Identifying the Influence of Dangerous Intersections in Measuring Accessibility for Children's Independent Mobility, A Case Study in Montreal, Canada
Bibliographic record
Abstract
ABSTRACT: Background: In transportation planning, children are marginalized and often neglected, as the focus is primarily on adult needs and abilities. The daily travel destinations of children are also different from those of adults. Walking speeds and abilities limit the distances that children can travel. Additionally, fear of traffic danger can also prevent children from traveling independently. Intersections are junctions that allow children to change direction but are also locations where conflict between road users can be frequent, which can limit children's travel. As such, their impact on children's independent travel is important. Objective: The objective of this study is to understand to what extent children's independent travel accessibility could be limited by the level of traffic danger at intersections. Methodology: Using open data, the methodology for this study has two key steps. First, “dangerous” intersections for children (aged 8 to 12) were identified in a specific area of the city of Montreal based on three major traffic danger components available in open datasets: speed limit, road class and design, and traffic control. Weights were given to each of these components based on experts’ prioritization. The second step involved the calculation of children's accessibility on foot (measured as the number of destinations within a specific distance) with and without dangerous intersections considered as barriers. Results: Among all intersections in the selected neighborhoods, 1673 dangerous intersections were identified. Accessibility without considering traffic danger ranges between 1 to 45 destinations. When considering traffic danger, accessibility drops to between 0 and 19. This reflects a 20% decrease in children's walking service areas. Conclusion: Our results demonstrate that children's accessibility is different when traffic danger is taken into account, which could be a major deterrent to choosing to walk to destinations, either by the children themselves or with their parents.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".