This paper analyses a class of route choice models with closed-form
probability expressions, namely, Generalized Multivariate Extreme Value (GMEV)
models. A large group of these models emerge from different utility formulas
that combine systematic utility and random error terms. Twelve models are
captured in a single discrete choice framework. The additive utility formula
leads to the known logit family, being multinomial, path-size, paired
combinatorial and link-nested. For the multiplicative formulation only the
multinomial and path-size weibit models have been identified; this study also
identifies the paired combinatorial and link-nested variations, and generalizes
the path-size variant. Furthermore, a new traveller's decision rule based on
the multiplicative utility formula with a reference route is presented. Here
the traveller chooses exclusively based on the differences between routes. This
leads to four new GMEV models. We assess the models qualitatively based on a
generic structure of route utility with random foreseen travel times, for which
we empirically identify that the variance of utility should be different from
thus far assumed for multinomial probit and logit-kernel models. The expected
travellers' behaviour and model-behaviour under simple network changes are
analysed. Furthermore, all models are estimated and validated on an
illustrative network example with long distance and short distance
origin-destination pairs. The new multiplicative models based on differences
outperform the additive models in both tests
