TECHBRIEF Safety Effectiveness of the

HAWK Pedestrian Crossing

Treatment

FHWA Publication No.: FHWA-HRT-10-045

FHWA Contact: Ann Do, HRDS-07, (202) 493-3319, 

ann.do@dot.gov

This document is a technical summary of the Federal Highway 

Administration report, Safety Effectiveness of the HAWK 

Pedestrian Crossing Treatment, FHWA-HRT-10-042.

Objective

The city of Tucson, AZ, developed the High intensity Activated 

crossWalK (HAWK) pedestrian crossing beacon in the late 

1990s to assist in pedestrian crossings, especially at major 

arterials with minor street intersections.(1) Previous research 

found driver yielding percentages above 95 percent for the 

HAWK treatment, even on major streets with multiple lanes 

or higher speeds.(2) Because of the limited number of treatments with high yielding rates for major arterials, the Federal 

Highway Administration (FHWA) sponsored this current study 

to evaluate the safety effectiveness of the HAWK device. 

This TechBrief provides a summary of the research findings,

while the corresponding main technical report (FHWA-HRT10-042) provides additional details.(3)

Background

While several roadway treatments are available to address 

pedestrian concerns, only a few are appropriate for high-speed 

or wide-crossing conditions. The HAWK beacon was developed to address these conditions. At a HAWK crossing, drivers 

receive multiple cues to emphasize the potential presence of 

a pedestrian. These cues include a unique configuration of 

the HAWK beacon (two red lenses over a single yellow lens), 

high-visibility crosswalk markings (ladder-style markings as

opposed to only two transverse white lines), a stop bar 

approximately 50 ft from the crosswalk, 8-inch solid lane lines 

between through travel lanes, signs that can be illuminated

and read “CROSSWALK,” and School Warning signs. When 

activated, the HAWK uses a red indication to inform drivers 

to stop, thereby creating a time period for pedestrians to cross 

Research, Development, and 

Technology

Turner-Fairbank Highway 

Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

www.tfhrc.gov
2

the major roadway. Figure 1 shows an example 

of the current head configuration for the HAWK. 

At the time of this study, HAWKs were used 

at more than 60 locations in Tucson, AZ. 

The HAWK beacon is not illuminated until it 

is activated by a pedestrian, triggering the 

warning flashing yellow lens on the major 

street. After a set amount of time, the indication changes to a solid yellow light to inform 

drivers to prepare to stop. The beacon then 

displays a dual solid red light to drivers on 

the major street and a walking person symbol to pedestrians. At the conclusion of the 

walk phase, the beacon displays an alternating 

flashing red light, and pedestrians are shown 

an upraised hand symbol with a countdown 

display informing them of the time left to 

cross. During the alternating flashing red 

lights, drivers can proceed after coming to a 

full stop and checking that pedestrians have 

already crossed their lane of travel. Each successive driver is legally required to come to a 

full stop before proceeding during the alternating flashing red phase.

The alternating flashing red phase allows the

driver delay to match the actual crossing needs

of the pedestrian. Drivers can proceed with

a stop-and-go operation during the flashing 

red phase if a pedestrian walks faster than the

assumed walking speed and clears the lanes

or roadway, as appropriate. If pedestrians need

more time, then the drivers remain stopped

until they finish crossing. The ability to balance the needs of the pedestrians with driver

delay is a valuable component of the HAWK

treatment. Concerns have been expressed 

regarding driver behavior and understanding

of the dark phase (not illuminated) and the 

Figure 1. Example of a HAWK treatment in Tucson, AZ.
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flashing red phase. Experiences in Tucson, AZ, 

have demonstrated that, with proper education 

and with experience, drivers understand when 

they should stop and when they should resume 

travel. The city has conducted public campaigns and increased enforcement to teach 

and encourage appropriate driver and pedestrian behavior at HAWK crossings as well 

as at all pedestrian crossings.

Following the completion of this study but 

prior to publication, the 2009 Manual on Uniform Traffic Control Devices (MUTCD) was 

released.(4) The 2009 MUTCD includes information about the pedestrian hybrid beacon, 

which is similar to the HAWK. The material in 

the 2009 MUTCD differs from the HAWKs included in this safety study in the following ways:

•	 Section 4F.02 of the 2009 MUTCD has 

the following guidance statement: “When 

an engineering study finds that installation of a pedestrian hybrid beacon is 

justified, then: 

A. The pedestrian hybrid beacon 

should be installed at least 100 feet 

from side streets or driveways that are 

controlled by STOP or YIELD signs.”(4)

All 21 HAWKs included in this study were 

located either at a minor intersection 

(where the minor street was controlled 

by a STOP sign) or at a major driveway 

(where the driveway was controlled by a 

STOP sign).

•	 The 2009 MUTCD includes an R10-23 sign 

with the symbolic red circle and a white 

background for the crosswalk section of 

the sign.(4) The signs typically used at the 

HAWK locations in Tucson, AZ, do not 

have the symbolic red circle, and the 

background is yellow for the crosswalk 

section of the sign.

Methodology

This TechBrief presents the findings from an 

FHWA study that determined safety benefits 

of the HAWK device. Before-after evaluations 

compared crash predictions for the after period 

(which assumed the treatment had not been 

applied) to the observed crash frequency for 

the after period (with the treatment installed) 

using an empirical Bayes (EB) method.(5)

Development of Safety Performance Functions

The first step in the before-after EB method 

was to develop and calibrate safety performance functions (SPFs) using data from a reference group. Development of the SPFs 

involved determining which predictor variables 

should be used in the model, how variables 

should be grouped, and what model form 

should be used. The major street and minor 

road average daily traffic (ADT) values are 

often the key variables in developing SPFs for 

intersections. In addition, pedestrian volumes 

are likely to play an important role in pedestrian crashes. To account for additional intersection-to-intersection variability (other than 

that caused by the differences in traffic volumes 

and pedestrian volumes), intersection type, 

median refuge presence, number of lanes, and 

major street speed limit were also considered 

in the SPF predictions.

Site Selection and Geometric Data

The city of Tucson, AZ, provided the research 

team with a list of all HAWKs that were installed 

or planned. Sites planned or installed less than 

18 months prior to this study were not evaluated. Only sites with the current head configurations (see figure 1) that were newly installed 

(21 locations) were included in the before-after 

study. The previous head configuration had a 

similar appearance as a vertical traffic signal.

Crash evaluations benefit when a reference 

group of similar sites without treatment is identified. Researchers identified two potential reference groups—reference group 1 and reference 

group 2—and derived the safety effectiveness 

estimate for HAWKs using each reference group. 

Reference group 1 included 36 signalized and 

35 unsignalized intersections. Because of concerns with including signalized intersections, 

reference group 2 was developed and consisted 

of 102 unsignalized intersections. 
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Traffic Counts

Several sources were used to obtain vehicle 

counts, including traffic counts (or historical

maps) available on the Web and historical 

counts from the Pima Association of Governments.(6,7) Vehicle counts from existing sources 

were identified for most of the major streets of 

the intersections. None of the existing sources 

had pedestrian counts available. Therefore, 

2-h pedestrian counts were collected during

spring 2008 and spring 2009. The city of 

Tucson, AZ, provided the research team with 

24-h video surveillance of five HAWK sites. 

The number of pedestrians crossing for each 

hour was counted from the videos, and pedestrian crossing distributions were determined 

and used to adjust the 2-h count into 24-h 

pedestrian counts. Appropriate seasonal variations were determined from Traffic Volumes 

Map, which noted that winter visitors and 

college students contributed to higher volumes 

during the spring.(7)

Study Periods

For the before-after study, the goal was to have 

36 months of before data and 36 months of 

after data. The before period reflected month 38

to month 2 prior to the installation date of the 

HAWK. The calculations assumed 2 months 

prior to the installation date as construction. 

The 2 months following installation of the 

HAWK were assumed to be a learning period for 

drivers to become familiar with the treatment. 

The after period occurred 2 to 38 months following the installation of the HAWK or until 

December 31, 2007, which was the limit of 

crash data available.

The number of months in the after period for 

the 21 HAWK sites varied depending on 

when the HAWK was installed. The majority 

of the sites had a 32-month or greater after 

period, with more than 80 percent of the sites 

having at least a 28-month after period. Reference group sites had the same time period in 

their before and after periods as their corresponding HAWK site.

Crash Data

Crash data were supplied by the city of 

Tucson, AZ, and street names were used to 

match crashes with the geometric database. 

Identifying all crashes with matching street 

names should capture the crashes that could 

be influenced by the intersection’s traffic control; however, it may also capture crashes that 

would not have been influenced. The intersection-related (IR) variable may provide insight 

into whether the crash is related to the intersection’s traffic control. The permitted responses 

for IR crashes were “yes,” “no,” or blank, and

about 1/3 of the crashes had this field blank.

A comparison of the number of IR crashes for 

a sample of intersections to material available in a previous study indicated that the IR 

variable may be too restrictive. Therefore, the 

following two crash datasets were used in the 

evaluations:

•	 Intersecting street name (ISN) crashes 

were identified by matching the street 

names for the intersection.

•	 IR crashes were identified as the crashes 

in the ISN crash dataset with “yes” for the 

IR code.

The following types of crashes from each of 

the crash datasets were considered:

•	 Total crashes included all crashes identified. 

•	 Severe crashes included all crashes with 

an injury severity code of possible injury, 

nonincapacitating injury, incapacitating 

injury, or fatal injury.

•	 Pedestrian crashes included all crashes 

with the type of collision coded as 

pedestrian.

Aggregating Crash Data

In developing the SPFs, the crash counts at reference sites can be treated as aggregated data 

over the entire study period (including both the 

before and after periods) or as disaggregated 

data with two crash counts from each intersection—one for the before period and one 

for the after period. Aggregating the data is 
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one way to account for the correlations that 

might be present in the crash counts when the 

intersections are included twice (once for the 

before period and once for the after period) in 

estimating the SPFs. Disaggregating the data 

provides the opportunity to account for a general time trend (if any exists) within the two 

periods. For the disaggregated data, it is desirable to use the generalized estimating equation approach in estimating the SPF coefficients 

to incorporate the potential correlation in the 

before and after crash counts from the same 

intersection. Both approaches were explored in 

this study.

Observations

Table 1 summarizes the number of crashes 

by control type. HAWKs are installed to assist 

pedestrians in crossing the roadway; therefore,

installing the device should have a notable 

impact on pedestrian crashes. The impact on 

total or severe crashes is not as intuitive. Using 

IR crashes, the HAWK sites experienced 

a decrease in the total crash rate of about 

35 percent after installation. The 102 unsignalized intersections in reference group 2 experienced a 9 percent decrease, and signalized intersections in reference group 1 had a 17 percent

decrease. These observations indicate that 

citywide conditions may have contributed to 

the reduction in crashes since all types of intersection control showed reductions in total IR 

crashes. 

The HAWK sites experienced a large decrease 

of 86 percent in the pedestrian IR crash rate 

after installation. The 102 unsignalized intersections experienced an increase in pedestrian 

crashes of 143 percent. Therefore, if citywide 

Table 1. Crash data for before-after study by groups.

Treatment

Group Measure

ISN Crashes IR Crashes

Before After

Percent 

Change (%) Before After

Percent 

Change (%)

HAWK sites (21)

Frequency 11.0 9.2 -17 5.0 3.3 -34

Total crashes/MEV&P 0.748 0.618 -17 0.341 0.223 -35

Severe crashes/MEV&P 0.265 0.210 -21 0.138 0.094 -32

Pedestrian crashes/MEV&P 0.029 0.005 -83 0.017 0.002 -86

Pedestrian crashes/MEP 3.081 0.511 -83 1.826 0.255 -86

Reference group 1: 

Signalized 

intersections (36)

Frequency 44.9 41.9 -7 19.6 16.8 -14

Total crashes/MEV&P 1.953 1.788 -8 0.854 0.716 -16

Severe crashes/MEV&P 0.549 0.503 -8 0.294 0.241 -18

Pedestrian crashes/MEV&P 0.020 0.016 -23 0.010 0.008 -16

Pedestrian crashes/MEP 2.051 1.546 -25 1.025 0.839 -18

Reference group 1: 

Unsignalized 

intersections (35)

Frequency 4.2 4.3 3 1.6 1.3 -17

Total crashes/MEV&P 0.285 0.292 2 0.108 0.090 -17

Severe crashes/MEV&P 0.098 0.088 -10 0.043 0.038 -10

Pedestrian crashes/MEV&P 0.006 0.009 52 0.003 0.004 42

Pedestrian crashes/MEP 1.383 2.078 50 0.615 0.866 41

Reference group 2:

Unsignalized 

intersections (102)

Frequency 5.9 6.1 3 2.4 2.1 -9

Total crashes/MEV&P 0.418 0.430 3 0.166 0.150 -9

Severe crashes/MEV&P 0.140 0.141 0 0.060 0.056 -6

Pedestrian crashes/MEV&P 0.006 0.011 93 0.001 0.003 143

Pedestrian crashes/MEP 1.233 2.297 86 0.257 0.602 134

Crashes/MEV&P = Type of given crash (total, severe, or pedestrian crashes) per million entering vehicles and pedestrians.

Pedestrian crashes/MEP = Pedestrian crashes per million entering pedestrians.

Note: Frequency is expressed as the average annual number of total crashes for a site with the given intersection control 

and study period.
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conditions were contributing to reductions in 

total crashes, these conditions were not having

the same impact on pedestrian crashes, or 

other factors were contributing to a rise in pedestrian crashes at unsignalized intersections 

but not at signalized and HAWK intersections.

From table 1, it is observed that HAWK locations have crash rates higher than unsignalized intersections. For this dataset, the HAWK 

locations were associated with a slightly greater 

number of crashes per million entering vehicles 

and pedestrians, as compared to the nearby 

unsignalized intersections. This observation 

does not imply that if the HAWK was removed, 

the crash rate for a given intersection would 

be similar to the crash rate identified for the 

neighboring unsignalized intersections. When 

the sites were unsignalized intersections (i.e., 

before the HAWK was installed), the crash rate 

at the sites exceeded the crash rate for nearby 

unsignalized intersections. Therefore, conditions at the HAWK locations before the treatment was installed were generating crashes in 

greater numbers than the unsignalized intersections. This indicates that those intersections 

were associated with conditions that resulted in 

a higher number of crashes. Addressing those 

conditions with a HAWK resulted in a decrease 

in total crashes and a large decrease in pedestrian crashes. The following section provides 

the findings from the statistical evaluation.

Results

To account for the effects of variables in crash 

reduction as well as the potential regressionto-the-mean bias, the EB approach was employed to identify the safety effectiveness of 

the HAWK treatment. The corresponding main 

report includes the reasonable SPFs identified 

in this study.(3) Although the magnitude of the 

safety effectiveness estimate varied to some 

extent as different predictors were included in 

the SPFs, the results did not seem to change 

materially. Table 2 summarizes the percent reduction and whether the finding was significant 

at the 95 percent confidence level from the 

different approaches used in evaluating the 

HAWK beacon.

The results for total crashes were similar regardless of the approach used to evaluate the data. 

The reduction was about 16 percent for ISN 

crashes (14 to 19 percent) and 27 percent for 

IR crashes (23 to 29 percent), all of which were 

significant at the 95 percent confidence level. 

There were similar results for pedestrian 

crashes, with the disaggregate approach resulting in higher reductions (65 or 69 percent) than 

the aggregate approaches (between 51 and 

59 percent). As seen in several evaluations, 

severe crash results were not at the desired 

confidence levels. The smaller sample size 

probably affected the results. 

Although the safety effectiveness estimate did 

not change significantly with which reference 

group was used, reference group 2 was chosen 

as the more appropriate reference group, since 

in most cases, the HAWK was installed at a 

previously unsignalized intersection. 

Table 2. Summary of results. 

Reference Group 

(Aggregation)

Percent Reduction (Significant at the 95 Percent Confidence Level)

Total Crashes Severe Crashes Pedestrian Crashes

ISN Crashes

1 (aggregated) 15 (Yes) 11 (No) 57 (Yes)

2 (aggregated) 14 (Yes) 13 (No) 59 (Yes)

2 (disaggregated) 19 (Yes) 14 (No) 69 (Yes)

IR Crashes

1 (aggregated) 29 (Yes) 18 (No) 56 (No)

2 (aggregated) 29 (Yes) 15 (No) 51 (No)

2 (disaggregated) 23 (Yes) 8 (No) 65 (Yes)
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Summary and Conclusions

The objective of this study was to evaluate the 

safety effectiveness of the HAWK device. This 

study used a before-after EB method which 

accounted for possible regression-to-the-mean 

bias as well as traffic, weather, citywide public relations campaigns, and other factors that 

changed over time. SPFs were developed using 

reference site data consisting of nearby intersections that did not have HAWK treatments. 

The study included 21 intersections where a 

HAWK had been installed and two reference 

groups. Evaluation approaches explored the 

following:

•	 Three types of crashes (total, severe, and 

pedestrian).

•	 Two methods for identifying crashes (ISN 

and IR).

•	 Two reference groups (reference group 1

with 36 signalized and 35 unsignalized 

intersection and reference group 2 with 

102 unsignalized intersections).

•	 Two ways to combine the reference group 

before and after data (aggregated and 

disaggregated).

The crash prediction during the before period 

was calculated from SPFs and combined with 

the observed crash count for the before period 

by using a weighted average to control for 

regression-to-the-mean bias. This weighted 

average was adjusted for differences in duration 

and traffic volumes (and general time trends 

if any existed) between the before and after 

periods. This lead to a crash prediction for the 

after period had the treatment not been applied. 

EB then compared this predicted value to the 

observed crash frequency for the after period. 

Two crash datasets were used in the beforeafter evaluation. In theory, the IR crash dataset

should have better represented those crashes 

that would be affected by the traffic control at 

the intersection. A closer review revealed that 

the IR code was not used in over a 1/3 of the 

crashes; therefore, too many crashes may have 

been eliminated. The ISN crash dataset, however, may include crashes that are not related 

to the traffic control. Therefore, both datasets 

were considered. The IR crashes were considered as the more appropriate dataset for 

total and severe crashes. The ISN crash dataset, 

however, may be more representative of the 

change in pedestrian crashes since the HAWK 

device could induce pedestrians to walk an 

additional distance to benefit from an activated 

traffic control device.

The before-after evaluation results were as 

follows:

•	 There was a 29 percent reduction in total 

crashes, which is statistically significant at 

the 95 percent confidence level. 

•	 There was a 69 percent reduction in 

pedestrian crashes, which is statistically 

significant at the 95 percent confidence 

level.

•	 There was a 15 percent reduction in 

severe crashes, which is not statistically 

significant at the 95 percent confidence 

level. 

The prime objective of a HAWK is to provide 

pedestrians with safe crossing opportunities. 

As such, a reduction in pedestrian crashes 

would be expected to be associated with the 

HAWK, and a statistically significant reduction 

in pedestrian crashes was found. The installation of the HAWK was also found to be associated with a statistically significant reduction 

in total crashes. It should be noted that the 

HAWK treatment, just like any other warning 

traffic control device, may not work as effectively if it is overused. In addition, such high 

crash reductions identified in this study may 

not be achieved at future locations if the site 

has different characteristics, such as less pedestrian activity.

References

1. Nassi, R.B. and Barton, M.J. (2008). “New 

Traffic Control for an Old Pedestrian Crossing Safety Problem,” APWA Reporter, 44–49. 
8

Researchers—This study was performed by Kay Fitzpatrick and Eun Sug Park, both of the Texas 

Transportation Institute, which is a member agency of The Texas A&M University System.

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

distribution is being made to the Divisions and Resource Center.

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

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

http://www.tfhrc.gov/safety.

Key Words—HAWK, Pedestrian treatment, Pedestrian crashes, Unsignalized intersection, and 

Pedestrian hybrid beacon.

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 highquality information to serve the Government, industry, and public in a manner that promotes 

public understanding. Standards and policies are used to ensure and maximize the quality, 

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

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

june 2010 FHWA-HRT-10-045

HRDS-07/06-10(2M)E

2. Fitzpatrick, K., Turner, S., Brewer, M., 

Carlson, P., Lalani, N., Ullman, B., Trout, N., 

Park, E.S., Lord, D., and Whitacre, J. (2006). 

Improving Pedestrian Safety at Unsignalized Crossings, TCRP/NCHRP Report 112/

562, Transportation Research Board, 

Washington, DC.

3. Fitzpatrick, K. and Park, E.S. (2010). Safety 

Effectiveness of the HAWK Pedestrian Crossing Treatment, FHWA-HRT-10-042, Federal 

Highway Administration, Washington, DC.

4. Federal Highway Administration. (2009). 

Manual on Uniform Traffic Control Devices

(MUTCD). Obtained from: http://mutcd.fhwa.

dot.gov/kno_2009.htm. Site last accessed 

March 5, 2010.

5. Hauer, E. (1997). Observational Before-After 

Studies in Road Safety, Pergamon Press, 

Oxford, England.

6. Tucson Department of Transportation. Traffic 

Data for the City of Tucson, Tucson, AZ. 

Obtained from: http://www.dot.ci.tucson.

az.us/traffic/adt.cfm. Site last accessed June 

26, 2008. 

7. Pima Association of Governments. Traffic 

Volumes Map, Tucson, AZ. Obtained from:

http://www.pagnet.orgRegionalDataTransportationTrendsandDataTrafficVolumes/

Maps/tabid/446/Defaultaspx. Site last

accessed June 26, 2008.