Performance Evaluation of Subway Signage:Part I - Methodology
Bibliographic record
Abstract
An integrated, format consistent and accessibility facile information signage system is not only a basic feature of any subway station, but also responsible for the smooth and well-organized operation of the subway service. To identify the deficiency and drawback of existing signs in a subway station and further recover and improve the signage function, a signage performance evaluation is recommended. The performance evaluation should consider three aspects: signage integration, standard format and optimal visibility. In this study a methodology performed in three stages provides support for consistent evaluation of signage systems of subway stations, with respect to the first aspect – signage integration. Utility zone is introduced as a new concept to identify various areas with traveling functions during a given trip. Signs are classified into three major categories while more categories can be defined as one may seek to improve the signage. Guidelines for signage implementation are prescribed based on the signage definitions of each category. The methodology proposed here potentially can be applied to other transportation facilities. Due to space limitation a companion paper titled, Performance Evaluation of Subway Signage: Part II – A Case Study, demonstrates how the methodology proposed here is applied to a subway station in Montreal, Quebec.
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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 | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".