Network Screening Methods to Identify Roadway Sites for Safety Investigation: An Examination of Some Critical Issues
Bibliographic record
Abstract
Traffic accidents are responsible for about 3,000 deaths and $25 billion in economic losses annually in Canada. One way for transportation authorities to improve safety is to identify potentially hazardous roadway elements through network screening. The process of network screening is a low-cost statistical analysis of highway safety data, which yields a ranked list of sites to be investigated in detail. Critical issues of two network screening methods are investigated in this thesis. The first method is a peak-searching algorithm for screening roadway segments, with attention focused on threshold values of a key user-selected variable, namely the coefficient of variation. The second method examined is a method of screening for high proportions of specific accident types. For this method, parameter estimation techniques are compared, and the effect of the 'critical proportion,' a key user-selected variable in the method, on site rankings is investigated. In addition to the two network screening methods, an investigation is carried out into some aspects of safety performance function calibrated using negative binomial regression. Specific attention is given to how the negative binomial dispension parameter changes over the range of some independent variables.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".