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.

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: Feb 4, 2016
• Journal: Transportation Research Part A: Policy and Practice
• Print ISSN: 0965-8564
• Electronic ISSN: 1879-2375
• 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