Advancing Crash Prediction Models Based on Simulated Conflicts and Exploring Their Predictive Capabilities and Transferability
Bibliographic record
Abstract
Evaluating the impacts of planned or implemented road safety treatments could be challenging as limited information on crash effects may be accessible. Thus, surrogate measures for safety assessments could be considered as an alternative approach in evaluating the effects of various road safety treatments. The main objective of this study was to investigate various approaches for developing crash prediction models for four-legged signalized intersections in the City of Toronto based on simulated traffic conflicts, including the speed of conflicting vehicles, a variable that has received little emphasis in previous research. A safety evaluation of these intersections with automated vehicles (AVs) was conducted and the transferability of the models to two Canadian jurisdictions was investigated. Results indicate that the safety of intersections may improve with the presence of AVs in cautious operation mode and that these types of models may be transferred for use with caution in other jurisdictions. The primary outcome of this study was the establishment of improved relationships between surrogate safety measures and crashes to swiftly evaluate planned or implemented road safety treatments with and without the presence of AVs.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".