JOINT MIXED LOGIT MODELS OF STATED AND REVEALED PREFERENCES FOR ALTERNATIVE-FUEL VEHICLES

In this paper, the authors compare multinomial logit and mixed logit models for data on California households' revealed and stated preferences for automobiles. The stated preference (SP) data elicited households' preferences among gasoline, electric, methanol, and compressed natural gas vehicles with various features. The mixed logit models provide improved fits over logit that are highly significant, and show large heterogeneity in respondents' preferences for alternative-fuel vehicles (AFV). The effects of this heterogeneity are demonstrated in forecasting exercises. The AFV models presented here also highlight the advantages of merging SP and revealed preference (RP) data. RP data appears to be critical for obtaining realistic body-type choice and scaling information, but this data is plagued by multicollinearity and difficulties with measuring vehicle attributes. SP data is critical for obtaining information about attributes not available in the marketplace, but pure SP models with this data give implausible forecasts.

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  • English

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  • Accession Number: 00794479
  • Record Type: Publication
  • Files: TRIS, ATRI
  • Created Date: Jun 8 2000 12:00AM