IMPROVING INTERSECTION SAFETY: REDUCING CASUALTIES AT HIGH-RISK INTERSECTIONS IN SURREY, RICHMOND AND VANCOUVER
Bibliographic record
Abstract
In British Columbia (BC), over 20,000 injuries and 400 fatalities occurred each year at intersections between 2000 and 2004. Using Ordinary Least Squares regression analysis, the objective of this study is to identify which of the 12 intersection characteristics play a role in predicting intersection safety. Using data from 19 intersections from Surrey, Richmond and Vancouver from the years 2000 to 2004, the findings reveal that traffic volume, restricted left-turns, permissive left-turns and right-turn lanes are positive predictors of intersection casualties. Five policy alternatives are proposed: 1) status quo, 2) reducing traffic volume, 3) eliminating the use of restricted left-turns, 4) using protected over permissive left-turns and 5) prohibiting right-turn on red. The policies are evaluated using three criteria 1) cost; 2) reduced casualty and 3) time delay. Based on the evaluation, status quo emerges as the most effective recommendation for reducing intersection casualties and improving intersection safety.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".