Exploring Diversity of Activities on Shared-Use Paths: Factors and Implications for Planning and Design
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.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".