What do we know about pedal assist e-bikes? : A scoping review to inform future directions
Bibliographic record
Abstract
Pedal-assist e-bikes (PAEB), are increasing in popularity. There are several significant benefits that can be attained by increasing use of PAEB. The purpose of this review was to synthesize the literature available on PAEB and to identify future directions for research, and policy and infrastructure development, that ensures an inclusive approach. We conducted a scoping review of the literature that led to the identification of 107 articles that included PAEB. Studies were grouped according to themes: Energy and Emissions, Bike Sharing, Violations and Accidents, Physical Activity, Active Commuting, and Perceptions. Overall, it appears that the uptake of PAEB leads to a modal shift such that overall car use is decreased. PAEB use is associated with lower emissions compared to cars, but requires physical effort that classifies use of a PAEB as moderate intensity physical activity. Cost appears to be prohibitive, thus sharing or rental programs, and subsidies may be beneficial. Several additional barriers related to lack of infrastructure were also noted. Importantly, violations, injuries, and crashes appear to be similar between PAEB users and traditional bicycle users. PAEB offer an opportunity to improve health and mobility in an eco-friendly manner compared to cars. Infrastructure and policies are needed to support this modal shift. There is an immediate need to clearly define PAEBs, and to ensure regulations are similar between PAEB and traditional bicycles. Future research is needed to better understand long-term behaviour change with regards to commuting, and to identify the effect of implementing better infrastructure and policies on PAEB uptake.Acknowledgments: SSHRC Knowledge Synthesis and Infrastructure Canada Funding
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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 | 0.014 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 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".