Safety Performance Functions for Signalized Intersections in Large Urban Areas
Bibliographic record
Abstract
This paper describes the development of safety performance functions (SPFs) for 1,950 urban signalized intersections on the basis of 5 years of collision data in Toronto, Ontario, Canada. Because Toronto has one of the largest known, readily accessible, urban signalized intersection databases, it was possible to develop reliable, widely applicable SPFs for different intersection classifications, collision severities, and impact types. Such a comprehensive set of SPFs is not available for urban signalized intersections from data for a single jurisdiction, despite the considerable recent interest in use of these functions for analyses related to network screening, and the development, prioritization, and evaluation of treatments. The application of a straightforward recalibration process requiring relatively little data means that the SPFs calibrated can be used by researchers and practitioners for other jurisdictions for which these functions do not exist and are unlikely to exist for some time. The value of the functions is illustrated in an application to evaluate a topical safety measure—left-turn priority treatment for which existing knowledge is on a shaky foundation. The results of this empirical Bayes evaluation show that this treatment is quite effective for reducing collisions, particularly those involving left-turn side impacts.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".