Sharing space on Granville Island: An assessment of shared street performance
Bibliographic record
Abstract
Shared space, or shared streets, is an urban design approach encouraging pedestrians and drivers to share a common surface by minimizing segregation features. Advocates contend that the concept generates extensive social, cultural and economic benefits. Scholarship investigating schemes and purported benefits have been limited primarily to European and New Zealand applications. Identifying the need to study shared space operations in the Canadian context, this research offers a quantitative evaluation of road user behaviour and shared street performance on Granville Island in Vancouver, Canada. Utilizing video survey, data was collected at three diverse sample sites between December 2015 and February 2016. Data was analyzed using univariate and bivariate statistical analysis and overall shared space performance was quantified using the Karndacharuk (2014) Analytical Hierarchy Process (AHP) index. The behavioural analysis revealed that the majority of pedestrian movements transpired outside the vehicle path and road space. Contiguous land uses were an important predictor of road user behaviour, as higher frequencies of pedestrians crossing the vehicle path were positively correlated with higher densities of commercial uses. Regression analysis calculated that vehicle path crossings were also a statistically significant predictor of vehicle speeds and interaction occurrences with vehicles. During interaction occurrences with vehicles, pedestrians were deemed to have priority. Regarding shared space performance, Granville Island managed both pedestrian and driver mobility effectively. Calculated performance in the Place function was substandard, as shared space design failed to inspire pedestrian reclamation of the street space. A key finding, the AHP index was recalibrated, enabling a custom, quantitative evaluation of pedestrian reclamation of road space on Granville Island. Results corroborated the importance of local traffic conditions noted in shared space literature. Future research should be undertaken to study the qualitative aspects of shared space on Granville Island, as well as an appraisal of street performance and road user behaviour under disparate conditions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".