A Dirichlet process mixture model of discrete choice: Comparisons and a case study on preferences for shared automated vehicles

Rico Krueger, Taha H. Rashidi and Akshay Vij

Journal of choice modelling, 2020, vol. 36, issue C

Abstract: This paper i) compares parametric and semi-parametric representations of unobserved heterogeneity in hierarchical Bayesian logit models and ii) applies these methods to infer distributions of willingness to pay for features of shared automated vehicle (SAV) services. Specifically, we compare the multivariate normal, the finite mixture of normals and the Dirichlet process mixture of normals (DP-MON) mixing distributions. The latter promises to be particularly flexible regarding the shapes it can assume, and unlike other semi-parametric approaches does not require that its complexity is fixed before estimation. We evaluate the different mixing distributions, using simulated data and real data from a stated choice study on preferences for SAVs in New York City. In the considered data settings, the DP-MON mixing distribution provides an excellent data fit and performs at least as well as the other methods at out-of-sample prediction. The DP-MON mixing distribution also offers substantive behavioural insights into the adoption of SAVs. We find that preferences for in-vehicle travel time by SAV with ride-pooling are strongly polarised. Whereas one-third of the sample is willing to pay between 10 and 80 USD/h to avoid pooling a vehicle with strangers, the remainder of the sample is either indifferent to ride-pooling or even desires it. We also estimate that vehicle automation and powertrain electrification are relatively unimportant to travellers. Consequently, travellers may primarily derive indirect, rather than immediate benefits from these new technologies through increases in operational efficiency and lower operating costs.

Keywords: Shared automated vehicles; Willingness to pay; Mixed logit; Dirichlet process; Nonparametric methods (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1755534520300282
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:eejocm:v:36:y:2020:i:c:s1755534520300282

DOI: 10.1016/j.jocm.2020.100229

Access Statistics for this article

Journal of choice modelling is currently edited by S. Hess and J.M. Rose

More articles in Journal of choice modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().