Stated Preference Analysis of Autonomous Vehicles Among California Residents Using Probabilistic Inferences

As technology advances, improvements in the way people travel also change. In 2017, the National Renewable Energy Laboratory (NREL) conducted a travel survey in California to understand the residents' perception of several mobility aspects. This study used the data collected by NREL to understand various factors associated with the safety perception and acquisition of autonomous vehicles among California residents. Bayesian Networks (BNs) was used to learn the probabilistic interrelationship among autonomous vehicles' aspects . The predicted probabilities for the safety concern of self-driving vehicles, purchase of vehicles with auto-drive assistance, and purchase of self-driving vehicles were determined after learning the BN structure and parameters from the data. The study found that there is a strong relationship between the acquisition of autonomous vehicles and vehicles with auto-drive assistance. The BN model predicted that residents who are interested in purchasing vehicles with auto-drive assistance are about 95% likely also to purchase self-driving vehicles. Moreover, the ridesharing, number of vehicles in the household, housing type, and Plug-in Vehicle (PEV) ownership are among the factors that play a great role in the acquisition and safety perception of autonomous vehicles. Residents who are currently participating in ridesharing and living in the apartments are more likely to purchase the vehicles with auto-drive assistance. Residents who either own the PEV or have three or more vehicles are more likely to have safety concerns with self-driving vehicles. Additionally, residents who do not have safety concerns with self-driving vehicles are about 45% likely to purchase them.


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 16p

Subject/Index Terms

Filing Info

  • Accession Number: 01763997
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-03698
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:17AM