Crash prediction modelling at intersections in New Zealand 1990 to 2009
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it