Johanna Zmud

Self-Driving Vehicles

This study gathered empirical evidence on adoption patterns of self-driving vehicles, their likely use, and how that use might influence the amount of travel, mode choice, auto ownership, and other travel behavior decisions. Because self-driving vehicles were 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, Texas, to determine intent to use. Four intent-to-use categories were determined: extremely unlikely, 18%; somewhat unlikely, 32%; somewhat likely, 36%; and extremely likely, 14%. Of those who indicated intent to use, qualitative interviews were conducted to ascertain the impact on their travel behavior. Most respondents would rather own a self-driving vehicle than use one such as Car2Go or Uber taxi. In addition, respondents reported that using a self-driving vehicle would make no change in where people would choose to live in Austin (80%), no change to their annual vehicle miles of travel (66%), and no change to the number of vehicles owned (55%).

Research to Examine Behavioral Responses to Automated Vehicles

This chapter provides a discussion of the important research topics for understanding behavioral responses to highly automated vehicles (AVs) as discussed at a breakout session at the Automated Vehicle Symposium (AVS) 2018. The session, and thus this chapter, highlights the need for valid behavioral data on which to base assumptions, models, forecasts, and impacts to inform AV adoption behaviors, the pathways of AV ownership and use, and the potential impact of AVs on human activity-travel behaviors and longer-term location choices.

Future Travel Demand in China: Scenarios for Year 2030

What might the future of mobility be in China in 2030? “Mobility” is defined as the ability to travel from one location to another regardless of mode or purpose. Researchers at RAND Corporation working with the German Institute for Mobility Research developed two scenarios that address this question with a six-step process: (a) select influence areas (domains that affect mobility directly) and factors, (b) elicit projections on factors (via expert workshops in Washington, D.C., and Beijing), (c) integrate these projections into scenario frameworks (using two analysis methods and a computer-based tool), (d) produce scenario narratives (based on the clusters produced by the tool), (e) draw qualitative consequences for future mobility, and (f) create a wild-card scenario (by looking at events that might disrupt trends). The two resulting scenarios—The Great Reset and Slowing but Growing—are differentiated by three key drivers: economic growth, environmental issues, and constraints on vehicle ownership and driving. Because of a lack of available data on past travel trends, some types of estimates of future travel demand, such as vehicle miles traveled, could not be developed. The scenarios include estimates of future vehicle ownership and production as well as more qualitative estimates for demand on other modes.