Adoption of Highway Safety Manual Predictive Technologies for Canadian Highways
Bibliographic record
Abstract
This paper is based on recent research projects for Transport Canada and the Ministry of Transportation that assessed implementation requirements for the Highway Safety Manual (HSM) predictive methods. It serves partly as an illustration of what it takes for jurisdictions to assess their implementation requirements. The focus is on two applications of predictive methods: a) evaluation of the safety impacts of alternate design scenarios using an algorithm that applies baseline safety performance functions (SPFs) and collision modification factors (CMFs) and b) estimation of the safety benefits of proposed or implemented countermeasures. For the first application, the transferability of the HSM algorithm for urban signalized intersections in Toronto is explored by assessing both the base SPFs and CMFs. In general, the recalibration exercise was successful. However, for individual variables, there is some bias indicating that the CMFs could be improved upon. For the second application, an SPF knowledge base was developed to enable the selection by Canadian jurisdictions of the appropriate SPF for a specific countermeasure and site type. These crash type SPFs, would be used in the economic appraisal of contemplated countermeasures and in the evaluation of implemented treatments. The use of a spreadsheet developed to facilitate the SPF selection process is illustrated. For the covering abstract of this conference see ITRD number 201211RT334E.
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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".