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Efficient public transportation management is essential for the development of large urban centers, providing several benefits such as comprehensive coverage of population mobility, reduction of transport costs, better control of traffic congestion, and significant reduction of environmental impact limiting gas emissions and pollution. Realizing these benefits requires a deeply understanding the population and transit patterns and the adoption of approaches to model multiple relations and characteristics efficiently. This work addresses these challenges by providing a novel dataset that includes various public transportation components from three different systems: regular buses, subway, and BRT (Bus Rapid Transit). Our dataset comprises daily information from about 700,000 passengers in Salvador, one of Brazil's largest cities, and local public transportation data with approximately 2,000 vehicles operating across nearly 400 lines, connecting almost 3,000 stops and stations. With data collected from March 2024 to March 2025 at a frequency lower than one minute, SUNT stands as one of the largest, most comprehensive, and openly available urban datasets in the literature.

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In modern, high-speed work settings, the significance of mental health disorders is increasingly acknowledged as a pressing health issue, with potential adverse consequences for organizations, including reduced productivity and increased absenteeism. Over the past few years, various mental health management solutions, such as biofeedback applications, have surfaced as promising avenues to improve employees' mental well-being.

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This paper explores the main challenges and barriers to VR/AR adoption and categorizes common activities performed with these technologies, explaining each specific factor affecting them. After reviewing literature on metaverse retailing, channel strategies, VR/AR technologies, and user experiences, a conceptual framework was developed. Data from the “Voice of the Consumer: Digital Survey” (2020–2024) in over 20 countries was analyzed, using Pearson’s correlation, factor analysis, and multiple linear regressions. The results point that key challenges for VR/AR adoption include security, privacy, content, price, headset-free experiences, digital fatigue, and poor experiences. Gaming is the most common VR/AR activity, while metaverse retailing activities like shopping and virtual try-ons have fewer users. Practical considerations drive metaverse retailing, unlike gaming, which is mainly hedonic. Privacy concerns, safety risks, poor experiences, and lack of knowledge surprisingly increase VR/AR usage for metaverse retailing, indicating informed consumers or threshold characterization of these variables. Additional insights were found for tourism, hospitality, and gaming activities. Theoretical implications, insights, and potential actions for retailers and tech companies are discussed, along with limitations and suggestions for further research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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Introduction: Urban centers face increasing congestion and pollution due to population growth driven by jobs, education, and entertainment. Promoting active modes like walking and cycling offers healthier and less polluting alternatives. Understanding perceptions of comfort (green areas, commercial areas, crowd density, noise, thermal sensation, air quality, allergenics), safety and security (street illumination, traffic volume, surveillance, visual appearance, and speed limits) are crucial for encouraging active modes adoption. This study categorizes user groups based on these indicators, supporting policymakers in the development of targeted strategies. Methods: We developed a questionnaire to support our empirical study and collected 653 responses. We have analyzed the data using clustering methods such as Affinity Propagation, BIRCH, Bisecting K-means, HAC, K-means, Mini-Batch K-means, and Spectral clustering. The best performing method (K-means) was used to identify the user groups while a random forest model evaluated the relative importance of indicators for each group. Results: The study identified five user groups based on urban mobility indicators for safety and security, comfort, and distance and time. Conclusions: These groups, distinguished by sociodemographic features, include: Street Aesthetes (young men valuing visual appeal), Safety Seekers (employed men prioritizing speed limits), Working Guardians (employed men focused on surveillance and green spaces), Urban Explorers (young women valuing air quality and low traffic), and Comfort Connoisseurs (employed women prioritizing noise reduction and aesthetics).