Biking the First Mile: Exploring a Cyclist Typology and Potential for Cycling to Transit Stations by Suburban Commuters
Bibliographic record
Abstract
Regional commuter rail has become an important means of traveling to urban employment centers across North America, but planners are faced with the challenge of connecting commuters from their origin or destination locations to a train station. Cycling may be an efficient and low-cost way of taking these transit-access trips. However, cycling behavior of rail commuters, particularly in a suburban context, remains understudied. This research examined perceptions of cycling and current cycling behavior of 257 transit users from three suburban commuter rail stations in the Toronto region, Canada. Using a cluster analysis approach, four distinct cyclist types were identified, namely: recreational cyclists (29%), all-purpose cyclists (10%), safety-conscious occasional cyclists (33%), and facility-demanding occasional cyclists (28%). Differences between these groups included different mode-choice motivations, tolerance for adverse weather conditions, comfort bicycling in various hypothetical traffic/infrastructure conditions, and current frequency of cycling for transportation and recreational purposes. The safety-conscious group included a higher percentage of women compared to other groups. Overall, 32.5% of regional transit users would be interested in cycling more often to rail stations. A higher proportion of recreational cyclists (compared to other groups) were “interested first-mile cyclists”, whereas the safety-conscious group had a significantly greater proportion of “uninterested” respondents. With careful planning of bicycle infrastructure and awareness campaigns targeting perceptions of cycling, there is much potential for cycling to accommodate a greater proportion of transit-access trips in suburban communities, reducing demand for automobile parking at transit stations.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".