Abstract
ABSTRACTThe random regret minimization (RRM) model considers the relative performance of the alternatives and is therefore context-dependent. In RRM, an individual, when choosing between alternatives, is assumed to minimize anticipated regret as opposed to maximize his/her utility. There are three variants of RRM, the classical CRRM, the µRRM, and the P-RRM. There is also a further approach called relative advantage maximization (RAM). We compare multinomial logit with the four mentioned alternatives. We use stated choice data sets which include mode choice, location choice, parking choice, carpooling, car-sharing. We compare the performance of those five models by their model fit, values of travel time savings (VTTS), and elasticities. Looking at the model fit, RAM outperforms the other models in five cases, whereas the PRRM does so in two cases and µRRM only for one case. The VTTS and elasticities vary substantially which is relevant for cost–benefit analysis or simplified modeling approaches.
Published Version
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