Forecasting Adoption of Ultra-Low-Emission Vehicles Using Bayes Estimates of a Multinomial Probit Model and the GHK Simulator

In this paper, the authors use Bayes estimates of a multinomial probit model with fully flexible substitution patterns to forecast consumer response to ultra-low-emission vehicles. In this empirical application of the probit Gibbs sampler, they use stated-preference data on vehicle choice from a Germany-wide survey of potential light-duty-vehicle buyers using computer-assisted personal interviewing. They show that Bayesian estimation of a multinomial probit model with a full covariance matrix is feasible for this medium-scale problem and provides results that are very similar to maximum simulated likelihood estimates. Using the posterior distribution of the parameters of the vehicle choice model as well as the GHK simulator, the authors derive the choice probabilities of the different alternatives. They first show that the Bayes point estimates of the market shares reproduce the observed values. Then they define a base scenario of vehicle attributes that aims to represent an average of the current vehicle choice situation in Germany. Consumer response to qualitative changes in the base scenario is subsequently studied. In particular, the authors analyze the effect of increasing the network of service stations for charging electric vehicles as well as for refueling hydrogen. The result is the posterior distribution of the choice probabilities that represent adoption of the energy-efficient technologies.

• Record URL:
  https://doi.org/10.1287/trsc.2013.0464
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/1767714
• Supplemental Notes:
  • Abstracts reprinted with permission of INFORMS (Institute for Operations Research and the Management Sciences, http://www.informs.org).
• Authors:
  • Daziano, Ricardo A
  • Achtnicht, Martin
• Publication Date: 2014-11

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 671-683
• Serial:
  • Transportation Science
  • Volume: 48
  • Issue Number: 4
  • Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
  • ISSN: 0041-1655
  • Serial URL: http://transci.journal.informs.org/

Subject/Index Terms

• TRT Terms: Bayes' theorem; Environmental protection; Forecasting; Multinomial probits; Pollutants; Vehicle design
• Geographic Terms: Germany
• Subject Areas: Highways; Vehicles and Equipment; I90: Vehicles;

Filing Info

• Accession Number: 01546210
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 2 2014 9:30AM