Effect of Major Life Events on Travel Behaviours: A Scoping Review
Bibliographic record
Abstract
The transportation sector accounts for about a quarter of global greenhouse gas emissions. Previous research suggests that major life events may be “windows of opportunity” for travel behaviour change. Our scoping review examined the effects of seven events (transitions to secondary school, post-secondary studies, labour market, marriage, parenthood, retirement, and relocation) on travel behaviours. Five databases were searched (MEDLINE, APA PsycINFO, Web of Science, SportDISCUS, and ProQuest Dissertations and Theses) and 80 articles met inclusion criteria. Relocation was the most commonly examined event (with 51 studies). Findings illustrate that moving to compact neighbourhoods (with shorter commute distance/travel time, greater walkability/access to destinations) was associated with shifts towards sustainable travel modes (e.g., walking, cycling, and transit). Relocation might be particularly conducive to implementing scalable sustainable transportation interventions, as all six interventions with appropriate statistical power were effective. Entry into the labour market was generally associated with increased car use and declines in sustainable transportation. Qualitative studies suggested that attitudes towards cycling may become negative during adolescence, while attitudes towards driving improve, highlighting a need for concerted action. Evidence for other events was less consistent. Research in developing countries remain scarce and further intervention research is needed to enhance quality of evidence.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".