Rule compliance and desire lines in Barcelona’s cycling network
Bibliographic record
Abstract
A major challenge in the development of new cycling infrastructure is the design of intersections that are safe, appropriately used, and inclusive. In this paper we study how cyclists interact with existing street design at intersections in Barcelona. We observed rule compliance (n = 5,063) and desire lines (n = 5,082) at six intersections over 12 weekdays. We find that 78.9% of cyclists comply with intersection rules. Rule incompliance is associated with the gender of the cyclists, the directionality of the bike lanes that intersect, traffic signals, and performing a turn. Our analysis of desire lines through the intersections illustrate that incompliant behavior is driven by a need for uninterrupted travel, and highlight systemic and design features that contribute to incompliance. We suggest ways to improve intersection design and safety: i) prioritize unidirectional bike lanes; ii) optimize traffic lights, and; iii) anticipate cyclists’ desired trajectories when designing new cycling infrastructure.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".