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But the weight of each factor is different state-by-state and week-by-week".
Third, it identifies the weight of each factor through mathematical modeling approach.
The relative weight of each factor can be defined through opinions by a panel of experts.
From the data collected, the weight of each factor was directly calculated, using the fuzzy analytic hierarchy process (FAHP).
In these methods, the weight of each factor index directly determines the accuracy to a certain extent.
As used in Table 1, normalization is the weight of each factor divided by the total of the factors' weights.
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The weights of each factor were computed statistically using multi-influencing factor (MIF) technique followed by heuristic approaches/knowledge driven method for assigning ranks to each sub-classes of factor maps.
Processed by extracting key components and to better interpret the variable weights in factors, an orthogonal factor rotation using the Varimax method was used, in which significant weights were high and all others were close to zero, i.e., the objective is to maximize the variation between the weights of each factor, hence the name Varimax (Hair JR et al., 2006).
This averaging was weighted using estimated population weights, formed by multiplying together a one-way table of weights for each factor, containing the proportions of cases recorded in each of its levels.
The value of the weights for each factor depends on the environment of the construction site and the type of transportation vehicle, and so it should be modified as required by engineers.
The effective weight of each polygon is obtained using the following formula: w = left( {frac{{P_{text{r}} P_{text{w}} }}{v}} right) times 100 (6)wherefersefers to 'effective' weight for each factor, P r and P w are the rating and weighting values of each factor, and v is defined previously.
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