Using Macrolevel Collision Prediction Models to Conduct Road Safety Evaluation of Regional Transportation Plan
Bibliographic record
Abstract
This paper describes the application of previously developed macro-level collision prediction models (CPMs) in a case study to evaluate the road safety of a regional transportation plan for the Greater Vancouver Regional District (GVRD) in British Columbia (BC), Canada. The research objective was to present and test model-use guidelines in a regional road safety planning application. The data used describes over 20 traits in each of over 400 GVRD neighborhoods, aggregated according to the traffic analysis zones (TAZs) used in the GVRD’s classic four-step regional transportation model, which runs on Emme/2 software (1). The CPMs were run to assess the resulting difference in 3-year collision predictions between a short-term regional transportation plan scenario, and a base “do-nothing” scenario. A review of the results found a lower predicted collision frequency region-wide due to the proposed transportation plan, versus a do-nothing scenario. These findings have been discussed, and recommendations have been made for future use of the CPMs in regional road safety planning applications, including interpretation of results. The application of macro-level 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".