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Effects of trip-level characteristics on autonomous vehicle ownership: a US analysis

About

Welcome to our autonomous vehicle (AV) ownership project!

We examined how trip-level characteristics may influence autonomous vehicle ownership decisions. Using 639 stated preference survey responses in the US, we developed an integrated choice and latent variable model linking trip- and consumer-level characteristics to AV ownership choices.

If you are interested in autonomous vehicle policies and ownership modeling, this project is for you! 🖖 👀

Paper page: https://www.sciencedirect.com/science/article/pii/S1361920922001493?dgcid=rss_sd_all

Team

  • Yuanjie (Tukey) Tu, PhD candidate, University of Washington, US
  • Parastoo Jabbari, PhD, University of Washington, US
  • Nazmul Arefin Khan, PhD, Argonne National Lab, US
  • Don MacKenzie, PhD, University of Washington

Highlights

  • We incorporated trip-level characteristics into AV ownership choice modeling
  • We examined elasticities of vehicle ownership market shares
  • We simulated future vehicle ownership decisions under three policy scenarios

Main takeaways

  • At the trip level, parking cost, travel cost, and travel time are important predictors for vehicle ownership
  • At the consumer level, monthly payment, car ownership importance, and AV safety are significant predictors
  • While individual-level characteristics have the most prominent effect on vehicle ownership decisions, trip-level characteristics have a small but measurable effect
  • Among trip-level characteristics, parking cost has the largest impact on vehicle ownership decisions, followed by travel cost of ridehailing, and transit travel time. Transit travel cost has almost no effect on vehicle ownership decisions

Future directions

  • Incorporate more details of the tour structure, land patterns such as urban/rural divide, and attitude change into the model

Acknowledgement

The work was supported by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) under the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Laboratory Consortium, an initiative of the Energy Efficient Mobility Systems (EEMS) Program. The submitted manuscript has been created by University of Washington and the UChicago Argonne, LLC, Operator of Argonne National Laboratory (Argonne). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.

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