Much-Anticipated Marriage of Cycling and Transit
Bibliographic record
Abstract
In response to the environmental, economic, and social costs associated with overreliance on the automobile, planners and transportation professionals are promoting sustainable alternatives such as walking, cycling, and public transit, either as single modes or in combination. It has been argued that the marriage between cycling and transit presents opportunities for synergy by enlarging catchment areas of transit stations while drawing in new users to both of these green modes. However, because of the marginality of combining cycling and transit in North America, there is a shortage of reliable empirical studies in this area. The present study addressed this gap through an analysis of travel behavior and preferences related to cycle–transit (C-T) integration. An online survey was conducted in the region of Montreal, Canada, during the summer of 2010. The questionnaire included a section on Montreal's public bicycle sharing system, Bixi (bicycle taxi), and its potential for integration with transit. Three current or potential C-T user groups were identified through a factor–cluster analysis: current parking bike-and-riders, Bixi users, and car drivers. Bringing a bicycle on transit was the preferred form of integration; however, scenarios involving bicycle parking (or using a public bicycle) were likely to be used more regularly. To accommodate the greatest number of bicycle–transit trips, measures that facilitated parking at transit stops and those that enabled the bringing of bicycles on board transit vehicles were recommended in tandem.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".