Young adults’ acceptance of shared autonomous vehicles in an urban-university setting

Etminani-Ghasrodashti, Roya; Patel, Ronik Ketankumar; Pamidimukkala, Apurva; Kermanshachi, Sharareh; Rosenberger, Jay Michael; Foss, Ann · 2025 · DOAJ

DOI: 10.3389/fbuil.2025.1613232

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Summary

This study investigates the factors influencing young adults' acceptance of shared autonomous vehicles (SAVs) within an urban-university setting. Motivated by the need to understand adoption among early technology adopters and the gaps in literature regarding actual SAV experiences versus simulated preferences, the research focuses on the RAPID pilot project in Arlington, Texas. This initiative integrates SAVs with on-demand rideshare services to improve mobility and equity in areas with high poverty rates and limited private vehicle access. The researchers collected survey data from 259 respondents living, working, or studying in the service area, which included Downtown Arlington and the University of Texas at Arlington campus. The methodology employed a two-step analytical approach. First, cross-tabulations and chi-square tests identified sociodemographic differences between SAV users and non-users. Second, exploratory factor analysis (EFA) was conducted to identify latent factors related to attitudes, travel modes, and residential accessibility. These factors were then integrated into a structural equation model (SEM) to test the causal relationships between sociodemographic characteristics, attitudes, travel behaviors, and SAV acceptance, defined by both ridership experience and willingness to ride. The results indicate that younger individuals and those with lower incomes are more prone to adopt SAVs. Specifically, individuals with higher education levels, fewer vehicles, and Asian ethnicity showed higher acceptance rates. The SEM revealed that favorable perceptions regarding SAV convenience, safety, and ease significantly increased acceptance, while negative attitudes regarding cybersecurity and road confusion decreased it. Crucially, heavy car users and those with high dependency on private vehicles were significantly less interested in SAVs. Conversely, individuals who utilized public transit, rideshare services, or active travel modes were more likely to accept SAV technology. The model also found that age negatively impacted positive attitudes toward AVs, whereas higher education positively influenced them. These findings suggest that SAV integration is most viable for populations already engaged in shared or public mobility systems, rather than those reliant on private car ownership. The study implies that integrating SAVs with existing on-demand rideshare services can enhance accessibility for low-income individuals and students. For service providers and policymakers, the results highlight that targeting early adopters requires addressing specific attitudinal barriers and leveraging the existing habits of non-car users. The research provides empirical evidence from a real-world demonstration, offering a more accurate perspective on market potential than studies based solely on hypothetical scenarios.

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• acceptance adoption

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• Empirical Findings: self report data