Challenges to Bicycle Usage in Columbus, Ohio: A Seasonal Analysis of Central Ohio Greenway Mode Choice
Bibliographic record
Abstract
In the United States, bicycle ridership is generally lower than in other cities across the globe. American cities lag behind other countries, especially some cities in Western Europe such as Copenhagen and Amsterdam. A number of factors may contribute to this decrease in ridership such as reduced infrastructure and a lack of cycling culture. Another factor may be weather, which is a focus of this study. How does weather impact ridership in U.S. cities, and what can those cities do about it? To explore this question, I measure how temperature and precipitation impact trail use and cycling in Columbus, Ohio. During the winter months between December 21, 2020 and March 20, 2021, I collected original broad data on trail usage modeled after a study conducted by the Mid-Ohio Regional Planning Commission. Two North American case studies were also done on Portland, Oregon and Montréal, Canada in order to understand how higher ridership is maintained with adverse weather conditions. I argue that there is a greater potential for usage in Columbus. Of the total number of users observed on the trails, 18% were bicyclists and 77% were pedestrians. Temperature and precipitation also played key roles. As temperature increased, bicycle ridership increased. Similarly, on days with little or no precipitation, ridership was higher than days with heavier rainfall or snow. Columbus is projected to continue growing in the long-term, and investing in a more interconnected, well-maintained, and widely accessible bike network has the potential to create cultural change in the city for bicycles that would significantly change mode choice for those living in the region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".