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Evaluating the Effectiveness of Red Light Running Camera Enforcement in Cedar Rapids and Developing Guidelines for Selection and Use of Red Light Running Countermeasures

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Camera and radio frequency (RF) antennae setup on mast arm

Researcher(s)

Principal investigator: Shauna Hallmark, 515-294-5249, shallmar@iastate.edu (project list)

Co-principal investigator: Tom McDonald, 515-294-6384, tmcdonal@iastate.edu (project list)

Project status

Completed

Start date: 02/01/10
End date: 06/01/11

Publications

Report: December 2011, Evaluating the Effectiveness of Red Light Running Camera Enforcement in Cedar Rapids and Developing Guidelines for Selection and Use of Red Light Running Countermeasures 3.32 mb (*pdf)

Related publications:

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Sponsor(s)/partner(s)

Sponsor(s):Iowa Department of Transportation
Midwest Transportation Consortium

About the research

Abstract:

Red light running (RLR) is a problem in the US that has resulted in 165,000 injuries and 907 fatalities annually. In Iowa, RLR-related crashes make up 24.5 percent of all crashes and account for 31.7 percent of fatal and major injury crashes at signalized intersections.

RLR crashes are a safety concern due to the increased likelihood of injury compared to other types of crashes. One tool used to combat red light running is automated enforcement in the form of RLR cameras. Automated enforcement, while effective, is often controversial.

Cedar Rapids, Iowa installed RLR and speeding cameras at seven intersections across the city. The intersections were chosen based on crash rates and whether cameras could feasibly be placed at the intersection approaches.

The cameras were placed starting in February 2010 with the last one becoming operational in December 2010. An analysis of the effect of the cameras on safety at these intersections was determined prudent in helping to justify the installation and effectiveness of the cameras.

The objective of this research was to assess the safety effectiveness of the RLR program that has been implemented in Cedar Rapids. This was accomplished by analyzing data to determine changes in the following metrics: