The future of urban cycling: A predictive framework for climate change
Bibliographic record
Abstract
Urban cycling plays a vital role in sustainable urban mobility. It reduces the environmental impact of transportation and promotes public health. While urban cycling is key to combating climate change, future climate conditions may significantly influence active transportation. In this context, this study proposes a predictive framework that integrates weather-based ridership models with downscaled climate projections. The framework aims to predict cycling demand under various climate scenarios. Using Montréal as a case study, we explore how projected climate changes could affect urban cycling in a cold-climate North American city. By the 2050s, Montréal is expected to experience warmer and drier conditions, with ridership projected to increase by 8.7% to 19.9% across different scenarios. The most notable growth is anticipated during shoulder months, such as April, October, and November, due to more favorable weather conditions. These findings emphasize the need to adapt bicycle infrastructure and services to accommodate evolving demand in a changing climate.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".