Sustainable Transport Safety : a review of barriers to, and promising engineering tools for, promoting safer bicycling and walking in Canadian communities
Bibliographic record
Abstract
The low level of physical activity in Canadian cities has encouraged planners/engineers to investigate the fundamentals of forming livable communities characterized by more active and healthier lifestyles. A major factor that hinders promoting healthier communities is our auto-oriented developments. Another factor appears to be a lack of integrated, system-based approaches to community planning and Active Transportation (AT) infrastructure design that provides a safe environment for AT users. Therefore, the objectives of this research were to 1) identify the barriers communities face when trying to promote healthy communities, 2) review Canadian practices for encouraging a more active lifestyle and healthy living, and 3) recommend a tool for planners to assess developments in terms of health and safety. Thirty-six one-hour interviews were conducted to collect publicly available data from key stakeholders in randomly selected cities to identify challenges for promoting AT. In addition, the functional ability of the Healthy Development Index (HDI) was examined by conducting a “beta test” case study on nine developments in major urban centers. Moreover, the effectiveness of the Fused Grid (FG) community design was evaluated using the HDI and Sustainable Transport Safety principles. Based on the interviews with stakeholders, it was found that discontinuity of the AT infrastructure system, caused mainly by a lack of available AT funding, is a main barrier to AT use in the majority of cases. It was also found that low density and uncontrolled land development are other main barriers. Conventionally, the focus is more concentrated on providing AT infrastructure within new developments; rather, the focus should also be on integrating the development within the existing larger community, and to enhance high density and good land-use. While the HDI has a comprehensive set of measures that capture most of these factors, the functional ability evaluation identified several drawbacks including: 1) the data collection process to perform the HDI analysis is a difficult and time consuming process for older, existing communities and 2) there is a gap in both the literature and the index on the relationship between intersection density (a vehicle connectivity metric) and, the safety of pedestrians/cyclists and the connectivity of pedestrian/cyclists off-road paths. Moreover, the effectiveness evaluation of the FG community illustrated how it can be a successful model for building sustainably healthy and safer communities.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.020 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".