### Effects o f Red Light Camera Enforcement o n Fatal Crashes i n Large U S Cities

Wen Hu Anne T. McCartt Eric R. Teoh

February 20111 

### Abstract Objective : To estimate the effects of red light camera enforcement on per capita fatal crash rates at intersections with signal lights

. Methods : From the 99 large US cities with more than 200,000 residents in 200 8 , 14 cities were
identified with red light camera enforcement programs during 2004

- 08 but not during 1992

- 96 , and 48
cities were identified without camera programs during either period. A nalys e s compared the citywide per capita rate of fatal red light running crashes and the citywide per capita rate of all fatal crashes at signalized intersections during the two study periods, and rate changes then were compared for cities with
and without cameras progr a m s . Poisson regression was used to model c rash rates as a function of red light camera enforcement, land area, and population density.
Results : The average annual rate of fatal red light running crashes declined for both study groups, but the decline was larger for cities with red light camera enforcement programs than for cities without

camera program s ( 35 vs. 14 percent). The average annual rate of all fatal crashes at signalized
intersections decreased by 14 perce nt for cities with camera programs and increased slightly (2 percent)
for cities without cameras. After controlling for population density and land area , t he rate of fatal red light running crash es during 2004

- 08 for cities with camera program s was an est imated 24 percent lower
than what would have been expected without cameras. The rate of all fatal crashes at signalized intersections during 2004

- 08 for cities with camera programs was an estimated 1 7 percent lower than
what would have been expected witho ut cameras.
Conclusions : Red light camera enforcement programs reduce the citywide rate of fatal red light running crash es and, to a less er but still significant extent, the rate of all fatal crash es at signalized
intersections. Cities wishing to reduce f atal crash es at signalized intersections should consider red light
camera enforcement

.2 

### 1. Introduction
M ore than 2.2 million police

- reported motor vehicle crashes in the United States in 2009 occurred
at intersection s or were intersection related , account ing for a bout 41 percent of all police

- reported crashes

. These crashes resulted in 81,112 serious nonfatal injuries and 7,358 deaths

. About one
- third of the deaths occurred at intersections with signal lights ( Insurance Institute for Highway Safet y, 2010 a )

. Running a red light is a common traffic violation. A study of traffic at 19 intersections in 4 states reported an average of 3.2 red light running events per hour per intersection (Hill and Lindly, 2003)

. In a
national telephone survey conducted in 2010, 93 percent of drivers said i t is unacceptable to go through a red light if it is possible to stop safely, but one

- third reported doing so in the past 30 days ( AAA Foundation for Traffic Safety, 2010)

. The safety consequences of running red lights are considerable. A study of urban crashes reported that running red lights and other traffic controls was the most common type of crash (22 percent). Injuries occurred in 39 percent of crashes in which motorists ran traffic controls (Retting et al., 1995). In 2009, 676 people were killed and 113,000 were injured in crashes in which police were able to establish that drivers ran red lights

. Sixty

- four percent of these deaths were people other than the red light

runners , including passengers in the red light running vehicles, occupants of the other vehicles,

p edestrians , and bicyclists

. C ompared with the drivers involved in these crashes wh o did not violate the signal , red light runners were more likely to be male, to b e younger than 30, and to have prior crashes,
alcohol

- impaired driving convictions, or citations for speeding or other moving violations. V iolators also

were much more likely to have been speeding or alcohol impaired at the time of the crash, and less lik ely
to have had a valid driver’s license ( Insurance Institute for Highway Safety, 2010b )

. A high likelihood of apprehension helps convince motorists to comply with traffic laws, b ut many enforcement agencies have insufficient personnel to mount effective enforcement programs using traditional police patrols. Red light cameras can supplement traditional methods of enforcement at
intersections , especially at times of the day and on roads where traditional enforcement can be difficult or
hazardous

. Studies have reported reductions in red light violations of 40

- 96 percent after the introduction3 

### of red light cameras (Retting et al., 1999a , 1999b ; Retting et al., 2008) , and reductions o ccurred not only

at camera

- equipped sites but also at signalized intersections with out cameras. A study of the impact of red light camera enforcement on crashes in Oxnard, California, one of the first US communities to employ such cameras, reported signif icant citywide reductions in crashes at intersection s with traffic signals, with injury crashes reduced by 29 percent (Retting and Kyrychenko, 2002)

. Right

- angle
collisions , the crash type most closely associated with red light running , at these intersect ions declined by 32 percent, and right

- angle crashes involving injuries fell by 68 percent

. Some studies have reported that even though red light cameras reduce front

- into

- side collisions and overall injury crashes, they can increase rear

- end crashes. A study evaluat ing red light camera programs in 7 communities reported a 25 percent reduction in right

- angle crashes , whereas rear
- end
crashes increased by 15 percent. Because the types of crashes prevented by red light cameras tend to be more severe and more costly than the additional rear

- end crashes that can occur, the study estimated a positive social benefit of more than $18.5 million in the 7 communiti es (Council et al., 2005)

. N ot all studies have reported increases in rear

- end crashes. A review of 10 controlled before

- after studies of red light camera effectiveness that adjust ed for regression to the mean , spillover effects, or both, reported an

est imated 13

- 29 percent reduction in all types of injury crashes , a 24 percent reduction in right

- angle injury crashes , and a nonsignificant 18 percent reduction in rear
- end injury crashes (Aeron

- Thomas and Hess, 2005). Red light c ameras have proven to be c ontroversial in some US communities, but the number of
communities that implemented camera program s during 1992

- 2010 has increased dramatically , from no
communities i n 1992 to 25 communities in 2000 and 501 communities in 2010 (Figure 1)

. N umerous studies have examined the effects of red light camera enforcement on all crashes or crashes involving injury, but few if any studies have examined the e ffects on fatal crashes

. The present study evaluate d the effect of camera enforcement on per ca pita fatal crash rates for large US cities. Changes in per capita rates of fatal red light running crashes were compared for cities with and without

camera programs. Because prior research reported citywide effects of red light cameras on all crashes at4 

### signalized intersections , the present study also examined changes in the rates of all fatal crashes at signalized inte rsections in these cities.

2. Method
L arge US cities were defined in this study as those with more than 200,000 residents; there were 99 such cities in 2008 (US Census Bureau, 2009 ). Information on red light camera program s in these 99 cities was obtained from news reports and calls to city police departments or public works departme nts.
For cities with camera enforcement , program start and end dates were obtained

. Other historical information was sought but was not available for all cities, including the number of cameras and number of signa lized intersections over time.
Calendar y ears 2004

- 08 , the latest 5 years for which fatal crash data were available, represented the “after” study period. Calendar years 1992

- 96 represented the “before” study period; very few US
communities had camera program s during this time ( Figure 1 )

. The 1 4 cities with camera program s during 2004

- 08 but not during 1992

- 96 comprised the camera group. The 48 cities without camera
progra ms during either time period comprised the comparison group. Of the remaining cities, 4 cities implemented camera program s prior to 1997, and 33 cities had camera program s for some but not all of
the 2004

- 08 period

. These 37 cities were excluded from analyses. Data on fatal crashes at intersections with signal lights were extracted for 1992

- 96 and 2004

- 08 from the Fatality Analysis Reporting System (FARS), which c ontains detailed information on all fatal
motor vehicle crashes occurring on US public roads ( National Highway Traffic Safety Administration,
1992

- 96, 2004

- 08 ). F atal red light running crash es were defined as the s ubset of these crashes that
involved a driver traveling straight who was assigned the driver level contributing factor of “ failure to obey traffic control devices

. ” This definition was developed jointly by the Insurance Institute for Highway Safety and Fe deral Highway Administration so that consistent estimates of red light running crash losses would be produced (Retting, 2006).5 

### Annual population estimates were obtained for each city from the US Census Bureau (1997, 2009). For each city in each study peri od and for each crash measure, the average annual per capita fatal crash rate (crashes per million population) was calculated by summing fatal crashes across the 5

- year period and then dividing by the sum of the annual population counts. This resulted in two observations ( one each for the before and after periods) per city for the rate of fatal red light running crash es and for the rate of all fatal crashes at signalized intersections. To study the citywide effect of camera enforcement

on fatal crash rates, the per capita crash rates were computed for each study group for the 2004

- 08 period , aggregating crashes and population across the cities in each group, and these rates were compared with
th ose for the 1992

- 96 period

. Using the city

- s pecific data, Poisson regression models were used to more rigorously examine the relationship of camera enforcement and other variables with fatal crash rates. The Poisson models accounted for the covariance structure due to repeated measures because each independent unit of analysis (city) had two observations (before and after periods). Separate models were developed for the rate of fatal red light running crashes and the rate of all fatal crashes at signalized intersections. Independent variables in the model were population density ( in thousands of people per square mile for each study period ) , land area ( in square miles for each study period ) , study period ( after vs. before ) , and city group ( cities with camera program s during the after period vs. cities without cameras). L and area
was included because large area changes potentially could confound the relationship between camera
enforcement and fatal crash rates. Census i nformation on cities’ land area s is available only from the
decennial reports (US Census Bureau, 1990, 2000) . Therefore, t he 1990 land area data were used for the before period and the 2000 data were used for the after period

. The population density during the before period was calculated as the average annual population d uring 1992

- 96 divided by the 1990 land area, and the population density during the after period was calculated as the average annual population during
2004

- 08 divided by the 2000 land area. An interaction variable for study period and city group test ed
wh ether crash trends were different for cities with and without camera program s. The difference in
modeled crash trend between cities with camera program and those without was taken as the primary6 

### measure of effectiveness. It was interpreted as the change in fatal crash rate for cities with camera programs beyond what would have been expected absen t the programs. Variables with p

- values less than 0.05 were taken as statistically significant.

3. Results
The 6 2 large US cities studied accounted for 10 percent of the US population, 14 percent of all fatal red light running crashes, and 15 percent of all fatal crashes at signalized intersections in 2008. Figure s 2 and 3 show the percentage changes in average annual per capita fatal crash rates for cities with and without red light camera enforcement programs, respectively. D etailed population and
crash data for each city are listed in Appendix A

. All but two of the 14 cities with camera programs

exp erienced reductions in the rate of fatal red light running crash es, and all but three experienced
reductions in the rate of all fatal crashes at signalized intersections (Figure 2 ). Among t he cities with camera programs that experienced reductions in both fatal crash rates, all but one city had percentage
reduction s for fatal red light running crash es that were l arger than those for all fatal crashes at signalized intersections. Among the 48 cities without camera programs, the pattern of changes in crash rates was much more variable

. A bout half of the cities experienced reductions in the rate of fatal red light running crashes, and about half experienced increases. More than one

- third of the cities experienced reductions in
the rate of all fatal crashes at signalized intersections (Figure 3 )

. Table 1 lists combined results for the camera and comparison groups
. The average annual rate of fatal red light running crashes declined for both study groups, but the decline was larger for citi es with camera programs than for cities without cameras ( 35 vs. 14 percent). T he average annual rate of all fatal crashes at signalized interse ctions decrease d by 14 percent for cities with camera programs and increase d slightly ( 2 percent ) for cities without cameras. For cities with camera programs, the percent age decline in
the annual average rate of fatal red light running crash es was much higher than the decline in the rate of
all fatal crash es at signalized intersections ( 35 vs. 14 percent )

.7 

### Table 2 lists results of the Poisson regression model that estimate d the effects of red light camera
enforcement and other predictors on the per capita rate of fatal red light running crashes

. N o significant
effect was associated with land area

. After accounting for the effects of other predictors , an increase in population density ( in thousands of people per square mile ) reduced the rate of fatal red light running
crash es by an estimated 4 percent ( [exp(

- 0.0371)

- 1] ×100 ) , a marginally significant difference

. After accounting for the interaction of study period and city group , t he fatal crash rate during the before period

was an estimated 65 percent higher ( [exp(0.4998)

- 1] ×100 ) for cities that later implemented camera programs compared with cities that did not . The rate of fatal red light running crashes between 1992

- 96
and 2004

- 08 was reduced by an estimated 16 percent ( [exp(

- 0.1709)

- 1] ×100 ) for cities without camera

program s and by an estimated 36 percent ( [exp(

- 0.1709

- 0.2809)

- 1] ×100 ) for cities with camera s
. The
estimated effect of camera enforcement on the rate of fatal red light running crashes was obtained by interpreting the interaction term for study period and camera use directly

. Based on this parameter, t he
rate of fatal red light runni ng crash es during 2004

- 08 for cities with cameras programs was 24 percent
lower ( [exp(

- 0.2809)

- 1] ×100 ) than what would have been expected without cameras.
Table 3 lists results of the Poisson regression model that estimate d the effects of red light camera enforcement and other predictors on the per capita rate of all fatal crashes at signalized intersections. After accounting for the effects of other predictors, neither land area nor population density was significantly ass ociated with the crash rate. After accounting for the interaction of study period and city group, the per capita rate of all fatal crash es at signalized intersections during the before period was an estimated 32 percent higher ([exp(0.2812)

- 1]×100) for ci ties that later implemented camera programs compared with cities that did not. The rate of all fatal crashes at signalized intersections between 1992

- 96
and 2004

- 08 changed only minimal ly for cities without camera program s and was reduced by an
estimated 16 percent for cities with cameras ([exp(0.0112

- 0.1822)

- 1]×100). Based on the interaction term for study period and camera use, the actual per capita rate of all fatal crash es at signalized intersections during 2004

- 08 for cities with camera program s was 17 percent lower ([exp(

- 0.1822)

- 1]×100) than what would have been expected without cameras.8 

### L and areas for 19 of the 62 study cities (4 camera cities and 15 comparison cities) increased by more than 10 percent between 1990 and 2000. Additional Poisson reg ression models were conducted that
excluded these cities , and results changed little.

4. Discussion Red light running is a frequent traffic violation , and the safety consequences have been
established. Enforcing red light laws is important, but many communities do not have the resources for police to patrol intersections as often as would be needed to ticket most motorists who run red lights. Traditional police enforcement also pose s special difficulties for police, who in most cases must follow a violating vehicle through a red light to stop it. This can endanger motorists and pedestrians as well as
officers.
Before

- after studies in communities that have implemented red light camera enforcement

programs have reported reductions in red light running, not only at camera
- equipped intersections but also at other signalized intersections without cameras ( Retting et al., 1999 a , 1999 b ) , as well as citywide crash
reductions at signalized intersections ( Retting and Kyrychenko, 2002 )

. The current study extends this research by examining the effects of camera enforcement on fatal crashes in large US cities. Based on Poisson regression model s , camera programs were associated with statistically significant citywide
reductions of 2 4 percent in the rate of fatal red light running crash es and 17 percent in the rate of all fatal crashes at signalized intersections, when compared with rates that would have been expected without

cameras. The larger effect of camera enforcement on the ra te of fatal red light running crashes would be expected because these are the crashes targeted by cameras. The significant reduction in the rate of all types of fatal crashes at signalized intersections indicate s that cameras have a general ized effect on driver
behavior at intersections that extends beyond running red lights. Other factors also were found to influence fatal crash rates. Higher population densities were
associated with lower fatal crash rates

. A p ossible explanation is that denser popul ation s generally lead to lower travel speeds and thus fewer fatal crashes ( Cerrelli, 1997 )

. Rates of f atal crash es during the9 

### baseline period were higher for cities that subsequently implemented red light camera programs than for cities that did not imple ment camera programs. It is to be expected that cities with larger red light running problems should have been more likely to implement camera enforcement programs
. Several limitations of the study are worth noting. The definition of red light running crashes excluded some crashes such as those involving a driver making an illegal turn on red. Other factors not considered may have influenced fatal crash rates for the camera cities but could not be examined due to
limitations in the data. A ttempts were made to obtain historical information on the number of red light cameras in the study cities, but information on the scope of red light programs could not be obtained for

many of the cities. Historical information also was sought on the number of signali zed intersections but was unavailable in many cities. Red light cameras are not the only countermeasure for reducing crashes at signalized
intersections. C onvert ing traditional intersections to roundabouts eliminate s the need for traffic signals as
well as cameras. It has been reported that conversion of traditional intersections to roundabouts reduces fatal crashes by 81

- 90 percent, injury crashes by 25

- 87 percent, and overall crashes by 37

- 61 percent (Federal Highw ay Administration, 2000; Persaud et al., 2001; Schoon and van Minnen, 1994; Troutbeck, 1993). However, it is not feasible to replace every traffic light with a roundabout, and not every intersection is appropriate for a roundabout. Better enforcement of traffic signals using cameras is a solution that can be implemented quickly on a large scale. In tallying the costs and benefits of camera enforcement, communities should factor in the considerable social and economic benefits of successfully reducing cras hes. Besides foregone medical
costs, vehicle repair bills, travel delays, and lost income, citizens in communities with camera
enforcement experience direct savings in terms of reduced police time to investigate and report crashes, lessened need for emerg ency response service, and lower roadway cleanup costs. National surveys of drivers and surveys conducted in cities with and without red light camera

program s have found that a large majority support camera enforcement (Garber et al., 2005 ; National Highwa y Traffic Safety Administration , 2004; Retting and Williams, 2000 ). Despite the widespread10 

### support and the safety benefits of red light camera enforcement, cameras remain controversial in some communities where opponents raise concerns about “big brother ” government tactics and claim that
violators are victims of revenue

- generating government schemes. In th e current s tudy , the cities that implemented red light camera programs had higher baseline crash rates , suggesting that government
officials were motiv ated by safety concerns. Although automated traffic enforcement is not a panacea, the current study adds to the large body of evidence that red light cameras can p revent the most serious
crashes. This evidence should be considered by communities seeking to reduce crashes at intersections.

Acknowledgements The authors appreciate the assistance of Nat han Oesch in obtaining information about the study cities and red light camera programs and the contributions of Ivan Cheung in developing the study approach. This work was supported by the Insurance Institute for Highway Safety.11 

### References
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- R ed) E nforcement P rograms in Virginia: A R eport in R esponse to a R equest by Virginia’s Secretary of Transportation . Charlottesville, VA: Virginia Transportation Resea rch Council. Hill, S. E. & Lindly, J. K. ( 2003 )

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- light running crash es. ITE Journal , 76 , 20

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. Retting, R. A., Ferguson, S. A., & Farmer, C. M. (2008). Reducing red light running through longer yellow signal timing and red light camera enforcement: results of a field investigation. Accident Analysis and Prevention , 40, 327

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### Retting, R. A. & Kyrychenko, S. Y. (2002). Reductions in injury crashes associated with red light camera enforcement in Oxnard, California. American Journal of Public Health , 92, 1822

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. US Census Bureau

. ( 1997 )

. 1990

- 96 C ities and P laces P opulation E stimates

. Washington, DC: US Department of Commerce.
US Census Bureau

. ( 1990 )

. 1990 Census of Population and Housing

. Washington, DC: US Department of Commerce.
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. ( 2000 )

. 2000 Census of Population and Housing

. Washington, DC: US Department of Commerce.
US Census Bureau. ( 2009 )

. Annual Estimates of the Resident Population for Incorporated Places over 100,000, Ranked by July 1, 2009 Population: April 1, 2000 to July 1, 2009 . Washington, DC: US Department of Commerce.13 Table 1 Average annual per capita rates of fatal red light running crashes and all fatal crashes at signalized intersections for cities with and without red light camera enforcement program s, 1992

- 96 and 2004

- 08 14 cities with camera programs 48 cities without camera programs
1992

- 96 2004

- 08 Percent change 1992

- 96 2004

- 08 Percent change Average annual population (million) 9.02 10.08 11.7 17.07 19.08 11.7 Number of fatal red light running crashes 323 235

- 27.2 409 391

- 4.4 Number of all fatal crashes at signalized

intersections 739 707

- 4.3 1112 1266 13.8 Average annual rate of fatal red light running

crashes per million population 7.16 4.66

- 34.9 4.79 4.10

- 14.4 Average annual rate of all fatal crashes at

signalized intersections per million population 16.38 14.02

- 14.4 13.02 13.27 1.9

Table 2 Poisson model of the effects of red light camera enforcement on average annual per capita rate of fatal red light running crashes Parameter Estimate Standard error p v alue Intercept 1.7050 0.1547 <0.0001 Land area in square miles 0.0001 0.0003 0.6391 Population density (thousands of persons per square mile)

- 0.0371 0.0191 0.0527 After period (2004

- 08) vs. before period (1992

- 96)

- 0.1709 0.0678 0.0117 Cities that implemented red light cameras vs. cities that did not 0.4998 0.1436 0.0005 Interaction of study period and city group

- 0.2809 0.1079 0.0092

Table 3 Poisson model of the effects of red light camera enforcement on average annual per capita rates of all fatal crashes at signalized intersections Parameter Estimate Standard error p v alue Intercept 2.5994 0.1314 <0.0001 Land area in square miles 0.0002 0.0002 0.3805 Population density (thousands of persons per square mile)

- 0.0187 0.0160 0.2428 After period (2004

- 08) vs. before period (1992

- 96) 0.0112 0.0564 0.8426 Cities that implemented red light cameras vs. cities that did not 0.2812 0.1284 0.0285 Interaction of study period and city group

- 0.1822 0.0914 0.046214 Fig
. 1. US communities with red light camera enforcement programs , 1992

- 2010

Fig. 2. Percent change in average annual per capita fatal crash rates for 14 large US cities with red light camera enforcement programs, 2004

- 08 vs. 1992

- 9615 Fig. 3: Percent change in average annual per capita fatal crash rates for 48 large US cities without red light camera enforcement programs, 2004

- 08 vs. 1992

- 9616 Appendix A Population, crash counts, per capita crash rates, and changes in per capita crash rates for each study city for fatal red light running crashes and all fatal crashes at signalized intersections, 2004

- 2008 vs. 1992

- 1996 Average annual population Fatal red light running crashes Percent change in crash rate All fatal crashes at signalized intersections Percent change in crash rate 5

- year total crash counts Annual crash rate per 100,000 population 5

- year total crash counts Annual crash rate per 100,000 population 1992

- 96 2004

- 08 1992

- 96 2004

- 08 1992

- 96 2004

- 08 1992

- 96 2004

- 08 1992

- 96 2004

- 08 Cities with red light camera programs Bakersfield , CA 203,797 301,102 7 16 0.69 1.06 55 14 28 1.37 1.86 35 Baltimore , MD 699,943 640,054 14 11 0.40 0.34

- 14 32 44 0.91 1.37 50 Chandler , AZ 119,198 241,729 7 3 1.17 0.25

- 79 16 8 2.68 0.66

- 75 Chicago , IL 2 , 799,671 2 , 824,206 69 47 0.49 0.33

- 32 175 170 1.25 1.20

- 4 Garland , TX 187,241 215,403 7 6 0.75 0.56

- 25 13 11 1.39 1.02

- 26 Long Beach , CA 430 ,595 464,451 14 10 0.65 0.43

- 34 32 23 1.49 0.99

- 33 Phoenix , AZ 1 ,098,702 1 , 509,114 100 76 1.82 1.01

- 45 197 190 3.59 2.52

- 30 Portland , OR 497 ,777 541,682 18 8 0.72 0.30

- 59 42 31 1.69 1.14

- 32 Raleigh , NC 241,617 364,026 3 9 0.25 0.49 99 6 24 0.50 1.32 165 Sacramento , CA 400,480 452,320 15 8 0.75 0.35

- 53 24 24 1.20 1.06

- 11 San Diego , CA 1 , 161,107 1 , 291,335 26 11 0.45 0.17

- 62 76 51 1.31 0.79

- 40 Santa Ana , CA 298,297 336,783 11 7 0.74 0.42

- 44 21 18 1.41 1.07

- 24 Toledo , OH 322,241 316,835 10 6 0.62 0.38

- 39 25 24 1.55 1.51

- 2 Washington , DC 563,014 584,461 22 17 0.78 0.58

- 26 66 61 2.34 2.09

- 11 Cities without red light camera programs Akron , OH 218,976 209,668 2 2 0.18 0.19 4 8 4 0.73 0.38

- 48 Anaheim , CA 282,074 330,345 12 13 0.85 0.79

- 7 24 20 1.70 1.21

- 29 Anchorage , AK 249,365 278,125 9 11 0.72 0.79 10 20 23 1.60 1.65 3 Arlington , VA 173,359 202,500 3 1 0.35 0.10

- 71 9 3 1.04 0.30

- 71 Aurora , CO 242,283 303,791 5 7 0.41 0.46 12 17 22 1.40 1.45 3 Birmingham , AL 256,388 231,578 14 7 1.09 0.60

- 45 25 24 1.95 2.07 6 Boise , ID 154,806 201,372 0 1 0.00 0.10 N/A 3 4 0.39 0.40 3 Boston , MA 553,977 617,749 5 3 0.18 0.10

- 46 21 11 0.76 0.36

- 53 Buffalo , NY 316,662 275,641 4 4 0.25 0.29 15 26 24 1.64 1.74 6 Chesapeake , VA 179,792 217,583 0 2 0.00 0.18 N/A 5 7 0.56 0.64 16 Chula Vista , CA 146,629 211,660 2 4 0.27 0.38 39 6 11 0.82 1.04 27 Cincinnati , OH 352,050 332,341 2 4 0.11 0.24 112 8 9 0.45 0.54 19 Colorado Springs , CO 315,112 395,544 11 10 0.70 0.51

- 28 27 34 1.71 1.72 0 Detroit , MI 1 , 007,094 918,776 46 20 0.91 0.44

- 52 111 68 2.20 1.48

- 33 Durham , NC 160,985 211,713 3 5 0.37 0.47 27 8 10 0.99 0.94

- 5 Fort Wayne , IN 200,085 251,663 5 4 0.50 0.32

- 36 14 10 1.40 0.79

- 4317 Average annual population Fatal red light running crashes Percent change in crash rate All fatal crashes at signalized intersections Percent change in crash rate 5

- year total crash counts Annual crash rate per 100,000 population 5

- year total crash counts Annual crash rate per 100,000 population 1992

- 96 2004

- 08 1992

- 96 2004

- 08 1992

- 96 2004

- 08 1992

- 96 2004

- 08 1992

- 96 2004

- 08 Henderson , NV 86,311 239,939 1 3 0.23 0.25 8 5 8 1.16 0.67

- 42 Hialeah , FL 204,090 220,141 3 3 0.29 0.27

- 7 21 24 2.06 2.18 6 Honolulu , HI 390,745 374,348 5 2 0.26 0.11

- 58 27 34 1.38 1.82 31 Indianapolis , IN 745,367 793,282 18 23 0.48 0.58 20 48 51 1.29 1.29 0 Jacksonville , FL 664,626 795,745 13 7 0.39 0.18

- 55 38 79 1.14 1.99 74 Jersey City , NJ 229,201 237,973 4 0 0.35 0.00

- 100 15 6 1.31 0.50

- 61 Kansas City , MO 434,600 469,728 15 13 0.69 0.55

- 20 33 39 1.52 1.66 9 Laredo , TX 152,870 210,741 2 3 0.26 0.28 9 5 12 0.65 1.14 74 Las Vegas , NV 334,750 550,914 10 22 0.60 0.80 34 33 47 1.97 1.71

- 13 Lexington , Fayette , KY 236,005 283,144 6 8 0.51 0.57 11 13 27 1.10 1.91 73 Lincoln , NE 204,472 244,961 4 2 0.39 0.16

- 58 5 11 0.49 0.90 84 Louisville , KY 670,350 706,926 17 18 0.51 0.51 0 28 47 0.84 1.33 59 Madison , WI 204,138 226,575 3 1 0.29 0.09

- 70 9 6 0.88 0.53

- 40 Memphis , TN 619,267 680,035 36 27 1.16 0.79

- 32 73 69 2.36 2.03

- 14 Miami , FL 362,845 407,606 5 14 0.28 0.69 149 35 67 1.93 3.29 70 Milwaukee , WI 606,704 602,397 14 17 0.46 0.56 22 37 44 1.22 1.46 20 Nashville , TN 502,398 585,422 8 13 0.32 0.44 39 34 43 1.35 1.47 9 Newark , NJ 271,809 276,721 12 6 0.88 0.43

- 51 39 25 2.87 1.81

- 37 Norfolk , VA 246,229 237,800 4 2 0.32 0.17

- 48 8 6 0.65 0.50

- 22 Oklahoma City , OK 459,474 539,146 1 5 0.04 0.19 326 12 29 0.52 1.08 106 Omaha , NE 371,308 437,344 15 17 0.81 0.78

- 4 29 32 1.56 1.46

- 6 Pittsburgh , PA 358,173 314,869 1 4 0.06 0.25 355 12 22 0.67 1.40 109 Reno, NV 148 , 367 209 , 923 4 3 0.54 0.29

- 47 19 9 2.56 0.86

- 67 Rochester, NY 225,908 209,022 2 3 0.18 0.29 62 12 17 1.06 1.63 53 Saint Paul, MN 262,938 277,799 7 6 0.53 0.43

- 19 13 8 0.99 0.58

- 42 Saint Petersburg, FL 237,878 246,461 13 6 1.09 0.49

- 55 28 28 2.35 2.27

- 3 San Antonio, TX 1,068,009 1,292,560 27 25 0.51 0.39

- 23 68 64 1.27 0.99

- 22 San Jose, CA 813,785 921,760 13 14 0.32 0.30

- 5 29 40 0.71 0.87 22 Tampa, FL 283,464 330,769 8 10 0.56 0.60 7 26 42 1.83 2.54 38 Tulsa, OK 376,458 383,293 9 7 0.48 0.37

- 24 15 18 0.80 0.94 18 Wichita, KS 322,887 358,229 5 7 0.31 0.39 26 9 22 0.56 1.23 120 Winston, Salem, NC 167,987 220,383 1 2 0.12 0.18 52 2 6 0.24 0.54 129