Built Environmental Correlates of Cycling Accidents Involving Fatalities and Serious Injuries in London, UK
Bibliographic record
Abstract
Introduction: Approximately 2552 individuals were killed or seriously injured through cycling accidents in the Greater London Area between 2010-2015. The purpose of this study is to investigate a wide range of built environmental correlates of cycling accidents resulting in KSI so that we can identify potential areas for targeted interventions. Methods: We performed a cross-sectional analysis to examine the association between serious cycling injuries (2010-15), using road segment as the unit of analysis, and a wide range of built environmental characteristics. Multilevel models were used to account for potential spatial clustering. Results: Serious cycling injuries were independently associated with higher commercial and residential densities, higher distance to speed camera, higher bus, car, and 2-wheeled (motorcycle and moped) traffic, and higher density of alcohol outlets. Greenspace was associated with decreased odds of injuries up to the 3rd quartile, but roads adjacent to the highest levels of green space (4th quartile) had increased odds of injuries. Findings from our study point to the potential of urban planning interventions to reduce serious cycling injuries (e.g. speed cameras, improving safety near alcohol outlets and in parks and recreational areas, etc.). Further research using quasi-experimental approaches is required to evaluate whether the implementation of interventions leads to injury reductions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".