Will Reducing Parking Standards Lead to Reductions in Parking Supply?
Bibliographic record
Abstract
To promote land-efficient development that supports nonautomobile modes of transportation, many municipalities are trying to implement parking policies that minimize parking oversupply and use existing parking supply more effectively. A commonly proposed strategy is for municipalities to lower their minimum parking standards. However, parking supply decisions are based on many factors, and experience shows that reducing parking standards does not always lead to corresponding reductions in parking supply. Using the results of an extensive commercial parking survey conducted across the City of Toronto, Canada, this study develops an empirical approach to determine whether reductions in parking standards are likely to lead to reductions in the amount of parking supplied by new development. It is proposed that the proportion of existing sites supplying less parking than existing standards require can be used as an indicator of the likelihood of developers to respond to reductions in parking standards by providing less parking. This assumes that the development characteristics of surveyed sites can be considered representative of current development practices. Applying such an analysis to Toronto, it is expected that reducing the parking standards for general office, medical office, and general retail uses will be a successful strategy in encouraging new development to provide fewer parking spaces on average. Such a strategy will be less successful for bank and large grocery uses, which tend to provide more parking and are less sensitive to minimum parking standards.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".