Integrating Observational and Traffic Simulation Models for Priority Ranking of Unsafe Intersections
Bibliographic record
Abstract
Observational models based on reported crash history are the most common measures for identifying unsafe sites for priority intervention. Observational models are good for predicting higher-severity crashes but ignore higher-risk vehicle interactions (e.g., near misses) that failed to result in crashes that are reported in historical data. Proponents of microscopic simulation models argue that failure to recognize these higher-risk interactions can significantly understate the safety problem at a given site and lead to misallocation of scarce treatment funds. This paper takes the position that a complete understanding of the safety problem at a given site can emerge only if both crash potential and traffic conflicts are taken into account. A priority-ranking model that integrates estimates from observational crash prediction models into an analysis of traffic conflicts is presented. Traffic conflicts were based on simulated vehicle interactions and deceleration requirements for different traffic scenarios. The suitability of the approach for priority ranking of sites was assessed with six ranking approaches: crash frequency, empirical Bayes, potential for safety improvement, conflict frequency, conflict rate (sum and cross product of traffic volume), and integrated model. Priority ranking was evaluated with five test criteria: site consistency, method consistency, rank difference, total rank score, and sensitivity and specificity. These models were applied to a sample of 58 signalized intersections from Toronto, Ontario, Canada, for the period from 1999 to 2006. The integrated model was found to yield better results for the five evaluation criteria; this result suggests that the proposed approach has promise.
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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 | 0.016 |
| 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 | 0.000 |
| 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 | 0.000 |
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".