Using Community-Based Macrolevel Collision Prediction Models to Evaluate Safety Level of Neighborhood Road Network Patterns
Bibliographic record
Abstract
The enormous social and economic burden imposed on society by injuries due to road collisions is a major global problem. Authorities worldwide have been researching ways to reduce this burden using more proactive road safety planning approaches that build on traditional reactive approaches. Results of recent research suggest that a major cause of the road safety problem lies in a built community form that nurtures over dependence on auto use, leading to increased exposure, risk, and road collisions. In response, the Canada Mortgage and Housing Corporation (CMHC) has begun research on a new model for sustainable subdivision development ¨C The Fused Grid road network pattern. Results of initial traffic flow and accessibility studies suggest that this model has potential to promote increasingly sustainable development patterns by combining several redeeming features from pre-existing models. Subsequently, the study reported on in this paper was initiated to evaluate the relative road safety level of the Fused Grid compared with four other networks, including: commonly-used 1) grid and 2) culs-de-sac patterns, and, recently developed 3) 3-way offset and 4) Dutch sustainable road safety (SRS) patterns. Using community-based, macro-level collision prediction models (CPMs) developed with data from three Canadian regions ¨C Vancouver, Ottawa, and Victoria - the road safety level of each pattern was evaluated. Analysis involved testing theoretical road network modules as well as comparisons with existing neighborhoods. Statistically significant results were obtained, and suggested that neighborhoods built following CMHCi¯s Fused Grid road network pattern would realize a 30 to 60% higher level of road safety than the commonly-used grid and culs-de-sac patterns, and a level of safety comparable to the 3-Way Offset pattern. Next steps in this research involve built-form experiments to validate these significant but still theoretical road safety results, as a precursor to wider implementation by land use and transportation planners to sustainable road safety improvement in communities worldwide.
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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 | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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".