Pedal preferences: GPS-based panel data insights into bike share traffic flow across membership groups
Bibliographic record
Abstract
Bike share systems promote sustainable transportation and active mobility. Understanding spatiotemporal usage patterns and influencing factors is crucial for equitable and effective policies. This study analyzes a full year of Global Positioning System-tracked bike share trip data from Hamilton, Ontario, to examine the travel behaviors of three membership types: Monthly and Seasonal Members, Pay-As-You-Go riders, and McMaster Monthly Pass holders. We employ descriptive statistics to analyze trip start times and the most frequently used routes, alongside a two-way fixed-effect binary logistic model to investigate bike share traffic at the road-and-day level, providing detailed insights into the determinants of bike share usage. Findings reveal that different membership types exhibit distinct spatiotemporal usage patterns and preferences regarding land use, infrastructure, sociodemographics, and events affecting bike-share road traffic. Only Monthly and Seasonal Members display consistent commuting patterns throughout the year. McMaster Monthly Pass holders dominate during the school semester following the introduction of a discounted pass for undergraduate students. Furthermore, Monthly and Seasonal Members are more likely to cycle on roads adjacent to parks, while McMaster Monthly Pass holders show lower sensitivity to extreme temperatures. Precipitation, darkness, slope, and holidays consistently deter bike share usage. Policy recommendations include expanding fare discount programs, improving wayfinding, organizing cycling events during holidays, and enhancing winter road maintenance for heavily used cycling routes. This study highlights differences in usage patterns, distinct preferences, and varying sensitivities to factors affecting bike share traffic flow among membership types, offering robust insights through a long study period and detailed road-level data.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.004 | 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".