Understanding Perceived Barriers and Enablers to Using Public Transport for Different Groups of Users
Bibliographic record
Abstract
Public transport (PT) usage is influenced by various perceived barriers, which vary by individual characteristics, local context, and PT system attributes. Despite previous research focusing on specific groups like the elderly, women, students, and new immigrants, there remains a lack of comprehensive understanding of common perceived barriers and enablers across different populations. Additionally, research often overlooks the relative importance of these perceived barriers and their impact on travel experiences and behaviors. This thesis addresses these gaps by examining perceived barriers and enablers from a global perspective and using the context of Saskatoon, Canada. The study objectives were reached through a systematic review of recent academic work and the development of the USask Mobility Survey to evaluate the University of Saskatchewan individuals' perceptions of PT barriers and enablers. The results of this research will contribute to a better understanding of where improvements in the PT system are required, integrating the population’s perception and enriching the PT planning process. Furthermore, exploring how perceived barriers to public transport relate to various personal factors across different populations offers a better understanding of the topic. The thesis can serve as a model for future research, tracking changes in perceptions and providing insights for improving PT services. Additionally, by focusing on Saskatoon's extreme cold weather, the thesis offers valuable data to inform targeted strategies and enhance sustainability efforts to the city and other cities with similar weather.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".