Examining Heterogeneity in Public Attitudes Toward Shared Autonomous Vehicles by User Experience

Term Start:

June 1, 2025

Term End:

May 31, 2026

Budget:

$131,660

Keywords:

Emerging Technologies, Mode Choice, Shared Autonomous Vehicles, Travel Behavior

Thrust Area(s):

Understanding User Needs

University Lead:

California State Polytechnic University, Pomona

Researcher(s):

Yongping Zhang

The rapid development of autonomous driving (also called self-driving or driverless) technologies is transforming the landscape of urban mobility. As a byproduct of autonomous vehicles, the emerging Shared Autonomous Vehicles (SAVs) are increasingly positioned at the intersection of economic viability and competitiveness, time savings and productivity, technological innovation, and transport policy. SAVs are fleet-operated, fully autonomous vehicles that provide on-demand transportation services to users without private ownership. A couple of regional pilot projects have been conducted to test the viability of SAVs and their impact on existing transportation systems, whereas relatively few models (e.g., Waymo, Cruise, and Zoox) operate SAVs commercially at scale in the U.S. At this stage, public attitudes and concerns toward this emerging travel mode remain predominantly perception-based, with limited grounding in real-world SAV ride experience. This leads to a significant gap in understanding the heterogeneity of public attitudes toward SAVs between individuals with and without direct experience using commercial SAV services.

The 2024 California Vehicle Survey, conducted by the California Energy Commission, provides a unique opportunity for this research, as commercial SAVs services like Waymo have already been operating in San Francisco and Los Angeles before the survey’s completion in November 2024. The survey collected respondents’ socio-demographic characteristics, vehicle ownership information, and mode choice data, and particularly introduced a new focus on perceptions, opinions, intentions, and motivations related to autonomous vehicles and SAVs. The survey indicates that approximately one in four respondents has experience using on-demand driverless ride-hailing services. Leveraging the 2024 survey data, this project attempts to answer the following questions. First, from an overall perspective, what segments of the population are most likely to adopt SAV’s services, and what are the driving factors influencing the intention to use SAVs? Second, focusing on the user groups, what are the significant differences in attitudes toward SAVs between groups with and without direct ride experience? Statistical models like Structural Equation Models and Fixed Effects model will be established to control the effects of a series of latent factors. The results will facilitate the understanding of both perceived and observed barriers to adopting SAVs.

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