THE IMPACT OF AN ULTRA LOW EMISSIONS ZONE IN CENTRAL
LONDON
Paul Metcalfe
PJM Economics and City University
Chris Heywood and Rob Sheldon
Accent
1
INTRODUCTION
London's air quality has improved significantly in recent years. However, it is
currently in breach of European Union (EU) legal limits on nitrogen dioxide
(NO2). In response to air pollution concerns, the Mayor of London announced
in February 2013 his intention to create the world’s first Ultra Low Emission
Zone (ULEZ) to ensure all vehicles driving in the centre of London during
working hours would be zero or low emission from 2020. The ULEZ is aimed
at reducing air quality pollutant (NOx and PM10) emissions without increasing
CO2 emissions.
In this paper, we investigate how current road users of different types in central
London are likely to respond to the ULEZ scheme upon its introduction, and
how sensitive their responses would be to a range of scheme variations,
including differences in the compliance criteria, differences in scheme operating
times, and differences in the level of charges that would be applied. A stated
preference (SP) survey of 955 owner-drivers (which included cars, small vans,
LGVs and HGVs) was undertaken which included two exercises: the first
focused on the decision over whether or not to replace one’s main vehicle with
a compliant vehicle; the second focused on what the respondent would do, for
up to three specific journeys, if they had a non-compliant vehicle.
Our modelling framework uses a random effects logit specification to model the
vehicle replacement choice, followed by a random parameters logit
specification to model the journey behaviour choice. These models are used
to generate elasticities of vehicle replacement and journey behaviour choice
with respect to each of the scheme parameters and, ultimately, predictions
concerning the impact of the scheme on vehicle stock projections, by class of
vehicle, and on journey patterns.
The remainder of this paper is structured as follows. Section 2 describes the
survey design; Section 3 gives details of the survey administration and
descriptive data on the sample obtained; Sections 4 and 5 present our analyses
of vehicle replacement and journey choices respectively; and Section 6 draws
conclusions.
© AET 2016 and contributors
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2
SURVEY DESIGN
2.1
Overview
The key objective of the research was to gain an understanding of how current
road users of different types in central London would be likely to respond to the
ULEZ scheme upon its introduction, and how sensitive their responses would
be to a range of scheme variations.
To this end, a private vehicle owner-driver survey questionnaire was designed
around the use of two SP exercises.
 SP1 focused on the decision over whether or not to replace one’s main vehicle
with a compliant vehicle if the ULEZ scheme were implemented.
 SP2 focused on what the respondent would do, for up to three specific journeys
into the Congestion Charging zone that they had made recently, if the ULEZ
scheme was in operation and they had a non-compliant vehicle.
The choices were separated in this way because the vehicle replacement
decision would affect all journeys made by a respondent, whereas their specific
journey choices may have differed for different journey types. Figure 1 gives an
outline of the questionnaire structure as a whole.
Figure 1: Questionnaire outline

Vehicle type questions, and questions about replacement plans

Current travel patterns

For up to three specific journeys: purpose and timing, costs, duration and who
paid; costs and duration of next best alternative journey if unable to drive into
CC zone

Information on ULEZ context and policy objectives

Questions on awareness of the ULEZ policy and attitudes towards it

SP1 (vehicle replacement) choice exercise

SP2 (specific journeys) choice exercise

Diagnostic questions

Demographics (age, gender, employment status, income etc).
In the following we focus on each of the SP exercises in turn. The materials
shown to respondents concerning the ULEZ context and policy objectives, in
addition to show material specifically supporting the SP1 (vehicle replacement)
© AET 2016 and contributors
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and SP2 (specific journeys) choice exercises, are available from the authors on
request.
2.2
SP1 Exercise Design (Vehicle Replacement)
The SP1 exercise was designed to gauge how many people would change their
vehicle to become compliant on account of the introduction of the ULEZ policy,
and to understand how sensitive the responses would be to scheme variations.
For cars and vans, two types of ULEZ scheme were defined: the first was based
on an “Ultra Low” option, and the second was based on a “Near Zero” option.
For HGVs, one level of compliance was to be tested: the so-called “Greener
Fleets” option. The following figure gives details of the core schemes tested in
the research. In all cases, the ULEZ schemes were defined on the basis that
they would be introduced in 2020.
Figure 2: Vehicle Type Restrictions Tested
Ultra Low Scheme (Cars and Vans Only)

All petrol cars and vans made in 2005 or later, all diesel cars made in 2014 or
later, and all electric and plug-in hybrid cars would meet the standard.

Applicable in the Congestion Charging zone 24 hours per day, seven days per
week

Other cars and vans would have to pay a £15 charge per day, in addition to the
congestion charge, to be allowed to drive in the congestion charge zone.
Near Zero Scheme (Cars and Vans Only)

Only electric and plug-in hybrid cars and vans would meet the standard

Applicable in the Congestion Charging zone 24 hours per day, seven days per
week

Other cars and vans would have to pay a £15 charge per day, in addition to the
congestion charge, to be allowed to drive in the congestion charge zone
Greener Fleets Scheme (HGVs Only)

All Euro VI HGVs (ie made in 2014 or later) would meet the standard

Applicable in the Congestion Charging zone 24 hours per day, seven days per
week

Other HGVs would have to pay a £150 charge per day, in addition to the
congestion charge, to be allowed to drive in the congestion charge zone.
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In addition to testing respondent’s responses to the core schemes as defined
above, the research also sought to explore how sensitive responses would be
to scheme variations. In particular, the following variations were tested.
Operating For cars and vans only, a variation of the ULEZ scheme involving
times
charging only between the hours of 7am and 6pm on weekdays,
rather than 24 hours a day seven days per week, was also tested.
Charges
For cars and vans, the initial charges shown to respondents as
the cost per day for driving into the Congestion Charging zone
during operating hours varied across the sample from the set{£5,
£15, £25}
For HGVs, the initial charges shown to respondents as the cost
per day for driving into the Congestion Charging zone during
operating hours varied across the sample from the set {£50, £150,
£300}
The decision over whether to replace one’s vehicle with a compliant one was
potentially a complex choice for respondents. This was particularly so in the
case of the Near Zero scheme option where the nature of the vehicles available
and the supporting infrastructure for electric vehicle charging might have been
unfamiliar, as well as being expected to rapidly evolve before the scheme’s
introduction in 2020. (In the case of the Ultra Low scheme option, the
characterisation was straightforward as the compliance threshold simply related
to the date of registration of the vehicle.)
A key challenge for the survey design was to try and characterise the vehicle
replacement choice for the Near Zero option in such a way as to capture the
complex range of factors that might drive the respondent’s decision in reality,
but to do so in such a way as to make the choice simple enough for them to
respond to in an accurate preference-reflecting way within the confines of a
survey interview situation.
From the literature on vehicle choice, we know that there are many vehicle
attributes that potentially influence this choice. These include: purchase price,
retained value, vehicle excise duty, insurance costs, fuel consumption, fuel
type, market segment, manufacturer, engine size, CO2 band, number of
airbags, transmission type, number of gears, vehicle size, number of doors,
horsepower, acceleration, weight, reliability, air conditioning, alloy wheels, Antilock Braking System (ABS). Although not all of these attributes will be relevant
to each respondent, they would all be available, amongst others, to an
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individual, or business, looking to purchase a new vehicle, and so excluding
any of them necessarily represents an omission of a potentially relevant
explanatory factor. On the other hand, the nature of a survey interview requires
that only a limited range of factors can be presented to respondents.
The information shown was designed to capture the most important elements
necessary to assist respondents with their choice over whether or not to choose
a compliant vehicle. This material included background information on electric
and plug-in hybrid vehicles generally plus photos and core details on a selection
of currently available vehicles that would be compliant with the Near Zero
standard, including purchase prices, running costs, range, and time to charge.
Comparisons were given of the running costs of petrol and diesel cars, and
respondents were informed that a wider range of electric vehicles could be
expected to appear on the market by 2020, and that the comparative costs of
electric vehicles may be less than they are currently.
The principle followed when designing this show material was that it should
contain sufficient information to allow the respondent to make a considered
choice, without omitting important details that would affect decisions in reality,
but that it should also allow the respondent to make a reasonably quick choice,
rather than have to wade through too much detail, which could be off-putting as
well as time-consuming. (The material shown to respondents is available from
the authors on request.)
The text below reproduces the initial question that was asked of respondents in
the SP1 exercise, following presentation of the show material described above.
Figure 3: SP1 Question Format
If the policy was announced today, do you think you would replace your vehicle
with one that meets the standards by 2020?
Yes
No
Don’t know
In addition, the SP1 vehicle replacement choice exercise in the owner-driver
survey included a follow-on question which either doubled or halved the initial
cost amount depending on their response to the initial charge. Those
respondents who said “yes” to whether they would replace their vehicle with a
compliant one were asked whether they would choose to replace their vehicle
with a compliant one if the charge were half the amount they were originally
asked about. If they said “no” to the initial question they were asked whether
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they would choose to replace their vehicle with a compliant one if the charge
were twice the amount they were originally asked about.
This form of questioning is known as the “double-bounded” method in the SP
literature. The advantage of the method over single-bounded questions which
don’t have the follow-on, is an improvement in statistical performance due to
the fact that more data is obtained. The disadvantage, however, as is well
known in the contingent valuation literature, is that responses to the second
question are not independent of the cost amount shown in the first question. To
take account of this dependence we adopted the analysis technique proposed
by Alberini, Kanninen and Carson (1997). This is described in Section 4.
As described above, the specific parameters of the ULEZ policy, including the
compliance criteria option, operating times, and ULEZ charges were varied
across the sample, and, in addition, multiple choices were asked of each
respondent. Furthermore, respondents were allocated across versions
according to their main vehicle type, and according to their answers to
preceding questions.
The survey was designed to allocate respondents to one of four versions, for
cars and vans, with slightly tailored text to distinguish between the car and van
versions. Versions 1 and 2 were asked of respondents who had a car or van
that was currently not compliant with the Ultra Low criteria, and included vehicle
replacement choice questions for the Ultra Low policy option followed by the
Near Zero policy option. Versions 3 and 4, by contrast, were asked only of
respondents who had a car or van that was currently already compliant with the
Ultra Low criteria, and hence only asked them about the Near Zero option.
The differences between Version 1 and Version 2, and between Version 3 and
Version 4, related solely to the order of the questions with respect to the ULEZ
operating times. In Version 1 and Version 3, respondents were initially told that
the ULEZ would operate between 7am and 6pm on weekdays only, and were
only asked about the 24/7 charging times if they indicated in their initial
response that they would not replace their vehicle with a compliant one.
Conversely, in Version 2 and Version 4, respondents were initially told that the
ULEZ would operate 24/7, and were only asked about the weekdays 7am to
6pm policy if they indicated in their initial response that they would replace their
vehicle with a compliant one.
An additional feature of all the questions in the exercise was the use of a
double-bounded SP question where the ULEZ charge was varied in a follow-up
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question depending on the respondent’s answer to the first question, as
described above.
For HGV owner-drivers, there was only one ULEZ compliance level being
tested: the Greener Fleets policy. Only vehicles not already compliant with this
policy were questioned, and these were asked only about this policy. The
double-bounded questioning on the cost levels was used for HGV ownerdrivers in the same way as for car/van owners.
2.3
SP2 Exercise Design (Specific Journey Choices)
The SP2 exercise was designed to focus on the choices the respondents would
make between pre-specified alternatives if the ULEZ policy was in force
conditional on them not owning a compliant vehicle. This exercise was
answered by car and van drivers only; HGV owner-drivers were excluded.
The alternatives for the second choice exercise included the following.
1)
Change the route(s) to avoid entering the charging zone
2)
Change the destination(s)
3)
Change the mode(s) of travel
4)
Stop travelling altogether
5)
Pay a charge in order to be allowed to continue to use the ULEZ without
changing their current vehicle(s) or behaviour(s).
Where the ULEZ scheme was defined as being in operation 7am to 6pm during
weekdays only, as opposed to 24/7, the set of options offered included one
additional alternative:
6)
Change the time(s) of travel to be outside the ULEZ affected times.
Since the choices made to this exercise would be journey-specific, respondents
were asked to make their choices for up to three specific journeys that they had
made within the past three months. The specific journeys to be asked about
were selected via a process involving a sequence of questions prior to the SP
exercise.
For car and van drivers, the respondent was asked how many trips, both within
Congestion Charging hours and outside these hours, were made within the past
three months for each of five journey purpose categories (Commuting,
Employer’s business, Personal business (e.g. seeing lawyer, going to doctor,
etc.), Shopping, Leisure.) The design then automatically chose up to three
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specific journey types according to a priority ordering designed to counteract
an often-observed bias in survey recruitment that leads to a shortage of
commuting and employer’s business trips in the sample in comparison with
population figures, and also to give priority order to trips made outside
Congestion Charging hours for personal business, shopping and leisure trips.
Once the specific journey purposes to be explored further were identified,
respondents were asked to think about “the most recent typical” journey of each
purpose in turn, and were then asked a sequence of questions about that
specific journey. These questions included day and time of travel, costs and
duration of journey and who paid, and costs and duration of next best alternative
journey if unable to drive into the charging zone. This allowed for the analysis
of the SP2 exercise to incorporate these factors into the modelling of journey
behavioural responses.
3
SURVEY ADMINISTRATION AND DATA
A sample size of 1000 car, van and HGV owner-drivers was targeted for the
survey, with this sample designed to over-represent commercial vehicles in
comparison with their frequency in the population due to the fact that separate
estimates were required for these sub-populations.
Respondents were recruited from a combination of a purchased panel sample,
and TfL’s databases of registered congestion charge payers and invited to
complete the survey online. Respondents were considered in scope for the
survey if in the last three months they had driven into or through the charging
zone at any time of day on any day of the week.
The prime motivation for restricting the sample in this way was that it would give
respondents a chance to accurately recall their journey experience, and hence
result in more reliable data than if the whole of the past year was in scope for
journeys into or through the charging zone. The restriction could potentially bias
the sample, however, towards more high frequency drivers in comparison with
the population of vehicles. This is because those that travel into the charging
area only once or twice per year, for example, would be part of the target
population but potentially restricted from taking part in the survey. On the other
hand, this restriction could be expected to lead to a sample more closely
matching the population of journeys because higher frequency travellers
provide more information on journey population statistics than lower frequency
travellers, all else equal.
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One of the aims of the survey was that it should allow results to be obtained on
journey choices by journey purposes and whether inside or outside charging
hours. No quotas were set for individual journey purposes or time-of-travel
segments, however. Instead, the survey sought to collect data from
respondents on multiple types of trip by purpose and whether inside or outside
charging hours from each respondent according to a set of rules defined so as
to enrich the sample of journeys with as informative a selection of data as
possible for the purposes of obtaining the required segment-specific results.
The panel sample was purchased based on the broad catchment area of drivers
into the charging zone, with quotas by geographical area (Inner London, Outer
London, Southeast England, Elsewhere). From the panel sample 11,848 survey
invitations were sent. From these, 469 private vehicle owners completed the
survey.
The database of registered congestion charge payers who had agreed to be
contacted for research purposes was also used as a sampling frame. Although
this was focused on those who drive into central London during congestion
charge operating hours, it was expected that many would have also driven into
the area outside these hours. The source was therefore considered to be an
effective sampling frame.
The response to the TfL-provided sample of registered congestion charge users
was very low in the pilot and at the beginning of the main phase. To address
this, in addition to a prize draw of £1,000 (five prizes of £200), every respondent
was offered a £5 Amazon voucher (or a £5 donation to charity) for completing
the survey.
In total 5,764 invitations were sent out for the main stage survey to this sample.
From these, 486 completed the survey. Approximately 300 were undeliverable,
either because the email address was invalid or because the respondent was
not available during the fieldwork period (ie on leave), 4,756 did not enter the
survey, and 522 entered but did not complete.
Fieldwork took place between 31 March and 21 April 2014. The average
questionnaire length was 16 minutes. Diagnostic feedback from respondents
suggested that the stated preference components of the questionnaire were
easy to understand and complete, and that the choice scenarios appeared
realistic.
The final sample contained 955 owner-drivers (which included 801 cars, 51
small vans, 70 LGVs and 33 HGVs).
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Weighting Vehicle Observations
In the present research, the sample was designed with two target populations
in mind: the population of unique vehicles entering the Congestion Charging
zone, and the population of unique journeys involving travel into or through the
Congestion Charging zone. Since these populations are different, it would be
impossible for a single sample to adequately represent them both.
The key measure that was identified to compare the sample with the population
was journey frequency. This variable relates the two target populations in the
sense that the total number of journeys equals the number of vehicles multiplied
by the frequency with which they travel into the charging zone. Also, and
importantly, it is expected in theory to influence a user’s decision over whether
or not to replace their vehicle with a compliant one on account of the ULEZ
policy. This is because the total amount paid in ULEZ charges given a noncompliant vehicle will be the sum of the number of journeys made by the vehicle
multiplied by the ULEZ charge. The greater the total amount paid in ULEZ
charges given a non-compliant vehicle, the greater the financial incentive, all
else equal, associated with switching to a compliant vehicle.
As usual, the two sets of weights were calculated such that the weighted
proportion of observations in each category was representative of the target
population. Weights were then applied throughout the analysis.
4
SP1 ANALYSIS OF VEHICLE REPLACEMENT CHOICES
SP1 Analysis Methodology
The choices to the SP1 exercise were modelled using a random effects logit
framework. In this framework, the probability that respondent i chooses to
replace their vehicle in choice situation t, given explanatory variables xit, is given
by:
Pr(yit=1|xit) = exp(xitβ + vi) / (1 + exp(xitβ + vi))
for i = 1,..,N respondents and t=1,..,T choice situations. The term xitβ is the
deterministic component of utility, and vi is a random utility effect, which is
assumed to be independently and identically distributed (iid) (N(0,σ 2v).
The choice variable for the exercise was defined as follows:
 SP1choice - a {0,1} dummy variable indicating whether or not the respondent
chose to replace their vehicle with a compliant one in the choice scenario
shown.
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The choice scenarios were themselves defined by the following variables:
 Scheme = {Ultra low, Near zero}
 SP1cost = {£2.50, £5, £7.50, £10, £15, £25, £30}, the charge per day for
entering the London congestion charging zone during charging hours
 ULEZ24 = {0 if charging hours=7am-6pm weekdays, 1 if charging hours = 24/7}
The Ultra low and Near zero scheme choices were modelled separately in the
analysis rather than being combined into a single model. This was for two
reasons: firstly, because the Ultra low and Near zero schemes represented
fundamentally different choices from one another, and secondly because the
sample size for the Ultra-low scheme was substantially smaller than the sample
size for the Near-zero scheme due to the fact that many vehicles were already
compliant with the Ultra low scheme criteria.
A further feature of the choice situation design to be accounted for in the
modelling was the “double-bounded” nature of the questioning. Specifically, the
values of the SP1cost and the ULEZ24 attributes were not independent of the
choices made by respondents and so could not be treated as exogenous
variables.
Following the procedure of Alberini, Kanninen and Carson (1997), we account
for the effects of question order on choices by incorporating dummy variables
to indicate question order. These variables include:
 choice1 = {0,1}, a dummy variable indicating whether or not the choice situation
was the first one asked for the scheme in question.
 choice1_24 ={0,1}, a dummy variable equal to the interaction of choice1 and
ULEZ24.
The inclusion of the choice1_24 variable in the utility specification allows
separate coefficients to be obtained for the impact of the ULEZ24 variable on
vehicle replacement choice depending on whether the 24/7 scheme or the
congestion charging scheme hours only scheme was asked about first.
To complete the utility specification, a range of respondent and vehicle
covariates were explored. Table 1 provides a list of the variables tested in the
modelling, and the expected influence, if any, on the likelihood of replacing
one’s vehicle with a compliant one.
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Table 1: Covariates used in discrete choice modelling
Variable
Description
Expected influence
hhinc31_low
hhinc32_med
hhinc33_high
Household income
Less than £20k
£20k to £50k
More than £50k
Higher incomes more likely to replace vehicle
with compliant one, all else equal, because
less likely to be cash constrained.
persinc_low
persinc_med
persinc_high
agelt34
age35_44
age45_54
age55_64
age65plus
female
londonres
londonresCCZ
drivecomp
comppay
tdaysccs
tdaysnonccs
howgot_new
howgot_used
howgot_other
petrol
diesel
otherfuel
supermini
lowermed
uppermed
exec
luxury
sports
suv
mpv
van
vintagelt2
vintage2_5
vintage5_8
vintage8_12
vintage12plus
Personal income:
Less than £20k
£20k to £50k
More than £50k
Respondent age:
Less than 34
35-44
45-54
55-64
65+
Respondent gender = female
Resident in London, anywhere
Resident in London, inside
charging zone
Drive company car
Company pays for travel
No. days per year respondent
drives into the charging zone
during charging hours.
No. days per year respondent
drives into the charging zone
outside of charging hours.
How current vehicle was acquired
Bought new
Bought used
Acquired another way
Current vehicle fuel type
petrol
diesel
other fuel type
Current vehicle class:
Supermini
Lower-medium
Upper-medium
Executive
Luxury
Sports
Sports utility vehicle (4x4)
Multi-purpose vehicle (MPV)
Van
Current vehicle vintage:
Less than 2 years old
2 to 5 years old
5 to 8 years old
8 to 12 years old
More than 12 years old
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Higher incomes also expected to be less
sensitive to the size of the ULEZ charge,
hence also interact with SP1cost.
(As above)
No prior.
No prior.
Residents inside the charging zone might be
expected to be more likely to replace their
vehicle with a compliant one.
No prior.
No prior
More frequent drivers into the charging zone
are expected to be more sensitive to the size
of the ULEZ charge, hence interact this
variable with SP1cost.
As above, but only applicable if ULEZ
scheme is operating 24/7, hence interact this
variable with ULEZ24 and SP1cost.
Those who bought a new vehicle may be
more likely to replace with a compliant one.
No prior.
No prior.
No prior.
The higher the intended replacement vehicle
spend, the more likely that a compliant
vehicle will be bought.
repfreqlt3
repfreq3_5
repfreq6_10
repfreq10plus
Intended spend on next vehicle:
Less than £10k
£10k to £15k
£15k to £20k
£20k to £25k
More than £25k
Vehicle replacement frequency:
More often than every 3 years
Every 3, 4 or 5 years
Every 6 to 10 years
Less often than every 10 years
reppetrol
repdiesel
repother
Intended fuel type of next vehicle:
Petrol
Diesel
Other
ULEZgreat
ULEZgood
ULEZneither
ULEZbad
ULEZterrible
Attitude to ULEZ:
A great idea
A good idea
Neither good nor bad
A bad idea
A terrible idea
If the intended next vehicle is to be diesel
this might have a negative influence on the
likelihood of choosing an Ultra-Low compliant
vehicle because a greater proportion of
diesel vehicles for sale will be non-compliant
in comparison with other petrol and other fuel
types.
Those thinking the ULEZ policy is a good
idea may be more likely to replace their
vehicle with a compliant one all else equal.
repvallt10k
repval10k_15k
repval15k_20k
repval20k_25k
repval25kplus
The more frequently the respondent changes
their vehicle, the more likely they will be to
replace to a compliant one.
A key principle underlying our modelling procedure was that we should aim to
use covariates to control for the base likelihood of replacing one’s vehicle with
a compliant one by 2020, ie the probability even without the introduction of the
ULEZ policy, so that sensitivity to the ULEZ charge, and to ULEZ charging
hours, could be estimated holding this base probability constant.
From a practical perspective, adjacent categories of variables from the above
table were grouped together in cases where this grouping was found to improve
model fit, and so the final model contains additional grouped variables not
shown in the above table.
Variables that were insignificant at the 10% level were excluded from the final
model unless excluding them caused the model fit to be worsened in respect of
one or more other variables.
SP1 Main Discrete Choice Models - Cars
The following table presents our main SP1 discrete choice model results for the
Ultra Low scheme, and the table beneath presents comparable results for the
Near Zero scheme.
Considering Table 2 first, the model shows the impact of each of the variables
on the probability of vehicle replacement, so for example a positive coefficient
indicates that the variable had a positive impact on the decision to replace one’s
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vehicle with a compliant one by 2020. The coefficients in this model are all
statistically significant at the 5% level. The sizes of the coefficients are not
meaningful in themselves, and only signal importance in relation to one another.
The first variable in Table 2, howgot_other, has a positive coefficient, indicating
that in comparison with respondents whose current vehicles were bought new
or used, respondents whose current vehicles were acquired another way
(leased, acquired as a gift, chosen as a company car, shared or hired) were
more likely to choose to replace their vehicle with an Ultra Low compliant one
by 2020 all else equal.
The second variable repval5plus also has a positive coefficient indicating that
respondents whose intended spend on their next vehicle was more than £25k
would be more likely to replace their vehicle with an Ultra Low compliant one
by 2020 than other respondents. This finding is as expected because Ultra Low
compliant vehicles will tend to be more expensive than non-compliant ones all
else equal.
The third variable hhinc31 has a negative coefficient, which shows that low
income households (less than £20k per year) tend to be less likely to choose a
compliant vehicle than higher income households all else equal. This finding is
as expected because low income households are more likely to be capital
constrained.
The fourth variable, drivecomp, has a negative coefficient indicating that
company car drivers are less likely than others to replace their vehicle with an
ultra-low compliant one by 2020 all else equal.
The fifth variable, uleztimes, has a positive coefficient, indicating that drivers
would be more likely to replace their vehicle with a compliant one if the scheme
was 24/7 rather than congestion charging scheme hours only. This result is as
expected because more drivers would have to pay the charge and there would
be less opportunity to avoid the charge under a 24/7 scheme.
The sixth variable, SP1cost_tdaysccs is perhaps the most important of all the
variables as it measures the sensitivity of vehicle replacement choice to the
level of the ULEZ charge. The charge itself, SP1cost, was interacted with
(multiplied by) the number of days per year that the respondent drives into the
London congestion charging zone per year to create the included variable
SP1cost_tdaysccs . The interaction variable is thus equal to the annual amount
that the customer would need to pay out in ULEZ charges if their travel
behaviour stayed the same.
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The coefficient on SP1cost_tdaysccs is positive, indicating that, as expected,
higher ULEZ charges would cause more people to choose to replace their
vehicle with a compliant one so as to avoid the charges. Furthermore, the
functional form employed means that more frequent drivers into the London
congestion charging zone are predicted to be more sensitive to the scale of the
ULEZ charge.
The seventh and eighth variables choice1_UL and choice1_UL_24 are
dummies that capture order effects in responses. The first of these,
choice1_UL, indicates that the choice was the first one asked about in relation
to the Ultra Low scheme. The choice1_UL_24 variable also indicates that the
choice was the first one asked about in relation to the Ultra Low scheme, but in
this case only if that question was about the 24/7 charging times variant of the
scheme.
The coefficients on these two variables are to be interpreted in conjunction with
one another. The coefficient on the choice1_UL variable indicates the effect of
being the first question for the 7am-6pm weekdays version, and the sum of the
coefficients on the choice1_UL and choice1_UL_24 variables indicates the
effect of being the first question for the 24/7 version.
The size and sign of these effects is consequential in the sense that they impact
on the predicted response to the ULEZ policy. A decision must therefore be
taken as to whether the first choice or the subsequent choices is more valid.
Typically, one would consider the first choice to be most valid because it is safer
to assume in this case that respondents considered the cost shown to be the
true costs rather than forming a judgement as to the true charges from the
present and previous questions combined. (See Carson and Groves, 2007, for
a full discussion of these issues.)
The final variable is the constant term, _cons. This has a negative coefficient,
which simply balances out the probabilities so that the average of the error
terms is zero.
In addition, the reported /lnsig2u, sigma_u and rho coefficients are statistics
pertaining to the random effects. The /lnsig2u is the original parameter
estimated, while the sigma_u and rho statistics are more meaningful
transformations of this parameter. The sigma_u statistic is the standard
deviation of the random effects, and rho is the proportion of the total variance
accounted for by the random effects. The fact that rho is close to one indicates
that the majority of the variation in vehicle replacement probability takes place
cross-sectionally rather than across choice situations within respondent choice
sequences.
© AET 2016 and contributors
15
Table 2: Main SP1 discrete choice model for Ultra Low scheme (Cars)
Random-effects logistic regression
Group variable: urn2
Number of obs
Number of groups
=
=
864
370
Random effects u_i ~ Gaussian
Obs per group: min =
avg =
max =
1
2.3
3
Integration method: mvaghermite
Integration points =
12
Log likelihood
Wald chi2(8)
Prob > chi2
= -419.45504
=
=
334.36
0.0000
---------------------------------------------------------------------------------SP1choice |
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
-----------------+---------------------------------------------------------------howgot_other |
13.80402
1.472836
9.37
0.000
10.91731
16.69072
repval5plus |
12.45216
.989389
12.59
0.000
10.51299
14.39133
hhinc31 | -6.354005
3.175053
-2.00
0.045
-12.577
-.131015
drivecomp | -5.596991
1.595039
-3.51
0.000
-8.72321
-2.470772
uleztimes |
3.616005
.8531085
4.24
0.000
1.943943
5.288066
SP1cost_tdaysccs |
.0048719
.0008993
5.42
0.000
.0031094
.0066345
choice1_UL | -1.511142
.7285899
-2.07
0.038
-2.939152
-.0831317
choice1_UL_24 |
3.091162
.9826299
3.15
0.002
1.165242
5.017081
_cons | -3.051732
.8061823
-3.79
0.000
-4.63182
-1.471644
-----------------+---------------------------------------------------------------/lnsig2u |
5.730003
.2222305
5.294439
6.165567
-----------------+---------------------------------------------------------------sigma_u |
17.54908
1.94997
14.11474
21.81905
rho |
.9894305
.002324
.983755
.993137
---------------------------------------------------------------------------------Likelihood-ratio test of rho=0: chibar2(01) =
422.09 Prob >= chibar2 = 0.000
Now turning to Table 3, this table shows comparable model results, but for the
Near Zero scheme. The first variable in the table is howgot_other. As for the
Ultra Low model, this has a positive coefficient, indicating that in comparison
with respondents whose current vehicles were bought new or used,
respondents whose current vehicles were acquired another way (leased,
acquired as a gift, chosen as a company car, shared or hired) were more likely
to choose to replace their vehicle with a Near Zero compliant one by 2020 all
else equal.
The second variable in Table 3, repother, has a positive coefficient, indicating
that in comparison with those intending their next vehicle to be petrol or dieselfuelled, those those intending their next vehicle to have another fuel type are
more likely to choose to replace their vehicle with a Near Zero compliant one.
This finding is as expected because Near Zero compliant vehicles will all be
one of the “other” fuel types.
The third variable, hhinc31, has a negative coefficient, as was the case in the
Ultra Low model, which shows that low income households (less than £20k per
year) tend to be less likely to choose a compliant vehicle than higher income
© AET 2016 and contributors
16
households all else equal. This finding is as expected because low income
households are more likely to be capital constrained, and Near Zero compliant
vehicles in particular tend to be expensive.
The fourth and fifth variables att234 and att5 measure attitudes to the ULEZ
policy. The omitted category att1 indicated that the respondent thought the
ULEZ policy was “a great idea”, so the coefficients on att234 (“a good idea”,
“neither good nor bad, or “a bad idea”) and att5 (“a terrible idea”) are measured
relative to this.
The coefficients on att234 and att5 are both negative, with the att5 coefficient
having a substantially more negative value than the att234 coefficient. These
results are as expected, and indicate that those with a more negative attitude
towards the ULEZ policy tend to be those that are less likely to replace their
vehicle with a compliant one by 2020.
The sixth variable in Table 3, drivecomp, has a negative coefficient, as was the
case in the Ultra Low model. This indicates that company car drivers are less
likely than others to replace their vehicle with an ultra-low compliant one by
2020 all else equal.
The seventh variable, uleztimes, has a positive coefficient, indicating that
drivers would be more likely to replace their vehicle with a compliant one if the
scheme was 24/7 rather than congestion charging scheme hours only. This
result is as was found for the Ultra Low model, and is as expected.
The eighth variable, SP1cost_tdaysccs is defined in the same way as for the
Ultra Low model and equals the annual amount that the customer would need
to pay out in ULEZ charges if their travel behaviour stayed the same. Its
coefficient is positive, as in the Ultra Low model, indicating that, as expected,
higher ULEZ charges would cause more people to choose to replace their
vehicle with a compliant one so as to avoid the charges. Furthermore, the
functional form employed means that more frequent drivers into the London
congestion charging zone are predicted to be more sensitive to the scale of the
ULEZ charge.
The ninth variable in the Near Zero model, Choice1_NZ_24, has a positive
coefficient. Although the coefficient is not statistically significant itself at the 10%
level, it was included because the model fit worsened significantly if the variable
was removed.
The final variable is the constant term, _cons, which has a negative coefficient.
This simply balances out the probabilities so that the average of the error terms
is zero.
© AET 2016 and contributors
17
Table 3: Main SP1 discrete choice model for Near Zero scheme (Cars)
Random-effects logistic regression
Group variable: urn2
Number of obs
Number of groups
=
=
1480
654
Random effects u_i ~ Gaussian
Obs per group: min =
avg =
max =
1
2.3
3
Integration method: mvaghermite
Integration points =
12
=
=
169.91
0.0000
Log likelihood
Wald chi2(9)
Prob > chi2
= -501.43202
---------------------------------------------------------------------------------[95% Conf. Interval]
P>|z|
z
Std. Err.
Coef.
SP1choice |
-----------------+---------------------------------------------------------------9.847968
1.72667
0.005
2.79
2.071798
5.787319
howgot_other |
3.070207
.5568806
0.005
2.83
.6411665
1.813544
repother |
-.14148
-4.445958
0.037
-2.09
1.098101
hhinc31 | -2.293719
-7.524252
-10.78145
0.000
-11.02
.8309325
-9.15285
att234 |
-14.71452
-51.01114
0.000
-3.55
9.259512
att5 | -32.86283
-2.035045
-11.61634
0.005
-2.79
2.444253
drivecomp | -6.825693
2.627331
.9629504
0.000
4.23
.4245947
1.795141
uleztimes |
.0040477
.0001684
0.033
2.13
.0009896
.002108
SP1cost_tdaysccs |
1.573813
-.165465
0.113
1.59
.4437016
.7041742
choice1_NZ_24 |
.2239495
-3.338658
0.087
-1.71
.9088453
_cons | -1.557355
-----------------+---------------------------------------------------------------4.868095
4.28763
.1480806
4.577862
/lnsig2u |
-----------------+---------------------------------------------------------------11.40495
8.531924
.7303625
9.864389
sigma_u |
.9753314
.9567598
.0046844
.9672962
rho |
---------------------------------------------------------------------------------405.66 Prob >= chibar2 = 0.000
Likelihood-ratio test of rho=0: chibar2(01) =
SP1 Response Predictions and Elasticities - Cars
The above models were used to generate predicted probabilities of user
segments choosing a compliant vehicle given the introduction of the ULEZ
policy, at various scheme parameters. The models were also then used to
generate the elasticities of vehicle replacement with respect to the scheme
charge.
In deriving the response predictions and elasticities we obtained unweighted
results, and results weighted to the population of unique vehicles and results
weighted to the population of unique journeys.
 The vehicles-weighted results are appropriate if one is interested in an
understanding of all vehicles that drive into the congestion charging zone at
least once per year, with no extra weight given to those that travel frequently
from those that travel very rarely.
© AET 2016 and contributors
18
 The journeys-weighted results are appropriate if one is interested in an
understanding of the impact of the ULEZ policy on traffic within the London
congestion charging zone.
In order to obtain the results for the full target populations, those that already
owned an Ultra Low-compliant vehicle in the case of the Ultra Low results, and
those that already owned a Near Zero-compliant vehicle in the case of the Near
Zero results, were assumed to have a probability of one of having a compliant
vehicle in 2020.
In addition, since the models included order effect variables: choice1_UL and
choice1_UL_24 in the case of the Ultra Low model, and choice1_NZ_24 in the
case of the Near Zero model, a range of estimates were obtained that were
conditional on whether they were consistent with the first or with subsequent
questions asked. In selecting the results to report from this range we had regard
for the base predictions provided by TfL for uptake of compliant vehicles in the
absence of the ULEZ policy (“ULEZ clarification material for Accent”, p.20.).
The lower end of the range we obtained for the case where the ULEZ charge
was equal to £0 appeared to match reasonably closely to the base predictions
provided by TfL and so this end of the range was adopted in generating the
remainder of the results. This range was consistent with the responses in
relation to subsequent, rather than initial questions.
The following tables present the main results obtained in respect of predicted
proportions of vehicles, and journeys, that would be compliant with the ULEZ
policy under various scheme parameters. The first table presents the results in
respect of the population of unique vehicles entering the congestion charging
zone at least once per year. The second table presents the results in respect of
the population of unique journeys entering the congestion charging zone.
Results are presented for All vehicles, Petrol vehicles and Diesel vehicles.
The results in Table 4 show that at the proposed ULEZ scheme parameters,
(with the Ultra Low criteria, 24/7 operating hours, and with a charge of £15 per
day for non-compliant vehicles), 85% of unique vehicles currently entering the
London congestion charging zone at least once per year could be expected to
own a compliant vehicle by 2020. As expected, the proportion of current petrol
vehicle owners that would be compliant (91%) is greater than the proportion of
current diesel vehicle owners that would be compliant (75%).
The results show only a small degree of sensitivity to the ULEZ charge, but a
much greater sensitivity to whether the scheme operates 24/7 or during
congestion charging scheme hours only, and an even greater sensitivity to
© AET 2016 and contributors
19
whether the Ultra Low or Near Zero criteria apply. If for example, the Near Zero
scheme were implemented during congestion charging scheme hours only,
then at a charge of £15, only 6% of vehicles could be expected to be compliant
by 2020.
Table 4: Predicted probability of owning a compliant vehicle in 2020, by
ULEZ scheme type (Cars, unique vehicle-weighted)
Predicted probability of owning compliant vehicle
ULEZ Scheme Type
All cars
Petrol
Diesel
Ultra Low (24/7, £5)
83%
90%
73%
Ultra Low (24/7, £15)
85%
91%
75%
Ultra Low (24/7, £25)
86%
91%
77%
Ultra Low (CCS hours, £5)
61%
76%
38%
Ultra Low (CCS hours, £15)
62%
77%
39%
Ultra Low (CCS hours, £25)
63%
77%
41%
Near Zero (24/7, £5)
11%
13%
7%
Near Zero (24/7, £15)
11%
14%
7%
Near Zero (24/7, £25)
12%
14%
8%
Near Zero (CCS hours, £5)
6%
8%
3%
Near Zero (CCS hours, £15)
6%
8%
4%
Near Zero (CCS hours, £25)
7%
9%
4%
Notes: Estimates shown are based on the econometric models shown in Table 2 and Table 3,
weighted to the population of unique vehicles entering into the London congestion charging
zone at least once per year.
The results in Table 5 are for the population of unique journeys entering the
congestion charging zone. This table shows that a somewhat greater proportion
of journeys could be expected to be made by a compliant vehicle upon the
introduction of the ULEZ policy than indicated by the proportion of compliant
vehicles (91% cf 85% at the central case policy parameters). This finding is as
expected since those that travel more frequently into the congestion charging
zone have a greater financial incentive to switch to a compliant vehicle, and
indeed were predicted to do so by the models in Table 2 and Table 3.
Furthermore, in comparison with the results in Table 4, the results in Table 5
show a greater degree of sensitivity to the ULEZ charge, although this
sensitivity is still dwarfed by the sensitivity to whether the scheme operates 24/7
or during congestion charging scheme hours only, and to whether the Ultra Low
or Near Zero criteria apply. If the Near Zero scheme were implemented during
congestion charging scheme hours only, then at a charge of £15, 11% of
journeys could be expected to be made by compliant vehicles by 2020 on the
basis of these results.
© AET 2016 and contributors
20
Table 5: Predicted probability of owning a compliant vehicle in 2020, by
ULEZ scheme type (Cars, unique journey-weighted)
Predicted probability of owning compliant vehicle
ULEZ Scheme Type
All cars
Petrol
Diesel
Ultra Low (24/7, £5)
87%
92%
78%
Ultra Low (24/7, £15)
91%
94%
84%
Ultra Low (24/7, £25)
92%
95%
87%
Ultra Low (CCS hours, £5)
68%
81%
44%
Ultra Low (CCS hours, £15)
73%
83%
54%
Ultra Low (CCS hours, £25)
76%
85%
60%
Near Zero (24/7, £5)
13%
15%
8%
Near Zero (24/7, £15)
16%
18%
11%
Near Zero (24/7, £25)
21%
24%
14%
Near Zero (CCS hours, £5)
8%
10%
4%
Near Zero (CCS hours, £15)
11%
13%
6%
Near Zero (CCS hours, £25)
15%
18%
9%
Notes: Estimates shown are based on the econometric models shown in Table 2 and Table 3,
weighted to the population of unique journeys into the London congestion charging zone.
The results from Table 2 and Table 3 have also been used to generate
elasticities of responses to the ULEZ charges. These elasticities are equal to
the percentage change in the predicted proportion of vehicles that would be
compliant, on a vehicle-weighted or journey-weighted basis, given a 1% change
in the scheme charge.
Elasticities are shown in Table 6 on a unique vehicles-weighted basis, and are
shown for Ultra Low and Near Zero schemes, operating 24/7 and congestion
scheme charging hours only. Results are presented for All vehicles, Petrol
vehicles and Diesel vehicles.
The results in Table 6 verify the findings from Table 4 that the sensitivity of
vehicle replacement to the ULEZ scheme charge is small. The elasticities are
under 0.1 for all scheme variations except the Near Zero congestion charging
scheme hours only scheme.
For the central case Ultra Low 24/7 scheme, the elasticity of vehicle
replacement with respect to the ULEZ charge is 0.019, which means that a 10%
increase in the ULEZ charge, from £15 to £16.50, would be expected to lead to
a 0.19% increase in the proportion of cars that would become compliant by
2020.
© AET 2016 and contributors
21
Table 6: Predicted elasticity of owning a compliant vehicle in 2020, by
ULEZ scheme type (Cars, unique vehicle-weighted)
Elasticity of probability with respect to ULEZ charge
All cars
Petrol
Diesel
Ultra Low (24/7)
0.019
0.011
0.032
Ultra Low (CCS hours)
0.024
0.013
0.057
Near Zero (24/7)
0.075
0.070
0.096
Near Zero (CCS hours)
0.127
0.119
0.163
Notes: Estimates shown are based on the econometric models shown in Table 2 and Table 3,
weighted to the population of unique vehicles entering the London congestion charging zone
at least once per year.
Table 7 shows the corresponding elasticities on a journey-weighted basis.
Here, the elasticities are larger, particularly for the Near Zero variants of the
ULEZ policy, although still small for the Ultra Low variants.
For the central case Ultra Low 24/7 scheme, the elasticity of vehicle
replacement with respect to the ULEZ charge is 0.026, which means that a 10%
increase in the ULEZ charge, from £15 to £16.50, would be expected to lead to
a 0.26% increase in the proportion of journeys made by cars that would become
compliant by 2020.
Table 7: Predicted elasticity of owning a compliant vehicle in 2020, by
ULEZ scheme type (Cars, unique journey-weighted)
Elasticity of probability with respect to ULEZ charge
All cars
All cars
All cars
Ultra Low (24/7)
0.026
0.018
0.044
Ultra Low (CCS hours)
0.060
0.033
0.140
Near Zero (24/7)
0.455
0.475
0.500
Near Zero (CCS hours)
0.610
0.582
0.841
Notes: Estimates shown are based on the econometric models shown in Table 2 and Table 3,
weighted to the population of unique journeys into the London congestion charging zone.
SP1 Main Discrete Choice Models - Vans
The following table presents our main SP1 discrete choice model results for the
Ultra Low scheme for owner-driven vans, and the table beneath presents
comparable results for the Near Zero scheme for owner-driven vans. The SP1
exercise for vans only tested the congestion charging hours variant of the
policy, and so no sensitivities to charging hours were obtained in this case.
Considering Table 8 first, the model shows the impact of each of the variables
on the probability of vehicle replacement, so for example a positive coefficient
indicates that the variable had a positive impact on the decision to replace one’s
vehicle with a compliant one by 2020. The number of observations used to
© AET 2016 and contributors
22
estimate this model is lower than that for the comparable cars model, and
accordingly the statistical significance of the coefficients is correspondingly
lower.
The first variable in Table 8 is drivecomp, a dummy variable indicating the
vehicle was a company van. The variable has a positive coefficient, which
indicates that company vans are more likely to be replaced with compliant vans
than non-company vans all else equal. The coefficient is not statistically
significant at the 10% level; the reason for including it was that the model fit
was significantly worse if it was excluded.
The second variable in Table 8 is SP1cost_tdaysccs, which is defined in the
same way as for the cars models. The coefficient on SP1cost_tdaysccs is
positive, as expected, indicating that higher ULEZ charges would cause more
people to choose to replace their vehicle with a compliant one so as to avoid
the charges. Furthermore, the functional form employed means that more
frequent drivers into the London congestion charging zone are predicted to be
more sensitive to the scale of the ULEZ charge. The coefficient is not
statistically significant at the 10% level, however; the reason for including it was
again that the model fit was significantly worse if it was excluded.
The third variable in Table 8 is SP1cost_tdaysccs_comp. This variable is equal
to the interaction between SP1cost_tdaysccs and drivecomp. It has a negative
coefficient, which indicates that company drivers are less influenced by the
ULEZ charge than non-company drivers. The coefficient is again not statistically
significant at the 10% level, and the reason for including it was again that the
model fit was significantly worse if it was excluded.
The fourth and fifth variables att23 and att45 measure attitudes to the ULEZ
policy. The omitted category att1 indicated that the respondent thought the
ULEZ policy was “a great idea”, so the coefficients on att23 (“a good idea” or
“neither good nor bad) and att45 (“a bad idea” or “a terrible idea”) are measured
relative to this.
The coefficients on att23 and att45 are both negative, with the att45 coefficient
having a substantially more negative value than the att23 coefficient. These
results are as expected, and indicate that those with a more negative attitude
towards the ULEZ policy tend to be those that are less likely to replace their
vehicle with an Ultra Low compliant one by 2020. The two coefficients are jointly
statistically significant at the 5% level.
© AET 2016 and contributors
23
The final variable is the constant term, _cons, which has a positive coefficient.
This simply balances out the probabilities so that the average of the error terms
is zero.
Table 8: Main SP1 discrete choice model for Ultra Low scheme (Vans)
Random-effects logistic regression
Group variable: urn2
Number of obs
Number of groups
=
=
186
78
Random effects u_i ~ Gaussian
Obs per group: min =
avg =
max =
1
2.4
3
Integration method: mvaghermite
Integration points =
12
=
=
41.64
0.0000
Log likelihood
Wald chi2(5)
Prob > chi2
= -62.745059
--------------------------------------------------------------------------------------[95% Conf. Interval]
P>|z|
z
Std. Err.
Coef.
SP1choice |
----------------------+---------------------------------------------------------------13.09201
-1.821208
0.139
1.48
3.804462
5.6354
drivecomp |
.0012891
-.0002451
0.182
1.33
.0003914
.000522
SP1cost_tdaysccs |
.000543
-.0029337
0.178
-1.35
.0008869
SP1cost_tdaysccs_comp | -.0011954
.6827234
-7.19062
0.105
-1.62
2.008543
att23 | -3.253948
-9.705848
-19.54635
0.000
-5.83
2.510379
-14.6261
att45 |
10.66108
3.909963
0.000
4.23
1.722256
7.285522
_cons |
----------------------+---------------------------------------------------------------4.864921
3.220898
.4194012
4.04291
/lnsig2u |
----------------------+---------------------------------------------------------------11.38686
5.005059
1.583093
7.5493
sigma_u |
.9752549
.8839163
.0216396
.9454252
rho |
--------------------------------------------------------------------------------------42.03 Prob >= chibar2 = 0.000
Likelihood-ratio test of rho=0: chibar2(01) =
Table 9: Main SP1 discrete choice model for Near Zero scheme (Vans)
Random-effects logistic regression
Group variable: urn2
Number of obs
Number of groups
=
=
249
106
Random effects u_i ~ Gaussian
Obs per group: min =
avg =
max =
1
2.3
3
Integration method: mvaghermite
Integration points =
12
Log likelihood
Wald chi2(3)
Prob > chi2
= -77.982526
=
=
38.32
0.0000
-----------------------------------------------------------------------------------------SP1choice |
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------------------+---------------------------------------------------------------SP1cost_tdaysccs_noncomp |
.000526
.0004074
1.29
0.197
-.0002725
.0013245
att23 | -9.040281
1.898224
-4.76
0.000
-12.76073
-5.31983
att45 | -14.06591
2.312512
-6.08
0.000
-18.59835
-9.533475
_cons |
7.106845
1.4039
5.06
0.000
4.355252
9.858437
-------------------------+---------------------------------------------------------------/lnsig2u |
3.283375
.4130346
2.473842
4.092908
-------------------------+---------------------------------------------------------------sigma_u |
5.163876
1.06643
3.44499
7.740404
rho |
.8901748
.0403798
.7829591
.9479482
-----------------------------------------------------------------------------------------Likelihood-ratio test of rho=0: chibar2(01) =
57.86 Prob >= chibar2 = 0.000
© AET 2016 and contributors
24
Table 9 shows comparable model results for the Near Zero scheme for van
owner-drivers. This model only contains three variables in addition to the
constant term.
The first of these variables is SP1cost_tdaysccs_noncomp. This variable is
equal to SP1cost_tdaysccs, the total ULEZ charge payable by the driver given
no change in travel behaviour, multiplied by noncomp, a dummy equal to one if
the driver was driving a non-company van. The implication of modelling the
ULEZ charge in this way is that the ULEZ charge was restricted to have a zero
impact on company vans’ probability of replacing their van with a compliant
version by 2020. This restriction was motivated by the preliminary modelling
work which showed the ULEZ charge to have no statistical significance for
company van drivers even at the 20% level.
The coefficient on SP1cost_tdaysccs_noncomp is positive, which indicates that
non-company van drivers are more likely to choose to replace their vehicle if
they drive more frequently into London and if the ULEZ charge is higher. The
coefficient is not statistically significant at the 10% level however.
The second and third variables att23 and att45 measure attitudes to the ULEZ
policy, and these variables do enter the model with statistically significant
coefficients (at the 1% level). The omitted category att1 indicated that the
respondent thought the ULEZ policy was “a great idea”, so the coefficients on
att23 (“a good idea” or “neither good nor bad) and att45 (“a bad idea” or “a
terrible idea”) are measured relative to this.
The coefficients on att23 and att45 are both negative, with the att45 coefficient
having a substantially more negative value than the att23 coefficient. These
results are as expected, and indicate that those with a more negative attitude
towards the ULEZ policy tend to be those that are less likely to replace their
vehicle with a Near Zero compliant one by 2020.
The final variable is the constant term, _cons, which has a positive coefficient.
This simply balances out the probabilities so that the average of the error terms
is zero.
SP1 Response Predictions and Elasticities - Vans
The above models were used to generate predicted probabilities of user
segments choosing a compliant van given the introduction of the ULEZ policy,
at various scheme parameters. The models were also then used to generate
the elasticities of vehicle replacement with respect to the scheme charge.
© AET 2016 and contributors
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In deriving the response predictions and elasticities we obtained unweighted
results, and results weighted to the population of unique vehicles and results
weighted to the population of unique journeys.
In order to obtain the results for the full target populations, those that already
owned an Ultra Low-compliant vehicle in the case of the Ultra Low results, and
those that already owned a Near Zero-compliant vehicle in the case of the Near
Zero results, were assumed to have a probability of one of having a compliant
vehicle in 2020.
The following tables present the main results obtained in respect of predicted
proportions of vehicles, and journeys, that would be compliant with the ULEZ
policy under the Ultra Low and Near Zero variants. The first table presents the
results in respect of the population of unique vehicles entering the congestion
charging zone at least once per year. The second table presents the results in
respect of the population of unique journeys entering the congestion charging
zone.
Results are presented for All vans, Small vans and Large vans.
The results in Table 4 show that at the proposed ULEZ scheme parameters,
(with the Ultra Low criteria, and with a charge of £15 per day for non-compliant
vans), 77% of unique vehicles currently entering the London congestion
charging zone at least once per year could be expected to own a compliant
vehicle by 2020. The proportion of small van owners that would be compliant
(86%) is greater than the proportion of current large van owners that would be
compliant (71%).
The results show very little sensitivity to the ULEZ charge, but a much greater
sensitivity to whether the Ultra Low or Near Zero criteria apply. If for example,
the Near Zero scheme were implemented, then at a charge of £15, 23% of vans
could be expected to be compliant by 2020.
© AET 2016 and contributors
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Table 10: Predicted probability of owning a compliant vehicle in 2020, by
ULEZ scheme type (Vans, unique vehicle-weighted)
Predicted probability of owning compliant vehicle
ULEZ Scheme Type
All vans
Small vans
Large vans
Ultra Low (£5)
77%
86%
71%
Ultra Low (£15)
77%
86%
71%
Ultra Low (£25)
77%
86%
71%
Near Zero (£5)
22%
22%
22%
Near Zero (£15)
23%
23%
24%
Near Zero (£25)
25%
23%
26%
Notes: Estimates shown are based on the econometric models shown in Table 8 and Table 9,
weighted to the population of unique vehicles entering into the London congestion charging
zone at least once per year.
The results in Table 5 are for the population of unique journeys entering the
congestion charging zone. This table shows that a somewhat greater proportion
of journeys could be expected to be made by a compliant vehicle upon the
introduction of the ULEZ policy than indicated by the proportion of compliant
vehicles (85% cf 77% at the central case policy parameters). This finding is as
expected since those that travel more frequently into the congestion charging
zone have a greater financial incentive to switch to a compliant vehicle, and
indeed were predicted to do so by the models in Table 2 and Table 3.
The results in Table 5 show again little sensitivity to the ULEZ charge, but a
large degree of sensitivity to whether the Ultra Low or Near Zero criteria apply.
If the Near Zero scheme were implemented, then at a charge of £15, 45% of
journeys could be expected to be made by compliant vans by 2020 on the basis
of these results.
Table 11: Predicted probability of owning a compliant vehicle in 2020, by
ULEZ scheme type (Vans, unique journey-weighted)
Predicted probability of owning compliant vehicle
ULEZ Scheme Type
All vans
Small vans
Large vans
Ultra Low (£5)
85%
85%
85%
Ultra Low (£15)
85%
85%
85%
Ultra Low (£25)
85%
84%
85%
Near Zero (£5)
41%
29%
46%
Near Zero (£15)
45%
31%
50%
Near Zero (£25)
50%
33%
55%
Notes: Estimates shown are based on the econometric models shown in Table 8 and Table 9,
weighted to the population of unique journeys into the London congestion charging zone.
The results from Table 8 and Table 9 have also been used to generate
elasticities of responses to the ULEZ charges. These elasticities are equal to
the percentage change in the predicted proportion of vehicles that would be
© AET 2016 and contributors
27
compliant, on a vehicle-weighted or journey-weighted basis, given a 1% change
in the scheme charge.
Elasticities are shown in Table 6 on a unique vehicles-weighted basis, and are
shown for Ultra Low and Near Zero schemes.
Results are presented for All vans, Small vans and Large vans.
The results in Table 6 verify the findings from Table 4 that the sensitivity of
vehicle replacement to the ULEZ scheme charge is small. The elasticities are
under 0.1 for all scheme variations except the Near Zero scheme for large vans.
For the central case Ultra Low scheme, the elasticity of vehicle replacement
with respect to the ULEZ charge is 0.00, which means that no increase in the
ULEZ charge could be expected impact on the proportion of vans that would
become compliant by 2020. This result does not rule out the possibility that
larger ULEZ charges outside of the range tested in the survey might have an
impact on vehicle replacement choices.
Table 12: Predicted elasticity of owning a compliant vehicle in 2020, by
ULEZ scheme type (Vans, unique vehicle-weighted)
Elasticity of probability with respect to ULEZ charge
All vans
Small vans
Large vans
Ultra Low
0.00
0.00
0.00
Near Zero
0.08
0.03
0.12
Notes: Estimates shown are based on the econometric models shown in Table 8 and Table 9,
weighted to the population of unique vehicles entering the London congestion charging zone
at least once per year.
Table 7 shows the corresponding elasticities on a journey-weighted basis.
Here, the elasticities are again very small for both ULEZ scheme variants.
For the central case Ultra Low scheme, the elasticity of vehicle replacement
with respect to the ULEZ charge is 0.00, which means that increases in the
ULEZ charge would have no effect at all on the proportion of journeys made by
vans that would become compliant by 2020. This result again does not rule out
the possibility that larger ULEZ charges outside of the range tested in the survey
might have an impact on vehicle replacement choices.
© AET 2016 and contributors
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Table 13: Predicted elasticity of owning a compliant vehicle in 2020, by
ULEZ scheme type (Vans, unique journey-weighted)
Elasticity of probability with respect to ULEZ charge
All vans
Small vans
Large vans
Ultra Low
0.00
-0.01
0.00
Near Zero
0.15
0.14
0.15
Notes: Estimates shown are based on the econometric models shown in Table 8 and Table 9,
weighted to the population of unique journeys into the London congestion charging zone.
SP1 Analysis – HGV Owner-Drivers
For HGV owner-drivers, there were too few non-compliant vehicles in the
sample to perform an econometric analysis. There were 33 HGVs in total in the
owner-driver sample. Of these, 11 already owned Euro VI vehicles – the
compliance standard for the “Greener Fleets” ULEZ scheme. Of the remaining
22, 20 stated that they expected to be compliant by 2020 even without the ULEZ
scheme being introduced. The remaining two HGVs were not encouraged to
switch their choice by the introduction of the ULEZ scheme.
On the basis of these descriptive results, our best estimate of the proportion of
HGV owner-drivers that could be expected to be compliant with the Euro VI
standard by 2020 is 94% (=31/33). This estimate is based on a small sample,
however, and so should be treated with due caution accordingly.
5
SP2 ANALYSIS OF JOURNEY CHOICES
SP2 Analysis Methodology
A mixed logit framework was employed to model the SP2 journey choice data.
This methodology involves specifying a utility function which can incorporate
random parameters.
We estimate separate models for Cars and Vans, and separate models for the
24/7 and congestion charging scheme hours only variants of the ULEZ scheme.
This is because the alternatives for the latter scheme included an additional
option to change the time of travel that was not present in the 24/7 variant.
In each case, for cars, we examine two models: the first combines all journey
purpose segments together into a single set of coefficients; the second
estimates separate coefficients for each of the journey purpose segments.
SP2 Main Discrete Choice Models – Cars
The model below includes alternative specific constants for three out of the four
alternatives: chmode refers to “change mode of travel”; chroute refers to
© AET 2016 and contributors
29
“change route or destination”; and paycharge refers to “pay the ULEZ charge
and continue to drive as into the congestion charging zone as before”. The
omitted alternative against which the utilities are set is the “Do not travel”
alternative.
Each of the three alternative specific constants enters the model with a random
coefficient, modelled with a normal distribution, and resulting in the mean and
standard deviations (SD) reported in the table. The size of the mean coefficients
on each of the variables indicates the relative attractiveness of that alternative,
all else equal. The standard deviations of the coefficients are a measure of how
variable preferences are in the population.
In addition to the alternative specific constants, the model also includes the
variable paycharge_ulezcost, which is equal to the size of the ULEZ charge for
the paycharge alternative and equal to zero otherwise. The coefficient on this
variable is held fixed, in line with common practice for choice models.
The coefficient on paycharge_ulezcost is negative as expected, which indicates
that respondents are less likely to stay and pay if the charge is higher than if it
is lower.
Two further variables are included in this model. The variable
chmoderoute_reldur is equal to the relative duration of the next best alternative
journey chosen by the respondent earlier in the questionnaire prior to the SP
exercises, if that alternative involved a change of mode or route. If the relative
duration is greater, we would expect these alternatives to be less attractive and
so we expect the variable to have a negative sign.
Related to chmoderoute_reldur , the final variable in the model
chmoderoute_reldurdk is a dummy variable equal to one if the relative duration
of the next best alternative was answered as “don’t know”, and the alternative
is either change mode or change route. The mean coefficient on this variable is
negative and statistically significant, suggesting that “don’t know” may be an
indicator that the alternative journey may be arduous in comparison with the
usual journey.
© AET 2016 and contributors
30
Table 14: SP2 main discrete choice model – cars (24-7), combined journey
purposes
Mixed logit model
Number of obs
Wald chi2(6)
Prob > chi2
Log likelihood = -789.89349
Robust
Std. Err.
z
P>|z|
=
=
=
2592
20.50
0.0023
choice
Coef.
[95% Conf. Interval]
Mean
paycharge_ulezcost
chmode
chroute
paycharge
chmoderoute_reldur
chmoderoute_reldurdk
-.0730015
.4243236
.7575738
.5378749
-.0125848
-3.545141
.0300666
.3976058
.3608556
.4817774
.0070243
.9188408
-2.43
1.07
2.10
1.12
-1.79
-3.86
0.015
0.286
0.036
0.264
0.073
0.000
-.1319309
-.3549695
.0503098
-.4063915
-.0263522
-5.346036
-.0140722
1.203617
1.464838
1.482141
.0011826
-1.744246
2.80499
2.210641
2.516473
-.0295465
2.765802
.711659
.7364765
.5468878
.0078084
1.604205
3.94
3.00
4.60
-3.78
1.72
0.000
0.003
0.000
0.000
0.085
1.410164
.7671732
1.444593
-.0448506
-.3783817
4.199816
3.654108
3.588353
-.0142424
5.909987
SD
chmode
chroute
paycharge
chmoderoute_reldur
chmoderoute_reldurdk
Pseudo R2= .121
The next table shows a similar model but which includes separate coefficients
on each of the alternative specific constants, and on the paycharge_ulezcost
interaction, for each of the journey purpose segments except for commuting
which is treated as the omitted base category. This model allows separate
predictions and elasticities to be obtained for each of the journey purpose
categories.
© AET 2016 and contributors
31
Table 15: SP2 main discrete choice model – cars (24-7), segmented by journey purpose
Mixed logit model
Log likelihood =
Number of obs
Wald chi2(22)
Prob > chi2
-764.2828
Robust
Std. Err.
z
=
=
=
P>|z|
2592
47.26
0.0014
choice
Coef.
[95% Conf. Interval]
empbus_chmode
persbus_chmode
shopping_chmode
leisure_chmode
empbus_chroute
persbus_chroute
shopping_chroute
leisure_chroute
empbus_paycharge
persbus_paycharge
shopping_paycharge
leisure_paycharge
paycharge_ulezcost
empbus_paycharge_ulezcost
persbus_paycharge_ulezcost
shopping_paycharge_ulezcost
leisure_paycharge_ulezcost
chmode
chroute
paycharge
chmoderoute_reldur
chmoderoute_reldurdk
.5354534
-1.052125
-.2370786
.1437084
.6996755
-2.271364
-1.014802
-.9749838
3.36769
-1.270742
-3.844269
-.7691581
-.072849
-.1016456
.0463724
.1738611
-.0222768
.5332489
1.579736
.8220799
-.0159455
-4.130476
.7229191
.6934215
.676284
.5173591
.6060698
.7093318
.7121783
.489122
1.113232
1.175669
1.732281
.9523213
.0569134
.0639462
.0899542
.1160723
.062761
.6405125
.4890229
.8196003
.0081159
1.556408
0.74
-1.52
-0.35
0.28
1.15
-3.20
-1.42
-1.99
3.03
-1.08
-2.22
-0.81
-1.28
-1.59
0.52
1.50
-0.35
0.83
3.23
1.00
-1.96
-2.65
0.459
0.129
0.726
0.781
0.248
0.001
0.154
0.046
0.002
0.280
0.026
0.419
0.201
0.112
0.606
0.134
0.723
0.405
0.001
0.316
0.049
0.008
-.8814419
-2.411206
-1.562571
-.8702968
-.4881994
-3.661629
-2.410645
-1.933645
1.185795
-3.57501
-7.239477
-2.635674
-.1843972
-.2269778
-.1299346
-.0536365
-.1452861
-.7221327
.6212685
-.7843071
-.0318524
-7.18098
1.952349
.3069566
1.088414
1.157714
1.88755
-.8810996
.3810422
-.0163224
5.549584
1.033527
-.4490605
1.097357
.0386993
.0236867
.2226794
.4013587
.1007325
1.78863
2.538203
2.428467
-.0000386
-1.079973
2.797062
2.303948
2.808166
-.0350679
-3.639892
1.016592
.7434906
.622276
.009315
2.072442
2.75
3.10
4.51
-3.76
-1.76
0.006
0.002
0.000
0.000
0.079
.8045776
.8467333
1.588527
-.0533249
-7.701803
4.789545
3.761163
4.027804
-.016811
.4220187
Mean
SD
chmode
chroute
paycharge
chmoderoute_reldur
chmoderoute_reldurdk
Pseudo R2= .150
The tables below show similar results to the above two models but for the
congestion charging scheme hours only variant of the ULEZ scheme, rather
than the 24-7 variant.
© AET 2016 and contributors
32
Table 16: SP2 main discrete choice model – cars (congestion charging
scheme hours only), combined journey purposes
Mixed logit model
Log likelihood =
Number of obs
Wald chi2(7)
Prob > chi2
-630.4708
Robust
Std. Err.
z
P>|z|
=
=
=
2275
32.78
0.0000
choice
Coef.
[95% Conf. Interval]
Mean
paycharge_ulezcost
chtime
chmode
chroute
paycharge
chmoderoute_reldur
chmoderoute_reldurdk
-.1414611
-2.432991
.1004487
.1747993
1.782345
-.0093187
-1.776204
.0474301
.9493626
.4107244
.3781514
.5937996
.0198401
.4676629
-2.98
-2.56
0.24
0.46
3.00
-0.47
-3.80
0.003
0.010
0.807
0.644
0.003
0.639
0.000
-.2344223
-4.293708
-.7045562
-.5663638
.6185189
-.0482046
-2.692807
-.0484998
-.572275
.9054537
.9159624
2.946171
.0295671
-.859602
4.68914
1.928954
1.389354
2.320475
.0466008
.503404
1.140013
.4793169
.529305
.7560198
.0593573
.7324377
4.11
4.02
2.62
3.07
0.79
0.69
0.000
0.000
0.009
0.002
0.432
0.492
2.454755
.9895103
.3519356
.8387034
-.0697375
-.9321475
6.923525
2.868398
2.426773
3.802247
.162939
1.938956
SD
chtime
chmode
chroute
paycharge
chmoderoute_reldur
chmoderoute_reldurdk
Pseudo R2= .137
© AET 2016 and contributors
33
Table 17: SP2 main discrete choice model – cars (congestion charging
scheme hours only), segmented by journey purpose
Mixed logit model
Number of obs
Wald chi2(27)
Prob > chi2
Log likelihood = -608.06921
Robust
Std. Err.
z
=
=
=
P>|z|
2275
47.85
0.0080
choice
Coef.
[95% Conf. Interval]
empbus_chmode
persbus_chmode
shopping_chmode
leisure_chmode
empbus_chtime
persbus_chtime
shopping_chtime
leisure_chtime
empbus_chroute
persbus_chroute
shopping_chroute
leisure_chroute
empbus_paycharge
persbus_paycharge
shopping_paycharge
leisure_paycharge
paycharge_ulezcost
empbus_paycharge_ulezcost
persbus_paycharge_ulezcost
shopping_paycharge_ulezcost
leisure_paycharge_ulezcost
chtime
chmode
chroute
paycharge
chmoderoute_reldur
chmoderoute_reldurdk
.252882
-.8789681
-.898368
-.442173
-1.17705
-1.188463
-.4790945
-.6607078
.7863798
-2.724152
-1.599235
.1440222
1.149362
-.2057793
-1.443366
-.3484374
-.1310557
-.0280984
-.0379216
.0216267
-.1215112
-1.866459
.5078402
.351702
2.031039
-.0112323
-2.085624
.6421388
.7811992
1.020036
.5691631
.9435329
1.132528
.9984353
.7830558
.6830926
1.127649
.8497208
.596558
1.312914
1.517689
2.430609
1.246118
.0762708
.0828458
.1052445
.1906296
.1189781
1.075753
.5495821
.5802447
1.069543
.0140764
.4757129
0.39
-1.13
-0.88
-0.78
-1.25
-1.05
-0.48
-0.84
1.15
-2.42
-1.88
0.24
0.88
-0.14
-0.59
-0.28
-1.72
-0.34
-0.36
0.11
-1.02
-1.74
0.92
0.61
1.90
-0.80
-4.38
0.694
0.261
0.378
0.437
0.212
0.294
0.631
0.399
0.250
0.016
0.060
0.809
0.381
0.892
0.553
0.780
0.086
0.734
0.719
0.910
0.307
0.083
0.355
0.544
0.058
0.425
0.000
-1.005687
-2.41009
-2.897601
-1.557712
-3.026341
-3.408177
-2.435992
-2.195469
-.5524571
-4.934304
-3.264657
-1.02521
-1.423902
-3.180396
-6.207273
-2.790784
-.2805437
-.1904731
-.244197
-.3520005
-.3547039
-3.974895
-.5693209
-.7855568
-.0652279
-.0388216
-3.018004
1.511451
.6521541
1.100865
.6733662
.6722401
1.031251
1.477803
.8740534
2.125217
-.5139999
.0661875
1.313254
3.722625
2.768837
3.32054
2.093909
.0184323
.1342764
.1683539
.3952538
.1116815
.2419776
1.585001
1.488961
4.127305
.0163569
-1.153244
4.771295
2.048621
1.594726
2.750761
.0471796
.4686824
1.226031
.5418768
.4632476
.8085675
.0273902
.6616896
3.89
3.78
3.44
3.40
1.72
0.71
0.000
0.000
0.001
0.001
0.085
0.479
2.368317
.9865624
.6867774
1.165998
-.0065043
-.8282054
7.174272
3.11068
2.502674
4.335525
.1008635
1.76557
Mean
SD
chtime
chmode
chroute
paycharge
chmoderoute_reldur
chmoderoute_reldurdk
Pseudo R2= .168
SP2 Response Predictions and Elasticities – Cars
The following two tables present probabilities of choosing each of the
alternatives given a non-compliant vehicle. Table 18 shows results on the basis
that the scheme operates 24-7, with a £15 charge, while Table 19 shows
comparable results on the basis that the scheme operates during congestion
charging scheme hours only, but again with a £15 charge.
The results in Table 18 show that Employer business journeys were most likely
to stay and pay the charge (40%), whereas Personal business and Leisure
journeys were most likely to not travel (30% and 26% respectively).
© AET 2016 and contributors
34
Table 18: Predicted probability of choosing journey alternatives given a
non-compliant vehicle (Cars, ULEZ 24-7 scheme, with £15 charge)
Journey alternative
Change mode
Change route/destination
Pay the charge
Do not travel
TOTAL
Predicted probability of choosing journey alternative
Commut- Employer Personal
All
ing
business business Shopping
Leisure
27%
22%
19%
23%
31%
29%
28%
33%
31%
19%
32%
26%
24%
26%
40%
27%
16%
19%
22%
19%
10%
30%
21%
26%
100%
100%
100%
100%
100%
100%
Notes: Estimates shown are based on the econometric models shown in Table 14 and Table
15. Data are weighted in inverse proportion to the likelihood of owning a compliant vehicle, and
proportionally to the number of trips made of the journey purpose type in question.
The results in Table 19 show that for those journeys made in congestion
charging scheme hours, a significant proportion (20% on average) would
change the time of travel so as to avoid the charge.
Table 19: Predicted probability of choosing journey alternatives given a
non-compliant vehicle (Cars, ULEZ congestion charging scheme hours
only scheme, with £15 charge)
Journey alternative
All
Change time
Change mode
Change route/destination
Pay the charge
Do not travel
TOTAL
Predicted probability of choosing journey alternative
Commut- Employer Personal
ing
business business Shopping
Leisure
20%
20%
18%
24%
19%
100%
25%
23%
19%
10%
23%
100%
16%
24%
27%
16%
17%
100%
22%
24%
5%
13%
37%
100%
24%
26%
13%
5%
31%
100%
20%
22%
26%
9%
23%
100%
Notes: Estimates shown are based on the econometric models shown in Table 16 and Table
17. Data are weighted in inverse proportion to the likelihood of owning a compliant vehicle, and
proportionally to the number of trips made of the journey purpose type in question.
The econometric models can also be used to derive elasticities of journey
choices with respect to the size of the ULEZ charge. For the 24-7 scheme, the
own price elasticity of demand for the stay and pay alternative ranges from 0.567 in the case of Employer business to an anomalous +0.705 in the case of
Shopping. (The Shopping elasticity suggests that an increase in the charge
would lead to more people staying and paying.)
© AET 2016 and contributors
35
Table 20: Elasticity of journey choice probabilities with respect to the
ULEZ charge (Cars, ULEZ 24-7 scheme)
Journey alternative
Change mode
Change route/destination
Pay the charge
Do not travel
TOTAL
Elasticity of journey choice probabilities with respect to the ULEZ
charge
Commut- Employer Personal
All
ing
business business Shopping
Leisure
0.082
0.089
-0.381
0.200
100%
0.083
0.070
-0.320
0.226
100%
0.260
0.295
-0.567
0.824
100%
0.034
0.039
-0.131
0.068
100%
-0.103
-0.097
0.705
-0.222
100%
0.077
0.089
-0.498
0.184
100%
Notes: Estimates shown are based on the econometric models shown in Table 14 and Table
15. Data are weighted in inverse proportion to the likelihood of owning a compliant vehicle, and
proportionally to the number of trips made of the journey purpose type in question.
The price elasticities are larger, as absolute values, and uniformly in line with
expectation in the case of the congestion charging scheme-hours only scheme.
In this case, Shopping has the largest (negative) price elasticity at -1.323,
indicating that a 1% increase in the ULEZ charge would lead to a 1.3% lower
probability of choosing to stay and pay rather than one of the other alternatives
for Shopping journeys.
Table 21: Elasticity of journey choice probabilities with respect to the
ULEZ charge (Cars, ULEZ congestion charging scheme hours only
scheme)
Elasticity of journey choice probabilities with respect to the ULEZ
charge
Commut- Employer Personal
Journey alternative
All
ing
business business Shopping
Leisure
Change time
0.126
0.075
0.136
0.086
0.037
0.059
Change mode
0.184
0.089
0.185
0.142
0.055
0.104
Change route/destination
0.214
0.114
0.199
0.191
0.077
0.108
Pay the charge
-0.705
-1.207
-1.163
-1.202
-1.323
-1.257
Do not travel
0.369
0.275
0.413
0.255
0.110
0.192
TOTAL
100%
100%
100%
100%
100%
100%
Notes: Estimates shown are based on the econometric models shown in Table 16 and Table
17. Data are weighted in inverse proportion to the likelihood of owning a compliant vehicle, and
proportionally to the number of trips made of the journey purpose type in question.
SP2 Main Discrete Choice Models (24/7) – Small Vans
The following two tables present similar models to those shown for cars (not
segmented by journey purpose). The first shows results for the 24-7 variant of
the ULEZ scheme and the following table shows results for the congestion
charging scheme hours only variant.
The results in both cases are in line with expectation insofar as the coefficient
on the paycharge_ulezcost variable is negative in each case.
© AET 2016 and contributors
36
Table 22: SP2 main discrete choice model – vans (24-7 scheme)
Mixed logit model
Number of obs
LR chi2(5)
Prob > chi2
Log likelihood = -73.023895
Std. Err.
z
P>|z|
=
=
=
324
39.99
0.0000
choice
Coef.
[95% Conf. Interval]
Mean
paycharge_ulezcost
chmode
chroute
paycharge
chmoderoute_reldur
chmoderoute_reldurdk
-.0935391
-9.89345
2.914671
3.652786
-.4037225
-20.49816
.1173455
6.634722
2.371573
2.633608
.2802969
14.05289
-0.80
-1.49
1.23
1.39
-1.44
-1.46
0.425
0.136
0.219
0.165
0.150
0.145
-.3235321
-22.89727
-1.733527
-1.508991
-.9530943
-48.04132
.1364538
3.110366
7.562869
8.814563
.1456494
7.045011
12.77687
6.342258
4.624408
-.7997381
5.867353
6.762988
3.574436
2.607373
.5136543
6.855866
1.89
1.77
1.77
-1.56
0.86
0.059
0.076
0.076
0.119
0.392
-.4783417
-.6635086
-.4859489
-1.806482
-7.569897
26.03208
13.34802
9.734764
.2070058
19.3046
SD
chmode
chroute
paycharge
chmoderoute_reldur
chmoderoute_reldurdk
Pseudo R2= .350
Table 23: SP2 main discrete choice model – vans (congestion charging
scheme hours only)
Mixed logit model
Number of obs
LR chi2(6)
Prob > chi2
Log likelihood = -87.029053
Std. Err.
z
P>|z|
=
=
=
340
9.33
0.1558
choice
Coef.
[95% Conf. Interval]
Mean
paycharge_ulezcost
chtime
chmode
chroute
paycharge
chmoderoute_reldur
chmoderoute_reldurdk
-.0809182
-.2505297
-3.352548
-.0333649
2.07978
-.0391977
-14.85787
.0448863
.6101777
3.618568
1.069784
.7888204
.0297624
26.87531
-1.80
-0.41
-0.93
-0.03
2.64
-1.32
-0.55
0.071
0.681
0.354
0.975
0.008
0.188
0.580
-.1688937
-1.446456
-10.44481
-2.130102
.53372
-.0975309
-67.53252
.0070573
.9453966
3.739715
2.063372
3.625839
.0191356
37.81678
-.6827784
-3.809676
2.239916
.7652089
-.0238351
7.936421
.9848841
2.941923
1.352736
.6941805
.0301951
14.57145
-0.69
-1.29
1.66
1.10
-0.79
0.54
0.488
0.195
0.098
0.270
0.430
0.586
-2.613116
-9.575739
-.4113982
-.5953598
-.0830164
-20.6231
1.247559
1.956388
4.89123
2.125778
.0353462
36.49595
SD
chtime
chmode
chroute
paycharge
chmoderoute_reldur
chmoderoute_reldurdk
Pseudo R2= .225
© AET 2016 and contributors
37
SP2 Response Predictions and Elasticities – Vans
The results for vans show that the majority would stay and pay the charge in
the case of the 24-7 scheme, or the congestion charging scheme hours only
scheme ((54% and 50% respectively).
Table 24: Predicted probability of choosing journey alternatives given a
non-compliant vehicle (Vans, ULEZ 24-7 scheme, with £15 charge)
Predicted probability of
choosing journey
Journey alternative
alternative
Change mode
6%
Change route/destination
15%
Pay the charge
54%
Do not travel
25%
TOTAL
100%
Notes: Estimates shown are based on the econometric model shown in Table 22. Data are
weighted in inverse proportion to the likelihood of owning a compliant vehicle, and proportionally
to the number of trips made by the respondent.
Table 25: Predicted probability of choosing journey alternatives given a
non-compliant vehicle (Vans, ULEZ congestion charging scheme hours
only scheme, with £15 charge)
Predicted probability of
choosing journey
alternative
Journey alternative
Change time
19%
Change mode
2%
Change route/destination
6%
Pay the charge
50%
Do not travel
21%
TOTAL
100%
Notes: Estimates shown are based on the econometric model shown in Table 23. Data are
weighted in inverse proportion to the likelihood of owning a compliant vehicle, and proportionally
to the number of trips made by the respondent.
The own price elasticity of journey choice for the stay and pay alternative is 0.159 for vans in the 24-7 scheme or -0.487 in the congestion charging scheme
hours scheme.
© AET 2016 and contributors
38
Table 26: Elasticity of journey choice probabilities with respect to the
ULEZ charge (Vans, ULEZ 24-7 scheme)
Elasticity of journey
choice probabilities with
respect to the ULEZ
charge
Journey alternative
Change mode
0.017
Change route/destination
0.039
Pay the charge
-0.159
Do not travel
0.315
Notes: Estimates shown are based on the econometric model shown in Table 22. Data are
weighted in inverse proportion to the likelihood of owning a compliant vehicle, and proportionally
to the number of trips made by the respondent.
Table 27: Elasticity of journey choice probabilities with respect to the ULEZ charge
(Vans, ULEZ congestion charging scheme hours only scheme)
Elasticity of journey
choice probabilities with
respect to the ULEZ
Journey alternative
charge
Change time
0.518
Change mode
0.197
Change route/destination
0.227
Pay the charge
-0.487
Do not travel
0.588
Notes: Estimates shown are based on the econometric model shown in Table 23. Data are
weighted in inverse proportion to the likelihood of owning a compliant vehicle, and proportionally
to the number of trips made by the respondent.
6
CONCLUSIONS
This paper has reported results from a stated preference survey of 955 private
vehicle owner-drivers in relation to the impact of introducing an ultra low
emissions zone in central London. The key results include a set of estimated
elasticities for vehicle replacement and journey behaviour choice with respect
to each of the scheme parameters and a suite of predictions concerning the
impact of the scheme on vehicle stock projections, by class of vehicle, and on
journey patterns.
Results from the analysis are intuitively reasonable. The econometric models
show correctly signed effects for all variables. Furthermore, the predictions of
vehicle replacement and travel behaviour impacts all show plausible
magnitudes.
The outputs from the present research have been used by Transport for
London, in conjunction with other commissioned research, to appraise how the
© AET 2016 and contributors
39
proposed ULEZ scheme would impact London. The scheme details have now
been published (https://tfl.gov.uk/modes/driving/ultra low-emission-zone) and
the policy will come into force in London in 2020, leading to reduced exhaust
emissions of NOx and particulate matter PM10/PM2.5, and thereby making
central London a more pleasant place to live, work and visit.
ACKNOWLEDGEMENTS
The paper is based on a project conducted by Accent and PJM Economics, and
commissioned and steered by Transport for London.
REFERENCES
Alberini, A., Kanninen, B. and Carson, R. (1997) “Modeling Response Incentive
Effects in Dichotomous Choice Contingent Valuation Data”, Land Economics,
73(3), 309-324
Carson, R. and Groves, T. (2007) “Incentive and Informational Properties of
Preference Questions”, Environmental and Resource Economics, 37, 181-210
© AET 2016 and contributors
40