Cycling Network Discontinuities as Indicators for Performance Evaluation: Case Study in Four Cities
Bibliographic record
Abstract
ABSTRACT: There are several existing evaluation methods for cycling networks, each with its set of indicators, none of which provides a complete picture of the cycling network performance. For example, most studies have relied only on the coverage as an indicator for network performance, while others focused on accessibility. Reviewing existing evaluation methods, it further appears that connectivity or discontinuity indicators have not been systematically identified and are missing from many evaluation methods. Discontinuities can be either intrinsic to the cycling facilities and the cycling network, such as changes in the type of facility or end of facilities, or related to changes in the cycling network environment, in particular the usually adjacent road network and motorized traffic. This paper formalizes the concept of discontinuities in the cycling network and the various causes of discontinuities, proposes a set of indicators to measure cycling network connectivity and the methodology to calculate them, including automated methods for geospatial data with the code available under an open-source licence. The automated method is applied to the comparison of the cycling network connectivity of four North American cities: Montreal and Vancouver in Canada, Portland, and Washington D.C. in the United States.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".