What about cycling conditions in the 15-min city? A comprehensive assessment of bikeability in the urban area of Strasbourg, France.
Bibliographic record
Abstract
In a context where the Covid pandemic, the energy and climate crises have rattled the world in close succession, redesigning urban organization and moving towards a sustainable mobility system is a crucial issue. The concept of 15-min city addresses these challenges proposing an urban model where all residents are able to access their daily needs within a 15-minute walk or cycle from home (Moreno, 2021). This model is thus based on active mobility and a close proximity of services and amenities. It also highlights the importance of the quality of the traveler experience. Accordingly, most of city administrations try to develop bike-friendly environments. Recent researches explore, in different geographical contexts, whether the 15-minute city is within reach. They assess the 15-minute accessibility by cycling to one or several destination types (Hosford et al., 2022; Knap et al., 2022). However, looking for insights on the potential of cycling as an alternative to carbon-based modes, the question should not only be « is it possible to access these services and amenities by cycling? » but also « at which travel conditions are they accessible? ». Within the URFé project, we develop a bikeability analysis to measure the travel conditions by bicycle to daily resources (shopping, leisure, education, healthcare...) in the Strasbourg urban area. In a comprehensive assessment of bikeability (Castañon and Ribeiro, 2021), we consider the capacity to access the destinations, as well as the presence of facilities dedicated to cycling (bicycle paths, bicycle parking), the quality of the urban environment (green and aquatic areas), and the travel safety (road accidents). Innovatively in the bikeability field, we assess these indicators in 5-minute cycling access zones. These zones are calculated from each cell centroid of the INSEE[1] grid (nearly 2800 cells of 200m x 200m) covering the Strasbourg urban area. Our indicators are combined into a synthetic indicator defining a level of cyclability for each 5-minute cycling access zone. Resulting values are mapped on the grid squares in order to analyze spatial disparities and to promote a sustainable mobility policy for the entire urban area. References: Castañon U. N., & Ribeiro P. J. G. (2021). Bikeability and Emerging Phenomena in Cycling. Sustainability, 13(4). Hosford K., Beairsto J., & Winters M. (2022). Is the 15-minute city within reach? Evaluating walking and cycling accessibility to grocery stores in Vancouver. Transportation Research Interdisciplinary Perspectives, 14. Knap E., Ulak M. B., Geurs K. T., Mulders A., & van der Drift, S. (2023). A composite X-minute city cycling accessibility metric and its role in assessing spatial and socioeconomic inequalities. Journal of Urban Mobility, 3. Moreno C., Allam Z., Chabaud D., Gall C., & Pratlong F. (2021). Introducing the “15-Minute City”: Sustainability, Resilience and Place Identity in Future Post-Pandemic Cities. Smart Cities, 4(1). [1] National Institute of Statistics and Economic Studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".