E-bikes and travel behaviour change: systematic review of experimental studies with meta-analyses
Bibliographic record
Abstract
Transitioning from private cars to active modes of transport is key to reducing transport related greenhouse gases emissions and promoting physical activity. Electrically assisted bicycles, often referred to as e-bikes, play a pivotal role in facilitating this shift. However, the environmental and health benefits of e-bikes depend on the modes they substitute, with the highest benefits obtained when private cars are replaced. This systematic review and meta-analysis targets quasi-experimental (i.e. pre–post measures of travel behaviours without control group) and experimental (i.e. pre–post measures of travel behaviours with control groups) studies assessing the impact of acquiring an e-bike on overall travel behaviour changes expressed in both distances and mode share (in % of kilometres travelled). Ten studies, all conducted in Northern Europe, were included. Results from the narrative synthesis and meta-analysis show that: (i) when participants have access to an e-bike, either through a free loan programme or a purchase, they engage in e-cycling; the meta-analysis reveals a significant difference of 5 km travelled daily after the interventions between participants that got access to an e-bike compared to those from control groups with no e-bikes, reflecting a substantial increase of 26% in e-bike mode share; (ii) e-bikes can substitute for all other modes of transport, but car use appears to be the most affected in both the systematic review and meta-analyses difference (2.4 km fewer travelled per day by car between the intervention and control groups at follow-up, reflecting to a 10% decrease in car mode share); (iii) baseline travel behaviours may influence modal shift, with e-bikes substituting for the most prevalent means of transport in baseline. E-bikes have the potential to be a reliable and competitive alternative to cars in a healthier and more sustainable transport system and our study brings new empirical evidence to support this claim.
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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 | 0.023 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".