TECHBRIEF

Research, Development, and 

Technology

Turner-Fairbank Highway 

Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

www.fhwa.dot.gov/research

Safety Evaluation of 

Red-Light Indicator Lights 

(RLILs)

FHWA Publication No.: FHWA-HRT-17-078

FHWA Contact: Roya Amjadi, HRDS-20, (202) 493-3383, 

roya.amjadi@dot.gov

This document is a technical summary of the Federal 

Highway Administration report Safety Evaluation of RedLight Indicator Lights (RLILs) (FHWA-HRT-17-077).

Objective

The Federal Highway Administration (FHWA) established 

the Development of Crash Modification Factors (DCMF) 

program in 2012 to address highway safety research 

needs for evaluating new and innovative safety strategies (improvements) by developing reliable quantitative 

estimates of their effectiveness in reducing crashes. The 

goal of the DCMF program is to save lives by identifying 

new safety strategies that effectively reduce crashes and 

promote those strategies for nationwide implementation 

by providing measures of their safety effectiveness and 

benefit–cost (B/C) ratios through research. State transportation departments and other transportation agencies 

need to have objective measures for safety effectiveness 

and B/C ratios before investing in broad applications 

of new strategies for safety improvements. Forty State 

transportation departments provide technical feedback on 

safety improvements to the DCMF program and implement new safety improvements to facilitate evaluations. 

These States are members of the Evaluation of Low-Cost 

Safety Improvements Pooled Fund Study, which functions 

under the DCMF program.

This study investigated the safety effectiveness of redlight indicator lights (RLILs). This effort was intended to 
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reduce the frequency of crashes resulting 

from drivers disobeying traffic signals by 

providing a safer and more efficient means 

for police to enforce the red interval and 

educate drivers about the existence and 

purpose of RLILs.

Few studies have explored the safety effectiveness of RLILs; specifically, studies have 

not shown the crash-based safety effectiveness for four-legged intersections. This 

study sought to fill this knowledge gap.

Introduction

RLILs—also known as signal indicator lights, 

enforcement lights, rat lights or boxes, or 

tattletale lights—are mounted on the signal 

head or mast arm and activate simultaneously with the red interval. This allows an 

enforcement officer downstream to identify 

if a vehicle has violated the red interval.

In its series on innovative intersection 

safety treatments, the FHWA presented 

a summary on enforcement lights.(1) The 

summary states that enforcement lights can 

provide safety, efficiency, and/or cost benefits compared to other enforcement methods, including the following:

• Allows red-light running monitoring 

from downstream of any leg of an 

intersection.

• Eliminates the need for unsafe pursuit 

from an officer positioned upstream. 

The officer would normally need to 

cross the intersection during the red 

interval.

• Allows one officer to conduct 

downstream enforcement (instead of 

requiring two officers), resulting in 

increased efficiency.

• Has lower installation and maintenance costs than automated enforcement systems (e.g., red-light camera 

enforcement). 

• Does not use controversial automated 

photography.

A literature review revealed that white 

enforcement lights allowed police officers 

in Hillsborough County, Florida, to operate 

more effectively by reducing the required 

manpower for red-light enforcement 

by half.(2) Prior to installation, it took two 

officers to enforce red lights (one upstream 

to observe the red light and the other 

downstream to apprehend the offending 

driver). The number of red-light running 

citations increased from 17,561 per year 

to 24,551 per year after treatment. The 

researchers documented that police officers 

found the white lights made the task of 

red-light enforcement simpler and safer. 

The red-light violation data collected at the 

study intersections showed a statistically 

significant reduction at the 90-percent 

confidence level in violations, from 759 to 

567, after white-light installation.

A review of the crash data indicated an 

average of 828 crashes per year at the sites 

before treatment and 860 crashes per year 

after treatment. Further analysis determined an average of 56 disregarded traffic 

signal crashes per year in the before period 

and 52 crashes per year in the after period. 

Considering only the approaches with white 

lights, red-light running crashes decreased 

from 40.17 crashes per year to 28 crashes 
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per year after treatment. The authors noted 

an increase in all crashes countywide during the study period, while the trend in redlight running crashes stopped increasing in 

2002, the year that white-light installation 

began. 

Methodology

This research examined the safety impacts 

of the application of RLILs at signalized, fourlegged intersections in Florida. Installations 

included blue-light-emitting diode and 

white incandescent indicators mounted 

directly on the signal head or mast arm.

The objective of this study was to estimate 

the safety effectiveness of this strategy as 

measured by crash frequency. The authors 

only evaluated intersection-related crashes 

and the following subtarget crash types:

• Total crashes (all types and severities 

combined).

• Fatal and injury crashes (K, A, B, and 

C injuries on KABCO scale) (K is fatal 

injury, A is incapacitating injury, B is 

non-incapacitating injury, C is possible 

injury, and O is property damage only).

• Right-angle crashes (all severities

combined).

• Left-turn crashes (all severities 

combined).

• Rear-end crashes (all severities 

combined).

• Crashes in which driver(s) disobeyed 

traffic signal (all severities combined).

• Nighttime crashes (all severities 

combined).

The researchers used the empirical 

Bayesian (EB) methodology for observational before–after studies for the 

evaluation.(3) This methodology is considered rigorous in that it accounts for 

regression to the mean using a reference 

group of similar but untreated sites.

In the process, the use of safety performance functions (SPFs) addressed the 

following:

• Overcame the difficulties of using 

crash rates in normalizing for volume 

differences between the before and 

after periods.

• Accounted for time trends.

• Reduced the level of uncertainty in the 

estimates of safety effect.

• Properly accounted for differences in 

crash experience and reporting practice 

in amalgamating data and results from 

diverse jurisdictions.

• Provided a foundation for developing 

guidelines for estimating the likely 

safety consequences of a contemplated strategy.

The researchers estimated the SPFs used 

in the EB methodology through generalized linear modeling assuming a negative 

binomial error distribution, which is consistent with the state of research in developing these models. In specifying a negative 

binomial error structure, the research team 

iteratively estimated an overdispersion 

parameter from the model and the data and 

used this parameter in the EB calculations. 

For a given dataset, smaller values of this 

parameter indicate relatively better models.
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The full report includes a detailed explanation of the methodology, including a 

description of how the estimate of safety 

effects for target crashes was calculated.

Results

Based on the data for all three districts 

combined, this brief presents results in 

two parts. The first part contains aggregate 

results, and the second part is based on a 

disaggregate analysis that attempted to discern factors that may be most favorable to 

the installation of enforcement lights.

Aggregate Analysis

Table 1 shows the aggregate results for all 

three districts and provides the estimates of 

expected crashes in the after period without treatment, the observed crashes in the 

after period, and the estimated CMF and 

its standard error (SE) for all crash types 

considered.

The results in table 1 indicate statistically 

significant reductions at the 95-percent 

confidence level for all crash types 

analyzed except rear-end crashes, for 

which the negligible increase (CMF = 1.02) 

was not statistically significant at the 

95-percent confidence level. The crash 

type with the smallest CMF (which 

translates to the greatest reduction) was 

left-turn crashes with a CMF of 0.600. For 

all crash types combined, the researchers 

estimated a CMF of 0.94. The CMFs 

for fatal and injury, right-angle, disobeyedsignal, and nighttime crashes were 0.86, 

0.91, 0.71, and 0.89, respectively. 

Disaggregate Analysis

The disaggregate analysis sought to

identify those conditions under which 

the treatment is most effective. Since 

total, fatal and injury, right-angle, and 

disobeyed-signal crashes were the focus 

of this treatment, these crash types are 

the focus of the disaggregate analysis. The 

results in table 2 suggest the RLILs become 

more effective with time. This was evident, 

as the CMFs for total, fatal and injury, 

and right-angle crashes become smaller 

as additional time accumulates for the 

treatment.

Statistic Total Fatal and 

Injury

RightAngleLeftTurnRearEndDisobeyedSignal Nighttime

EB estimate of crashes 

expected in the after 

period without strategy

5,337.4 2,816.0 1,023.3 507.3 2,291.6 470.8 1,673.8

Count of crashes 

observed in the after 

period

5,012 2,411 927 305 2,329 336 1,495

Estimate of CMF 0.939* 0.856* 0.905* 0.600* 1.016 0.713* 0.892*

SE of estimate of CMF 0.022 0.027 0.042 0.041 0.033 0.048 0.034

Table 1. Aggregate results.

*Indicates CMF estimates that are statistically significant at the 95-percent confidence level.
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For enforcement and education practices, 

the research team disaggregated the results 

by district, as shown in the last two columns 

of table 2. Across all crash types, the CMFs 

were smallest for district 1. Local agencies 

in district 1 responded to the research team 

regarding the enforcement of the indicator 

lights. Several counties and cities reported 

initial advertisements in local newspapers and participation in awareness campaigns. Additionally, a few agencies in this 

district noted that they utilized the lights and 

increased enforcement after their application. No agencies in districts 2 or 5 reported 

awareness campaigns or increased enforcement. The CMF estimates for districts appear 

to support these implementation practices. 

Further analyses indicated RLILs appear to 

be more effective for total, fatal and injury, 

and right-angle crashes in rural areas; at 

signalized intersections with lower total 

Crash Type Treatment 

Duration CMF (SE) District CMF (SE)

Total crashes 1 1.024 (0.037) 1 0.736 (0.077)*

Total crashes 2 0.963 (0.027) 2 0.995 (0.033)

Total crashes 2+ 0.939 (0.022)* 5 0.934 (0.031)*

Fatal and injury crashes 1 0.917 (0.047) 1 0.676 (0.082)*

Fatal and injury crashes 2 0.888 (0.035)* 2 0.895 (0.044)*

Fatal and injury crashes 2+ 0.856 (0.027)* 5 0.868 (0.037)*

Right-angle crashes 1 0.989 (0.079) 1 0.756 (0.112)*

Right-angle crashes 2 0.944 (0.057) 2 1.036 (0.075)

Right-angle crashes 2+ 0.905 (0.042)* 5 0.856 (0.054)*

Disobeyed-signal crashes 1 0.748 (0.099)* 1 0.368 (0.086)*

Disobeyed-signal crashes 2 0.784 (0.074)* 2 0.797 (0.088)*

Disobeyed-signal crashes 2+ 0.713 (0.048)* 5 0.750 (0.066)*

Table 2. Results disaggregated by treatment duration and district. 

*Indicates CMF estimates that are statistically significant at the 95-percent confidence level.
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entering volume; and a lower proportion 

of entering traffic from the minor road. The 

researchers found the opposite to be true 

for disobeyed-signal crashes where RLILs 

appear to be more effective in urban areas, 

at signalized intersections with higher total 

entering volume, and a higher proportion of 

entering traffic from the minor road. Results 

showed positive correlation between total 

entering volume, number of RLILs, proportion entering from the minor road, and area 

type. These factors should not be combined 

for quantitative analysis, but they could be 

considered when prioritizing intersections 

for treatment.

Economic Analysis

For the purposes of the economic analysis, 

the research team conservatively assumed 

the treatment as the installation of RLILs 

for which the recommended combined 

CMF was 0.94 for total crashes (table 1). 

The analysis assumed that the installation 

of RLILs cost $3,000 per intersection. In 

total, 108 intersections received the RLIL 

treatment at an estimated cost of $324,000. 

The analysis assumed the useful service 

life for safety benefits was 5 years. This is 

likely conservative, since it is the minimum 

service life reported from several vendors, 

who indicated potential service lives of up 

to 10 years.

The FHWA Office of Safety Research and 

Development suggests that, based on the 

Office of Management and Budget Circular 

A-4, a real discount rate of 7 percent be 

applied to calculate the annual cost of 

the treatment for the 5-year service life.(4)

With this information, the capital recovery 

factor is 4.1 for all intersections.

For the benefit calculations, the researchers applied the most recent FHWA mean 

comprehensive crash costs, at the time the 

research was performed, disaggregated by 

crash severity and crash geometry type as 

a base.(5)

The researchers calculated the total crash 

reduction by subtracting the actual crashes 

in the after period from the expected 

crashes in the after period had the treatment 

not been implemented. They then divided 

the total crash reduction by the average 

number of after-period years per site to 

compute the total crashes saved per year. 

The number of total crashes saved per year 

was 58.7 for all intersections. Considering 

the number of treated intersections, this 

resulted in an average savings of 0.54 

crashes per intersection per year. 

The researchers obtained the annual benefits (i.e., crash savings) by multiplying 

the crash reduction per site year by the 

cost of a crash, all severities combined. The 

research team calculated the B/C ratio as 

the ratio of the annual benefit to the annual 

cost. The B/C ratio is 92:1 for all signalized intersections. The U.S. Department of 

Transportation (USDOT) recommends that 

sensitivity analysis be conducted by assuming values of a statistical life of 0.57 and 

1.41 times the recommended 2014 (when 

the research was performed) value.(6) 

These factors can be applied directly to 

the estimated B/C ratios to get a range of 

53:1 to 130:1 for all signalized intersections. 
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These results suggest that the strategy,

even with conservative assumptions on 

cost, service life, and the value of a statistical life, can be cost effective for reducing total crashes at signalized intersections. 

While the resulting B/C ratio is very high, 

users should keep in mind the low-cost 

nature of this strategy and that implementing other strategies with lower B/C ratios 

may result in larger reductions in crashes.

Summary and Conclusions

The objective of this study was to undertake 

a rigorous before–after evaluation of the 

safety effectiveness, as measured by crash 

frequency, of RLILs. The research team 

recommended the CMFs shown in table 3 

for various crash types.

A disaggregate analysis of the results indicated that RLILs are almost immediately 

effective, and the effect is sustained for 

disobeyed-signal crashes. For other crash 

types, CMFs decrease over the first few 

years of treatment, suggesting that they 

are more effective for reducing crashes 

as drivers become accustomed to them. 

The only district with agencies that noted 

increased enforcement and public awareness campaigns found the smallest CMFs. 

The researchers found no significant difference between indicator types used.

Additionally, RLILs appear to be more effective for total, fatal and injury, and rightangle crashes in rural areas; at signalized 

intersections with lower total entering volume; and a lower proportion of entering 

traffic from the minor road. The researchers 

found the opposite to be true for disobeyedsignal crashes where RLILs appear to be 

more effective in urban areas, at signalized 

intersections with higher total entering volume, and a higher proportion of entering 

traffic from the minor road. The analysis 

showed that quantitative analyses should 

not combine these factors but can consider them when prioritizing intersections 

for treatment. 

The B/C ratio, estimated with conservative 

cost and service life assumptions and considering the benefits for total crashes, was 

92:1 for all signalized intersections. With the 

USDOT recommended sensitivity analysis, 

this value could range from 53:1 to 130:1. 

These results suggested that the strategy—

even with conservative assumptions on 

cost, service life, and the value of a statistical life—can be cost effective.

Variable Total Fatal and 

Injury

RightAngleLeftTurnRearEndDisobeyedSignal Nighttime

Estimate of CMF 0.939* 0.856* 0.905* 0.600* 1.016 0.713* 0.892*

SE of estimate of CMF 0.022 0.027 0.042 0.041 0.033 0.048 0.034

Table 3. Results for Florida.

*Indicates CMF estimates that are statistically significant at the 95-percent confidence level.
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NOVEMBER 2017 FHWA-HRT-17-078

HRDI-20/11-17(200)E

Researchers—Scott Himes, Frank Gross, Bhagwant Persaud, and Kimberly Eccles.

Distribution—This TechBrief is being distributed according to a standard distribution. Direct 

distribution is being made to the Divisions and Resource Center.

Availability—This TechBrief may be obtained from the FHWA Product Distribution Center by 

e-mail to report.center@dot.gov, fax to (814) 239-2156, phone to (814) 239-1160, or online at 

http://www.fhwa.dot.gov/research.

Key Words—Red-light indicators, low-cost, safety improvements, safety evaluations, 

empirical Bayesian.

Notice—This document is disseminated under the sponsorship of the U.S. Department of 

Transportation in the interest of information exchange. The U.S. Government assumes no 

liability for the use of the information contained in this document. The U.S. Government does 

not endorse products or manufacturers. Trademarks or manufacturers’ names appear in this 

report only because they are considered essential to the objective of the document.

Quality Assurance Statement—The Federal Highway Administration (FHWA) provides 

high-quality information to serve the Government, industry, and public in a manner that promotes public understanding. Standards and policies are used to ensure and maximize the 

quality, objectivity, utility, and integrity of its information. FHWA periodically reviews quality 

issues and adjusts its programs and processes to ensure continuous quality improvement.

References

1. Federal Highway Administration. (2009). 

Red-Signal Enforcement Lights, Report 

No. FHWA-SA-09-005, Federal Highway 

Administration, Washington, DC. 

Available online: http://safety.fhwa.dot.

gov/intersection/resources/techsum/

fhwasa09005/, last accessed July 6, 2010.

2. Reddy, V., Adbel-Aty, M., and Pinapaka, 

S. (2008). Evaluation of Innovative 

Safety Treatments: A Study of the 

Effectiveness of White Enforcement 

Lights, Florida Department of 

Transportation State Safety Office, 

Tallahassee, FL. Available online: http://

www.fdot.gov/research/Completed_

Proj/Summary_SF/BD500/BD500_v1_

rpt.pdf, last accessed July 6, 2010.

3. Hauer, E. (1997). Observational 

Before–After Studies in Road Safety—

Estimating the Effect of Highway 

and Traffic Engineering Measures 

on Road Safety. Elsevier Science, 

Incorporated, Amsterdam, Netherlands. 

4. Office of Management and Budget. 

(2003). Circular A-4: Regulatory 

Analysis. USDOT, Washington, DC.

5. Council, F., Zaloshnja, E., Miller, 

T., and Persaud, B. (2005). Crash 

Cost Estimates by Maximum PoliceReported Injury Severity Within 

Selected Crash Geometries, Report No. 

FHWA-HRT-05-051, Federal Highway 

Administration, McLean, VA.

6. U.S. Department of Transportation. 

(2014). Guidance on Treatment of the 

Economic Value of a Statistical Life 

(VSL) in U.S. Department of Transportation Analyses—2014 Adjustment,

Memorandum, U.S. Department of 

Transportation, Office of the Secretary 

of Transportation, Washington, DC. 

Available online: http://www.dot.

gov/sites/dot.gov/files/docs/VSL_

Guidance_2014.pdf, last accessed 

November 12, 2014.