D5 Annex report WP 4 - ETIS plus
D5 Annex report WP 4 - ETIS plus 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. 22 Document3 27 May 2004
D5 Annex WP 4: ETIS DATABASE METHODOLOGY AND DATABASE USER MANUAL – PASSENGER DEMAND Figure 4.2 Iterative process of matrix adjustment Iteration process dateline data validation data gap handling Generation Distribution Model Adjustments Matrix Bounding Basic Statistics Evaluation Matrix Evaluation dateline data Modal Split Mode Choice Assessment validation data gap handling Assignment Link Load Assessment The resulting matrices (composed out of model and bounding matrix) are the base for a modal split procedure following. Assigning the resulting mode specific flows to the mode specific networks (road, rail, air) and compare these assignment figures with values received from link counts, airport statistics, IATA on flight stages, etc. allows to set up an iterative process to calibrate the underlying matrix and modal split results. The iterative sequence ensures a converging of modelled data and the observed information and will improve the quality of the base year matrix compared to a single modelled output. 4.6.1 Model calibration As we are confident that the models have been estimated on a data base that is sufficient for the intended task, their general structure is assumed to be appropriate to capture the overall structure of the transport patterns. Nevertheless, a number of trip relations are not covered explicitly within the samples. This holds especially for Southern Europe as we currently do not have any access to surveys that might be undertaken in this area. To cope with this ‘outofsample’ problem we tried to adjust the model structure e.g. by dummy variables. Another way of calibrating models is the adjustment of model parameters. This was necessary to obtain a better fit of the functional shape to the observed structures that are not covered appropriately by the estimation sample. This was done to adjust the trip distribution for short distances. Due to the level of aggregation (zoning system) an model adjustment ‘by hand’ can be useful to correct errors in intrazonal trip distance distributions that are remaining because of missing details in the estimation data base. Document3 27 May 2004 23
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- Page 63: ANNEX A DESCRIPTION OF INDICATORS C
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<strong>D5</strong> <strong>Annex</strong> <strong>WP</strong> 4: <strong>ETIS</strong> DATABASE METHODOLOGY AND DATABASE USER<br />
MANUAL – PASSENGER DEMAND<br />
Figure 4.2<br />
Iterative process of matrix adjustment<br />
Iteration process<br />
dateline<br />
data<br />
validation<br />
data gap<br />
handling<br />
Generation<br />
Distribution<br />
Model Adjustments<br />
Matrix Bounding<br />
Basic Statistics Evaluation<br />
Matrix Evaluation<br />
dateline<br />
data<br />
Modal Split<br />
Mode Choice Assessment<br />
validation<br />
data gap<br />
handling<br />
Assignment<br />
Link Load Assessment<br />
The resulting matrices (composed out of model and bounding matrix) are the base for a modal<br />
split procedure following. Assigning the resulting mode specific flows to the mode specific<br />
networks (road, rail, air) and compare these assignment figures with values received from link<br />
counts, airport statistics, IATA on flight stages, etc. allows to set up an iterative process to<br />
calibrate the underlying matrix and modal split results.<br />
The iterative sequence ensures a converging of modelled data and the observed information and<br />
will improve the quality of the base year matrix compared to a single modelled output.<br />
4.6.1 Model calibration<br />
As we are confident that the models have been estimated on a data base that is sufficient for the<br />
intended task, their general structure is assumed to be appropriate to capture the overall<br />
structure of the transport patterns.<br />
Nevertheless, a number of trip relations are not covered explicitly within the samples. This<br />
holds especially for Southern Europe as we currently do not have any access to surveys that<br />
might be undertaken in this area. To cope with this ‘outofsample’ problem we tried to adjust<br />
the model structure e.g. by dummy variables.<br />
Another way of calibrating models is the adjustment of model parameters. This was necessary<br />
to obtain a better fit of the functional shape to the observed structures that are not covered<br />
appropriately by the estimation sample. This was done to adjust the trip distribution for short<br />
distances. Due to the level of aggregation (zoning system) an model adjustment ‘by hand’ can<br />
be useful to correct errors in intrazonal trip distance distributions that are remaining because of<br />
missing details in the estimation data base.<br />
Document3<br />
27 May 2004 23
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