• Developed a immersive, scalable survey using GSV Time-Machine for street preferences. • The tool enables temporal analysis using real panoramic images. • The tool follows the GSV Terms of Use being replicable for other applications. • Parents and non-experts show a higher preference for car-oriented interventions. • Support for active modes grows with experience; less impact seen for bus interventions. Urban spaces are changing in response to the current paradigm shift in mobility planning, which is moving away from car-oriented planning, prioritizing public transport and active modes. The decision-making process is shifting from a solely expert-led and politician-driven model to one that integrates citizens’ preferences and expectations, acknowledging the importance of a better understanding of public attitudes toward street design reconfigurations. Current research relies on resource-intensive and time-consuming experiments, rarely considering the element of change. Consequently, the main contribution of this work is the development of a visual stated preference survey methodology designed to evaluate individuals’ preferences regarding street interventions by comparing conditions before and after their implementation. The approach uses Google Street View’s ‘Time Machine’ feature to represent real-world changes, embedding iframe-based visualizations that enable pairwise comparisons between pre- and post-intervention scenarios. The visual stated preference survey was designed to capture preferences in different street interventions types focusing on prioritizing car, bus or active modes among diverse population groups. This framework not only ensures compliance with Google’s usage policies and is reproducible but also provides respondents with an immersive 360-degree environment that allow a more complete visualization of the transformations on the street. Although the survey was disseminated world-wide, respondents primarily resided in Portugal (53.2%) and had an urban planning background (54.8%). The methodological contribution is reproducible, scalable, and can be deployed across diverse geographic contexts, helping to mitigate current sampling limitations in future applications. Although the results are not generalizable to all contexts, car-oriented interventions had the lowest preference rate, and parents and nonexperts showed comparatively higher support for these interventions. These group effects were not observed in active-modes-oriented interventions, which had a near-unanimous preference for the ”after” scenarios. Bus-oriented interventions received a more moderate level of support. In particular, suburban residents consistently preferred all types of transitions. Results highlight the importance of inclusive consultation and tailored communication strategies to address the difference in perceptions of specific groups.
Lopes et al. (Tue,) studied this question.