Passenger Satisfaction across Multiple Public Transit Modes
Bibliographic record
Abstract
Understanding how the passenger satisfaction varies across multiple Public Transit (PT) modes is helpful to identify different needs of service users and make improvements accordingly. This study investigates customer satisfaction in three PT modes (bus rapid transit, metro, and jitney) using a set of consistent customer satisfaction surveys; providing a comparable approach in the surveying tool, defined variables, and model structure across different modes. Additionally, the effects of a wide set of variables influencing satisfaction such as personal and trip characteristics, and perceptions towards service quality attributes have been incorporated in the modeling process. A total of 1,808 valid responses from PT passengers in Tehran have been used to develop ordered logit models. The findings indicate that bus rapid transit and metro users are respectively more satisfied with their trips compared to jitney users, and the reasons have been explored. Also, an importance-performance analysis has been applied on the modeling results as an application of the study to prioritize improvements in the service quality attributes of all three PT modes aiming towards allocating limited resources more efficiently. The transit agencies can benefit from this study to find specific strategies for each PT mode and increase competitiveness within the transit system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".