Age-Friendly Cycling Infrastructure—Differences and Preferences among 50+ Cyclists
Bibliographic record
Abstract
In this paper, the needs, attitudes, and perceptions of older (50+) cyclists were examined with the aim of determining the level of comfort, safety, and the way of using different types of cycling infrastructure. Considering that by 2050, 1 in 6 people will be over the age of 65, and that this category of users (particularly cyclists) still receives insufficient attention, the authors believed that in this way, a significant contribution can be made to the existing literature. Data from 389 50+ cyclists were collected through a survey, including Canada, the United States (USA), and Serbia, and analyzed using visual preference testing (VPT), ANOVA, and Kruskal–Wallis test. The countries were chosen to include certain similarities (traffic characteristics), as well as differences (cultural characteristics), in order to enable an adequate exchange of knowledge, good practice, and experience. The results indicate the existence of differences between these countries, especially regarding the perception of safety and the way of using certain infrastructure in Serbia (e.g., major urban collectors and shared space). Based on the obtained results, a set of general guidelines was proposed for countries with similar traffic and cultural characteristics on how to treat and provide sustainable infrastructure for older cyclists.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".