTTS by mode may vary due to the different characteristics of the modes interms of comfort, cleanliness, reliability, level of personal control and other quality attributes.

Mackie et al. (2003) argued that, the last point aside, individuals should theoretically have the same VTTS
for a given trip regardless of the mode used. This favours an approach which takes into account the first
three points through the income, socio-economic characteristics, and trip and purpose characteristics ofthe traffic modelled to the various sub-markets. Any remaining variation in VTTS should then reflect
comfort effects.
The Department interpreted the modal ordering of values observed in the study as self-selectivity because
it generally corresponded to the users’ average incomes. This suggests that the analysis undertaken in the
study might not have fully captured the role of income in variations in values between users of different
modes. This is clearest in the absence of a relationship between income and the VTTS in the bus
commuting model.
For non-work trips, the study attempted to control for income-based differences in values. It used theincome elasticities from the behavioural model to calculate the appraisal values of travel time savings at aconstant, average level of income. This did not undo the modal ordering observed above. The
Department’s view was that self-selectivity could have biased the income elasticities downwards, as thosewith a higher-than-average VTTS for their level of income are more likely to opt for a quicker, more
expensive mode, whereas those with a lower-than-average VTTS for their income are more likely to do the
opposite. As the income elasticities were estimated separately by mode and purpose in the behaviouralmodel, it is possible that the true, stronger, “global” income effect was masked, as illustrated in Figure 5.
Figure 5. Income relationships estimated jointly and separately by mode

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For work trips, on the other hand, distance variation was found to dominate modal variation in theappraisal values. Rail values are generally greater than car values, especially for longer trips, and theDepartment’s view was that self-selectivity remained at play. Furthermore, the distance variation wasconsidered to be a function of both the behavioural model parameters for time and cost sensitivity (which,given that time, cost and distance are highly correlated, capture the relationship between the VTTS andtrip distance for a given individual, holding all other trip and traveller attributes constant) and the sample
enumeration process (where the variation in VTTS by distance would largely be driven by the variation in
typical trip and traveller attributes for journeys of different lengths as observed in NTS).
For appraisal purposes, the Department was content to recommend distance segmentation for businessvalues, on the basis that these values offered a valid representation of the change in social welfare
resulting from quicker business journeys (arising from the economic surplus earned by firms, combinedwith the individual welfare impacts perceived by their employees). Because it is not generally possible toidentify the ultimate beneficiaries of this surplus (i.e. the owners of the factors of production) in a transport
appraisal context, the Department treated this as a benefit to the overall economy without any
distributional impacts. There are, however, practical difficulties of applying modally-segmented VTTS in avalid way using the rule-of-a-half, where the variation in values does not unambiguously relate to comfort
or other quality attributes relating to the use (as opposed to the current users) of a particular mode (Mackie
et al., 2003, section 8.3). Specifically, in such conditions, mode-switchers are treated anomalously in theappraisal. Little empirical work has been done to understand the practical implications of this.

Variations in values over time
New national VTTS studies are conducted infrequently, typically with significant time gaps between
studies. In the case of the United Kingdom, 20 years separated the surveys underpinning the two mostrecent studies: the survey work for AHCG (1999) was conducted in 1994, whereas the survey work for
Arup, ITS Leeds and Accent (2015a) was conducted in 2014. In the intervening period, the official VTTS is
typically updated alongside GDP growth using a specified income elasticity. It is often believed that thisincome elasticity should be equal to one, but this convention has its origins in the CSA approach where the
VTTS should increase at the same pace as the wage rate.
The means and conditions of travel change over time and user preferences change as a result. One can
expect, then, that the income-adjusted growth in the VTTS does not capture changes in the VTTS. This
concern is particularly relevant if one believes that the productivity of travel time (or more generally thedisutility of travel time) is likely to change in response to improved connectivity and comfort. Against thisbackground, DfT recently commissioned a scoping study from ITS Leeds and Arup (2018) to set out aframework for maintaining a robust VTTS over time.
The development of such a framework is ambitious and moves away from the default option in Western
European countries, including the United Kingdom, to update using only the income elasticity. A simple
extension of the default approach would be to combine uprated income data with new NTS data throughthe aforementioned implementation tool, such that the uprated VTTS values would also capture changes
in travel patterns while assuming that the underlying behavioural framework remains fit-for-purpose. A
question that remains is whether these two approaches can be combined without the risk of double
counting (since part of the observed changes in travel behaviour could be a result of income growth).
More frequent data collection from smaller sample groups is, however, required to test the validity of the
2014-15 behavioural framework over time. This could be done in one of two ways. The first relies on
incorporating emerging empirical evidence into the well-developed meta-analytical frameworks on the24© OECD/ITF 2020

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VTTS in the United Kingdom and Europe (e.g. ITS Leeds, Arup and URS, 2013). The meta-analyticalframework provides an independent source of information against which the outcomes of the default
uprating procedure can be statistically verified. However, this option is only feasible when the metamodel(s) are regularly updated to continually provide sufficient new evidence. The second option allows
for more direct empirical testing of the temporal stability of the assumed behavioural framework throughthe annual collection of new SP data using the same methodology as the original study. Since annual
sample sizes of around 10-20% of the original data are perhaps practicable, it is recommended that the
new data are jointly analysed with the original dataset while controlling for shifts in key VTTS parameters.
A benefit of this approach is that narrower confidence intervals can be obtained, as long as the behavioural
framework is judged appropriate (see also Wheat and Batley, 2015).
Frequently collecting new data within the United Kingdom, and possibly in other countries using acomparable framework, would help establish better inter-temporal and cross-country comparisons. This
would facilitate empirical identification of changes in the VTTS, e.g. due to increased productivity of travel
time. If and when such changes are identified, more detailed studies can be commissioned to seek
understanding of what might be driving such changes.

Modelling, forecasting, appraisal and land use
policy implications: DfT case studies
The updated WebTAG guidance released in July 2016 represented a step-change in appraisal for the
Department. For the first time, employers' business values of travel time savings were directly groundedin WTP valuations, which the Department viewed as a major step forward in response to the ongoingdebate around business values (and the influence of productive time use in particular). Implementing the
new values in live appraisal work has had a material impact on the appraisal of many major investment
projects in the Department’s portfolio.
Overall, given the journey purpose split of passenger travel and the reductions to business and other nonwork VTTS, the updated guidance led to a moderate (around 10%) reduction in scheme benefits, but thismasked significant variation on a scheme-by-scheme basis. Reductions in benefits were larger (sometimes
up to 40%) where investments largely benefitted leisure or shorter-distance business travel. However, thenew values also had a small to moderate positive impact on scheme benefits, where the investments
predominantly benefitted commuters and longer-distance business trips.
There was also a pronounced modal pattern in the impacts, with larger reductions in benefits being moreprevalent for road schemes compared to rail. The two major scheme case studies below illustrate some ofthese patterns. In addition, some results of simple Departmental portfolio testing are presented in Annex A– illustrating the overall distribution of impacts.

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Case study 1: High-speed two
High-speed two (HS2) is a new high-speed rail network for the United Kingdom, connecting London withmajor cities in the Midlands and the north of England. It is a Y-shaped network that will be delivered inseveral stages. Trains will also run beyond the Y network to serve places such as Liverpool, Preston,
Newcastle and Scotland. The impact of the new VTTS guidance (i.e. based on the Arup, ITS Leeds andAccent (2015a) study) on the economic appraisal of HS2 has been tested by applying the old VTTS guidance(i.e. based on the Mackie et al. (2003) study, but updated for income growth) to the latest modelling
outputs. The impact of a revision of the wait time multiplier from 2.5 to 2.0 has been separated out fromthe impact of changes in the basic values of travel time saving so to isolate the impact of the new VTTS.
Table 6. HS2 appraisal impacts
Present value GBP billion (2015/16 priceand discounting base year)
Breakdown of impacts

Old VTTS,
wait
multiplier

New VTTS,
oldmultiplier

New VTTS,
new
multiplier

Overall %difference
between new
and old
results

Transport user benefits (business)

56.6

63.5

61.2

8%

Transport user benefits (other)

20.0

18.3

17.1

-14%

Other quantifiable benefits (excluding carbon)

0.4

0.4

0.4

0%

Loss to government of indirect taxes

-4.1

-4.1

-4.1

0%

Net transport benefits (PVB)

73.0

78.1

74.6

2%

Wider Economic Impacts

16.3

18.3

17.6

8%

Net benefits including WEIs

78.6

84.4

80.7

3%

Revenues

43.6

43.6

43.6

0%

Capital costs

55.8

55.8

55.8

0%

Operating costs

27.6

27.6

27.6

0%

Cost to the broad transport budget

39.8

39.8

39.8

0%

Benefit-cost ratio

1.8

2.0

1.9

2%

Benefit to cost ratio (with wider economic impacts)

2.2

2.4

2.3

3%

The journey purpose split of the market expected to be served by HS2 is quite atypical of passenger travelin the United Kingdom generally (across all modes as well as rail specifically), with approximately 45%
business travel, 45% other non-work and the remainder commuting. Given this fact, the fall in VTTS forother non-work travel, and the marked rise for long-distance rail travel, engender two strong opposing
influences on the benefit to cost ratio (BCR) (Table 6). However, as the Department’s guidance for
estimating wider economic impacts (WEIs) partially pivots off the value of business user benefits (which isprimarily composed of travel savings), the boost to business benefits induces a comparable percentage
increase in WEIs, lifting the overall BCR. The reduction of the wait time multiplier partially offsets this, as
HS2 is forecast to cut waiting times, which are valued relatively less under the new guidance.

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Case study 2: Roads Investment Strategy 1
The first roads investment strategy (RIS1) outlines the Department’s long-term programme for motorways
and major roads and is intended to put in place the stable funding needed to plan ahead. A cornerstoneof RIS1 is a multi-year investment plan to improve the strategic road network (SRN). Covering the period
2015-20, this comprises a GBP 15 billion programme of over 100 major schemes. For appraisal purposes,
modelling was carried out by the Department in 2014 to support the case for investment. The impact of
the new VTTS guidance on these results has been tested by applying the new VTTS to the original modelling
results (Table 7).
Table 7. RIS1 appraisal impacts
Breakdown of impacts

Present value GBP million (2010 price anddiscounting base year)% difference

Old VTTS

New VTTSTime savings: commuters

4 769

6 916

45%

Time savings: other non-work

12 830

9 568

-25%

Time savings: business

12 120

8 175

-33%

Time savings: freight

9 699

9 699

0%

Vehicle operating costs

1 220

1 220

0%

Greenhouse gases (CO2)

-758

-758

0%

Local air quality (nitrogen oxides andparticulate matter PM10)

-23

-23

0%

Accidents

-386

-386

0%

Noise

-31

-31

0%

Wider public finances (indirect taxation
revenues)

902

902

0%

Present value of benefits (PVB)

40 34235 282-13%

Present value of costs (PVC)

8 757

8 757

0%

Initial BCR

4.6

4.0

-13%

Reliability: commuters

1 845

1 338

-27%

Reliability: other non-work

4 976

1 855

-63%

Reliability: business

5 070

1 710

-66%

Reliability: freight

3 749

1 875

-50%

Total reliability

15 640

6 778

-57%

Wider economic impacts

5 655

6 007

6%

Landscape

-339

-339

0%

Adjusted PVB

61 29847 729-22%

Adjusted BCR

7.0

5.5

-22%

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As with the HS2 case, the revision to other non-work VTTS places downwards pressure on the BCR, withsome off-setting increases to commuting benefits. However, the comparative lack of long-distancebusiness travel results in a substantial fall in business user benefits which, coupled with the halving of thereliability ratio, causes a large fall in the adjusted BCR (the initial BCR, under UK guidance, omits reliability,
landscape and WEIs). As the economic case for RIS1 was originally very strong, however, the case forinvestment was deemed by the Department to remain robust and the overall value for money rating of
the package remains “very high”. Under DfT guidance (DfT, 2017b), this is the ranking reserved for schemeswith BCRs in excess of 4 (including consideration of non-monetised impacts, risk and uncertainty).

Summary and synthesis
In 2014-15, Arup, ITS Leeds and Accent conducted the first substantive national study of VTTS in some
20 years. In the period 2015-17, the UK Department for Transport assimilated the findings of the study,
consulted stakeholders on proposed changes to appraisal guidance and, following some refinements(especially in terms of distance-varying values), published updated guidance.
In accordance with direction imposed by ITF, this paper has covered insights and conclusions from the UK
experience, including the following:•

the rationale for the investigation particularly around productive time use and the relationshipwith VTTS•

the approaches investigated and the reasons for the selected willingness-to-pay (WTP) approach•

the key findings from the 2014-15 study•

the approach to implementation and lessons for other countries•

modelling, forecasting, appraisal and land use policy implications due to distance-based VTTS andpotentially time-varying VTTS if the level of time use increases with technologies over time.

Overall, considering the case studies and wider portfolio testing, it is apparent that introducing distancevarying business VTTS has strengthened the case for investment in long-distance, inter-urban travel wherebusiness and commuting are the dominant trip purposes. There are, however, grounds for challenging the
Department’s decision to use distance variation in practical appraisal guidance in the absence of detailed
testing of alternative dimension of variation, such as journey time and cost. Given the strength of theseother variables in the behavioural modelling, where distance itself was usually not significant, they may
present a more plausible basis for application in appraisal. All other things being equal, segmenting by
journey time would be expected to worsen the case for investing in travel time savings on already fastroutes, as they would have shorter travel times for a given trip distance than slower routes. A
corresponding argument could be made for segmenting by journey cost.
In terms of the debate around productive or worthwhile time use, the updated guidance has perhaps not
had the effect that one might have expected. Valuations for long-distance rail travel have actually
increased, whereas sceptics might have expected the new valuations to be lower because of controlling
for time use. There are a number of intuitive reasons why time savings might be worth more on longer
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trips, as outlined in the Department’s October 2015 consultation document. However, this finding does
necessarily dismiss the underlying view that productive or worthwhile time use should reduce unit
valuations of VTTS.
There are a number of possible reasons why the 2014-15 study did not detect statistically significant
effects of time use. For example, there is a self-selectivity argument that those who get bored easily or
who have the greatest working time constraints, and therefore a higher prior VTTS, may be more inclined
to undertake leisure or work while travelling. This could induce downwards bias on the time use
coefficients in the modelling, making it more difficult to estimate significant parameters. Anotherpossibility is that respondents, when intercepted for the survey, were not always fully cognisant of theactivities they are able to undertake during travel. Time use was not included directly as a choice attribute,but indirectly as a reference trip attribute. It is possible that an alternative valuation approach could yield
different results.
Despite these concerns, the Department is confident that the updated VTTS within WebTAG provides adefensible basis for the appraisal of business travel time savings. They robustly represent the average WTP
for time savings, given current market conditions and the state of technology. In any case, there would besignificant practical implementation challenges associated with any move to business VTTS explicitly
segmented by time use. Firstly, there is no obvious source of data currently available to provide robust
assumptions on how travel time is used at present, let alone a method for forecasting this into the future.
Secondly, travel time use itself is not in the domain of policy interventions. Rather, it is the opportunity to
use travel time more advantageously that investment can confer.
Moving forwards, with the dawn of autonomous vehicles (AVs) there is likely to be an increasing need to
expand the evidence base around the impact of travel time use on VTTS. The Department has tentatively
taken early studies by Wadud, MacKenzie and Leiby (2016) and Kolarova et al. (2017) to indicate an
approximately 25% reduction in the VTTS with AVs compared to conventional cars. The evidence to dateis thin, however, and there is no authoritative figure to use in policy appraisal. Given the extremely low
penetration of the technology, this issue is presently expected to remain on the research frontier.
However, the Department is of the view that concerted effort will be needed in the medium term todevelop defensible VTTS estimates for policy appraisal involving AVs.

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Notes

1 This paper uses the terms “DfT” and “the Department” interchangeably.

2 As noted by Batley et al. (2017), the VTT calculations were dependent upon the size of the time change from the reference (denoted Δt).
Therefore, in translating these behavioural values to appraisal values, it was necessary to make a definitive assumption concerning Δt. In
correspondence with recent assumptions applied in Denmark and Sweden and after conducting several sensitivity tests, the authors of this paper

eventually settled upon a Δt of ten minutes.

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06.010.

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Annex A. DfT portfolio testing
Figure A.1. Adjusted benefit to cost ratios for a range of strategic road network schemes(error bars indicate low/high range)
1098Adjusted BCR

7
6
5
4
3
2
1
0
Old values

New values

BCR = 2 (High VfM)

Range of impacts with revised VTTS proposals

Figure A.2. Adjusted benefit to cost ratios for a range of rail schemes(error bars indicate low/high range)
76

Adjusted BCR

5
4
3
2
1
0
-1
-2
Old values
BCR = 2 (High VfM)

© OECD/ITF 2020

New values
Range of impacts with revised VTTS proposals

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Figure A.3. Adjusted benefit to cost ratios for a range of local road schemes(error bars indicate low/high range)

14
12

Adjusted BCR

10
8
6
4
2
0
Old values
BCR = 2 (High VfM)

34

New values
Range of impacts with revised VTTS proposals© OECD/ITF 2020

Worthwhile Use of Travel Time andApplications in the United Kingdom
This paper outlines the rationale, methodologies, key findings andpolicy implications of the national Value of Travel Time Savings(VTTS) study conducted in Britain during 2014-15. The study found
VTTS varied with distance, trip purpose and mode of travel but notwith worthwhile use of travel time. Using two case studies, the paper
discusses the approach to implementation of the new VTTS estimatesand lessons for other countries.

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Published: 03/2020 | Photo credit: metamorworks | Shutterstock

All resources from the Roundtable on Zero Value of Time are available
at: www.itf-oecd.org/zero-value-time-roundtable.