Exploring User Perspectives to Increase Winter Bicycling Mode Share in Edmonton, Canada
Bibliographic record
Abstract
Over the last decade many municipal governments in the United States and Canada have proactively invested in bicycling infrastructure, ranging from physically separated bikeways to greenways and bike-share programs. Evidence points to gradually increasing bicycle use, though this growth is small in absolute numbers, particularly in winter cities. Two interrelated issues are noteworthy: i) research shows that bicycling rates go down in cold weather, yet little is known about how those who still bicycle adapt to road conditions, and ii) while cities rely on manuals and best practices when creating bicycling policies, there is limited direct evaluation of bicycling infrastructure in severe winters. Relying on semi-structured interviews with winter cyclists in Edmonton, Canada, this paper investigates riders’ adaptation strategies. This study posits that bicycling infrastructure would be better utilized if riders’ observations and experiences informed policy, thus increasing the odds of expanding winter cycling to the next most likely group. A methodological contribution of this work is using mapping voice to uncover user preferences. In Edmonton, winter cyclists use specific corridors, both with and without bike lanes. They ride with traffic on streets or on sidewalks since bike lanes are often covered with snow windrows. Findings suggest that a group of policies including consistent snow clearing, separated bikeways, network connectivity, destination amenities, and public/driver education would make cycling more convenient and safe during the snow season. These insights have the potential to increase winter bicycling by moving the infrastructure supply paradigm from a best practice to a best fit approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".