Introduction: Shared automated vehicles (SAVs) could significantly enhance public transport by addressing urban mobility challenges. However, public acceptance of SAVs remains under-studied, particularly regarding how informational factors and individual personality traits influence acceptance. Methods: This study explores SAV acceptance using data from an experimental survey of 1902 respondents across Norway. Participants were randomly presented with different informational conditions about SAV services, manipulating vehicle autonomy (fully autonomous vs. steward onboard), seating orientation (facing direction of travel vs. facing other passengers), and ethnicity of co-passengers. Personality traits from the Five Factor Model (FFM) and Social Dominance Orientation (SDO) were assessed. The General Acceptance Factor (GAF), derived from the Multi-Level Model of Automated Vehicle Acceptance (MAVA), was used as the primary outcome measure. Results: No significant main or interaction effects were found from the experimentally altered information conditions. However, personality traits significantly influenced acceptance. Specifically, higher openness and agreeableness positively predicted SAV acceptance, while higher neuroticism and social dominance orientation negatively predicted acceptance. Discussion: The absence of experimental effects suggests either a limited role of the manipulated factors or insufficiently robust manipulations. Conversely, the substantial impact of personality traits highlights the importance of psychological factors, particularly trust, openness, and social attitudes, in shaping SAV acceptance. These findings emphasize the need for tailored communication strategies to enhance SAV uptake, addressing specific psychological profiles and fostering trust in automation.

Shared autonomous shuttles (SASs) could improve the mobility infrastructure in the worlds’ growing cities. This novel service could reduce congestion and improve both mobility and sustainability. To facilitate the implementation of SASs, more research is needed on the psychological aspects of sharing a small, intimate shuttle with strangers. The current study is among the first to use open-ended questions to investigate SAS acceptance. This investigation is based on the Multi-Level Model on Automated Vehicle Acceptance (MAVA). We had 236 participants answer short-form interviews including both open-ended questions and quantitative items. Quantitative data were analyzed using descriptive statistics and correlations, and qualitative data analyzed with directed content analysis. Respondents seem very positive about the proposed new transport service. We found that perceived usefulness, hedonic motivation, trust, and social influence shared large correlations with intentions to use. Other factors such as demographics, technology savviness and use of public transport did not share a linear relationship with intentions to use. Qualitative analysis suggests that, while most people do not mind sharing shuttles with strangers, some could find the social situation deterring. People seem most concerned with availability, effectiveness, travel cost and safety. The reported positive attitudes towards the service seem predicated upon trust in the government regulation and proper testing of the technology, that many think of as immature. Regulation and thorough testing may be paramount in keeping people positive. This study emphasizes the importance of trust and safety to adoption of SAS, while suggesting new factors that need further investigation.

The primary aim of this study was to develop an accurate measure of acceptance for shared autonomous vehicles (SAVs) and to assess whether this measure can predict intentions to use SAVs. One leading model for explaining technology uptake is the UTAUT (Unified theory of acceptance and use of technology). This model is extensive and has received numerous suggested extensions and revisions, even being developed into a Multi-Level Model of Autonomous Vehicle Acceptance (MAVA). The challenge is to consolidate a model that effectively measures SAV acceptance and to determine which extensions capture the unique social situation within SAVs. The current study used survey data from 1902 respondents. The sample was split into two: one half underwent a principal component analysis (PCA) and the other half a confirmatory factor analysis (CFA). We found that the 24 items we included were reducible to a single general acceptance factor (GAF), with three additional factors measuring interpersonal security, sociability, and attractivity. The GAF was, by a large margin, the most efficacious predictor of intention to use SAVs. The GAF could be further reduced to as little as two predictors, trust and usefulness, accounting for over 70 % of the variance in intention to use. However, there is also an argument to be made that the other components of SAV acceptance may capture different nuances of the service, particularly relating to the social situation. Interaction terms show differences between genders in their rating of sociability and how this impacts intentions to use SAVs. Our findings carry significant implications for future research in this field. They underscore the pivotal roles of trust and usefulness while corroborating the notion that SAV acceptance is best represented by a single latent component. However, further investigation is warranted to explore individual-level moderating effects on the other components, potentially offering novel insights for the design of future SAV services.

This study investigates acceptance of shared autonomous shuttles (SASs) in a suburban area. A model where contextual variables were mediated through trust in SASs and technology optimism was tested. We examined intentions to use SASs without a steward and the significance of social distancing. Data were collected at the start and end of a 2020–2021 pilot involving 922 and 608 participants respectively, operating at SAE level 3. Findings indicate that trust and technological optimism significantly influence the willingness to use SASs, though contextual variables show minimal impact. Older adults and women displayed lower trust and optimism, reducing their usage intentions. These two groups also feel that it is more important to be able to keep social distance while riding SASs. The study suggests that future pilots should avoid negative impacts from using immature technology and address the social needs of specific groups.

Background Autonomous shuttles (AS) could grow to be more efficient, greener, safer and cost-efficient than current transport solutions. To harvest the full potential of future transport, we depend on their public adoption. The employment of shared, stewardless electric AS will create a novel social situation which there is a paucity of knowledge about. The main public transport provider in the Oslo-region, Ruter, seek to develop a mobility-as-a-service using small stewardless robot taxis for 6-8 passengers. Their shuttles are modified vans that might feel intimate. This novel social situation of being picked up at home by a stewardless AS, may impact the adoption of such services. Previous research has explored theoretical frameworks such as the Unified theory of acceptance and use of technology (UTAUT), which recently was molded into the Multi-Level Model on Automated Vehicle Acceptance (MAVA). No research has yet investigated the role of the social situation for AS acceptance or tested the MAVA experimentally. The current study will explore how the social situation affects acceptance of AS using an experimental design. Experimental conditions are based on key constructs identified in the MAVA and the theory of Social Dominance Orientation (SDO). Possible moderating effects of personality traits are also studied. Method This study will build on previous research from pilots near Oslo that show ambivalent reactions to sharing AS with strangers. We will gather data using a large-scale experimental online survey to the general Norwegian public. Experimental elements could be presented by manipulating text, images or video. Likely manipulations are seat orientation, presence of a steward, number of passengers present, and safety measures such as emergency stop buttons. Results & Conclusions The study is ongoing, results will be available at the conference. Analyses will focus on exploring the role of the social situation in acceptance of AS. We will seek to experimentally test factors from the MAVA, and how this relates to other theoretical constructs that explore the social situation. The conclusions drawn from this study will impact the integration of autonomous transport systems in Norway and internationally.

Background Automated shuttles are tested in many places worldwide. They are typically electrical minibuses operating at SAE level 3 (i.e. with an operator on-board). Low speed, stereotypical driving and compliance with traffic rules characterize their driving. Their software models are able to solve pre-defined traffic situations. However, in situations they have not been trained for, the shuttles might react in unexpected ways. Both predictable and non-predictable reactions of shuttles have impact on human behavior towards them and may affect traffic safety. As safety is a major concern for society and for implementation of automated vehicles, it is essential to understand the safety consequences of human-shuttle interactions. Method This study utilizes video clips of human-shuttle interactions in regular, mixed traffic. Videos were captured in the Oslo region within several TØI projects, at locations such as a shared space, residential areas or signalized intersections. The analyses were conducted in several steps. First, video clips were categorized by a road safety researcher (based on variables such as location and type of involved traffic participants and their maneuvers). Second, a team of experts with diverse backgrounds (such as traffic psychology, game theory, traffic conflict studies, accident analyses, behavioral observations) evaluated the interactions and discussed human behavior towards the shuttles. Utilizing various perspectives of the team members enabled us to better identify the contributory factors to humans’ responses to automated shuttles and to evaluate potential safety issues. Results This study is currently on-going. We identified two major categories of human responses. The first category is characterized by misusing the shuttles’ operational characteristics, e.g., drivers and cyclists overtaking slow shuttles in a risky matter or not giving way to the shuttles. The second category is connected to misinterpreting/not trusting shuttles’ reactions which can result for example in drivers wrongly giving way to the shuttles. Conclusions The preliminary results suggest that an introduction of automated shuttles into urban traffic leads to several types of behavioral adaptations, which can affect the traffic safety. Misusing of shuttles’ operational characteristics and misinterpreting of shuttles’ reactions contribute to the occurrence of these adaptations.

The current paper presents the results of behavioural observations in a field experiment with automated shuttles in Oslo, Norway. Video observations were conducted at five fixed locations along a challenging 1.2 km automated shuttle line with varying traffic conditions. Observed interactions between vulnerable road users and automated shuttles were coded using a predefined codebook, which allowed a structured quantitative analysis. The paper identified several potentially risky types of situations in which the automated shuttles did not always behave according to the traffic rules. Generally, the automated shuttles failed to give way to pedestrians at pedestrian crossings in 26%–50% of the interactions. Right-turning shuttles failed to yield to cyclists going straight in 38% of the interactions at observation Site 1 (the only location where the automated shuttle takes a right turn). In majority of same direction interactions between cyclists and automated shuttles, the interactions resulted in the cyclist overtaking the automated shuttle, usually on the left-hand side. Generally, the paper found little evidence of road users trying to bully or otherwise take advantage of the defensive driving style of the automated shuttles and identified only a limited number of interactions in which a vulnerable road user behaved ignorant or aggressive towards the automated shuttles. In addition, the paper found very little indication of temporal effects that suggest changes in the interaction patterns over time.