Transferring Matters: Analysis of the Influence of Transfers on Trip Satisfaction
Bibliographic record
Abstract
Conventional wisdom in public transport planning suggests that transfers should be minimized because of the negative perceptions associated with them. However, little is known about how transferring affects overall satisfaction levels. This study aims to answer the following three research questions: (1) Are people that require transfers on their daily commute less satisfied with their trips compared with their non-transferring counterparts? (2) How many transfers appear to be too many transfers to remain satisfied with a trip? (3) Do mode-specific transfers have different impacts on overall satisfaction levels? Using data from a 2017/18 commuting survey of students, faculty, and staff at McGill University, Montreal, Canada, this study tries to answer the above questions through two statistical models, general and mode-specific. The general model showed that compared with trips involving zero transfers, no statistical difference in trip satisfaction was observed for one-transfer trips, whereas trip satisfaction declines by 32% when a rider must transfer at least two times. The mode-specific transfers showed that transferring between bus routes, and between a bus and subway, negatively affects trip satisfaction. However, transferring between subway lines did not show an impact in the models. These results show that transferring between high-frequency routes does not affect total trip satisfaction levels in the same way as transfers involving low-frequency services. Findings from this study are expected to contribute to both scholarly and practical discussions of the relationship between transferring and customer satisfaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".