Application of Fuzzy Linguistic Rating and Entropy-Based GRA to Address Uncertainty in Safety Performance Index Construction
Bibliographic record
Abstract
As the need to assess the level of road safety grows, there is a noticeable tendency of experts to use one overall composite index that contains information on a number of safety performance indicators (SPIs). Indicators commonly used in road safety assessment are numerical, and their natural uncertainty and vagueness are often overlooked. However, there are also SPIs that are rather linguistic, such as data on driver behavior, which are most often collected through questionnaires and are considered qualitative, imprecise, and fuzzy. Together with inappropriate selection of weighting and aggregation methods, such data can be a source of uncertainty and can lead to unreliable results and erroneous conclusions. In this regard, the present study provides a systematic and efficient hybrid method that integrates three different procedures to deal with unavoidable uncertainty in each step of index construction. The application of fuzzy linguistic rating grasp insight into the ambiguity that is intrinsic in drivers’ self-assessment. Entropy describes each observed behavior by quantifying the disorder of a system. Grey relational analysis aggregates behavioral indicators into a composite index, doubting their sufficiency and completeness. A case study of Montenegro has been provided to demonstrate the practical applicability of the proposed method in safety assessment under uncertainty. Results abstracted not wearing the seatbelt as the most common negative behavior among drivers in Montenegro, followed by using the telephone while driving, speeding, and driving under the influence of alcohol. In addition, municipalities are ranked according to the level of road safety.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".