Shared electric bicycles: who are the potential users? An examination of survey results from urban and suburban neighbourhoods in the Greater Golden Horseshoe area
Bibliographic record
Abstract
Bicycle sharing systems based on electric bicycles (e-bikes) have the potential to provide users with unique benefits compared to conventional bike sharing systems and privately owned e-bikes. It follows that their use-patterns and motivations would also be unique. Through an online transportation survey, this research examines the socio-demographic, attitudinal, and environmental factors that influence a respondent’s propensity to consider using a shared e-bike. It was revealed that a similar proportion of people living in urban and suburban areas are willing to consider this micro-mobility option. Additionally, it appears that in urban environments, shared e-bike systems are more likely to replace transit and walking trips, while in suburban environments, they are more likely to replace car trips. The results of the analysis indicate that all respondents with income less than $50,000 (OR=1.08), suburban respondents who already own a bicycle (OR=1.06), suburban respondents who valued active, environmentally friendly, cost effective, and flexible transportation modes (OR=1.07), urban respondents who felt they had connective cycling infrastructure near them (OR=1.09), urban respondents who felt the streets were not too congested (OR=1.08), and suburban respondents who felt walking and cycling were practical ways of getting to their destinations (OR=1.11) were more likely to consider use of shared e-bikes. All respondents who do not travel for work or school (OR=0.89) and urban respondents whose primary commute modes were active (walking or cycling) (OR=0.93) were less likely to consider shared use of shared e-bikes. The findings reported in this study can be useful for transportation planners in evaluating the feasibility of implementation, and optimizing the strategic placements of shared e-bike schemes in urban and suburban areas. Key Words: Active Transportation, Electric Bicycle, Bike Share, Greater Golden Horseshoe Area, Micro mobility
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".