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In this paper, we contrast parametric and semi-parametric representations ofunobserved heterogeneity in hierarchical Bayesian multinomial logit models andleverage these methods to infer distributions of willingness to pay forfeatures of shared automated vehicle (SAV) services. Specifically, we comparethe multivariate normal (MVN), finite mixture of normals (F-MON) and Dirichletprocess mixture of normals (DP-MON) mixing distributions. The latter promisesto be particularly flexible in respect to the shapes it can assume and unlikeother semi-parametric approaches does not require that its complexity is fixedprior to estimation. However, its properties relative to simpler mixingdistributions are not well understood. In this paper, we evaluate theperformance of the MVN, F-MON and DP-MON mixing distributions using simulateddata and real data sourced from a stated choice study on preferences for SAVservices in New York City. Our analysis shows that the DP-MON mixingdistribution provides superior fit to the data and performs at least as well asthe competing methods at out-of-sample prediction. The DP-MON mixingdistribution also offers substantive behavioural insights into the adoption ofSAVs. We find that preferences for in-vehicle travel time by SAV withride-splitting are strongly polarised. Whereas one third of the sample iswilling to pay between 10 and 80 USD/h to avoid sharing a vehicle withstrangers, the remainder of the sample is either indifferent to ride-splittingor even desires it. Moreover, we estimate that new technologies such as vehicleautomation and electrification are relatively unimportant to travellers. Thissuggests that travellers may primarily derive indirect, rather than immediatebenefits from these new technologies through increases in operationalefficiency and lower operating costs.

Semi-Parametric Hierarchical Bayes Estimates of New Yorkers' Willingness to Pay for Features of Shared Automated Vehicle Services