Effect of Street Pattern on Road Safety: Are Policy Recommendations Sensitive to Aggregations of Crashes by Severity?
Bibliographic record
Abstract
In the past 50 years, the loops-and-lollipops design has become the basic building block of many urban neighborhoods. In the field of traffic engineering, this combination of cul-de-sacs and loop streets is designed to discourage through traffic and improve road safety, and thus it has the support of many traffic engineers. Perhaps because of its intuitive appeal, few studies have examined the impact of this design on road crashes. The city of Calgary, Alberta, Canada, was used as a case study to examine the effects of neighborhood street patterns on the number of reported crashes. In the study, crashes were converted into equivalent property-damage-only crashes using various weighting factors to check the sensitivity of the finding. Results suggest that currently popular road patterns such as warped parallel, loops, and lollipops are safer than the traditional gridiron pattern. Moreover, this result is quite robust regarding severity weights or aggregation schemes, albeit with some variations in the absolute values of the estimated effects. However, changing the aggregation scheme had a significant effect on some of the control variables used in the model, especially the socioeconomic characteristics, although most of the road features and land use estimates remained robust.
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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 | 0.036 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".