Comprehensive Evaluation and Classification of Interchange Diagrammatic Guide Signs’ Complexity
Bibliographic record
Abstract
The effectiveness of interchange diagrammatic guide signs has significant meaning in traffic safety and driver’s understanding. This paper presented a comprehensive evaluation and classification of interchange diagrammatic guide signs’ complexity. The effectiveness of interchange diagrammatic guide signs relies on how well road users can understand those diagrams. This study tested 37 types of diagrams on the visual recognition complexity degree in three levels, general level, partial level, and detailed level, and finally seven indexes are selected to evaluation and classification of interchange diagrammatic guide signs’ complexity. These indexes can be used to conduct quantitative evaluation and classification. And the result of diagram complexity range is between −1.366 and 2.046, which have a correlation with graph cognition complexity, including perspective of distribution, diagram character, essential element expression manner, and utilization degree, and K -means clustering method was used in the analysis. Based on the presented method, 37 types of diagrams are separated into three categories according to their complexity score: low complexity, medium complexity, and high complexity. This study not only presents a theoretical approach for quantitative evaluation of guide signs’ complexity and effectiveness but also can be a reference for traffic sign design and application.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".