Traffic Conflict–Based Before–After Study with Use of Comparison Groups and the Empirical Bayes Method
Bibliographic record
Abstract
Road safety evaluations mainly rely on the analysis of crash data that are challenged by well-recognized availability and quality issues. The statistical models used to predict the safety level of road sites—that is, safety performance functions—have recently been successfully developed with the use of traffic conflict observations instead of crashes. As such, it is possible to adopt and transfer the statistical techniques used in crash-based road safety analysis to conflict-based analysis. The use of statistically rigorous techniques in crash-based before-and-after (BA) studies is essential for evaluation of the effectiveness of road safety countermeasures. In particular, the use of Bayesian methods, such as the empirical Bayes (EB) technique, is vital to control for confounding factors that can operate simultaneously with the countermeasure and may affect road safety performance. The main objective of this paper was to estimate the treatment effectiveness of two traffic signal (visibility) improvement projects in the city of Edmonton, Alberta, Canada, with a conflict-based BA study using the comparison group and the EB methods. More than 300 h of video data with traffic conflict observations was automatically collected and analyzed by computer vision techniques for two treatment intersections and two control (untreated) intersections before and after the signal improvement projects. The results of the comparison group method showed a statistically significant 24% reduction in the average number of rear-end conflicts per hour, whereas the EB method showed a statistically significant 24.5% reduction in the average number of total conflicts per hour.
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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 | 0.036 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".