D5 Annex report WP 3: ETIS Database methodology ... - ETIS plus
D5 Annex report WP 3: ETIS Database methodology ... - ETIS plus D5 Annex report WP 3: ETIS Database methodology ... - ETIS plus
D5 Annex WP 3: DATABASE METHODOLOGY AND DATABASE USER MANUAL – FREIGHT TRANSPORT DEMAND Ideally, it would have been possible to use just the 1996 survey, which was partly funded by the STEMM project (DGVII), but experience has shown that this survey contains deficiencies. In order to complete the freight modelling work within the STEMM project, the survey data was enhanced by contributing data from other sources such as the 1996 International Road Haulage Survey (IRHS) and the Railfreight Distribution database. Finally it was grossed up using UK trade data for 1996, and given greater regional strength by incorporating parts of the 1991 ODIT, which had a stronger methodology. (See STEMM Report). The ODIT source is essentially a record of unitised trade flows only as the consolidation of bulks in port silos and tanks makes it very difficult to match origins to destinations using a questionnaire approach. It was not considered sensible to estimate the inland origins and destinations of such commodities. Regional data is collected at the county level (NUTS3). Problems have been encountered in mixing basic data from different years, as a series of changes have occurred in the definition of county boundaries. However, in aggregating to ten NUTS1 standard regions, (excluding Northern Ireland) these inconsistencies have been removed. Building RegionRegion Matrices: The Problem The final stage has been to tackle one of the classic transport problems which is how to convert data of the form: Region R1 (belonging to Country C1) to Country C2: T1 tonnes. Region R2 (belonging to Country C2) to Country C1: T2 tonnes. Into: Region R1(in C1) to Region R2 (in C2) : T3 tonnes. The problem can be observed as an origindestination matrix where the row and column totals are known, but the individual cells are unknown. 138 Document2 27 May 2004
D5 Annex WP 3: DATABASE METHODOLOGY AND DATABASE USER MANUAL – FREIGHT TRANSPORT DEMAND For example: Orig/ Dest Dest1 Dest2 Dest3 Total Orig1 100 Orig2 200 Orig3 300 Total 150 250 200 600 For this study, the problem can be seen as a series of large matrices, one for each commodity. The origins and destinations are a mixture of countries and regions. Country to/from country and country to/from region flows will be known, but subsets of the matrices will appear as above, e.g. France to/from Spain. For example, Iron and Steel Trade, Orig/ Dest Champagne Picardie Hte Normandie Total Galicia 100 Asturias 200 Cantabria 300 Total 150 250 200 600 There are various iterative procedures for finding "solutions" to these problems, but these are essentially computer algorithms that have too many degrees of freedom to reach conclusive results. They fit but they are not necessarily right. It is possible to "seed" the matrix by filling it with particular values before the iteration solves it. The seeding biases the results, so if the seeding is performed according to a valid theory of what the matrix represents, it should improve the result. One possibility is the socalled "Gravity Model" which takes into account the distance between any two regions in the matrix. By introducing seeded values inversely proportional to the distance (interpreted as the attraction between cells) between the regions it ought to be possible to improve accuracy. Further accuracy might be achieved by extending the analysis to using Generalised Cost instead of pure distance, and other factors such as language compatibility, common currency, joint membership of trade bloc, and so on. Many sophisticated procedures can be hypothesized, but without any base matrices to test the results against, it is difficult to judge their validity. In these circumstances, if the base matrix were known, there would be no practical reason for trying to estimate it. This is the problem. Document2 27 May 2004 139
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<strong>D5</strong> <strong>Annex</strong> <strong>WP</strong> 3: DATABASE METHODOLOGY AND DATABASE USER MANUAL –<br />
FREIGHT TRANSPORT DEMAND<br />
Ideally, it would have been possible to use just the 1996 survey, which was partly funded by the<br />
STEMM project (DGVII), but experience has shown that this survey contains deficiencies. In<br />
order to complete the freight modelling work within the STEMM project, the survey data was<br />
enhanced by contributing data from other sources such as the 1996 International Road Haulage<br />
Survey (IRHS) and the Railfreight Distribution database. Finally it was grossed up using UK<br />
trade data for 1996, and given greater regional strength by incorporating parts of the 1991<br />
ODIT, which had a stronger <strong>methodology</strong>. (See STEMM Report).<br />
The ODIT source is essentially a record of unitised trade flows only as the consolidation of<br />
bulks in port silos and tanks makes it very difficult to match origins to destinations using a<br />
questionnaire approach. It was not considered sensible to estimate the inland origins and<br />
destinations of such commodities.<br />
Regional data is collected at the county level (NUTS3). Problems have been encountered in<br />
mixing basic data from different years, as a series of changes have occurred in the definition of<br />
county boundaries. However, in aggregating to ten NUTS1 standard regions, (excluding<br />
Northern Ireland) these inconsistencies have been removed.<br />
Building RegionRegion Matrices: The Problem<br />
The final stage has been to tackle one of the classic transport problems which is how to convert<br />
data of the form:<br />
Region R1 (belonging to Country C1) to Country C2: T1 tonnes.<br />
Region R2 (belonging to Country C2) to Country C1: T2 tonnes.<br />
Into:<br />
Region R1(in C1) to Region R2 (in C2) : T3 tonnes.<br />
The problem can be observed as an origindestination matrix where the row and column totals<br />
are known, but the individual cells are unknown.<br />
138<br />
Document2<br />
27 May 2004