Developing macro-level collision prediction models to enhance traditional road safety improvement programs and evaluate bicycle safety in the City of Vancouver
Bibliographic record
Abstract
To encourage greener cities while reducing transportation impacts such as climate change, traffic congestion, and road safety issues, governments have been investing in sustainable transportation modes such as cycling. A safe and comfortable cycling environment is critical to encourage bicycle trips, since cyclists are subject to greater safety risks and represent the highest share of severe and fatal road collisions. Traditionally, engineering approaches have addressed road safety in reaction to existing collision histories. For bicycle collisions, which are rare events, a proactive approach is more appropriate. This study described the development of bicycle related macro-level (i.e. neighbourhood or traffic analysis zone level) Collision Prediction Models (CPMs) and tested the models as empirical tools for bicycle road safety evaluation and planning. This study was unique in its usage of the bicycle exposure variable represented by Bicycle Kilometers Travelled (BKT) as a lead exposure variable in the models. The macro-level CPMs that were developed for bicycle-vehicle collisions were applied to a case study of the City of Vancouver at the zonal level. The objectives of the study were to: (1) identify bicycle data safety indicators, (2) develop bicycle macro-level CPMs using generalized linear regression modeling (GLM), (3) demonstrate model use by applying them to a case study of the City of Vancouver through a macro-reactive road safety application, and (4) identify potential safety countermeasures for the highest ranked Collision Prone Zones (CPZs). The models were effective in enhancing traditional road safety initiatives and identifying and ranking dangerous CPZs in the City of Vancouver. The top three collision prone areas were then brought forward for diagnosis and remedy analysis. This case study effectively demonstrated the use of the models to proactively enhance bicycle safety.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".