Public adoption of novel transport technology depends on the willingness of potential users to engage with unfamiliar systems. Trust is a key determinant of this willingness, yet little is known about how trust in shared autonomous vehicles (SAVs) can be shaped. The current pre-registered study investigates to what degree trust predicts the intention to use SAVs and whether static and dynamic social norms can influence the degree of trust in SAVs. Static norms convey the current consensus in a population, while dynamic norms highlight that attitudes or behaviors are changing over time. An online experimental survey was conducted in which participants (n = 1,032; n = 630 after filtering) were randomly assigned to one of three conditions: control, static norm, or dynamic norm. Each condition presented distinct normative statements embedded in informative vignettes about SAVs. The moderating effect of propensity to trust (interpersonal and automation-specific) on the relationship between normative framing and subjective trust was also examined. One-way ANOVA and regression analyses revealed that both static and dynamic normative framings led to significantly higher subjective trust in SAVs compared to the control group. No significant difference was found between the two normative conditions. Propensity to trust (both interpersonal and automation-specific) had direct positive effects on subjective trust but did not moderate the effect of normative framing. Safety evaluation emerged as the strongest predictor of subjective trust. Subjective trust, in turn, was strongly associated with intention to use SAVs. These findings indicate that norm-based interventions providing social cues about others’ trust can effectively shape public attitudes toward SAVs, even in the absence of firsthand experience. However, safety perceptions remain paramount, suggesting that normative strategies should complement, rather than substitute for, efforts to demonstrate and communicate the safety of autonomous public transport.

The deployment of shared automated vehicles (SAVs) has the potential to transform urban mobility, yet public acceptance remains a critical barrier. Most acceptance research relies on hypothetical scenarios and quantitative surveys. There is a need for real-world, theory-informed qualitative studies that capture user experience. Rather than a tech-demo or a specialized closed-loop pilot test, this study investigates early adopters of a SAE Level 2 SAV service integrated into the public transport system, with a visible safety driver. Drawing on 12 semi-structured interviews, we apply reflexive thematic analysis informed by the SAVA model, comprising trust, utility, and social comfort, as well as Social Practice Theory (SPT). The aim is to explore how psychological mechanisms and socially embedded practices intersect in shaping acceptance. Four overarching themes were identified: everyday mobility contexts, utility, trust, and social comfort. Acceptance was not determined by isolated attitudes but by how well the service integrated into existing routines and infrastructures. Trust was found to be layered and anchored in vehicle behavior, human operators, and institutional reputation. Utility hinged on dynamic factors such as pick-up/drop-off design, route transparency, and pricing logic. Social comfort was shaped by ambiguous norms around sharing, spatial intimacy, and service framing. Across all themes, the perceived fit between the SAV and users' lived practices emerged as central to acceptance. Our findings extend current models of SAV acceptance by demonstrating how individual experiences are mediated by competence, materiality, and meaning. We propose that trust, utility, and social comfort function as interdependent practice elements rather than isolated predictors. Design and policy implications include clearer social signaling, accessible service design, and better alignment with everyday geographies.

Shared automated vehicles (SAVs) may transform urban mobility but face strong public resistance. Existing acceptance research is fragmented and often relies on complex frameworks. We introduce a simplified shared automated vehicle acceptance (SAVA) model, identifying trust, utility and social comfort as core predictors of SAV acceptance. Using structural equation modelling, we tested whether these factors form a general acceptance factor (GAF) or operate as distinct but correlated predictors of intention to use. A 2 × 2 × 2 experimental design further assessed whether targeted informational interventions could increase trust, utility and social comfort. Monte Carlo-based power analyses indicated a minimum of 800 participants to detect small effects (Cohen’s f = 0.20) with 80% power at α = 0.05; we recruited 1250 respondents after data cleaning, ensuring adequate power. Results show that trust, utility and social comfort are best modelled as distinct but correlated constructs, implying rather than establishing a GAF. Utility exerted the strongest effect. Experimental manipulations had no significant impact, suggesting stronger interventions are needed to shift acceptance. The SAVA model provides a parsimonious, testable framework explaining intention to use SAVs. This recommended registered report advances theory and offers practical insights for policymakers and providers seeking to improve SAV acceptance.

IntroductionShared 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.MethodsThis 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.ResultsNo 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.DiscussionThe 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.

If autonomous vehicles are to have beneficial impacts on society, people must be willing to use them in their everyday lives. Many studies have engaged in questions regarding the technology of automation and how drivers will interact with it. However, little research has focused on the social situation arising from small shared autonomous shuttles (SASs) used in public transportation. This study aims to investigate a conceptual framework suggested by previous research and the MAVA-model. We tested a conceptual model where the background variables’ impact is mediated through trust in SASs and technology optimism. Our two dependent variables were the intention to use SASs with strangers without a steward onboard and the importance of social distance. The current article uses data collected using two identical online surveys conducted in 2020 (n=922) and 2021 (n=608). The data were collected before and after a pilot using SAS was employed in a suburban area outside Oslo. Examining the same population before and after the pilot gives us crucial insight into the development of attitudes toward automated vehicles when exposed to them in regular traffic.We find that trust in SASs and technological optimism positively predict willingness to use SAS. However, the passage of time had a negative effect on trust and tech-optimism, which in turn lowered the intentions to use. The background variables have little effect on the mediators. Contrary to previous research, we find that familiarity with the pilots predicted lower technological optimism and thus lower intentions to use. Older participants and women reported less trust in SASs and less tech-optimism compared to others. In the next step, these mediators lowered the intention to use SASs. These two groups also feel that it is more important to be able to keep social distance while riding SASs. The participants who use active transport modes think it is less important with social distance. The ongoing COVID-19 pandemic may also impact the results. The proposed model was less suited for predicting desire for social distance than for intentions to use. Our results suggest that future pilots should take care not to leave a negative impression by employing immature technology in neighborhoods, as this may be detrimental to the perception of SASs. Furthermore, transportation providers should take care to meet the social needs of exposed groups in the novel social context created by SASs.

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.

As the pressure on urban mobility rises, shared autonomous vehicles (SAVs) offer enhanced transportation efficiency and safety. This study investigates the valuations of additional services in SAVs, examining demographics’ impact on preferences for different service features. We use data from 1723 Norwegian respondents to an online survey. We find that women put increased value on a safety host. Younger respondents value fast travel time. More tech-savvy individuals showing a higher valuation for services enhancing personal utility and comfort. Intention to use strongly predicts valuation of utilities, but not social factors.

The technology behind shared autonomous vehicles (SAVs) is developing rapidly and may revolutionize public transport in metropolitan areas. To take full advantage of the potential benefits, it is paramount to understand the public acceptance of this new technology. One of the leading models for explaining technology uptake is the UTAUT (Unified theory of acceptance and use of technology). This model is vast and has received numerous suggested extensions and revisions, even being developed into the Multi-Level Model of Autonomous Vehicle Acceptance (MAVA). More research is needed to consolidate the model to best measure the acceptance of SAVs, and to determine which extensions capture the unique social situation arising within SAVs. The current study used survey data from 1902 respondents to perform a principal component analysis (PCA) of key constructs suggested by the MAVA. We found that these items were reducible to a single general acceptance factor (GAF), with three additional constructs measuring interpersonal security, sociability, and attractivity. The GAF was, by a large margin, the most efficacious predictor of intention to use SAVs. The overlap between GAF and intention to use may suggest that these are best conceptualized as a single component. 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. There is, however, also an argument to be made that the other three components of SAV acceptance may be important for capturing different nuances of the service. Interaction terms show that there is differences between genders in their rating of sociability, and how this impacts intentions to use SAVs. Our results have important implications for future research within the field. It cements the importance of trust and usefulness and corroborates the claim that acceptance of SAVs is best represented by a single latent component. However, more research should investigate the individual level moderating effects on the other components, as this may unlock new insights about how best to design a future SAV service.

•Game theoretic basis for studying interaction between autonomous shuttles and ordinary road users in mixed traffic.•Repeated field surveys in the two Norwegian cities with AV shuttle pilots.•Pedestrians and cyclists are considerate and tend to yield to the AV shuttle at both sites.•Cyclists in Oslo are less considerate towards the AV shuttle over time.

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.

There is great political motivation to improve conditions for cyclists to help solving the transport needs of the future. We used eye-tracking to collect data and analysed it using a novel machine learning approach. 40 cyclists in total were tasked with navigating a set route through the Oslo city centre. One group before the new infrastructure was in place and one group after. The analysis focused on developing a method that could be used to investigate how a new signage strategy impacted cyclists in Oslo. Improving signage could create safer traffic conditions for cyclists, while avoiding adding distracting elements. The algorithms developed were able to detect and categorize a variety of important objects. The signage system itself seemed to result in some route change among cyclists, but not all followed the suggested route. Qualitative analyses suggests that those who deviated cycled faster and looked less at signs, than those who chose the suggested route. The paper discusses strengths and weaknesses involved in this approach. While useful, one should be careful to conclude that gaze behaviour reflects the true inner consciousness of cyclists.

Automated shuttles are already seeing deployment in many places across the world and have the potential to transform public mobility to be safer and more accessible. During the current transition phase from fully manual vehicles toward higher degrees of automation and resulting mixed traffic, there is a heightened need for additional communication or external indicators to comprehend automated vehicle actions for other road users. In this work, we present and discuss the results from seven studies (three preparatory and four main studies) conducted in three European countries aimed at investigating and providing a variety of such external communication solutions to facilitate the exchange of information between automated shuttles and other motorized and non-motorized road users.

The Norwegian authorities want to limit the extent of car use in city areas to existing levels. Such a limitation would help combat climate change, improve health of citizens, and alleviate congestion. This implies that any further increase in transport needs will have to be met by walking, cycling and use of public transport. Reaching this ambitious goal requires knowledge about cyclists' preferences concerning operation and maintenance (M&O) of roads and foot/cycle paths. Previous research suggests that M&O have great implications for travel mode choice, bicycle route/path choice, safety, security, and comfort. With the need to serve bicyclists of all ages and genders, this study additionally explores which M&O of roads and foot/cycle the different demographic groups perceive positively or negatively. This article reports results from a nationwide survey in the summer of 2019. Two thousand three hundred seventy-six cyclists across Norway (55% male; 29% <40; 17% >60) participated to determine the cyclists' perceptions about year-round M&O of roads and foot/cycle paths. Respondents, rather than being randomly selected, completed an internet-linked survey. The variables included maintenance of foot/cycle paths in terms of salt and snow plowing and operation and maintenance of roads in terms of glass, holes/bumps, and conditions. Our results suggest that female cyclists suffer more from adverse conditions than do males. We also find that males are more likely to cycle during winter, which is an additional indication that adverse conditions affect women and men differently. Surprisingly, older cyclists report to be less affected by poor conditions than younger cyclists. Self-selection to participate in the survey among older cyclists might be an important explanation for this result. Cycling conditions vary greatly between geographical areas, reflecting the large climatic variations across Norway. Most respondents have experienced a cycle accident where conditions contributed, and many sometimes forfeit cycling due to adverse conditions. Implications for future research and practice of M&O are discussed.