01590158

Component

https://doi.org/10.3141/2565-07

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%).

01609830

16-1832

English

pp 57–64

2016

Transportation Research Record: Journal of the Transportation Research Board

9780309369879

Print

Figures (1) ; References (20) ; Tables (2)

Attitudes; Automobile ownership; Intelligent vehicles; Mode choice; Surveys; Technological innovations; Travel behavior

Austin (Texas)

Data and Information Technology; Highways; Planning and Forecasting; Vehicles and Equipment; I90: Vehicles

TRIS, TRB, ATRI

Jan 12 2016 4:47PM

• Effect of Traveler’s Nationality on Daily Travel Time Expenditure Using Zero-Inflated Negative Binomial Regression Models: Results from Belgian National Household Travel Survey
• Full-Scale Controlled Comparison of Alternative Information Formats for Stated Choice Exercises
• Handling Intrahousehold Correlations in Modeling Travel: Comparison of Hierarchical Models and Marginal Models
• Incentivizing Modal Change: Exploring Effect of Multimodal Information and Ticketing Systems for Medium and Long Distances in Europe
• It’s a Matter of Time: Assessment of Additional Time Budgeted for Commuting to McGill University Across Modes
• Life-Oriented Approach of Modeling Commute Mode Loyalty and Transition Behavior
• Modeling Travel Tool Ownership of the Elderly Population: Latent Segmentation-Based Logit Model
• Short-Term Adaptations as a Response to Travel Time Increases: Results of Stated-Adaptation Experiment
• Travelers’ Risk-Taking Behaviors in Day-to-Day Dynamic Evolution Model
• Valuation of Travel Time Reliability Accommodating Heterogeneity of Route Choice Behaviors
• Weakening Obstacles to Transit Use: Changes in Relationships with Child Rearing and Automobile Access from 2000 to 2010