Using Meta Analysis Techniques to Assess the Safety Effect of Red Light Running Cameras
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Using Meta Analysis Techniques to Assess the Safety Effect of Red Light Running Cameras

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    • Abstract:
      Automated enforcement programs, including automated systems that are used to enforce red light running violations, have recently come under scrutiny regarding their value in terms of improving safety, their primary purpose. One of the major hurdles to overcome in assessing the value of automated enforcement programs is the limited amount of before/after crash and violation data available. The authors of this paper were able to conduct a wide reaching literature review and, in addition, were able to gather together crash data from a few sites in the United States. This study draws on several techniques, termed meta-analysis techniques, to evaluate the effect of automated enforcement cameras on reducing crashes and violations at signalized intersections. Meta-analysis techniques are a family of statistical techniques developed to allow researchers to quantitatively synthesize the findings of a set of evaluation studies that were conducted over a span of years, conducted in several countries, and published in several formats (Elvik, 1999). Meta-analysis techniques summarize the results of several studies and provide an estimate of the average effect of a measure, in this case the effect of automated enforcement systems in reducing crashes at signalized intersections. The small number of identified studies that reported useable crash frequency data limited the number of meta-analysis techniques performed in this study. Even so, the tests that could be conducted demonstrated that the available data appear to be consistent. The mean, mode, and median effect of automated enforcement cameras on reducing combined right-angle and rear-end crashes were each found to be very close to 26%. This means that data from the two studies included indicate the probability of these types of accidents occurring in the after period was reduced by approximately 26%. Given the data limitations, these results should not be emphasized. Rather, the value of this research effort is the demonstration of meta-analysis methods applied to assess the impact of an ITS application. Additional data are needed to fully determine the effect of automated red light enforcement systems on safety at signalized intersections.
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