Community-Based Macrolevel Collision Prediction Models for Evaluating Road Safety Levels of Left-Turn and On-Street Parking Restrictions on Major Urban Corridors
Bibliographic record
Abstract
The enormous social and economic burden imposed on society by injuries due to road collisions is a major global problem. Road authorities worldwide are researching ways to reduce this burden. Due to this ongoing work, a possible road collision pattern was discovered by the Insurance Corporation of British Columbia (ICBC) regarding factors related to unsignalized intersections. This pattern involved turning movements onto and off of major arterial routes at intersections without signalization as well as parking adjacent to such intersections. This study was commissioned to evaluate the road safety impacts on neighborhoods from banning left-turn and/or parking along urban arterial corridors at unsignalized intersections. Using recommended generalized linear regression modeling (GLIM) techniques, community based, macro-level collision prediction models (CPMs) were developed. Data was based on four arterial corridors in the City of Vancouver - Knight Street, Granville Street, Broadway Avenue, and 12th Avenue/Grandview Highway, including: collision claim, road attribute, and adjacent neighborhood trait data from 44 Traffic Analysis Zones (TAZs). Four land use stratifications were made in the data: Total, Residential, Mixed, and Commercial. Assuming a negative binomial residual distribution and standard statistical goodness of fit tests at a 95% level of confidence, ten new CPMs were successfully developed for total and rush hour (AM and PM) collision types. The models revealed that decreased collisions were associated with increases in restricted parking hours and left-turn hours at unsignalized intersections on urban arterial corridors. Further research is recommended, including application of these new models.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".