Characterizing, measuring, and managing transit service quality
Bibliographic record
Abstract
Summary Recent studies to evaluate the quality of transit service are generating a good amount of renewed interest in an old idea, the passenger's perspective; this new interest stems from recognizing that transit service quality should be characterised, measured, and managed by parameters capturing both passenger and transit operator perspectives. However, although the selected parameters are user‐oriented in their input, the output may not be as user‐oriented as considered, and the number or the percentage of passengers is often neglected. As a result, the findings are often misleading because the perspectives of transit operators dominate. Therefore, academics and practitioners must rethink their strategies of quality analysis of public transportation by stressing more on the role of passengers. These challenges are addressed in this paper with a practical, simple, and holistic framework, for Transit Quality (TRANSQUAL). This framework provides for the involvement of all stakeholders in the characterisation, measurement, and management of the stages of quality monitoring, which is jointly analyzed at different planning levels. In the characterization stage, the framework supports the selection of parameters to be monitored. The measurement stage sets and measures four quality areas in terms of percentage of passengers who expect a predefined level of service, for whom the service is designed, who receive the planned service, and who perceive the service as delivered. The management stage computes the differences between these percentages, points out criticalities, and recommends corrective actions. These stages are investigated in‐depth, integrated, and discussed in a real‐life case study. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".