Do Traffic Countermeasures Improve the Safety of Vulnerable Road Users at Signalized Intersections? A Combination of Case-Control and Cross-Sectional Studies Using Video-Based Traffic Conflicts
Bibliographic record
Abstract
Driven by the vision of eliminating road fatalities, Vision Zero initiatives have been widely adopted by many cities around the world, with significant investment of resources in various safety countermeasures. However, there is still a lack of reliable quantitative evidence on the effectiveness of those countermeasures on the traffic conflict frequency at intersections. This research attempts to address this challenge with a combination of case-control and cross-sectional studies, aiming at quantifying the safety effects of three commonly applied Vision Zero countermeasures, namely, leading pedestrian interval, no right turn on red, and installation of a dedicated bicycle lane. A case study was conducted using video trajectory data from 10 signalized intersections in the City of Toronto, Canada. The traffic interactions between vehicles and vulnerable road users were extracted using a video data processing platform, and two surrogate measures of safety, including post-encroachment time and conflict speed, were obtained, and then used to classify the conflict severity into different levels. A comparative analysis using mixed-effects negative binomial regression was conducted to quantify the impacts of different treatments on the frequency of traffic conflicts under specific road weather and traffic conditions. The results show that these three types of traffic countermeasures can effectively reduce the frequency of high-risk and moderate-risk traffic conflicts, moderated by various traffic exposure and weather and environmental conditions and accessible pedestrian signals. These findings could help road safety engineers and decision makers make better informed decisions on their road safety initiatives and projects.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".