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This study gathered empirical evidence on adoption patterns of self-driving vehicles, people's likely use of them, and how that might influence amount of travel, mode choice, auto ownership, and other travel behaviour decisions. Because self-driving vehicles are not yet on the market, a car technology acceptance model was applied to understand adoption and use. Researchers implemented a two-stage data collection effort. An online survey was conducted with 556 residents of metropolitan Austin to determine intent to use. Based on results, four “intent to use” categories were determined: (1) extremely unlikely = Rejecters (18%); (2) somewhat unlikely = Conservatives (32%); (3) somewhat likely = Pragmatists (36%); (4) extremely likely = Enthusiasts (14%). Individuals with a higher level of intent to use have any physical conditions that prohibit them from driving; use technology – smartphone, text messaging, Facebook, transportation apps – and are not concerned with data privacy about using online technology; think using self-driving vehicles would be fun, decrease accident risk, and easy to become skilful at using; and believe people whose opinions are valued would like using them. Among those who indicated intent to use, qualitative interviews were conducted to ascertain the impact on their travel behaviour. Most respondents would rather own self-driving vehicles (59%) than just use one (41%), like a Car2Go or Uber taxi. Additionally, respondents reported that using one would have no change on where people would choose to live in Austin (80%), no change to their annual VMT (66%), and no change to the number of vehicles owned (61%).
Zmud et al. (Sun,) studied this question.