Evaluating Safety Performance of an Interchange in the Design Process: Turcot Complex Case Study
Bibliographic record
Abstract
Located in the southwest quadrant of the Island of Montreal, the Turcot Interchange is a major component of the City's highway system. A detailed assessment of the overall condition of this structure conducted by MTQ authorities in 2004 concluded that the full reconstruction of this major crossroads is necessary. Subsequently, a consortium that includes CIMA+ was given the mandate to provide a detailed functional assessment of the interchange and the highways flowing through it. Several scenarios were developed, and the preferred solution involves lowering the profile of the existing elevated roadways, the disenclavement of the urban area underneath and the reconfiguration of the La Verendrye and Angrignon interchanges. In order to build a safer structure, the MTQ wishes to develop a quantitative assessment method reflecting the specific characteristics of the Turcot Interchange. This method will demonstrate the gains in road safety achieved by the proposed scenario, in comparison with the existing configuration. As part of the overall project, the CIMA+ team was given the mandate to develop a road safety assessment methodology. Of all approaches considered, the Bayesian Empirical Method (EB) was selected. This project was nominated for the TAC 2008 Road Safety Engineering Award. This paper was originally published in French in the 2009 TAC Annual Conference as La reconstruction du complexe Turcot: Une nouvelle approche pour estimer le niveau de securite.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".