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

Boeri, M.; Scarpa, R.; Chorus, C.G.

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


Authors

M. Boeri

C.G. Chorus



Abstract

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.

Citation

Boeri, M., Scarpa, R., & Chorus, C. (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, 121-135. https://doi.org/10.1016/j.tra.2014.01.003

Journal Article Type Article
Acceptance Date Jan 3, 2014
Online Publication Date Feb 4, 2014
Publication Date Mar 1, 2014
Deposit Date Jan 21, 2015
Publicly Available Date Mar 29, 2024
Journal Transportation Research Part A: Policy and Practice
Print ISSN 0965-8564
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 61
Pages 121-135
DOI https://doi.org/10.1016/j.tra.2014.01.003
Keywords Random regret minimization, Random utility maximization, Discrete choice experiment, Latent classes, Traffic calming schemes.
Public URL https://durham-repository.worktribe.com/output/1446961

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