Cycle Accessibility and Level of Traffic Stress: A Case Study of Toronto
Bibliographic record
Abstract
This paper examines the level of traffic stress for cyclists on the street and path network in the City of Toronto. Link as well as intersection stress is calculated to develop a citywide network of cycling stress. The cumulative opportunities reachable at four levels of cycling stress are calculated for each dissemination area in the city. The results show a low level of cycling access (<5000 jobs) across most of the city at low levels of stress (LTS ≤ 2). Only at level of stress three, where cyclists may be required to negotiate with vehicles and may be in proximity of high-speed traffic, does cycling accessibility rise above 15, 000 jobs for a sizeable section of the city. The link between low-stress access to jobs and the decision to cycle from home is investigated using a binary logit model. The results indicate that the cycling accessibly measure has a significant effect on choosing cycling as the travel mode with larger effect for low-stress access. The low stress cycling accessibility to subway stations is calculated as an example of the practical applications of this method and illustrates the limited cycling access to many stations in the Toronto network. Further, a theoretical scenario analysis is undertaken comparing different bicycle network scenarios. Three scenarios are tested 1) removing all cycle tracks, 2) upgrading all bike lanes to cycle tracks, and 3) decreasing street speeds by 10 km/h on the road network entire network. Scenario 3 in particular drastically increases cycling access to jobs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".