TRANSIT-SUPPORTIVE PARKING POLICIES: NORTH AMERICAN EXPERIENCE MODEL PRACTICES FOR MUNICIPALITIES
Bibliographic record
Abstract
This study explores specific experiences with transit-supportive parking policies (e.g. requirements for new developments, overall parking policies, parking pricing policies), and develops model policies that might be used in other municipalities. It is intended to complement an earlier CUTA literature synthesis of North American experiences with transit-supportive parking policies, namely, STRP Report 12-1, Developing Transit-Supportive Parking Policies: A Synthesis of Key Concepts from the Literature. The study is based on a series of interviews with officials in cities where significant efforts have been made to reduce automobile commuting and encourage transit use through parking supply and pricing policies. The focus of the interview was to obtain updates on the policies and measures that have been tried and assess their effectiveness in influencing the modal choice of commuters. The results of the interviews have been summarized, with some of the more notable experiences presented as case studies. The report develops six parking supply-related policies, and four pricing-related policies, that should have good potential for application in Canadian municipalities. The report also discusses issues related to implementation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".