Methodology for Conducting Safety Performance Measurement of Interchange in Transportation Design Process: Case Study of Turcot Complex
Bibliographic record
Abstract
The main objective of this study is to supplement qualitative findings of Road Safety Audits (RSA) with results of a quantitative approach that will allow verification of RSA’s findings for a real project to better understand the impacts of a new design on safety implications. To that end, a scientific safety analysis has been conducted in order to quantitatively measure safety performance of existing design of Turcot complex - a major interchange located in the City of Montreal, in the Province of Quebec, Canada - and its proposed alternative design with a great extent of details. To achieve this goal, the Empirical Bayes Method was employed to use Safety Performance Functions (SPFs), historical collision data, and different Collision Modification Factors (CMFs) for each element and consequently to obtain long-term expected number of collisions for all elements of the existing and proposed designs. Two existing and proposed designs were compared to each other, both in terms of total number of expected collisions for the whole site – obtained by summing number of collisions for the individual links of the complex - and also in terms of total number of expected collisions on “movement” level. Eventually the results of this analysis were compared to the findings of conducted Road Safety Audit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".