TECHBRIEF

Research, Development, 

and Technology

Turner-Fairbank Highway 

Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

https://highways.dot.gov/research

Driver Yielding with LEDEmbedded Pedestrian- and 

School-Crossing Signs

FHWA Publication No.: FHWA-HRT-23-013

FHWA Contact: Ann Do (ORCID: 0000-0001-9932-7422), 

HRSO-30, (202) 493-3319, ann.do@dot.gov

This document is a technical summary of the Federal 

Highway Administration report Driver Yielding with 

LED-Embedded Pedestrian- and School-Crossing Signs.

INTRODUCTION

Traffic agencies are looking for ways to address the rise in pedestrian 

fatalities. One traffic control treatment gaining in popularity is the 

pedestrian-crossing or school-crossing warning sign with light-emitting

diodes (LEDs) embedded in its borders (called LED-Em in this report).

An example of the treatment is provided in figure 1. The LED-Em 

treatment is activated by a pedestrian push button, so the LEDs only 

flash when a pedestrian is attempting to cross the street. Due to the 

increased interest in the LED-Em treatment, guidance on where to 

install the devices is needed.

Figure 1. Photo. Example of LED-Em treatment on a four-lane divided street.

© 2022 Texas A&M Transportation Institute.
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STUDY OBJECTIVE

This study focused on evaluating the operational 

performance of the LED-Em treatment with respect to 

the characteristics of roadway and traffic control devices. 

While recent studies have considered the effectiveness 

of the LED-Em treatment, those studies only included 

a limited number of study sites. This Federal Highway 

Administration (FHWA) project attempted to address 

the sample size limitation by collecting data at a greater 

number of sites across multiple States. The objective of 

this research was to identify site and traffic control device 

characteristics, associated with high and low driveryielding values, in places where the LED-Em treatment 

has been applied. This information would provide insights 

into whether the LED-Em treatment is appropriate for 

specific locations.

SITE IDENTIFICATION

The criteria established for the inclusion of a study site 

in this FHWA study were as follows:

• The sign is a pedestrian- (W11-2), school- (S1-1), or 

trail- (W11-15) crossing warning sign with LED-Em.

• The sign is at a marked crosswalk.

• The sign is activated by a pedestrian push button.

• The crossing could have other pedestrian-related 

treatments such as in-pavement lights, and these 

supplemental features will be considered in the 

evaluation.

• The crossing can have advance-warning treatments.

The research team used several approaches to identify 

potential study sites. These approaches included making 

presentations at national meetings, sending emails to 

groups like the American Association of State Highway 

and Transportation Officials and State bicycle and 

pedestrian coordinators, and engaging in discussions 

with the regional offices of team members. Most of the 

11 States identified with a treatment had only a few 

device installations. Only Texas and California had 

more than 10 installations. Both States were selected 

for inclusion in this study.

DATA COLLECTION

Researchers collected data during four periods. Data were 

collected at 9 Texas sites in fall 2020, 31 California sites 

in late spring and early summer 2021, and 6 California 

sites and 3 Texas sites in fall 2021. The data for 13 Texas 

sites collected in spring 2019 or winter 2020, as part of 

previous Texas Department of Transportation projects, 

were also available.(1,2)

 The data collection periods 

were influenced by weather, temperature (with the goal 

of avoiding the hotter parts of the year for the region), 

travel restrictions, and when the sites were identified.

Researchers employed a staged pedestrian-crossing 

approach in this study to obtain a sufficient sample of 

pedestrian-crossing observations. The staged pedestrian 

was trained to approach the crossing in a similar manner 

at every location to minimize the effects of pedestrian 

behavior on drivers and maintain consistency among 

study locations in how pedestrians approached a crossing. 

A video recording was made during data collection. 

Researchers used the video to count the number of 

vehicles driving across the crosswalk in both directions 

for 1 min before each staged pedestrian crossing. The 

1-min increment provides an estimate of the amount of 

traffic present just before the specific pedestrian crossing.

This study included about 250 h of video recordings 

containing data for 7,805 drivers and 3,675 pedestrian 

crossings at 62 sites.

ANALYSIS APPROACHES

The data were prepared in two levels: persite and 

perdriver. The per-site analysis was performed based on 

driver-yielding rates averaged by each site, using a normal 

linear model, specifically the analysis of covariance 

(ANCOVA) model. An ANCOVA model was employed 

since many of the predictor variables, which are either 

continuous or categorical, are site-based or traffic-control 

device based (e.g., sign face) rather than individual 

crossing event based. The average driver-yielding rates 

also satisfied the underlying assumptions for using the 

ANCOVA model.

The per-driver analysis was also to explore the relationship 

between driver yielding and site and traffic control device 

variables. The nature of the per-driver analysis permits 

more detailed consideration of the traffic volume present 

when the staged pedestrian is attempting to cross the street. 

The dataset contains the individual driver response to the 

crossing pedestrian (1 if yielding or 0 if not yielding), the 

site and traffic control device characteristic variables, and 

the two-way hourly volume estimate. The estimate is based 

on a 1-min count for each crossing. Logistic regression 

was used with this dataset.

FINDINGS

The ANCOVA considered per-site mean yield rates while 

the logistic regression was able to consider the decision 

made by an individual driver. The nature of ANCOVA 

modeling permits easier and more intuitive interpretation of 

the results. Three efforts were conducted using ANCOVA:
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Table 1. Overview of significant variables in the statistical models.

VARIABLE STATISTICAL ANALYSIS APPROACH

PANDEMIC 

DATASET 

ANCOVA

ALL 

DATASET 

ANCOVA

INITIAL 

DATASET 

ANCOVA

PANDEMIC WITH 

VIDEO DATASET 

LOGISTIC

Posted speed limit − − − −

Vehicle volume NS − − −

Additional treatment [no] − − − Base

Additional treatment [yes] Base Base Base +

Bike lane [2 sides] Base NS NS +

Bike lane [none] − Base Base Base

Yield bar present [no] NS + NS NS

Yield bar present [yes] Base Base Base Base

LED flash length NS NS NS +

Curb to curb and median crossing distance − NS NS −

Sign location [right only] Base Base Base +

Sign location [right and median] + + NS +

Sign location [right and left] − − NS Base

Period [pandemic] NA + NS NS

State [California] NS NS NS +

State [Texas] Base Base Base Base

+ or − = variable (or at least one level of the variable) was significant, with + indicating a positive relationship and – indicating a negative 

relationship; Base = variable level represents the base condition in the model; NA = variable not applicable to the dataset; NS = variable was 

not significant in the model; Datasets include:

• Pandemic dataset = 49 sites where data were collected between fall 2020 and fall 2021.

• All dataset = 62 sites where data were collected between spring 2019 and fall 2021.

• Initial dataset = 53 sites where data were collected between spring 2019 and summer 2020.

• Pandemic with video dataset = 7,805 drivers within 48 sites where data were collected between fall 2020 and fall 2021.

Table 2. Per-site average driver yielding by posted speed limit and periods.

POSTED 

SPEED 

LIMIT (MPH)

PANDEMIC 

DATASET, N

PANDEMIC 

DATASET, 

DRIVER 

YIELDING (%)

PREPANDEMIC 

DATASET, N

PREPANDEMIC 

DATASET, 

DRIVER 

YIELDING (%)

ALL 

DATASET, N

ALL DATASET, 

DRIVER 

YIELDING (%)

25 8 78 0 NDY 8 78

30 12 75 6 56 18 68

35 19 61 4 26 23 55

40 6 39 0 NDY 6 39

45 4 55 1 16 5 48

50 0 NDY 2 16 2 16

Total 49 64 13 38 62 58

N = number of sites; NDY = no driver yielding value because 0 sites had the given posted speed limit; Datasets include:

• Pandemic dataset = 49 sites where data were collected between fall 2020 and fall 2021.

• Prepandemic dataset = 13 sites where data were collected between spring 2019 and fall 2019.

• All dataset = 62 sites where data were collected between spring 2019 and fall 2021.

• Sites available early in the project (53 sites, called 

“initial dataset”).(3)

• Sites where data had been collected by the research 

team within the past 3 yr (62 sites, called “all dataset”).

• Sites where data were collected during the pandemic 

(49 sites, called “pandemic dataset”).

Table 1 summarizes the findings from the different 

ANCOVA and logistic regression analyses.

There is variation where variables are significant within 

the three datasets using ANCOVA and the dataset for 

the logistic regression analysis; however, in all cases the 

posted speed limit is significant. The use of the LED-Em 

treatment on higher speed roads is clearly associated with 

lower driver yielding rates. Table 2 provides the average 

driver yielding rates by posted speed limit for the sites, 

which, in addition to the findings from the ANCOVA and 

logistic regression analyses, clearly shows the trend of 

lower driver yielding on higher speed roads.
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November 2022 FHWA-HRT-23-013

HRSO-30/11-22(WEB)E

When using an automated stepwise variable selection 

procedure with ANCOVA, the volume variable was 

insignificant. While vehicle volume had a statistically 

significant relationship with driver yielding—when 

included in the model by itself—it became statistically 

insignificant when posted speed limit was added to the 

model. When variable selection was guided by the research 

team, the vehicle volume variable was in the model with a 

negative coefficient. The negative coefficient indicates that 

as the vehicle volume increases, driver yielding decreases. 

Another variable that has been found to be significant 

in some of the models was the crossing distance for the 

pedestrian. As the crossing distance (roadway width) 

increases, driver yielding decreases.

The results of the research indicate that the LED-Em 

treatment is effective in certain conditions. Those 

conditions include:

• Lower volume roads.

• Lower posted speed limits.

• Narrower roads.

• Supplemental traffic control devices are present, such 

as in-street pedestrian crossing warning signs, inroadway lights, or additional crossing warning signs.

The length of time the LED-Em flashed was significant 

in the logistic regression model; however, at most of 

the sites, that length of time was within a narrow range 

(between 20 and 30 s). Increasing the length of time of 

the LED-Em flashing can be associated with higher driver 

yielding, but that change is not expected to offset the 

lower driver yielding associated with higher speed, higher 

volumes, or longer crossing distances.

The presence of bike lanes on the street was associated 

with higher driver yielding. Bike lanes can communicate to 

drivers the likelihood of vulnerable road users being present. 

Future research could investigate what street characteristics 

are associated with communicating a pedestrian-friendly 

or bicyclist-friendly environment to drivers. Defining what 

is pedestrian or bicyclist-friendly is also necessary. Driver 

yielding or driver operating speed are two examples of 

metrics that could be used to develop the criteria.

REFERENCES

1. Finley, M. D., K. Fitzpatrick, E. Rista, M. A. Brewer, 

R. Avelar, A. Pike, T. P. Barrette, and S. Datta. 

2020. Traffic Control Device Analysis, Testing, and 

Evaluation Program: FY2019 Activities. Report 

No. FHWA/TX-19/0-6969-R2. Austin, TX: Texas 

Department of Transportation.

2. Fitzpatrick, K., and E. S. Park. 2021. “Nighttime 

Effectiveness of the Pedestrian Hybrid Beacon, 

Rectangular Rapid Flashing Beacon, and LED-Embedded 

Crossing Sign.” Journal of Safety Research 79: 273–286.

3. Fitzpatrick, K, E. S. Park, and N. Johnson. 2022. 

“A Multistate Analysis Investigating Roadway 

Characteristics that Affect Driver Yielding at LEDEmbedded Pedestrian- and School-Crossing Signs.” 

Transportation Research Record: Journal of the 

Transportation Research Board 2676, no. 8.

Researchers—This study was conducted by Principal Investigator Kay Fitzpatrick (ORCID: 0000-0002-1863-5106), 

along with Eun Sug Park (ORCID: 0000-0001-6224-7007) and Neal Johnson (ORCID: 0000-0001-9167-9132) of 

Texas A&M Transportation Institute under contract HRDS30180007PRPR-DTFH6116D00039.

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

made to the division offices and Resource Center.

Availability—This TechBrief may be obtained at https://highways.dot.gov/research.

Key Words—Pedestrian crossings, driver yielding, traffic control device.

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the interest of information exchange. The U.S. Government assumes no liability for the use of the information contained 

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Recommended citation: Federal Highway Administration, Driver Yielding

with LED-Embedded Pedestrian- and School-Crossing Signs

(Washington, DC: 2022) https://doi.org/10.21949/1521956