Comparative Observational Assessment of Cyclists’ Interactions on Urban Streets with On-Street and Sidewalk Bike Lanes
Bibliographic record
Abstract
The competition for urban space and the debate about where people can and should ride their bicycles began not long after this new form of mobility was introduced to the public. For two centuries, we have debated and eventually investigated whether bike lanes belong on the sidewalk or if they should be on the street alongside the vehicular roadway. Existing research has provided evidence of preferences for bike lane alignment based on perceived safety or comfort as well as objective measures of comparative safety based on available crash and hospital data. Much of the existing research has been driven by deductive assumptions or is limited by the lack of data describing near-miss events and the subtle everyday interactions cyclists experience when using different types of cycle facilities. To help us understand better what role everyday interactions play in the relative functionality of sidewalk and on-street bike lanes, an observational study was conducted using a new qualitative–quantitative grounded theory-driven method for identifying and interpreting the outcome of cyclists’ interactions. Using data gathered from 2,583 interactions observed at four case study street segments in Munich, Germany, four outcomes were identified: no reaction; adjusting or yielding; lane exiting; or multiple reactions. Based on inferential analyses of these outcomes, this paper presents an assessment of the safety, directness, and access afforded or hindered by the spatial conditions of observed interactions. The results of this assessment revealed a trade-off between frequent, but minor interactions in sidewalk bike lanes and infrequent, but less safe interactions in on-street bike lanes.
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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 | 0.002 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".