Enhancing public transport use: The influence of soft pull interventions
Bibliographic record
Abstract
Public transport (PT) success depends on targeted interventions, ranging first from push measures that discourage car use to pull measures that encourage PT use, and second from hard measures that intervene at physical infrastructures to soft measures that intervene at psychological elements of individuals’ behaviors. Focusing on soft-pull policy measures, and through a scoping review of 36 publications, we categorize these measures into three overarching groups: 1) Internally motivating strategies that gradually but firmly instill pro-sustainability attitudes and norms in people’s mind; 2) Satisfaction increasing strategies that primarily help retain current users especially those who feel forced to use PT and secondary attract new riders by improving the service factors and modifying travelers’ inaccurate perceptions of the service; 3) Stimulating PT-use and car-habit disrupting strategies such as attractive incentives and tailored information that encourage auto-drivers to give PT a try and break their car-habit. This review provides an analytical evaluation of each approach, offering recommendations for policy makers and PT service providers, along with identifying research gaps and suggesting future research directions.
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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".