This study investigates the factors influencing the public’s acceptance of shared automated electric vehicles (SAEVs) as a practical alternative to public transit during peak travel times in London, UK. Advanced statistical models, including mixed multinomial logit (MMNL) and Bayesian multinomial logit (BMNL), analyze mode preferences and determinants. The MMNL model underscores the pivotal role of cost, travel attributes and demographic considerations in shaping preferences, emphasizing SAEVs as a sustainable alternative to private vehicles and public transport. Incorporating random coefficients and case-specific variables enhances the model’s accuracy, revealing distinct preferences among individuals with driver’s licenses and certain gender groups during high-demand periods. The BMNL model explores intricate relationships between gender, driver’s licenses, employment variables, and mode choices. Despite limitations of Bayesian uninformative priors, the study highlights its potential for future research and encourages further exploration of random effects in Bayesian modeling using the framework’s flexibility by incorporating priors, utilizing new information to update subjective beliefs over time. Research findings predict a substantial 63% likelihood of individuals choosing SAEVs during peak travel, providing valuable guidance for policymakers aiming to reduce private vehicle use. The study emphasizes the importance of addressing public concerns about safety and privacy through targeted educational campaigns, advocating for a holistic approach to promote SAEV adoption. Overall, this research contributes to a comprehensive understanding of the factors influencing mode choices in urban transportation using advanced MMNL and BMNL statistical models, and providing insights for transportation planning interventions when scaling new automated transport technologies.