A trip distribution model involving spatial and dominance attributes

Modeling the destination choice is a difficult task and very often it represents the weakest step in travel demand modeling. This weakness is mainly due to the high number of potential alternatives and to the very limited number of available attributes. Indeed, the alternative destinations in a distribution model are generally all the (hundreds in a medium large size city) traffic zones identified in the zoning phase. Moreover, the most widely adopted specification of random utility (RU) destination choice models introduces just two categories of attributes: attractiveness attributes of the destination zone and “impedance” attributes reproducing the origindestination generalized cost. Through this kind of attribute alone it is quite difficult reproducing the real choice context faced by the decision maker, who generally knows only a limited part of the study area with sufficient detail to evaluate its attractiveness and the generalized transport costs of reaching it. In this regard, our article proposes two sets of new dummy-like attributes to be used within the destination choice models to identify, within the whole choice set, a smaller subset of zones (those with nonzero value of dummy-like attributes) more/less likely to be perceived. The former are generated by extending and applying the concept of dominance among alternatives to the framework of RU theory and can be used to identify a set of alternatives less likely to be perceived (exclusion variables) whose systematic utility will be penalized as a function of these variables. The latter are spatial variables reproducing better knowledge of zones with a privileged spatial position and can be used to identify a set of alternatives more likely to be perceived (selection variables) whose systematic utility will be improved as a function of these variables. These new attributes are tested on empirical data related to nonsystematic trips in Rome, Italy. It is also important to underline that the proposed dominance variables can be conveniently used in any other choice context.