Discrete choice models have been a major part of the transport analysts' toolbox for decades. These models are able to accommodate diverse requirements and they have a firm theoretical foundation in utility theory. Discrete choice models with additive independent error terms pose the problem that the scale of the error terms is not identified. Earlier models assumed the problem away by requiring the scale to be constant. Later contributions have allowed the scale to vary across data sets and individuals. Insteada multiplicative specification of discrete choice models is proposed thatcircumvents the problem by making the scale irrelevant. It can thus be random and have any distribution. This specification is applicable in situations where there is a priori information about the sign of the systematic utility. The multinomial logit (MNL) model has been very successful, due to its computational and analytical tractability. Later, generalized extreme value (GEV) models and mixtures of MNL and GEV models have gained popularity due to their flexibility and theoretical results relating these models to random utility maximization. So far, most applications of these models have used an additive specification where the random utility for an alternative is specified. Improvements to these models resulting in a very large improvement in the goodness of fit are described. For the covering abstract see ITRD E137145.
Abstract