This paper presents the development and estimation of a novel semi-compensatory model for residential choice. The model assumes that apartment seekers engage in a two-stage process, consisting of a non-compensatory strategy to retain only alternatives that meet search-criteria thresholds,...

Spatial choices entailing many alternatives (e.g., residence, trip destination) are typically represented by compensatory models based on utility maximization with exogenous choice set generation, which might lead to incorrect choice sets and hence to biased demand elasticity estimates....

To analyse the diversion from auto modes to combined modes such as park and ride, it is common to develop mode choice models based on discrete choice theory. In most cases, park and ride is modelled as an access mode to a main transit mode. This paper proposes an approach to test similarities...

Spatial choices entailing many alternatives (e.g., residence, trip destination) are typically represented by compensatory models based on utility maximization with exogenous choice set generation, which might lead to incorrect choice sets and hence to biased demand elasticity estimates....

The estimation of semi-compensatory models is gaining momentum in transport planning in recent years. However, traditional survey methodologies focus on collecting solely compensatory choice data, which leads to information loss when semi-compensatory models are estimated. The present study...

In decisions involving many alternatives, such as residential choice, individuals conduct a two-stage decision process, consisting of eliminating non-viable alternatives and choice from the retained choice set. In light of the potential of semi-compensatory discrete choice models to...

This study proposes a two-stage method to elicit consumers' price acceptability range. The method combines a conjunctive stage to elicit price acceptability limits with a utility-based stage to choose a preferred product variation. The method is efficient in choice situations entailing many...