Community-Based, Macrolevel Collision Prediction Model Use with a Regional Transportation Plan
Bibliographic record
Abstract
This paper describes the application of previously developed community-based, macrolevel collision prediction models (CPMs) to evaluate the road safety of a regional transportation plan. The research objective was to present and test model-use guidelines in a regional road safety planning application. The data was extracted from over 400 Greater Vancouver neighborhoods in British Columbia, Canada, including output from the regional transportation model. The CPMs predicted a lower mean collision frequency region-wide due to a proposed three-year transportation plan, versus a do-nothing scenario. Recommendations have been made for future use of the CPMs in regional road safety planning applications. The application of macrolevel CPMs to this regional case study proved a solid step in the development of new and improved empirical tools for planners and engineers to include road safety in the planning process. It is hoped that these models and model-use guidelines will facilitate improved decisions by community planners and engineers, and ultimately, facilitate improved neighborhood traffic safety for residents and other road users.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".