An evaluation of pedestrian crash risk factors at urban intersections in a developing country: Comparing the classification accuracy of methods accounting for unobserved heterogeneity
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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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".