Are californians willing to use shared automated vehicles (SAV) & renounce existing vehicles? An empirical analysis of factors determining SAV use & household vehicle ownership

Behram Wali, Paolo Santi and Carlo Ratti

Technological Forecasting and Social Change, 2023, vol. 195, issue C

Abstract: Future smart transportation systems are anticipated to integrate automation and sharing of vehicles. Responding to the expected changes in shared mobility services, this study used representative data from over 4100 households in California to examine consumers' affinity to use shared automated vehicles (SAV) and their willingness to renounce existing vehicle(s) in the presence of SAVs - two interlinked factors determining the long-term success of SAVs. Results of the study showed that around 50 % of the households reported willingness to use SAVs but unwillingness to renounce their current vehicle(s). As innovators, another 9.1 % stated their affinity to use SAVs and renounce existing vehicle(s) but with heterogeneity across vehicle ownership levels. Random parameter logit models revealed that households using existing vehicles to work for ride hailing companies were significantly less likely to use SAVs and renounce their current vehicle(s). Households who had high awareness about self-driving cars, possessed full electric vehicles, used sustainable work travel modes (bike, e-bike, bikeshare), had greater number of leased vehicles and ridesharing trips were more likely to use SAVs and renounce current vehicle(s). Significant unobserved preference heterogeneity was recorded in the effects of behavioral and sociodemographic correlates. The study contributes by shedding new light on the behavioral determinants of the vehicle ownership impacts of SAVs. We discuss policy implications of the key findings.

Keywords: Automated vehicles; Shared automated vehicles; Consumer affinity; Household vehicle ownership; Shared mobility (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:195:y:2023:i:c:s0040162523004420

DOI: 10.1016/j.techfore.2023.122757

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