Keep ’Em Separated: Desire Lines Analysis of Bidirectional Cycle Tracks in Montreal, Canada
Bibliographic record
Abstract
As cities worldwide try to increase the adoption of the bicycle as a legitimate mode of urban transportation, the perception of danger plays a significant role in deterring potential new users. In a study conducted in Montreal, Quebec, Canada, bicycle users claimed to perceive intersections with bidirectional cycle tracks twice as negatively as they perceived either similar protected facilities midblock or intersections with painted bicycle lanes. This study aimed to understand this negative perception through a fine-grained analysis and observation of the interplay between infrastructure design and bicycle users’ behavior at these intersections. Researchers used the Desire Lines Analysis tool pioneered by Copenhagenize Design Company and developed recommendations and design interventions for two intersections with bidirectional facilities in the city of Montreal. Study results demonstrated that most users followed the prescribed routes of the street design through each intersection and shone light on users who did not—more than a quarter of users. The trajectories of bicycle users that were questionably legal resulted in observed conflicts at both bidirectional intersections. Conflicts were grouped into three major observed themes: counterflow interactions, priority confusion, and directional awareness. Recommendations made in this paper aim to address each one of these observed themes with appropriate designs that are choreographic, prioritized, and predictable for all road users. Planners, engineers, and urban designers can gain significant insight into best-practice bicycle infrastructure through techniques, such as desire lines analysis, that observe behavior and design accordingly.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".