Do frequent satisfying trips by public transport impact its intended use in later life?
Bibliographic record
Abstract
Abstract Previous studies have indicated that factors such as the built environment, attitudes and past behaviour can influence travel behaviour. However, the possible effect of travel satisfaction on travel mode choice remains underexplored, despite many studies focusing on travel satisfaction over the past years. It is likely that individuals experiencing satisfying trips with a certain travel mode will use this mode (more) frequently for future trips. In this study—using data from 984 students from Laval University, Canada—we analyse how satisfaction with public transport and the frequency of public transport use affect the intention to use public transport in later life stages. Our results indicate that public transport frequency, public transport satisfaction and the interaction between these two factors (i.e., the frequency of (dis)satisfying public transport trips) significantly affect people’s intentions to use public transport in later life, although variations in effect sizes exist between different life stages. Making public transport more pleasant and increasing ridership of children and young adults (e.g., by giving them free public transport passes) may consequently result in a higher public transport frequency in later life stages. We argue that travel satisfaction can play an important role in the formation of habitual mode use, and that satisfying trips (if undertaken frequently) are likely to be repeated in the future.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".