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Cochrane Database Syst Rev. 2005 Apr 18;(2):CD003862.

Red-light cameras for the prevention of road traffic crashes.

Author information

1
RoadPeace, PO Box 2579, Harlesden, London, UK, NW10 3PW. amy.aeronthomas@roadpeace.org

Abstract

BACKGROUND:

Road crashes are a prime cause of death and disability and red-light running is a common cause of crashes at signalised intersections. Red-light cameras are increasingly used to promote compliance with traffic signals. Manual enforcement methods are resource intensive and high risk, whereas red-light cameras can operate 24 hours a day and do not involve high-speed pursuits.

OBJECTIVES:

To quantify the impact of red-light cameras on the incidence and severity of road crashes and casualties, and the incidence of red-light violations.

SEARCH STRATEGY:

We searched the following electronic databases: TRANSPORT (NTIS, TRIS, IRRD,TRANSDOC), Cochrane Injuries Group Specialised Register, Cochrane Controlled Trials Register, MEDLINE, EMBASE and the Australian Transport Index. We checked the reference lists of relevant papers and contacted research and advocacy organisations.

SELECTION CRITERIA:

Randomised or quasi-controlled trials and controlled before-after studies of red-light cameras. For crash impact evaluation, the before and after periods each had to be at least one year in length. For violation studies, the after period had to occur at least one year after camera installation.

DATA COLLECTION AND ANALYSIS:

Two reviewers independently extracted data on study type, characteristics of camera and control areas, and data collection period. Before-after data were collected on number of crashes by severity, collision type, deaths and injuries, and red-light violations. Rate ratio was calculated for each study. Where there was more than one, rate ratios were pooled to give an overall estimate, using a generic inverse variance method and a random-effects model.

MAIN RESULTS:

No randomised controlled trials were identified but 10 controlled before-after studies from Australia, Singapore and the USA met our inclusion criteria. We grouped them according to the extent to which they adjusted for regression to the mean (RTM) and spillover effects. Total casualty crashes: the only study that adjusted for both reported a rate ratio of 0.71 (95% CI to 0.55, 0.93); for three that partially adjusted for RTM but failed to consider spillover, rate ratio was 0.87 (95% CI to 0.77, 0.98); one that made no adjustments had a rate ratio of 0.80 (95% CI 0.58 to 1.12). Right-angle casualty crashes: rate ratio for two studies that partially addressed RTM was 0.76 (95% CI 0.54 to 1.07). Total crashes: the study addressing both RTM and spillover reported a rate ratio of 0.93 (95% CI 0.83 to 1.05); one study that partially addressed RTM had a rate ratio of 0.92 (95% CI 0.73 to 1.15); the pooled rate ratio from the five studies with no adjustments was 0.74 (95% CI 0.53 to 1.03). Red-light violations: one study found a rate ratio of 0.53 (95% CI 0.17 to 1.66).

AUTHORS' CONCLUSIONS:

Red-light cameras are effective in reducing total casualty crashes. The evidence is less conclusive on total collisions, specific casualty collision types and violations, where reductions achieved could be explained by the play of chance. Most evaluations did not adjust for RTM or spillover, affecting their accuracy. Larger and better controlled studies are needed.

PMID:
15846684
PMCID:
PMC6492462
DOI:
10.1002/14651858.CD003862.pub2
[Indexed for MEDLINE]
Free PMC Article

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