D5 Annex report WP 4 - ETIS plus

D5 Annex WP 4: ETIS DATABASE METHODOLOGY AND DATABASE USER
MANUAL – PASSENGER DEMAND
While detailed information of the last bullet point of the above is not available neither on
European nor on national level, the disaggregated model approach (and its results) has to be
considered as appropriate in general. A better indication of the quality of data could be obtained
from the comparison of aggregated model output with available statistics as well as results
coming from other models/ projects.
The Figure 4­2 shows the general structure of the iterative procedure that was undertaken to
adjust the computed matrices to statistics and traffic counts. The iterative process starts with the
generation and distribution stages. Their results are compared with basic statistics and (sub­)
matrices collected in the first project stages. The findings of these evaluations affect the
adjustments of the models involved. Basic adjustments are related to the model parameters and
correct e.g. the shape of the trip distance distributions. This is done by varying the influence of
distance measures on the model results. Additionally we apply dummy variable to consider
‘barriers’ like national borders. Dummy variables are model components that possess the value
‘1’ if a certain condition is true and the value ‘0’ is it is wrong. This kind of model ‘switch’
allows adding specific information to the model under well­defined conditions.
If an improved fit is no longer possible by model parameter calibration, the uncovered effects
are captured by constraints in a set of additional matrices. These matrices contain upper and/or
lower limits of transport flows corresponding to observed figures.
We named these structures ‘bounding matrices’ as they bound the base year flows to a welldefined
interval. The bounding matrices could generally cover all information that have not to
be modelled necessarily as appropriate observations are available. The combination of observed
information and modelled data fills the remaining gaps in the travel patterns and allows the
prediction of future situations. The matrix generation process considers these bounds while
setting up the distribution stage. For details see the corresponding section below.
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Document3
27 May 2004