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Among urban public transport systems, light rail, mass transit, and tram systems offer sustainable travel options. However, many of these systems, particularly in developed countries, fail to meet user needs and the expectations of transport authorities. Increasing the demand for urban rail systems as an alternative to private cars is essential for achieving net zero targets and Sustainable Development Goals. This study investigates the factors influencing urban rail demand using qualitative data analysis, with a focus on thematic analysis. A systematic review of 53 studies from the UK, Europe, and worldwide, including journal articles and transport research reports, was conducted and coded using NVivo Version 15 software. Six main categories emerged: land use and accessibility, service quality, user benefits, governance, sustainability aspects, and user-focused elements. These categories, along with their themes and sub-themes, were analysed using cross-tabulations to compare attributes across domains. The key findings indicate that accessibility and intermodal connectivity are crucial for encouraging urban rail use, while ticketing, station facilities, walkability, travel costs, ventilation, and security also moderately influence user preferences. This study provides essential guidelines for policymakers and transport providers to improve urban rail systems and informed the development of a questionnaire to explore the interrelationships of these factors, discussed in a forthcoming paper. 

Abstract Electric shared mobility hubs, called eHUBs, offer users access to a range of shared electric vehicles, including e‐bikes, e‐cargobikes, and e‐cars. Through the diversity of modes offered, eHUBs provide mobility solutions for different target groups and trip purposes. In this study, potential users’ willingness to use shared electric vehicles from eHUBs as either a commute or food shopping trip alternative was analysed using logistic regression methods. Results indicated that half of respondents were willing to use shared electric vehicles for at least a few of their regular commute or food shopping trips, although this proportion dropped substantially if considering the use of shared vehicles in combination with public transport. Across modes and trip purposes, holding a pro‐shared mobility attitude and belonging to the youngest age group strongly increased the willingness to use shared modes. Yet, while eHUBS may offer a potential alternative for at least some of people's regular commute or food shopping trips, cross‐mode shifts may be limited. That is, car drivers show a greater interest in shared e‐cars, whereas cyclists show a greater interest in e‐bikes and e‐cargobikes with public transport. Further influential factors, as well as implications for both shared mobility providers and local authorities, are discussed. 

ABSTRACT The accepted gap—the time or distance a driver deems sufficient to enter or cross an intersection—is a key indicator of traffic risk, particularly at uncontrolled three‐legged intersections. Smaller accepted gaps are linked to higher risk due to an increased chance of vehicle conflicts. This study investigates the relationship between accepted gaps and risk and proposes a method to quantify the level of risk and severity (LORS) to guide targeted safety interventions. Data on vehicle speed, accepted gap and critical gap were collected from six rural intersections in India. Using a binary logit regression model and clustering techniques, the LORS was estimated and validated against actual accident data, yielding a predictive accuracy of up to 83%. The significance of this study lies in its novel data‐driven approach to safety assessment using parameters easily measured in the field. Designed for heterogeneous traffic conditions, the method provides traffic engineers and planners with a practical tool to assess intersection safety, recommend specific remedial measures and prioritise interventions based on risk and severity levels. With potential for automation and scalability, this research contributes to the development of safer road systems, particularly in low‐resource settings where conventional crash data is limited or unavailable.