Low Traffic Neighbourhoods in London reduce road traffic injuries: a controlled before-and-after analysis (2012–2024)
Bibliographic record
Abstract
BACKGROUND: Between 2015 and 2024, 113 Low Traffic Neighbourhoods (LTNs) were implemented across Greater London, with 27 subsequently removed. We investigated their impacts on road traffic injuries inside LTNs and on 'boundary roads' immediately surrounding the LTNs. METHODS: We matched police-recorded injuries from STATS19 data to Ordnance Survey road links that were spatially intersected with LTNs/boundary roads. Conditional fixed-effects Poisson regression models used the number of injuries per road link per quarter of each year (January 2012 to June 2024) to test whether LTN implementation was associated with changes in injury rates. RESULTS: LTN implementation was associated with a 35% (95% CI 29% to 40%; p<0.001) decrease in all injuries and a 37% (95% CI 24% to 48%; p<0.001) decrease in people Killed or Seriously Injured (KSI). Injuries decreased across a range of casualty and LTN characteristics. However, there was evidence of a smaller benefit in LTNs implemented in Outer London since 2020. Following the removal of an LTN, injury numbers increased back to pre-intervention levels. On boundary roads, there was no evidence of a change in total injury numbers (estimate -2%, 95% CI -5% to +2%) or KSI injury numbers (estimate 0%, 95% CI -7% to +8%). This reflected decreased numbers of injuries on boundary roads for cyclists and motorcyclists, and no change for pedestrians and other motor vehicle users. CONCLUSION: LTNs in London reduced road traffic injuries among all road users inside the LTN areas, with no evidence of overall impact (and for cyclists and motorcyclists a benefit) on boundary roads.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".