Usage Patterns of Electric Bicycles: An Analysis of the WeBike Project
Bibliographic record
Abstract
This paper presents an analysis of data collected through the Waterloo WeBike project: a field trial in which over 30 sensor-equipped electric bicycles (e-bikes) were given to members of the University of Waterloo for personal use. Our dataset includes e-bike trips and battery charging sessions spanning nearly three years, from summer 2014 until spring 2017. We also conducted three surveys both before and during the trial. Our main findings were that the primary purpose of the e-bikes in our trial was for commuting, with most trips lasting less than 20 minutes and most trips taking place in the summer months. Our battery charging analysis revealed no evidence of range anxiety, and our analysis of survey results showed little correlation between anticipated and actual use. Furthermore, when asked about their opinions about various modes of transportation, our participants rated regular bicycles higher than e-bikes even after becoming familiar with e-bikes through the field trial. Based on our analysis, we draw several conclusions, including the fact that the general population in Canada is still unaware of e-bikes and their potential. Moreover, e-bike manufacturers should target sales to nonbike users, such as seniors, rather than trying to displace sales of regular bicycles.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".