Macro-Level Collision Prediction Models Related to Bicycle Use
Bibliographic record
Abstract
The growing automobile transport results in severe traffic congestion, pollution and road safety problems. Bicycling, as one of sustainable transportation mode, is encouraged in most developed countries for its attributes of convenience, low cost, non-fuel use, and zero-emissions. It is generally accepted that increasing bicycle use could improve road safety. Based on a comprehensive literature review, this paper discusses potential factors influencing bicycle use and bicycle collisions. Understanding these bicycle-related factors is useful to develop new bicycle-related Collision Prediction Models (CPMs) with generalized linear regression. These CPMs can support economic justification of much-needed major bicycle infrastructure investments, and also help policy makers to promote bicycling in an effective and economic manner. Also, a brief methodology of developing such macro-level CPMs is suggested. Based on a case study of City of Kelowna, BC, Canada, several new macro-level CPMs are proposed. Results reveal that the increase of bicycle use can lead to a decrease in total collisions despite an increase in bicycle collisions, which is consistent with the actual case. Also, the bicyclerelated exposure variable, bicycle lane length, has a significantly positive relationship with dependent variables: total collision frequency. In this case, it is concluded that increasing bike lanes (on-street and off-street) can be a good measure to improve road safety. Still, aimed on the research gap, this paper identifies potential works of high interests in future.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".