Exploring SAEVs in the UK: A Behavioral Attitudes and Ridesharing Analysis for Sustainable Urban Mobility

Shared Automated Electric Vehicles (SAEVs) could revolutionize urban mobility in London due to its complex terrain and traffic congestion. However, there remains a gap bridging this new mobility with wider public acceptance of SAEVs that could induce congestion and foster social inequity. This study explores the potential adoption of SAEVs by investigating behavioral attitudes and willingness to rideshare. This study employs a statistical-utility maximization approach, including sociodemographic, socioeconomic, travel, and psychological factors, utilizing a Stated Preference Discrete Choice Experiment (SPDCE) with 446 Londoners. The analysis employs Ordinal Logit Models (OLM) and Kruskal–Wallis (H-tests) to assess the link between socio-demo-econo-travel factors, psychological variables, and ridesharing with strangers. Additionally, we specify a Multinomial Logit Model (MNL) to understand mode choice and preferences. The results reveal a general reluctance among respondents to rideshare in SAEVs with strangers from diverse backgrounds. Gender influences the importance of SAEVs’ self-charging capability, while ethnicity and prior Dynamic Ride Sharing (DRS) experience impact the importance of comfort in SAEVs. H-test results indicate potential future SAEV appreciation, especially considering psychological factors like Technological Acceptance and Use (TAU) during peak and off-peak periods. The MNL model highlights the preference of license holders for SAEVs in mode choice decisions. This study offers crucial insights into ridesharing hesitations and acceptance, benefiting transport agencies and operators in integrating new mobility technologies while maintaining transport resilience.