Speaker
Description
The application of street experiments is not new, with early examples in Europe dating back to the 1960s. However, their large-scale implementation as a tool for supporting rapid urban transformations is a more recent phenomenon. Various experimental techniques have been employed in public spaces, including urban acupuncture (Lerner, 2006), tactical urbanism (Lydon and Garcia, 2015), and street experiments (Bertolini, 2020). These techniques are often applied to the two primary functions of streets: facilitating the movement of people and goods and serving as central spaces for urban public life—commonly referred to as the movement and place functions of streets (Mehta, 2015; von Schönfeld and Bertolini, 2017).
Assessing the long-term impact and lessons learned from these interventions is crucial for understanding their effectiveness. Several methodologies have been developed for such evaluations. For instance, Zhao, Sun, and Webster (2023) introduced a spatial model using data from COVID-19-induced interventions, while Kinigadner et al. (2024) proposed a qualitative approach based on goals, characteristics, impacts, and lessons learned.
This study builds upon existing evaluation frameworks by employing a Latent Class Logistic Regression approach to analyze a database of street interventions implemented during the COVID-19 pandemic. The research aims to identify the key factors influencing the permanence of these interventions in the post-pandemic context. The expected outcomes will establish relationships between the dependent variable (intervention permanence) and both physical attributes (e.g., size, scale, affected infrastructure) and procedural aspects (e.g., implementation approach, leading stakeholders, and engagement strategies).
By providing empirical insights into the determinants of intervention permanence, this study offers valuable support for decision-makers, enabling them to make more informed and efficient choices, streamline decision-making processes, and optimize resource allocation for future urban mobility initiatives.
References
Bertolini, L. (2020). From “streets for traffic ” to “streets for people ”: Can street experiments city street experiments. Journal of Urban Mobility, 2 , Article 100015 .
Kinigadner,J. & Büttner,B. & Rivas de Gante, A. & Aumann, S. (2024) How to transform urban spaces and mobility: a framework for analysing street experiments, Journal of Urban Design, 29:5, 536-556, DOI: 10.1080/13574809.2024.2320918
Lerner, J. (2006). Acupuntura Urbana. In Investigaciones geográficas (Issue 61).
Lydon, M., & Garcia, A. (2015). Tactical urbanism: Short-term action for long-term change.
Mehta, V. (2015). The street as ecology. In Incomplete streets: Processes, practices, possibilities
von Schönfeld, K. C., & Bertolini, L. (2017). Urban streets: Epitomes of planning challenges Washington, D.C.: Island Press .
Zhao, J. & Sun, G. & Webster, C. (2024) Global Street Experiment: A Geospatial Database of Pandemic-induced Street Transitions, Landscape and Urban Planning, Volume 242, 104931, ISSN 0169-2046, https://doi.org/10.1016/j.landurbplan.2023.104931.
| Keywords | Street Experiments; Latent Class Logistic Regression; Evaluating Interventions |
|---|---|
| Best Congress Paper Award | No |
