Tools for Improving Safety. Integration of Data Capture, Storage, Safety Analysis, and GIS in Collision Reduction
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 expensive and time consuming. Since the funds for safety improvements are limited, it is important to spend the resources as effectively as possible. This paper presents a unique initiative with the Region of Halton in Canada that involved all municipalities within the region, and the Region of Halton itself. The project entailed the development of safety performance functions (SPF) and network screening tools. This allowed for the automation of ranking processes involved in determining locations with the largest potential for safety improvements, within each municipality involved in the project. This paper expands on the processes used for data capture, storage, and safety analysis utilizing geographic information system (GIS) technology, SPF, and collision over-representation through the use of the Traffic Engineering Software (TES).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".