Examination of Clusters for Better Understanding Commuter Cyclists’ Travel Behavior: Insights from a Longitudinal Panel Survey
Bibliographic record
Abstract
This study examined commuter cyclists’ travel behavior, particularly in response to changes in weather conditions and situational factors. The motivators, deterrents, and preferences of commuter cycling were also examined. To reach greater understanding of commuter cyclists’ decision making, cyclists were clustered. Disaggregated travel data were collected from a longitudinal panel survey (sample size: 355) and analyzed to identify the influence of weather conditions and situational factors on commuters’ day-to-day cycling decisions. The baseline survey (a one-off survey) provided information about the work characteristics, travel characteristics, motivational factors, and sociodemographics of the participants. Generalized estimating equations were used to compare the responses between groups on the day-to-day influences of weather conditions and situational factors. Binary logistic regression was used to predict commuters’ cycling decisions (whether or not to cycle). Commuter cyclists’ average decision (over the longitudinal survey period) was modeled with work characteristics, travel characteristics, motivational factors, and sociodemographics as explanatory variables. The results indicate that parameter estimates vary significantly between groups. Part-time commuter cyclists and commuter cyclists who do not plan their travel behavior in advance were more affected by adverse weather conditions than were the comparison groups. Situational factors were larger deterrents for part-time commuter cyclists and cyclists who traveled longer distances to work. However, off-road paths encouraged long-distance commuters to cycle more often. The paper includes a discussion on how the implications of these results can influence government policies and strategies in an effort to increase commuter cycling.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".