Integrating Traffic Conflict Frequency and Severity Indicators to Estimate Pedestrian Safety Performance Functions for Signalized Intersections
Bibliographic record
Abstract
Assessing intersection safety for pedestrians based on crashes can be challenging because of the paucity of crashes. This is why surrogate measures of safety, typically traffic conflicts, have been used as an alternative approach for such assessments. Insufficient sample size has also presented a challenge in developing conventional safety performance functions (SPFs) for estimating pedestrian crash frequency in a variety of Highway Safety Manual applications. This challenge can also be resolved with the surrogate measures approach, in that these measures can logically capture the effects of geometric and operational variables that influence safety, thereby providing a useful complementary approach for developing SPFs, especially where sample sizes are small. This paper develops SPFs for pedestrian crashes at signalized intersections based on traffic conflicts, and in the process, it advances the methodology for this approach. Machine learning was used to develop a data-driven safety index that integrates frequency and severity indicators. This index was developed to classify conflicts into groups using a database of pedestrian conflicts derived from video observations at 44 intersections in five Canadian cities. The frequencies of conflicts in these groups, and crashes at these intersections, were then utilized to estimate SPFs. The results are promising in that they demonstrate the potential of using machine learning to estimate SPFs using surrogate measures. The approach is especially important given the focus on pedestrian crashes in Vision Zero plans and the reality that crash samples are typically too small for estimating pedestrian SPFs that directly capture the effects of multiple variables.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 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".