Title: Quantifying the causal effect of speed cameras on road traffic accidents via an approximate Bayesian doubly robust estimator
Organisation: Imperial College London (Centre for Transport Studies)
Date uploaded: 5th April 2017
Date published/launched: March 2017
The study, led by Professor Dan Graham from the Department of Civil Engineering at Imperial College London, is based on data for 771 camera sites in eight areas across England - Cheshire, Dorset, Greater Manchester, Lancashire, Leicester, Merseyside, Sussex and the West Midlands. For control sites the researchers randomly sampled 4,787 points on the network across the same eight areas.
The researchers developed an approximate Bayesian doubly-robust estimation method to quantify the causal effect of speed cameras on collisions.
The paper says that previous empirical work on speed camera effectiveness, which shows a ‘diverse range of estimated effects’, is based largely on ‘outcome regression models’ using the Empirical Bayes approach, or on simple before and after comparisons.
The paper’s conclusion reads as follows: “We have developed an approximate Bayesian doubly robust approach for estimation of average treatment effects to analyse the impact of speed cameras on road traffic accidents. This is the first time such an approach has been applied to study road safety outcomes.
“The method we propose could be used more generally for estimation of crash modification factor distributions.
“Our case study results indicate the speed cameras do cause a significant reduction in road traffic accidents, by as much as 30% on average for treated sites.
“This is an important result that could help inform public policy debates on appropriate measures to reduce RTAs.
“The adoption of evidence based approaches by public authorities, based on clear principles of causal inference, could vastly improve their ability to evaluate different courses of action and better understand the consequences of intervention.”
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