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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".