Spiral Plot Analysis of Variation in Perceptions of Urban Public Transport Performance between International Cities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a method for comparing perceptions of transit service attributes across different customer groups. It compares customer perceptions across 22 service attributes in nine major world cities (Toronto, Ontario, Canada; New York City; San Francisco, California; Boston, Massachusetts; Sydney, Brisbane, Perth, and Melbourne, Australia; and London) by using an importance–performance analysis (IPA) framework. This paper proposes a new approach to displaying results of IPA, a spiral plot analysis (SPA), to highlight similarities and differences across a large range of attributes between disaggregate groups in the case cities. Results showed a general consistency between cities in the importance of service attributes. Greater variation in performance of attributes was found. The IPA suggested the average target area (high importance–low performance) attributes for the nine cities were (in order): “feeling safe traveling on public transport at night,” “the ability of operators to deal with service disruptions quickly,” “unexpected service disruptions don't happen very often,” “quality of service on public transport,” “public transport operating frequently,” and “having public transport travel options available when and where I need them.” Results stressed how important unplanned disruptions were to passengers in all cities. Results for some individual cities were slightly different, although these attributes were critical for all. The SPA method more concisely illustrated similarities and differences between cities as well as highlighted which attribute scores were more important to customers. The SPA illustrated that Melbourne had some of the largest gaps between expectations and performance, whereas New York City tended to have the smallest. Areas for future research are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it