How Can We Make Shared E-scooter Systems Safer? Municipal Perspectives on Safety in Shared E-scooter Programs
Bibliographic record
Abstract
Dockless shared electric scooters (e-scooters) are quickly becoming a popular mobility choice in urban areas. E-scooter systems have spread rapidly to cities across North America since 2017. Their ubiquity in urban centres has significant impacts on transportation ecosystems in cities, and has raised significant concerns around safety, public health, accessibility, and public space use. There is a need to better understand how e-scooters can be integrated into existing transportation systems while considering public health and safety. This study examines how municipalities perceive and are responding to issues of public health and safety posed by e-scooters by interviewing municipal staff who oversee e-scooter programs. The results show that municipalities describe similar experiences to those documented in the academic literature. In particular, they identify four safety challenges: e-scooter riders as a new category of vulnerable road users, getting accurate safety data, issues with sidewalk riding and improper parking, and rider behaviour. Recommendations are provided to aid municipal planners in developing local escooter policies around safety in Canada.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".