Identifying High Collision Locations Without Traffic Volume Data
Bibliographic record
Abstract
Safety network screening is used to identify road locations (particularly intersections and roadway segments) which exhibit an abnormally high number of expected collisions or an unusually high proportion of a certain configuration of collisions. The current state-of-the-art network screening methods rely on safety performance functions (SPFs) that require traffic volume as an input, but many cities in Canada, including the City of Saskatoon, do not collect traffic volume for every single segment within the city limits. Lack of traffic volume data for a study network severely restricts the applicability of a SPF-based network screening method. The binomial and the beta-binomial tests, however, are formal collision diagnosis tests that can be used to screen roadway networks that include roadway segments for which traffic volume data are not available. Unfortunately, previous studies have applied these two collision diagnosis tests without explicitly defining the circumstances that indicate which test is preferable. This study uses a formal statistical test known as the “overdispersion test” to determine when there is a need to apply the beta-binomial test instead of the binomial test to screen a roadway network. The study targeted uncontrolled major arterial segments in Saskatoon using five years (2005-2009) of collision data for the two most frequent collision configurations: rear end collision and side swipe same direction collision. The authors used ArcGIS to develop collision maps that visually display the screening results. The collision map will facilitate the governing agencies’ decision-making processes when selecting appropriate safety countermeasures to reduce target collision configurations at screened locations.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".