Safety Assessment of Road Network Using Traffic Engineering Software (TES). Application of Generalized Estimating Equations and Empirical Bayes
Bibliographic record
Abstract
This paper describes how identifying sites with potential for safety improvements, network screening is the initial step that is usually taken by many transportation agencies in their safety management programs. However, identifying and conducting detailed engineering studies of candidate improvement sites is very time consuming and very expensive. Since the funds for safety improvements are limited, it is important to spend the resources as effectively as possible. The purpose of network screening is to review the entire roadway network under the jurisdiction of a particular agency and identify and prioritize those sites that have promise as sites for potential safety improvements. This paper details the development of an automated ranking tool using Traffic Engineering Software (TES) for identifying and prioritizing problem intersections and road segments for the Region Municipality of Halton, Canada. The proposed approach uses the Empirical Bayes method and collision prediction models for estimating the potential safety improvement that can be achieved for each intersection and roadway segment. The generalized estimating equations technique with the assumption of negative binomial error distribution was used for development of the collision prediction 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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".