Assessing Safety Improvements to Pedestrian Crossings Using Automated Conflict Analysis
Bibliographic record
Abstract
Surrogate safety measures, such as the traffic conflict technique, have been promoted as an alternative or complementary approach to evaluate road safety from a broader perspective than collision statistics alone. The before-and-after evaluation of safety treatments is one application that can significantly benefit from the use of surrogate safety measures. This study demonstrated the use of automated traffic conflict analysis to conduct before-and-after safety evaluations. The objective was to conduct a time-series (before-to-after) safety evaluation for an intersection in Surrey, British Columbia, Canada, where several pedestrian-related countermeasures were implemented. Treatments included one or more of the following: use of protected-only turns for left-turning vehicles, installation of pedestrian countdown timers, crosswalk realignment, and use of drop-down sidewalks. Results indicated a significant decrease in both pedestrian conflict frequency and severity at the intersection following the treatments. A greater reduction of conflicts was exhibited on the western portion of the intersection where several countermeasures were implemented, including a protected left-turn phase, dual drop-down ramps, and a shift in the crosswalk further west. This variation in conflict reduction along different segments of the same intersection gives a particularized view of the impact of the adopted countermeasures. The outcome of this research provides evidence that surrogate safety indicators can be used effectively to diagnose safety problems and evaluate countermeasures at intersections.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".