Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

PUBLICATION NO. FHWA-HRT-23-038 JUNE 2023 

Driver Yielding with LED-Embedded 

Pedestrian- and School-Crossing Signs
FOREWORD

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

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

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

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. ELCSI-PFS studies provide a crash 

modification factor and benefit-cost analysis for each targeted safety strategy identified as a 

priority by member States of the PFS.

In the last decade, the number of pedestrian fatalities increased at a greater rate as compared to 

overall traffic fatalities. A traffic control treatment being used at pedestrian-crossing locations is 

the pedestrian- or school-crossing warning sign with light-emitting diodes (LEDs) embedded in 

the borders (called LED-Em in this report). The objective of this project was to determine the 

effectiveness of the LED-Em treatment. Included in the report are details of the methods used, 

and statistical findings of numerous test scenarios that support the researchers’ determinations. 

The results of the research indicate that the LED-Em treatment can be effective at increasing 

driver yielding on lower traffic volume roads, lower posted speed limit roads, and narrower 

roads. This report may be of interest to roadway safety professionals, local road operators, and 

pedestrian & school safety advocates.

Brian P. Cronin, P.E. 

Director, Office of Safety and Operations 

Research and Development

Notice

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

(USDOT) 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 DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-23-038

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

4. Title and Subtitle

Driver Yielding with LED-Embedded Pedestrian- and School-Crossing Signs

5. Report Date

June 2023

6. Performing Organization Code

HRSO-30

7. Author(s)

Kay Fitzpatrick (ORCID: 0000-0002-1863-5106), Eun Sug Park (ORCID: 0000-

0001-6224-7007), Neal Johnson (ORCID: 0000-0001-9167-9132), Katherine Falk

8. Performing Organization Report No.

9. Performing Organization Name and Address

Texas A&M Transportation Institute

The Texas A&M University System

TAMU 3135

College Station, TX 77843-3135

10. Work Unit No. (TRAIS)

HRSO-30

11. Contract or Grant No.

DTFH61-16-D-00039

12. Sponsoring Agency Name and Address

Office of Safety Research and Development

Federal Highway Administration

6300 Georgetown Pike

McLean, VA 22101

13. Type of Final Report and Period 

Covered

Final Report; January 2020—August 

2022

14. Sponsoring Agency Code

HRSO-30 and HSST-1

15. Supplementary Notes

The contracting officer’s representative was Ann Do (HRSO-30; ORCID: 0000-0001-9932-7422). 

16. Abstract

A traffic control treatment being used at pedestrian-crossing locations is the pedestrian- or school-crossing warning sign with lightemitting diodes (LEDs) embedded in the borders (called LED-Em in this report). The LED-Em treatment, which includes a 

pedestrian pushbutton, is pedestrian activated, so the LEDs flashes only when a pedestrian is present. For this research study, 

researchers considered more than 7,800 drivers involved in more than 3,675 staged pedestrian crossings at 62 sites. The average 

driver-yielding rates at those sites were analyzed using analysis of covariance models. Logistic regression was also used to 

evaluate individual driver decisions on whether to yield or not to yield to a pedestrian. These analyses considered both roadway 

characteristics and traffic control device characteristics. The statistical analyses showed that posted speed limit, vehicle volume at 

the time of the crossing, crossing distance (roadway width), and the presence of supplemental traffic control devices influenced a 

driver’s decision to yield to a pedestrian attempting to cross a street when the LED-Em treatment was active. Higher posted speed 

limits, higher vehicle volumes, and longer crossing distances are associated with low driver yielding.

17. Key Words

Pedestrian crossings, driver yielding, traffic control device, 

LED-Embedded treatment

18. Distribution Statement

No restrictions. This document is available to the public through 

the National Technical Information Service, 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

38

22. Price

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
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iii

TABLE OF CONTENTS

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

Introduction ............................................................................................................................. 1

Study Objective ....................................................................................................................... 2

Study Approach ...................................................................................................................... 3

CHAPTER 2. LITERATURE ..................................................................................................... 5

Literature Review Overview .................................................................................................. 5

Findings .................................................................................................................................... 5

Literature Review Summary ................................................................................................. 6

CHAPTER 3. DATA COLLECTION AND REDUCTION ..................................................... 7

Site Identification .................................................................................................................... 7

Site Selection ............................................................................................................................ 9

Data Collection Periods .......................................................................................................... 9

Data Collection Protocol......................................................................................................... 9

Site Characteristics ............................................................................................................... 10

LED-Em Flash Time, Current Practice .............................................................................. 15

Video Data Reduction ........................................................................................................... 16

Driver Yielding Rate Calculation ........................................................................................ 16

CHAPTER 4. ANALYSIS .......................................................................................................... 19

Approach to Analyses ........................................................................................................... 19

Per-Site Analysis............................................................................................................... 19

Per-Driver Analysis .......................................................................................................... 19

Results .................................................................................................................................... 20

Per-Site Analysis, Initial Sites .......................................................................................... 21

Per-Site Analysis, All Sites ............................................................................................... 21

Per-Site Analysis, Sites Collected During the Pandemic.................................................. 23

Per-Driver Analysis .......................................................................................................... 24

CHAPTER 5. SUMMARY AND CONCLUSIONS................................................................. 27

Summary................................................................................................................................ 27

Conclusions ............................................................................................................................ 28

REFERENCES ............................................................................................................................ 31
iv

LIST OF FIGURES

Figure 1. Photo. Example of LED-Em treatment at a four-lane divided site. ................................. 1

Figure 2. Photo. Closeup of active LED-Em treatment where the LEDs are illuminated as 

short bars. ......................................................................................................................... 1

Figure 3. Photo. Closeup of active LED-Em treatment where the LEDs are illuminated as 

points. ............................................................................................................................... 2

Figure 4. Graph. Range of per-site driver yielding by treatment type and light level 

(daytime and nighttime periods).(7) .................................................................................. 6

Figure 5. Equation. Driver-yielding rate. ...................................................................................... 16

Figure 6. Graph. Distribution of per-site driver yielding by State and time period. ..................... 17

Figure 7. Equation. Log-odds of probability of driver yielding. .................................................. 20

Figure 8. Equation. Conditional probability of driver yielding. ................................................... 20

Figure 9. Equation. Log-odds of probability of driver-yielding from analysis. ........................... 25

LIST OF TABLES

Table 1. Variable descriptions. ..................................................................................................... 11

Table 2. Summary statistics for numerical variables used in the analysis. ................................... 12

Table 3. Summary statistics for categorical variables used in the analysis. ................................. 12

Table 4. Number of sites by roadway characteristic, State, and data collection period. .............. 13

Table 5. Number of sites by sign characteristic, State, and data collection period. ..................... 14

Table 6. Number of sites by pavement traffic control device characteristic, State, and data 

collection period. .......................................................................................................... 15

Table 7. Summary statistics by select variables, State, and data collection period. ..................... 15

Table 8. ANCOVA model with key variables and per-site mean yield rates. .............................. 21

Table 9. ANCOVA model for per-site mean yield rates with key variables fitted based on 

all available sites (n = 62). ............................................................................................ 22

Table 10. ANCOVA model for per-site mean yield rates with key variables fitted based on 

sites collected during pandemic (n = 49). ..................................................................... 23

Table 11. Number of drivers considered in per-driver analysis along with hourly traffic 

volume. ......................................................................................................................... 24

Table 12. Logistic regression model coefficients estimated by GEE. .......................................... 24

Table 13. Overview of significant variables in models. ............................................................... 27

Table 14. Per-site average driver yielding by posted speed limit and time period. ...................... 28
v 

LIST OF ABBREVIATIONS

AIC Akaike information criterion

ANCOVA analysis of covariance

BIC Bayes information criterion

FHWA Federal Highway Administration

GEE generalized estimating equations 

LED-Em light-emitting diode-embedded 

LEDs light-emitting diodes 

MUTCD Manual on Uniform Traffic Control Devices 

PHB pedestrian hybrid beacon 

RRFB rectangular rapid-flashing beacon

TRB Transportation Research Board

TxDOT Texas Department of Transportation
1 

CHAPTER 1. INTRODUCTION

INTRODUCTION

Traffic agencies are seeking solutions to the rise in pedestrian fatalities. A traffic control 

treatment that is gaining use is the pedestrian- or school-crossing warning sign with 

light-emitting diodes (LEDs) embedded in the borders (called LED-Em in this report). Figure 1

shows an example of an installation. Figure 2 and figure 3 show closeups of the LED-Em sign. 

© 2022 Texas A&M Transportation Institute. 

Figure 1. Photo. Example of LED-Em treatment at a four-lane divided site. 

© 2022 Texas A&M Transportation Institute 

Figure 2. Photo. Closeup of active LED-Em treatment where the LEDs are illuminated as 

short bars. 
2 

© 2022 Texas A&M Transportation Institute 

Figure 3. Photo. Closeup of active LED-Em treatment where the LEDs are illuminated as 

points. 

Pressing a pedestrian pushbutton activates the LED-Em treatment, so the LEDs flash only when 

a pedestrian is present. This treatment is typically less costly than other pedestrian-crossing 

treatments, such as pedestrian hybrid beacons (PHBs), and is similar in cost to rectangular rapid 

flashing beacons (RRFBs). 

LEDs are embedded in traffic signs to enhance the conspicuity of the signs. Section 2A.07 of the 

Manual on Uniform Traffic Control Devices (MUTCD) lists the provisions for retroreflectivity 

and illumination for use of LEDs in traffic signs; however, advice about when to use such a 

treatment is not provided within the MUTCD.

(1) With the increased interest in the LED-Em 

treatment, guidance on when or where to install the device is desired. 

With the growing interest in using this treatment, understanding its effectiveness and ways its 

effectiveness changes based on the roadway environment is important. Are there situations, such 

as wide roads or streets with high posted speed limits, that make the treatment less effective? 

Several methods are available for evaluating the effectiveness of a pedestrian-crossing treatment, 

including analyzing the number of crashes and safety surrogates (e.g., number of conflicts)

associated with treatment locations. Given the relative newness of this treatment, a safety 

surrogate, rather than crashes, was used due to the lack of sufficient number of study sites and 

length of time the signs have been installed. This study used the safety surrogate of driver 

yielding, which can be expressed as the percentage of drivers who yielded to a pedestrian 

attempting to cross the street.

STUDY OBJECTIVE 

The focus of this study was to evaluate the operational performance of the LED-Em treatment 

with respect to roadway and traffic control device characteristics. This Federal Highway 

Administration (FHWA) project attempted to address sample size limitation by collecting data at 

a greater number of sites as well as collecting data in multiple States. The objective of this 
3 

research was to identify site and traffic control device characteristics associated with high and 

low driver-yielding values to provide insights as to where and when this treatment is effective. 

STUDY APPROACH 

The project began by reviewing previous research and identifying the variables of high interest 

for study. Next, the researchers identified locations where LED-Ems were installed and then 

selected study locations based on the number of installations in a region. Researchers recorded 

the road and traffic control device characteristics for each site. Researchers employed a staged 

pedestrian-crossing approach in this study to obtain a sample of pedestrian-crossing 

observations. The data were evaluated using multiple statistical analysis techniques. The results 

were considered when developing recommendations on when to consider the LED-Em for a site.

5 

CHAPTER 2. LITERATURE

LITERATURE REVIEW OVERVIEW

This chapter presents a summary of the identified literature on the LED-Em. 

FINDINGS

Driver yielding counts the number of drivers who did and did not yield to a crossing pedestrian, 

resulting in a driver-yielding percentage.

Most of the previous studies for the LED-Em signs included only a few sites. A Des Moines, IA,

case study examined driver and nonstaged pedestrian behavior at two sites.(2) The activation of 

the lights was associated with an increase in the driver-yield rate from 24 and 33 percent to 72 

and 63 percent at the two sites, respectively. A Vermont case study examined the effectiveness 

of the treatment on a two-lane road with a posted speed limit of 35 mph.(3) The results showed 

the highest increase in yield rate during the 1-yr period after installation and a slight decrease in 

yield rate from yr 1 to yr 4 after installation. The overall yield rate 4 yr after installation

remained 12 percent high than the yield rate measured before installation. A case study in Maple 

Grove, MN, included 54 pedestrian crossings for the preinstallation period and 41 pedestrian 

crossings for the post-installation period.(4) The results showed no improvement in driver-yield 

rates and reported that less than 20 percent of pedestrians activated the treatment during 

crossings.

A 2019 Texas Department of Transportation (TxDOT) study is the largest study to date of the 

LED-Em treatment.(5,6) The study included data at 13 locations and found an average driver-yield 

rate of 40 percent. High hourly volumes, posted speed limits 45 mph and greater, lack of 

sidewalks, and 12-ft lanes (no deviation from the baseline 12-ft lane width) were found to 

adversely affect yield probability. The research team concluded that based on the findings, 

LED-Em would be a suitable pedestrian treatment at sites with sidewalks, lower operating 

speeds, lower traffic volumes, and narrower lanes.

A 2020 TxDOT study also investigated the performance of several pedestrian traffic control 

treatments during both daytime and nighttime conditions.(7,8)

 Two statistical evaluations were 

used on the staged pedestrian data: analysis of covariance (ANCOVA) models that considered 

per-site mean yield rates, and logistic regression that considered the individual driver response to 

the crossing pedestrian. The research found the LED-Em to be more effective during the day

than at night. Using the results from the logistic regression evaluation, researchers observed 

higher driver yielding at LED-Em sites in the low speed limit group (30 or 35 mph), with two 

lanes (rather than four lanes), with narrow lanes of 10.5- or 11-ft widths (rather than 11.5- or 

12-ft widths), and with low hourly volumes. The results from the ANCOVA model for LED-Em 

also showed a statistically significant difference for yield lines (high yielding when present).

Another key outcome from the 2020 TxDOT study was a comparison of driver-yielding 

differences between day and night, along with a comparison among three pedestrian treatments 

(RRFBs, PHBs, and LED-Em).
6 

Figure 4 shows the comparison of daytime and nighttime driver yielding for the PHB, RRFB, 

and LED-Em. Overall, the average daytime driver yielding was 31 percent for the LED-Ems, 

77 percent for the RRFB, and 97 percent for the PHBs. 

© 2019 Texas A&M Transportation Institute.

Figure 4. Graph. Range of per-site driver yielding by treatment type and light level

(daytime and nighttime periods).(7)

LITERATURE REVIEW SUMMARY

With most of the previous research on the LED-Ems being conducted at only a few sites and in 

only one State, a larger study that includes multiple sites in different regions could provide a 

better understanding of the range of site conditions influencing driver behavior. Based on 

previous findings, the following variables should be included: posted speed limit, number of 

lanes (or crossing distance), and hourly vehicle volume. 
7 

CHAPTER 3. DATA COLLECTION AND REDUCTION

SITE IDENTIFICATION 

The criteria established for a study site to be included in this FHWA study are as follows: 

• Sign is a pedestrian (W11-2), school (S1-1), or trail (W11-15) crossing warning sign with 

LED-Em.

• Sign is at a marked crosswalk.

• Sign is activated by a pedestrian pushbutton. 

• Crossing could have other pedestrian-related treatments, such as in-pavement lights, and 

these supplemental features will be considered in the evaluation. 

• Crossing can have advance-warning treatments. 

The research team used the following approaches to identify potential study sites:

• Polled a research team member’s regional offices to identify locations known to their 

employees. 

• Conducted a presentation at the Transportation Research Board (TRB) Pedestrian 

Committee in January 2020. 

• Held discussions with vendors at the American Traffic Safety Services Association

meeting in late January 2020.

• Prepared the following email request: “FHWA is starting a new project to investigate the 

safety performance for the MUTCD W11-2 Pedestrian Crossing Warning and S1-1 

school-crossing signs with embedded LEDs. The research team is looking for locations 

where these signs have been installed and would greatly appreciate information on those 

sites. Please provide the intersection or street address (or latitude/longitude) for the 

installation to the research team.”

• Sent an email request to the following groups: the chair of the National Committee on 

Uniform Traffic Control Devices Pedestrian Task Force for distribution to members, 

chairs of the TRB Pedestrian Committee for distribution to members, American 

Association of State Highway and Transportation Officials State bicycle and pedestrian 

coordinators (54 members), Walk Friendly Communities (64 members), FHWA for 

distribution to the traffic control device pooled fund members, and National Association 

of City Transportation Officials (NACTO) staff to distribute to NACTO members. 

• Distributed the request via social media outlets and email listservs. 
8 

This outreach helped identify several potential study sites. The research team examined each site

with the Google® EarthTM Street View function, reviewing the pedestrian-crossing signs for 

evidence that they had embedded LEDs (e.g., the sign had a solar panel, or the embedded LEDs 

were present) and a pedestrian pushbutton. Based on what could be viewed in the Google Earth 

Street View, the research team used the following criteria to categorize the sites into groups that 

reflected whether a site could be considered for this study: 

• Groups that could be considered for this study:

o LED-Em: These sites appear to meet the criteria of this study. They have pedestrianor school-crossing signs with embedded LEDs that are activated by a pedestrian 

pushbutton. 

o LED (add treat): These sites appear to meet the criteria for this study but with 

additional pedestrian treatments present. They have pedestrian- or school-crossing 

signs with embedded LEDs that are activated by a pedestrian pushbutton. In addition, 

they have a supplemental pedestrian treatment feature, typically in-pavement lights or 

advance warnings. Inclusion of these sites in the study requires adding a variable to 

control for the supplemental pedestrian treatments present at the crossing.

• Sites that were grouped into “LED (NotFound)”: These sites reportedly had an 

installation of pedestrian- or school-crossing signs with embedded LEDs, but when the 

research team examined them with Google Earth Street View, a pedestrian pushbutton 

was not seen, possibly indicating the signs were installed after the latest Google Earth

Street View image. The research team planned to contact the city of the provided location 

for additional information if an insufficient number of sites were identified that fit the 

study’s requirements.

• Groups that would not be considered for this study: 

o LED (24/7): These signs with embedded LEDs are at an intersection but flash 24 h/d

7 d/wk. 

o LED (signal): These signs with embedded LEDs are at an intersection controlled with 

a signal. 

o LED (stop): These signs with embedded LEDs are at an intersection with 

all-way-stop control. 

o LED (approach): These signs with embedded LEDs are on an approach to an 

intersection. They are assumed to flash 24/7 or were confirmed as flashing 24/7. 

The outreach efforts identified 153 sites. Of the sites identified, 78 sites satisfied the key criteria

for the study (pedestrian- or school-crossing signs with embedded LEDs that are activated by a 

pedestrian pushbutton). An additional 19 sites satisfy the key criteria but with supplemental 

pedestrian-related treatments. These 97 sites are located in 11 States.
9 

SITE SELECTION 

The goal of the study was to collect data at a minimum of 35 sites located in at least two States. 

For the 11 States identified with a treatment, most had only one installation. Only Texas and 

California had more than 10 installations and, therefore, were selected for inclusion in this study. 

In California, most of the identified sites could logically be grouped into three areas: Oakland, 

Sacramento, and San Diego. For these sites, locations with high posted speed limits or great

number of lanes had priority for data collection. For Texas, almost every site identified would be 

eligible for inclusion. Some of the Texas-based sites’ data were collected from previous TxDOT 

studies to expand the sample size. Data were also collected in Texas for the FHWA study at a 

group of recent installations in Colleyville, TX, which is in the Dallas/Fort Worth, TX, 

metropolitan area, and in Houston, TX. 

DATA COLLECTION PERIODS 

Researchers collected data during four periods. 

• Fall 2020—9 Texas sites. 

• Late spring and early summer 2021—31 California sites. 

• Fall 2021—6 California sites and 3 Texas sites. 

• Spring 2019 and winter 2020—13 Texas sites and previous TxDOT projects.

(5,8)

The data collection periods were influenced by the weather, temperature (with the goal of 

avoiding the hotter parts of the year for the region), COVID-19 restrictions, and timing of when 

the sites had been installed or identified. 

The COVID-19 pandemic affected the timing of data collection for this FHWA project. The nine 

Texas site studies were completed in the fall of 2020 when conditions in the State were near 

normal as businesses reopened and viral cases fell. The research team planned to collect data at

sites in California in the fall of 2020 as well; however, they were postponed due to 

COVID-19-related travel and business restrictions that resulted in traffic volume and patterns 

that those familiar with local activity felt were different from the norm. Therefore, California 

data collection was completed in the late spring and early summer of 2021 as the pandemic 

conditions improved, businesses reopened, and travel restrictions lessened. Changes in 

pedestrian volumes did not affect the data collection because of the use of a staged pedestriancrossing approach to measure driver yielding. Any reduction in the vehicle traffic volume was 

also accounted for in the 1-min traffic counts collected for each crossing from the video footage

collected at the site. The details of the data collection approach are discussed in the next 

subsection. 

DATA COLLECTION PROTOCOL

Researchers employed a staged pedestrian-crossing approach in this study to obtain a sufficient 

sample of pedestrian-crossing observations. Researchers based the protocol for data collection on 

the experiences gleaned from several previous research projects, including research on the PHB 

and the RRFB.

(9,10) The protocol used a team of two, with one staff member playing the role of 
10 

the staged pedestrian while the other staff member recorded the driver-yielding behavior. Before 

collecting data in the field, inexperienced data collection teams received a copy of the written 

protocol along with the data collection sheets. After their review, an experienced team trained the 

new teams on the staged pedestrian protocol.

The staged pedestrian was trained to approach the crossing in a similar manner for each location 

to minimize the effects of pedestrian behavior on drivers and to maintain consistency among 

study locations in how the pedestrian approaches a crossing. The staged pedestrian wore a gray 

t-shirt or sweatshirt, blue jeans, and predominantly dark shoes. A baseball cap and sunglasses 

were permitted. The staged pedestrians activated the LED-Em pedestrian-crossing sign while 

vehicular traffic was approaching. Once the vehicles stopped, the pedestrian cross the street. The 

staged pedestrian waited until all queued vehicles from the previous crossing had cleared before 

beginning another staged crossing so that drivers did not observe two consecutive actuations. 

Researchers used a video camera to record data collection effort. The recordings served as a 

backup for the yielding data collected and was used to obtain the 1-min volume vehicle counts 

before each pedestrian crossing. 

SITE CHARACTERISTICS 

Researchers used aerial photographs to identify the roadway geometric characteristics, which

were confirmed in the field as needed. Table 1 lists the descriptions of the variables considered 

in the statistical analysis. Additional variables were collected for each site, such as one-way or 

two-way operations; however, those variables were either uniform for all sites or were 

determined in the preliminary analyses not to be influential with respect to driver yielding.
11 

Table 1. Variable descriptions. 

Variable Name Description

Driver Yielding Average driver yielding for the site (percent) 

HourVol Estimated hourly volume: Estimated from counting vehicles for 

approximately 1 min prior to the pedestrian crossing the street, total of 

both directions (vehicles/hour)

M:Bike Main: Is a bike lane present? (0 = none, 1 = one side, 2 = both sides) 

M:Curb2Curb+Med Main: Calculated distance between curbs including median, bike lanes, 

and parking lanes, if present (feet)

M:CurbExten Main: Is a curb extension present? (yes or no) 

M:MT Main: Median type (TWLTL, raised (offset, short), none) 

M:Parking Main: Is on-street parking present? (0 = none, 1 = one side, 2 = both 

sides)

M:SW Main: Is a sidewalk present? (0 = none, 1 = one side, 2 = both sides) 

MTCD:AddTreat Main TCD: Are supplemental features present at the crossing, for 

example in-pavement lights or multiple sets of LED-Em signs? 

(yes/no) 

MTCD:CW_Mark Main TCD: Crosswalk marking type (e.g., continental, ladder, 

transverse, other)

MTCD:FaceLED Main TCD: Combination of sign face and LED style in sign 

MTCD:Flash Main TCD: Flash duration (seconds) 

MTCD:InPaveLights Main TCD: Are in-pavement warning lights present? (yes or no)

MTCD:LED-InSign Main TCD: LED style in the sign face (bar or dot) 

MTCD:PSL Main TCD: Posted speed limit (mph)

MTCD:SignFace Main TCD: Type of sign (e.g., pedestrian or school crossing) 

MTCD:SignLoc Main TCD: Location of signs (right only, both right and median, both 

right and left)

MTCD:SZ Main TCD: Is crossing within a school zone? (yes or no) 

MTCD:YieldBar Main TCD: Is advance-yield bar present? (yes or no) 

S:Dev Site: Development type (commercial, residential, or mixed) 

S:Legs Site: Number of legs/approaches

S:ID Site: Number for site

TWLTL = two-way left-turn lane. TCD = traffic control device.

Table 2 and Table 3 provide the summary statistics for the numerical variables or the number of 

sites for the categorical variables. Table 4 provides additional details on the number of sites by 

roadway characteristics. For traffic control devices, table 5 provides details about the signs at the 

sites, and Table 6 provides the number of sites with pavement markings. Table 7 shows the 

summary statistics for crossing distance, hour volume, and length of time the treatment flashed 

after activated by the pedestrian. At most of the sites, the LED-Em flashed for between 20 and 

30 s. In California, one of the sites only flashed for 10 s, while a site in Texas flashed for 80 s, 

the longest recorded in this study. Placement of LEDs on signs vary. In some cases, the LEDs 

looked like dots, and in other cases they are in a row that resembled an LED bar. Another sign 

variation is where the LEDs are placed on the sign, for example either in the sign’s black border 

or in the yellow portion of the sign. 
12 

Table 2. Summary statistics for numerical variables used in the analysis. 

Variable Name Minimum Maximum Mean Std. Deviation 

DataDY:#Crossings 10 121 59.27 21.24 

DataDY:Driver Yielding 0.05 0.95 0.58 0.24 

HourVol 14 1745 465.15 378.52 

M:Curb2Curb+Med 20 128 54.19 22.70 

MTCD:PSL 25 50 34.03 6.13 

S:Flash 10 80 27.11 10.65 

Table 3. Summary statistics for categorical variables used in the analysis. 

Variable Name Value (Number of Sites)

M:Bike? 0 (40), 2 (22) 

M:CrDis2Group <40 ft (22), >40 ft (40)

M:CurbExten Yes (6), No (56) 

M:MT None (22), Raised (29), TWLTL (11) 

M:PK? 0 (51), 1 (2), 2 (9) 

Main:PSL PSL_25 (8), PSL_30 (18), PSL_35 (23), PSL_40 and above (13) 

MTCD:AddTreat Yes (20), No (42) 

MTCD:AdvSignTreat Yes (26), No (36) 

MTCD:SignFace S1-1 (19), W11-15 (1), W11-2 (42) 

MTCD:SignLoc Right and left (11), Right and median (13), Right only (38) 

MTCD:SZ? Yes (21), No (41) 

MTCD:YieldBar Yes (30), No (32) 

Period Prepandemic (49), pandemic (13) 

S:Dev Commercial (11), Mix (21), Residential (30)

S:Legs 2 (20), 3 (23), 4 (19) 

S:ST CA (37), TX (25) 

Note: The number in parentheses corresponds to the frequency of that variable. 
13 

Table 4. Number of sites by roadway characteristic, State, and data collection period. 

Variable 

Name1

Variable 

Level2

CA: 

Pandemic

TX: 

Pandemic

TX: 

Prepandemic All

M:Bike

0 18 12 10 40 

1 0 0 0 0 

2 19 0 3 22 

M:CurbExten No 31 12 13 56 

Yes 6 0 0 6 

M:MT

None 12 5 5 22 

Raised 17 4 4 25 

Raised, offset 1 0 0 1 

Raised, short 2 1 0 3 

TWLTL 5 2 4 11 

M:Parking

0 26 12 13 51 

1 2 0 0 2 

2 9 0 0 9 

M:SW

0 0 0 4 4 

1 2 4 1 7 

2 35 8 8 51 

S:Dev

Commercial 7 0 4 11 

Mix 17 2 2 21 

Residential 13 10 7 30 

S:Legs

2 13 3 4 20 

3 11 8 4 23 

4 13 1 5 19 

Total All sites 37 12 13 62 1

Descriptions of variables are available in Table 1. 2

Column includes variable level. 
14 

Table 5. Number of sites by sign characteristic, State, and data collection period. 

Variable 

Name1 Variable Level2

CA: 

Pandemic

TX: 

Pandemic

TX: 

Prepandemic All

MTCD:Face

LED

PedXing_bars in black (8 

or 16) 

7 0 0 7 

PedXing_bars in yellow 

(8) 

7 0 0 7 

PedXing_dots (12) 0 0 2 2 

PedXing_dots (16) 5 0 0 5 

PedXing_dots (8) 8 7 7 22 

Sch_bars in black (8) 6 0 0 6 

Sch_bars in yellow (8) 3 0 0 3 

Sch_dots (5) 1 5 4 10 

MTCD:LEDInSign LED bars in black (16) 1 0 0 1 

LED bars in black (8) 11 0 0 11 

LED bars in yellow (8) 11 0 0 11 

LED dots (12) 0 0 2 2 

LED dots (16) 5 0 0 5 

LED dots (5) 1 5 4 10 

LED dots (8) 8 7 7 22 

MTCD:PSL 25 8 0 0 8 

30 3 9 6 18 

35 19 0 4 23 

40 3 3 0 6 

45 4 0 1 5 

50 0 0 2 2 

MTCD:SignF

ace

S1-1 (school crossing) 8 5 4 17 

SW24-2 (school crossing 

with arrow)

2 0 0 2 

W11-15 

(bicycle/pedestrian 

crossing)

1 0 0 1 

W11-2 (pedestrian 

crossing)

26 7 9 42 

MTCD:Sign

Loc

Right only 8 3 11 

Right and left 11 2 13 

Right and median 18 9 11 38 

MTCD:SZ No 28 7 6 41 

Yes 9 5 7 21 

Total All sites 37 12 13 62 1

Variable description are available in Table 1. 2

Column includes variable level: PedXing = pedestrian-crossing warning sign; Sch = school-crossing warning sign.
15 

Table 6. Number of sites by pavement traffic control device characteristic, State, and data 

collection period. 

Variable Name1 Variable Level

CA: 

Pandemic

TX: 

Pandemic

TX: 

Prepandemic All

MTCD:AddTreat No 19 10 13 42 

Yes 18 2 0 20 

MTCD:CW_Mark

Continental 13 0 1 14 

Continental, split 3 0 0 3 

Ladder 16 6 11 33 

Ladder, diagonal 1 0 0 1 

Ladder, split 1 0 0 1 

Transverse 3 6 1 10 

MTCD:InPaveLights No 24 10 13 47 

Yes 13 2 0 15 

MTCD:YieldBar No 13 9 10 32 

Yes 24 3 3 30 

Total All sites 37 12 13 62 1

Descriptions of variables are available in table 1. 

Table 7. Summary statistics by select variables, State, and data collection period. 

Variable Name1

Variable 

Level2

CA: 

Pandemic

TX: 

Pandemic

TX: 

Prepandemic All

MTCD:Flash

(seconds) 

Min 10 20 29 10 

Max 40 30 80 80 

Ave 23 28 38 27 

M:Curb2Curb+Med 

(feet)

Min 20 20 22 20 

Max 126 78 87 126 

Ave 58 47 50 54 

HourVol 

(vehicles/hour)

Min 14 38 168 14 

Max 1,745 987 1,640 1,745 

Ave 453 420 541 465 1

Descriptions of variables are available in table 1. 2

Column includes: min = minimum; max = maximum; ave = average. 

The goal was to collect data at sites with high posted speed limits and wider crossings. For this 

group of sites, the posted speed limit ranged from 25 to 50 mph. (See table 5 for the distribution 

of number of sites by posted speed limit.) The crossing distance was measured curb to curb and 

included the width of the median and bike lanes or parking lanes when present. The crossing 

distance generally ranged from 20 ft to 87 ft, except for one site with a crossing distance of 

126 ft wide.

LED-EM FLASH TIME, CURRENT PRACTICE

As shown in the data (table 7), the length of time the LEDs flash varies by location, which is to 

be expected, since the crossing lengths vary. The 2009 edition of the MUTCD does not provide 

information regarding the length of flash time.

(1)

 The calculated pedestrian clearance time from 
16 

MUTCD, chapter 4, “Pedestrian Control Features,” Section 4E.06 can be used to determine the

minimum length of time for the LED-Em flash. As stated in the MUTCD, “the pedestrian 

clearance time should be sufficient to allow a pedestrian crossing in the crosswalk who left the 

curb or shoulder...to travel at a walking speed of 3.5 feet per second of at least the far side of the 

traveled way or to a median of sufficient width for a pedestrian to wait.”(1) The MUTCD allows 

characteristics of the pedestrian population and signal technology factors to increase the walking 

speed to 4 ft/s or low it to 2.8 ft/s. 

VIDEO DATA REDUCTION

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 volume of traffic present just before the specific pedestrian crossing. When there 

are many vehicles, drivers may be hesitant to stop for the pedestrian because of a concern with 

their vehicles being hit from behind. In a few cases, researchers used a time slightly longer than 

1 min to avoid starting the count with a vehicle in the crosswalk. In a few cases, researchers used 

a shorter period to accommodate the start time of the video file. They also converted the 1-min 

traffic counts into hourly volumes by using the exact number of seconds reflected in the vehicle 

count. 

This study included about 250 h of video recordings, resulting in data for 7,805 drivers and 

3,675 pedestrian crossings at 62 sites. 

DRIVER YIELDING RATE CALCULATION

Each driver responding to a staged pedestrian crossing was coded as being either 1 (for yielding) 

or 0 (for not yielding). Figure 5 shows the calculation used to determine the average driveryielding rate (DYR). 

Figure 5. Equation. Driver-yielding rate.

A large range of per-site driver yielding is present for the 62 sites. As illustrated in figure 6, the 

range of per-site driver yielding was 5 to 95 percent with the range being smaller for California 

sites (38 to 95 percent) than Texas sites (5 to 90 percent). Both States had sites with very high 

driver yielding (90 percent or more). The average driver yielding for California sites was 

66 percent, while it was 58 percent for data collected at Texas sites during the pandemic and 

38 percent for data collected at Texas sites prior to the pandemic. While the prepandemic 

average driver yielding is much low compared to the pandemic average driver yielding, the 

difference is not due to the pandemic. Rather, it is due to the type of sites represented in both 

groups. The Texas prepandemic sites included many sites that had high speed limits (3 sites had 

45 or 50 mph posted speed limits) and a rural appearance.
17 

© 2022 Texas A&M Transportation Institute. 

Figure 6. Graph. Distribution of per-site driver yielding by State and time period. 

19 

CHAPTER 4. ANALYSIS

This chapter presents the results of the evaluation of the per-site data and the per-driver data. 

APPROACH TO ANALYSES 

Per-Site Analysis

The objective of the per-site analysis was to explore the relationship between site-level driver 

yielding (i.e., average driver yielding rates) and traffic control device and site characteristic 

variables. Analyses were performed using a normal linear model, specifically the ANCOVA 

model, applied to driver-yielding rates averaged by each site. An ANCOVA model was 

employed because 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, and because the average driver-yielding rates satisfy the underlying assumptions for 

ANCOVA models. The average driver-yielding rates approximately follow a normal distribution 

because they are the averages computed based on much more than 30 crossings. The 

independence and equal variance assumptions for ANCOVA were also checked by examining 

the residual plot and the Durbin–Watson test, although those are not included in this report. 

Table 1 shows the list of variables used in the per-site analysis, including driver yielding rates, 

site characteristics, and traffic volume variables, along with summary statistics. Note that not all 

potential predictors in table 1 may have a significant relationship with driver yielding rates. 

Although it is possible to keep all variables in the full model if the coefficients are physically 

meaningful, researchers also performed variable selection on candidate predictors shown in table 

1 to develop more parsimonious models (having important predictors only). Several ANCOVA 

models were explored based on adjusted R2

. The automatic stepwise (forward, backward, or 

mixed) model selection procedure available in the statistical package JMP® (SAS® product) 

was also utilized to perform variable selection with diverse selection criteria. Specifically, the 

following three stopping rules were used in implementing a stepwise model selection procedure 

in JMP: p-value threshold, minimum Akaike information criterion (AIC), and minimum 

Bayesian information criterion (BIC). Both AIC and BIC are penalized-likelihood criteria for 

model selection that balance the choice of model by adding a penalty for the number of 

predictors used to the residual sum of squares.

(11)

 Although a low AIC or BIC means a better 

model, a simpler model can be selected by the principle of parsimony whenever there is not 

much difference in AIC or BIC values among competing models. One needs to keep in mind, 

however, that the best choice of a model may also depend on how useful the model is in the 

given context, not just on statistical model selection criteria. The models chosen by an automatic 

stepwise model selection procedure did not include the HourVol variable. While HourVol had a 

statistically significant relationship with Driver Yielding rates when included in the model by 

itself, it became statistically insignificant with the addition of MTCD:PSL to the model.

Per-Driver Analysis 

Like the per-site analysis, the objective of the per-driver analysis was to explore the relationship 

between driver yielding (expressed as the probability of yielding for each driver), traffic control 
20 

device, and site characteristic variables, including traffic volume, and to assess their effects on 

the probability of driver yielding. The nature of the per-driver analysis permits 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 along with the site and traffic control device characteristic variables and the 

two-way hourly volume estimate based on a 1-min count for each crossing. 

Because the outcome variable is dichotomous with 1 for Yield, 0 for Not Yield, researchers 

employed the logistic regression analysis for the per-driver analysis. The log-odds of the 

probability of driver yielding given the value of predictor variables (X), P(Y = Yield x) are

expressed in figure 7. 

Figure 7. Equation. Log-odds of probability of driver yielding. 

Where:

g(x) is the logit (log-odds) 

x = value of the predictor variables X1,,Xk , e.g., HourVol, S_Flash, (e.g., MTCD_PSL, 

MTCD_YieldBar, MTCD_AddTreat, etc.). 

β0 = baseline level of the logit.

βk = change in the logit that occurs with a unit change in Xk. 

Note that the logit, g(x), is linear in its parameters. The intercept β0 represents the baseline level 

of the logit, and βk represents the change in the logit that occurs with a unit change in Xk. 

Figure 8 shows the conditional probability that the driver yields at site i in jth pedestrian 

crossing. 

Figure 8. Equation. Conditional probability of driver yielding. 

To account for possible correlation in the outcome variable obtained for multiple crossings from 

the same site, the generalized estimating equations (GEE) was employed as an estimation 

method. 

RESULTS

ANCOVA was used with 3 datasets: sites available early in the project (53 sites, called “initial 

sites”), all sites where data had been collected by the research team within the past 3 yr (62 sites, 

called “all sites”), and those sites where data were collected during the pandemic (49 sites, called 

“pandemic sites”). The findings from the analysis of the initial sites were also reported 

elsewhere.(12)
21 

Per-Site Analysis, Initial Sites

The initial analyses used 53 sites whose data were available for preliminary analyses. Table 8. 

ANCOVA model with key variables and per-site mean yield rates. provides the results for the 

model that used a minimum number of variables and that had only statistically significant 

variables. Posted speed limit and hourly volume are significant and indicate lower driver yielding 

for higher posted speed limits and hourly volumes. For this model, the research team included a 

variable to describe if supplemental treatments were present at the site, such as in-pavement 

warning lights or extra signs for the LED-Em treatment, which would help draw additional 

attention to the crossing. The variable, MTCD:AddTreat, was significant, indicating that for this 

set of sites, the additional treatments helped increased the conspicuity of the crossing, resulting 

in more drivers yielding to the crossing pedestrians. The coefficient for posted speed limits 

indicates a 6.7-percent decrease in driver yielding for each 5-mph increment increase and a 

7.6-percent decrease in driver yielding for an increase of 250 vehicles/hour. 

Table 8. ANCOVA model with key variables and per-site mean yield rates.

Parameter Estimates Estimate Std Error t Ratio Prob > |t| 

Intercept 1.2057807 0.129366 9.32 <0.0001*

MTCD:PSL −0.013417 0.003772 −3.56 0.0008*

HourVol −0.00029 8.012e-5 −3.62 0.0007*

MTCD:AddTreat[No] −0.071914 0.025492 −2.82 0.0069*

Std error = standard error; t ratio = test statistic used for the t-test; prob > |t| = p-value for the t-test; * = statistically 

significant at alpha=0.05. 

Note: summary of fit: R2 = 0.466614, R2 adjusted = 0.433958. Root mean square error = 0.158903, mean of response 

= 0.583585, observations (or sum weights) = 53

Per-Site Analysis, All Sites

The per-site analysis then considered all sites with the LED-Em treatment whether the data 

collection occurred prepandemic or during the pandemic. Researchers explored several 

ANCOVA models and the model that, overall, performed better in terms of model selection 

criteria (smaller AIC or BIC) along with variables with a p-value greater than 0.2 removed. The 

fixed-effect test for the model (table 9) provides the estimated model coefficient and model 

fitting results. Researchers did not include State variable in the model as it was not a significant 

variable. However, the period variable was significant and may explain some of the variability 

that the State variable would have explained since all the prepandemic sites were in one State 

(Texas). 
22 

Table 9. ANCOVA model for per-site mean yield rates with key variables fitted based on 

all available sites (n = 62). 

Parameter Estimates Estimate Std Error t Ratio Prob > |t| 

Intercept 1.1185094 0.120545 9.28 <0.0001* 

Period[Pandemic] 0.1052489 0.026105 4.03 0.0002* 

HourVol −8.376e-5 5.639e-5 −1.49 0.1433 

MTCD:PSL −0.016147 0.003416 −4.73 <0.0001* 

MTCD:YieldBar[No] 0.0505354 0.022224 2.27 0.0270* 

MTCD:SignLoc[Right & Left] −0.108768 0.035567 −3.06 0.0035* 

MTCD:SignLoc[Right & 

Median]

0.0548842 0.037438 1.47 0.1484 

MTCD:AddTreat[No] −0.096238 0.023542 −4.09 0.0001* 

Std error = standard error; t ratio = test statistic used for the t-test; prob > |t| = p-value for the t-test; * = statistically 

significant at alpha=0.05. 

Note: summary of fit: R2 = 0.734238, R2 adjusted = 0.655075. Root mean square error = 0.140689, mean of response 

= 0.583871, observations (or sum weights) = 62.

In some cases, the coefficient for the variable may be counterintuitive. For example, yielding

should be higher when a yield bar is present. For this set of sites, the presence of an advance 

yield bar was associated with slightly lower driver yielding, which, perhaps, is a reflection that 

yield bars were added at sites where pedestrian visibility is a concern.

The location of the warning sign generated a counterintuitive result and an expected result. The 

crossing warning signs could be located on the right only, on both the right and left sides of the 

street, or on the right side of the street and in the raised median. The MTCD:SignLoc variable 

indicates the location of the crossing warning signs that an approaching driver can see. Right 

only reflects the condition when the driver can see the face of the sign on the right side of the 

road only. Right and left reflects the condition when the driver can see the face of the sign on 

both the right side and on the far-left side of the road. Typically, this situation occurs when 

crossing warning signs are located on both sides of the signpost so that drivers coming from both 

approaches to the crossing can see the warning sign. The condition of having a right and a 

median sign reflects the thought that having a sign closer to the approaching driver on the left as 

compared to when the sign is located on the left-side of the road. 

The coefficients for the variable MTCD:SignLoc had an expected positive response when the 

signs were right and median, but a counterintuitive response for when the signs are located right 

and left. The theory is that in having more than one sign face visible, drivers will be more aware 

of the crossing and more likely to yield. Having the warning sign on the right is believed by the 

Research Team members to have the lowest driver yielding; however, the coefficients indicate 

that lower driving yielding occurs when the signs are located on both the right-side and the leftside of the road. Having signs located both on the right side of the roadway and in the median, 

which places the sign closer to the approaching driver, resulted in the highest driver yielding.

Another factor impacting driver yielding may be the distance the warning signs are from the 

driver. While having an additional warning sign on the left side of the road for an approaching 

driver to see should be of value, the cross-section width could result in the sign being outside the 

typical field of view for the driver. The sites with pedestrian crossing warning signs on the right 
23 

and left had wider cross sections compared to pedestrian crossings with right only warning signs. 

Future research is needed to fully explore the benefits of having multiple crossing warning signs 

visible as a function of the number of lanes at the crossing. The presence or absence of a raised 

median is another factor that also needs to be considered. Within this study’s site sample, the 

median type was not significant; however, the sign location variable may explain some of the 

variation in driver yielding that could be attributed to the median presence. 

The street’s posted speed limit was significant, with lower driver-yielding rates as the posted 

speed limit increased. This coefficient had the strongest impact on driver yielding estimates. 

Assuming a site has the warning signs in the median and on the right along with supplemental 

treatments, and an hourly volume of 650 vehicles/h, the estimated driver yielding is 81 percent 

for a 20-mph road, 48 percent for a 40-mph road, and a low of 32 percent for a 50-mph road. 

Per-Site Analysis, Sites Collected During the Pandemic

With concerns expressed by members of the review panel that driver behavior was different 

during the pandemic compared to prepandemic, a per-site analysis was conducted using the 49 

sites where the data were collected during the pandemic. Again, researchers explored several 

ANCOVA models and included in this report the findings of the model that, overall, had the best

measures (small AIC or BIC) and most interesting results. 

Previous research(10,12) indicated that the crossing distance for the pedestrian, the posted speed of 

the street, and the traffic volume during the time of the crossing are important. The model shown 

in table 10 includes those variables along with other selected variables with a p-value of less than 

0.2. 

Table 10. ANCOVA model for per-site mean yield rates with key variables fitted based on

sites collected during pandemic (n = 49). 

Parameter Estimates Estimate Std Error t Ratio Prob > |t| 

Intercept 1.3491735 0.172545 7.82 <0.0001* 

M:Bike?[0] −0.043511 0.025956 −1.68 0.1011 

MTCD:PSL −0.017571 0.00597 −2.94 0.0053* 

MTCD:SignLoc[Right & Left] −0.101612 0.037148 −2.74 0.0091* 

MTCD:SignLoc[Right & Median] 0.0570401 0.044624 1.28 0.2082 

MTCD:AddTreat[No] −0.085798 0.02593 −3.31 0.0019* 

M:Curb2Curb+Med −0.001976 0.001391 −1.42 0.1630 

Std error = standard error; t ratio = test statistic used for the t-test; prob > |t| = p-value for the t-test; * = statistically 

significant at alpha=0.05. 

Note: summary of fit: R2 = 0.560158, R2 adjusted = 0.497324. Root mean square error = 0.149282, mean of response 

= 0.639184, observations (or sum weights) = 49. 

The results demonstrate that for the LED-Em treatment, the variables—street’s posted speed 

limit and crossing distance (M:Curb2Curb+Med)—are both significant, with lower driveryielding rates as those variables increase. The coefficient for posted speed limit indicates about 

8.8 percent decrease in driver yielding for each 5-mph increment increase. The presence of bike 

lanes and additional treatments were significant, with higher driver yielding occurring for those 

sites with a bike lane or with additional treatments such as in-pavement warning lights or 
24 

additional warning signs. The location of the crossing warning signs was significant with slightly 

higher driver yielding rates when the sign was located both to the right and in the median. For 

this dataset, the hourly traffic volume and several other variables were not statistically 

significant. 

Per-Driver Analysis 

The per-driver analysis focused on those crossings where the traffic volume for the 1-min prior 

to the staged pedestrian crossing was available. It also only considered the sites where the data 

were collected during the pandemic. For data at the 49 sites collected during the pandemic, the 

video for 1 of the sites was accidentally erased, so data for 48 sites were available. Table 11 lists 

the number of drivers considered in the analysis along with the range of hourly traffic volume 

per State. Each of the 5,003 drivers made a choice to yield (2,834 drivers) or not to yield 

(2,169 drivers).

Table 11. Number of drivers considered in per-driver analysis along with hourly traffic 

volume. 

State

Number 

of Sites

Number of 

Drivers

Min Vehicle 

Volume (vehicles/h)

Max Vehicle 

Volume (vehicles/h)

Average Vehicle 

Volume (vehicles/h)

CA 36 3,519 60 667 7,920 

TX 12 1,484 60 622 2,100 

Both 48 5,003 60 654 7,920 

Several logistic regression models were explored, and the model that included only variables 

with at least one level significant at the 0.05 level is provided in this report. Table 12 provides 

the estimates of the model coefficients. 

Table 12. Logistic regression model coefficients estimated by GEE. 

Variable Level Estimate 

Standard

Error

95% Confidence 

Limits Z Pr > |Z| 

Intercept – 2.0411 0.7079 0.6536 3.4286 2.88 0.0039

S:Flash – 0.0606 0.0156 0.0300 0.0911 3.89 0.0001

M:Curb2Curb+Med – −0.0160 0.0051 −0.0261 −0.0059 −3.11 0.0019

MTCD:PSL – −0.1005 0.0265 −0.1524 −0.0485 −3.79 0.0001

S:ST CA 0.5903 0.2571 0.0863 1.0942 2.30 0.0217

S:ST TX 0.0000 0.0000 0.0000 0.0000 . . 

M:Bike? 2 0.5761 0.2390 0.1078 1.0445 2.41 0.0159

M:Bike? 0 0.0000 0.0000 0.0000 0.0000 . . 

MTCD:SignLoc Right 

only

0.4258 0.2710 −0.1053 0.9569 1.57 0.1161

MTCD:SignLoc Right and

median

0.7034 0.3063 0.1029 1.3038 2.30 0.0217

MTCD:SignLoc Right and

left

0.0000 0.0000 0.0000 0.0000 . . 

MTCD:AddTreat Yes 0.4678 0.2244 0.0280 0.9075 2.08 0.0371

MTCD:AddTreat No 0.0000 0.0000 0.0000 0.0000 . . 

− = No data; . = not applicable (the value is not relevant since this level is the base for the variable).
25 

The estimated logit (the log-odds of the probability of driver yielding) is given by the following 

expression (figure 9): 

Figure 9. Equation. Log-odds of probability of driver-yielding from analysis.

Like the linear regression, the estimated logistic regression coefficient in figure 9 represents the 

expected change in the logit per unit change (for a continuous predictor such as S:Flash) or going 

from the base level (the level not shown in figure 9) to the specified level (for a categorical 

variable such as S:ST). 

Note that for S:Flash, a one-unit change (i.e., an increase of length of time the beacons are 

flashing by 1 s) may be too small to be considered important. A change of 10 s might be more 

useful. Increasing S:Flash by 10 s multiplies the odds of driver yielding by e(0.0606÷10) (=1.8331). 

That is, the estimated odds ratio for an increase of 10 s is 1.8331, which indicates that for every 

increase of 10 s in S:Flash, odds of driver yielding increase 1.8 times. 

When the predictor variable is dichotomous (i.e., measured at two levels), the regression 

coefficient (β) represents the difference in the logit, and the interpretation can be given in terms 

of the odds ratio (OR), reflected as OR = exp(β), the ratio of the odds for the specified level 

(non-base level) to the odds for the base level). The odds ratio for MTCD:AddTreat can be 

estimated by exp(MTCD:AddTreat) = exp(0.4678) = 1.5965, which suggests that driver yielding 

is 1.6 times as likely to occur where additional treatments are present comparted to the sites with 

no additional treatments. 

When the predictor variable is polychotomous (i.e., having more than two categories), the odds 

ratio is given for each non-base level, using one of the categories as the base level. For example, 

the variable MTCD:SignLoc is coded at three levels (right only, right and median, and right and 

left) as shown in table 12 that right and left is currently designated as a base level (reference 

group). The coefficients for right only (0.4258) and right and median (0.7034) of 

MTCD:SignLoc represents the log of the odds ratio for each of right only and right and left. The 

odds ratios for MTCD:SignLoc = right only (compared to MTCD:SignLoc = right and left) is 

given by exp(0.4258) = 1.5308, which indicates that driver yielding is 1.5 times as likely to 

occur for right only sites than right and left sites. Similarly, the odds ratio for MTCD:SignLoc = 

right and median (compared to MTCD:SignLoc = right and left) is given by exp(0.7034) = 

2.0206, which indicates that driver yielding is 2.0 times as likely to occur for right and median 

sites than right and left sites. 

27 

CHAPTER 5. SUMMARY AND CONCLUSIONS

SUMMARY

For this research analysis, researchers considered over 7,800 drivers involved in more than 3,675 

staged pedestrian crossings. Both ANCOVA and logistic regression were used in this study. 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 

easy and intuitive interpretation of the results. Three efforts were conducted using ANCOVA: 

sites available early in the project (53 sites, called “initial sites”), all sites where data had been 

collected by the research team within the past 3 yr (62 sites, called “all available sites”), and sites 

where data were collected during the pandemic (49 sites, called “pandemic sites”). Table 13

summarizes the findings from these efforts. 

Table 13. Overview of significant variables in models.

Variable 

Pandemic 

Dataset All Dataset 

Initial 

Dataset 

Pandemic 

with Video 

Dataset 

Statistical analysis approach ANCOVA ANCOVA ANCOVA Logistic 

MTCD:PSL − − − −

HourVol NS − − −

MTCD:AddTreat[No] − − − Base

MTCD:AddTreat[Yes] Base Base Base + 

M:Bike?[2] Base NS NS + 

M:Bike?[0] − Base Base Base

MTCD:YieldBar[No] NS + NS NS

MTCD:YieldBar[Yes] Base Base Base Base

S:Flash NS NS NS + 

M:Curb2Curb+Med − NS NS −

MTCD:SignLoc[Right Only] Base Base Base + 

MTCD:SignLoc[Right & 

Median]

+ + NS + 

MTCD:SignLoc[Right & Left] − − NS Base

Period[Pandemic] NA + NS NS

S:ST[CA] NS NS NS + 

S:ST[TX] 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. 

There was some variation in which variables were significant from the three datasets using 

ANCOVA along with the dataset for the logistic regression analysis; however, in all cases, the 
28 

posted speed limit was significant. For the LED-Em treatment, use on high-speed roads is clearly 

associated with low driver yielding rates. Table 14 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 low driver yielding on high-speed roads. 

Table 14. Per-site average driver yielding by posted speed limit and time period.

Posted 

Speed 

Limit 

(mph)

Pandemic

(N) 

Pandemic, 

Driver 

Yielding

(percent)

Prepandemic 

(N) 

Prepandemic, 

Driver 

Yielding

(percent) All (N) 

All, 

Driver 

Yielding 

(percent)

25 8 78 0 NS 8 78 

30 12 75 6 56 18 68 

35 19 61 4 26 23 55 

40 6 39 0 NS 6 39 

45 4 55 1 16 5 48 

50 0 NS 2 16 2 16 

Grand 

Total 49 64 13 38 62 58 

N = number of sites; NS = driver yielding not provided because there were no sites. 

The various statistical approaches along with the various datasets used in the evaluation of the 

LED-Ems’ effectiveness identified variables as being significant in the models. When using an 

automated stepwise variable selection procedure with ANCOVA, the HourVol variable was not 

significant. While HourVol had a statistically significant relationship with driver-yielding rates 

when included in the model by itself, it became statistically insignificant when MTCD:PSL was 

added to the model. When variable selection was guided by the research team, the team included 

the HourVol variable in the model with a negative coefficient, indicating that as the traffic 

volume increases, driver yielding decreases. Another variable found to be significant in some of 

the models was crossing distance for the pedestrian. As the crossing distance (roadway width) 

increases, driver yielding decreases. 

CONCLUSIONS

The results of the research indicate that the LED-Em treatment is effective in certain conditions, 

including: 

• Lower traffic volume roads.

• Lower posted speed limits roads. 

• Narrower roads.

• Presence of supplemental traffic control devices, such as in-street pedestrian crossing 

warning signs, in-roadway lights, or additional advance crossing warning signs. 

Previous research showed higher percent driver yielding on roads with lower hourly traffic 

volumes.(12)

 In a low-volume environment, drivers may be more likely to see and react to LEDEm signs and the pedestrian waiting to cross. The findings from this research confirmed that 

conclusion. It showed that higher posted speed limits are associated with lower driver yielding 
29 

with about an 8.8-percent decrease in driver yielding for each 5-mph increase. Regarding the 

impact of crossing distance on driver yielding, this research showed lower driver yielding on 

wider roads with about a 4-percent decrease in yielding for each additional 12 ft in crossing 

distance. One reason for this finding is that narrower roadway width may create an environment 

conducive to seeing pedestrians and yielding at crossings with LED-Em signs as a result. The use 

of supplemental traffic control devices increased driver yielding, likely because they provide 

increased conspicuity of the crossing. 

The length of time the LED-Em flashed was significant in the logistic regression model; 

however, at most of the sites, the LED-Em flashed between 20 s and 30 s. While increasing the 

length of time the LED-Em flashing may be associated with higher driver yielding, the 

researchers caution that change may not offset the lower driver yielding associated with higher

speed, higher traffic volumes, or wider crossing distances. Roadways with these characteristics

(e.g., higher posted speed, higher traffic volume, or wider crossing distances) may require

increased attention from driver, and LED-Em flashing signs are harder to recognize and react to 

as part of the driver’s tasks.

The presence of bike lanes on the street was associated with higher driver yielding. Having a 

bike lane helps to communicate to the driver the likelihood that vulnerable users may be present. 

Future research could investigate the street characteristics associated with communicating to 

drivers a pedestrian-friendly or bicyclist-friendly environment. 

Research also is needed to answer the following questions: 

• What is the best way to define pedestrian-friendly or bicyclist-friendly environments, and 

how should the impact that the environment has on driver yielding be measured? Driver 

operating speed is a potential measure.

• How does the number of crossing warning signs within the driver’s field of vision 

influence driver yielding? 

• How beneficial is placing the crossing warning signs on both the right and left sides of 

the road as a function of the road’s cross section? 

• What cross sections should have an overhead crossing warning sign in addition to a 

right-side, ground-mounted sign? 

When using the per-driver data only, this research found that the length of time the LED-Em 

flashes was associated with higher driver yielding. To better explore the impact of flash time on 

driver yielding, a future study could systematically vary the flash time during the study while 

gathering driver yielding data for select sites. For example, an initial effort could collect data 

with a 30-s flash time, while an effort several days or weeks later could collect data at the same 

site with a 40-s flash time. This approach will hold other roadway and surrounding 

characteristics constant, which would result in a better likelihood of the study determining the 

influence of flash time on driver yielding. The study should start with identifying different 

approaches to establishing the flash time length for different crossing distances, including 
30 

whether a median is present with or without a pedestrian pushbutton. The study could consider 

both LED-Ems and RRFBs. 

Finally, laws regarding crosswalks continue to evolve, and future research could examine their 

impacts on driver yielding. The study sites included in this research were in two States, 

California and Texas. California law, section 21950 requires the driver of a vehicle to yield the 

right-of-way to a pedestrian crossing the roadway within any marked crosswalk or within any 

unmarked crosswalk at an intersection.

(13)

 In September 2021, revisions to Texas law (Title 7, 

Sec. 552.003) became effective.

(14) It requires drivers to come to a full stop for pedestrians or 

cyclists who are properly in an intersection. Before the new law, drivers were required to yield to 

pedestrians, but not required to come to a full stop. Across the country, laws vary along with 

their requirements such as basing the required driver behavior on where the pedestrian is located 

within the roadway (e.g., within an adjacent lane or on the other half of the roadway). With the 

increased focus on pedestrian safety, uniform laws across the country, as well as uniform traffic 

control devices, will all play a role in improving safety. 
31 

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HRSO-30/06-23(WEB)E Recycled

Recyclable

Recommended citation: Federal Highway Administration, 

Driver Yielding with LED-Embedded Pedestrian- and School-Crossing Signs

(Washington, DC:2023) https://doi.org/10.21949/1521982