A Novel Technique to Identify Hot Zones for Active Commuters’ Crashes
Bibliographic record
Abstract
This paper presents an approach to identify and rank accident-prone (hot) zones for active transportation modes. The approach aims to extend the well-known empirical Bayes (EB) potential for safety improvement (PSI) method to cases where multiple crash modes are modeled jointly (multivariate modeling). In this study, crash modeling was pursued with a multivariate model, incorporating spatial effects, using the full Bayes (FB) technique. Cyclist and pedestrian crash data for the City of Vancouver (British Columbia, Canada) were analyzed for 134 traffic analysis zones (TAZs) to detect active transportation hot zones. The hot zones identification (HZID) process was based on the estimation of the Mahalanobis distance, which can be considered an extension to the PSI method in the context of multivariate analysis. In addition, a negative binomial model was developed for cyclist and pedestrian crashes, where the EB PSI for each mode crash was quantified. The cyclist and pedestrian PSIs were combined to detect active transportation hot zones. Overall, the Mahalanobis distance method is found to outperform the PSI method in terms of consistency of results; and discrepancy is observed between the hot zones identified using both approaches.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".