Cutting across the curb – A review of recent developments in municipal curb management policy in America and Canada
Bibliographic record
Abstract
The curb is perhaps the most complex and dynamic space within the urban street right-of-way. With the rise of urban freight, ride-hailing, cycling, outdoor dining and other uses, competition for curb space is increasing. In response to this pressure for space, municipalities are developing new policies to manage these demands. To inform this new policy development, this research focuses on analyzing the content of 26 recent curb management policies from American and Canadian municipalities. Specifically questioning to what extent the policies: identify the range of potential uses of the curb; recognize the competing interests; and/or acknowledge the dynamic nature of demand for curb space? We find that policies are rethinking the use of the curb beyond parking and are clearly seeking to manage demand for: urban freight, ride-hailing, and transit. Despite these policy innovations, there are opportunities to improve considerations for: bicycle accommodation; outdoor dining; mobility challenges; emergency services; utilities, and equity and inclusion among curb. Our research findings are limited to the analysis of existing curb management policies in America and Canada. Further research could explore how policies are operationalized, how streets are reconfigured, and how other similar municipal policies relate to curb management. Planning and transportation practitioners are uniquely positioned to bridge divides between competing interests and find equitable solutions for managing the curb. • Recent curb management policies are primarily conceptualized to address the increased demand for freight and delivery; pick-up and drop-off for ride-hailing; and technology integration. • In identifying the range of curb uses addressed in policies we find that: freight and delivery; pick-up and drop-off for ride-hailing; technology integration; and transit accommodations are addressed in the majority of recent policies. • There is much to be learned from the recent policy development, and hundreds of cities are interested in curb management strategies. • The siloed nature of municipal governance will continue to pose a substantial challenge for developing holistic curb management policies and strategies for their implementation especially with regard to negotiating between conflicting uses.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.029 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".