Comparison of Alternative Methods for Identifying Sites with High Proportion of Specific Accident Types
Bibliographic record
Abstract
Modern road safety management programs are required to meet a number of objectives within the overall goal of reducing accidents on the road network. One of these objectives is the identification of hazardous locations through network screening. Often screening is focused on specific accident types to be mitigated by specific programs, for example, red light cameras for reducing right-angle accidents at signalized intersections. Recent methods developed for network screening utilize safety performance functions and the empirical Bayes methodology to overcome limitations in existing methods such as ignoring regression to the mean in using accident counts or rates as a ranking measure. These newer methods often are referred to as screening for the potential for safety improvement (PSI). In one version of the PSI method, sites are ranked according to their expected accident frequency; in another, sites are ranked by the expected excess frequency measured as the difference between the expected accident frequency and that expected at similar sites. This study focuses on a relatively untested method that screens for high proportions of specific accident types by using estimates of the probability that a site's observed proportion of an accident type is truly above a given critical proportion. The impacts on the method of selection for the critical proportion are examined and the efficiency of this method is evaluated by comparing its application with that of the two PSI methods for a data set of stop-controlled intersections. Results indicate that screening for high proportions may be a reasonable alternative where PSI-type approaches are not possible.
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 | 0.033 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".