Benefit–Cost-Based Method to Determine When Safety Performance Functions Should be Redeveloped for Use in Intersection Network Screening
Bibliographic record
Abstract
Network screening is the process of identifying the sites within the road network that are of highest priority for detailed safety audits and interventions. Contemporary road safety network screening methods rely on safety performance functions (SPFs), which are statistical models relating site characteristics to the number of crashes. SPFs are developed on the basis of historical crash and traffic volume data and should be periodically redeveloped to accurately reflect changes in the underlying relationships that result from changes in the network, traffic behavior, operational performance of vehicles, rules of the road, and other contributing factors. However, presently, there is no guidance available to practitioners to enable them to objectively decide when redevelopment of SPFs is warranted. In this study, we present a practical method to provide this guidance for network screening of intersections that reflects both the expected benefits of SPF redevelopment in relation to improved network screening accuracy and the cost of redevelopment. The data required for this method are either already available in municipalities which have locally developed SPFs or can be easily estimated. The estimation models for the method are created based on real data sets and were successfully tested on validation data sets.
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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 | 0.023 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".