Investigation of Violation Reduction at Intersection
Approaches with Automated Red Light Running
Enforcement Cameras in Clive, Iowa, Using
a Cross-Sectional Analysis
Eric J. Fitzsimmons, S.M.ASCE
1
; Shauna L. Hallmark, A.M..ASCE
2
; Massiel Orellana
3
;
Thomas McDonald
4
; and David Matulac
5
Abstract: Red light running causes more than 100,000 crashes and 1,000 fatalities annually and results in an estimated economic loss of
over $14 billion per year in the United States. Red light running is a significant safety issue facing communities which rarely have the
resources to place additional law enforcement in the field. As a result, communities are increasingly turning to automated red light running
camera-enforcement systems to address the problem. The effectiveness of red light running cameras in reducing the number of drivers
who run the red light violations in an Iowa community was evaluated. The number of red light running violations at camera-enforced
intersection approaches were compared to violations at approaches at intersections where cameras were not used within the same
metropolitan area using a cross-sectional analysis. A Poisson lognormal regression was used to evaluate the effectiveness of the cameras
in reducing violations. Results indicated that red light running cameras substantially reduced the number of violations at camera-enforced
approaches as compared to control approaches.
DOI: 10.1061/ASCETE.1943-5436.0000079
CE Database subject headings: Traffic signals; Traffic management; Iowa; Intersections.
Introduction
The Federal Highway Administration FHWA estimates that red
light running causes more than 100,000 crashes and 1,000 fatali-
ties annually which results in an estimated economic loss of over
$14 billion per year in the United States Federal Highway Ad-
ministration FHWA2006. Retting et al. 1995 and Moham-
edshah et al. 2000 indicated that occupant injuries occurred in
45% of red light running crashes as compared to all other urban
intersection crashes. Red light running crashes account for 16–
20% of total crashes at urban signalized intersections Federal
Highway Administration FHWA 2006. Red light running can
be particularly dangerous since many red light running crashes
are right-angle collisions. Bonneson and Zimmerman 2004 es-
timated that red light running costs the state of Texas approxi-
mately $2 billion each year in societal costs. In Iowa alone, a
statewide analysis of red light running crashes, 2001 to 2006,
indicated that an average of 1,682 red light running crashes occur
at signalized intersections every year Fitzsimmons et al. 2007.
Red light running poses a significant safety issue for commu-
nities. However, communities rarely have the resources to place
additional law enforcement in the field to combat the problem so
they are increasingly using automated red light running camera-
enforcement systems at signalized intersections. As with all coun-
termeasures, agencies want to understand the effectiveness of
camera enforcement before committing resources. The effective-
ness of the cameras can be measured in two ways. Reduction in
total crashes or specific crash types is the most useful since cam-
eras are a safety treatment. However, it is difficult to evaluate the
impact of the systems in the short term since crash analyses usu-
ally require several years of data after the treatment is installed.
As a result, the effectiveness of red light running cameras can be
also be evaluated using reductions in the number of red light
running violations. A summary of the known effectiveness of both
methods is described in the following sections.
Crash Reduction
The best measure of the impact of red light running cameras is
reduction in crashes. Several studies have been conducted to com-
pare the crash reduction potential of red light running cameras.
1
Graduate Research Assistant, Dept. of Civil, Construction, and Envi-
ronmental Engineering, Institute for Transportation, Center for Transpor-
tation Research and Education, Iowa State Univ., Ames, 2711 South Loop
Dr., Suite 4700, IA 50010. E-mail: efitz@iastate.edu
2
Associate Professor, Dept. of Civil, Construction, and Environmental
Engineering, Insitute for Transportation, Center for Transportation
Research and Education, Iowa State Univ., 2711 South Loop Dr., Suite
4700, Ames, IA 50010 corresponding author. E-mail: shallmar@
iastate.edu
3
Graduate Research Assistant, Dept. of Statistics and Dept. of
Agronomy, Institute for Transportation, Center for Transportation
Research and Education, Iowa State Univ., 2711 South Loop Dr., Suite
4700, Ames, IA 50010. E-mail: massiel@iastate.edu
4
Safety Circuit Rider, Institute for Transportation, Center for Trans-
portation Research and Education, Iowa State Univ., 2711 South Loop
Dr., Suite 4700, Ames, IA 50010. E-mail: tmcdonal@iastate.edu
5
Traffic Operations Engineer, Iowa Dept. of Transportation, 800
Lincoln Way, Ames, IA 50010. E-mail: david.matula@dot.iowa.gov
Note. This manuscript was submitted on July 21, 2008; approved on
June 18, 2009; published online on June 22, 2009. Discussion period
open until May 1, 2010; separate discussions must be submitted for indi-
vidual papers. This paper is part of the Journal of Transportation Engi-
neering, Vol. 135, No. 12, December 1, 2009. ©ASCE, ISSN 0733-
947X/2009/12-984–989/$25.00.
984 / JOURNAL OF TRANSPORTATION ENGINEERING © ASCE / DECEMBER 2009