Sensitivity of a real-time freeway crash prediction model to calibration optimality
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
Real-time crash prediction models are often structured as general log-linear categorical models which must be calibrated using an extensive database. However, there is no method to optimally select the number of categories and the values that define the boundaries between categories when representing continuous measures as categorical variables within the log-linear model. This raises the question of how important the calibration is to the safety impacts estimated when using the crash prediction model. In this paper, we examined the impact that the process used to calibrate the crash prediction model has on estimates of safety impacts of a variable speed limit system. Two calibration methods were compared, namely a heuristic ad hoc method and a nearoptimal method. Both methods were applied to calibrate a crash prediction model using the same set of data from an urban freeway in Ontario, Canada. The calibrated crash prediction models are used to evaluate the safety benefits of a candidate variable speed limit system under three different traffic demand levels (Peak, Near-Peak, and Off-Peak). It was found that safety improvements estimated by the two calibrated crash prediction models are within approximately 13% of each other for the Peak and Near-Peak scenarios, but differ by a larger amount for the Off-Peak scenario. However, despite these differences in the estimated magnitude of the safety impacts, the sign of the impact (i.e. increase versus decrease in safety) were consistent irrespective of the calibration method used. The results suggested that the safety impacts provided by the crash prediction model are robust in that they are relatively insensitive to the optimality of the calibration.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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