Diagnosis of Safety Problems Using Safety Analyst for Efficient and Effective Safety Management
Bibliographic record
Abstract
The Safety Analyst software was released by the American Association of State Highway and Transportation Officials (AASHTO) in 2009. The software enables road agencies to automate their safety management programs. All four modules of Safety Analyst are being configured for the Province of Ontario. Diagnosis and Countermeasure Selection Module enables the Ministry to conduct in-service road safety reviews (ISRSR) more efficiently by providing tools to identify collision patterns at a site and guiding traffic analysts during site visits. Identification of collision patterns is an important step to diagnose potential safety problems at a site. For collision pattern identification, Safety Analyst provides capabilities to develop collision diagrams, generate collision summaries, and conduct statistical tests (test of proportion and test of frequency). This paper discusses and recommends a methodical approach to objectively identify collision patterns utilizing the Safety Analyst capabilities. Additionally, Safety Analyst is equipped with an system which guides the analyst towards appropriate office and field investigations. The expert system includes diagnostic scenarios for intersections and road segments. This paper provides a process through which additional diagnostic scenarios were developed for freeway and ramp sections for the Province of Ontario. The process includes identification of major collision patterns based on analysis of historical collisions (for all sites in the Province), engineering parameters as well as human factors principles. For the covering abstract of this conference see ITRD record number 201310RT334E.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".