national road traffic forcast 2009

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Road Transport Forecasts 2009

Results from the Department forTransports National TransportModel

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ROAD TRANSPORT FORECASTS 2009

EXECUTIVE SUMMARY 4

1 INTRODUCTION TO THE NATIONAL TRANSPORT MODEL 6

1.1 Summary ................................................................................................................ 6

1.2 Strategic transport modelling and appraisal ...................................................... 6

1.3 A technical overview of the National Transport Model...................................... 7

1.4 Updating the National Transport Model ............................................................ 10

1.5 The Model Base Year .......................................................................................... 111.6 Performance against observed data.................................................................. 11

2 ROAD TRANSPORT FORECASTS 13

2.1 Summary .............................................................................................................. 13

2.2 Forecast Limitations ........................................................................................... 13

2.3 Summary of key forecasts.................................................................................. 13

2.4 Forecast Traffic 2009 by vehicle type................................................................ 15

2.5 Traffic Growth by Vehicle Type, Area Type and Road Type............................ 16

2.6 Distance Travelled............................................................................................... 18

2.7 Congestion........................................................................................................... 192.8 Emissions............................................................................................................. 22

2.9 Other Government Forecasts of Transport CO2 ............................................... 24

3 KEY DRIVERS OF THE NTM FORECASTS 26

3.1 Summary .............................................................................................................. 26

3.2 Population and employment .............................................................................. 26

3.3 GDP....................................................................................................................... 26

Car Ownership ....................................................................................................... 27

Forecasts for GDP ................................................................................................. 28

3.4 Fuel economy and the costs of driving............................................................. 28Fuel economy......................................................................................................... 28

Projected Oil Prices............................................................................................... 30

3.5 Road Capacity...................................................................................................... 33

3.6 Applying the key drivers..................................................................................... 33

4 SENSITIVITY ANALYSIS AND SCENARIOS 35

4.1 Summary .............................................................................................................. 35

4.2 Oil Price Scenarios.............................................................................................. 35

4.3: High/Low Demand.............................................................................................. 37

4.4: High/Low CO2...................................................................................................... 39ANNEX: SUMMARY OF KEY FORECASTS FOR WALES 41

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EXECUTIVE SUMMARY

Road Transport Forecasts 2009 (RTF09) presents the latest results from the

Department for Transports National Transport Model (NTM), which producesforecasts of road traffic growth, vehicle tailpipe emissions1, congestion andjourney times. The key results are presented in Chapter 2 of this report. Theestimates presented are mainly for England and are provided up to 2035.Forecasts for Wales are included in the annex.

Road transport forecasts have been published by the Department since the1970s, with varying frequency, reflecting both changing demand for forecastsand improved modelling capability. In 2009 the NTM was also used to informthe Impact Assessment of the Departments Carbon Reduction Strategy(CRS), which sets out the Departments key strategies for the transport sector

in reducing greenhouse gases in line with European and UK targets.2 TheNTM has also been used to inform the Committee for Climate Change (CCC)report on Building a Low Carbon Economy3, which seeks to adviseGovernment on the 2050 reduction targets on greenhouse gases and on thethree legally binding carbon budgets.

Road Transport Forecasts 2009 represents an update of Road TransportForecasts 2008: Results from the Department for Transports NationalTransport Model. The key changes this year have been to revise assumptionson growth in Gross Domestic Product (GDP), fuel prices, and fuel efficiency.

This years forecast incorporates new assumptions that are consistent withthe CRS including the EU long-term target on new car carbon dioxide (CO2)emissions of 95 grammes of CO2 per kilometre, as well as the RenewableEnergy Directive 2020 target for biofuel content within transport fuel. Both ofthese policies have impacts on vehicle fuel efficiency, particularly the new carCO2 targets which if met will be achieved by an increase in the fuel efficiencyof cars. In the future, as ultra-low emission cars become a larger part of thecar fleet, the scope for further emissions reductions will need to beconsidered as part of the Road Transport Forecasts.

The key results this year are: an expected lower overall rate of traffic growthand congestion than the previous years forecast, due to lower GDP andhigher oil prices; and, a faster decline in CO2, which is mainly attributable toCRS policies. Chapter 3 explains the key assumptions that lead to theseresults, and illustrates the impact of each key driver.

1 All references to vehicle emissions in this document are to tailpipe emissions only, andexclude emissions of pollutants produced in the production of the fuel or the vehicle2 Links to both the report and the impact assessment can be found on the DfT website:

http://www.dft.gov.uk/pgr/sustainable/carbonreduction/3 Links to the report can be found on the CCC website: http://www.theccc.org.uk/reports/

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As with all forecasts, there is uncertainty around the outturn of key inputvariables, such as expected future GDP growth, fuel prices and population.The forecasts presented in Chapter 2 should therefore be read as theexpected trends for traffic, congestion and emissions, given the most likelypath of the input variables. To account for key uncertainties around the

forecasts Chapter 4 of this report includes a range of scenarios, whichcombine sensitivity tests on key variables. The aim of these scenarios is toshow how the forecasts change when the key input variables are variedwithin reasonable bounds.

The rest of the document is structured as follows:

Chapter 1 gives an overview of the structure of the National TransportModel and describes in detail how the model works.

Chapter 2 presents the main forecasts of traffic, congestion, CO2 and air

pollutants and describes the key drivers of the results. Chapter 3 discusses the key drivers for the change in traffic, congestion

levels and CO2 emissions between now and 2035, and shows how applyingthem one by one in a sequence would lead to the overall changes in theforecast.

Chapter 4 covers the scenario tests that have been carried out to takeinto account uncertainties regarding input variables such as GDP, fuelprices and vehicle efficiency.

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1 INTRODUCTION TO THE NATIONAL TRANSPORTMODEL

1.1 Summary

This chapter describes the NTM in detail. The results from running the modelare presented in the next chapter.

1.2 Strategic transport modelling and appraisal

The NTM is a highly disaggregated multi-modal model of land-based transportin Great Britain (GB)4. It comprises six modes - car driver, car passenger, rail,

bus, walk and cycle. The NTM has two main objectives:

to produce forecastsin a future year of the main road transport indicators -traffic, congestion, carbon dioxide and pollutants

to provide a policy and scenario testing tool by estimating the impact of atransport policy scenario or a change in forecasting assumption.

The NTM combines a wealth of information taken from a range of sources. Ituses data of both a cross-sectional and time-series nature to capture theimpact of factors affecting travel patterns and the impact of time on theprojections.

Observed data - This data is usually measured from surveys, the mostimportant of which are the National Travel Survey, the population census and theroad traffic and goods vehicle censuses.

Forecast Data the model requires forecast data or assumptions on futuretrends, the most important of which are assumptions on population, oil pricesand GDP. This data is often provided by other Government Departments such asthe Office for National Statistics (ONS), the Treasury (HMT) and the Departmentof Energy and Climate Change (DECC) and, therefore, the NTM is based on thesame planning assumptions as used elsewhere across Government.

The NTM uses this time-series data to reflect differences through time, andcross-sectional data to capture the diversity of factors that, at any time,determine the travel patterns of people in Great Britain. The model also has a'welfare module' which aims to appraise the overall impact, in cost benefit

4 Although, the NTM is a model of land-based transport in Great Britain, forecasts aregenerally presented at the England level. The forecasts for Wales are provided in theannex.Domestic air travel is not currently modelled.

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terms, of one modelled scenario against another. Thus, at a relatively highlevel the NTM could be described as having three distinctive elements:

Time series elements - The model forecasts the change in demand for travelover time in terms of the number of trips. This change in demand for travel

depends on changing population and employment patterns, changing levels ofincome, changing prices, patterns of where people live and work, car ownershipand social trends.

Cross-sectional elements - For any point in time, the model projects modesplit based on the relative generalised cost5 of travelling to different destinationsby different modes. This is essentially where the supply side of the model, whichincludes both the time and money costs of making a journey interacts with thedemand side to determine destination and mode choices. Congestion, forinstance, that increases journey times on a part of the road network, would makea person more likely to chose an alternative destination, or another mode. The

NTM models every trip and every vehicle kilometre and the costs associated withthese trips. Furthermore, it projects how generalised costs change with theintroduction of various transport interventions, including providing additional roadcapacity, changing public transport fares and frequencies, or change in motoringcosts.

Economic elements The Welfare module summarises the main outputs of theNTM to give the overall cost and benefit implications, including some of theenvironmental ones, of a policy intervention relative to some base case in aparticular future year. The key determinants of overall welfare remain thejourney times for trips, which are themselves determined by comparing demand

levels with capacity and identifying (any) congestion/crowding.

1.3 A technical overview of the National Transport Model

The National Transport Model6 uses what is known as a 4 stage behaviouralmodelling approach to forecast the demand for travel. This approachestimates the demand for travel from the bottom up. Firstly, it estimates thenumbers of trips people make; secondly, it allocates those trips to actual journeys made between specific origins and destinations; thirdly, it allocatesthose journeys to specific modes; and finally, it allocates the journeys being

made via a particular mode to specific routes across the transport network.The basic structure of the model through which this is done is illustrated inFigure 1.

5 The generalised cost of a journey is a combination of time (multiplied by an individualsvalue of time) plus the monetary costs associated with the trip. The NTM works on a'Generalised Time' basis - the concept is the same as generalised cost but expressed inunits of time rather than money.

6 More details about the model available at: http://www.dft.gov.uk/pgr/economics/ntm/

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Figure 1: Outline Structure of the National Transport Model

Trip endmodel

Carownership

model

Total numberof trips

Land use

Employment

Population

Income (GDP)

Driving licenses

Car purchase price

Bus

CycleWalk

Carpassenger

Car

driver

Multi Modal

DemandModel

RailRail

ModelFORGE

Trafficrowth Rail trips

Congestedcar costs

Overcrowded rail

FreightModel

% HGV& LGVtrafficgrowth

RailPolicy

RoadPolicy

Local TransportPolic

FreightPolicy

At the top of the diagram, the Car Ownership7 and Trip End Models estimate

the number of trips made in each future year as a function of demographicand land use inputs and various economic forecasting assumptions. The CarOwnership Model, National Trip End Model and the demographic and planninginput assumptions are collectively known as TEMPRO8. TEMPRO calculatesthe number of trips starting in each zone (trip productions) and the numberof trips finishing in each zone (trip attractions) for both the base and futureyear. Trip productions are primarily generated by the location and structure ofhouseholds and trip attractions by the location and structure of employment,schools, shops and leisure facilities.

In the centre of the system is the main Demand Model 9 that first determines

the geographic distribution of the trips and then the mode by which they aremade. The inputs to the demand model are total numbers of trip ends (whichare taken to be largely invariant to cost as shown by the National TravelSurvey) as calculated by the National Trip End Model and the generalisedcosts of travelling between each origin and destination for each mode. Forany chosen year, the Demand Model then uses these generalised costs todetermine how the trip ends are joined together to form trips betweenorigins and destination area types and the mode they are made by (car

7

http://www.dft.gov.uk/pgr/economics/ntm/carownership8 http://www.tempro.org.uk9 http://www.dft.gov.uk/pgr/economics/ntm/demandmodel

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driver, car passenger, bus, rail, cycle and walk). The outputs are numbers oftrips by each mode segmented by origin, destination area type, trip length,trip purpose and person type.

The Demand Model is not geographically detailed but it is highly segmented

by trip length, trip purpose, and person type. For road and rail, separate andmore detailed geographic models are then used to determine the specificroute across the network by which a trip is made. The Demand Model iscalibrated to replicate behaviours as observed from the National TravelSurvey10. It is highly segmented by user class because different user classesare known to have different responses to changes in generalised costs.

On the left of the diagram, a specialist highway model FORGE11 links with theDemand Model to provide a more detailed estimate of highway traffic flows,congestion and pollution. FORGE is not a traditional assignment model; rather

it uses observed data on the current level of traffic using each link of the roadnetwork and then applies elasticities derived from the Demand Model toforecast future levels of traffic.

To understand how traffic is currently distributed across the road network,FORGE takes data from the national road traffic database which is populatedfrom count censuses of every major road and a sample of minor road sitesacross Britain. For each of the road types modelled in FORGE a relationshipknown as a speed flow curve links the average speed on that section of theroad to the level of traffic flow. A similar kind of relationship between speed

and vehicle emissions is used to determine pollution.

Although the NTM is essentially a passenger transport model, freight roadtraffic is modelled for the purpose of assessing the impact of freight vehicleson congestion. Heavy Goods Vehicle (HGV)12 traffic growth is modelled usingthe Great Britain Freight Model (GBFM). This takes base year data from 2004on international and domestic freight movements for a range of differentcommodities. The model then grows this traffic over time by modelling theeffect of changes in macroeconomic variables and also changes in generalisedcost. Light Goods Vehicle (LGV)13 traffic is projected by a separate timeseries model relating LGV kilometres in a given year to the levels of GDP andfuel price.

On the opposite side of the diagram, the National Rail Model14 assigns railpassenger trips from the demand model to a detailed geographic network of

10 http://www.dft.gov.uk/pgr/statistics/datatablespublications/personal/11 Fitting On of Regional Growth and Elasticities. Seehttp://www.dft.gov.uk/pgr/economics/ntm/nationaltransportmodelntmsummary12 Goods vehicles of over 3.5 tonnes Gross Vehicle Weight13 Includes mainly but not exclusively vans not over 3.5 tonnes

maximum permissible gross vehicle weight14 http://www.dft.gov.uk/pgr/economics/ntm/railmodellingframeworkfullreport

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rail services. The resulting journey time, overcrowding costs, and rail fareoutputs are sent back to the demand model so that the interactions betweenroad and rail can be modelled.

The blue boxes at the bottom of the diagram indicate that the NTM can be

used to examine the impact of various policy assumptions, which are fed intothe different sub-models. In general, changing policy assumptions changesthe relative costs and/or time (generalised cost) of travelling by the variousmodes. However the forecasting assumptions feeding into the car ownershipand trip end models can also be altered to represent the impact of differentpolicies and scenarios.

Although the National Transport Model covers the whole of Great Britain (itexcludes Northern Ireland) the majority of forecasts set out in this publicationare for England. Some results for Wales are provided in the annex.

1.4 Updating the National Transport Model

Peer review and external validation have consistently shown that the NationalTransport Model (NTM) follows best practice, provides robust results and is fitfor purpose. Nevertheless, the assumptions and methodologies used by theNTM are kept under review. For example, many of the main forecastingassumptions, such as forecasts of GDP and oil prices have been updated sincethe 2008 forecasts were published and the forecasts set out in this paperhave made use of these.

Further to this, over the last few years a major research programme toupdate the underlying methodology of some of the modules making up theNTM has been in progress. This programme has been further increased inambition to include new additional objectives.

The aims of the integrated update and development programme can besummarised as follows:

To increase the capability of the NTMs central Demand Model. The

behavioural responses in the Demand Model are being recalibrated tomore recently available National Travel Survey data, and thegeographic and population segmentation of the model improved.

To improve the modelling of non-car modes and include new modesrepresenting metro and taxi. Work is underway to replace the NationalRail Model by linking the NTM to the Network Modelling Framework(NMF) a strategic rail model which was used to appraise options in therail industrys High Level Output Specification (HLOS).

To provide a new detailed assignment model of the GB road networkthat will work alongside FORGE and allow more detailed analysis of

road traffic forecasts.

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To increase the efficiency of the model running process and help NTMcustomers to better understand its capabilities by improving userinterfaces and documentation.

The new components of the model are currently being tested, and it isintended that later this year the new model will be ready to be fully used toproduce multi-modal transport forecasts.

1.5 The Model Base Year

All forecasting models require a base year on which predictions can be based.The choice of an appropriate base year is driven by a range of criteria - it isnot necessarily best practice to pick the most recent year for which data isavailable.

First of all, required data needs to be final to minimise the risk that futurerevisions will occur. Given that the NTM requires a wide range of data, forexample, on behavioural observations, traffic volumes, economic series,prices and emissions, the scope for having a very recent base year is limited.

In addition, base year data should be uninteresting in the sense that thelikelihood of external shocks influencing the data is minimal. Governmentbudget documents often look at years where the output gap of the economyis small suggesting the economy is operating at capacity (see HMT, 2008c

which shows 2003 as having a low output gap). We also need to choose ayear where there are no significant shocks. Such shocks would includewidespread fuel protests (e.g. in 2000), outbreaks of animal diseases (e.g.foot and mouth in 2001), recessions or large variations in oil prices.

Finally, frequently updating the base year reduces the comparability betweensubsequent forecasts.

The NTM base year is currently 2003, and has been chosen taking all of thesefacts into account.

1.6 Performance against observed data

The NTM is designed to forecast long-term trends (currently 2015, 2025 and2035) rather than individual years. It therefore does not provide insight intothe precise path between the base year and forecast years. Figure 2compares this years short-term forecast to 2015 to observed TSGB data ontraffic.

The short-term forecast to 2015 follows the trend of the observed data to

2008 closely. The short-term forecast implies that, on average, traffic willgrow by around 0.5% per annum between 2008 and 2015. It is likely that in

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any individual year the growth rate could deviate considerably from thisaverage. Given the macroeconomic circumstances of 2009, a further fall intraffic could reasonably be expected for 2009, as occurred in the recession ofthe early 1980s.15 Conversely, the return to economic growth predicted tobegin in 2010 and to gain strength in 2011 is likely to lead to traffic growing

more strongly than the average annual rate forecast for the 2008-2015period.

Figure 2: NTM 2015 Forecast and Observed Traffic Data, England

95

100

105

110

115

120

125

1990 1995 2000 2005 2010 2015

Index

(1995=

100)

2015 Forecast

Observed2003 Baseline

Source: Traffic data from DfT (2009); Traffic forecast from the NTM

15 This years forecast is based on HMT forecasts of GDP published in Budget 2009. In the2009 Pre-Budget Report these numbers were revised further down for 2009.

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2 ROAD TRANSPORT FORECASTS

2.1 Summary

Transport is vital to the economy and the way we live. Taking a long-termview of the likely trends in key metrics such as traffic, congestion andenvironmental impacts is important to enable policy decisions to be madeearly enough to have an impact.

This chapter presents the central NTM forecasts of traffic demand growth andassociated emissions over the period to 2035 and discusses changes toprevious forecasts.

This report updates last years publication of NTM forecasts. The majorchanges to the assumptions used in the model are revised projections of GDPand fuel prices. We have also incorporated more stringent targets for car fuelefficiency and biofuels, as set out in the EU new car CO2 regulation and theRenewable Energy Directive. For RTF09 we have also extended the forecastperiod by ten years - adding an additional forecast year of 2035 - anddropping the 2010 forecast year making the new forecast years 2015, 2025and 2035.

The key results for this years forecast are lower traffic growth due to higher

fuel prices and lower GDP, and a faster decline in CO2 emissions due tostricter targets both at the EU level and domestically.

2.2 Forecast Limitations

As with all forecasts, there is uncertainty around the outturn of key inputvariables, such as expected future GDP growth, fuel prices and population.The forecasts presented in this chapter should therefore be read as theexpected trends for traffic, congestion and emissions, given the most likelypath of the input variables. To account for key uncertainties around theforecasts Chapter 4 of this report includes a range of scenarios, whichcombine sensitivity tests on key variables. The aim of these scenarios is toshow how the forecasts change when the key input variables are variedwithin reasonable bounds.

2.3 Summary of key forecasts

Table 1 summarises the key central forecasts for the years 2015, 2025 and2035. Compared to last years forecast this represents a lower growth in

traffic, mainly due to a lower growth in GDP and much higher expected fuelprices. The NTM forecasts reflect the revised GDP forecast published in

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Budget 2009 (HM Treasury, 2009), which projected a 3.5% reduction in GDPin 2009.

Table 1: Summary of Key Forecasts

Road TrafficTotal EmissionsEngland, Forecast

Change comparedto 2003

Year

Traffic

(Vehiclekm)

Congestion

(Lost

time/km)

Journey

Time

(time/km)

CO2 PM10 NOx

2015 7% 6% 1% -11% -55% -60%

2025 25% 27% 4% -22% -50% -59%Central Forecast

2035 43% 54% 9% -22% -41% -54%

Source: NTM 2009

Congestion on a road occurs when the number of vehicles on a linkapproaches road capacity. Due to lower forecast traffic growth this year,congestion is lower in both 2015 and 2025 than in last years forecasts, andwe therefore also see a slower increase in journey times.

Figure 3 shows the trends in traffic and emissions based on recent historicdata (1995 to 2007), as well as the forecasts for the period 2003 to 2035.

Figure 3: Historic and Forecast Traffic and Emissions, England

0

20

40

60

80

100

120

140

160

180

1995 2000 2005 2010 2015 2020 2025 2030 2035

Index(19

95=100)

Historic Data

TrafficForecast

CO2

NOx

PM10

Source: Historic data from DfT (2009); Historic emissions from Defra (2009); forecasts

produced by the NTM.

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The NTM forecasts show that traffic is expected to continue to increase overthe period to 2035, whilst CO2 will begin to decline and air pollutants willcontinue their decline.

2.4 Forecast Traffic 2009 by vehicle type

Figure 4 shows forecast traffic by vehicle type. The majority of traffic on theroads (79% in 2008) is car traffic, and as can be seen from the diagram, totaltraffic closely follows car traffic.

Figure 4: Forecast Growth in Traffic by Vehicle Type, England

Car

LGV

HGVPSV

0

20

40

60

80

100

120

140

160

180

1995 2000 2005 2010 2015 2020 2025 2030 2035

Index

(To

talTra

fficin1995=

100)

ForecastHistorical Data

Source: Historic traffic data from DfT (2009); forecasts from the NTM 2009.

In line with recent trends, LGV traffic is forecast to grow the most rapidly,doubling over the period to 2035. Growth in LGV traffic has generally followedgrowth in GDP in the past and this is projected to continue. Despite this, LGVsare still expected to account only for a relatively small proportion of totaltraffic in 2035.

Over the past 20 years, HGV traffic has grown more slowly than car trafficand the NTM forecasts a continuation of this trend. HGV traffic constitutesaround 5% of all traffic in 2035. One of the determinants of HGV trafficforecasts is the trend in the average length of haul, which has flattened offconsiderably in recent years. HGV traffic is concentrated on strategic roads

and is forecast to grow around 23% between 2003 and 2035.

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2.5 Traffic Growth by Vehicle Type, Area Type and Road Type

Table 2 shows forecast traffic growth by vehicle type, area type and roadtype. Overall, the highest traffic growth for 2003-2035 is forecast in rural

areas and on motorways and trunk roads. The slowest growth can be seen inalready congested urban areas. In the NTM, increased congestion leads to re-routing, where road users chose to travel at different time periods and ondifferent parts of the road network, and mode switching (which is usually asmall part of the overall response). Congestion has the biggest impact onforecast traffic growth in peak times and on those parts of the network thatare already the most congested.

Table 2: Forecast traffic by vehicle type, road type, and area Type

Vehicle kms,Change from2003

Year London LargeUrbanAreas*

OtherUrbanAreas

RuralAreas

AllAreas

Motor-way

All HATrunkRoads

2015 0% 4% 5% 4% 4% 4% 4%

2025 17% 20% 20% 23% 21% 25% 25%Cars

2035 31% 34% 34% 38% 36% 42% 41%

2015 31% 30% 31% 31% 31% 29% 30%

2025 61% 63% 63% 63% 63% 63% 63%LGV

2035 100% 103% 103% 104% 103% 104% 104%

2015 6% 3% 7% 7% 6% 5% 5%

2025 12% 10% 14% 16% 15% 14% 14%HGV

2035 18% 18% 21% 26% 23% 24% 24%

2015 5% 7% 8% 7% 7% 6% 7%

2025 23% 24% 25% 27% 25% 27% 28%All Traffic

2035 40% 41% 41% 44% 43% 46% 46%

Source: NTM 2009. (*)Large urban areas include metropolitan areas and towns and citieswith a population of more than 250,000.

Due to the already high levels of congestion, the lowest traffic growth isforecast in London, especially in the shorter run. The largest increases inLondon traffic are forecast over the weekend and before and after the

weekday peaks. This reflects the fact that these times are currently relativelyless congested. The NTM, however, is currently being updated and trafficforecasts for London will be reviewed in the context of this update and in thelight of slower than forecast traffic growth in London over the past few years.

Detailed regional traffic growth and speeds forecasts from the model areincluded as an annex.

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Box 1: Historical Traffic

The table below shows the average annual growth rate for all traffic for eachof the last six decades alongside the average annual growth rates for oilprices and GDP. Traffic grew very strongly in the 1950s and 60s. This was

fuelled by strong growth in households owning cars for the first time. The1970s were marked by two oil price shocks which pushed up fuel priceshelping trigger a recession. Consequently, traffic growth was significantlybelow the rates of the previous two decades.

After the steep recession in the early 1980s the economy grew strongly andoil prices fell. Unsurprisingly, therefore, average annual traffic growth wasstronger than it had been in the 1970s, but nevertheless it was below therates of the 1950s and 60s. The period since 1990 has been one of lowerand more stable traffic growth. The average rate, since the last recession

ended in the early 1990s, has been about 1.6% per annum.

Historic Growth in Traffic, GDP and Oil Prices, Average Annual GrowthDecade Traffic Oil Prices GDP Comments

1950s 8.4% -0.5% 2.4% Strong increase in 1st carownership

1960s 6.3% -3.7% 3.1% Strong increase in 1st carownership

1970s 2.9% 24.4% 2.4% Oil Crises

1980s 4.7% -10.3% 2.3% Strong growth post 1982, falling oilprices

1990s 1.4% -2.9% 2.1% Early 90s recession, fuel dutyescalator

2000-2007 1.2% 15.4% 2.8% Steady traffic growth, rapidlyincreasing oil prices

Source: GDP from ONS, Traffic from DfT, Oil Prices to 1999: Energy InformationAdministration, US. Post 1999: Brent crude, DECC

The rate of traffic growth has generally been declining over the last 50 to 60

years. This trend is expected to continue to some extent, with averagegrowth of around 1.1% per annum forecast over the period to 2035. Thispartly reflects slowing car ownership growth and an ageing population(pensioners currently make fewer and shorter distance trips than those inwork). Also congestion itself, and the impact it has on journey times, will actto dampen demand, but we have not been able to estimate in isolation theextent to which congestion reduces total traffic demand.

Figure 5 shows trends in traffic in Great Britain over the period 1980 to 2008along with the NTM forecasts for Great Britain to 2035. As can be seen, traffic

has historically grown year on year, with the exception of the early 1990srecession where growth was negligible. Since 1995 traffic has grown at a

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slower rate than the previous 10-15 years. The negative GDP growth of 2009has reduced the forecast traffic growth for 2003-2015, reflecting the fact thattraffic falls could be seen in some years. In the longer term, however, trafficis forecast to continue to grow at a similar rate as to the whole of the 1990s,but at a slower rate than witnessed before that.

Figure 5: Trends in Total Traffic, 1980 2035, Great Britain

80

100

120

140

160

180

200

220

240

260

1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035

Index

(1980=

100)

Historic traffic

Traffic forecast

Source: Historic data from DfT (2009); forecasts from NTMNote: This figure differs from the majority of charts presented in this report as the data andforecasts are for Great Britain rather than for England.

2.6 Distance Travelled

Table 3 presents forecast growth of total distance travelled by differentmodes from 2003 to 2015, 2025 and 2035. Total passenger kilometres areforecast to increase by 27% between 2003 and 2035.

Table 3: Change in total distance travelled by modeEngland, Passenger kms % change to

2015% change to

2025% change to 2035

Car total 6% 16% 26%

Driver 4% 21% 35%

Passenger 9% 8% 10%

Bus 29% 31% 36%

Walk/Cycle 11% 16% 24%

Total 8% 17% 27%

Source: Forecasts from the NTM

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In 2003, car travel accounted for 81% of total movements and it is forecastto reduce its share very slightly to 77% by 2035. This is mainly due to thepopulation growth being concentrated in urban areas where congestion andshorter journeys make alternative modes to the car more attractive.

Figure 6 shows projections of the change in average trip length over time, aswell as variation in trip lengths by journey purpose. Between 2003 and 2035,the average trip length is forecast to increase by around 4% from about 11kilometres to just under 12 kilometres.

Figure 6: Average Trip Length, England

0

5

10

15

20

25

30

35

Commuting Employer's Business Private Business Discretionary All Trips

Average

Trip

Leng

th(km

)

2003 2015 2025 2035

Source: NTM

2.7 Congestion

Congestion can be measured in several ways, including in terms of averagelost time, or as changes to journey times. For the purposes of this forecast,

congestion is measured as seconds lost per vehicle kilometre

16

relative to freeflow speeds17. In contrast, journey time per kilometre reflects what roadusers actually experience. As traffic rises, journey time measures will involvesmaller percentages than changes to congestion as congestion typically startsat a lower overall level.18

16This includes all vehicle kilometres, including those travelling in free flow conditions.

17 Where free flow speeds are the speeds that vehicles would travel at, subject to speedlimits, if there where no other traffic on the road.18

If the free flow speed was 70 mph, travelling at 60mph represents about 5 second lost perkm (congestion) and 37 sec/km (journey time). A reduction to 50mph increases bothmeasures by 7s/km. This is a 20% rise in journey time but a 140% increase in congestion.

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The impact of rising traffic on congestion depends on the pattern of trafficgrowth, how it is spread across the network and across time periods, and theextent to which road capacity is changing.

Our forecasts suggest that congestion across the English network as a whole

will increase by about 54% between 2003 and 2035. This represents anaverage increase in time spent travelling of 9% (6 seconds) for eachkilometre travelled.

Table 4 highlights how increased traffic across different area types leads tovarying rises in congestion and changes in average speeds. The impact ofadditional traffic on congestion is markedly higher in places where congestionis already significant. In smaller urban areas, however, increases in trafficimpact on congestion by less than one for one, whilst in London therelationship is closer to one to one and a half. It also shows the proportion of

traffic on congested roads19by area type.

Table 4: Forecast Change in Traffic and Measures of Delay

England(change from

2003)Year London

Large UrbanAreas

Other UrbanAreas

RuralAreas

All

2015 5% 7% 8% 7% 7%

2025 23% 24% 25% 27% 25%Traffic

(veh km)2035 40% 41% 41% 44% 43%

2015 8% 7% 6% 2% 6%

2025 35% 26% 24% 24% 27%Congestion(lost time/km)2035 67% 54% 41% 58% 54%

2015 -3% -2% -1% 0% -1%

2025 -12% -5% -3% -1% -4%Vehicle Speed

2035 -20% -10% -6% -3% -8%

2003 24% 14% 9% 3% 8%

2015 26% 14% 10% 2% 9%

2025 32% 17% 13% 3% 11%

Proportion of alltraffic travelling

in verycongestedconditions 2035 41% 21% 16% 5% 14%

Source: NTM

London has the highest proportion of traffic in congested conditions, with over40% of traffic travelling in very congested conditions by 2035. It is important tonote, however, that this represents the proportion of distance travelled incongested conditions and not time, and so the proportion of time spent incongested conditions will be higher.

Figure 7 shows the average seconds lost per kilometre by area type in thebase and forecast years. For London, the time lost per kilometre travelled bythe average vehicle is projected to increase from a level of just over 50

19 Defined as conditions where the ratio of the volume of traffic relative to road capacity isabove 0.8.

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seconds in 2003 to more than 85 seconds in 2035. On rural routes, however,hardly any time is lost to congestion; compared to free-flow conditions,vehicles lost 3 seconds per kilometre in 2003 and are expected to lose just 4seconds in 2035.

The NTM takes into account congestion costs when estimating traffic.Congestion increases travel time, and leads to lower demand than free-flowconditions.

Figure 7: Congestion in 2003, 2015, 2025 and 2035 by Area Type,England

0

10

20

30

40

50

60

70

80

90

London Large Urban Areas Other Urban Areas Rural Areas All

Secon

ds

los

tperve

hiclekm

2003 2015 2025 2035

Source: NTM

Figure 8 shows total lost time per year from congestion for freight traffic, cartraffic by journey purpose and light goods vehicles for the years 2003, 2015,2025 and 2035. Total lost time for businesses travel20 in 2035 amounts to 875million lost hours, an increase of 154% over 2003 levels. Businesses accountfor a little more than one quarter of all lost time, with commuting taking a

similar share. Other road users experience a little less than half of the totaltime lost.

20 Freight, car trips for employers business and working LGV (88% of LGV are assumed to bein work time following DfT guidance).

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Figure 8: Total Lost Time from Congestion for Freight and Car Traffic,England

0

100

200

300

400

500

600

700

800

900

Commuters Employer's

Business

Education and

Personal

Business

Home based

Recreation and

Holiday

Non home

based

Recreation and

Holiday

LGV's HGV's

TotalLostTime(millionhour

s)

2003 2015 2025 2035

Source: NTM

2.8 Emissions

The environmental outputs of the NTM are vehicle tailpipe emissions of the

greenhouse gas CO2, and the local air pollutants, NOX and PM10. Figure 9shows the central forecasts along with the recently observed trends.

As the historic data in Figure 9 shows, CO2 emissions have grown over thelast decade or so, though at a slow rate. The forecasts suggest that CO2emissions will fall by 11% to 2015 and 22% by 2025, relative to 2003 levels,and then stabilise. This represents a larger reduction compared to last yearsforecast. The main factors driving this reduction are the expected greater useof biofuels within road transport fuels and increasing vehicle efficiency. Thisyear we have modelled the EU targets on new car CO2, which will be

delivered by increasing new vehicle efficiency, as well as the RenewableEnergy Directive.

As part of reflecting the EU long term CO2 emissions target we haveintroduced in this years forecast a provisional assumption that a certainproportion of the vehicle fleet in each forecast year will be hybrids. Post 2020there are no targets in place for vehicle efficiency. This, coupled withcontinuing traffic growth, explains the levelling out of CO2 emissions at theend of the forecast period. At present there are no assumptions in the NTMregarding the role electric vehicles (EVs) could play in reducing emissions.This is an area in which we are hoping to develop the NTMs capability in for

future forecasts.

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Figure 9: Tailpipe21 CO2, NOX and PM10 Transport Emissions: Historyand Forecast, England

0

20

40

60

80

100

120

1995 2000 2005 2010 2015 2020 2025 2030 2035

Index(1995=100)

Historic Data

Forecast

CO2

NOx

PM10

Source: Historic emissions data from DEFRA (2009); forecasts from the NTM

The trend over time of emissions of local air pollutants NOX and PM10 are alsoshown in Figure 9. This highlights the significant progress that has been made

in reducing emissions of pollutants. Emissions of NOx and PM10 have fallensince the early 1990s. Road Transport Forecasts 2009 includes Euro 4emissions standards for light vehicles implemented in 2006 and Euro IVstandards for HGVs. Inclusion of these standards in the NTMs modelling ofemissions shows that the trend of declining emissions of NOx and PM10 is setto continue until 2015 as newer vehicles that meet the standard continue toenter the fleet. The NTM will soon be updated with later emissions standards,which are likely to show a faster and more sustained decline.

21Emissions from biofuels are in this context only counted at the combustion

stage of biofuel lifecycle, i.e. only tailpipe emissions are considered. This isbecause the only emissions created through using biofuel for transport are at thecombustion stage; all other emissions (earlier in the biofuels lifecycle, e.g.refining/processing) are accounted for within other sectors/industries.

Biofuel combustion is considered to only emit tailpipe carbon emissions equivalentto the carbon absorbed by the feedstock when it was grown, as tailpipe carbon

emissions are limited by the amount of carbon used by the feedstock duringgrowth. Therefore, the net tailpipe emissions from biofuels are considered to bezero, in line with IPCC guidance. However, this is an emissions accounting

convention for transport, rather than a full lifecycle.

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2.9 Other Government Forecasts of Transport CO2

Within Government, forecasts of CO2 emissions from transport are produced

both by the DfT and by the Department of Energy and Climate Change(DECC). These forecasts are produced by two separate models, the DfTsNTM and the DECC UK Energy Model.

The DECC Energy Model is a time series econometric top-down model thatdirectly forecasts energy use and emissions for all sectors across the UnitedKingdom including transport. The DECC model provides for consistent wholeeconomy modelling of energy use and associated emissions, of whichtransport is a part. Such a model is needed for the purposes of cross-Government strategies.

The NTM on the other hand uses a bottom-up approach starting from data onthe trips that people make and distinguishing between area, person andhousehold types as well as including a representation of the road network.Emissions are derived taking into account the type of vehicles and the speedthey are forecast to be travelling at. The DfT model allows for a greater levelof detail in modelling the transport sector specifically and is better suited tothe modelling of a range of transport policies.

Nevertheless, there are a number of significant common assumptionsbetween the two models. For example, assumptions on economic growth,fuel prices, vehicle fuel economy, population, cars per household, and the CO2impact of biofuels are the same in both models.

Considered together the two models may provide different insights and agreater understanding of the key influences on the transport sector. But, dueto the different nature and purpose of the two models, we would not expectidentical forecasts.

Further, NTM forecasts are only available at a Great Britain level, whereasDECC forecasts are published on a UK basis. Published forecasts, therefore,

cannot usually be directly compared on a levels basis and so Figure 10compares the trends in growth only.

The latest projections from the DECC model were published in the updatedenergy and CO2 emissions projections in July 2009

22. In order to comparethese DECC forecasts relating to the UK with the NTM forecasts relating toGreat Britain, as presented below, both have been indexed, while NTMforecasts have also been scaled up to reflect CO2 emissions at a UK level.Figure 10 shows these projections along with the trend in actual UK roadtransport CO2 emissions from 1980 to 2003. It should be noted that while the

22 Available at http://www.decc.gov.uk/en/content/cms/publications/lc_trans_plan/lc_trans_plan.aspx

24
http://www.decc.gov.uk/en/content/cms/publications/lc_trans_plan/lc_trans_plan.aspxhttp://www.decc.gov.uk/en/content/cms/publications/lc_trans_plan/lc_trans_plan.aspx


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DECC model computes transport energy demand and emissions on an annualbasis, the NTM only models the years 2015, 2025 and 2035 emissions,therefore, may not follow a straight line between these points.

Figure 10: DfT & DECC CO2 Forecasts, UK

60

65

70

75

80

85

90

95

100

105

1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

Index

(2003=

100)

Historic

CO2

DECC forecast

NTM forecast

Source: Historical data from DEFRA, DECC forecast from DECC (2009), DfT forecasts fromthe NTM 2009

The data series between 2007 and 2007 shows a break from the past trend ofrising road transport CO2 emissions. Both DECC and the NTM are expectingthis trend to continue with CO2 emissions falling to the mid 2020s. Thisreflects policies such as regulation on the CO2 emissions of new cars and theRenewable Energy Directive (biofuels). The forecasts themselves are also verysimilar with any differences occurring within what would be considerednormal margins of modelling uncertainty. In 2015, the DECC forecasts arearound 2.4% higher than the NTMs and, therefore, fall well within theforecast fan produced in the sensitivity analysis reported in Chapter 4.

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3 KEY DRIVERS OF THE NTM FORECASTS

3.1 Summary

The key drivers of traffic growth, congestion and changes in CO2 emissions inthe NTM are changes in GDP, population, vehicle fuel efficiency and the costsof fuel. Sections 3.2, 3.3, 3.4, and 3.5 below discuss what is forecast happento these key drivers, and how that compares with the assumptions madeabout them in last years forecasts.

3.2 Population and employment

As with many transport models, the Departments Trip End ModelPresentation Program (TEMPRO 5.4) provides demographic and land useplanning assumptions for the NTM.

Population is assumed to rise by 21% between 2003 and 2035. Employmentpatterns and other determinants of traffic growth are likewise expected to riseover the same period. Employed people tend to make more trips and travelfurther.

Offsetting this growth impetus is the fact that the population is expected to

age over time, with the proportion of over-65 year olds set to grow. This hasimportant implications for our forecasts as pensioners currently make fewerand shorter distance trips than other segments of the population. Evidencefrom the National Travel Survey (NTS) suggests pensioners make on average50% fewer trips a week than adults of working age. In 2000 the over 65population resident in households23 was around 16% of the population, but in2025 this is forecast to grow to over 23%.

3.3 GDP

Growth in travel is also closely associated with increasing incomes. In athriving economy, people travel more and businesses move more goodsacross transport networks.

Rising GDP impacts on traffic growth in two ways:

Firstly, increasing GDP and its impact on personal disposable income isclosely associated with increasing levels of car ownership. As might beexpected, the impact of having a greater availability of cars is to increasethe number and length of car trips, and decrease car vehicle occupancy.

23 Source: TEMPRO 5.4

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Secondly, increasing disposable income causes people to change theirjourney patterns to travel to more attractive destinations which are furtheraway.

Car Ownership

Table 5 shows National Travel Survey data on car ownership rates perhousehold in 2007. As can be seen, for those households in the two highestincome quintiles, the average number of cars per household is nearly threetimes that of those in the lowest income quintile range. Furthermore, 54% ofhouseholds in the lowest income quintile band do not own a car, whereasonly 10% of those households in the highest income quintiles do not.

Table 5: Proportion of Households Owning No, One or More Cars

No car One carTwo or more

cars

Cars per

household

Lowest real income 54% 37% 8% 0.56

Second level 36% 46% 18% 0.85

Third level 17% 49% 34% 1.26

Fourth level 10% 42% 48% 1.51

Highest real income 10% 39% 51% 1.51

All incomes 25% 43% 32% 1.14

Source: National Travel Survey

It is likely that there is a saturation point in car ownership. This occurs whenhouseholds with high incomes reach a point where they are unlikely to want

to own another car even if their incomes continue to rise. We continue toinvestigate whether we are reaching saturation levels, but even if somesections of the market are nearing saturation, there currently appears to bescope for further growth amongst other sections of the population.

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Forecasts for GDP

Real GDP is forecast to grow as follows:

Table 6: Real GDP and real GDP per capita growth, 2003 to 2035

Period Real GDP GDP per capita

2003-2015 28% 18%

2003-2025 60% 39%

2003-2035 102% 66%

Source: GDP growth rates implied by HM Treasury forecasts, Budget 2009

To compare these rates with those used in last years forecasts, real GDPgrowth used in the central scenario for last years forecasts in the period 2003to 2025 was 71%, whereas the equivalent figure for the same period used inthe RTF09 central case is 60%, which is slightly less than the growth used inthe low scenario (63%) for last years forecasts.

3.4 Fuel economy and the costs of driving

The rate at which vehicles use fuel is a key determinate of road transport CO2

emissions, and the cost of driving. Later in this section, the expected changesto the costs of driving are discussed.

Fuel economy

The assumed rate of increase in car fuel efficiency and the proportion ofbiofuels used in the road transport fuel mix in RTF09 are higher than used inlast years forecasts and reflect the EU new car CO2 targets and theRenewable Energy Directive.

Road Transport Forecasts 2009 includes the EU new car CO2 regulation,including the mid-term target of 130 grammes of CO2 per kilometre by 2015and the long-term target of 95 grammes of CO2 per kilometre by 2020.Although the long-term target has been agreed, this target will reviewed by2013. Given that these CO2 targets will need to be achieved by increasing carfuel efficiency the car fuel economy assumptions used in the NTM areconsistent with these targets.

When modelling fuel economy we have taken into account the biofuels energypenalty that biofuels have lower energy content so more fuel is needed to

drive the same mileage. Despite this energy penalty, car fuel economy isexpected to increase dramatically, translating into a 51% increase in fleet fuel

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economy for petrol cars and a 37% increase for diesel cars between 2003 and2035. This translates to an expected 47% increase in fuel efficiency of theaverage car in the fleet between 2003 and 2035.

There are currently no targets on car fuel economy in place for the period

post 2020. It is expected that the regulatory framework will remain in placeand is likely to be tightened. However, in the absence of policy the NTMassumption is for no further improvement in new car fuel economy post 2020.This is also consistent with the Impact Assessment of new car CO2 in the CRSanalysis.

Total fleet efficiency will continue to improve beyond 2020 due to newvehicles replacing old ones, refreshing the fleet with more economicalvehicles. The regulatory framework is therefore expected to deliver increasedcar fuel efficiency long after the long term CO2 target is implemented. At

present there are no assumptions in the NTM regarding the role electricvehicles (EVs) could play in reducing emissions. This is an area in which weare hoping to develop the NTMs capability in for future forecasts.

The assumptions for LGV fuel economy for the purposes of calculatingemissions are the same as used in last years forecasts. These reflect the factthat like cars, van fuel economy is expected to improve, but in the absence ofagreed current regulation, fuel economy is expected to improve more slowlythan for cars.

For this years forecast we have updated our assumptions on HGV fueleconomy. Without policy, the assumption is that average fuel efficiency ofnew HGVs will improve at an annual rate of 0.5%. However, the rate of fueleconomy improvement of HGVs operating in the fleet is expected to beincreased due to policies such as driver training. Post 2013 it is expected thatnew HGV efficiency will worsen slightly due to the introduction of a Eurovehicle standard on air quality pollutants (Euro VI) which comes into force2013/14.

Taking the impacts of all these policy and technical developments intoaccount along with the rate at which they feed into the HGV fleet we obtainthe following overall assumptions on HGV fleet fuel economy improvements:

Table 7: Fleet Plan Fuel Consumption per Kilometre2003-10 -0.66%2010-15 -0.54%2015-25 0.06%2025-35 -0.32%

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The costs of driving

The cost of driving per kilometre is a key determinate of traffic andcongestion levels.

The cost of driving includes various elements such as the costs of purchasinga vehicle, insurance, fuel costs and servicing costs. Assumptions on non-fueloperating costs are unchanged from last years forecasts. The assumptionsregarding fuel prices and fuel economy are combined to produce car fleetaverage costs of driving per kilometre for each year.

Between 2003 and 2015 the fleet average cost of driving per kilometre isexpected to increase by 15%, as increasing fuel costs outweigh improvementsto fuel economy during this period. After 2015 fuel economy increases feedmuch more rapidly through to the car fleet and the costs per kilometre of

driving fall. Table 8 shows these assumptions over time.

Table 8: Percentage changes to the average cost of driving per kilometre

2003-2015 15%

2003-2025 -11%

2003-2035 -23%Source: DfT 2009

The sections that immediately follow outline the underlying trends in oil prices

that are causing the cost of driving to change over time.

Projected Oil Prices

The underlying driver for changes to pump fuel prices, in the absence ofchanges to rates of taxation, is oil prices. Road Transport Forecasts 2009 isbased on the latest DECC crude oil price projections, published in April 2009.DECC has produced four oil price scenarios (low, central, high and high high)to 2030, represented in Figure 11. 24 Post 2030 the prices assumed in theforecasts remain at 2030 prices levels (in real terms). Given the impossibilityof forecasting the future oil price with real certainty, the range of outcomescovered is intentionally wide. Figure 11 indicates the width of those oil priceprojections.

As Figure 11 shows, the high and high-high scenarios project oil prices to riserapidly, then level off in 2020 and 2016 at $114 and $142 per barrelrespectively (in 2006 prices). The low scenario rises by a smaller amount froma lower base, stabilising at $57 per barrel from 2016. The central scenariorises gradually to 2030 at $85 per barrel.

24 Published in April 2009

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Box 2: Historic Oil Prices

Oil prices hit historical highs in 2008, and have since then fluctuated strongly.Nevertheless, the average for 2008 ended near $100/ bbl compared to $73 bbl in2007 and $65 bbl in 2006. It is also worth looking at historical oil prices as these

show that even prices of $50 a barrel are significantly above the long term averageprice.

The chart below shows annual average oil prices from 1861 to 2008 in constantprices25 along with the range of DECC oil price projections out to 2035. As can beseen, there was an impressive spike in the 1860s that was similar to the price spikesof the 1970s and the one over the last few years.

After peaking in 1864 at over $100, prices gradually trended down until the late1870s. They then remained in a relatively tight $20 range (between about $10 and

$30) for the following 100 years until the first of the 1970s oil price crises of 1974.Prices then remained high for a few years before falling steeply in the early 1980s,returning to the long term range during the period 1986 to 2003.

Prices again broke out of this long term range in 2004 and continued higher until July2008, reaching the most recent nominal peak of $147. However, they havefluctuated substantially since then, falling below $50 in December 2008, before risingback to $75-$80 in January 2010.

Oil Prices 1861 to 2008 (annual average)

0

20

40

60

80

100

1861 1871 1881 1891 1901 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001

$/Ba

rrel(2006Prices)

Data source to 1999: Energy Information Administration, US. Post 1999: Brent crude, DECC

25 2006 prices

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3.5 Road Capacity

The NTM has a representation of the road network that is updated in line withthe Highway Agencys and the eight England Regions published road

investment plans. Strategic road investment is dominated by roadimprovements in rural areas, such as rural motorways and dual carriageways,mainly by widening existing roads and improving junctions.

The impact of this years assumptions is to increase planned additions to roadcapacity by 22% to create total additional capacity of 1,924 lane kilometres inEngland over the period 2003-2015. The increase in 2015 is due to therevised roads programme (announced in January 2009)26 which increased thenumber of Hard Shoulder Running (HSR) schemes assumed to be completedover the period.

No investment decisions have been made by DfT for the period 2025-2035. Itis assumed for the purposes of these projections that in this period roadinvestment policies will broadly continue. It is therefore assumed that theincrease in road capacity through to 2035 adding 3,624 lane kilometres overthe period 2003-2035. This should not be interpreted as indicating a DfTpreference for or against such a policy.

3.6 Applying the key drivers

Figure 12 below shows the results of a key driver analysis carried out on theRoad Transport Forecasts 2009 to analyse what impact individual variableshave on the outputs. The analysis has been carried out for the main forecastyear, 2035, and includes the following ten drivers: population growth, GDP,fuel prices, car fuel efficiency, mileage split, freight efficiency without policy,freight efficiency with policy, road build and public transport.

This analysis has been carried out by setting up a model run for 2035 with allbasic settings, but excluding the 2035 assumptions for the key drivers.Instead, base year values have been inserted into the model for these

variables. In subsequent runs, 2035 input assumptions have been addedincrementally, in the order illustrated in Figure 12. The three lines in thefigure hence show the cumulative impact on traffic, congestion and CO2. Thedifference between each point shows the cumulative impact of theassumption that has been added. However, as each point shows thecumulative impact, the order in which the assumptions have been added willhave an effect on the magnitude of each driver.

26The programme announced can be found in the report released with the announcement,

available at: http://www.dft.gov.uk/pgr/roads/network/policy/motorways/

33
http://www.dft.gov.uk/pgr/roads/network/policy/motorways/http://www.dft.gov.uk/pgr/roads/network/policy/motorways/http://www.dft.gov.uk/pgr/roads/network/policy/motorways/


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As can be seen, population growth and increased GDP both serve to push uptraffic, congestion and CO2 emissions.

Figure 12: Key Driver Analysis (2003-2035)

0

45

90

135

180

225

1. 2003

Base

2.

Population

Growth

3. GDP 4. Fuel Price 5. Car Fuel

Efficiency

6. Mileage

Split

7. Freight

Do Nothing

8. Freight

Policy

9. Road

Build

10. Public

Transport

11. 2035

Central

Traffic

Congestion

CO2

Population growth

2003-2035: 21%

GDP growth

2003-2035: 103%

Fuel Price Increase

2003-2035: 45%

Car Efficiency Increase

2003-2035: 47%

Biofuels 5% by 2015,

10% by 2020

Diesel Share

2003: 23%

2035: 56%

HGV efficiency

increase

2003-2035: 8%

HGV efficiency

increase

2003-2035: 9%

By 2035: 3624

additional Lkms

The key messages that can be inferred from Figure 12 are the following:

Population and GDP growth together explain most of the forecastgrowth in traffic and congestion;

Congestion increases proportionately more than traffic for the reasonsdiscussed in section 2.7 of this report;

With no other changes than growth in population and GDP, CO2emissions would also rise;

Increased fuel prices are expected to mitigate growth in traffic,congestion and emissions;

Increased fuel efficiency means that CO2 emissions are expected tofall, but vehicle fuel efficiency improvements also act to reinforceincrease of traffic and congestion;

Adding road capacity in line with the assumptions used in the NTMwould reduce congestion, and demand would respond accordingly,with traffic increasing slightly.

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4.3: High/Low DemandThe central assumed level of growth and the variation in the key measuresconsidered were:

GDP: The central estimate is for total income, for the period 2003 to2035, to grow by 102%, or just over 2.2% per annum. In the lowscenario, GDP growth is 88% (0.25%-points less p.a. post 2010) and inthe high 112% (0.25%-points more p.a. post 2010).

Oil Prices: These are varied according to the range of projections aboveusing the low, central and high prices.

Fuel Economy: The central scenario is that fleet average car fuelconsumption (litres of fuel consumed per kilometre) falls by 47% between2003 and 2035. This reflects EU fuel efficiency legislation and targets as

laid out in the DfT Carbon Reduction Strategy. HGVs improve by anaverage of 0.3% per year, 9% to 2035. For cars, LGVs and HGVs in thehigh scenario, the annual rate of fuel economy increase is increased by50%; in the low scenario it is decreased by 50%.

For the purpose of assessing the maximum variation around the centralforecast, we model scenarios based on a combination of these sensitivities.The high demand scenario combines high GDP, low oil prices and high fueleconomy. Each of these assumptions cause demand for transport to rise. Thelow demand case assumes low GDP, the high oil price and a low fuel

economy setting. Each of these assumptions cause demand for transport tofall.

As shown in Figure 14, combining the variations in key assumptions results ina forecast range of between 31% and 50% growth in vehicle kilometres for2035 compared to 2003. The central forecast lies in between at 43%.

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Table 10 also shows the impacts of the demand scenarios on congestion. Thedifference between the central and the high/low scenarios illustrates the non-linearities in congestion. Adding an additional vehicle on an already congestedlink will have a much greater impact on speed than if that vehicle was addedto a relatively uncongested link, which is why the congestion response in the

high demand scenario is much greater than in the low demand scenario

4.4: High/Low CO2

Those assumptions that reduce the amount of CO2 have been combined,namely all the settings for the low demand scenario, except for the fueleconomy settings, where the high fuel economy setting is used. While high oilprices and low fuel economy make driving both more expensive and,therefore, reduce demand, the lowest CO2 emissions are achieved when high

fuel prices are matched with high fuel economy settings. The reduction inemissions per kilometre more than offsets the higher demand arising from thehigher fuel economy improvements. Table 11 shows the summary resultsfrom these scenarios.

Table 11: Summary Results of High and Low CO2Road Traffic

Total EmissionsEngland, ForecastChange compared

to 2003Scenario

Traffic(Vehicle

km)

Congestion(Lost

time/km)

JourneyTime

(time/km)CO2 PM10 NOx

Low CO2 43% 54% 9% -36% -44% -54%

Central 43% 54% 9% -22% -41% -54%2035 forecasts

High CO2 42% 53% 9% -4% -40% -54%

Source: NTM 2009

Figure 15 shows the result against the recent history of road transport CO2

emissions. This suggests that with the low carbon combination of sensitivities,emissions are forecast to fall to 32% below 1990 levels in 2035. The highcombination would lead to CO2 being 3% above 1990 levels.

Traffic and congestion growth are similar between the three scenarios. This isbecause the variations to GDP, oil prices and fuel economy assumptions in thehigh and low scenarios away from the central case, cancel each other out interms of their overall impact on traffic and congestion in 2035.

In the low CO2 scenario high fuel economy improvements lead to lower CO2emissions, but higher oil prices and lower GDP growth than assumed in the

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central case mean that fuel economy improvements do not cause traffic andcongestion to rise.

In the high CO2 scenario low fuel economy improvements are counter-actedby lower oil prices and higher GDP growth than in the central case, so traffic

and congestion levels would be similar to the central case.

Figure 15: Central, High and Low CO2 Forecasts, England

65

70

75

80

85

90

95

100

105

110

115

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035

Index(1995=1

00) Historic CO2

central CO2

low CO2

high CO2

Forecast

Source: Historic CO2 from DfT (2009), forecast from NTM

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ANNEX: SUMMARY OF KEY FORECASTS FOR WALES

Table 12: Summary of Key Forecasts for Wales

Road TrafficTotal Emissions

ForecastChange

compared to2003

Year

Traffic

(Vehiclekm)

Congestion

(Losttime/km)

Journey

Time(time/km)CO2 PM10 NOx

2015 7% 7% 1% -12% -53% -61%

2025 22% 22% 1% -24% -47% -60%Central

Forecast2035 39% 45% 3% -25% -37% -55%

national road traffic forcast 2009

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