Comparing the Highway Safety Manual's Safety Performance Functions with Jurisdiction-Specific Functions for Intersections in Regina
Bibliographic record
Abstract
The first edition of the Highway Safety Manual (HSM) includes a number of safety performance functions (SPF), which can be used to identify collision-prone locations on a roadway network. The HSM recommends that these SPFs be calibrated in order to more accurately reflect a specific jurisdiction's unique roadway characteristics, driver behavior, etc. Another alternative is the creation of jurisdiction-specific SPFs. For this study, negative binomial regression was used to develop a set of models using five years of collision data (2005-2009) from the city of Regina, Saskatchewan. Three intersection categories were investigated: 3-leg unsignalized, 4-leg unsignalized, and 3 and 4-leg signalized. The SPFs provided in the HSM were also calibrated using this data, and a set of calibration factors were produced. Statistical goodness of fit (GOF) tests were performed in order to determine the best-fitting SPFs for the study region. In addition to the statistical tests, CURE (cumulative residual) plots were utilized to perform two comparisons: between candidate jurisdiction-specific model forms, and between the jurisdiction specific SPFs and the HSM's SPFs (both calibrated and un-calibrated). It was found that the jurisdiction-specific SPFs provided the best fit to the data used in this study, and would be the best SPFs for predicting collisions at 3 and 4-leg intersections in the City of Regina. For the covering abstract of this conference see ITRD record number 201211RT334E.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".