Calibration and Transferability of Accident Prediction Models for Urban Intersections
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Accident prediction models, also known as safety performance functions, have several important uses in modern-day safety analysis. Unfortunately, calibration of these models is not straightforward. A research effort was undertaken that demonstrates the complexity of calibrating these models for urban intersections. These complexities relate to the specification of the functional form, the accommodation of the peculiarities of accident data, and the transferability of models to other jurisdictions. Toronto data were used to estimate models for three- and four-legged signalized and unsignalized intersections. Then the performance of these models was compared with that of models for Vancouver and California that were recalibrated for Toronto using a procedure recently proposed for the application in the Interactive Highway Safety Design Model (IHSDM). The results of this transferability test are mixed, suggesting that a single calibration factor as is currently specified in the IHSDM procedure may be inappropriate and that a disaggregation by traffic volume might be preferable.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it