Project REsource
Road pricing
Predicting the responses of drivers to road pricing
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oad pricing has emerged as one of the most
prominent policy measures for managing
traffic congestion. While the theory behind
road pricing is well established, only more
recently has the theory been implemented in
practice in cities as diverse as London, Singapore
and Stockholm.
The increasing prominence of road pricing
in the transport debate comes from the continued
growth in traffic and the accompanying congestion in many cities and countries, as well as
increasing awareness of the impact that traffic is
having on the environment. Pricing for road use
based on when and where one chooses to drive
has become more feasible with the rapid development of technologies suitable for area-wide road
pricing schemes.
If road pricing is to achieve its potential,
then careful modelling is essential to ensure
that schemes are the most appropriate for each
particular location and meet the specific policy
objectives. Traditional traffic modelling and
forecasting approaches may be found wanting in
investigating road pricing schemes because of the
complex behavioural responses that may occur.
However, there exist techniques, which are both
feasible and proven, to address some of these
issues and which we outline in the next sections.
Disaggregate modelling
Disaggregate models segment the population
into a large number of groups, each of which may
exhibit different characteristics. Disaggregate
approaches are key to capturing the variation in
responses to charging that may occur across the
population. For example, it may be expected that
people from lower income households would be
more sensitive to pricing than those from higher
income households. Understanding the way such
households respond to road pricing schemes is
important not only in the near term, but also
in the longer term for forecasting purposes, as
average incomes change and the distribution of
income within the population alters.
Abstract
Road pricing is moving up the policy agenda
as an option to relieve traffic congestion and
to reduce the environmental impact of traffic.
While the effectiveness of road pricing in relieving congestion has long been demonstrated,
both in theory and now in practice in a
number of cities, policymakers need reliable,
flexible transport models to forecast the impact
of road pricing schemes. Road pricing schemes
place additional demands on our modelling
techniques, which require specialist expertise
and experience. In this REsource we describe
some of the work that RAND Europe has been
undertaking with UK clients to investigate
road pricing.
Positive and negative impacts of road pricing
will vary across the various population groups.
An understanding of these implications is critical
to addressing any potential inequities of a scheme
and in developing policies to ameliorate these
impacts.
Stated preferences
Where available, data based on existing choices
provide the best indicator of future choices.
However, in many cases such data do not exist
for proposed policies such as road pricing. Stated
preference (SP) techniques provide a means of
quantifying choices based on hypothetical choice
data when no existing revealed preference (RP)
data are available. Typically, surveys are undertaken in which respondents are presented with
a series of hypothetical alternatives and asked to
choose which they prefer. Discrete choice models
are estimated from such data in order to be able
to quantify likely behavioural responses.
SP choice experiments are one of the most
flexible and robust methods for analysing choices
in complex choice contexts such as road pricing.
The charge may well differ depending on when
and where a journey is made, making a host of responses
possible, including:
• switching to other modes of travel;
• making fewer trips;
• switching the time of travel; and
• choosing to travel to another destination.
SP techniques can be used to quantify these likely
responses. Discrete choice models estimated from SP data
can be fed into disaggregate model systems so that the
responses across population segments, as well as in aggregate, can be predicted.
Theory into practice: London congestion
charging
RAND Europe has worked intensively with Transport
for London (TfL) to develop a disaggregate model capable
of examining a wide range of potential congestion charging scheme enhancements, including cordon charging,
time-of-day-based charging and emissions-related charging.
A spreadsheet-based forecasting system, developed by
RAND Europe, allows rapid evaluation of the potential
impact of congestion charging policies on traffic flows in
the charging zone.
SP experiments were undertaken to examine driver
responses to these future policies. Specifically, we designed
and tested three SP experiments examining the following:
1. drivers’ values of time across travel purposes and socioeconomic groups;
2. time-of-day-based charging to determine drivers’ willingness to shift journey departure times to avoid higher
charges for peak hour travel;
3. interest in a cordon charging scheme, which would be
offered in addition to the current area licence-based
charge. In a cordon charging scheme, drivers would pay
every time they cross the cordon.
For time-of-day-based charging to be effective, the charge
differential would need to be significant in order to encourage
travellers to alter the timing of their journeys. In particular, we
found that travellers would be more willing to alter the timing
of the return leg of their journey rather than that of the outward
leg. Furthermore, changes that had implications for the
duration of stay at the destination were valued very negatively.
Emissions-related charging
In a related study, TfL commissioned a team including
RAND Europe to forecast the impact of road user charges
that would be linked to vehicle carbon dioxide (CO2)
emissions. TfL was specifically interested in the impact
that emissions-related charges would have on the choice of
car used by household members already having a variety to
choose from, and on longer-term car purchasing decisions.
As before, we used SP techniques to present respondents
with hypothetical scenarios in which they could: continue
to use their current vehicle at the specified charge; use
another vehicle in the household (if they have one); or buy
another car with lower emissions to reduce the impact of
an emissions-related charge. The experiment was tailored to
each individual. An example showcard is provided below.
Use one of your current cars
Range Rover
Ford Escort
Size
Buy a new car
Car 1
2
Car 2
2
5
2
Purchase price
£8,000
£20,000
Acceleration time
(0–60 mph)
14 sec
10 sec
Top speed (mph)
110 mph
140 mph
Fuel economy
55 mpg
30 mpg
Free
£8
Congestion charge
(per day)
£25
£10
I would choose...
I would use another means of transport to Central London
I would not travel to Central London
Example of SP showcard from emissions-related charging
experiment
Findings
The resulting values of time varied by household income,
journey purpose and journey length. Journeys made for
employer’s business had significantly higher values of time
than for other purposes. Drivers from higher income
households valued their time more highly (in other words,
they were less price sensitive) than drivers from lower income
households. We also found that values of time tended to
decrease as journey times increased.
A preference for the existing area charge was evident,
perhaps in part due to a degree of familiarity with the current
scheme. We found that for a cordon charge to be equally
attractive to an area charge, the cordon charge would need to
be substantially cheaper; how much cheaper would depend
on the individual and journey purpose.
Findings
A forecasting system was developed to test the impact of
different emissions-based charging scenarios. The models
predicted a small decrease in trips by the most polluting cars,
charged at £25 and a more substantial increase in trips by
the least polluting cars, where there was either no charge for
the lowest CO2 emission cars or £8 for average-emitting
ones. These effects were partly a result of drivers choosing
less polluting cars in their household, if available, and if not,
then purchasing new cars. Our findings suggest that an
emissions-related charge may be effective in altering the
vehicle composition, but that care will be required to ensure
that congestion benefits are not reduced. O
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RB-9288-RE (2007)
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