Valuing Public Transport Customer Amenities: International Transit Agency Practice
Bibliographic record
Abstract
Public transport customer amenities cover a range of measures that can enhance the quality of the passenger experience, such as information provision and station quality. While much research has determined the value that users place on amenities, there is little understanding of current practice in the use of customer amenity valuations in project appraisal. A survey of transit agencies in 11 cities (Melbourne, Sydney, Brisbane, Perth, Auckland, London, Paris, Toronto, Vienna, Oslo and Singapore) was undertaken showing that Australasian cities, albeit Melbourne, generally have widespread inclusion of customer amenities as part of advanced appraisals for all relevant types of public transport projects. Australasian practice tends to include customer amenities more frequently in project appraisal than London, Singapore and Oslo. Paris, Toronto and Vienna, although they adopt advanced appraisals for some projects, rarely (if at all) include customer amenities in these appraisals. While agencies generally use published sources of customer amenity values specific to their country, Toronto and Singapore tend to use customer amenity values from London, highlighting a lack of local customer amenity values.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".