An evaluation of pedestrian crash risk factors at urban intersections in a developing country: Comparing the classification accuracy of methods accounting for unobserved heterogeneity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pedestrian safety has always been a concern at urban intersections, especially in low-income developing countries with higher casualty rates. As one of the cities with the highest pedestrian fatality rates in Iran, Mashhad lacks studies that pinpoint the causes of these crashes. The choice of appropriate methodology was guided by the two-fold objective of the study: first, disaggregating crashes into homogeneous clusters; and second, examining the effects of risk factors on pedestrian crashes while accounting for the inherent unobserved heterogeneity in crash data. The study compared the classification accuracy of modeling approaches using receiver operating characteristic analysis. By analyzing three years (2015–2017) of pedestrian crashes in Mashhad, this study identified risk factors associated with higher severity of vehicle–pedestrian crashes at intersections. The results show that models incorporating the heterogeneity effect, such as the cluster-aggregated model and the random parameter model, have higher classification accuracy for crashes than models that do not consider heterogeneity. Based on the risk factors associated with increasing fatal crashes, several low-budget and immediate countermeasures are suggested in the hope of improving pedestrian safety at intersections.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it