Configuration of SafetyAnalyst Software for Efficient and Effective Safety Management
Bibliographic record
Abstract
In the summer of 2009, American Associated of State Highway and Transportation Officials released the first version of SafetyAnalyst software, and in 2010 published the Highway Safety Manual. The Highway Safety Manual provides road safety knowledge and tools in a practical form to facilitate improved decision making based on safety performance. SafetyAnalyst software incorporates methodologies set forth in the Highway Safety Manual for road safety management in computerized analytical tools. These tools support the identification of safety improvement needs and the decision making process for developing a system-wide program of safety improvement projects. The Ministry of Transportation of Ontario has initiated a project to configure SafetyAnalyst to meet their needs in managing the road safety analysis of their highway network. In this initiative, all six SafetyAnalyst modules including the Network Screening Tool, Diagnosis Tool, Countermeasure Selection Tool, Economic Appraisal Tool, Priority Ranking Tool, and Countermeasure Evaluation Tool are configured for the Province of Ontario road network. The main goal of this paper is to summarize the lessons learned in this initiative to assist other road authorities in their prospective undertakings related to SafetyAnalyst. This paper highlights challenges associated with compiling infrastructure data, traffic volume data, collision data, importing data into SafetyAnalyst while keeping the road authority's databases unchanged, Safety Performance Functions in SafetyAnalyst, and customized values for various modules in SafetyAnalyst. This paper provides solutions to address these challenges. It also recommends the necessary steps for road authorities before starting an initiative to configure SafetyAnalyst for their network. For the covering abstract of this conference see ITRD record number 201210RT334E.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".