Crash prediction modelling at intersections in New Zealand 1990 to 2009
Bibliographic record
Abstract
A large number of crash prediction models have been developed in New Zealand, for different road elements and for different speed limits. These models provide insight into crash causing mechanisms, which can in turn assist engineers in diagnosing safety problems. In conjunction with other road safety research (e.g. results ofbefore and after studies) they can also be used to predict the change in crashes that might result from an engineering improvement, whether good or bad. The crash modeling methods used in New Zealand are based on best practice overseas, from the UK, Canada and the USA, with some local enhancements. The research to date has produced a number of interesting and thought-provoking outcomes including thesafety-in- numbers effect for cyclists and pedestrians and that reducing visibility can lead to safety gains at roundabouts. This paper profiles the models that have been developed for low and high speed traffic signals, roundabouts and priority intersections in New Zealand. In addition to presenting the crash models and the modeling methods, the paper will show how the models are used to compare various forms of control at an intersection. It will highlight the importance of using the models within the prescribed flow ranges. The models are less accurate when used to extrapolate to traffic volumes that are not typical for the intersection type, for example, for low volume traffic signals and high volume priority intersections.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".