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By Kenneth Small, Erik Verhoef

The ebook applies together constant versions of call for, rate, pricing, funding standards and business association, with a purpose to be mixed to explain equilbria of the complete city transportation procedure.

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While no one factor explains very much of the variation, all of them taken together make a significant difference. To illustrate, the authors predict annual vehicle use for a national sample of households if they all lived in metropolitan areas with specified characteristics related to land use and transportation supply. If the characteristics are those of Atlanta, their model predicts 16,900 vehicle-miles per household; if the characteristics are changed to those of Boston, the same households would travel 25% less.

In such a case, an ordered response model might be assumed. In this model, the choice of * individual n is determined by the size of a “latent variable” yn = β ′zn + ε n , with choice j occurring if this latent variable falls in a particular interval [mj – 1, mj] of the real line, where m0 = – ∞ and mJ = ∞. The interval boundaries m1, ... , mJ–1 are estimated along with b, except that one of them can be normalized arbitrarily if b′zn contains a constant term. ) is the cumulative distribution function assumed for en.

5. 09 (b1/w). 25(b1/w), more than twice the true value. Again, the existence of variety reduces the actual sensitivity to changes in independent variables, in this case because there are only a few travelers (those with extreme values of e1n – e 2n) who have a close enough decision to be affected. McFadden and Reid (1976) derive a more formal result illustrating this phenomenon in the case of a binary probit model where the independent variables are normally distributed in the population. 27) where –z and s 2 are the average of z and the variance of b¢z, respectively, within the population.

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