Cyclists and Infrastructure: A Supervised Research Project in two parts I: If You Build it, Who Will Come? A Travel Behavior Analysis of Urban Cycling Facilities in Montreal II: Build it. But where? The Use of Geographic Information Systems in Identifying Optimal Location for New Cycling Infrastructure
Bibliographic record
Abstract
Despite growing interest in active transportation, little is known about the detailed travel behavior associated with on-street bicycle facilities. The core questions this study seeks to understand are: 1)what personal factors influence cycling facility usage and 2) how do specific facility types and theirspatial characteristics affect route choice? This study is based on analysis of an online survey of 2917 cyclists in Montreal, Quebec, Canada. Respondents more frequent home-based trips are modeled alongwith travel bicycle facilities, if used. Distance decay analyses, a binary logit model and ordinary leastsquare regressions are used to address the central research questions. The study demonstrates that thereare cogent travel patterns associated with different types of utilitarian cyclists, who demonstate varyingusage patterns. Overall, cyclists are observed to add greater distance to their trips for facilities that aresegregated from vehicle traffic; however, the associated diversions can be better explained by spatialfactors such as facility length and location. Bicycle facilities are associated with greater levels ofcycling, and can increase the distance that people are willing to travel. When considering new utilitarianbicycle infrastructure, it is recommended for planners to aim for long, continuous facilities,before settling on a particular design. It is also important to recognize that different facility designs appeal to different types of cyclists, and consequently to select a facility type with maximum appeal
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".