Energy: Strategic transport models output vehicle flows and average link speeds on road and public transport network links. Flows are usually categorised by vehicle type, typically passenger cars; and commercial vehicles (disaggregated by light, medium and heavy commercial vehicles), as well as bus flows and bus speeds. Average speed and fuel consumption relationships may be used to estimate total fuel consumption on each link. National evaluation guidelines in Australia recommend typical values for a number of evaluation parameters, including fuel consumption estimation.
There are several reasons to predict fuel consumption impacts of transport/land use options being assessed using strategic transport models, namely:
– To estimate the total energy impact of projects or strategies;
– To estimate the changes in transport modal energy efficiency levels; and
– To estimate the greenhouse gas impacts either at the total study area aggregation or at more disaggregated levels (in both time and space).
Transport model outputs may be tailored for each of those purposes, by:
– Allowing for fuel estimates based on forecast traffic volumes and average travel speeds for each link (road and public transport trips). VKTs by link for each main vehicle class (passenger cars, medium sized truck; heavy trucks and buses) can be readily converted into litres of fuel.
– Allowing for estimates of fuel consumption by main daily time period, with most models built on the basis of peak (a.m. and p.m.) and off-peak periods. VKTs by main vehicle type for each time period can be readily obtained.
– Converting vehicle trip fuel efficiency into modal fuel efficiency estimates based on passenger-kms obtained using forecasts vehicle load factors. The impact of strategies aimed at changing those load factors for specific trip purposes and time periods can be tested in this way.
Climate change and air quality: The carbon footprint from transport demand may be disaggregated for the purposes of evaluating specific projects and strategies. The latter may include a range of travel demand management measures, such as parking supply and pricing; road pricing; public transport priority and incentive schemes. Land use measures to increase the level of employment containment may also need to be evaluated.
Greenhouse gas implications of different strategies may focus on the carbon footprint by mode: contribution of each mode for all trip purposes and all time periods. Specific local footprints may also be obtained by estimating total emissions to/from individual transport analysis zones. Carbon footprints for specific activity nodes (for example: special generators) such as airports; ports; and large shopping centres, may also be estimated.
Emission factors for particle and gaseous pollutants, by vehicle class, road type and average speed are needed to make full use of strategic modelling outputs. The most harmful emissions from human health impacts are the ultra-fine particles. It is therefore recommended that both particle number and particles mass for such emissions be estimated.
Having obtained estimates of total pollutants for each link by summing over VKTs for each vehicle class, it is possible to use detailed estimates of exposure by linking emission estimates with dispersion models and land use adjacent to transport links, to arrive at exposure impacts on local population. As an intermediate stage, it may be worthwhile to arrive at measures of environmental impact by weighting link based emissions according to the likely population exposure. This would mean associating each link with an additional attribute which measures likely population exposure within a given distance from the link. The most commonly used exposure, from a health impact perspective is a 300 m distance from the vehicle source.
There area several ways in which emissions impacts can be evaluated using model outputs, in addition to estimating overall totals for each pollutant, such as:
1. Showing differences from the base case (ie: base case minus the option being evaluated) for each pollutant. Could show only links where the differences are greater than X %.
2. As in 1. above but showing only results for links with high levels of exposed population as measured by the land uses within the 300 m boundary. This requires an estimate of likely exposed population, given the types of land use and the residential densities.
3. Showing the number of households likely to be exposed to a level of pollution higher than a given threshold, using the 300 m boundary criterion. (or the showing the differences from the base-case, for the same measure).
BTRE (2003) Urban pollutant emissions from motor vehicles: Australian Trends to 2020. Report prepared for Environment Australia, Bureau of Transport and Regional Economics, Canberra, June 2003.
Ferreira, L (2007) Guidelines on the use of transport models to evaluate options. Portfolio Transport Modelling Team, Queensland Transport, Brisbane.
IFEU & SGKV (2002) Comparative analysis of energy consumption and co2 emissions of road transport and combined transport road/rail. Institute for Energy and Environmental research (IFEU, Heidelberg) and Association for study of combined transport (SGKV, Frankfurt
Litman, T (2005) Efficient vehicles versus efficient transportation. Comparing transportation energy efficient strategies. Transport Policy, 12 (2), 121-129.
Van Essen, H; Bello, O; Dings, J and Van den Brink, R (2003) To shift or not to shift, that’s the question. The environmental performance of the principal models of freight and passenger transport in the policy making context. Faculty of Policy Technology and Management, University of Delft.
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