assembled. In two to X fittings, data is adjusted to perfectly fit the city’s transportation system or match its official reporting. The necessary fitting parameters are
not hard coded, but part of questionnaire and fed to the model via calculation
setups. The level of parameterization is the result of constant adaptations to the
needs of numerous cities. In order to avoid having to feed thousands of parameters
to the model for each calculation, most likely cases are defined, which can be
combined and controlled with much fewer parameters. The hard-coded cases in the
model consist of extracts from external databases such as HBEFA, which were
imported via EcoSpold format previous to actual projects. This way, the number of
commonly necessary data points was reduced to just 250 (not including forecasting
parameters). Global project parameters define the scope of the model, which
observation year to balance or the “flux” of transportation volumes of individual
processes. Local process parameters are set to adapt individual processes. As one of
the most relevant process categories, the mode type passenger cars for example are
categorized by fuel type and conversion technology (combustion, hybrid…).
Parameterization includes the average fuel consumption or tank to wheel GHG
emissions, the EURO-Class, the difference in yearly vehicle mileage between diesel
and petrol vehicles, as well as newer versus older vehicles and the average journey
distance for cold start emission determination.
In addition to the parameters necessary for baselining, lever specific parameters
are defined to obtain the current development or state of an individual infrastructure. Others identify the maximum applicability of a lever. One Parameter is set for
each lever, to trigger it and set the degree to which it is implemented at a certain
time.
2.4 Forecasting
The model is built as a decision support too for city administrations. When making
long term infrastructure decisions, it is of little use to know impacts of a lever if it
was in place today, if it takes 10 years in real world to construct the necessary
infrastructure. The cause therefore requires forecasting options to determine the
future baseline situation and lever impacts during the years the levers will actually
be in place.
Forecasting elements are separated into three categories and treated differently:
• Forecasting parameters which are mainly influenced by the market development
or can only be impacted by national or state union level governance are
implemented by default or by case selection. This concerns technology
improvements and fleet turnovers.
– Example 1: Average ages of vehicle technology are automatically forecasted
based on vehicle fleet distributions in EURO classes.
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F. A. Jaeger et al.
not hard coded, but part of questionnaire and fed to the model via calculation
setups. The level of parameterization is the result of constant adaptations to the
needs of numerous cities. In order to avoid having to feed thousands of parameters
to the model for each calculation, most likely cases are defined, which can be
combined and controlled with much fewer parameters. The hard-coded cases in the
model consist of extracts from external databases such as HBEFA, which were
imported via EcoSpold format previous to actual projects. This way, the number of
commonly necessary data points was reduced to just 250 (not including forecasting
parameters). Global project parameters define the scope of the model, which
observation year to balance or the “flux” of transportation volumes of individual
processes. Local process parameters are set to adapt individual processes. As one of
the most relevant process categories, the mode type passenger cars for example are
categorized by fuel type and conversion technology (combustion, hybrid…).
Parameterization includes the average fuel consumption or tank to wheel GHG
emissions, the EURO-Class, the difference in yearly vehicle mileage between diesel
and petrol vehicles, as well as newer versus older vehicles and the average journey
distance for cold start emission determination.
In addition to the parameters necessary for baselining, lever specific parameters
are defined to obtain the current development or state of an individual infrastructure. Others identify the maximum applicability of a lever. One Parameter is set for
each lever, to trigger it and set the degree to which it is implemented at a certain
time.
2.4 Forecasting
The model is built as a decision support too for city administrations. When making
long term infrastructure decisions, it is of little use to know impacts of a lever if it
was in place today, if it takes 10 years in real world to construct the necessary
infrastructure. The cause therefore requires forecasting options to determine the
future baseline situation and lever impacts during the years the levers will actually
be in place.
Forecasting elements are separated into three categories and treated differently:
• Forecasting parameters which are mainly influenced by the market development
or can only be impacted by national or state union level governance are
implemented by default or by case selection. This concerns technology
improvements and fleet turnovers.
– Example 1: Average ages of vehicle technology are automatically forecasted
based on vehicle fleet distributions in EURO classes.
196
F. A. Jaeger et al.