Does easy mean happy? Exploring the impact of ease of travel on travel satisfaction
Bibliographic record
Abstract
Abstract Many studies have examined the determinants of travel satisfaction. However, how the perceived ability to travel, i.e., ease of travel (EoT), influences travel satisfaction has not been analysed in a comprehensive way. In this study, we will analyse how EoT, which is comprised of travel motivation, travel skills, travel options and travel quality, impacts satisfaction with travel to campus of 2593 students and staff members of University College London (UCL). One-way ANOVAs show that respondents with high levels of EoT are more satisfied with their trips to campus compared to those with lower EoT levels. Based on linear regressions (per mode and all modes combined), we found that all EoT elements seem to positively affect travel satisfaction, even after controlling for socio-demographics and trip characteristics. This indicates that EoT may be regarded as an important predictor of travel satisfaction. Apart from EoT, also age, mode choice, weather conditions and levels of crowding and congestion were found to significantly impact travel satisfaction. Somewhat surprisingly, effects of travelling alone, trip duration, and travel disabilities on travel satisfaction – which were often found in existing studies – were weak, suggesting that these effects may be partly explained/moderated by variations in EoT elements. In order to make public transport and active travel trips more satisfying, we recommend policy makers to focus on (1) improving the quality of public transport services and active travel infrastructure, and (2) helping people to improve their skills required to easily walk, cycle or use public transport.
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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 | 0.008 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".