Perceived Quality of Bus Transit Services: A Route-Level Analysis
Bibliographic record
Abstract
Passengers’ perceptions of transit quality depend on their interactions with the service. However, given the varied operational features in any transit network, the perceived service quality is expected to vary between different types of operation. Recently, there has been an emphasis on addressing this issue and quantifying the variation in the perceived service quality at route level. In this respect, this study quantifies the perceived quality of bus services across different route types and user groups. A two-step cluster analysis is developed to classify bus routes based on their operational features, which is followed by a series of importance-performance analysis (IPA) models corresponding to each route type. The study is supported by a primary dataset collected from 1,883 users through an online survey in Hamilton, Canada. The emerging results indicate four predominant route types: core, standard, express, and local routes, each exhibiting a unique set of characteristics. The IPA models show an apparent variation in the perceived service quality from each route-type. In addition, there are clear indications of differential perception between passengers who use the service very frequently and other less frequent users. These results call for the consideration of variations in route level and user type in informing service quality improvements.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".