Assessing International Transferability of Highway Safety Manual Crash Prediction Algorithm and Its Components
Bibliographic record
Abstract
The Highway Safety Manual (HSM) provides an algorithm and associated knowledge to predict crashes on different types of facilities. This algorithm requires calibration to current local conditions through a procedure prescribed in the HSM to enhance its transferability. However, no procedure assesses the transferability. To fill this void, this paper focuses on a methodology to assess the transferability of the key HSM algorithm components—the baseline safety performance function and the crash modification factors (CMFs)—and uses the Italian road network as an illustrative case study. The calibration of the HSM crash prediction model is investigated with a data set for two-lane two-way rural highways to demonstrate some tools that could be used by jurisdictions around the world to assess the validity and compatibility of the CMFs and the base models, as well as the performance of the complete algorithm. A comparison with the results from a similar study carried out in Canada is provided to supplement the conclusions on the transferability of the HSM algorithm outside the United States.
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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 | 0.033 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".