Bibliographic record
Abstract
Recent research has advocated the use of linear intervention models developed within a hierarchical full Bayes context to conduct road safety evaluations. These models acknowledge that the effects of a safety treatment (intervention) do not occur instantaneously but are spread over future time periods. Despite the demonstrated advantages of such models, the manner in which the implemented countermeasures affect safety at the treated locations according to their novelty, direct effects, and indirect effects is not completely understood. A novel nonlinear intervention model was proposed to better understand how safety countermeasures work. To demonstrate the proposed model's capabilities, linear and nonlinear (Koyck) models were applied to estimate the effectiveness of the installation of shoulder rumble strips on a number of highway segments in the province of British Columbia, Canada. In addition to providing the best fit, the nonlinear Koyck model provided valuable insight into the effectiveness of shoulder rumble strips. This model showed an immediate 24.9% reduction of off-road-right collisions after 1 year that decreased with time and a 19.2% reduction in collisions as a result of permanent treatment. Overall, the findings from this study can have a significant impact on the economic evaluation of safety programs and countermeasures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".