Adoption of autonomous and shared autonomous vehicles: a multi-criteria decision-making approach

• AHP and FAHP were used to evaluate AV/SAV adoption criteria in Amman. • A novel hierarchical model integrates UTAUT and TAM frameworks. • User Perception emerged as the top criterion across all expert groups. • Safety, infrastructure, and policy readiness are key adoption barriers. • Gradual AV/SAV integration was favored by non-drivers for smoother adoption. This research investigates the adoption of autonomous vehicles (AVs) and shared autonomous vehicles (SAVs) in Amman, Jordan, utilizing a multi-method approach. A comprehensive literature review was conducted, synthesizing existing research on AV and SAV adoption factors within Amman’s transportation landscape. Subsequently, a local community feedback survey was conducted to understand their perspectives on AV and SAV adoption. Using multi-criteria decision-making (MCDM) techniques, specifically the analytic hierarchy process (AHP) and fuzzy AHP (FAHP), this study assesses local community acceptance and adoption levels of AVs and SAVs. The novel hierarchical model is derived from the unified theory of acceptance and use of technology (UTAUT) and technology acceptance model (TAM). The findings highlight the critical importance of user perception (UP), particularly performance expectancy (PE), across all expert groups. Safety concerns, infrastructure readiness, and regulatory frameworks emerged as pivotal factors shaping public acceptance and adoption of AVs and SAVs in Amman, emphasizing the vital role of governmental support and well-structured regulatory frameworks that are crucial for facilitating the integration of AVs and SAVs into the existing transportation infrastructure. The study provides insights and recommendations for policymakers and stakeholders, emphasizing the necessity of collaborative efforts, pilot programs, and targeted public outreach to promote AV and SAV adoption. These measures are essential for advancing urban mobility and mitigating traffic congestion in Amman.