Network screening for specific collision types at urban signalized intersections - conventional and spatial methods
Bibliographic record
Abstract
Transportation authorities are always looking for ways to improve road safety since vehicle collisions cost Canada 25 billion dollars of capital loss and around 2800 deaths each year. An important step in improving road safety is to sieve out the problem sites through network screening processes. screening for specific accident types is discussed in this thesis, using signalized intersections in Toronto in an illustrative application. each such collision type is associated with corresponding countermeasures, which allows the engineer to rank the entities with specific remedies in mind. In this way, the effectiveness of road network screening can improved through targeted treatments. Three different screening methods are introduced and compared; procedures for selecting entities from screening results by different methods are also presented. A process for ranking jurisdictions by regions is proposed. This is a method which combines the conventional network screening techniques with geographic information system (GIS) tools. The GIS can integrate the spatial information of a selected area with the conventional accident and road characteristic data and facilitate network screening by region.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".