Effectiveness of interventions for modal shift to walking and bike riding: a systematic review with meta-analysis
Bibliographic record
Abstract
Background Identification of priority interventions to support modal shift to walking and bike riding is challenged by the myriad of interventions available, and a lack of synthesised evidence for what types of interventions are most effective. With increasing investments in active travel, there is substantial demand for synthesised evidence of efficacy between intervention types. This systematic review aimed to measure the effectiveness of interventions to increase active travel with a primary outcome of modal shift.Methods The electronic databases MEDLINE, PsycINFO and Web of Science were searched. Eligible study designs included randomised and non-randomised studies of interventions with specific study design features that enabled the estimation of causality with minimal risk of bias. Studies were categorised by intervention types described within the Behaviour Change Wheel.Results 106 studies that assessed the impact of an intervention on walking, cycling or active transport overall were included. Findings demonstrate that physical environmental restructure interventions, such as protected bike lanes and traffic calming infrastructure, were most effective in increasing cycling duration (OR = 1.70, 95% CI 1.20–2.22). Other intervention types, including individually tailored behavioural programmes, and provision of e-bikes, were also effective (OR = 1.33, 95% CI 1.23–1.43, OR = 1.13, 95% CI 1.02–1.22). An intensive education programme intervention demonstrated the greatest impact on walking (OR = 1.96, 95% CI 1.68–2.21). This body of research would benefit from more rigors in study design to limit lower quality evidence with the potential for bias.Conclusion This review provides evidence for investment in high-quality active transport infrastructure, such as protected bike lanes, to improve cycling and active transport participation overall. It also provides evidence for investment in other non-infrastructure interventions. Active transport research needs to move towards trials with consistent outcome measures to inform which combinations of interventions (including disincentives) are most effective.Study registration PROSPERO CRD42023445982
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.046 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".