Transport Scenarios
2
2.1
URBAN TRANSPORT SCENARIO DEVELOPMENT
Introduction
This chapter describes the urban transport scenarios addressed by the project, and how they were
represented in the model system. The original intention of the research was to investigate the air quality
and health implications of the following development and policy scenarios:
(a) A base or 'Business as Usual' scenario to assess the implications of projected regional traffic
growth, assuming no other management interventions;
(b) Road network development;
(c) Road user charging; and
(d) Leeds "corridor initiatives" comprising arterial routes with guided bus (A61 and A64) and high
occupancy vehicle lanes (HOVL) on the A647.
However, since the project was awarded the government announced that the Leeds Supertram, a £487
million three line 28 km light rail network with terminal park and rides, was to be funded. This meant
that the SATURN networks (see 2.2) required to address scenario (d) were not to be developed, and
hence were unavailable to the research team. To compensate, demand management effects were
investigated through a greater range of road user charging scenarios than originally envisaged, and a
completely new scenario, the wider adoption of clean fuelled vehicles (CFV's) was added.
2.2
Traffic modelling
To assess the impact of the predicted demand for road use, the interactive simulation and assignment
model SATURN (Simulation and Assignment of Traffic to Urban Road Networks) was used. SATURN
(Van Vliet, 1982) was developed at the Institute of Transport Studies, University of Leeds and is used
in thirty countries, including application by 80 UK local authorities. SATURN is a tactical transport
model that estimates the traffic volume on each link of a road network assuming a fixed trip matrix.
This type of model is distinguished from assignment models used to model strategic transport by its
very detailed representation of the road network and the modelling of turning movements at junctions.
SATURN requires two basic sets of input data: (i) a trip matrix, and (ii) road network data. The trip
matrix, (also known as the origin-destination matrix), expresses the demand for travel and gives the
number of trips between pairs of zones, i.e. between trip origins and destinations. Trips are usually
expressed in passenger car units (PCU), defined as unity for cars and light duty vehicles, 0.5 for
motorcycles, and 2 for buses and heavy-duty vehicles. Bus trips by public transport are not included in
the trip matrix, but in the network data, because they follow fixed routes and cannot be subject to the
assignment procedure. For the Leeds SATURN application, the road network is represented at two
levels, with an inner simulation network containing data about road links and junctions (including
detailed descriptions of roundabouts, priority and signalled junctions), and a buffer network
(surrounding the simulation network), with data on roads but not junctions. The parameters required for
nodes (junctions), links and turns include:
•
number of links at the node;
•
number of entry lanes for each link;
•
traffic signals data (number of stages, duration of each stage, turning movements allowed at each
stage);
•
minimum gap for give-way turns at priority junctions and roundabouts; and
•
free-flow speed on the link, link length, and saturation flow for each turn.
2-1
Transport Scenarios
SATURN is first run using the trip matrix for a selected traffic period (AM, PM or intermediate
periods), which transforms the origin-destination matrix from ASCII to binary format. A separate
module of SATURN is applied to transform corresponding network data to the binary format. The
SATALL module within SATURN is then run with the origin-destination matrix ("demand for trips")
and network binary files ("supply of routes"), which causes the assignment and simulation loops to run
iteratively, until an equilibrium point is reached at which the costs (e.g. times) are optimised. This
procedure considers parameters such as driver vehicle-vehicle minimum gap acceptance, junction type,
number of lanes, turn data, traffic signal stages and cycle time, which all impact upon time spent at
junctions, the key parameter fed back to the cost optimising routine. The final result is a detailed spatial
representation of the traffic patterns on the road network. A full SATURN specification, including
current input data requirements, is given in the user manual (Van Vliet and Hall, 1998).
2.3
Network Development
The influence of major new road schemes on air quality and health was investigated through three
Leeds SATURN network configurations, described in Table 2.1. The 1993 network, used as the
'standard' network in Leeds SATURN modelling for some time, has recently been supplemented by a
variety of networks, developed for Leeds City Council to allow appraisal of the East Leeds Link road,
and Stage 7 of the inner ring road development. These networks address several base years (2003, 2005,
2018, 2020), and a range of road developments included in the networks. Two of these networks were
selected for application in the air quality assessment, both with a base year of 2005. This year was
chosen as 2005 is the target year for National Air Quality Strategy assessments.
Table 2.1 Major Road Schemes Represented Network Development Scenarios
Network
Year
Description
Output
Network 1
1993
Network 2
2005
Network 3
2005
The Leeds road network as it appeared in 1993,
Vehicle flow (in
prior to major road developments.
PCU's), and mean
'Do-minimum'. As network 1, plus the A1-M1 link
speed (km/h) per link,
road and all city centre road schemes.
for the AM peak hour.
'Do-All'. As network 2, plus the East Leeds Link
road and Stage 7 of the inner ring road works.
The 'Do-minimum' network includes a number of city centre road developments and the A1-M1 link
road which extends the M1 from junction 43 south of Leeds to junction 48 at Hook Moor, to the east of
Leeds. The motorway link was opened in 2000, and completes the motorway network chain in
Yorkshire. A number of minor city centre road schemes implemented since 1993, included those
associated with the completion of the city centre inner loop, are also included in the 'Do-Min' network.
The 'Do-All' network includes two major road developments recently approved by DETR: the inner ring
road Stage 7 development and the East Leeds Link road. The Stage 7 development is a £20.7 million
project comprises 2.7 km of main road in the south east of the city, linking the Stage 6 development
(completed in 2000) at South Accommodation road (near the Royal Armouries) to the M621. The
significance of the Stage 7 development is that it will complete the Leeds inner ring road and so remove
through traffic from the city centre.
2-2
Transport Scenarios
Figure 2.1 Leeds SATURN networks showing the three principal networks studied.
A1-M1 link road
East Leeds
link road
Final stage (7) of
inner ring road
_____ _
1993 links;
_____
Additional links in 2005 Do-Min network;
2-3
_____
Additional links in 2005 Do-All
Transport Scenarios
The East Leeds Link road is a £35.6 million development that will link South Accommodation road
near the city centre to junction 45 of the M1-A1 link road to the East of the city via the existing Cross
Green industrial estate and key brownfield development sites. The road will follow an existing but very
minor route, and will comprise 3.9 km of dual carriageway to relieve congestion in large densely
populated areas of east Leeds, and enable access to key economic development sites within the Aire
valley employment area. The A63 Selby road, an existing major east Leeds radial route is to be
downgraded by traffic calming and other measures in character with secondary road status. Figure 2.1
illustrates the SATURN network for Leeds, showing the various 2005 road schemes described above,
superimposed onto the network as it appeared in 1993.
These SATURN maps are topologically, but not geographically accurate representations of the Leeds
road network. This is not usually significant in typical SATURN applications, where the analyst is
seeking to quantify vehicle flows on network links under alternative conditions. However, when
applied in air quality modelling applications, with emissions calculated from characteristics of road
links such as vehicle flow, it is important that the link is geographically accurate, else the emission
location is incorrect. To ensure that all mobile emission sources were correctly located, the SATURN
network was 'vector edited' so that link nodes mapped onto the correct national grid co-ordinates. This
is usually a highly laborious process, but one that has been automated in the TEMMS model (see
Appendix A and the description under the ROADFAC-SATURN-MapInfo interface, options 2-4).
2.4
'Business as usual'
2.4.1
Introduction
The impact on air quality and health of long run changes in traffic volume was assessed through a
'business as usual' scenario. In this scenario it is assumed that there are no management interventions to
transport in Leeds, hence any changes to air quality are attributable to changes in traffic volume and
fleet characteristics which may be expected over the forecast period. Assessments were conducted for:
•
1993, using the standard Leeds SATURN network;
•
2005 using the 'Do-minimum' network; and
•
2015 using the 'Do-minimum' network.
Ideally, the assessment would have been conducted using the same network throughout. However, we
decided to use the 1993 and 2005 networks, which differ slightly, as (a) the results would be a better
reflection of reality, and (b) the resource constraints of the dispersion modelling mean that a limited
number of model runs is possible, and the above selections enable the widest range of comparisons with
the other modelled scenarios. Thus in interpreting the results it is important that the effect of the
network changes between 1993 and 2005 (essentially the development of the A1-M1 link road) is
accounted for. In developing the 'business as usual' scenarios, there are two key considerations,
discussed further below:
•
the traffic volume (vehicle flow per link) for the forecast year; and
•
the fleet characteristics (distribution by type, emission factors) for the forecast year.
2.4.2
Volume of trips
The traffic flow on links for the 1993 and 2005 Leeds networks were derived through application of the
SATURN tactical transport model (see 2.2). This application was made possible by Leeds City Council
(LCC) Highways Department, who provided the description of the Leeds networks for 1993 and 2005
(.UFN files) and corresponding trip origin-destination matrix files (.UFM files). These files were input
2-4
Transport Scenarios
to the iterative simulation and assignment procedure in SATURN so as to produce link based flows in
PCU's for the AM peak. The origin-destination data were produced by LCC, with the 1993 matrix
verified through conventional procedures recommended in the DMRB Volume 12 (DTLR, 1996), that
is using roadside interviews adjusted according to vehicle counts for the same link. The DMRB
recommends that trip demand data greater than six years old is avoided where possible, particularly in
areas that have experienced significant land use change or economic development, as is the case with
Leeds. Therefore, LCC conducted further roadside interviews and vehicle counts to update the verified
1993 matrix to 2000. This matrix was then projected to 2005 through the application of traffic growth
factors.
The 2015 trip matrix was similarly derived by application of regional traffic growth factors (to the 2005
.UFM acting as the base matrix). The traffic growth factors were derived from the DETR Highways
Economic Transport and Appraisal groups TEMPRO 3.1 model which provides district level factors.
TEMPRO 3.1 is consistent with the 1997 National Road Traffic Forecasts, produced by the National
Trip End Model. The derivation of the forecasts is described in detail in DMRB Volume 12, section 2.3
(DTLR, 1996). The factors provided are representative of high and low growth scenarios, based on
economic and demographic projections. Table 1 presents the input data and output growth factors from
TEMPRO 3.1 for the Leeds district. The low growth scenario produces a traffic growth factor over the
forecast period of 1.116 compared to a national value of 1.113 and a regional (Yorkshire and
Humberside) value of 1.108. Under the high growth scenario the Leeds traffic growth factor (1.173)
falls between the regional (1.169) and national projections (1.178).
Table 2.2 TEMPRO 3.1 assumptions and traffic growth predictions for Leeds, 2005-15
Variable
Low Growth Scenario
High Growth Scenario
Base Year
(2005)
Forecast year
2015
Base Year
(2005)
Forecast year
2015
Cars/household
Population
0.905
725,549
0.971
726,261
0.985
725,549
1.067
726,261
Households
320,193
338,334
320,193
338,334
Workers
Jobs
314,115
357,196
315,987
356,683
314,115
357,196
315,987
356,683
Total Traffic growth
factor (2005-2015)
1.116
1.173
The TEMPRO 3.1 model was last updated in 1996, and examination of the population and economic
forecasts underlying the traffic growth forecasts indicated that the estimates may no longer adequately
reflect the strong economic growth in Leeds over recent years. For the 2005-2015 period TEMPRO 3.1
assumes that in Leeds population will grow by 0.1%, households by 5.7%, workers by 0.6% and that
jobs will fall by 0.1%. These figure appear conservative given the recent economic growth in Leeds,
and also in comparison to other projections. For example, Cambridge Econometrics predicted that total
employment in Leeds would grow by 11% over the period 2000-2010 (LCC economic fact book, 1999).
We therefore investigated alternative source of Leeds traffic/trip end projections. The government
office for Yorkshire and Humberside, who are co-ordinating the South and West Yorkshire Multi
Modal Study (SWYMMS) indicated that improved projections may be available under SWYMMS,
derived from both land use-transport interaction modelling, and from an updated but as yet unpublished
version of TEMPRO. In the event, these forecasts were unavailable in advance of their wider
publication hence the TEMPRO 3.1 high growth forecasts were used. 1
1
TEMPRO 4.2 projections for Leeds became available in 2002, and forecast a car driver growth of 19% 2005-15.
2-5
Transport Scenarios
In addition to the trip forecasts, vehicle fleet characteristics for the forecast years are required. This
includes a description of the fleet composition (see section 3.2.1) and emission factors for each class of
vehicle within the fleet (see sections 3.2.2).
2.5
2.5.1
Road User Charging
Introduction
In December 1998 the government published 'Breaking the Logjam' (DETR, 1998) which consulted on
means to combat traffic congestion and pollution through the application of road user charging and
workplace parking levies. This exercise was part of a commitment to a 'A New Deal for Transport', the
governments vision for an improved and more integrated transport system, as set out in the 1998
transport white paper. In February 2000, the results of the consultation were published, indicating that
the public were least supportive of the proposed measures, business had reservations but were generally
in favour, and local authorities were strongly supportive. Following the consultation, the Transport Act
2000 was passed giving local authorities powers to introduce road user charging and workplace parking
levies (under schedules 12 and 13) to tackle congestion and pollution. Thus road user charging in the
UK has moved from a technical option to a real possibility.
Now that the principle of road user charging is accepted in the UK, the next step is to devise effective
schemes that meet the broader integrated transport strategy objectives in local areas. However, the
practical and technical issues associated with introducing road user charging are not fully resolved,
hence charging is not likely to be implemented widely in the near future. Whilst 24 local authorities
indicated in their provisional Local Transport Plan (LTP) that they were interested in the new powers,
and 11 indicated in their full LTP that they were considering using charging as a solution to local
transport problems, only London has so far made a firm commitment to date.
Leeds is one local authority that considers road user charging in its LTP. The authority was part of the
governments 'Charging Development Partnership', receiving £2.5 million to further develop charging
measures and proposals, and is to be a demonstration site for a forthcoming road user charging
technology trial (WYLTP, 2000). The view of the local authority, as expressed in the LTP, is that road
user charging should not be introduced in Leeds until the end of the first plan period, or more likely
during the second plan period (2006-2011), when a number of major schemes have been committed and
substantially built, including the East Leeds Link, inner Ring Road Stage 7 and the Leeds Supertram.
The authority also indicated that any revenues raised by user charging must be truly additional, and
should preferably be hypothecated to local transport for longer than the ten year period indicated in the
Transport Act 2000.
2.5.2
Leeds Road User Charging Tests
The impact of road user charging on air quality and health in Leeds was investigated through
application of SATURN and the TEMMS integrated model system. During the course of the research,
the government announced that it was to support the Leeds Supertram proposal, whilst the local
authority announced that substantial completion of this development (projected to be fully operational
by 2007) would be a precondition for the introduction of road user charging. Thus the charging
measures investigated in the research (i.e. without consideration of the Supertram) are indicative of the
likely air quality impacts of road user charging on a medium sized city, rather than an analysis intended
to support making of specific policy in Leeds.
Four road user charging systems have been proposed to date, based on cordon crossing, distance
travelled, time spent travelling and time spent in congestion. Most road pricing studies assume that
cordon charging would be implemented, as it is the simplest system to implement, but offers flexibility
2-6
Transport Scenarios
in that charges can vary by travel direction (e.g. inbound/outbound) and time of day, whilst several
cordons and screenlines can be set up. Those systems already implemented (Singapore, Norway) adopt
a cordon approach. However, the cordon approach has been criticised because the location of charging
points cannot easily be moved if conditions change, they can add congestion to routes just outside the
cordon, and because charges are inequitable, with the same charge levied for short or long journeys
(Oldridge, 1990). For these reasons, other charge mechanisms have been suggested, although none have
yet been implemented anywhere. Table 2.3 summarises the relative merits of the different charging
approaches, based on May and Milne (1999) and May and Milne (2000).
May and Milne (op.cit.) examined the relative impacts of the four charging regimes on network
performance, judged from changes to total trips made, total distance travelled across the network, total
time spent travelling across the full network, and the generalised cost. The assessment was made using
SATURN applications for Leeds, York and Cambridge. On the basis of these tests, they demonstrated
that time and distance based charging were the most effective in improving network performance, and
cordon pricing the least effective. They concluded that, given the concerns over potential driver risk
taking with time based charging, further analysis of road user charging should usefully focus on
distance based charging. The relatively poor performance of cordon pricing was a concern given that it
is the simplest to implement and enforce. However, they also noted that cordon pricing was particularly
sensitive to the siting of cordons and screenlines, and the balance of charges between them, and that in
principal the appropriate cordon configuration could deliver improvements in network performance
comparable to distance based charging. Given these conclusions, it was decided to focus our road user
charging tests on distance based and cordon charging: the former being most effective in terms of
network performance without the driver risk associated with time charging, whilst the latter is
technologically proven, and is the simplest to implement in practice.
Table 2.3. Relative Merits of alternative road user charging systems
Charge
Basis
Advantages
Disadvantages
Cordon
•
Charge can be varied by direction
Charge can be varied by time of day
Simplest to implement in practice
Charging technology proven
Relates reasonably well to delays
Greatest reduction in total trips/trip
time
Relates well to contribution to delays
Greatest reduction in total trips/trip
time
•
Relates well to contribution to delays
•
•
•
•
Distance
•
•
Time
•
•
Delay
•
•
•
•
•
•
•
•
•
•
•
Immobile charging locations
Charges are independent of trip length
Relates least well to delays
Least reduction in total trips/trip times
Charging technology difficulties remain
Charges are not known in advance
Could encourage added driver risk taking
Charging technology difficulties remain
Charges are not known in advance
Could encourage added driver risk taking
Charging technology difficulties remain
Encourages re-routing to minor roads
Source: Derived from May and Milne (1999, 2000).
The effect of road user charging in Leeds was simulated in SATURN through the application of the
SATTAX module (Milne and Van Vliet, 1993). SATTAX uses the SATEASY elastic assignment
algorithm to model the response of traffic demand to changes in the generalised cost. This generalised
2-7
Transport Scenarios
cost comprises the cost of time spent travelling (in pence per minute) and the distance travelled (vehicle
operating cost of the trip, in pence per kilometre). This can be expressed as real behavioural values, but
is usually expressed as a ratio of one cost to the other. The values used were 7.63 pence per minute, and
5.27 pence per kilometre, giving a ratio of 1:0.69. Trip demand for each link is then determined from a
simple inverse elasticity function with a demand sensitivity coefficient of -0.0027, and the generalised
cost value for the relevant link.
Using SATTAX, road user charging is simply represented by adding a charge to the variables in the
generalised cost function. For example, the cost of passing a fixed point, as in a cordon charge, is added
to the vehicle operating cost for the appropriate link, and then represented as a generalised cost as
normal. Similarly, distance based charging is represented by adding a fixed cost to the operating cost
parameter for all links that fall within the charging area. In practice, the generalised cost parameter is
usually expressed as a cost in seconds, hence a value of time of 1.13 pence per minute is used to convert
tolls to generalised seconds.
The model response is to affect route choice and to transfer trips off the road. The off road transfer
comprises: (a) trips that take place at alternative times; (b) change to an alternative mode, including
switch from driver to passenger; and (c) a lower long run equilibrium in trip frequency, that is, trip
cancellation.
Six road user charging scenarios (plus a reference case) were investigated in terms of their impact on
urban air quality in Leeds. Of these, three related to cordon charging using different charge and cordon
configurations and three to distance charging, each using a different charge rate (Table 2.4). Charges
were levied according to PCU's and no attempt was made to differentiate between different vehicle
types (e.g. higher charges for HGV's). SATURN outputs were flow (PCU/hr) and speed (kph) as the
flow weighted average of link and turn speed for the appropriate movement.
Table 2.4. Road User Charging Scenarios for Leeds, 2005
Charge Type
Charge
Network
City Centre Cordon (inner ring road)
£3.00; zero
Do-Minimum
City Centre Cordon (inner ring road)
£3.00; zero
Do-All
Double Cordon (inner+outer ring
road)
Outer ring road £2, inner ring road
£1; zero
Do-Minimum
Distance based charge (inside IRR)
20p/km; 10p/km; 2p/km; zero
Do-Minimum
The cordon scenarios included a single cordon placed just inside the Leeds inner orbital road, and a
double cordon, with the inner cordon supplemented by a second cordon just inside the outer orbital
road. Figure 2.2 illustrates the positions of the cordon. The inner cordon is represented by the transition
from the inner city zone (denoted in black) to the urban zone (denoted in red). The inner cordon
encompasses and area of approximately 4 km2 . The outer cordon is represented by the transition from
the urban zone to the exterior zone (also in black). Distance charges are applied to the region within the
outer cordon, .
The toll selected was £3 to cross the inner cordon, a value that the local authority indicated as a
politically acceptable maximum charge, were they to implement a cordon scheme. For the double
cordon scheme we then decided on £1 to cross the outer cordon, and £2 to cross the inner cordon, so
that the same £3 charge was levied for entering the city centre as in the single cordon schemes.
2-8
Transport Scenarios
The revenue generated under the cordon scheme was used to set the distance based charges. This was
done by multiplying the number of trips crossing the cordon in the reference case (i.e. no trip
suppression) by the charge level, and dividing this by total PCU-km travelled in the area that would be
charged. For the Do-minimum network, the revenue raised under either the single (and also double)
cordon options is approximately £97 000, whilst the distance travelled within the single cordon is
470 000 PCU kms, giving a distance based charge of approximately 20 p/km. This is a value consistent
with that applied in other distance based charge tests [e.g. values applied by May and Milne (op.cit.) to
Cambridge ranged from 10-37p/km], but generated a much greater trip suppression (see Chapter 6) than
expected. This trip suppression is likely to be far from an economic optimum, even one that valued
environmental externalities highly. Therefore, the remaining distanced based charges were set at lower
level. These were 2 p/km, an order of magnitude less, and a mid value of 10 p/km.
Figure 2.1. Cordon lines and distance charge zones, Leeds Do-all network.
Inner cordon at boundary of
central and urban zones;
Outer cordon at boundary of
urban and suburban zones;
Distance charge area includes
central and urban zones
Central zone
Urban zone
Suburban zone
For all the cordon schemes, charges were assumed to apply only for the inbound trip. This implies that
different charges would have to be applied to inbound and outbound trips (outbound would be zero),
which is difficult to do in SATURN. To overcome this, half the charge is levied, and it is assumed that
drivers respond to the sum of the inbound and outbound charges. This modelling solution estimates the
trip demand response adequately, but is thought to underestimate the route choice response in the main
charging period, likely to be the AM peak (Milne, pers. comm.). Distance based charging has no
directional bias hence charges are levied for any link travelled that falls within the charge area. Figure
2.2 (a-f) presents SATURN networks illustrating changes in flow/hour from the reference case for the
Leeds road user charging tests.
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Transport Scenarios
Finally, note that for non-road user charging test we used the preferred SATURN output of 'actual' flow,
but that for the road user charging tests we only have available, output as the 'demand' flow (DF), a
product of the way in which SATTAX is applied to the demand elasticity function. Both demand and
actual flow variables are in PCU's. These output measures reflect differences in the way queues at
junctions are addressed when quantifying link flows (see section 17.2 of the SATURN manual).
Comparison of actual and demand flow outputs from common networks shows a maximum flow
difference of 5% for any link. The impact of these differences on air quality was investigated through
separate tests (e.g. SC-1 cf. SC-5 base; SC-2 cf. SC-6 base - see Table 4.4) to permit better comparison
of road user charging with the other tests.
2.6
2.6.1
Alternative Fuelled Vehicles
Introduction
The environmental impacts of conventional road vehicles has encouraged the development of
alternative road fuels and vehicle technologies. Table 2.6 details the alternative fuels that are available,
with a brief summary of their main characteristics and barriers to implementation.
Table 2.6 Alternative Vehicle Fuel Technology
Fuel
Main characteristics relevant to UK.
Key barriers to implementation
Natural Gas
(CNG) and
Liquefied
Petroleum Gas
(LPG)
Battery Electric
Vehicle (BEV)
•
High octane, low VOC;
Can operate dual fuel with diesel;
Reduced emission and noise;
CNG sourced in UK (North Sea),
LPG a refining by-product.
Zero emission at point of use
Most suited to predictable urban
drive cycles;
Quiet;
Efficient in stop-start driving;
Braking energy recovery possible
•
Comparable performance to
internal combustion engine (ICE);
Onboard ICE produces electricity
to drive electric motors;
Reduced fuel use and emissions;
Zero emission over limited range
Hydrogen fuel burnt to create
electricity giving performance
comparable to ICE;
Ultra low point of use emission;
Quiet;
Life cycle emission depends on
hydrogen source.
Produced by crop fermentation or
esterification of seed oils.
•
•
•
•
•
•
•
•
•
Hybrid Electric
Vehicle (HEV)
•
•
•
•
Fuel Cell
Electric Vehicle
(FCEV)
•
•
•
•
Bio-fuels (biodiesel, ethanol,
methanol)
•
Source: Compiled from Cleaner Vehicles Task Force (2000).
2-10
•
•
•
•
•
•
•
•
•
•
•
•
High capital vehicle cost;
Lack of refuelling infrastructure;
Heavy fuel storage system
reducing payload
High capital vehicle cost; and few
BEV available;
Few public recharge points;
Slow recharge and heavy battery
pack;
Limited range, payload and
performance
High capital vehicle cost;
Limited vehicle supply
High capital cost of fuel cell;
Limited vehicle supply;
Undeveloped hydrogen
infrastructure and uncertain in
vehicle fuel delivery;
Low fuel efficiency;
Not significant in UK due to
limited land area for crop growth.
Transport Scenarios
Some of these fuels and technologies remain largely experimental, but others are technologically
proven and commercially available. The Cleaner Vehicle Task Force (2000) concluded from their
assessment of the alternative fuels and vehicle technology market, that petrol and diesel fuels would
remain the dominant fuel source well into the 21st century, in part because emissions from
conventionally fuelled vehicles were improving significantly. For example, in 2005 low sulphur petrol
will be mandated by European legislation, and the tighter Euro 4 vehicle emission standards will come
into effect. However, the Task Group concluded that alternative fuels do have a role to play in particular
applications (e.g. certain vehicle types) or geographically sensitive areas.
In 1996 the Powershift programme was launched in the UK. Run by the Energy Savings Trust and
financed by government, the aim of Powershift is to encourage the UK market for clean fuel
technologies so as to reduce greenhouse gas emissions and limit emission of local air pollutants. With a
budget of £30 million for 2001-2004, the Powershift programme provides grant aid to purchase clean
fuel vehicles that offer emission benefits and which are technologically viable. These include vehicles
that run on natural gas (compressed or liquid, CNG and LNG respectively), liquefied petroleum gas
(LPG), and electric vehicles, including hybrids.
2.6.2
Revised fleet composition
The impact of uptake of clean fuelled vehicles on air quality in Leeds was investigated through
manipulation of emission factors and fleet composition using the ROADFAC model within TEMMS.
The analysis was restricted to those alternative fuels and technologies that are currently being promoted
through the Powershift programme: natural gas, electric vehicles, hybrid electric vehicles and fuel cell
electric vehicles. It was assumed that clean fuel technology would have limited impact by 2005. We
therefore chose to conduct a clean fuel analysis for 2015, a year for which we have a reference scenario
for comparison (from the analysis of trip growth impacts), and when the technology is expected to have
achieved a small, yet significant market penetration.
For each of the clean fuel technologies included in the analysis, information on fleet composition and
emission factors were required. This data was derived from MEET (1999), the report of the EC group
on traffic emissions and energy use in transport. Appendix B presents the projected fleet composition,
including clean fuelled vehicles, for 2015. Electric vehicles have been present for many years, but have
limited appeal. MEET estimated that sales of electric vehicles would not be significant until the second
half of the century, and could account for 5-10% of the new car market by 2020. Hybrid electric
vehicles (HEV's) are thought likely to make a more significant impact in the near future, and the Toyota
Prius, the first commercial HEV has sold above its projected rate since its introduction in 1997. Honda
recently released its commercial hybrid passenger car, the Insight. HEV's are thought likely to account
for 1-2% of sales by 2010, and 5-10% by 2020. The fuel cell electric vehicle (FCEV) is further from
production, and MEET estimate that significant growth will not begin until 2010, but rise rapidly to 1020% market share by 2020.
MEET projected the market penetration rates of these technologies, based on continued economic and
social conditions and evolutionary rather than revolutionary technology development. Their projections
are necessarily highly speculative, and one technology could come to dominate the new fuel market.
The number of alternative fuelled vehicles in future years was estimated using the fleet ageing
technique described in 2.4.3 above. From this analysis, high and low case projection are presented. We
selected the high growth option, so as to consider a "best case" scenario. Table 2.7 presents the high
case penetration of alternative fuel new technology vehicles in Europe to 2015.
MEET no longer consider natural gas to be a new technology fuel, hence more detailed projections of
natural gas use are available in their report on a country by country basis (Table A64-79). These show
that only Italy and the Netherlands had a significant gas vehicle presence in 1995, where LPG vehicles
accounted for circa 2.5 % and 5 % of the total fleet respectively. The MEET projections show no gas
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vehicle usage for 2015 in the UK, a projection based on the very low levels of use in 1995. However,
the 2001 market report from the Powershift programme shows that gas vehicle sales in the UK are
significant and growing, particularly LPG (Table 2.8).
Table 2.7 High case penetration of new technology vehicles.
Technology Type
Percentage of fleet in 2015
Electric vehicle
Hybrid electric vehicle
2
3
Fuel cell electric vehicle
1
Total
6
Source: MEET (1999), Table A100.
Table 2.8 Sale of Alternative fuelled vehicles in the UK
Technology Type
Electric/hybrid vehicles
Natural Gas vehicles
Liquefied Petroleum Gas
vehicles
2000*
2001 (Aug 2000
survey)
2002 (Jan 2001 survey)
369
69
2,018
343
2,220
1,395
20,576
40,556
50,206
Source: Powershift Market Survey (2001) * Actual sales
In order to maintain consistency with the 'best case' scenario applied for the electric vehicles, we
examined the MEET projection for gas vehicles for all European countries addressed in the MEET
report. There are no projections for CNG (very small market share, similar emissions to LPG), so the
projections related to LPG only. The greatest uptake of LPG by 2015 in Europe was 7.2 % (of total
fleet) for the Netherlands, and 3.6 % for Italy. Based on these values, we chose a figure of 5 % to
represent a high growth 'best case' rate for LPG vehicles in the UK. This is of course, fairly arbitrary,
but we consider this a reasonable high growth projection based on known UK LPG sales, and LPG
vehicle projections for other European countries. Note however, that the projections relate only to LPG
vehicles < 2.5 t, for which emission factors are available.
Overall, the clean fuel scenario includes 6 % of electric or hybrid vehicles, and 5 % gas powered
vehicles, giving a total of 11 % non-conventional fuelled vehicles. The fleet composition data
(Appendix B) were modified to include these clean fuelled vehicle technology, with conventional fuel
vehicles reduced by 11 % in each class.
2.6.3
Clean Fuel Emission Factors
Emission factors for the clean fuelled vehicles were also derived from MEET (1999). The emission
factors for LPG (CO, NOx, VOC, CO 2 ) relate to vehicles of <2.5t, and are speed dependent, calculated
from test-bed observations of typical urban driving cycles (sees Chapter 5 for further details on
derivation and accuracy of emission factors). LPG factors were drawn from tables A20 and A21 of
MEET, which address emissions for uncontrolled and EURO I standard vehicles. MEET presents no
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emission factors for LPG EURO II-IV vehicles, and hence we used the EURO I factors for these
classes. Emission factors for the electric and hybrid vehicles were drawn from MEET tables A92
(Electric Vehicle emission coefficients), A93 (Gasoline hybrid electric vehicle emission coefficients)
and A95 (Methanol fuel cell electric vehicle emission coefficients). Emission coefficients are given for
CO2 , CO, NO x, PM and HC for all fuels and additionally SO 2 and CH4 for electric vehicles. Separate
coefficients are given for passenger cars, light duty vans and additionally urban buses for fuel cell
vehicles.
These coefficients are used in MEET equation A27, that calculates emissions as:
F = aV2 + bV + c
Where:
F
is the emission factor (g/km)
a, b, c are the coefficients given in tables A92-95 derived
from analysis of test cycle observations
V
is the average vehicle speed (km/h)
Note that the emission factors developed using this data are only meant as a guide, as they are
developed from very little data. Note also that for both the hybrid electric vehicles and fuel cell
vehicles, the a and b coefficients are zero, hence the emission factors for these fuels are not speed
dependent.
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