Ricardo A. Daziano

TL;DR: Semiparametric random parameter logit estimates suggest that the demand for automation is split approximately evenly between high, modest and no demand, highlighting the importance of modeling flexible preferences for emerging vehicle technology.

TL;DR: In this paper, the authors developed, implemented and applied a Markov chain Monte Carlo (MCMC) Gibbs sampler for Bayesian estimation of a hybrid choice model (HCM), using stated data on both vehicle purchase decisions and environmental concerns.

TL;DR: Gmnl as mentioned in this paper is a package for estimating multinomial logit models with unobserved heterogeneity across individuals for cross-sectional and panel (longitudinal) data, by allowing the parameters to vary randomly over individuals according to a continuous, discrete, or discrete-continuous mixture distribution, which must be chosen a priori by the researcher.

TL;DR: In this paper, the authors propose a general demand model for vehicle purchases at the individual level assuming that the necessary microdata is available, and discuss data sources and collection strategies for the different attributes of the model, especially for those characteristics that are nonstandard.

TL;DR: A unified framework to design, optimize and analyze MoD operations within a multimodal transportation system where the demand for a travel mode is a function of its level of service is proposed.