Safety Evaluation of Red Light

Indicator Lights at Intersections

PUBLICATION NO. FHWA-HRT-17-077 NOVEMBER 2017

Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296
FOREWORD

The research documented in this report was conducted as part of the Federal Highway 

Administration’s (FHWA) Evaluation of Low-Cost Safety Improvements Pooled Fund Study 

(ELCSI-PFS). FHWA established this PFS in 2005 to conduct research on the effectiveness of 

the safety improvements identified by the National Cooperative Highway Research Program 

Report 500 guides as part of the implementation of the American Association of State Highway 

and Transportation Officials Strategic Highway Safety Plan. The ELCSI-PFS studies provide a 

crash modification factor and benefit–cost (B/C) economic analysis for each of the targeted safety 

strategies identified as priorities by the pooled fund member States.

This study evaluated red-light indicator lights (RLILs). RLILs are auxiliary lights mounted on 

signal heads, mast arms, or poles that were connected to a traffic-control signal. This strategy is

to reduce the frequency of crashes resulting from drivers disobeying traffic signals by providing a 

safer means for police to enforce the red interval. The RLIL activates at the onset of the red phase 

and allows an enforcement officer to observe red-light running from downstream of the 

intersection. Results indicate statistically significant crash reductions for most crash types (i.e.,

total crashes, fatal and injury crashes, right-angle, and left-turn). The B/C ratio estimated with 

conservative cost and service life assumptions indicates this strategy was highly beneficial for 

four-legged signalized intersections. This report will benefit traffic engineers, enforcement 

personnel, and safety planners by providing insight for greater intersection safety.

Monique R. Evans

Director, Office of Safety

Research and Development

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 

government, industry, and the 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.
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-17-077

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

Safety Evaluation of Red-Light Indicator Lights (RLILs) at Intersections

5. Report Date

November 2017

6. Performing Organization Code

7. Author(s)

Scott Himes, Frank Gross, Bhagwant Persaud, and Kimberly Eccles

8. Performing Organization Report No.

9. Performing Organization Name and Address

VHB

8300 Boone Boulevard, Suite 700

Vienna, VA 22182-2626

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTFH61-13-D-00001

12. Sponsoring Agency Name and Address

U.S. Department of Transportation

Federal Highway Administration

1200 New Jersey Avenue, SE 

Washington, DC 20590-3660

13. Type of Report and Period Covered

Safety Evaluation

14. Sponsoring Agency Code

15. Supplementary Notes: 

The Federal Highway Administration (FHWA) Office of Safety Research and Development managed this study 

under the Development of Crash Modification Factors program. The FHWA Office of Safety Research and 

Development Program and Task Manager was Roya Amjadi.

16. Abstract

The Development of Crash Modification Factors program conducted the safety evaluation of red-light indicator 

lights (RLILs) at intersections for the Evaluation of Low-Cost Safety Improvements Pooled Fund Study. This study 

evaluated safety effectiveness of RLILs. RLILs are auxiliary lights mounted on signal heads, mast arms, or poles that 

are directly connected to a traffic-control signal. The RLIL activates at the onset of the red phase and allows an 

enforcement officer to observe red-light running from downstream of the intersection. This strategy is intended to 

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. Geometric, traffic, and crash data were obtained at treated fourlegged signalized intersections in Florida. To account for potential selection bias and regression-to-the-mean, an 

empirical Bayes before–after analysis was conducted using reference groups of untreated four-legged signalized 

intersections with characteristics similar to those of the treated sites. The analysis also controlled for changes in 

traffic volumes over time and time trends in crash counts unrelated to the treatment. Results indicate statistically 

significant crash reductions for most crash types. Disobeyed signal crashes had an estimated crash modification 

factor (CMF) of 0.71. Total crashes, fatal and injury crashes, right-angle, and left-turn crashes had estimated CMFs 

of 0.94, 0.86, 0.91, and 0.60, respectively. The benefit-cost ratio estimated with conservative cost and service life 

assumptions was 92:1 for four-legged signalized intersections. The results suggest that the treatment, even with 

conservative assumptions on cost, service life, and the value of a statistical life, can be cost effective. In addition to 

the crash-related benefits, RLILs can improve the efficiency and safety of red-light running enforcement efforts. 

While this study did not evaluate the efficiency and safety impacts with respect to enforcement, it should be noted 

that RLILs do allow police to observe violators from a downstream position, eliminating the need for a second 

observer (upstream) and the need to pursue a violator through the red light.

17. Key Words

Intersection, running red-light, red-light indicators, 

signal indicator lights, enforcement lights, low-cost, 

safety improvements, safety evaluations, empirical 

Bayesian.

18. Distribution Statement.

No restrictions. This document is available through the 

National Technical Information Service, 

Springfield, VA 22161.

http://www.ntis.gov

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

53

22. Price
ii
iii

TABLE OF CONTENTS

EXECUTIVE SUMMARY........................................................................................................... 1

CHAPTER 1. INTRODUCTION ................................................................................................ 3

BACKGROUND ON STRATEGY ......................................................................................... 3

BACKGROUND ON STUDY.................................................................................................. 4

LITERATURE REVIEW......................................................................................................... 5

CHAPTER 2. OBJECTIVE ......................................................................................................... 7

CHAPTER 3. STUDY DESIGN .................................................................................................. 9

SAMPLE SIZE ESTIMATION OVERVIEW ....................................................................... 9

CHAPTER 4. METHODOLOGY ............................................................................................. 15

CHAPTER 5. DATA COLLECTION....................................................................................... 17

INSTALLATION DATA........................................................................................................ 17

REFERENCE SITES.............................................................................................................. 17

ROADWAY DATA................................................................................................................. 18

CRASH DATA ........................................................................................................................ 18

TRAFFIC DATA..................................................................................................................... 18

TREATMENT COST DATA................................................................................................. 19

DATA CHARACTERISTICS AND SUMMARY................................................................ 19

CHAPTER 6. DEVELOPMENT OF SPFs............................................................................... 23

SPFs FOR SPILLOVER AND MIGRATION EFFECTS .................................................. 23

SPFs FOR COMBINED REFERENCE DATA ................................................................... 26

CHAPTER 7. BEFORE–AFTER EVALUATION RESULTS............................................... 29

AGGREGATE ANALYSIS.................................................................................................... 29

DISAGGREGATE ANALYSIS............................................................................................. 29

CHAPTER 8. ECONOMIC ANALYSIS .................................................................................. 33

CHAPTER 9. SUMMARY AND CONCLUSIONS................................................................. 35

APPENDIX. ADDITIONAL INSTALLATION DETAILS.................................................... 37

ACKNOWLEDGMENTS .......................................................................................................... 43

REFERENCES............................................................................................................................ 45
iv

LIST OF FIGURES

Figure 1. Photo. RLIL on signal head. .............................................................................................3

Figure 2. Equation. Estimated change in safety. ............................................................................15

Figure 3. Equation. EB estimated of expected crashes. .................................................................15

Figure 4. Equation. EB weight. ......................................................................................................16

Figure 5. Equation. Index of effectiveness.....................................................................................16

Figure 6. Equation. Standard deviation of index of effectiveness. ................................................16

Figure 7. Equation. SPF for spillover and migration. ....................................................................23

Figure 8. Graph. Yearly multiplier by year for reference groups. .................................................24

Figure 9. Equation. SPF for EB analysis........................................................................................26

Figure 10. Graph. Crash totals by year before treatment. ..............................................................28

Figure 11. Diagram. Assembly sheet for confirmation light with globe........................................39

LIST OF TABLES

Table 1. Before-period crash rate assumptions for signalized intersections in Florida. ................10

Table 2. Minimum required before-period intersection-years for treated intersections at the 

95-percent confidence level. ..............................................................................................11

Table 3. Minimum required before-period intersection-years for treated intersections at the 

90-percent confidence level. ..............................................................................................12

Table 4. Sample analysis for crash effects. ....................................................................................13

Table 5. Definitions of crash types.................................................................................................20

Table 6. Before and after data summary for treatment and reference sites....................................21

Table 7. Parameter estimates for the reference group SPF for total crashes..................................23

Table 8. Observed and predicted crashes for reference groups......................................................24

Table 9. SPF results with indicator variable for potential spillover/migration sites. .....................25

Table 10. SPF estimates for nearby treatment effect for group 2. .................................................26

Table 11. Parameter estimates and SEs for Florida signalized intersection SPF for total 

crashes. ...............................................................................................................................26

Table 12. SPF-generated yearly multipliers...................................................................................27

Table 13. Aggregate results............................................................................................................29

Table 14. Results disaggregated by treatment duration and district. .............................................30

Table 15. Results disaggregated by indicator type and area type. .................................................31

Table 16. Results disaggregated by entering volume and proportion entering on minor road 

approaches..........................................................................................................................31

Table 17. Crash costs and distributions by crash type and severity...............................................33

Table 18. Results of Florida ...........................................................................................................35
v

LIST OF ABBREVIATIONS

AADT average annual daily traffic 

B/C benefit–cost

CAR Crash Analysis Reporting (system)

CMF crash modification factor

DOT Department of Transportation

EB empirical Bayes 

FDOT Florida Department of Transportation

FHWA Federal Highway Administration 

KABCO Scale used to represent injury severity in crash reporting (K is fatal injury, A is 

incapacitating injury, B is non-incapacitating injury, C is possible injury, and O is 

property damage only)

LED light-emitting diode

RLILs red-light indicator lights

SE standard error

SPF safety performance function

USDOT U.S. Department of Transportation

1

EXECUTIVE SUMMARY

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 ultimate 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 (ELCSI-PFS), 

which functions under the DCMF program.

This study investigated the safety effectiveness of red-light indicator lights (RLILs). RLILs are 

auxiliary lights mounted on signal heads, mast arms, or poles that are directly connected to a 

traffic-control signal. The RLIL activates at the onset of the red phase and allows an enforcement 

officer to observe red-light running from downstream of the intersection. This strategy is 

intended to 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. Moreover, for 

the strategy to be effective, agencies should educate drivers of their existence and intent. Few 

studies have explored the safety effectiveness of RLILs; in particular, no studies have shown the

crash-based safety effectiveness for four-legged intersections.

The research team obtained geometric, traffic, and crash data at treated four-legged rural and 

urban intersections in Florida. To account for potential selection bias and regression-to-the-mean

(RTM), the research team conducted an empirical Bayes (EB) before–after analysis using

reference groups of untreated four-legged signalized intersections with characteristics similar to 

those of the treated sites. The analysis also controlled for changes in traffic volumes over time 

and time trends in crash counts unrelated to the treatment.

The results indicate reductions for all crash types analyzed except rear-end crashes. Reductions 

were statistically significant at the 95-percent confidence level for all crash types. The crash type 

with the smallest crash modification factor (CMF)—which translated to the greatest reduction—

was left-turn crashes with a CMF of 0.60. For all crash types combined, the research team 

estimated a CMF of 0.94. The CMFs for disobeyed signal, fatal and injury, right-angle, and 

nighttime crashes were 0.71, 0.86, 0.91, and 0.89, respectively. The research team estimated an 

insignificant CMF of 1.016 for rear-end crashes. 

A disaggregate analysis sought to identify those conditions under which the treatment was most 

effective. Because total, fatal and injury, right-angle, and disobeyed signal crashes were the focus 

of this treatment, these crash types were the focus of the disaggregate analysis. The disaggregate 

analysis showed that CMFs decreased over the first few years of treatment, indicating that they 

were more effective in reducing crashes as drivers became accustomed to them. The smallest 
2

CMFs (i.e., the greatest reductions) found were for the only district with agencies that noted 

increased enforcement and public awareness campaigns. The research team found no significant 

difference in effects between white incandescent and blue light-emitting diode (LED) indicators.

For total, fatal and injury, and right-angle crashes, RLILs appeared to be more effective in rural 

areas and at intersections with lower total entering volume and a lower proportion of entering 

traffic from the minor road. The data indicated that the opposite was true for disobeyed signal 

crashes; the research team found RLILs were more effective in urban areas and at intersections 

with higher total entering volume and a higher proportion of entering traffic from the minor road.

Owing to correlations among these factors, the disaggregate effects should not be combined for 

quantitative analysis; however, the indications can be considered 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 recommended U.S.

Department of Transportation (USDOT) sensitivity analysis, this value could range from 53:1 to 

130:1. These results suggest that the strategy—even with conservative assumptions on cost, 

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

In addition to the crash-related benefits, RLILs can improve the efficiency and safety of red-light 

running enforcement efforts. While this study did not evaluate the efficiency and safety impacts 

with respect to enforcement, it is important to note that RLILs do allow police to observe 

violators from a downstream position, eliminating the need for a second observer (upstream) and

the need to pursue a violator through the intersection during the red interval.
3

CHAPTER 1. INTRODUCTION

This chapter presents background information on the installation of RLILs at traffic signals. It 

also provides a brief overview of the ELCSI-PFS, of which the study reported here is a part, and 

the literature review conducted for this study.

BACKGROUND ON STRATEGY

This strategy involves installing RLILs—also known as signal indicator lights, enforcement 

lights, rat lights or boxes, or tattletale lights—at traffic signals. RLILs can be mounted on the 

signal head, as shown in Figure 1, or on the mast arm. The indicator activates simultaneously 

with the red interval, allowing an enforcement officer downstream to identify whether a vehicle 

has violated the red interval. While the lights should be visible for the enforcement officer to 

more safely conduct enforcement operations, the lights should be designed such that they do not 

confuse drivers (i.e., they should not be red, yellow, or green).

© VHB

Figure 1. Photo. RLIL on signal head.
4

In its series of reports on innovative intersection safety treatments, FHWA presented a summary 

on enforcement lights.

(1) The summary states that compared with other enforcement methods, 

enforcement lights can provide safety, efficiency, and/or cost benefits, 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 a single officer to conduct downstream enforcement (instead of requiring two 

officers), resulting in increased efficiency.

• Results in lower installation and maintenance costs than automated enforcement systems

(e.g., red light photo enforcement). 

• Does not use controversial automated photography.

The FHWA summary also includes several implementation considerations. Because RLILs are 

not traffic-control devices, there are no compliance issues with the Manual on Uniform Traffic 

Control Devices for Streets and Highways.

(2) However, it is worthwhile to consider the following 

points regarding implementation:

• RLILs should be visible to downstream enforcement officers but should minimize 

confusion or distraction to drivers. Covering the indicator in one or more directions may 

help reduce confusion and distraction.

• RLILs should be high enough to be visible over large vehicles and should be out of reach 

of vandals. 

• Wiring should connect to the controller output for the red interval to power the RLIL 

simultaneously.

• Warning and regulatory signs can be used to supplement RLILs to remind drivers of 

enforcement or fines.

• Attainment of judicial support for acceptance of the citations given based on the RLILs is 

critical. 

• Public awareness campaigns and increased enforcement may amplify RLIL effectiveness. 

BACKGROUND ON STUDY

In 1997, the American Association of State Highway and Transportation Officials Standing 

Committee on Highway Traffic Safety, with the assistance of FHWA, the National Highway 

Traffic Safety Administration, and the Transportation Research Board Committee on 

Transportation Safety Management, met with safety experts in the field of driver, vehicle, and 

highway issues from various organizations to develop a strategic plan for highway safety. These 

participants developed 22 key emphasis areas that affect highway safety.(3)
5

The National Cooperative Highway Research Program published a series of guides to advance 

the implementation of countermeasures targeted to reduce crashes and injuries. Each guide 

addresses one of the emphasis areas and includes an introduction to the problem, a list of 

objectives for improving safety, and strategies for each objective. The guides designate each 

strategy as proven, tried, or experimental. For many of the strategies discussed in these guides, 

no States or agencies have performed evaluations; the guides consider about 80 percent of the 

strategies tried or experimental.

In 2005, to support the implementation of the guides, FHWA organized a pooled fund study of 

States to evaluate low-cost safety strategies as part of this strategic highway safety effort. Over 

the years, the pooled fund has grown in size and now includes 40 States. The purpose of the 

ELCSI-PFS is to evaluate the safety effectiveness of high priority tried and experimental lowcost safety strategies selected by member States through scientifically rigorous crash-based 

studies. The use of RLILs was selected as a strategy to be evaluated as part of this effort. 

The ELCSI‐PFS conducts its research within the FHWA’s DCMF program, which is a 

comprehensive, long‐term safety research effort. FHWA established the DCMF program in 

November 2012 to support and complement the efforts of the ELCSI-PFS. The ultimate goal of 

the DCMF program is to save lives by identifying new safety countermeasures that effectively 

reduce crashes and promoting those countermeasures for nationwide installation by providing 

measures of their safety effectiveness, including B/C ratios, through research.

LITERATURE REVIEW

Reddy et al. conducted the leading study to date, examining the effectiveness of white 

enforcement lights in Hillsborough County, FL.

(4) They noted that white enforcement lights 

allowed police officers to operate more effectively because the required manpower could be cut 

in half. Prior to installation, it took two officers to enforce red lights (one upstream to observe 

the red light and the other downstream to stop the offending driver). Reddy et al. evaluated 

17 signalized intersections in Hillsborough County to determine the effectiveness of white lights 

in reducing red-light violations and associated crashes. They observed red-light violations in the 

a.m. and p.m. peak hours for 5 months prior to installation and 3 months after installation. In 

addition, they collected crash data from 2000 to 2005 from the Florida Department of 

Transportation (FDOT) Crash Analysis Reporting (CAR) system.

(4)

A review of the crash data indicated an average of 828 crashes per year at the treatment 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 per year after treatment. The authors 

noted an increase in all crashes countywide during the study period, while the trend in red-light 

running crashes stopped increasing in 2002, the year that white light installation began.(4)

The number of red-light running citations increased from 17,561 per year before treatment 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
6

confidence level in violations from 759 to 567 after white light installation. Owing to high 

variation, the results from the analyses of crash data were less conclusive than the results of the 

violation data.(4)
7

CHAPTER 2. OBJECTIVE

The research described in this report examined the safety impacts of the application of RLILs at 

signalized intersections. The objective was to estimate the safety effectiveness of this strategy as 

measured by crash frequency. The research team considered only intersection-related crashes, 

and further sub-target crash types included the following: 

• Total crashes (all types and severities combined).

• Injury crashes (K, A, B, and C injuries on KABCO scale).

1

• Right-angle crashes (all severities combined).

• Left-turn crashes (all severities combined).

• Rear-end crashes (all severities combined).

• Nighttime crashes (all severities combined).

• Crashes in which driver(s) disobeyed traffic signal (all severities combined).

A further objective was to address questions of interest, such as the following:

• Do effects vary by traffic volume?

• Do effects vary by intensity of treatment (e.g., number of enforcement lights per 

intersection)?

• Do effects vary depending on the exact type of indicator light (e.g., blue LED versus

white bulb)?

• Do effects vary by posted speed limit on the major route?

• Are the effects short-lived?

• Are spillover effects evident?

The evaluation of overall effectiveness included the consideration of the installation costs and 

crash savings in terms of the B/C ratio. 

Meeting these objectives placed some special requirements on the data collection and analysis 

tasks, including the need to do the following:

• Select a large enough sample size to detect with statistical significance what may be 

small changes in safety for some crash types.

• Identify appropriate untreated reference sites. This included reference sites both adjacent 

and not adjacent to treated sites in order to consider potential spillover and migration 

 

 

1The KABCO scale is used to represent injury severity in crash reporting (K is fatal injury, A is incapacitating 

injury, B is non-incapacitating injury, C is possible injury, and O is property damage only).
8

effects. The term spillover is used to describe the effect of the treatment at nearby 

locations because motorists may be unsure of the exact locations of the treatment and 

may change their behavior at the nearby locations as well. Crash migration may occur if 

motorists change their behavior at the treatment sites and then compensate at other 

locations, or if motorists choose alternate routes to avoid the treatment. 

• Properly account for changes in safety due to changes in traffic volume and other 

nontreatment factors.
9

CHAPTER 3. STUDY DESIGN

The study design involved a sample size analysis and prescription of needed data elements. The 

sample size analysis assessed the size of sample required to statistically detect an expected 

change in safety and also determined what changes in safety could be detected with likely 

available sample sizes.

SAMPLE SIZE ESTIMATION OVERVIEW

When planning a before–after safety evaluation study, it is vital to ensure that enough data are 

included such that it is statistically possible to detect the expected change in safety. While the 

expected change in safety is unknown in the planning stages, it is still possible to estimate the 

number of required sites (i.e., intersections) based on the best available information about the 

expected change in safety. Alternatively, one could estimate the change in safety that one could

statistically detect for the number of available sites. Chapter 9 of Hauer provides a detailed 

explanation of sample size considerations and estimation methods.

(5) The sample size analysis 

presented in this section addresses two cases: (1) how large a sample would be required to

statistically detect an expected change in safety and (2) what changes in safety could be detected 

with available sample sizes.

For case 1, the research team assumed that a conventional before–after study with comparison 

group design would be used because available sample size estimation methods were based on 

this assumption. The sample size estimates from this method would be conservative in that the

EB methodology would likely require fewer sites. To facilitate the analysis, it was also assumed 

that the number of comparison intersections was equal to the number of treatment intersections

and the durations of the before and after periods were equal, which was again a conservative 

assumption.

Table 1 provides the crash rate assumptions. Initially, the research team assumed that the 

locations of interest for this treatment would be four-legged and three-legged signalized 

intersections. However, very few three-legged intersections were treated, and therefore, these 

intersections were dropped from the dataset. The research team used a central Florida study as 

the basis for assumptions on intersection crash rates.

(6)

Intersection crash rates differ 

substantially depending on a number of factors (e.g., traffic volume, geometric configuration, 

and area type). Therefore, the intersection crash rates assumed represent the general lower and 

upper end of the crash frequency spectrum in Florida. Rate C represents the intersection crash 

rate for before-period data for all intersections in Florida. 
10

Table 1. Before-period crash rate assumptions for signalized intersections in Florida.

Crash Type

Rate A:

Orange County

(2x2 Lane Signalized 

Intersections)1

Rate B:

Seminole County

(6x4 Lane or 6x6 Lane 

Signalized Intersections)1

Rate C:

Before-Period

(All Treatment Sites)2

All 2.47 28.45 9.33

Injury 1.79 4.8 4.84

Right-angle 0.55 3.93 1.82

Left-turn 0.55 1.6 0.95

Rear-end 0.89 17.2 3.97

Nighttime — — 2.92

Disobeyed signal — — 0.81

1Data source: Kowdla.(6)

2Data source: project database.

Rate = Crashes/intersection/year.

— Indicates no available data.

Table 2 and table 3 provide estimates of the required number of before- and after-period 

intersection-years for crash rates A, B, and C to achieve statistical significance at 95- and 

90-percent confidence levels, respectively. The minimum sample indicated the level for which a 

study seemed worthwhile (i.e., it was feasible to detect with the specified level of confidence the 

largest effect that one might reasonably expect based on current knowledge about the strategy). 

The research team based these sample size calculations on specific assumptions regarding the 

number of crashes per intersection and years of available data. Intersection-years is the number 

of intersections where the strategy was in effect multiplied by the number of years of data before 

or after implementation. For example, if a strategy was implemented at nine intersections and 

data were available for 3 years since implementation, then there was a total of 27 intersectionyears of after-period data available for the study.
11

Table 2. Minimum required before-period intersection-years for treated intersections at 

the 95-percent confidence level.

Crash Type

Expected Percent 

Reduction in Crashes Rate A Rate B Rate C

All

10 751 65 199

20 113 10 30

30 38 3 11

40 17 1 5

Fatal and injury

10 1,036 386 384

20 156 58 58

30 53 20 20

40 23 9 9

Right-angle

10 3,373 472 1,020

20 507 71 153

30 173 24 52

40 75 10 23

Left-turn

10 3,373 1,159 1,955

20 507 174 293

30 173 59 100

40 75 26 43

Rear-end

10 2,084 108 468

20 313 16 71

30 107 6 24

40 46 2 11

Nighttime

10 — — 636

20 — — 96

30 — — 33

40 — — 14

10 — — 2,290

Disobeyed

20 — — 344

30 — — 117

40 — — 51

Note: Assumes equal number of intersection-years for treatment and comparison 

intersections and equal length of before and after periods.

Boldface indicates the sample size values recommended in this study.

— Indicates no data.
12

Table 3. Minimum required before-period intersection-years for treated intersections at 

the 90-percent confidence level.

Crash Type

Expected Percent 

Reduction in Crashes Rate A Rate B Rate C

All

10 467 41 124

20 78 7 21

30 27 2 8

40 12 1 4

Fatal and injury

10 644 240 239

20 108 40 40

30 37 14 14

40 16 6 6

Right-angle

10 2,096 293 635

20 351 49 106

30 122 17 37

40 53 7 16

Left-turn

10 2,096 721 1,215

20 351 121 203

30 122 42 70

40 53 18 31

Rear-end

10 1,296 67 291

20 217 11 49

30 75 4 17

40 33 2 8

Nighttime

10 — — 395

20 — — 66

30 — — 23

40 — — 10

Disobeyed

10 — — 1,425

20 — — 238

30 — — 82

40 — — 36

Note: Assumes equal number of intersection-years for treatment and comparison 

intersections and equal length of before and after periods.

Boldface indicates the sample size values recommended in this study.

— Indicates no data.

The sample size values recommended for this study are highlighted in bold in table 2 and table 3. 

These were recommended based on conservative estimates of the anticipated effects of the 

treatment. As noted, the sample size estimates provided were also conservative in that the stateof-the-art EB methodology proposed for the evaluations would require fewer intersections than 

the less robust conventional before–after study with a comparison group. Estimates could be 

predicted with greater confidence or a smaller reduction in crashes would be detectable if there 

were more intersection-years of data available in the after period. The same holds true if the 

actual data used for the analysis had a higher crash rate for the before period than was assumed.

Case 2 considered the data collected for both the before and after periods. RLILs were installed 

at 108 signalized intersections between 2004 and 2010. The before and after periods varied
13

across intersections depending on the year of treatment installation. The total intersection-years 

of data available was 365 for the before period for two-lane major roadways and 599 for the after 

period. From this, one can estimate the minimum percent reduction detectable for the two

confidence levels (i.e., 90 and 95 percent). The results of these calculations are shown in table 4. 

The calculations are based on the methodology in Hauer.

(5)

Table 4. Sample analysis for crash effects.

Crash Type

IntersectionYears

in Before 

Period

IntersectionYears

in After 

Period

Minimum Percent

Reduction

Detectable for Crash 

Rate Assumption1

P = 0.10

Minimum Percent

Reduction

Detectable for Crash 

Rate Assumption1

P = 0.05

Total 365 599 5 10

Fatal and injury 365 599 10 10

Right-angle 365 599 10 15

Left-turn 365 599 15 15

Rear-end 365 599 10 10

Nighttime 365 599 10 10

Disobeyed 365 599 15 20

1Results were to nearest 5-percent interval, and the crash rate assumption was based on actual crash rate for the 

before period.

For the available data, the minimum percentage changes in crash frequency that could be 

statistically detected at the 95- and 90-percent significance levels were estimated using the 

before-period crash rates (Rate C) in table 1. The results indicate that the data should be 

sufficient for detecting the anticipated crash reduction effects highlighted in table 2 (i.e., 

10-percent reductions for all crash types except left-turn and disobeyed signal crashes, if such an 

effect were present). Using these results, the project team decided to proceed with the evaluation 

using the data available at the time.

15

CHAPTER 4. METHODOLOGY

This study employed the EB methodology for observational before–after studies.

(5) This 

methodology is considered rigorous in that it accounts for RTM using a reference group of 

similar but untreated sites. In the process, safety performance functions (SPFs) were used, which 

did 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.

Figure 2 shows the change in safety for a given crash type at a site in the EB approach.

Figure 2. Equation. Estimated change in safety.

Where:

= Predicted number of crashes that would have occurred in the after period without the 

strategy.

= Number of reported crashes in the after period. 

In estimating , the effects of RTM and changes in traffic volume were explicitly accounted for 

using SPFs, relating crashes of different types to traffic flow and other relevant factors for each 

jurisdiction based on untreated sites (reference sites). The research team calibrated annual SPF 

multipliers to account for temporal effects on safety (e.g., variation in weather, demography, and 

crash reporting).

In the EB procedure, the SPF is used to first estimate the number of crashes that would be 

expected in each year of the before period at locations (i.e., reference sites) with traffic volumes 

and other characteristics similar to the one being analyzed. The sum of these annual SPF 

estimates (P) is then combined with the count of crashes (x) in the before period at a strategy site 

to obtain an estimate of the expected number of crashes (m) before strategy. Figure 3 shows this 

estimate of m:

Figure 3. Equation. EB estimated of expected crashes.

 Safety = λ - π

π

m = w(P)+(1-w)(x),
16

Where w, the EB weight, is estimated from the mean and variance of the SPF estimate as seen in 

figure 4.

Figure 4. Equation. EB weight.

Where:

k = Constant for a given model, which is estimated from the SPF calibration process with the use 

of a maximum likelihood procedure. In that process, a negative binomial distributed error 

structure is assumed with k being the overdispersion parameter of this distribution.

A factor is then applied to m to account for the length of the after period and differences in traffic 

volumes between the before and after periods. This factor is the sum of the annual SPF 

predictions for the after period divided by P, the sum of these predictions for the before period. 

The result, after applying this factor, is an estimate of . The procedure also produces an 

estimate of the variance of .

The estimate of is then summed over all sites in a strategy group of interest (to obtain sum) 

and compared with the count of crashes observed during the after period in that group (πsum). The 

variance of is also summed over all sites in the strategy group. 

Figure 5 shows how to estimate the index of effectiveness ( ).

Figure 5. Equation. Index of effectiveness.

The standard deviation of is shown in figure 6.

Figure 6. Equation. Standard deviation of index of effectiveness.

The percent change in crashes is calculated as 100(1  ); thus, a value of  0.7 with a 

standard deviation of 0.12 indicates a 30-percent reduction in crashes with a standard deviation 

of 12 percent.

w =

1

1+ kP

,

θ

















2

( )

1

sum

sum

sum

sum

Var









 ,

θ

2

2

2 2

2

( )

1

( ) ( )

( )







































sum

sum

sum

sum

sum

sum

Var

Var Var

StDev















 ,

θ θ
17

CHAPTER 5. DATA COLLECTION 

FDOT provided the majority of the data for this strategy, including treatment sites, information 

regarding the enforcement lights (e.g., manufacturer specifications and law enforcement 

practices), screening for construction activity for many treatment sites, and access to its 

mainframe computer. This allowed the research team to query crash, roadway, and traffic data. 

Several city traffic engineering departments and police departments also provided information. 

Additional details about the design, installation, and maintenance of RLILs, as well as lessons 

learned, are provided in the appendix of this report.

INSTALLATION DATA

The FDOT central office undertook an initiative to deploy enforcement lights at signalized 

intersections. It provided the FDOT districts with enforcement light equipment (e.g., mounts and

bulbs) and lists of intersections for potential installations. Districts 1, 2, and 5 were able to 

implement this treatment widely. The engineering departments of these districts provided the 

following data for this evaluation:

• Treatment locations.

• Installation dates.

• Movements monitored (e.g., northbound through, westbound left).

• Enforcement light type (i.e., white incandescent or blue LED).

After receiving lists of all the potential treatment sites from the districts, the research team

selected treatment sites appropriate for this study. Initially, there were more than 300 potential 

treatment sites with installation dates varying from 2003 to 2010. Only those treatment sites with 

an installation date on or after 2004 allowed retrieval of before-period data from the available 

data in Florida’s CAR system. This excluded a number of potential sites from the study. The 

CAR did not contain complete crash data for sites located on other route types, thus excluding 

sites not located on U.S., State, or county routes. The research team identified the number of 

approaches using aerial imagery of the potential treatment sites and observed that only a small 

proportion were three‐legged intersections. Such a small sample size would not likely produce 

statistically robust results; consequently, the research team removed the three‐legged treatment 

intersections from further consideration in this study. The final treatment group for this strategy 

was composed of 108 four‐legged intersections located in three districts in Florida (districts 1, 2, 

and 5).

The study also solicited information on law enforcement practices and public awareness campaigns 

regarding the enforcement lights. A number of local agencies provided feedback regarding the 

enforcement practices and public awareness, particularly in district 1. The appendix of this report

provides the responses by city and county agencies tasked with red-light enforcement.

REFERENCE SITES

As noted in the study design, the potential for spillover or crash migration effects existed for this 

strategy. To address this potential issue, the research team formed two reference groups. The first 
18

group, a potential spillover reference group, consisted of signalized four‐legged intersections 

immediately adjacent to a treatment site. In other words, a selected spillover reference site would 

not have a signalized intersection between it and a treatment site. The second group, a non‐

spillover reference group, consisted of signalized four‐legged intersections that had one or more 

untreated signalized intersections separating them from a treatment site. If the research team 

detected no crash migration or spillover effects, then it pooled the two reference groups to form 

the reference group for the EB method. If crashes increased or decreased significantly at the 

spillover sites, then these sites would not help to identify the safety effectiveness of the strategy 

at treated sites reliably. This would also indicate effects of the strategy on adjacent intersections.

In all, the research team selected 30 reference sites. Sixteen were spillover reference sites, and the 

remaining 14 were non-spillover sites. For each reference site, the research team queried all 

available crash records between January 1, 2003, and December 31, 2012.

ROADWAY DATA

Using crash report data, FDOT provided roadway data for the installation and reference sites. 

Selected roadway data elements included the following:

• District.

• County.

• Shoulder type.

• Functional classification (which also defined area type).

• Surface width.

• Median width.

• On-curve presence.

• Posted speed limit.

CRASH DATA

FDOT’s crash database, the CAR system, provided the crash data for the treatment and reference 

sites using a two-stage process. First, the project team determined the node number for each site. 

This number was a unique number that FDOT assigned to each intersection within a county for 

identification purposes. The crash database also used these node numbers for location 

identification. After compiling the node numbers, the second stage was to query crash records. 

The research team queried records using the “around a node” option within the CAR system and

a defined influence area of a 250-ft radius around each intersection. 

TRAFFIC DATA

The crash reports that the CAR system generated also contained traffic volume data. Most 

important was the average annual daily traffic (AADT) values for the mainline and cross-street 

roadway sections for the intersections. In addition, the 2012 Florida Transportation Information 

(FTI) DVD provided traffic volumes for State roadways where no crashes occurred.(7) Finally, the 

research team estimated traffic volumes for cross streets with missing data based on nearby 

traffic counts, surrounding land use, roadside development, and interconnectivity. 
19

TREATMENT COST DATA

FDOT provided estimates of the costs and service lives of the treatments for use in conducting a 

B/C analysis of the treatment. Red-signal enforcement light sets (including housing) cost 

approximately $50 to $150 for traditional (rather than LED) bulbs. Generally, four to 

eight indicator sets were needed for a major intersection to cover all signals and intervals for a 

total intersection cost of $200 to $1,200. Cost depended substantially on whether LED or 

traditional bulbs were used; LED bulbs cost approximately three times as much initially but 

required much less power. The power cost was generally negligible in comparison with the power 

used by the signal, into which the indicator was directly wired. Therefore, the analysis ignored

the power cost.

In addition, installation cost and service life data were explored through vendors,2 State 

transportation departments, and newspaper articles. (See references 1 and 8–12.) The cost of

lights was dependent on color and hardware required for installation. The research team found 

that the lights cost between $77 and $300 per light. In addition, the installation of the indicator 

lights required manpower and equipment, and the associated cost depended on where the lights 

were mounted (i.e., on the back of signal head or on a pole). The cost per intersection ranged

from $1,000 to $3,000 for installation. The State transportation departments reported the life span

of the indicator lights was between 5 and 10 years. 

DATA CHARACTERISTICS AND SUMMARY

Table 5 defines the crash types used by Florida. Table 6 provides summary information for the 

data collected for the treatment and reference sites. Installations dates ranged from 2004 to 2010. 

The before and after periods varied by location with the installation year marking the change 

from before to after. Before periods started as early as 2003 and ended as late as 2009. After 

periods started as early as 2005 and ended in 2012. The information in table 6 should not be used 

to make simple before–after comparisons of crashes per site-year because it does not account for 

factors other than the strategy that might cause a change in safety between the before and after 

periods. Such comparisons require an EB analysis, as presented in chapter 7.

 

 

2Melvin Barrios of Industrial Traffic Solutions, e-mail correspondence, December 3, 2014, and

http://lanecontrols.com/tattle-tale/.
20

Table 5. Definitions of crash types.

Crash Types Definition

Total Identified as all crashes, without exclusion

Fatal and injury Resulted in the following:

5—Fatal Injury

4—Incapacitating Injury

3—Non-incapacitating Evident Injury

2—Possible Injury

Right-angle First Harmful Event coded as 03—Collision With MV in Transport (Angle)

Left-turn First Harmful Event coded as 04—Collision With MV in Transport (Left Turn)

Rear-end First Harmful Event coded as 01—Collision With MV in Transport (Rear-End)

Nighttime Light Condition coded as anything other than 01-Daytime or 88-Unknown

Disobeyed signal 1st Contributing Cause coded as 11—Disregarded Traffic Signal

MV = Moving vehicle.
21

Table 6. Before and after data summary for treatment and reference sites.

Variable Treatment

Reference

Spillover Non-Spillover Combined

Number of sites 108 19 11 30

Site-years before 365 190 110 300 Site-years after 599

Before total crashes 10.085 3.989 5.355 4.490 After total crashes 8.367

Before fatal and injury crashes 5.167 2.242 2.818 2.453 After fatal and injury crashes 4.025

Before right-angle crashes 1.986 0.621 0.891 0.720 After right-angle crashes 1.548

Before left-turn crashes 0.981 0.363 0.464 0.400 After left-turn crashes 0.509

Before rear-end crashes 4.386 1.711 2.345 1.943 After rear-end crashes 3.888

Before nighttime crashes 3.219 0.932 1.427 1.113 After nighttime crashes 2.496

Before disobeyed crashes 0.819 0.237 0.391 0.293 After disobeyed crashes 0.586

Before major AADT

Avg. 35,841

Min. 5,900

Max. 80,500 Avg. 35,341

Min. 10,900

Max. 80,500

Avg. 31,705

Min. 13,900

Max. 67,500

Avg. 34,008

Min. 10,900

Max. 80,500 After major AADT

Avg. 34,084

Min. 5,000

Max. 79,000

Before minor AADT

Avg. 13,934

Min. 845

Max. 62,666 Avg. 6,938

Min. 910

Max. 40,850

Avg. 10,327

Min. 1,048

Max. 28,500

Avg. 8,180

Min. 910

Max. 40,850 After minor AADT

Avg. 12,146

Min. 867

Max. 59,000

Note: Crash rates are presented as crashes/site/year.

Avg. = Average.

Min. = Minimum.

Max = Maximum.

23

CHAPTER 6. DEVELOPMENT OF SPFs

This section presents the SPFs developed for each crash type. The SPFs support the use of the EB 

methodology to estimate the safety effectiveness of the strategy.(5) The research team used 

generalized linear modeling to estimate model coefficients assuming a negative binomial error 

distribution, which was consistent with the state of research in developing these models. In 

specifying a negative binomial error structure, the dispersion parameter, k, was estimated 

iteratively from the model and the data. For a given dataset, smaller values of k indicate relatively 

better models (i.e., less dispersion).

SPFs FOR SPILLOVER AND MIGRATION EFFECTS

Before developing SPFs, the research team analyzed separate reference groups to identify 

potential crash migration and spillover effects. The project team used data from both reference 

groups to develop yearly multipliers for each group. Figure 7 provides the form of the SPF.

Table 7 presents the estimated coefficients, as well as the value of k, the overdispersion parameter 

of the model.

Figure 7. Equation. SPF for spillover and migration.

Where:

TotalEnter = Total entering volume.

PropAADTMin = Proportion of entering volume from minor route.

ShldT1 = Indicator for paved shoulder.

a, b, c, d = Parameters estimated in the SPF calibration process.

Table 7. Parameter estimates for the reference group SPF for total crashes.

Crash Type

Parameter Estimate

a b c d k

Total −5.019 0.598 1.534 −0.245 0.225

Table 8 presents the observed crashes versus predicted crashes for each of the two reference 

groups. In the table, group 1 is the non-spillover/migration reference group, and group 2 is the 

potential spillover/migration reference group. Yearly factors are the ratio of observed crashes to 

predicted crashes for the given group within the given year. Because the base model was 

independent of year, yearly fluctuations were not a consideration in predicted crashes. Crash 

spillover was evident when the ratio for the spillover group became smaller with increasing time 

(because treatments were in use from 2004 to 2010). Crash migration occurred when the ratio 

increased with time. However, table 8 and figure 8 illustrate that there was no noticeable trend for 

the potential spillover/migration group. The ratios for groups 1 and 2 follow a consistent trend, 

indicating that neither crash spillover nor crash migration was observable. However, there was a 

slight underprediction for the non-spillover/migration group and a slight overprediction for the 

crashes

year

= e

a × TotalEnter

b × PropAADTMinc × e

 ShldT1×d 
24

potential spillover/migration group. In addition, there was no apparent increasing or decreasing 

trend for either group, indicating that there was no observed districtwide crash migration or 

spillover effects.

Table 8. Observed and predicted crashes for reference groups.

Year

Observed Crashes Predicted Crashes Yearly Factors

Group 1 Group 2 Group 1 Group 2 Group 1 Group 2

2003 55 60 52.945 82.127 1.039 0.731

2004 65 97 53.146 81.677 1.223 1.188

2005 62 78 55.166 84.369 1.124 0.925

2006 57 82 54.400 82.999 1.048 0.988

2007 48 62 54.387 82.548 0.883 0.751

2008 68 75 53.389 83.822 1.274 0.895

2009 64 77 52.874 79.854 1.210 0.964

2010 68 72 52.045 80.265 1.307 0.897

2011 47 72 51.791 78.853 0.907 0.913

2012 55 83 52.011 78.185 1.057 1.062

Note: Group 1 is non-spillover/migration, and group 2 is potential spillover/migration.

Figure 8. Graph. Yearly multiplier by year for reference groups.

While the results shown in table 8 and figure 8 provided evidence that crash migration and 

spillover were not of concern, they were insufficient to definitively conclude that the analysis 

could combine the two groups into a single reference group. Therefore, the research team 

conducted a supplementary analysis to estimate a second SPF, including an indicator for group 2

(the potential spillover/migration group) after the application of the nearby treatments. Table 9
25

presents the SPF results. The model includes yearly indicators to account for annual fluctuation 

that other predictor variables do not capture.

Table 9. SPF results with indicator variable for potential spillover/migration sites.

Variable Coefficient SE P-Value

Log-total entering 0.643 0.098 < 0.001

Proportion AADT minor 1.045 0.463 0.024

Paved shoulder −0.204 0.096 0.034

Year 2004 indicator 0.369 0.171 0.031

Year 2005 indicator 0.187 0.176 0.290

Year 2006 indicator 0.204 0.182 0.264

Year 2007 indicator −0.020 0.198 0.919

Year 2008 indicator 0.231 0.192 0.230

Year 2009 indicator 0.242 0.193 0.210

Year 2010 indicator 0.242 0.193 0.210

Year 2011 indicator 0.105 0.197 0.595

Year 2012 indicator 0.231 0.194 0.233

Indicator for spillover/migration group −0.218 0.137 0.111

Group 2 with treatment indicator −0.001 0.145 0.994

Constant −5.478 1.022 < 0.001

Overdispersion 0.201 N/A N/A

SE = Standard error.

N/A = Not applicable.

The resulting SPF showed no statistical difference for group 2 compared with group 1. Posttreatment application in that the indicator variable was insignificant, with the direction of effect 

being negative. This indicates that no crash migration or spillover effects occurred in group 2 

after the application of the treatment at nearby sites. In addition, the research team included an 

indicator to account for systematic differences between the potential spillover/migration group 

and non-spillover/migration group (see table 8). Because the potential spillover/migration group 

consistently overpredicted crashes, the indicator should have been negative (meaning that fewer 

crashes would be predicted at potential spillover/migration sites compared to non-spillover/

migration sites). This was found to be the case; however, the indicator was not statistically 

significant even at the 90-percent confidence level (P > 0.10). This study found similar results for 

the other crash types considered. 

Table 10 presents SPF model results (similar to the SPF results in table 9) for the nearby 

treatment indicator for all crash types without providing the estimates for other variables. This 

shows that spillover/migration did not occur for any crash types. Overall, the research team 

concluded that the two potential reference groups could be combined to estimate the SPFs for the 

EB analysis.
26

Table 10. SPF estimates for nearby treatment effect for group 2.

SPF 

Estimates

Crash Type

Total 

Fatal and 

Injury 

RightAngle LeftTurn RearEnd Disobey 

Signal Nighttime 

Coefficient −0.001 −0.018 0.307 −0.346 0.014 −0.154 −0.327

SE 0.145 0.164 0.276 0.341 0.202 0.490 0.232

P-value 0.994 0.913 0.266 0.310 0.943 0.752 0.160

SPFs FOR COMBINED REFERENCE DATA

The form of the SPF for total crashes for combined reference groups is given by figure 9, and the 

results are presented in table 11, where k is also provided for each SPF.

Figure 9. Equation. SPF for EB analysis.

Where:

Curve = Indicator for intersection being on a horizontal curve.

Table 11. Parameter estimates and SEs for Florida signalized intersection SPF for total 

crashes.

Crash Type

Parameter Estimate

a b c d f k

Total −4.217 0.513 1.757 −0.343 −0.365 0.195

SE (1.047) (0.098) (0.422) (0.098) (0.109) N/A

Note: The letters for parameters in table 11 correspond with those in figure 9.

N/A = Not applicable.

In addition, the research team considered crash sample size for reference sites in the development 

of SPFs. Because total crashes ranged from a minimum of 110 crashes in 2008 to a maximum of 

162 in 2004, the research team developed an SPF for total crashes. For all other crash types, there 

were too few crashes per year to develop separate reliable SPFs. Therefore, the research team 

used the total crashes SPF for other crash types, along with a proportion factor relating the crash 

type in question with total crashes. The research team multiplied the prediction from the SPF by 

the proportion factor to determine the number of predicted crashes of each specific crash type. 

The following crash type proportions were used:

• Fatal and injury crashes = 0.570.

• Right-angle crashes = 0.152.

• Left-turn crashes = 0.132.

• Rear-end crashes = 0.391.

• Disobey signal crashes = 0.048.

• Nighttime crashes = 0.305.
27

Table 12 provides annual factors (i.e., multipliers) estimated from the total crashes SPF. For 

multipliers greater than 1.00, more crashes were predicted for that year than the base year. For 

multipliers less than 1.00, fewer crashes were predicted for that year than the base year. The base 

year for multipliers was 2003. All crash types used the annual factors from the total crashes SPF.

Table 12. SPF-generated yearly multipliers.

Crash Type 2004 2005 2006 2007 2008 2009 2010 2011 2012

Total 1.430 1.202 1.208 0.957 1.245 1.244 1.246 1.089 1.228

Based on the large difference in crash rates between the treatment and reference sites (see 

table 6), the team decided to calibrate the SPF to the treatment site data just before treatment. The 

difference in crash rates was too large to be explained by RTM bias. Therefore, the research team

used the treatment sites to account for the underprediction, using only the final year of crash data 

before treatment installation to calibrate the SPF. This was consistent with the approach used by 

Srinivasan et al.(13)

In the study by Srinivasan et al., the authors calibrated SPFs to be more representative of the 

treatment group using before-period data.(13) The authors plotted 6 consecutive years of crash 

data for treatment sites to look for evidence of randomly high crashes during the before period. 

The plot showed that the counts for 2, 3, and 4 years before treatment were higher than for 1, 5, 

and 6 years before treatment. The authors selected 5 or more years before treatment to calibrate 

the SPF.

Figure 10 provides a plot of the before-period crashes at the treatment sites for the current study. 

Figure 10 is based on 40 of 108 sites for which 4 years of before data were available. (Crash data 

were not available for 4 years before treatment for the other 68 sites.) The plot indicates that the 

year before installation was the least prone to randomly high crash counts, as was also the case 

for the Srinivasan et al. data.(13) This result is intuitive for two reasons. First, the timeframe to 

identify and treat the intersections generally ranged from 6 months to 1 year. Second, there was a 

lag between the end of a calendar year and the availability of crash data for that year. As such, it 

was unlikely that crash data were available for inclusion in the site-selection process for the year 

prior to RLIL installation. The large difference between predicted and observed crashes at 

treatment sites was similar in magnitude to the difference found by Srinivasan et al.(13)
28

Figure 10. Graph. Crash totals by year before treatment.

The research team developed calibration factors by dividing the observed number of crashes for 

the year prior to treatment by the predicted number of crashes in the same year. This involved 

2003 to 2009 data because installations occurred from 2004 to 2010. The research team 

developed calibration factors separately by crash type, which was consistent with Srinivasan et 

al.(13) The following calibration factors by crash type were used:

• Total crashes = 1.638.

• Fatal and injury crashes = 1.546.

• Right-angle crashes = 2.034.

• Left-turn crashes = 1.146.

• Rear-end crashes = 1.782.

• Disobey signal crashes = 3.083.

• Nighttime crashes = 1.639.
29

CHAPTER 7. BEFORE–AFTER EVALUATION RESULTS

This chapter presents the evaluation results, the aggregate results for all intersections, and the 

results disaggregated by treatment duration, district, indicator type, area type, entering volume,

and proportion of volume from the minor road.

AGGREGATE ANALYSIS

Table 13 provides the estimates of expected number of crashes in the after period without 

treatment, the observed crashes in the after period, and the estimated CMFs, and their SEs for all 

crash types considered. 

Table 13. Aggregate results.

Statistic

Crash Type

Total

Fatal and 

Injury

RightAngleLeftTurnRearEndDisobey 

Signal 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

Note: Boldface indicates CMF estimates that are statistically significant at the 95-percent confidence level.

The results in table 13 indicate statistically significant reductions at the 95-percent confidence 

level for all crash types analyzed except rear-end crashes, for which the negligible increase was 

statistically insignificant. 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, a CMF of 

0.94 was estimated. The CMFs for fatal and injury, right-angle, disobeyed signal, and nighttime 

crashes were 0.86, 0.91, 0.71, and 0.89, respectively. An insignificant CMF of 1.02 was 

estimated for rear-end crashes.

DISAGGREGATE ANALYSIS

The disaggregate analysis sought to identify those conditions under which the treatment was most 

effective. Because total, fatal and injury, right-angle, and disobeyed signal crashes were the focus 

of this treatment, these crash types were the focus of the disaggregate analysis. The research team 

identified several variables as being of interest, including treatment duration, indicator type, level 

of enforcement, number of indicators, area type, curve presence, major and minor approach 

traffic volumes, number of lanes, median width, surface width, and posted speed limit. The 

disaggregate CMFs may be used in prioritizing installation sites, but interpretations should be 

made with caution. While the research team conducted disaggregate analyses by variables of 
30

interest, these characteristics were likely not independent, and the research team does not advise 

combining disaggregate CMFs. However, based on the disaggregate analysis, one could consider 

several characteristics qualitatively when prioritizing sites for treatment. 

For treatment duration, as shown in table 14, RLILs became more effective with time. This was 

evident because the CMFs for total, fatal and injury, and right-angle crashes became smaller as 

additional time passed after the treatment. The CMFs for total crashes and right-angle crashes 

were not statistically significant after 1 or 2 years of implementation but became significant after 

the second year of implementation. While CMFs became smaller over time for most crash types, 

the CMF for disobeyed signal crashes was significant and stable after the first year of installation. 

Table 14. Results disaggregated by treatment duration and district. 

Crash Type

Treatment 

Duration (years) 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)

Note: Boldface indicates CMF estimates that are statistically significant at the 95-percent confidence level.

To assess enforcement and education practices, the research team disaggregated the results by 

district, as the last two columns of Table 14 show. 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. In addition, a few agencies in this 

district noted that they used the lights and had 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 (i.e., having publicity and 

awareness campaigns in combination with some increased enforcements result in smaller CMFs). 

For indicator type, as table 15 shows, there was no difference between use of white incandescent 

indicator lights and blue LED indicator lights for all crash types. For total, fatal and injury, and 

right-angle crashes, the CMF for white incandescent lights was slightly smaller than the CMF for 

blue LED lights; however, the difference was not significant at the 95-percent confidence level. 

Table 15 also presents the disaggregate results by area type. The results show that RLILs were 

more effective at rural intersections than urban intersections for total, fatal and injury, and rightangle crashes. These differences were all significant at the 95-percent confidence level. However, 
31

although the strategy appeared to be more effective at urban intersections for disobeyed signal 

crashes, the difference was not statistically significant at the 95-percent confidence level.

District 1 did not include any rural sites; all rural sites were located in districts 2 and 5. This 

implies that the differential effects are likely the result of higher enforcement or awareness 

campaigns for rural sites. 

Table 15. Results disaggregated by indicator type and area type. 

Crash Type Indicator Type CMF (SE) Area Type CMF (SE)

Total crashes White incandescent 0.921 (0.027) Rural 0.701 (0.051)

Total crashes Blue LED 0.975 (0.038) Urban 0.963 (0.024)

Fatal and injury crashes White incandescent 0.842 (0.034) Rural 0.580 (0.061)

Fatal and injury crashes Blue LED 0.880 (0.045) Urban 0.883 (0.030)

Right-angle crashes White incandescent 0.900 (0.053) Rural 0.477 (0.078)

Right-angle crashes Blue LED 0.911 (0.068) Urban 0.953 (0.046)

Disobeyed signal crashes White incandescent 0.729 (0.060) Rural 0.928 (0.150)

Disobeyed signal crashes Blue LED 0.678 (0.077) Urban 0.681 (0.050)

Note: Boldface indicates CMF estimates that are statistically significant at the 95-percent confidence level.

As shown in table 16, CMFs were significantly smaller for intersections with a total entering 

volume of less than 40,000 vehicles per day for total, fatal and injury, and right-angle crashes 

compared with intersections with a higher total entering volume. The strategy was more effective 

for disobeyed signal crashes at intersections with a higher total entering volume. In all cases, the 

differences were significant at the 95-percent confidence level. The same trend appears to be true 

for the proportion of the total entering volume on the minor approach. For intersections with less 

than 20 percent of the entering volume from the minor road approaches, the CMFs for total, fatal 

and injury, and right-angle crashes were smaller. However, the difference was not statistically 

significant at the 95-percent confidence level. For disobeyed signal crashes, the CMF was smaller 

when more than 20 percent of the entering volume was from the minor road; however, the 

difference was not significant at the 95-percent confidence level.

Table 16. Results disaggregated by entering volume and proportion entering on minor road 

approaches. 

Crash Type

Entering 

Volume

CMF

(SE)

Proportion from 

Minor Roads

CMF

(SE)

Total crashes < 40,000 0.749 (0.033) < 0.2 0.858 (0.041)

Total crashes 40,000+ 1.018 (0.029) 0.2+ 0.969 (0.026)

Fatal and injury crashes < 40,000 0.716 (0.041) < 0.2 0.813 (0.049)

Fatal and injury crashes 40,000+ 0.916 (0.035) 0.2+ 0.873 (0.033)

Right-angle crashes < 40,000 0.749 (0.061) < 0.2 0.882 (0.074)

Right-angle crashes 40,000+ 0.978 (0.055) 0.2+ 0.913 (0.050)

Disobeyed signal crashes < 40,000 0.911 (0.091) < 0.2 0.899 (0.092)

Disobeyed signal crashes 40,000+ 0.608 (0.054) 0.2+ 0.614 (0.054)

Note: Boldface indicates CMF estimates that are statistically significant at the 95-percent confidence level.

Further analysis considered the total number of RLILs present at intersections. There was a 

positive correlation between total entering volume and number of RLILs, indicating that there 
32

were more RLILs at intersections with a higher total entering volume. There was also a positive 

correlation with the number of RLILs and urban area type and proportion of entering volume 

from the minor route. In combination, this led to findings that showed fewer RLILs were more 

effective for total, fatal and injury, and right-angle crashes than most indicators. In addition, there 

was likely substantial positive correlation between area type and total entering volume, as well as 

between total entering volume and proportion entering from the minor road. Correlation between 

area type and entering volume prohibited combining these CMFs for the purpose of crash 

prediction.

In summary, the disaggregate analysis showed that RLILs were almost immediately effective in 

reducing disobeyed signal crashes and became more effective over time for all other crash types. 

In addition, RLILs appeared to be more effective for total, fatal and injury, and right-angle 

crashes in rural areas at signalized intersections with lower total entering volume and a lower 

proportion of entering traffic from the minor road. On the other hand, RLILs appeared 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 one should not 

combine these factors for quantitative analysis, but they could be considered when prioritizing 

intersections for treatment. The research team found no significant difference in the results 

between use of white incandescent bulbs and blue LED bulbs; however, the level of enforcement 

and the level of awareness campaigns conducted appeared to affect the effectiveness.
33

CHAPTER 8. ECONOMIC ANALYSIS

The research team conducted an economic analysis to estimate the B/C ratio for this strategy at 

signalized intersections. The team used the statistically significant reduction in total crashes as 

the benefit for this treatment strategy. On the cost side and in the absence of details of each 

installation, the analysis conservatively assumed that the installation of RLILs cost $3,000 per

intersection. In total, 108 intersections received RLIL treatments at an estimated cost of 

$324,000. As summarized in the appendix of this report, the cost of bulbs ranged from $50 to 

$300. The local agencies involved with the installation of RLILs noted that there was negligible 

cost for operation because the bulbs were directly wired into the existing traffic signals. The 

minimal operational cost was offset by the reduced enforcement cost and thus was not factored 

into this analysis in order to be more conservative.

The analysis assumed the useful service life for safety benefits was 5 years. This was based on 

information provided by vendors. This service life is likely conservative because 5 years is the 

minimum service life reported by several vendors, with some potential service lives estimated at 

up to 10 years.

The FHWA Office of Safety Research and Development suggested that, based on the Office of 

Management and Budget Circular A-4, a real discount rate of 7 percent should be applied to 

calculate the annual cost of the treatment for the 5-year service life.(14) With this information, the 

capital recovery factor was 4.1 for all intersections.

For the benefit calculations, the most recent FHWA mean comprehensive crash costs 

disaggregated by crash severity and crash geometry type were used as a base.

(16) Council et al. 

developed these costs based on 2001 crash costs and found the values in table 17 by crash type

and severity.

(16) Table 17 also provides the proportion of fatal and injury crashes and the 

proportion of property damage only (PDO) crashes for each crash geometry type. Considering the 

proportion of crashes by type and severity, the unit cost (in 2001 dollars) for crashes at signalized 

intersections in this study was $51,395. 

Table 17. Crash costs and distributions by crash type and severity. 

Crash Geometry Type Frequency

K/A/B/C 

Cost1

PDO 

Cost

K/A/B/C 

Percent1

PDO 

Percent

Multi-vehicle rear-end 2,362 $48,236 $9,919 49.1 50.9

Multi-vehicle head-on 137 $131,356 $4,980 59.1 40.9

Multi-vehicle crossing path 1,593 $108,401 $8,598 50.6 49.4

Multi-vehicle sideswipe 222 $138,339 $5,905 18.5 81.5

Multi-vehicle backing 32 $53,966 $4,579 9.4 90.6

Single-vehicle struck parked vehicle 21 $108,300 $4,587 47.6 52.4

Single-vehicle struck pedestrian 84 $173,191 $5,432 82.1 17.9

Single-vehicle struck bicycle 64 $173,191 $5,432 87.5 12.5

Single-vehicle struck object 145 $237,600 $5,618 49.7 50.3

Single-vehicle rolled over 12 $324,366 $13,331 91.7 8.3

Other/undefined 340 $316,501 $4,463 30.3 69.7

1K/A/B/C refers to the KABCO scale.
34

Because the analysis was performed in 2014, the unit cost in 2001 dollars was updated to 2014 

dollars by applying the ratio of USDOT 2014 value of a statistical life of $9.2 million to the 2001 

value of $3.8 million.

(15,16) Applying this ratio of 2.42 to the unit cost resulted in an aggregate 

2014 unit cost for total crashes of $124,377 for signalized intersections. 

The research team 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. The research team 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 research team 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 was 

92:1 for all signalized intersections. USDOT recommended a sensitivity analysis be conducted by 

assuming values of a statistical life of 0.57 and 1.41 times the recommended 2014 value.

(17)

These factors were applied directly to the estimated B/C ratios to obtain a range of 53:1 to 

130:1 for all signalized intersections. 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 was 

very high, users should keep in mind the low-cost nature of this strategy and that implementing 

other strategies with lower B/C ratios might result in larger reductions in crashes.
35

CHAPTER 9. SUMMARY AND CONCLUSIONS

The objective of this study was to undertake a rigorous before–after evaluation of the safety 

effectiveness of RLILs as measured by crash frequency. The study used data from Florida to 

examine the effects on specific crash types, including total, fatal and injury, right-angle, left-turn,

rear-end, nighttime, and disobeyed signal crashes. Based on the combined results, the research 

team recommends the CMFs shown in table 18 for the various crash types. 

Table 18. Results of Florida

Statistic

Crash Type

Total

Fatal and

Injury

RightAngleLeftTurnRearEndDisobey 

Signal 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

Note: Boldface indicates CMF estimates that are statistically significant at the 95-percent confidence level.

A disaggregate analysis of the results indicated that RLILs were almost immediately effective,

and the effect was sustained for disobeyed signal crashes. For other crash types, CMFs decreased

over the first few years of treatment, indicating that they were more effective for reducing crashes 

as drivers became accustomed to them. The smallest CMFs were for the only district with 

agencies that noted enforcement and public awareness campaigns. There was no indication of a 

notable increase in the level of enforcement. There were consistent reports that enforcement was 

based on intersections with high crash counts and that it was not focused on intersections with 

red-light indicators only. Some agencies focused awareness campaigns on the indicator lights,

while others focused on red-light running in general. The research team found no significant 

difference between indicator types used.

In addition, RLILs appeared to be more effective for total, fatal and injury, and right-angle 

crashes in rural areas at signalized intersections with lower total entering volume and a lower 

proportion of entering traffic from the minor road. The research team found the opposite was true

for disobeyed signal crashes, where RLILs appeared 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 one should not combine these factors for 

quantitative analysis 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 

suggest that the strategy—even with conservative assumptions on cost, service life, and the value 

of a statistical life—can be cost effective.

37

APPENDIX. ADDITIONAL INSTALLATION DETAILS 

This appendix presents additional details provided by FDOT and local agencies about the use of 

this strategy. The information presented here may be of use to other agencies interested in using 

this strategy. Participants were asked to provide responses to six questions. The following section 

provides the questions and responses.

1. Were there any notable variations in level of enforcement from location to location by district 

or any variations in driver awareness campaigns? Was the driving public made aware of what 

the indicator lights are and how they are used (e.g., supplemental warning and regulatory 

signs)?

District 1 provided the most feedback to this question, indicating that some level of awareness 

campaign was used. The Bartow Police Department noted that it participated in some awareness 

programs in conjunction with the Polk County Community Traffic Safety Team during the initial 

installation. However, after installation, the department did not participate in any awareness 

programs other than enforcement of the lights. Since installation, the Bartow Police Department 

noted that it has used the lights for the purposes of red-light violators, and they have been 

beneficial to the department’s enforcement efforts. The department began using the lights 

immediately after installation and has continued using them. 

The Charlotte County Sheriff’s Office (CCSO) noted that the white strobe lights were a very 

useful tool that helped with enforcement efforts, but since installation, CCSO has not increased 

its time of duration at those specific intersections. The CCSO increases its red-light enforcement 

or duration at intersections based on traffic crash statistics. The Collier County Sheriff’s Office 

noted that it used press releases when the indicators were first installed, using some minor 

coverage in print media. The Collier County Sheriff’s Office did not change levels of red-light 

enforcement or conduct special operations because of the indicator lights.

Highlands County indicated that it completed public awareness programs dealing with all topics 

involving traffic enforcement/education. However, the county did not single out red-light 

running. Most awareness programs focused on local news media or meetings held at different 

homeowners’ associations. The Lake Wales Police Department noted that a press release was 

issued when the lights were initially installed.

The Manatee County Sheriff’s Office indicated that it conducted red-light enforcement details 

three times per month, targeting 1 of the 10 most dangerous intersections based on crash 

statistics. The Sheriff’s Office also noted that the strobe installation made enforcement less 

manpower dependent. The Sheriff’s Office did not conduct any public awareness programs 

strictly targeting strobe enforcement; however, the office frequently had newspaper articles 

concerning red-light enforcement. The Naples Police Department distributed press releases on the 

indicator lights (e.g., an article printed in the local newspaper) and also talked about the lights on 

several talk radio shows on an AM station.

Districts 2 and 5 did not comment on any awareness campaigns or utilization of the indicators.
38

2. The installation dates for the treatment sites on our list range from 2004 to 2010. What types 

of indicator lights (e.g., white incandescent) were installed at these treatment sites? Were the 

indicator lights located on the signal heads, mast arms, or other locations? If you have 

standard drawings or specifications that addresses these questions, please pass them along.

Orange County noted that the first bulbs were incandescent, 69-watt pedestrian bulbs; later they 

were “corncob” style 360-degree blue LED bulbs. Additionally, Orange County noted that some

nearby agencies have mounted them to the mast arm, some have mounted them directly to the 

bottom of the green, and others have mounted the lights on top of the signal head. 

Orange County has mounted them above the signal in every case. If a mast arm was used, a slip 

fit-threaded 1.5-inch PVC coupler was used to place the fixture on top of the signal brackets to 

secure it with a tapped screw. The globe would face up and be visible 360 degrees. If mounted on

a span wire, agencies used typical pedestrian-style feet and tubes with the 90-degree elbows to 

turn them down. If the direction had an exclusive left and some standard ball indications, the 

confirmation lights were installed as far as possible from each other, left-most left-turn signal and 

right-most ball signal. In the area, conduit 90-degree elbows or a horizontal mounting installation 

to the pedestrian foot have been used. The pedestrian hardware increased the cost.

 © Pelco Products, Inc.

Figure 11 provides an assembly sheet for the confirmation light with globe.
39

 © Pelco Products, Inc.

Figure 11. Diagram. Assembly sheet for confirmation light with globe.
40

3. Were there any other requirements (e.g., minimum or maximum traffic volume, presence of 

turn lanes, etc.) for the installation of indicator lights?

Red-light indicator lights were installed on both State highways and municipal streets. Locations 

were requested by city agencies, police, or pedestrian safety committees. FDOT provided the 

bulb holder/socket with the local signal agency providing cable and additional necessary 

hardware for mounting. FDOT approved the locations and provided the hardware as part of a 

safety initiative.

4. Please describe any notable challenges related to the indicator light installation and how you 

overcame them.

A potential challenge is the presence of the indicator if the intersection goes to flash. The 

indicators are wired directly to the red bulb of the signal that they represent to simplify the wiring 

required. They flash with the signal indication, which means the intersection has red, yellow, 

white, and blue lights potentially if it goes to flash.

5. Please describe any notable challenges related to the indicator light maintenance and how you 

overcame them.

No agencies reported maintaining the indicators; several noted that the operations costs were

negligible.

6. What lessons learned or recommendations would you share with another State interested in 

the widespread application of red-light indicator lights?

During the daytime, both the white and blue bulbs can be hard to see. At night, there was no 

visibility problem, the blue being distinctly noticeable. Agencies reported that RLILs mounted 

globe down/socket up seemed to make them slightly more visible because the sunlight might be 

more obscured (depending on the time of day), and the plastic globe lens seemed to last slightly 

longer. In addition, a broken lens would not allow the socket to get very wet, but a poor 

installation could allow it to fill with water. 

The Florida two-point span wire gave agencies an attachment that typically made the light 

slightly obstructed from the front (drivers’ view), so all were mounted toward the back for the 

officer’s viewing. A box span wire design was very easy to plan, but a diagonal span intersection 

created problems due to use of two-way or greater assemblies and the ability to define the back of 

a signal. In addition, if the globe was mounted underneath the signal, the two-way (or greater) 

bracket might become impossible to install in a conventional fashion. Neighboring agencies 

installed additional hanger brackets adjacent to a two-way signal to indicate which signal they 

represented by associated physical location (i.e., left of the left signal and right of the right 

signal.) If the two-point style hanger were not used, as is common in Florida, one might be 

limited to use of the bottom of the signal. Being connected to the red means that the “special” 

indication on the bottom would operate in conjunction with the signal indication on the top.

One agency reported avoiding any five-section left-turns (unless part of a sequential side street 

movement) because of enforcement concerns; in addition, that agency avoided a two-way or 
41

greater signal combo because it was not clearly obvious what direction the supplement light 

indicated. The light has also been used on a blank-out no-turn-on-red symbol.

The public may inquire about what the lights mean. Even some police officers may not be aware 

of the purpose. (These officers are usually from an agency that has not been involved in the 

enforcement use in their jurisdiction.) Public awareness notification does not yet seem to exist in 

a user-friendly fashion.

43

ACKNOWLEDGMENTS

This report was prepared for the FHWA Office of Safety Research and Development under 

contract DTFH61-13-D-00001. The FHWA Contracting Officer’s Program and Task Manager for 

this project was Roya Amjadi. 

The research team gratefully acknowledges the participation of the following organizations and

their assistance in this study:

• FDOT.

• FDOT District 1 Traffic Operations/Safety Office.

• FDOT District 5 Traffic Operations.

• Department of Public Works—Engineering Division—Brevard County, FL.

45

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