Exploring Diversity of Activities on Shared-Use Paths: Factors and Implications for Planning and Design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The increased need for active transportation facilities coupled with the limited funding and space have influenced the prioritizing of shared-use paths (SUPs). Unlike other activity-specific facilities, the SUP can accommodate a wide range of users. With SUPs being relatively new facilities, less is known about the characteristics of the users and the key factors associated with the user type. This study explored the influential factors for SUP user diverse activities using multinomial regression on the survey data collected in Edmonton in 2018. The descriptive analysis revealed that walking was the activity with the highest frequency, followed by walking and cycling, and walking with pets, whereas cycling had the lowest priority. The multinomial model showed that as the age increases, residents are less likely to perform activities other than walking or cycling alone. Further, residents with higher education are more likely to either walk and cycle or walk, run, and cycle. Residents whose secondary mode of transportation is bicycle are less likely to walk and walk pets. Residents who own their house are likely to walk and walk pets. Furthermore, male residents, residents with children and those whose primary mode of transportation is not personal vehicles are more likely to walk, run, and cycle but less likely to walk and walk pets, compared with either walking or cycling alone. Planners can utilize the findings to understand the possible utilization of the planned SUPs and design them accordingly.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it