Microtransit is a new, technology-enabled, on-demand transportation mode in which small shuttles provide shared rides through flexible routing and scheduling in response to customers’ requests for rides. It can potentially offer greater efficiency and more equitable service than ride-hailing services, and it may fill gaps in traditional transit services. Thus far, the early shape of the microtransit customer market remains unclear. Specifically, why some people are interested in microtransit while others are not remains an open question. For people who have never used it, what factors could work as facilitators or barriers in their willingness to adopt microtransit? Who are early adopters of microtransit? Aiming to fill this gap, in 2021, researchers at the University of California, Davis conducted focus groups and an online survey of SmaRT Ride adopters and users of other means of transportation in the Sacramento area.

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In the development of new PET/CT systems, image quality assessment is critical. This study evaluated two aspects of system performance on a PET/CT scanner under development: the displacement of the source of radioactivity off the center of the field of view and the reconstruction algorithm employed for the correction of metal artifacts. Uniformity served as the principal property for image quality assessment in both studies. Our findings indicated that the displacement from the center resulted in increased non-uniformity, particularly at the edge, and the activity concentration and standard uptake value inaccuracies worsened with increased degree of displacement. Further, the in-built whole-body metal contrast reconstruction failed to correct for the metal artifact for metal plates with a thickness up to 3cm. These results shed light to the potential improvements of system image quality and provide grounds for future work on performance testing with better clinical representations.

Microtransit is an emerging, technology-enabled, on-demand transportation mode whereby small shuttles provide shared rides through flexible routing and scheduling in response to customers’ requests for rides. Given its potential to address the equity and accessibility needs of the public, public transportation agencies are experimenting with this service to fill gaps in traditional transportation in the US. However, why some people are interested in microtransit while others are not remains an open question. For people who have never used it, what factors could work as facilitators or barriers in their willingness to adopt microtransit? Who are the early adopters of microtransit? Guided by the theory of planned behavior, this study aims to fill the gap in knowledge by conducting a large-scale survey of microtransit adopters and users of other means of transportation in the Sacramento area of California in 2021. This study focuses on the microtransit service SmaRT Ride (SR), operated by the Sacramento Regional Transit District (SacRT). Focus groups and interviews were conducted before the largescale online survey to gather preliminary information, help develop survey questions, and improve understanding of research findings, given the novelty of microtransit. Discrete choice models, including binary logit and ordered logit models and latent class analysis, were employed to explore barriers to and facilitators of SR adoption, willingness to use it, and underlying subgroups of early adopters. Important findings include that people who like fixed-route transit are less likely to adopt microtransit. Social support plays an important role in explaining the willingness to use microtransit. The analysis reveals three salient classes of microtransit users: travel time savers with environmental awareness, riders with a neutral mindset, and pro-SR and travel cost savers.

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Self-driving vehicles, as a revolution in mobility, are emerging and developing rapidly. However, public attitudes toward this new unproven technology are still uncertain. Given the significant influence of attitude toward a new technology on the intention to use it, the question arises as to why some people are in favor of this technology whereas others are not. Additionally, questions about the key attitudes influencing self-driving technology acceptance, where these attitudes come from, and how they interact with each other have not yet been addressed.

This study aims to explore these research questions based on data collected from people who live or work in the West Village area of the University of California, Davis, campus after a self-driving electric shuttle was piloted in this area. structural equation modeling was employed to explore interactions between attitude elements. The results show that affect—i.e., liking or enthusiasm for self-driving shuttles—strongly explains the acceptance of self-driving technology. A higher level of affect could be formed by strengthening an individual’s trust. Additionally, trust works as an important mediator between perceived risk, usefulness, and ease of use on both affect and intention to ride self-driving vehicles. Perceived risk captured more security and functional concerns, reflecting uncertainty around current self-driving technology. The model identified important bi-directional influences between trust and affect. Significant effects of mental and physical intangibility were also shown, but each works differently on cognitive beliefs. Individuals’ socio-demographic, lifestyle, and mobility characteristics also exert influences on attitude and self-driving technology acceptance.

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Higher-occupancy self-driving shuttles could bring about the benefits of vehicle automation—improved safety, parking cost savings, greater mobility to those who cannot drive, and stress relief for drivers. At the same time, these shuttles would not bring the potential drawbacks of self-driving vehicle ownership, such as increases in vehicle miles traveled and associated energy use. Because they can only currently operate in relatively simple and closed environments, self-driving shuttles are likely to be deployed earlier than personal self-driving vehicles in open road environments. However, acceptance of the new technology remains uncertain. Whether people will use these services will be largely influenced by their attitudes toward self-driving technology.

Researchers at the University of California, Davis surveyed residents and employees of the West Village area of the UC Davis campus during the three-month pilot deployment of a self-driving, electric shuttle to understand attitudes toward self-driving technology. The researchers then applied existing theories of technology adoption to model how attitudes of residents and employees influenced their acceptance of the shuttle service. This policy brief summarizes findings from that research and provides policy implications of self-driving shuttles.

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The majority of bicycling in the US is for recreation rather than transportation purposes but few studies have examined the question of bicycling purpose. We use data from an online survey conducted in 2006 in six small cities in the western US to examine factors affecting bicycling for transportation compared to bicycling for recreation. The results indicate that individual, social-environment, and physical-environment factors have important influences on the balance between transportation and recreational bicycling and on miles of bicycling for each purpose. Bicycling comfort and an aversion to driving are associated with more transportation bicycling. A culture of utilitarian bicycling and short distances to destinations are also key factors for transportation bicycling. Bicycle infrastructure appears to play an indirect role through its effect on perceived bicycling safety and through the self-selection effect, by attracting bicycling-inclined people to bicycling-supportive communities.