An importance-performance analysis of public transport to the university campus based on best-worst scaling
Bibliographic record
Abstract
University campuses represent important transport attraction poles in cities due to the large number of students, faculty and administrative staff who commute to the campus daily. The campus location can significantly increase traffic around the area, especially during the class entry and exit times. Therefore, public transport systems are essential to facilitate access to the university campus worldwide, especially for students. This study aims to evaluate the level of importance and satisfaction with factors that affect public transport use among university students. In this context, a best-worst scaling experimental design is used to carry out an important performance analysis (IPA) of public transport services to university campuses in Gran Canaria by estimating a Mixed Logit model. Thus, it will be possible to determine what attributes should be prioritised when implementing policies for improving these services. The results showed that public transport managers and university authorities should primarily focus on providing direct services and improving punctuality and bus frequency. Our results also provide valuable insights into the search for the best policies that match students’ transport mobility preferences with the service provision.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".