Spiral Plot Analysis of Variation in Perceptions of Urban Public Transport Performance between International Cities
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".