Stated Preference Analysis of Automated Vehicles among California Residents Using Probabilistic Inferences

Abstract

As technology advances, improvements in the way people travel also occur. In 2017, the National Renewable Energy Laboratory (NREL) conducted a travel survey in California to understand residents’ perception of several aspects of mobility. This study used the data collected by NREL to understand various factors associated with safety perceptions and acquisition of automated vehicles among California residents. Bayesian networks (BNs) were used to learn the probabilistic interrelationships between aspects of automated vehicles. The predicted probabilities for safety concerns about vehicles with full driving automation (FAVs), purchase of vehicles with auto-drive assistance, and purchase of FAVs were determined after learning the BN structure and parameters from the data. The study found a strong relationship between the acquisition of automated vehicles and vehicles with auto-drive assistance. The BN model predicted that residents who are interested in purchasing vehicles with auto-drive assistance also have about 95% likelihood of purchasing FAVs. Moreover, ridesharing, number of vehicles in the household, housing type, and plug-in electric vehicle (PEV) ownership are among other factors playing a great role in the acquisition and safety perception of automated vehicles. Residents who are currently participating in ridesharing and living in apartments are more likely to purchase vehicles with auto-drive assistance. Residents who either own a PEV or have three or more vehicles are more likely to have safety concerns about FAVs. Additionally, residents who do not have safety concerns about FAVs have about a 45% likelihood of purchasing them. These results could provide valuable user opinion information for vehicle developers and other stakeholders.