Safety Evaluation of Converting 

Traffic Signals from Incandescent 

to Light-Emitting Diodes

Background

Across the Nation, many agencies have been replacing conventional incandescent light 

bulbs in traffic signals with light-emitting diodes (LED) (see figure 1 and figure 2). LEDs 

are primarily installed to reduce energy consumption and decrease maintenance. In addition, 

LEDs are expected to last much longer compared with incandescent bulbs and tend to 

age gradually.(1) However, a recent study revealed several potential problems with LEDs, 

including their inability to melt snow and issues related to visual discomfort caused by 

glare at night.(2)

Two recent studies, one in Middleton, OH, and the other in Memphis, TN, evaluated the 

safety effects of converting from incandescent bulbs to LEDs.(3,2) The Ohio study concluded 

that the LED conversion resulted in a 71-percent increase in crashes, while the Tennessee 

study concluded that the LED conversion resulted in a 47-percent increase in crashes. Both 

studies used before–after empirical Bayes (EB) evaluation techniques, but methodological 

issues associated with these two studies, which are discussed by Srinivasan et al., necessitate 

a cautious approach to interpreting and applying the results.(4) One of the most significant 

issues that these studies have in common is a very limited sample size—both studies use 

data from only eight intersections where incandescent bulbs were replaced by LEDs and 

two comparison/reference sites.

An Analysis Tool for 

Making Informed 

Safety Decisions

SUMMARY REPORT

Figure 1. Photo. Incandescent signal head. Figure 2. Photo. LED signal head.
2

Objectives

It is clear that LEDs are superior in terms of energy 

consumption and service life. In fact, this economic 

benefit is the primary reason for the changeover to 

the LEDs. While agencies do not typically make this 

change on the basis of expected safety benefits, they 

certainly do not expect a safety detriment. The effect 

on traffic safety has not yet been adequately determined. 

The objective of this study was to determine whether 

the conversion of traffic signals from incandescent 

bulbs to LEDs has an effect on the number of crashes, 

and, if so, to quantify that effect in terms of a crash 

modification factor (CMF). This evaluation used 

data from Charlotte, NC. In 2008, the Charlotte 

Department of Transportation (CDOT) contracted 

a firm to change the city’s signalized intersections 

from incandescent bulbs to LEDs. CDOT instructed 

contractors to change out all bulbs at an intersection, 

even if LEDs were already present. Most of the 

intersections were converted in 2008 and 2009.

Methodology

This study used an EB before–after evaluation that 

compared the actual number of crashes that occurred 

after the implementation of the LEDs with the estimate of the expected number of crashes in the after 

period if the LEDs had not been implemented. The 

EB method has been identified as the state of the art 

for conducting before–after evaluations. The EB 

method has been found to be effective in addressing 

possible bias owing to regression to the mean (RTM) 

if locations with a high number of accidents were 

selected. It also overcomes the difficulties of using 

crash rates in normalizing for volume differences 

between the before and after periods and is effective 

in accounting for trends owing to changes in crash 

reporting practices, weather, driver behavior, demographics, vehicle population, and technology over time.

Because the treatment involved a “blanket” LED 

conversion at all signalized intersections in Charlotte 

(rather than a selected group), any possible bias 

owing to RTM was minimal. Thus there was no 

need to identify an untreated “reference group” as 

is typically done to account for RTM in before–after 

studies. However, a comparison group was necessary 

to account for trends in crashes owing to changes in 

crash reporting practices, weather, driver behavior, 

demographics, vehicle population, and technology 

over time. Because all the signalized intersections 

were treated, it was impossible to identify a sample of 

untreated signalized intersections for the comparison 

group. The following options were considered for 

identifying an appropriate comparison group:

1. Stop-controlled intersections within Charlotte.

2. Signalized intersections from a city outside 

of Charlotte.

3. Other facilities within Charlotte, e.g., 

non-intersection roadway segments.

Option 2 would have required an assumption that the 

trends in these signalized intersections outside Charlotte 

are similar to the trends within Charlotte. Option 3 would 

have required an assumption that the trends at these other 

facilities (e.g., non-intersection locations) are similar 

to the trends at intersections. Option 1 would indicate 

trends of crashes within Charlotte (a drawback of 

option 2) and crashes related to intersections (a drawback of option 3), and thus was considered the best 

choice under the circumstances. A similar approach 

had been successfully used for a Federal Highway 

Administration (FHWA) safety evaluation of red light 

cameras.(5) In that study, because the presence of red 

light cameras at some intersections was expected to 

affect behavior at most signalized intersections in their 

vicinity, the researchers used a comparison group of 

unsignalized intersections from each jurisdiction to 

account for annual trends. The steps involved in this 

current EB evaluation, including the estimation of 

safety performance functions and annual calibration 

factors, can be found in Srinivasan et al.(4)

Data Collection

Site Identification

Treatment Sites

The treatment sites consisted of signalized three- and 

four-leg intersections that underwent conversion from 

incandescent to LED bulbs. Treatment sites were identified based on contractor notes obtained from CDOT 

staff. The initial list of treated intersections consisted of 

550 sites that were converted between January 2008 and 

September 2009. These sites were filtered to remove 

intersections where other changes may have occurred at 

about the same time that the LED bulbs were installed. 

The sites were also filtered to remove sites with red 

light cameras, sites at freeway ramps, sites with flashing 

yellow arrows, and sites with several other characteristics that might influence results. After all site drops, the 

treatment group consisted of 282 intersections.
3

Comparison Sites

The method chosen to identify unsignalized intersections 

for the comparison group was to select from the geographic information system (GIS) intersection inventory data provided to the Highway Safety Information 

System (HSIS) by CDOT. The 2008 inventory shows 

more than 26,000 unsignalized intersections. Of these, 

8,100 lie on major (non-local roads), which indicates 

that they are more suitable for comparison with the 

major signalized intersections. In addition, there was 

a much greater chance of having traffic volume information for the major road of the intersection. 

This initial selection was followed by a more detailed 

investigation to collect intersection characteristics 

data and determine which intersections were eligible 

for inclusion in the comparison group. The initial 

list was filtered by the applicable exclusion criteria 

used for the treatment sites (e.g., those locations that 

showed a major improvement during the analysis 

period), as well as other criteria specific to unsignalized intersections, such as the presence of flashing 

beacons. The final number of comparison intersections 

used was 3,375.

Treatment Information

The installation data for the treatment sites were 

obtained from CDOT staff. Initially, the data existed 

on worksheets (bulb change-out forms) that were filled 

out by the contractor responsible for converting the 

signal from incandescent bulbs to LEDs. Contractor 

notes indicated that all signal heads at an intersection 

were converted when the intersection was visited. 

The team manually entered the information into a 

spreadsheet for analysis. These data included installation date, location, and number of bulbs changed. 

Intersection Characteristics

Although an inventory of intersections was available, 

the details about the intersection characteristics were 

sparse. Aside from the type of traffic control (signalized versus stop controlled), all other intersection 

characteristics of interest, such as number of lanes, 

traffic volumes, and speed limit were obtained by 

linking to the roadway (segment) file in the GIS that 

is maintained in the HSIS database. Because there is 

no reliable linkage variable between the intersection 

point and the roadway line segments, the linking was 

accomplished through a spatial join in the GIS. This 

produced an association between intersection legs 

(line segments) and the intersection (point). Some 

additional processing was necessary to use these 

data in the analysis and determine which legs were 

opposite each other.

Traffic Data

Traffic volume data (annual average daily traffic 

(AADT)) were obtained from HSIS for the years 2005 

to 2010. The HSIS traffic volumes for Charlotte are 

based on a system of regular midblock 24- or 48-h 

counts performed by CDOT and converted to AADT. 

The counts are conducted on a rotating cycle such that 

each segment is counted once every 3 years. These 

point AADTs are distributed temporally (i.e., extrapolated backwards or forwards through time for years 

using a growth factor when counts were not conducted 

at the location) and geographically (i.e., the same 

AADT is carried down all segments of the road on 

which the count was taken). 

The other source of traffic volume information was 

the turning movement counts that CDOT conducts at 

its intersections on a regular basis. These counts are 

performed manually at all signalized intersections 

as well as a few unsignalized ones. Both the turning 

movement counts and the segment AADTs were used 

in the development of safety performance functions 

for the analysis.

Crash Data

Crash data were obtained from HSIS for the years 2005 

through 2010. Charlotte HSIS crash data are maintained spatially—every crash is geocoded and given 

latitude and longitude coordinates. For any crash 

occurring within 100 ft of an intersection, CDOT 

“snaps” it to the intersection, placing it exactly at the 

intersection of the road lines. 

Initially, the project team attempted to use a spatial 

query in which a count of crashes at an intersection was 

determined by counting the number of crashes within 

a certain radial distance from the intersection point 

(e.g., 150 ft). The advantage of this spatial approach 

was that it gave the analyst the ability to specify the 

distance to be used in the analysis. However, this spatial 

query approach proved problematic in two ways:

1. Crash mislocation. Upon examination of the crash 

locations, it became clear that some crashes had 

been mislocated. Evidently, some transformation or 

conversion of the GIS data at some point in the past 

had caused some crashes to be “shifted” from their 

true location by approximately 100 ft (see figure 3). 
While this could be somewhat rectified by using a 

larger spatial query radius, this change in radius 

conflicted with the second issue of double-counting. 

2. Double-counting. If two intersections were near 

each other, the crashes lying between them would 

be double-counted (i.e., counted for both intersections) (see figure 4). Using smaller spatial query 

radii decreased the effect of this problem, but 

excluded those crashes that had been mislocated. 

Thus, the team chose to use the location information in 

the crash record to determine which crashes occurred 

at which intersections. Each crash that was located on 

the map by CDOT staff has a street name or names to 

indicate where it occurred (in addition to the latitude/

longitude coordinates). For crashes determined to be 

intersection-related (as determined by the Charlotte 

staff), the field value appears as, for example, “PROVIDENCE_RD_MCKEE_RD,” indicating that the crash 

occurred at the intersection of Providence Road and 

McKee Road. This value was unique and so could be 

used to join crashes to intersections. 

Evaluation and Results

Table 1 shows summary statistics for the 282 treatment sites (84 that were three-leg intersections and 

198 that were four-leg intersections). The table shows 

the average traffic volume per intersection per year 

and average number of crashes (of different types) per 

intersection per year in the before and after periods. 

As shown in the table, eight different crash types were 

investigated:

• Total crashes.

• Injury and fatal crashes.

• Left turn, angle, and head-on crashes.

• Rear-end crashes.

• Total crashes during dark, dawn, and dusk.

• Injury and fatal crashes during dark, dawn, 

and dusk.

• Left-turn, angle, and head-on crashes during 

dark, dawn, and dusk.

• Rear-end crashes during dark, dawn, and dusk.

The initial goal was to investigate left-turn, angle, and 

head-on crashes separately. However, DOT staff indicated that some police officers code left-turn crashes 

as angle or head-on crashes. Consequently, these crash 

types were combined into one category. Another goal 

was to investigate the effect of the LEDs by time of 

day. However, time of day of the crash was not consistently coded during the before period. Hence, data on 

ambient lighting during the crash was used, and dark, 

dawn, and dusk were combined into one category. 

When comparing the crashes per intersection per year 

in the before and after periods, there seems to be a 

reduction in almost all the crash types, even though 

the traffic volume changed very little during that 

period. However, this change obviously cannot be 

attributed solely to the treatment because it is very 

possible that the reductions could be the result of 

changes in crash reporting practices, weather, driver 

behavior, demographics, vehicle population, and 

technology over time; that is precisely the reason for 

including the comparison group of stop-controlled 

4

Figure 3. Illustration. Crash mislocation.

Figure 4. Illustration. Crash double-counting.
5

Note: In 2007, crash type information was not coded consistently. Hence, the summary statistics for rear-end, left-turn, angle, and 

head-on crashes do not include data from 2007

intersections. In fact, the total numbers of crashes at 

the comparison sites by year are:

• 2005: 4,714.

• 2006: 5,033.

• 2007: 4,649.

• 2008: 4,488.

• 2009: 3,836.

• 2010: 3,403.

It is clear that the number of crashes decreased 

substantially in 2009 and 2010, although, collectively, 

these stop-controlled intersections experienced very 

few changes (apart from changes in traffic volumes) 

during this period. 

Discussion of Results

Table 2 shows the results of the evaluation conducted 

using the EB method. Included are the EB expected 

crashes in the after period without the treatment, the 

observed crashes in the after period, the CMF, and the 

standard error of the CMF. A CMF greater than one 

implies that the treatment may lead to an increase in 

crashes, whereas a CMF less than one implies that the 

treatment may lead to a reduction in crashes.

As shown in Table 2, for three-leg intersections, the 

CMFs for the various crash types and times of day 

range from 1.016 to 1.177, indicating a possible 

increase in crashes after the conversion to LEDs. 

However, none of the CMFs are statistically different 

from 1.0 at the 0.05 significance level. For four-leg 

intersections, the CMFs range from 0.827 to 1.091, 

and five out of the eight CMFs are less than 1.0. The 

CMFs for rear-end crashes during day and night and 

rear-end crashes during dawn, dusk, and dark time 

periods are lower than 1.0 and statistically significant, 

indicating a safety benefit from the changeover to 

LEDs for this crash type. 

The reason for the difference in the effectiveness of 

LEDs for three-leg and four-leg intersections is not 

known at this time. The analysis also revealed that 

there is wide variation in the individual CMFs across 

the sites, indicating substantial differences among 

the different sites in terms of the safety effects of the 

LEDs. Future research should investigate whether 

LEDs are more or less beneficial depending on the 

characteristics of the intersection, including type 

of area, sight distance, intersection lighting, traffic 

volume, and phasing scheme. This research could 

lead to crash modification functions that may provide 

further insight into the safety aspects of LEDs.

Table 1. Summary statistics for treatment sites.

VARIABLE BEFORE AFTER

Intersection Approach Through AADT (per intersection) 18,668 18,347

Intersection Approach Right Turn AADT (per intersection) 3,415 3,484

Intersection Approach Left Turn AADT (per intersection) 3,644 3,697

Total crashes per intersection per year 8.15 6.40

Injury and fatal crashes per intersection per year 2.55 2.39

Left-turn, angle, and head-on crashes per intersection per year 2.80 2.19

Rear-end crashes per intersection per year 3.09 2.54

Dawn, Dusk, and Dark: Total crashes per intersection per year 2.06 1.79

Dawn, Dusk, and Dark: Injury and fatal crashes per intersection per year 0.64 0.64

Dawn, Dusk, and Dark: Left-turn, angle, and head-on crashes per intersection per year 0.78 0.64

Dawn, Dusk, and Dark: Rear-end crashes per intersection per year 0.64 0.60
References

1. Butler, J.P., “California Says ‘Go’ to Energy-Saving Traffic Lights,” State Energy Program Case 

Studies, California Energy Commission, Sacramento, CA, 2004.

2. Jami, N., Ivey, S., Lipinski, M., and Golias, M.M., “LED Traffic Signal Retrofits: Implications for 

Intersection Safety,” Presented at the 91st Annual Meeting of the Transportation Research Board, 

Washington, DC, 2012.

3. Eustace, D., Griffin, V., and Hovey, P., “Analyzing the Effects of LED Traffic Signals on Urban 

Intersection Safety,” ITE Journal, 80,(4) pp. 22–27, 2010.

4. Srinivasan, R., Carter, D., Smith, S., and Lan, B., “Safety Evaluation of Converting Traffic Signals 

from Incandescent to LED Bulbs,” Transportation Research Record (In Press). Presented at the 

92nd Annual Meeting of the Transportation Research Board, Washington, DC, 2013.

5. Council, F., Persaud, B., Eccles, K., Lyon, C., and Griffith, M.S., Safety Evaluation of Red Light 

Cameras, Report FHWA-HRT-05-048, Federal Highway Administration, McLean, VA, April 2005. 

For More Information

The research was conducted by Raghavan Srinivasan, Daniel Carter, Sarah Smith, and Bo Lan, of 

the University of North Carolina Highway Safety Research Center. Further details about the evaluation can be found in Safety Evaluation of Converting Traffic Signals from Incandescent to LED Bulbs, 

which was presented at the 2013 Annual Meeting of the Transportation Research Board.(4) For more 

information about HSIS, please contact HSIS program managers Carol Tan, (202) 493-3315, carol.

tan@dot.gov, or Ana Maria Eigen, (202) 493-3168, ana.eigen@dot.gov, at the FHWA.

The Highway Safety Information System (HSIS) is a safety database that contains crash, roadway inventory, and traffic volume 

data for a select group of States and cities. The participating States of California, Illinois, Maine, Minnesota, North Carolina, Ohio, 

and Washington and the city of Charlotte were selected based on the quality of their data, the range of data available, and their 

ability to merge the data from various files. The HSIS database also contains historic data from Michigan and Utah. The HSIS 

is issued by FHWA staff, contractors, university researchers, and others to study current highway safety issues, direct research 

efforts, and evaluate the effectiveness of crash countermeasures.

AUGUST 2013

FHWA-HRT-13-070

HRDS-20/08-13(750)E McLean, VA 22101-2298 6300 Georgetown Pike Turner-Fairbank Highway Research Center Research, Development, and Technology www.HSISinfo.org 

Table 2. Results of before–after evaluation

INTERSECTION 

TYPE

TIME 

PERIOD CRASH TYPE

EEB EXPECTED 

CRASHES IN 

AFTER PERIOD 

WITHOUT 

TREATMENT

OBSERVED 

CRASHES 

IN AFTER 

PERIOD

CMF

STANDARD 

ERROR OF 

CMF

Three-Leg 

(84 sites)

Day and 

Night

TOTAL 516.9 539 1.042 0.051

Injury and fatal 175.9 206 1.170 0.094

Left turn, angle, 

and head on 159.1 162 1.016 0.094

Rear end 213.3 236 1.105 0.084

Dawn, 

Dusk, 

and Dark

TOTAL 120.6 134 1.109 0.106

Injury and fatal 40.0 45 1.122 0.179

Left turn, angle, 

and head on 33.2 36 1.078 0.194

Rear end 41.5 49 1.177 0.182

Four-Leg 

(198 sites)

Day and 

Night

TOTAL 2006.3 1971 0.982 0.026

Injury and fatal 698.8 732 1.047 0.045

Left turn, angle, 

and head on 638.5 697 1.091 0.049

Rear end 918.3 760 0.827† 0.036

Dawn, 

Dusk, 

and Dark

TOTAL 612.1 567 0.926 0.044

Injury and fatal 197.9 205 1.035 0.081

221.7 213 0.959 0.075 Left turn, angle, 

and head on

Rear end 226.7 188 0.828† 0.069

†Statistically different from 1.0 at the 0.05 significance level.