Green stormwater infrastructure and active mobility: A case study investigating the effects of bioswales on individuals’ perceptions
Bibliographic record
Abstract
• A case study is conducted in a small town to uncover the relationships between bioswales and active mobility. • Respondents are more satisfied with walking and cycling street designs with bioswales over grass strips or grey street. • Individuals who exhibit eco-friendly behaviour and value their neighbourhood's design are more likely to prefer bioswales. • Implementing bioswales can positively impact active travel by enhancing aesthetic appeal, perceived safety, and comfort. • Concerns about bioswales are primarily related to car transportation. Cities are increasingly designing streets with green stormwater infrastructure (GSI) to improve urban drainage systems, while providing secondary socio-environmental benefits. Yet, the relationship between GSI and active mobility remains underexplored. This study addresses this gap by conducting a case study analyzing the impact of GSI implementation on individuals’ perceptions of walking and cycling infrastructure, while identifying associated challenges and opportunities. The case study focuses on the redesign of five residential streets with bioswales in a small Canadian city. Data were collected through: (i) an online and in-person survey with 296 residents, (ii) interviews with 12 municipal stakeholders, residents, or workers, and (iii) two focus groups with children aged 10–11. Findings indicate that implementing bioswales within the right-of-way contributes to enhanced satisfaction with street design for walking and cycling. Bioswales have the potential to improve the comfort and safety of active travellers by reallocating space for pedestrians and cyclists, while segregating non-motorized and motorized traffic. However, satisfaction with bioswales varies significantly among individuals, following personal characteristics and attitudes. Factors such as exhibiting eco-friendly behaviour, valuing the aesthetics of the neighbourhood, and recognizing the socio-environmental and active mobility benefits of bioswales positively contribute to satisfaction. Conversely, limiting factors stem from changes experienced by car drivers due to the new street configuration and limited agreement or awareness regarding the socio-environmental benefits they provide. This paper is relevant to planners and researchers wishing to understand the challenges and opportunities associated with designing multifunctional streets to support sustainable urban drainage systems and active mobility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".