Scooting into place: How comfort on different infrastructure types influences shared e-scooter trip making
Bibliographic record
Abstract
Shared e-scooters are a growing disruptive micro-mobility technology. Several Canadian cities, including Calgary, have launched pilot programs to test these micro-mobility options. As part of the Calgarian pilot, a public engagement survey was launched to collect information about the public’s perception of shared e-scooters. The survey includes a set of questions related to users’ perception of comfort using shared e-scooters on various infrastructure types: roads, bike lanes, sidewalks and dedicated pathways, as well as a question related to the frequency of use of the e-scooters. Comfort in general and specific comfort on the type of infrastructure most used is expected to have a high degree of correlation with the frequency of use. We present a joint ordered choice model examining how different sociodemographic characteristics and typical travel patterns of the survey respondents influence perceptions of comfort on these different infrastructure types and their frequency of e-scooter use. The model estimation results show that e-scooter use is not uniform across age and gender demographics and that infrastructure, riding comfort, and e-scooter use are linked. E-scooter riding comfort was negatively associated with infrastructure types that led to more potential interactions with motor vehicles and pedestrians. These results will allow decision makers to tailor new policies and prioritize infrastructure to improve the overall usage of e-scooters and maximize the benefits these new technologies will provide.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".