Bibliographic record
Abstract
An information integrated, recognition accessible and format standardized signage system is not only a basic feature of a subway station, but also a factor that contributes to the smooth and well-organized operation of the subway service. To figure out the deficiency and limitation of existing signs in one subway station, and to further recover and improve the signage function, a comprehensive performance assessment of the signage system is necessary and compulsory, if not periodically, as least when major system modifications/enhancements are executed. In this study, a methodology is proposed to evaluate the signage performance from three aspects: information integration, visibility optimization and legibility standardization. \n \n\tInformation integration requires a complete signage system in the station offering demanded and mandatory information to the public. It is examined via a comparison between the existing signs and a standardized signage system, which is defined in three stages: station element and passenger flow identification, signage definition and classification, and signage implementation. Visibility optimization means the signs should be set and installed in a proper way regarding to the color, panel size, lighting, orientation and height, to maximize their ability of drawing and facilitating passenger’s attention and recognition. The visibility of one sign is evaluated as one of the three levels: optimized visibility, limited visibility and impaired visibility. Legibility standardization introduces guidelines on format displaying of signs with respect to typeface, color application and information presentation, to achieve for passenger’s easy acceptance and understanding of signage information. \n \n\tBased on the methodology, an implementation flowchart is developed for generic signage evaluation in one subways station. A case study (Berri-UQAM Metro station) in Montreal city is tested as a step-by-step application of this methodology in a real-world system. The absent signs and signs that need improvement are identified in detail and the evaluation result is summarized, as basis for further ameliorative measures.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".