Red Light Cameras Unsuccessful in Reducing
Fatal Crashes in Large US Cities
Barbara Langland-Orban, PhD
Etienne E. Pracht, PhD
John T. Large, PhD
Objective: In 2011, the Insurance Institute for Highway Safety (IIHS) evaluated changes in fatal
red light running (RLR) and total fatal crash rates in cities that both never used and used red
light traffic cameras (RLCs). The IIHS reported that RLC cities had larger decreases in both fatality
rates. We replicated the IIHS study and then corrected for methodological errors that violated
the IIHS study’s validity. Methods: Negative binomial models were executed for fatal RLR and
total fatal crashes, first excluding one extreme outlier RLC city and then using matched casecontrol cities with similar fatal RLR rates in the “before” period. Results: The camera variable was
not statistically significant in these models or in the replication of the IIHS total fatal crash rate
model. Conclusions: RLCs were not associated with reductions in fatal RLR or total fatal crash
rates. Cities that employ RLCs may not improve the safety of their communities.
Key words: highway safety, motor vehicle fatalities, red light cameras
Health Behavior & Policy Review. 2014;1(1):72-81
DOI: http://dx.doi.org/10.14485/HBPR.1.1.8
R
ed-light traffic cameras (RLCs) are proposed
as an intervention to modify driver behavior
for the purpose of reducing red-light running (RLR) crashes, injuries, and fatalities at signalized intersections. RLCs photograph vehicles
that enter an intersection on a red light, which becomes evidence to issue a traffic ticket that carries
a fine. This is meant to deter drivers from entering
an intersection on a red light. However, RLCs cannot influence accidental red light running, which
occurs when drivers cannot see or fail to notice the
red traffic signal, or cannot safely come to a stop.
Despite the stated purpose, RLCs are controversial. Seven studies were identified in a National
Highway Traffic Safety Administration (NHTSA)
compendium as the best in research design and
methods among 75 RLC studies reviewed.1 None
of the 7 studies documented a safety benefit associated with cameras. More importantly, 3 of the
studies documented an increase in injury or possible injury crashes.2 A study published in the Journal
of Trauma concluded that one city’s RLC program
was not associated with a reduction in crashes and
recommended pursuing other injury prevention
interventions.3 More recently, a publication in
Transport Policy explained how traffic signal timings at RLC sites can be used by municipalities to
increase RLC ticket revenue, and how such timings
can adversely impact safety.4
Currently, 24 states have RLC programs in operation. However, laws regarding their use differ,
meaning there are no uniform standards for RLC
enforcement. Nine states prohibit the use of RLCs,
and 20 states have no law regarding RLC enforcement. Ten states permit unrestricted RLC use,
whereas 11 states permit use with restrictions, such
as requiring a local ordinance or prohibiting use on
federal or state roads. Most states with RLC programs provide for ticketing the registered owner of
the photographed vehicle. Three states with RLC
programs do not have laws that identify whether
the vehicle owner or driver is to be ticketed (Arizona, Louisiana, and New Mexico). Two states (Arizona and California) assign points to the ticketed
Barbara Langland Orban, Associate Professor; Etienne Pracht, Associate Professor; John T. Large, Assistant Professor. Department of Health Policy
and Management, University of South Florida College of Public Health, Tampa, FL.
Correspondence Dr Orban; [email protected]
72
Orban et al
person’s driver’s license.5
Additional differences in state laws include signal
timings at RLC sites and provisions for ticketing
drivers over right turns. Some jurisdictions were
found to set signal timings to increase RLR, which
increases RLC tickets and revenue. In response,
Georgia and Tennessee have passed laws that prohibit shortening yellow light timings at RLC sites.
In addition, state laws differ on ticketing over right
turns. For example, a Tennessee state law was implemented in 2011 prohibiting RLC tickets for
right turns, which was followed by a decline in
RLC tickets by a factor of three-fourths.4
The present study replicated an analysis conducted by the Insurance Institute for Highway Safety
(IIHS), which was published in the Journal of Safety Research,6 and then corrected for obvious methodological errors in the IIHS study to determine if
the findings are valid. The IIHS study analyzed the
association of RLCs to 2 outcome variables: changes in fatal red light running (RLR) crash rates and in
total fatal crash rates at signalized intersections in 62
large US cities. Fourteen cities implemented RLC
programs between 2004 and 2008 whereas 48 cities did not. The prior time period used for comparison was 1992 to 1996 when none of the 62
cities had RLCs. The IIHS study also controlled for
each city’s land area and population density. It concluded that cities using cameras had an estimated
24% lower fatal RLR crash rate and an estimated
17% lower total fatal crash rate “than would have
been expected without cameras.”
The objective of the present analysis was to replicate the statistical results of the original IIHS study
and then to determine whether the estimates for
the RLC (camera) variable from the IIHS study
would remain statistically significant after correcting for obvious methodological flaws. Table 1
provides the means and variances for the variables
used in the IIHS study. To duplicate the exact values reported in the Hu, McCartt and Teoh6 study,
the “weighted” means were derived by weighing
the individual population adjusted fatality rates by
the total population. The weighted rates shown in
Table 1 were identical to those reported in Table 1
of the Hu, McCartt and Teoh study.
An inherent weakness to an unadjusted beforeand-after study is the likelihood of regression to the
mean. Therefore, the first methodological error in
the IIHS study was the lack of control for regression to the mean, allowing for the larger percent
reduction to occur in cities that used RLCs. As
reported in Table 1, the average annual RLR fatal
crash rate per one-million population (weighted)
was much lower in 1992-1996 for the cities that
did not adopt RLCs than the cities that did (4.79
and 7.16, respectively). Thus, the cities that used
RLCs averaged a 50% higher fatal RLR crash rate
at the outset. Similarly, the average annual total
fatal crash rate at signalized intersections per onemillion population was lower among cities that
did not use RLCs (13.03 and 16.38, respectively).
For internal validity, the means of the 2 groups
should have been similar in the baseline period
(1992-1996).
Further, one camera city, Phoenix Arizona, was
an extreme outlier from the other cities in terms
of fatality rates. Including all 62 cities in the study,
the average fatal RLR crash rate was 5.61 per million population with a variance of 13.15. Excluding Phoenix from the sample reduced the fatality
rate substantially to 5.05 and, more importantly,
reduced the variance by more than half to 6.45.
In other words, a single camera city accounted for
over half of the observed variance that the IIHS
study sought to explain in the sample. For internal validity, RLC and non-RLC cities should have
been matched such that the fatality rates were similar in the “before” period.
In addition to the obvious potential for regression
to the mean in the RLC-city group, 6 of the 48 nonRLC cities started with zero or a single RLR fatality in the 5-year “before” period, which essentially
precluded them from having a meaningful decrease
in the fatal RLR crash rate. Whereas extreme rates
can be used in research, they should be selected such
that the 2 groups (treated and comparison) are similarly extreme, either high or low.7
Figure 1 clearly illustrates the potential for a regression to the mean problem in these data. The
rate associated with Phoenix, AZ reflects the most
obvious case with a “before” period rate that was
well over 3 standard deviations removed from the
mean. There was no non-RLC city in the sample
with a comparable rate. In addition, 10 of the
14 RLC cities (71%) started with rates above the
mean, whereas only 23% of non-RLC cities were
above the mean in the “before” period. Because the
Health Behavior & Policy Review. 2014;1(1):72-81
DOI: http://dx.doi.org/10.14485/HBPR.1.1.8
73
Red Light Cameras Unsuccessful in Reducing Fatal Crashes in Large US Cities
Table 1
Variable Means and Variance for 1992-1996
14 Camera Cities
1992-1996
Area in square miles
Population density
Mean
Variance
Mean
Variance
126.91
13,384.74
171.72
74,912.96
5.63
12.34
4.45
11.43
Camera installation
0
0
0
0
644,549
486-B
355,689
49-B
Fatal RLR crashes (5-year average)
23.07
752.84
8.52
80.68
Fatal crashes (5-year average)
52.79
3589.87
23.17
403.55
RLR fatalities per 1M population (weighted)
7.16
19.05
4.79
8.10
All fatalities per 1M population (weighted)
16.38
65.84
13.03
35.10
Annual population (average)
14 Camera Cities
2004-2008
Area in square miles
48 Non-camera Cities
2004-2008
135.77
15,714.77
179.96
74,358.12
Population density
6.08
12.19
4.54
12.52
Camera installation
1.00
0.00
0.00
0.00
Annual population
720,250
51-B
397,417
57-B
Fatal RLR crashes
16.79
405.10
8.15
49.62
Fatal crashes
50.50
3235.81
26.38
399.52
RLR fatalities per 1M population (weighted)
4.66
7.62
4.10
4.39
All fatalities per 1M population (weighted)
14.02
31.61
13.27
35.78
cities that used RLCs had substantially higher average fatal RLR and total fatal crash rates in the
“before” period, this allowed for greater percentage
reductions.
As additional methodological issues, the use of
Poisson regression by the IIHS is problematic because data assumptions for using Poisson were not
met. Poisson is typically used for count data, such
as the integer number of total fatal RLR crashes or
total fatal crashes. The IIHS reported using the rate
of RLR and total crashes as their dependent variable. Alternatively, they could have used the integer
count of fatalities while controlling for the cities’
population on the right hand side, as a weight, in
the equation.
Further, a cursory examination of the data indicates that a critical assumption made by the
Poisson model was not met. The IIHS study used
Poisson regression to analyze fatal RLR crash rates
and total fatal crash rates at signalized intersections
74
48 Non-camera Cities
1992-1996
absent consideration of the means or variances of
the variables used in the model. The key assumption of a Poisson model is the relationship between
the conditional mean and the variance. When a
Poisson distribution is specified, it requires that the
conditional variance be equal to the conditional
mean. When this is not the case and the data are
over-dispersed, as is frequently the case, the Poisson estimator becomes inconsistent, and standard
errors may be underestimated, thereby inflating
statistical significance.8 Poisson regression was used
despite the data not meeting this key assumption
for using Poisson, as illustrated in Table 1. Because
the data were over-dispersed, the negative binomial
distribution should have been used.
This analysis replicated the IIHS study using the
same data, and then used improved models that
correct for the methodological flaws discussed to
determine if the IIHS “camera” variable was still
associated with a statistically significant reduction
Orban et al
Figure 1
Cities Sorted in Descending Order by Fatalities Per One-million Population, 1992-1996
Note.
Solid dots represent RLC cities whereas outlined dots represent non-RLC cities.
in fatal RLR and total crash rates at signalized intersections in cities that used RLCs.
METHODS
Data used in the present analysis were restricted
to that used in the 2 models in the IIHS study.6 The
dependent variables reported in the IIHS study
were the Fatal RLR Crash Rate and the Total Fatal Crash Rate at signalized intersections. Table 2
describes the 5 independent variables used in each
model. The rationale for their inclusion was provided in the publication of the IIHS study.
In the present study, each city’s population size,
camera use, and RLR and total crashes and fatality
rates were obtained from the IIHS study. The land
area data for the cities were obtained from the 1990
Health Behavior & Policy Review. 2014;1(1):72-81
and 2000 US Census data to match the sources
specified in the IIHS study. Because each city had
2 repeated observations for land area, one for each
period, the resulting correlation in the error term
was accounted for in the analysis. Statistical Analysis System software (SAS version 9.3) was used to
perform all statistical analyses.
To begin, the IIHS study was replicated using
their data and analytic method (Poisson regression)
to corroborate their findings. Then, to address overdispersion, a negative binomial distribution was
used because it does not rely on the same strong
assumptions as the Poisson concerning the equality of the conditional mean and variance. Negative
binomial regression is used to account for over dispersion that is evident in data.8 Thus, in addition
DOI: http://dx.doi.org/10.14485/HBPR.1.1.8
75
Red Light Cameras Unsuccessful in Reducing Fatal Crashes in Large US Cities
Table 2
Independent Variables Used in IIHS Study
Variable
Definition
Land area
City land area measured in square miles. The 1992-1996 time period used land area in 1990 and
the 2004-2008 time period used land area in 2000
Population density
1000 people per square mile. The 1992-1996 time period used the 1997 population and the 20042008 time period used the 2009 population.
Study period
0 = 1992-1996; 1 = 2004-2008
City implemented
0 = 48 cities that did not use cameras; 1 = 14 cities that used cameras during 2004-2008
Camera
0 = no camera; 1 = camera (identified in the IIHS study as the interaction of the study period and
city implemented)
to the Poisson regression replication of the IIHS
study, this analysis estimated the parameters, first
assuming the more appropriate negative binomial
for all 62 cities included in the IIHS model. Then,
to illustrate the influence of a single major outlier,
the negative binomial model was used for all observations with the exception of Phoenix (61 cities).
To correct for the difference in average fatal RLR
crash rates reported during 1992-1996 between
cities that did and did not use RLCs, a case-control model was developed. For each city that used
RLCs, control cities were identified that did not
use RLCs. Matching of camera cities and controls
was done based on RLR fatality rates and population density. Each RLC city was matched based
on having a fatality rate within one standard deviation and a population per square mile within one
standard deviation. From all nearest matches found
per case (camera city), indicating they were within
one standard deviation from one another, up to 3
control cities were selected using a random number
generator. Negative binomial models for fatal RLR
and total fatal crashes were then executed using the
identified case and control cities.
Table 3 provides the means and variances for the
case and control cities, and identifies the control cities. This case-control model included 13 RLC cities. Phoenix was the only RLC city excluded from
this analysis. From 1992 through 1996, Phoenix
had a fatal RLR crash rate per one-million population of 18.2, which was 2 standard deviations from
the mean of the highest RLR fatal crash rate among
non-RLC cities, specifically Memphis with a rate of
76
11.6. Thus, a control city did not exist in the data for
Phoenix. Among the 48 cities that did not use cameras, 31 were included in the final analysis, as they
had a similar RLR fatality rate and population density as a city using cameras. In this matched negative
binomial analysis, the case and control city groups
had equivalent fatal RLR crash rates (weighted) of
5.63 and 5.51 per one-million population, respectively, during 1992 through 1996.
As suggested in the IIHS publication, their Poisson regression models were as follows:
Fatality Rate = β0 + β1LandArea
β2PopulationDensity
+
β3StudyPeriod
β4CityImplemented + β5Camera
+
+
The Poisson regression model for rates is as
follows.9
log(u) = log(E(Yi)) = β' xi + log ti
In the present model, μ is the mean number fatalities for all subjects and Yi is the average fatality
count at city i. β is a vector of regression coefficients
and xi is a vector of covariates for subject i; and log
ti, referred to as an offset variable, which is needed
in the RLC study to account for the different population sizes (ti) of the different cities. In essence,
fatality rates are calculated and weighted by each
city’s population relative to the total of all cities’
population. In the models created with more statistical rigor, negative binomial was used with fatality
counts weighted by the population.
Orban et al
Table 3
Variable Means and Variance for Case-Control Cities, 1992-1996 and 2004-2008
13 RLC Cities (Case)
1992-1996
Area in square miles
31 Non-RLC Cities (Control)
1992-1996
Mean
Variance
Mean
Variance
104.38
6796.49
134.02
22,825.33
Population density
5.86
12.55
5.12
14.11
Camera installation
0
0
0
0
Annual population
609,614
508B
402,330
64B
Fatal RLR crashes
17.15
284.47
11.10
100.75
Fatal crashes
41.69
2022.56
28.55
496.26
RLR fatalities per 1M people (weighted)
5.63
2.43
5.51
7.54
All fatalities per 1M people (weighted)
13.68
15.06
14.19
35.60
13 Camera Cities
2004-2008
Area in square miles
31 Non-Camera Cities
2004-2008
104.38
6796.49
134.37
22777.59
Population density
6.30
12.45
5.54
15.56
Camera installation
1
0
0
0
Annual population
659,568
499B
451,036
74B
Fatal RLR crashes
12.23
124.19
10.10
59.62
Fatal crashes
39.77
1759.03
31.38
471.98
RLR fatalities per 1M people (weighted)
3.71
2.90
4.47
4.32
All fatalities per 1M people (weighted)
12.06
11.40
13.91
39.16
Control cities were Anaheim, CA, Arlington, VA, Aurora, CO, Birmingham, AL, Boston, MA, Cincinnati, OH, Detroit, MI, Fort Wayne, IN, Henderson, NV, Hialeah, FL, Indianapolis, IN, Jacksonville, FL, Jersey City, NJ, Kansas
City, MO, Las Vegas, NV, Lexington-Fayette, KY, Lincoln, NE, Louisville, KY, Memphis, TN, Miami, FL, Milwaukee,
WI, Newark, NJ, Omaha, NE, Reno, NV, Rochester, NY, Saint Paul, MN, Saint Petersburg, FL, San Antonio, TX, San
Jose, CA, Tampa, FL, and Wichita, KS.
RESULTS
The IIHS analysis was first replicated. Table 4
provides the results of the IIHS study and the replication of their study (Poisson, all observations) for
the dependent variable RLR fatal crash rate. The
values associated with the independent variables
are the estimated coefficients, the standard errors
are provided in parentheses, and the p-values in
square brackets.
For the replication of the IIHS study a choice
had to be made concerning the specification of the
dependent variable. The first option was to specify
it as the rate, a non-integer response variable, and
further weighing the regression by the annual population. The IIHS study described the dependent
variable as such. The second option was to specify
the count or integer value of fatal accidents, as is
conventional in the case of Poisson and negative
binomial regressions, and, subsequently, normalize
the number of fatalities by using the logarithm of
the population as an offset variable. The difference
between these specifications manifested itself in the
estimates of the intercept. Whereas the former produced almost exactly the intercept reported in the
IIHS study, the latter was deemed more appropriate as it avoided using a non-integer response variable in a Poisson and negative binomial regression.
More importantly, for the purposes of replicating
the IIHS results, none of the remaining variables
were affected by the choice of specifications as de-
Health Behavior & Policy Review. 2014;1(1):72-81
DOI: http://dx.doi.org/10.14485/HBPR.1.1.8
77
Red Light Cameras Unsuccessful in Reducing Fatal Crashes in Large US Cities
Table 4
Regression Results for Fatal Red Light Running Crashes
IIHS Study
Findings
Replication of
IIHS Study
Poisson,
All Obs
Poisson,
All Obs
All Obs
All Obs,
Less Phoenix
Matched
Intercept
1.7050
(0.1547)
[<.0001]
-10.515
(0.137)
[<.001]
-10.652
(0.140)
[<.001]
-10.647
(0.146)
[<.001]
-10.201
(0.152)
[<.001]
Land area
0.0001
(0.0003)
[.6391]
0.000
(0.000)
[.503]
0.000
(0.000)
[.210]
0.000
(0.000)
[.596]
-0.001
(0.000)
[.041]
Population density
-0.0371
(0.0191)
[.0527]
-0.037
(0.023)
[.111]
-0.019
(0.019)
[.316]
-0.014
(0.019)
[.445]
-0.040
(0.017)
[.020]
Study period
-0.1709
(0.0678)
[.0117]
-0.160
(0.081)
[.049]
-0.145
(0.072)
[.044]
-0.143
(0.071)
[.045]
-0.196
(0.085)
[.021]
City implemented
0.4998
(0.1436)
[.0005]
0.506
(0.256)
[.048]
0.499
(0.170)
[.003]
0.348
(0.128)
[.007]
0.155
(0.122)
[.204]
Camera
-0.2809
(0.1079)
[.0092]
-0.276
(0.106)
[.009]
-0.282
(0.136)
[.039]
-0.254
(0.153)
[.096]
-0.200
(0.148)
[.177]
scribed here.
In addition, Table 4 provides the results of the 3
negative binomial regression models for fatal RLR
crashes weighted by population. The first uses all observations from the IIHS study (62 cities), the second uses all observations except Phoenix (61 cities),
and the third uses the matched case-control cities
(44 cities). Consistent with assumptions for using
negative binomial, the standard error and p-value
for the camera variable were larger in the negative binomial model for all observations than in the IIHS
model. More importantly, the camera variable was
not significant in the negative binomial model at alpha=.05 when Phoenix was excluded (p = .096). The
p-value for the camera variable was even larger in the
matched case-control model (p = .177). In all models, the study period variable was statistically significant, which is consistent with fatal RLR crash rates
decreasing between the 2 periods in both RLC and
non-RLC cities. In only the matched model, the city
implemented variable was not significant because it
was the only model where cities had similar RLR
fatality rates in the “before” period.
Table 5 provides both the IIHS results and the
78
Negative Binomial
replication of the IIHS study analysis for Total Fatal Crash Rates at signalized intersections, as well
as the results of the 3 negative binomial models.
The replication of the IIHS study (Poisson, all observations) differed from the IIHS study regarding
the camera variable. Whereas the estimates for the
camera variable were somewhat similar, the p-value
in the replication was not significant at alpha=.05
(p = .070). The study findings from the replication and the 3 negative binomial models (all observations, all observations except Phoenix, and
matched cities) concluded the camera variable was
not statistically significant at alpha=.05.
The negative binomial model is a more general
case of the Poisson model. Whereas Poisson relies
on the assumption that the conditional variance
equals the conditional mean, negative binomial
does not require this criterion. When the data are
over dispersed, Poisson regression is inconsistent
and may underestimate the true standard errors,
producing lower p-values, which may influence the
conclusions of the model. This is evidenced in the
IIHS study replication. Using the same data, including all observations, the standard errors of the
Orban et al
Table 5
Regression Results for Fatal Crashes at Signalized Intersections
IIHS
Study Findings
Replication of
IIHS Study
Poisson,
All Obs
Poisson,
All Obs
All Obs
All Obs,
Less Phoenix
Matched
Intercept
2.5994
(0.1314)
[<.0001]
-9.646
(0.113)
[<.001]
-9.783
(0.121)
[<.001]
-9.777
(0.120)
[<.001]
-9.537
(0.133)
[<.001]
Land area
0.0002
(0.0002)
[.3805]
0.000
(0.000)
[.147]
0.000
(0.000)
[.045]
0.000
(0.000)
[.074]
0.000
(0.000)
[.888]
Population density
-0.0187
(0.0160)
[.2428]
-0.010
(0.020)
[.610]
0.015
(0.019)
[.446]
0.017
(0.019)
[.362]
-0.001
(0.018)
[.979]
Study period
0.0112
(0.0564)
[.8426]
0.015
(0.066)
[.819]
0.010
(0.059)
[.872]
0.012
(0.059)
[.836]
-0.030
(0.076)
[.689]
City implemented
0.2812
(0.1284)
[.0285]
0.262
(0.208)
[.207]
0.235
(0.150)
[.119]
0.118
(0.123)
[.340]
0.001
(0.125)
[.995]
Camera
-0.1822
(0.0914)
[.0462]
-0.175
(0.097)
[.070]
-0.206
(0.124)
[.096]
-0.173
(0.135)
[.200]
-0.122
(0.139)
[.378]
camera variable in Tables 4 and 5 in the Poisson
model were smaller than the standard errors in the
similar negative binomial model.
DISCUSSION
When using models with greater statistical rigor
to assess the association of RLCs with both fatal
RLR and total fatal crash rates at signalized intersections, RLCs were not associated with differences
in either fatal RLR or total fatal crash rates. Further,
the IIHS finding that RLCs were associated with
a significant reduction in total fatal crash rates at
signalized intersections could not be corroborated
because the replications of their model concluded
that the camera variable was not statistically significant. However, the reason for the difference is
unknown. Reasons may include the final specification of the dependent variable as the rate versus the
count of fatalities or a discrepancy in the land area
variable, which had to be collected independently.
It should be noted that the findings of a previous
IIHS study of RLC effectiveness were found to be
inaccurate. In 2002, IIHS researchers Retting and
Kyrychenko published an RLC study in the Ameri-
Health Behavior & Policy Review. 2014;1(1):72-81
Negative Binomial
can Journal of Public Health10 concluding that the
camera variable was statistically significant in reducing total crashes at signalized intersections in Oxnard, California. Subsequently, Burkey and Obeng
replicated the IIHS analysis and concluded the pvalue for the camera variable was not statistically
significant, contrary to what the IIHS researchers
reported.11 The IIHS researchers responded that
they deviated from the methods described in their
publication and had omitted a variable because it
was not statistically significant.12 Large, Orban and
Pracht also replicated the Retting and Kyrychenko
study and verified Burkey and Obeng’s finding,
confirming the p-value for the camera variable was
not statistically significant.13 In the IIHS study, the
p-value for the camera variable was reported as p
= .0281, whereas the 2 replications found the pvalue was .061, which was not significant at the
alpha=.05 level. In addition, the camera variable
was not statistically significant in the replication of
the reduced model that Kyrychenko and Retting
claimed to use after Burkey and Obeng reported the
discrepancy.13 Nonetheless, current IIHS researchers continue to cite the Retting and Kyrychenko
DOI: http://dx.doi.org/10.14485/HBPR.1.1.8
79
Red Light Cameras Unsuccessful in Reducing Fatal Crashes in Large US Cities
study as evidence of RLC effectiveness in “citywide
crash reductions at signalized intersections.”6
In a letter to the editor in the St. Petersburg Times,
the President of the IIHS responded to criticisms
of the research methods used in the IIHS Oxnard
RLC research study, writing the following.
“Meanwhile, peer-reviewed research of the Insurance Institute for Highway Safety is criticized as ‘unscientific’ for using sound methods to compare crashes before and after photo
enforcement began in Oxnard, Calif. One such method was
to include intersections without cameras in the analysis because photo enforcement reduces crashes communitywide,
not just at camera sites. Excluding intersections without
cameras dilutes crash reduction results.”14
The 2 replications of the IIHS Oxnard study did
not find a significant reduction in crashes at signalized intersections communitywide. Further, the
IIHS study design could not make any such conclusion because the comparison group was nonsignalized intersections in the same community,
recognizing these sites would not have any RLR
crashes. More importantly, studies that merge data
from RLCs sites with non-RLC sites can conceal any
increase in crashes and injuries at the camera sites.
The IIHS advocacy for RLCs should be viewed
with caution because it is funded by automobile
insurance companies and their related associations.
At the time the Centers for Disease Control and
Prevention (CDC) deemed motor vehicle safety as
one of the top 10 public health accomplishments
of the 20th century in 1999,15 automobile insurance revenues and profits had become stagnant. In
response, the automobile insurance industry developed new pricing models that allow for penalizing
(increasing) drivers’ premiums for every adverse
factor identified, thereby increasing profitability
over the previous pricing model that had used only
4 or 5 underwriting tiers.16 The IIHS has a potential
financial conflict of interest because camera tickets
may be used to justify insurance rate increases.
IMPLICATIONS FOR HEALTH BEHAVIOR
OR POLICY
Two percent of US traffic fatalities result from
red light running, and the number has been decreasing over time absent the use of cameras. Red
light running fatalities decreased from 937 to 676
from 2000 to 2009, a 28% decline. This improve-
80
ment occurred prior to the growth in the use of red
light running cameras.17
Whereas efforts to achieve further reductions are
indicated, the data from the IIHS study provide
evidence that RLCs were not associated with significant changes in fatal red light running or total fatal
crashes at signalized intersections. This finding is
consistent with a comprehensive 7-year RLC study
in Virginia that used empirical Bayes to analyze 6
jurisdictions. It concluded that RLCs were not associated with a significant change in RLR crashes.18 If
RLCs do not reduce RLR crashes, then they would
not be expected to reduce RLR fatalities. The Virginia findings and IIHS data are consistent with
RLR crashes and injuries occurring from accidental (unintentional) RLR and not from intentional
RLR, which is the focus of RLCs. Also, the Virginia study concluded RLCs were associated with
a 29% increase in crashes and an 18% increase in
injury crashes.18 Consequently, whereas RLCs may
be legal in many states, they are nonetheless an unethical intervention because the increase in crashes
and injuries violates the medical ethical principle of
nonmaleficence – first do no harm.
Human Subjects Approval Statement
Human subjects were not used in this analysis.
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Health Behavior & Policy Review. 2014;1(1):72-81
DOI: http://dx.doi.org/10.14485/HBPR.1.1.8
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