The 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.
Context-dependent models (CRRM, MuRRM, PRRM, RAM) versus a context-free model (MNL) in transportation studies: a comprehensive comparisons for Swiss and German SP and RP data sets
Transportmetrica A: Transport Science ; 15 , 2 ; 1487-1521
2019-11-29
35 pages
Article (Journal)
Electronic Resource
English
SYSTEM AND METHOD FOR CONTEXT DEPENDENT TRANSPORTATION USER EXPERIENCE
European Patent Office | 2019
|Begging: the global context and international comparisons
British Library Conference Proceedings | 1999
|Integrating context-free and context-dependent attentional mechanisms for gestural object reference
British Library Online Contents | 2004
|Putting Transportation Investments in Context
Online Contents | 1997
|