Adoption and willingness to pay for autonomous vehicles: Attitudes and latent classes

This paper presents a comprehensive analysis of the propensity toward autonomous vehicles (AVs). The main hypothesis of this study is that individuals’ decisions toward AVs vary by their modality style, and it is possible to identify a distinct set of attitudes toward AVs among people with distinct mobility profiles. A latent class clustering analysis model was applied to the survey data, and three distinct user classes (sub-datasets) were identified, including auto-dependent users, all-mode users, and non-drivers (passengers and transit users). Separate structural equation models were developed to identify a distinct set of attitudes for each user class and estimate their propensity toward AV technology. The results showed that attitudes play a critical role in users’ behavior toward AV adoption and willingness-to-pay (WTP). Moreover, the identified attitudes for each class and their contribution to the decisions were different among the three classes, confirming the necessity to develop separate models to account for the heterogeneity in their choice behavior. In view of attitudes, pro-technology showed significant positive impacts on both AV adoption and WTP for auto-dependent users and non-drivers. While self-driving features might motivate auto-dependent users to adopt AV technologies, driving assistance features seemed to be more important for all-mode users in their adoption and WTP decisions. Trip privacy and data privacy concerns presented potential barriers for auto users and all-mode users but did not show significant impacts for non-drivers, who were more likely to be discouraged from adopting AVs by the joy of driving. The findings contribute to the current literature by providing more in-depth insights into users’ attitudes toward AV technologies and a better understanding of their decisions to adopt and pay for AV technologies.


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  • Accession Number: 01759950
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Nov 19 2020 3:20PM