Do New Roads
Increase Traffic?
How can planners anticipate new choices by road users?
RAND Europe examines induced traffic effects.
RESEARCH APPROACH
BACKGROUND
It has long been known that new roads have a more complex impact on behaviour than drivers
merely changing routes. Travellers may reschedule trips, make additional trips, switch from public
transport to car, visit new destinations or even move home:
responses collectively known as 'induced traffic'. While total
The Scheme
traffic flows can be measured after a new road is built,
post-event data cannot tell us who has changed their
behaviour or in what ways. The challenge is to be able to
Manchester
predict the size of such impacts before new roads are
planned. The completion of the M60 Motorway Box around
Manchester in October 2000 provided an ideal opportunity
to analyse the size and extent of behavioural changes
because of new road infrastructure.
M66
M61
M61
M60
M62
M67
M60
M60
M56
INCREASE IN CAR TRAFFIC PREDICTED
After adjusting for increases due to demographic changes such as increasing population, employment
and car ownership, the model predicted an 8.8% increase in commuting and a 13.1% increase in social
and recreation travel by all modes in the area of the Scheme between 1999 (Before) and 2003 (After).
Looking specifically at additional car traffic, the model predicted a 12.4% increase in induced car
commute traffic and a 17.0% increase in induced social and recreation car traffic in the area of the
Scheme. The bulk of this extra car traffic comes from expected increases in journey length because of
improvements in accessibility, with a small predicted mode shift from public transport to car.
Increase in car traffic
20%
...and the reasons
10%
10%
5%
5%
0%
0%
Commute
Other
3%
Induced
car
traffic
3.7%
Commute
Other
Increase due to
destination
shifting
Percentage changes after completion of the new road
Time
Cost
Speed
-10%
-15%
Time period:
AM
peak
Posit utility formulations for each choice
alternative, including:
<
Journey time by car
<
Cost of driving, including parking
<
Journey time by public transport
<
Frequency of public transport services
<
Public transport fares
<
Walking and cycling distances
<
Household interview surveys
<
Network travel models
Increase due to
mode shifting
<
Apply utility maximisation to explain choices
<
Adjust model error between datasets
<
Weight to allow for survey data bias
<
Estimate parameters to give best explanation of
observed choices
Validate model
<
Ensure reasonable time-cost trade-offs
<
Replicate trip distributions
<
Check elasticities against published guidelines
Apply model to Before and After situations
Analysis of Before and After travel costs and times showed completion of the
motorway benefited car drivers, by increasing journey speeds and reducing
average journey times. Journey costs dropped substantially, partly due to increased
journey speeds, but also because petrol prices fell during the period of analysis.
0%
13.2%
3.9%
NEW ROAD BENEFITS FOR CAR DRIVERS
-5%
passenger, cycle, public transport)
<
Across 15 time period combinations
<
To a possible 559 destinations
Estimate model
8.8%
Increase due to
demographic
changes
10%
5%
Identify potential travel choices:
<
Among five modes of travel (walk, driver, car
and bus travellers, for Before and After periods
0.6%
15%
We built choice models for five travel purposes:
commuting, business, education, shopping and
social and recreational travel. The steps were to:
Use data on choices and services
15.7%
15%
<
To quantify different induced traffic effects
<
To quantify the total induced traffic effects
<
Intercept surveys of car drivers, train, metrolink
20%
17.5%
The Department for Transport commissioned RAND
Europe to develop a predictive choice model to
measure the induced traffic effects resulting from
the completion of the Manchester Motorway Box.
The project had two aims:
Interpeak
period
PM
peak
IMPACT
The model will help policymakers assess induced traffic impact in three ways:
<
Planning: Accurate forecasts of demand are essential to plan future transport
infrastructures to meet eventual needs.
<
Economic appraisal: There is a danger of overpredicting the benefits to the
economy of travel time savings if induced traffic is not taken into account
<
Environmental impact: Increasing concerns about limiting vehicle emissions
make it more important to understand induced traffic effects.
<
Use demographic data and data on network
changes to predict changes in traffic flows
PREDICTIONS BROADLY IN LINE WITH MEASURED CHANGES
We judged the model to be robust on the basis of highly significant
model parameters and replication of observed trip lengths. The resulting
elasticities align well with UK guidelines. The key test, however, is how
predictions compare with measured changes. For social and
recreational journeys, the predicted 17.5% total increase in car trips in
the area of the Scheme is close to the measured overall increase of
21.6%. For commuting, the model predicted a 15.7% increase in car
trips compared to the observed increase of 9.9%. It's possible that
commuting behaviour changes more slowly because of the more
complex changes required (e.g. moving jobs or home), and the model
will reflect a longer-term view. It is also possible that there are sampling
inconsistencies between the collection of Before and After data. Either
way, it is clear that 'route change only' assumptions are insufficient.
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