Biking to Ride: Investigating the Challenges and Barriers of Integrating Cycling with Regional Rail Transit
Bibliographic record
Abstract
Integrating bicycling with public transport can potentially benefit cyclists and transit operators. Successfully coordinating these transport modes, however, can be a difficult task when so little is known about the social and environmental barriers to this type of multimodal travel in the North American context. Using data derived from a survey of regional train service patrons in the Greater Toronto and Hamilton regions of Ontario, Canada, this study examines the challenges faced by those who cycle to/from the train, the barriers that keep passengers from commuting to/from the train by bicycle, and the sociodemographic characteristics of those cycling—and not cycling—to/from the train. Safety concerns, worries about bicycle security, and rules restricting when bicycles are permitted on trains were among the top challenges identified by individuals currently cycling to and/or from train stations. Among those who do not cycle to or from the train, appearance and comfort were the two primary concerns. Results also indicate that certain groups were more likely to cycle to/from the train than others. Notably, a large gender gap exists, approximately two-thirds (67%) of those cycling to their local train station were male. Results from this study may inform policy makers on how to successfully, and equitably, integrate cycling with regional rail transit.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".