As Automated Vehicles Lead to an Increase in Willingness to Take Longer Trips, Service Providers and Public Agencies Will Need to Reorient Information Campaigns Beyond Usual Geographic Confines.
The Impacts of Automated Vehicles on Five Dimensions of Short-term Activity Travel Choices Are Studied Using 1,127 Survey Responses.
Made Public Date


United States

Investigating Autonomous Vehicle Impacts on Individual Activity-Travel Behavior


The University of Texas used a survey-based modeling approach to investigate the impacts of automated vehicles (AVs) on individual travel behavior. The research project uses a confirmatory factor analysis and multivariate regressions to predict five main outcomes. Using individual socio-demographics with built environment variables and psycho-social variables, AV impacts on five dimensions of short-term activity-travel choices are predicted: “(1) Additional local area trips generated, (2) Trip distance to shop or eat-out activities in the local area, (3) Trip distance to leisure activities in the local area, (4) Additional long distance road trips beyond the local area, and (5) Commute travel time”. The data collection for this study included an emerging mobility survey in the Austin metropolitan area in Texas in 2019. A total of 1,127 respondents participated in the survey.

Lessons Learned

The survey responses and model results provided several insights on the travel behavior impacts of automated vehicles:

  • Reorient information campaigns and social/media advertisement beyond usual geographic confines as the age of AVs draws closer. AVs are expected to have an impact on an increasing trend of long-distance trips. Local area trips are expected to be longer, although AVs may not have a substantial impact on overall trip-making. This suggests that the intensity of trips to city tourist attractions may expand, thus expanding information campaigns and social/media advertisement is important.
  • Consider psycho-social latent constructs related to the adoption/use of current and mobility services in AV response models. Examples of psycho-social characteristics  include tech-savviness, safety-concern, variety-seeking lifestyle, and interest in the productive use of travel time. Studies do not often explicitly connect the need to address AV-related safety concerns to addressing AV transportation equity issues. To ensure no one is left behind, service providers and public agencies must plan for an equitable future with the consideration of heterogeneity in different population groups.
  • Understand the unique sensory, cognitive, and physiological uniqueness of older adults. The analysis suggests that, unless appropriate actions are taken, AVs will not deliver the much touted mobility benefits to older adults and will likely widen the relative schism between the young and the old in terms of accessibility to activity opportunities. Such an understanding can be beneficially used to promote AV use and plan for a more equitable future.
  • Dispel older adults’ distrust of technology through safety awareness campaigns as well as customized human machine interface design features. It is important to implement policy actions where necessary to reduce safety concerns among marginalized groups and AV design to quell some of these concerns.
  • Implement a non-linear-in-distance fare policy to restrict shared-AV usage over shorter distances. Added congestion from empty trips that are generated when AVs return home after a drop should be considered. Policies such as a non-linear-in-distance fare policy is suggested to be supplemented with residential densification, a good land use-mix, and high retail density as it can lead to containing the number of trips made, while also “compacting” the geographic footprint of trips.