Utilizing Shared Parking to Mitigate Imbalanced Supply in a Dense Urban Neighborhood: Case Study in Vancouver, British Columbia, Canada
Bibliographic record
Abstract
Excess off-street parking can have a range of impacts, including undesirable effects on housing costs, urban form, mode choice, and overall density. In urban residential areas, excess off-street parking can coexist with on-street parking congestion because of restrictions in parking access, nonmarket pricing, and other factors. This paper examines the potential for shared parking to address such an imbalance in parking supply by using a case study of the West End, a high-density residential neighborhood in Vancouver, British Columbia, Canada. The West End’s residential parking permit (RPP) program has faced parking shortages and congestion, with on-street parking consistently reaching 90% occupancy. At the same time, off-street residential parking facilities in the neighborhood have occupancy rates consistently less than 50%. This analysis uses the inventory and occupancy data for off- and on-street parking stalls to investigate the impacts of making off-street stalls available to RPP users in a shared-parking program. Results showed that on-street parking congestion could be greatly reduced by introducing a relatively small number of off-street stalls from select residential buildings to the RPP program. Methods to unlock currently underutilized off-street parking supply are also discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".