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Stated choices and benefit estimates in the context of traffic calming schemes : utility maximization, regret minimization, or both?

Boeri, M. and Scarpa, R. and Chorus, C.G. (2014) 'Stated choices and benefit estimates in the context of traffic calming schemes : utility maximization, regret minimization, or both?', Transportation research. Part A, policy and practice., 61 . pp. 121-135.


This paper proposes a discrete mixture model which assigns individuals, up to a probability, to either a class of random utility (RU) maximizers or a class of random regret (RR) minimizers, on the basis of their sequence of observed choices. Our proposed model advances the state of the art of RU–RR mixture models by (i) adding and simultaneously estimating a membership model which predicts the probability of belonging to a RU or RR class; (ii) adding a layer of random taste heterogeneity within each behavioural class; and (iii) deriving a welfare measure associated with the RU–RR mixture model and consistent with referendum-voting, which is the adequate mechanism of provision for such local public goods. The context of our empirical application is a stated choice experiment concerning traffic calming schemes. We find that the random parameter RU–RR mixture model not only outperforms its fixed coefficient counterpart in terms of fit—as expected—but also in terms of plausibility of membership determinants of behavioural class. In line with psychological theories of regret, we find that, compared to respondents who are familiar with the choice context (i.e. the traffic calming scheme), unfamiliar respondents are more likely to be regret minimizers than utility maximizers.

Item Type:Article
Keywords:Random regret minimization, Random utility maximization, Discrete choice experiment, Latent classes, Traffic calming schemes.
Full text:(AM) Accepted Manuscript
Available under License - Creative Commons Attribution Non-commercial No Derivatives.
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Publisher statement:© 2014 This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Date accepted:03 January 2014
Date deposited:04 February 2016
Date of first online publication:04 February 2014
Date first made open access:No date available

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