Investigation of surrogate measures for safety assessment of urban two-way stop controlled intersections
Bibliographic record
Abstract
Crash prediction models used to estimate safety of highway segments and intersections are traditionally developed using various traffic volume measures. There are issues with this approach and surrogate safety measures such as conflicts and delays have been proposed to overcome them. This study investigates the statistical relationships between crash frequencies and traffic volume, intersection delay, and simulated conflicts to explore and compare the viability of these models for estimating safety at urban two-way stop controlled intersections. The database used includes 78 three leg and 55 four leg intersections within the city of Toronto, Canada. Crash prediction models were developed and evaluated based on various goodness-of-fit measures. With the developed models, an alternate approach to crash based evaluations of intersection improvements is presented. A case study is developed to investigate and demonstrate the use of the models for estimating the safety impact of implementing a left turn lane on a major approach of an urban three leg stop controlled intersection. This case study and other results confirm the promise of the approach, suggesting that surrogate measures, such as the ones investigated, can capture effects of factors other than traffic volume alone.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".