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 Distance Decay by Commodity 120 Propensity to Trade (Index) 100 80 60 40 20 Ores and Scrap Scientific Machinery 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 Distance (Kms) The parameters calculated for these commodities indicate that distance decay is a relevant factor at the regional scale, with a high degree of impact between 200 and 1000 km. They also indicate that value density is a factor in determining the shape of the curve. The gravity model is used to estimate regionregion traffics which in turn, are then used to seed the O/D matrix and a furnessing algorithm is then used to constrain the matrix to its (known) row and column totals. The estimation of the ‘n’ and ‘m’ parameters depends on a multidimensional optimisation technique known as the downhill simplex method, or “amoeba”. 5 This is a relatively simple search algorithm that can be applied to a wide range of functional forms, such that the calling routines can specify parameter ranges, evaluation criteria and numerical precision. It does not involve the use of derivatives or statistical methods such as regression analysis. A test was carried out in which a version of the gravity formula was used to generate some ‘real’ data. A random number generator was then used to disturb these results within preset ranges, and these ‘sample’ points were then fed into a software routine that used the amoeba algorithm to detect the parameters. 5 Press WH, Teukolsky SA, Vetterling WT, Flannery BP, 1992, Numerical Recipes in C, Cambridge University Press 54 Document2 27 May 2004
D5 Annex WP 3: DATABASE METHODOLOGY AND DATABASE USER MANUAL – FREIGHT TRANSPORT DEMAND Tonnes Lifte d 100000.0 True Plus Error TRUE Estimated Tonnes 10000.0 50 100 150 200 250 300 350 400 450 500 550 Km s 600 650 700 750 800 850 900 950 1000 The figure shows the ‘true’ relationship between distance and traffic generated with the solid line. The ‘sample’ points are shown as dots (note that the scale is logarithmic), and the error term is as much as plus/minus 50% of the actual. The broken line represents the curve that the amoeba algorithm estimates from the sample so that the sum of the errors visavis the sample points is minimized. This indicates a very good approximation of the original function, from only 20 sample points, which improves as the error term is reduced. The test implies that this simple estimation technique provides an accurate and computationally effective method for estimating gravity model parameters where the functions are nonlinear, and where a large number of models for different commodities are required. Although parameters were calculated within CASSS it now makes sense to update these results with the method developed here and to use a new commodity classification system. Conversion tables could be used but recalculation is favoured allowing more base year data to be included in the estimation, and different functional forms to be compared. The matrix estimation method can be illustrated with trade data. The Year 2000 COMEXT database shows the following patterns of trade under the SITC heading ‘89’ Miscellaneous Manufactures, for ten countries. Document2 27 May 2004 55
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<strong>D5</strong> <strong>Annex</strong> <strong>WP</strong> 3: DATABASE METHODOLOGY AND DATABASE USER<br />
MANUAL – FREIGHT TRANSPORT DEMAND<br />
Tonnes Lifte d<br />
100000.0<br />
True Plus Error<br />
TRUE<br />
Estimated<br />
Tonnes<br />
10000.0<br />
50<br />
100<br />
150<br />
200<br />
250<br />
300<br />
350<br />
400<br />
450<br />
500<br />
550<br />
Km s<br />
600<br />
650<br />
700<br />
750<br />
800<br />
850<br />
900<br />
950<br />
1000<br />
The figure shows the ‘true’ relationship between distance and traffic generated with the solid line.<br />
The ‘sample’ points are shown as dots (note that the scale is logarithmic), and the error term is as<br />
much as <strong>plus</strong>/minus 50% of the actual. The broken line represents the curve that the amoeba<br />
algorithm estimates from the sample so that the sum of the errors visavis the sample points is<br />
minimized. This indicates a very good approximation of the original function, from only 20 sample<br />
points, which improves as the error term is reduced. The test implies that this simple estimation<br />
technique provides an accurate and computationally effective method for estimating gravity model<br />
parameters where the functions are nonlinear, and where a large number of models for different<br />
commodities are required.<br />
Although parameters were calculated within CASSS it now makes sense to update these results<br />
with the method developed here and to use a new commodity classification system. Conversion<br />
tables could be used but recalculation is favoured allowing more base year data to be included in<br />
the estimation, and different functional forms to be compared.<br />
The matrix estimation method can be illustrated with trade data.<br />
The Year 2000 COMEXT database shows the following patterns of trade under the SITC heading<br />
‘89’ Miscellaneous Manufactures, for ten countries.<br />
Document2<br />
27 May 2004<br />
55
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