Bibliographic record
Abstract
This paper presents an approach to analyze spatial and temporal (spatiotemporal) patterns of traffic accidents and to organize them according to their level of significance. This approach was tested using three years (2011-2013) of traffic accident data for Sherbrooke. The spatiotemporal patterns of traffic accidents were analyzed using kernel density estimation (KDE) for four different seasons. Two different crash measures were compared: simple crash counts and severity-weighted crash counts. The results show that severity-weighted crash counts reveal the effect of a single fatal/severe injury or light injury crash on the patterns. However, the lack of a significance test is the main drawback of the KDE. Therefore, this paper integrates the KDE with local Moran’s I to identify clusters of statistical significance for traffic accidents within each area. Thus, after the density is calculated by the KDE, it is then applied as the attribute (input value) for calculating local Moran’s I. Our findings show that the method was successful to detect traffic accident clusters and hazardous areas in Sherbrooke.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".