Evaluation of Pedestrian Hybrid Beacons and Rapid Flashing

Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

Evaluation of Pedestrian Hybrid Beacons

and Rapid Flashing Beacons

PUBLICATION NO. FHWA-HRT-16-040 JULY 2016

FOREWORD

The overall goal of the Federal Highway Administration’s (FHWA) Pedestrian and Bicycle

Safety Research Program is to improve safety and mobility for pedestrians and bicyclists.

The program strives to make it safer and easier for pedestrians, bicyclists, and drivers to share

roadways through the development of safer crosswalks, sidewalks, and pedestrian technologies

as well as through the expansion of educational and safety programs.

This report documents an FHWA project that includes four studies that investigated how

characteristics of rectangular rapid-flashing beacons (RRFBs) and pedestrian hybrid beacons

(PHBs) affected the likelihood of drivers yielding to a pedestrian. The results of this project

supported the development of two Manual on Uniform Traffic Control Devices official

interpretations for the RRFB: Official Interpretation #4(09)-41 (I)—Additional Flash Pattern for

RRFBs and Official Interpretation #4(09)-58 (I)—Placement of RRFB Units Above Sign.

(1–3)

The

overall 96 percent high yielding for PHBs identified in this research, along with findings from

previous studies, support the use of this device at a variety of locations, such as on high-speed

roads, wide roads, and at residential intersections.

This report should be of interest to engineers, planners, and other community authorities who

share an interest in safeguarding the lives of roadway users, especially pedestrians.

Monique R. Evans

Director, Office of Safety

Research and Development

Notice

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

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

the information contained in this document. This report does not constitute a standard,

specification, or regulation.

The U.S. Government does not endorse products or manufacturers. Trademarks or

manufacturers' names appear in this report only because they are considered essential to the

objective of the document.

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality information to serve

Government, industry, and the public in a manner that promotes public understanding. Standards

and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its

information. FHWA periodically reviews quality issues and adjusts its programs and processes to

ensure continuous quality improvement.

TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-16-040

2. Government Accession No.

3. Recipient’s Catalog No.

4. Title and Subtitle

Evaluation of Pedestrian Hybrid Beacons and Rapid Flashing Beacons

5. Report Date

July 2016

6. Performing Organization Code

7. Author(s)

Kay Fitzpatrick, Raul Avelar, Michael Pratt, Marcus Brewer,

James Robertson, Tomas Lindheimer, and Jeff Miles

8. Performing Organization Report No.

9. Performing Organization Name and Address

Texas Transportation Institute

The Texas A&M University System

College Station, TX 77843-3135

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTFH61-08-D-00032, Task Order #8

12. Sponsoring Agency Name and Address

Office of Safety Research and Development

Federal Highway Administration

6300 Georgetown Pike

McLean, VA 22101-2296

13. Type of Report and Period Covered

Technical Report:

October 2012–March 2016

14. Sponsoring Agency Code

15. Supplementary Notes

The Contracting Officer’s Technical Representative was Ann Do, HRDS-30.

16. Abstract

Two pedestrian treatments receiving national attention are the rectangular rapid-flashing beacon (RRFB) and the

pedestrian hybrid beacon (PHB). These devices have unique characteristics that produce improved vehicle stopping

and yielding to crossing pedestrians. This Federal Highway Administration (FHWA) project includes multiple

studies to help refine these devices. A closed-course RRFB study measured the time to determine the position and

direction of a cutout representation of a pedestrian on a crosswalk to identify conditions that produced faster and

more accurate recognition. Placing the beacons above rather than below the warning sign produced better

recognition. A following open-road study investigated driver yielding when the beacons were located above and

below the warning sign at 13 sites. Results indicated that any differences between the above and below positions

were minor and statistically insignificant. With the apparent benefits identified from the closed-course study

(i.e., lower discomfort and improved ability to detect the pedestrian) and the lack of difference in driver yielding,

locating the beacons above the sign could improve the overall effectiveness of this treatment. FHWA issued an

official interpretation in early 2016 to permit the placement of the beacons above the sign.

(3)

An open-road study was

also conducted to determine driver yielding for different RRFB flash patterns at eight sites, seven of which were

four-lane crossings with 40- or 45-mi/h speed limits. The patterns selected for evaluation were the 2-5 flash pattern

(two flashes on one side followed by five flashes on other side) that was currently in use, a pattern using a

combination of wig-wag and simultaneous (WW+S) flashes, and a pattern using a combination of long and short

flashes called “blocks.” The statistical analysis showed no statistical significant difference between patterns; in other

words, the newer patterns were as effective as the 2-5 flash pattern. As a result, FHWA issued an official

interpretation indicating the preference for the WW+S pattern.

(2)

In the final study, behaviors at PHBs were

investigated. The PHB has shown great potential in improving safety and driver yielding; however, questions have

been asked regarding actual driver and pedestrian behavior. For the 20 PHB sites in the open-road study, driver

yielding to pedestrians averaged 96 percent. Overall, 91 percent of the pedestrians pushed the pushbutton to activate

the PHB in the crosswalk. A greater percentage number of pedestrians activated the device when on 45-mi/h posted

speed limit roads as compared to roads with posted speed limits of 40 mi/h or less.

17. Key Words

Rectangular rapid-flashing beacon, Pedestrian hybrid

beacon, RRFB, PHB, Pedestrian crossing, Driver

yielding to pedestrians, Pedestrian crosswalk

18. Distribution Statement

No restrictions. This document is available to the public

through the National Technical Information Service,

Springfield, VA 22161.

http://www.ntis.gov

19. Security Classif.(of this report)

Unclassified

20. Security Classif.(of this page)

Unclassified

21. No. of Pages

165

22. Price

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

SI* (MODERN METRIC) CONVERSION FACTORS

APPROXIMATE CONVERSIONS TO SI UNITS

Symbol When You Know Multiply By To Find Symbol

LENGTH

in inches 25.4 millimeters mm

ft feet 0.305 meters m

yd yards 0.914 meters m

mi miles 1.61 kilometers km

AREA

square inches 645.2 square millimeters mm

square feet 0.093 square meters m

square yard 0.836 square meters m

ac acres 0.405 hectares ha

square miles 2.59 square kilometers km

VOLUME

fl oz fluid ounces 29.57 milliliters mL

gal gallons 3.785 liters L

cubic feet 0.028 cubic meters m

cubic yards 0.765 cubic meters m

NOTE: volumes greater than 1000 L shall be shown in m

MASS

oz ounces 28.35 grams g

lb pounds 0.454 kilograms kg

T short tons (2000 lb) 0.907 megagrams (or "metric ton") Mg (or "t")

TEMPERATURE (exact degrees)

F Fahrenheit 5 (F-32)/9 Celsius

or (F-32)/1.8

ILLUMINATION

fc foot-candles 10.76 lux lx

fl foot-Lamberts 3.426 candela/m

cd/m

FORCE and PRESSURE or STRESS

lbf poundforce 4.45 newtons N

lbf/in

poundforce per square inch 6.89 kilopascals kPa

APPROXIMATE CONVERSIONS FROM SI UNITS

Symbol When You Know Multiply By To Find Symbol

LENGTH

mm millimeters 0.039 inches in

m meters 3.28 feet ft

m meters 1.09 yards yd

km kilometers 0.621 miles mi

AREA

square millimeters 0.0016 square inches in

square meters 10.764 square feet ft

square meters 1.195 square yards yd

ha hectares 2.47 acres ac

square kilometers 0.386 square miles mi

VOLUME

mL milliliters 0.034 fluid ounces fl oz

L liters 0.264 gallons gal

cubic meters 35.314 cubic feet ft

cubic meters 1.307 cubic yards yd

MASS

g grams 0.035 ounces oz

kg kilograms 2.202 pounds lb

Mg (or "t") megagrams (or "metric ton") 1.103 short tons (2000 lb) T

TEMPERATURE (exact degrees)

C Celsius 1.8C+32 Fahrenheit

ILLUMINATION

lx lux 0.0929 foot-candles fc

cd/m

candela/m

0.2919 foot-Lamberts fl

FORCE and PRESSURE or STRESS

N newtons 0.225 poundforce lbf

kPa kilopascals 0.145 poundforce per square inch lbf/in

*SI is the symbol for th International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. e

(Revised March 2003

)

iii

TABLE OF CONTENTS

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

BACKGROUND ..................................................................................................................... 1

STUDY OBJECTIVE ............................................................................................................. 1

Impact of Rapid-Flashing Yellow Light-Emitting Diodes (LEDs) on Detecting

Pedestrians in a Closed-Course Setting .............................................................................. 1

Driver-Yielding Results for Beacons Placed Above or Below Crossing Sign in an

Open-Road Setting .............................................................................................................. 2

Driver-Yielding Results for Three Rectangular Rapid-Flash Patterns in an

Open-Road Setting .............................................................................................................. 2

PHB Study .......................................................................................................................... 2

APPROACH ............................................................................................................................ 2

REPORT ORGANIZATION ................................................................................................. 3

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

FHWA INTERIM APPROVAL OF RRFBS ....................................................................... 5

FHWA OFFICIAL INTERPRETATIONS .......................................................................... 5

RRFB ........................................................................................................................................ 7

PHB .......................................................................................................................................... 8

MULTIPLE PEDESTRIAN TREATMENTS ................................................................... 10

CHAPTER 3. IMPACT OF RAPID-FLASHING YELLOW LEDS ON DETECTING

PEDESTRIANS IN A CLOSED-COURSE SETTING ..................................................... 11

INTRODUCTION................................................................................................................. 11

Study Objective ................................................................................................................. 12

Overview of Study Approach ........................................................................................... 12

COURSE DEVELOPMENT ............................................................................................... 13

Riverside Campus ............................................................................................................. 13

Pedestrian Crossing Assemblies Selected for Study ......................................................... 13

Study Site .......................................................................................................................... 14

Cutout Pedestrian .............................................................................................................. 16

Flash Pattern for Assemblies ............................................................................................ 18

Brightness of LEDs ........................................................................................................... 21

Combinations Studied ....................................................................................................... 23

Concluding Survey............................................................................................................ 26

DATA COLLECTION ......................................................................................................... 31

Study Periods .................................................................................................................... 31

Participants ........................................................................................................................ 31

Participant’s Tasks ............................................................................................................ 32

Instrumented Vehicle ........................................................................................................ 32

Participant Intake .............................................................................................................. 33

Initial Button Push Training.............................................................................................. 34

Vehicle Review ................................................................................................................. 35

Data Collection at Study Site ............................................................................................ 35

DATA REDUCTION ............................................................................................................ 36

Participant Demographics ................................................................................................. 36

Data Cleaning.................................................................................................................... 37

Responses .......................................................................................................................... 37

Box Plots ........................................................................................................................... 38

Mosaic Plots ...................................................................................................................... 39

Potential Outliers .............................................................................................................. 39

FINDINGS ............................................................................................................................. 40

Detection Time to Correctly Identify Pedestrian Walking Direction ............................... 40

Accuracy of Detecting Pedestrian Direction..................................................................... 46

Discomfort ........................................................................................................................ 53

Concluding Survey............................................................................................................ 57

STATISTICAL ANALYSIS ............................................................................................ 61

Pedestrian Detection Time ................................................................................................ 61

Accuracy of Detecting Pedestrian Direction..................................................................... 68

Discomfort Glare .............................................................................................................. 77

CHAPTER 4. DRIVER-YIELDING RESULTS FOR BEACONS PLACED ABOVE

OR BELOW CROSSING SIGN IN AN OPEN-ROAD SETTING.................................. 87

INTRODUCTION................................................................................................................. 87

Study Overview ................................................................................................................ 87

Study Objective ................................................................................................................. 87

STUDY DEVELOPMENT ................................................................................................... 87

Study Sites ........................................................................................................................ 87

Study Assemblies .............................................................................................................. 90

Rotation ............................................................................................................................. 91

DATA COLLECTION AND REDUCTION ...................................................................... 91

Study Periods .................................................................................................................... 91

Staged Pedestrian Protocol ............................................................................................... 92

Driver Yielding ................................................................................................................. 93

RESULTS .............................................................................................................................. 94

COMPARISON OF BELOW TO ABOVE ........................................................................ 95

CHAPTER 5. DRIVER-YIELDING RESULTS FOR THREE RRFB PATTERNS

IN AN OPEN-ROAD SETTING ......................................................................................... 97

INTRODUCTION................................................................................................................. 97

Study Overview ................................................................................................................ 97

Study Objective ................................................................................................................. 97

STUDY DEVELOPMENT ................................................................................................... 97

Study Sites ........................................................................................................................ 97

Temporary Light Bar ........................................................................................................ 98

Flash Patterns .................................................................................................................... 99

Brightness of LEDs ......................................................................................................... 102

Sample Size ..................................................................................................................... 102

Flash Pattern Order ......................................................................................................... 102

DATA COLLECTION AND REDUCTION .................................................................... 103

Study Periods .................................................................................................................. 103

Staged Pedestrian Protocol ............................................................................................. 103

DATA REDUCTION .......................................................................................................... 103

RESULTS ............................................................................................................................ 104

Patterns Used with Temporary Light Bars...................................................................... 104

2-5 Flash Pattern ............................................................................................................. 106

CHAPTER 6. PHB STUDY ..................................................................................................... 107

INTRODUCTION............................................................................................................... 107

Study Objective ............................................................................................................... 109

STUDY SITES ..................................................................................................................... 109

DATA COLLECTION AND REDUCTION .................................................................... 113

DRIVER BEHAVIOR FINDINGS ................................................................................... 114

Driver Behavior During Dark Indication ........................................................................ 114

Driver Position Relative to Pedestrian Position During Steady or Flashing Red

Indications When the Driver Drove Across the Crosswalk ............................................ 115

Driver Yielding Behavior During Steady or Flashing Red Indications .......................... 117

Driver Behavior During Flashing Red Indication ........................................................... 119

Impact of PHB Actuation on Minor Movement Drivers ................................................ 119

PEDESTRIAN BEHAVIORS FINDINGS ....................................................................... 126

Pedestrian Departures by Indication ............................................................................... 126

Pedestrian Actuation of the PHB .................................................................................... 127

CONFLICTS FINDINGS ................................................................................................... 130

CHAPTER 7. SUMMARY/CONCLUSIONS, DISCUSSION, AND FUTURE

RESEARCH NEEDS .......................................................................................................... 133

OVERVIEW ........................................................................................................................ 133

CLOSED-COURSE STUDY ............................................................................................. 133

Summary/Conclusions .................................................................................................... 133

Discussion ....................................................................................................................... 137

ABOVE-BELOW (OPEN-ROAD) STUDY ..................................................................... 138

Summary/Conclusion ...................................................................................................... 138

Discussion ....................................................................................................................... 139

FLASH PATTERN (OPEN-ROAD) STUDY ................................................................... 139

Summary/Conclusions .................................................................................................... 139

Discussion ....................................................................................................................... 140

PHB STUDY ........................................................................................................................ 141

Summary/Conclusions .................................................................................................... 141

Discussion ....................................................................................................................... 142

FUTURE RESEARCH NEEDS......................................................................................... 143

ACKNOWLEDGMENTS ........................................................................................................ 149

REFERENCES .......................................................................................................................... 151

LIST OF FIGURES

Figure 1. Photo. Study assembly containing LEDs above, below, and within the sign ............... 13

Figure 2. Illustration. Route for closed-course study .................................................................... 14

Figure 3. Illustration. Layout for the study site ............................................................................ 15

Figure 4. Photo. View of the study assemblies ............................................................................. 15

Figure 5. Photo. Back view of study site ...................................................................................... 16

Figure 6. Photo. View of 54-inch cutout pedestrian used in study ............................................... 17

Figure 7. Photo. Researcher removing short cutout pedestrian after placing tall cutout

pedestrian ...................................................................................................................................... 17

Figure 8. Illustration. Plan view showing pedestrian cutout positions ......................................... 18

Figure 9. Photo. View at start of the driving video for the concluding survey for

queries 1 and 2 .............................................................................................................................. 27

Figure 10. Photo. Training example with pedestrian facing left................................................... 34

Figure 11. Photo. Training example with pedestrian facing right ................................................ 34

Figure 12. Photo. Training example with no pedestrian ............................................................... 35

Figure 13. Illustration. Box plot details ........................................................................................ 38

Figure 14. Illustration. Mosaic plot details ................................................................................... 39

Figure 15. Graph. Daytime detection time by LED location and target intensity ........................ 44

Figure 16. Graph. Nighttime detection time by LED location and target intensity ...................... 45

Figure 17. Graph. Daytime detection time by pedestrian position and target intensity ................ 45

Figure 18. Graph. Nighttime detection time by pedestrian position and target intensity ............. 46

Figure 19. Graph. Daytime correct detection rate by target intensity ........................................... 50

Figure 20. Graph. Nighttime correct detection rate by target intensity ........................................ 50

Figure 21. Graph. Daytime correct detection rate by flash pattern ............................................... 51

Figure 22. Graph. Nighttime correct detection rate by flash pattern ............................................ 51

Figure 23. Graph. Daytime detection rate by age ......................................................................... 52

Figure 24. Graph. Nighttime detection rate by age ....................................................................... 52

Figure 25. Graph. Older driver daytime discomfort rating for set I ............................................. 53

Figure 26. Graph. Younger driver daytime discomfort rating for set I......................................... 54

Figure 27. Graph. Older driver nighttime discomfort rating for set I ........................................... 55

Figure 28. Graph. Younger driver nighttime discomfort rating for set I ...................................... 55

Figure 29. Graph. Older driver nighttime discomfort rating for set II .......................................... 56

Figure 30. Graph. Younger driver nighttime discomfort rating for set II ..................................... 56

Figure 31. Graph. Results for survey queries 1 and 2 ................................................................... 57

Figure 32. Graph. Number of pulses by percent of participants ................................................... 61

Figure 33. Equation. Natural logarithm of detection time ............................................................ 62

Figure 34. Equation. Accuracy analysis ....................................................................................... 69

Figure 35. Equation. Accuracy rate .............................................................................................. 69

Figure 36. Equation. Logit model ................................................................................................. 69

Figure 37. Equation. Odds ratio corresponding to levels A and B of factor X

............................ 70

Figure 38. Graph. Daytime estimated accuracy rate by age and pedestrian position ................... 72

Figure 39. Graph. Close-up view of daytime estimated accuracy rate by age and

pedestrian position ........................................................................................................................ 73

Figure 40. Graph. Nighttime estimated accuracy rate by age and LED intensity ........................ 75

vii

Figure 41. Graph. Close-up view of nighttime estimated accuracy rate by age and

LED intensity ................................................................................................................................ 75

Figure 42. Graph. Idealized relationship between discrete and real discomfort scales ................ 78

Figure 43. Equation. Cumulative frequency ................................................................................. 79

Figure 44. Equation. Odds ratio for levels A and B of variable X

at a maximum level of

discomfort ..................................................................................................................................... 79

Figure 45. Equation. Revised odds ratio ....................................................................................... 79

Figure 46. Graph. Estimated cumulative probabilities by age of participant for the discrete

scale of discomfort when using 2,200 candelas of intensity and the 2-5 flash pattern ................. 85

Figure 47. Photo. Example of RRFB placed above the sign ........................................................ 90

Figure 48. Photo. Example of RRFB placed below the sign ........................................................ 90

Figure 49. Equation. Driver yielding rate ..................................................................................... 93

Figure 50. Photo. Installation of the light bar in field ................................................................... 98

Figure 51. Photo. CS-02 study site with installed temporary light bars and staged

pedestrian crossing ........................................................................................................................ 99

Figure 52. Illustration. Flash patterns studied ............................................................................. 101

Figure 53. Photo. Example of PHB installation in Tucson, AZ ................................................. 107

Figure 54. Photo. Example of PHBs being used in Austin, TX.................................................. 108

Figure 55. Photo. Example of sign used in Tucson, AZ ............................................................. 111

Figure 56. Photo. Example of internally illuminated sign used in Tucson, AZ ......................... 112

Figure 57. Photo. Sign used in Austin, TX ................................................................................. 112

Figure 58. Photo. Sign recommended by FHWA to address comprehension issues with the

flashing red phase ....................................................................................................................... 113

Figure 59. Photo. Example of advance warning sign used in Tucson, AZ ................................. 113

Figure 60. Illustration. Pedestrian and driver positions when the pedestrian is on the initial

approach and vehicles are present on the same approach and on the other approach ................ 116

Figure 61. Illustration. Minor movements at a PHB-controlled crosswalk ................................ 120

Figure 62. Graph. Volume cumulative distribution when pedestrian started the crossing ......... 127

Figure 63. Graph. Percentage of pedestrians pushing the button, by posted speed limit ........... 129

Figure 64. Graph. Percentage of pedestrians pushing the button, by crossing distance ............. 129

Figure 65. Graph. Percentage of pedestrians pushing the button, by 1-min volume counts

adjusted to hourly counts ............................................................................................................ 129

viii

LIST OF TABLES

Table 1. Summary of RRFB official interpretations released prior to 2014 ................................... 6

Table 2. Summary of RRFB official interpretation developed using the results of this

research project ............................................................................................................................... 7

Table 3. Flash patterns used with LEDs located in rectangular beacons above or below

the sign .......................................................................................................................................... 19

Table 4. Flash patterns used with LEDs within sign .................................................................... 20

Table 5. LED characteristics for set I ........................................................................................... 22

Table 6. LED characteristics for set II .......................................................................................... 23

Table 7. Number of variable combinations tested during the closed-course study ...................... 25

Table 8. Video assignments and flash patterns for each query by participant group ................... 28

Table 9. Flash patterns for queries 1 and 2 showing moving videos from driver perspective ..... 29

Table 10. Flash patterns used for queries 3 and 4 with the video showing a close-up view ........ 30

Table 11. Distribution of participants ........................................................................................... 32

Table 12. Demographic information for participants ................................................................... 37

Table 13. Daytime average detection time for set I ...................................................................... 41

Table 14. Nighttime average detection time for set I .................................................................... 42

Table 15. Nighttime average detection time for set II and combined total for sets I and II ......... 43

Table 16. Daytime accuracy of correct detection for set I ............................................................ 47

Table 17. Nighttime accuracy of correct detection ....................................................................... 48

Table 18. Results for survey queries 1 and 2 ................................................................................ 57

Table 19. Percent of participants who felt a sense of urgency to yield for signs with

no active LEDs and LEDs below the sign .................................................................................... 59

Table 20. Percent of participant results for sense of urgency to yield for LEDs above

the sign .......................................................................................................................................... 60

Table 21. Number of pulses on light bar ....................................................................................... 61

Table 22. Daytime ANOVA for detection time fixed effects ....................................................... 63

Table 23. Daytime detection time fixed effects coefficients ........................................................ 64

Table 24. Daytime simultaneous tests for general linear hypothesis of detection time

flash pattern effects ....................................................................................................................... 64

Table 25. Daytime magnitude of detection time intensity effect .................................................. 65

Table 26. Nighttime ANOVA for detection time fixed effects .................................................... 65

Table 27. Nighttime fixed effects coefficients for detection time ................................................ 66

Table 28. Nighttime simultaneous tests for general linear hypothesis of flash patterns on

detection time ................................................................................................................................ 66

Table 29. Nighttime magnitude of intensity effect on detection time .......................................... 67

Table 30. Nighttime simultaneous tests for effect of LED location on detection time ................ 67

Table 31. Nighttime magnitude of LED location effect on detection time .................................. 67

Table 32. Daytime analysis of deviance for accuracy fixed effects ............................................. 70

Table 33. Daytime accuracy fixed effects coefficients ................................................................. 71

Table 34. Daytime simultaneous tests for general linear hypothesis of flash pattern

accuracy effects ............................................................................................................................. 72

Table 35. Nighttime analysis of deviance for accuracy fixed effects ........................................... 73

Table 36. Nighttime accuracy fixed effects coefficients .............................................................. 74

Table 37. Nighttime odds ratio of correct detection intensity levels ............................................ 76

Table 38. Nighttime simultaneous linear hypotheses for pedestrian position effect

on accuracy ................................................................................................................................... 76

Table 39. Nighttime simultaneous linear hypotheses on LED location effect on accuracy ......... 76

Table 40. Daytime likelihood ratio tests for incremental discomfort fixed effects ...................... 80

Table 41. Daytime discomfort fixed effect coefficients ............................................................... 81

Table 42. Daytime odds ratios for higher level of discomfort by target intensity level ............... 81

Table 43. Nighttime likelihood ratio tests for incremental discomfort fixed effects .................... 82

Table 44. Nighttime discomfort fixed effect coefficients ............................................................. 82

Table 45. Nighttime odds ratios of higher discomfort by target intensity level ........................... 83

Table 46. Nighttime simultaneous hypotheses for flash pattern discomfort effect ...................... 84

Table 47. Nighttime simultaneous hypotheses on LED location discomfort effect ..................... 84

Table 48. Nighttime tests for simultaneous hypotheses on discomfort effect of pedestrian

location .......................................................................................................................................... 85

Table 49. Study site characteristics for above-below study .......................................................... 89

Table 50. Installation and data collection dates ............................................................................ 91

Table 51. Nighttime driver yielding rate by site and beacons position ........................................ 93

Table 52. Daytime driver yielding rate by site and beacon position ............................................ 94

Table 53. GLMM results comparing below to above ................................................................... 96

Table 54. List of sites for rapid flash pattern study ...................................................................... 98

Table 55. Brightness measurements ........................................................................................... 102

Table 56. Flash pattern order by test site location ...................................................................... 103

Table 57. Driver yielding rate by site and pattern ...................................................................... 104

Table 58. Linear mixed-effects model results for flash patterns used with temporary

light bars...................................................................................................................................... 105

Table 59. Simultaneous comparisons on flash pattern differences ............................................. 106

Table 60. LMM results comparing the 2-5 flash pattern with temporary and existing

equipment .................................................................................................................................... 106

Table 61. Site characteristics ...................................................................................................... 110

Table 62. Pedestrian position and vehicle approach during steady or flashing red

indications ................................................................................................................................... 117

Table 63. Driver yielding values for all 20 sites ......................................................................... 118

Table 64. Minor movements permitted at the sites ..................................................................... 121

Table 65. Minor movement vehicle distribution by site ............................................................. 121

Table 66. Minor movement vehicle distribution upon vehicle arrival........................................ 122

Table 67. Minor movement vehicle distribution upon vehicle departure ................................... 122

Table 68. Minor movement vehicle distribution by PHB indication .......................................... 123

Table 69. Minor-movement violation rates by site ..................................................................... 124

Table 70. Selected minor-movement violation rates by site and movement code ...................... 125

Table 71. Pedestrian departures by indication ............................................................................ 126

Table 72. Number of pedestrians by site who pushed, did not push, or did not push

because PHB was active ............................................................................................................. 128

Table 73. Indication when pedestrian departed for those that activated the PHB ...................... 130

Table 74. Vehicle and pedestrian conflicts by beacon indication and vehicle maneuver ........... 131

Table 75. Pedestrian-vehicle conflict rates ................................................................................. 132

Table 76. Summary of results for LED intensity and location along with flash pattern ............ 134

Table 77. Summary of results for pedestrian height and position and participant age ............... 135

LIST OF ABBREVIATIONS

ADT average daily traffic

ANOVA analysis of variance

BEC beyond end of cycle

CRFB circular rapid-flashing beacon

DF degrees of freedom

FHWA Federal Highway Administration

GLMM generalized linear mixed effects model

HAWK high-intensity activated crosswalk

IA interim approval

IQR interquartile range

LED light-emitting diode

LMM linear mixed effects model

LT left-turn movement originating from the major street

LT1 left-turn movement originating from the minor street

MOE measure of effectiveness

MUTCD Manual on Uniform Traffic Control Devices

NCHRP National Cooperative Highway Research Program

NPA Notice of Proposed Amendment

NCUTCD National Committee on Uniform Traffic Control Devices

PHB pedestrian hybrid beacon

RRFB Rectangular rapid-flashing beacon

RT2 right-turn movement originating from the minor street

SAE Society of Automotive Engineers

SSD stopping sight distance

STC Signals Technical Committee

TAMU Texas A&M University

TCS traffic control signal

TH1/TH2 through movements on the minor-street approaches

TTI Texas A&M Transportation Institute

TWLTL Two-way left-turn lane

WW+S wig-wag and simultaneous

CHAPTER 1. INTRODUCTION

BACKGROUND

Two pedestrian treatments receiving national attention are the rectangular rapid-flashing beacon

(RRFB) and the pedestrian hybrid beacon (PHB) (originally termed High-intensity Activated

crossWalK (HAWK) when developed). These devices have noteworthy characteristics that

produce improved vehicle stopping and yielding behavior to crossing pedestrians. Characteristics

include brighter indications, unique beacon arrangements and flash patterns, and activation only

when pedestrians are present. The PHB was added to the 2009 Manual on Uniform Traffic

Control Devices (MUTCD).

(1)

The Federal Highway Administration (FHWA) provided Interim

Approval 11 (IA-11) for the optional use of the RRFB at uncontrolled pedestrian and school

crosswalks on July 16, 2008.

(4)

The Signals Technical Committee (STC) of the National Committee on Uniform Traffic Control

Devices (NCUTCD) assists in developing language for chapter 4 of the MUTCD.

(1)

STC is

interested in research and/or assistance in the development or refinement of material on these

devices, especially the RRFB, which is being considered for the next edition of the MUTCD.

This FHWA project included studies that can help with refining these devices.

STUDY OBJECTIVE

The objectives of the four studies performed under this FHWA project were refined during the

course of the research. The revised objectives are based on proposed research plans that were

modified using comments from FHWA and the project panel. Specific objectives are highlighted

in the following subsections.

Impact of Rapid-Flashing Yellow Light-Emitting Diodes (LEDs) on Detecting Pedestrians

in a Closed-Course Setting

The objectives of the closed-course study were as follows:

• Quantify the effect of traffic control device brightness on drivers’ ability to detect

pedestrians in and around a pedestrian crossing, which is a measure of disability glare.

• Quantify the effect different flashing beacon assembly characteristics have on drivers’

ability to detect pedestrians in and around a pedestrian crossing, which is a measure of

disability glare.

• Quantify drivers’ perception of discomfort and relate it to their ability to detect

pedestrians in and around a pedestrian crossing, which is a measure of discomfort glare.

Driver-Yielding Results for Beacons Placed Above or Below Crossing Sign in an Open-

Road Setting

The objective of the open-road study was to identify motorist yielding rates for the different test

conditions selected at the conclusion of the closed-course study. Specifically, the test conditions

selected included placing the rectangular beacons above and below the sign.

Driver-Yielding Results for Three Rectangular Rapid-Flash Patterns in an Open-Road

Setting

The objective of the flash pattern study was to determine if simpler flash patterns than the

one that was tested prior to the issuance of IA-11 would be equally effective or more effective in

encouraging driver yielding at crosswalks.

(4)

PHB Study

The objective of the PHB study was to evaluate driver and pedestrian behaviors at PHB

installations. This study was to provide insight into the actual behavior of motorists, bicyclists,

and pedestrians at locations with a PHB.

APPROACH

The research was conducted in a series of tasks as follows:

• Task 1—Hold Kickoff Meeting and Develop Work Plans: The research team met with

FHWA staff to discuss the project direction, scope, and work plan.

• Task 2—Develop Research Plan for Each Countermeasure: The research team

revised and expanded the work plans using comments from FHWA and the panel.

• Task 3—Collect and Analyze Data: The research team conducted the four studies.

• Task 4—Develop Draft Marketing, Communications, and Outreach Plan: The

research team identified products that could be developed that would be useful to

engineers, planners, and other practitioners who have an interest in implementing

pedestrian and bicycle treatments.

• Task 5—Develop Technical Briefs and Conduct Final Briefing Meeting: The research

team developed a TechBrief for each of the studies. A final briefing meeting was held at

FHWA’s Turner-Fairbank Highway Research Center in McLean, VA, in November 2015

that included FHWA, members of the research team, and the panel.

• Task 6—Develop Final Deliverables: The research team developed the final

deliverables which included this comprehensive technical report that documents all

aspects of the project’s activities and findings.

REPORT ORGANIZATION

This report includes the following chapters:

• Chapter 1. Introduction: Presents general background information along with the

research objectives.

• Chapter 2. Literature Review: Presents background and recent findings on RRFBs and

a literature review of PHBs.

• Chapter 3. Impact of Rapid-Flashing Yellow LEDs on Detecting Pedestrians in a

Closed-Course Setting: Describes the methodology and results from the closed-course

study that examined LED brightness, position, and flash patterns.

• Chapter 4. Driver-Yielding Results for Beacons Placed Above or Below Crossing

road study that investigated the effects of the placement of yellow rapid-flashing beacons

above or below the pedestrian crossing sign.

• Chapter 5. Driver-Yielding Results for Three Rectangular Rapid-Flash Patterns in

an Open-Road Setting: Describes the methodology and results from the open-road study

that examined different flash patterns for use with yellow rapid-flashing beacons.

• Chapter 6. PHB Study: Describes the methodology and results from the study that

examined driver and pedestrian behavior at PHBs.

• Chapter 7. Summary/Conclusions, Discussion, and Future Research Needs: Provides

a summary and the conclusions of the research and presents future research needs.

CHAPTER 2. LITERATURE REVIEW

Efforts during the initial phase of this project included literature reviews of selected pedestrian

treatments as needed to build upon a previous FHWA study.

(5)

The previous FHWA study

contains a comprehensive literature review of pedestrian treatments being used at unsignalized

pedestrian crossings, and readers are encouraged to review that report if a review of the literature

is sought. This chapter provides background information on the RRFB and a review of recently

published literature that is relevant to the efforts within this project.

FHWA INTERIM APPROVAL OF RRFBs

On July 16, 2008, FHWA provided IA-11 for the optional use of the RRFB.

(4)

FHWA approved

the use of this device at uncontrolled pedestrian and school crosswalks. As defined in IA-11, the

RRFB is to consist of two rapidly and alternately flashing rectangular yellow indicators having

LED-array based pulsing light sources.

(4)

Within the IA-11, there are the following seven items

with subsections:

1. General conditions.

2. Allowable uses.

3. Sign/beacon assembly locations.

4. Beacon dimensions and placement in sign assembly.

5. Beacon flashing requirements.

6. Beacon operations.

7. Other.

FHWA OFFICIAL INTERPRETATIONS

As of November 2015, FHWA has released several official interpretations concerning the

interim approval of RRFBs, including the following:

• 4-376 (I) on overhead mounting of RRFBs (December 9, 2009).

(6)

• 4(09)-5 (I) on using RRFBs with the W11-15 sign (August 12, 2010).

(7)

• 4(09)-17 (I) on RRFB light intensity (January 9, 2012).

(8)

• 4(09)-21 (I) on clarification of RRFB flashing pattern (June 13, 2012).

(9)

• 4(09)-22 (I) on flashing pattern for existing RRFBs (August 8, 2012).

(10)

• 4(09)-24 (I) on daytime dimming of RRFBs (September 27, 2012).

(11)

• 4(09)-37 (I) on the definition of dimming (October 9, 2013).

(12)

• 4(09)-38 (I) on RRFB flashing extensions and delays (October 22, 2013).

(13)

• 4(09)-41 (I) on additional flash patterns for RRFBs (July 25, 2014).

(2)

Another interpretation letter that may be of interest is 4(09)-11 (I) on flashing beacons maximum

mounting height, which was released on June 29, 2011.

(14)

Table 1 summarizes key components for each of the official interpretations released prior to

2014. Table 2 summarizes the interpretations that were developed using results from this FHWA

research study.

Table 1. Summary of RRFB official interpretations released prior to 2014.

Number

Summary of Key Characteristics Relevant to this Study

4-376 (I)

(6)

Interpretation letter 4-376 (I) indicates that overhead mounting of the pedestrian

crossing (W11-2) warning sign or school crossing (S1-1) warning sign with a

RRFB is appropriate. When the W11-2 or S1-1 sign is mounted overhead, only a

minimum of one such sign per approach is required, and it should be located over

the approximate center of the lanes of the approach. It also indicates that “for

roadside signs, the MUTCD establishes no maximum mounting height. Therefore,

W11-2 or S1-1 signs with W16-7P plaques could be installed at a mounting height

much higher than the normal 7 feet, perhaps 15 to 17 feet or more, and still

comply with the MUTCD and the IA-11 technical provisions.”

(6)

(pg. 2)

4(09)-5 (I)

(7)

Interpretation letter 4(09)-5 (I) states that the “RRFB may be used to supplement a

W11-15 sign at a shared-use trail crossing if the W11-15 substitutes for the

W11-2 and is placed at the crosswalk.”

(7)

(pg. 1)

4(09)-11 (I)

(14)

Interpretation letter 4(09)-11 (I) states that “the maximum mounting height of a

flashing warning beacon mounted over the roadway shall be 25.6 ft, measured

from pavement surface to the top of the housing of the beacon.”

(14)

(pg. 1)

4(09)-17 (I)

(8)

Official interpretation number 4(09)-17 (I) clarifies that the light intensity of

RRFBs shall meet the minimum intensity requirements for class 1 optical warning

devices within SAE Standard J595, as opposed to classes 2 or 3 minimum

intensity requirements.

(15)

The SAE J595 peak luminous intensity requirements

for classes 2 and 3 are only about 25 and 10 percent, respectively, of the peak

luminous intensity requirement for class 1.

(15)

4(09)-21 (I)

(9)

A detailed review of the flash pattern used with the original RRFB installation

resulted in a change in the requirements. Official interpretation 4(09)-21 (I)

changes item 5b to read, “b. As a specific exception to 2003 MUTCD

Section 4k.01 requirements for the flash rate of beacons, RRFBs shall use a much

faster flash rate. Each of the two yellow indication of an RRFB shall have 70 to

80 periods of flashing per minute and shall have alternating, but approximately

equal, periods of rapid pulsing light emissions and dark operation. During each of

its 70 to 80 flashing periods per minute, the yellow indication on the left side of

the RRFB shall emit two slow pulses of light after which the yellow indication

on the right side of the RRFB shall emit four rapid pulses of light followed by a

long pulse.”

(9)

(pg. 2)

4(09)-22 (I)

(10)

Official interpretation 4(09)-22 (I) clarifies that agencies do not have to update the

flash pattern for devices already deployed in the field and that official

interpretation 4(09)-21 (I) only applies to new deployments.

4(09)-24 (I)

(11)

Official interpretation 4(09)-24 (I) states that “it is not acceptable to dim the

RRFB signal indications during daytime conditions and that the light output

from the RRFB signal indications must meet the SAE J595 requirements for

peak luminous intensity (candelas) for Class 1 at all times during daylight

hours.”

(11)

(pg. 1)

Information on SAE J595 is available in Surface Vehicle

Recommended Practice.

(15)

4(09)-37 (I)

(12)

Official interpretation 4(09)-37 (I) states that “It is the FHWA’s official

interpretation that dimming occurs only when the light output from a traffic

control signal indication or an RRFB signal indication falls below the minimum

specified intensity for daytime conditions.”

(12)

(pg. 1)

4(09)-38 (I)

(13)

Official interpretation 4(09)-38 (I) states that “It is the FHWA’s official

interpretation that the predetermined flash period should be initiated each and

every time that a pedestrian is detected either through passive detection or as a

result of a pedestrian pressing a pushbutton detector. This would include

pedestrians who are detected while the RRFBs are already flashing and who are

detected immediately after the RRFBs have ceased flashing.”

(13)

(pg. 1)

Table 2. Summary of RRFB official interpretation developed using the results of this

research project.

Number

Summary of Key Characteristics Relevant to this Study

4(09)-41

(2)

Official interpretation 4(09)-41 (I) states that, “…the FHWA favors the WW+S

(wig-wag plus simultaneous) flash pattern because it has a greater percentage of

dark time when both beacons of the RRFB are off and because the beacons are on

for less total time. The greater percentage of dark time is important because this

will make it easier for drivers to read the sign and to see the waiting pedestrian,

especially under nighttime conditions. The less total on time will make the RRFB

more energy efficient, which is important since they are usually powered by solar

energy.”

(2)

(pg. 1)

4(09)-58 (I)

(3)

Official interpretation 4(09)-58 (I) states that, “…it is the FHWA's official

interpretation that any new RRFB units that are installed under the terms of

Interim Approval 11 may be placed either above or below the crossing warning

sign. Existing RRFB units that are placed below the crossing warning sign

may be retained in their current position or may be relocated to be above the

sign.”

(3)

(pg. 1)

RRFB

RRFBs flash in an eye-catching sequence to draw drivers’ attention to the sign and the need to

yield to a waiting pedestrian. It may be located on the side of the road below the pedestrian

crosswalk or school crossing signs or overhead with a sign and can be activated actively

(pushing a button) or passively (detected by sensors) by pedestrians. Several studies have

examined the effectiveness of the device or elements contained within the device, including

the following:

• An FHWA study in the early 2000s included 22 RRFB sites.

(16)

• A 2009 FHWA study considered two sites in Miami, FL.

(17)

• A 2009 study reported on an uncontrolled trail crossing of a four-lane urban street in

St. Petersburg, FL.

(18)

• A 2011 study considered an uncontrolled crossing in Garland, TX.

(19)

• A 2011 Oregon Department of Transportation study examined three crosswalks in

Bend, OR.

(20)

• A 2013 pilot project in Calgary, Canada, included six sites.

(21)

• A 2014 Michigan study examined a bike trail crossing.

(22)

All of these studies used a before (none or continuously flashing beacon treatment) to after

(RRFB installed) design and found an improvement in driver yielding after the RRFBs were

installed. (See references 16–22.)

Other studies focused on examining how different features of the rapid-flashing beacons affect

driver yielding. A study of two sites in Santa Monica, CA, compared the effect of an RRFB and

a circular rapid-flashing beacon (CRFB) on yielding behavior at two crossings.

(23)

The RRFB

was installed at one site, and the CRFB was installed at the other. After an evaluation period,

they were switched and evaluated again. The study evaluated driver yielding rates both when the

beacons were actuated and when they were not actuated. In all cases, driver yielding rates were

higher when the beacons were activated.

An FHWA study also investigated differences between RRFBs and CRFBs.

(5,24)

Both were

installed at 12 sites located in 4 cities. The statistical results indicated that there were no

significant differences between the two beacon shapes.

For a subset of the 12 sites used in the FHWA study to evaluate the beacon shape, the luminous

intensity (also called brightness) of the beacons was measured.

(24)

For those sites, there was

evidence of an increasing yielding rate with increasing intensity at night.

Additional research was done at those 12 sites to evaluate the effect of the activation of the

beacons and traffic volumes on driver yielding behavior when a crossing pedestrian was

present.

(25)

The results of the analysis suggest that when a beacon—whether rectangular or

circular—was activated, a driver was 3.68 times more likely to yield to pedestrians than when it

was not activated. The results of an analysis of the relationship between traffic volume and driver

yielding suggested that driver yielding behavior was not influenced by traffic volume at the study

sites; however, the sample size available may have limited the ability to identify a relationship.

PHB

In a FHWA study, researchers conducted a before-after evaluation of the safety performance of

the PHB.

(26)

Using an empirical Bayes method, the evaluations compared the crash prediction for

the before period without the treatment to the observed crash frequency after installation of

the treatment. To develop the datasets used in the evaluation, researchers counted the crashes

occurring 3 years before and up to 3 years after the installation of the PHB. The crash categories

examined in the study included total, severe, and pedestrian crashes. From the evaluation

considering data for 21 treatment sites and 102 unsignalized intersections (reference group),

the researchers found the following changes in crashes following installation of the PHBs:

• A 29 percent reduction in total crashes (statistically significant).

• A 15 percent reduction in severe crashes (not statistically significant).

• A 69 percent reduction in pedestrian crashes (statistically significant).

In a 2006 study, drivers yielding at five PHBs (known as HAWK sites at the time of the study)

had an average driver yielding value of 97 percent.

(27,28)

For the sites included in the study, the

number of lanes (two, four, or six lanes) did not affect performance. The driver yielding was

very high compared to the other pedestrian devices included for the speed limits (either 35 or

40 mi/h) and intersection configurations (four-legged, T, offset T, or midblock crossings)

represented in the dataset.

PHBs generally rest in a dark mode. A concern has been expressed that drivers may believe there

is a power outage present and that the device is malfunctioning due to its dark resting mode,

resulting in the need to come to a complete stop at the crossing. A study of driver behavior in

Tucson, AZ, which had over 60 PHBs installed at the time of the study, investigated this concern

and did not find evidence of confusion.

(29)

Driver perception of PHBs was studied in Kansas to

identify drivers’ knowledge of each phase of the device.

(30)

Surveys were distributed to drivers in

stopped vehicles at a midblock PHB crossing and at a nearby signalized intersection. The results

of the survey showed that drivers understood the dark (94 percent) and steady red (91 percent)

signals well, understood the flashing yellow (76 percent) and steady yellow signals (67 percent)

moderately well, and had poor understanding of the flashing red signal (58 percent).

A study in Oregon was conducted where three 1-h visits were made to a PHB site.

(31)

Compliance was observed to be very high; however, no records were made. They noted that

drivers of queued vehicles sometimes proceeded through the crossing when the beacons changed

to flashing red “without checking to see if the crossing was clear.”

(31)

(pg. 67) Additionally, a

2014 Vermont study reported on a site near a hospital where, following installation of the PHB,

yielding compliance increased by 18 percent, and there was an 83 percent increase in the number

of vehicles slowing as they approached within 300 ft of the crosswalk.

(32)

PHB installation in

San Antonio, TX, resulted in driver yielding increasing from 0 (i.e., no drivers yielding to staged

pedestrians in 39 crossing attempts) to 95 percent for 60 staged pedestrian crossings.

(33)

All of

the non-staged pedestrians at this site activated the treatment. An increase in the number of

non-staged pedestrian crossings was observed after the PHB was installed. Finally, a study of

three PHB installations in Charlotte, NC, found an increase in the number of motorists yielding

to pedestrians.

(34)

Because data were collected for several periods after installation, they were

able to conclude that improvements seemed to be relatively more consistent 3 mo after the

installation of the PHB. In other words, it may take 3 mo for pedestrians and motorists to adapt

to the new device.

MULTIPLE PEDESTRIAN TREATMENTS

A Texas Department of Transportation study explored the factors associated with drivers

yielding to pedestrian crossings with traffic control signals (TCSs), PHBs, and RRFBs in

Texas.

(35,36)

The percentage of drivers who yielded to a staged pedestrian was collected at 7 TCS

sites, 22 RRFB sites, and 32 PHB sites. Overall, TCSs in Texas had the highest driver yielding

rates, with an average of 98 percent. The average driver yielding for RRFB in Texas was

86 percent, while the average for PHB was 96 percent. All of the RRFB sites had school crossing

(S1-1) signs. The number of devices within a city may have an impact on driver yielding. Those

cities with a greater number of a particular device (i.e., Austin, TX, for the PHB and Garland,

TX, for the RRFB) had higher driver yielding rates as compared to cities where the device

was only used at a few crossings. Comparing the number of days since installation revealed

statistically significant higher driver yielding rates for those PHBs that had been installed longer.

The authors concluded that based on the statistical evaluation of the 32 PHB sites, the results

support the use of the PHB on roadways with multiple lanes or a wide crossing. For RRFBs,

the posted speed limit, total crossing distance, one-way versus two-way traffic, and location were

all statistically significant. The data revealed a trend of lower driver yielding rates for wider

crossing distances as compared to shorter crossing distances. This finding indicates that there is a

crossing distance width where a device other than the RRFB should be considered.

CHAPTER 3. IMPACT OF RAPID-FLASHING YELLOW LEDs ON DETECTING

PEDESTRIANS IN A CLOSED-COURSE SETTING

INTRODUCTION

This chapter describes the methodology and results from the closed-course study that examined

LED brightness, position, and flash patterns. The brightness of LEDs, whether used within

beacons or embedded in a sign, can help draw drivers’ attention to a device and the area around

the device. However, LED brightness can also make it more difficult for drivers to see objects

around a device (disability glare) or result in drivers looking away from a device (discomfort

glare). Either condition—disability glare or discomfort glare—may result in drivers missing

hazards located near the source of the glare. In the case of LEDs used at pedestrian crossings,

this may affect drivers’ ability to detect pedestrians.

In general, disability glare impairs a driver’s ability to detect hazards near a device even in

situations where the driver is not experiencing discomfort glare. This results from light striking

photoreceptors within the eye in a manner that diminishes the eye’s ability to discern contrast. In

low-contrast situations, such as nighttime conditions, disability glare caused by bright LEDs may

affect drivers’ ability to detect pedestrians. Conversely, discomfort glare is the perceived

discomfort of the light source and may result in drivers looking away from a device.

To prevent devices from being set at brightness levels that produce disability or discomfort glare,

the profession needs to quantify the effect of bright traffic control devices on a driver’s ability

to detect pedestrians in and around the crosswalk. This closed-course study was designed to

examine drivers’ ability to detect pedestrians in and around crosswalks. Specifically, it examined

the effect of traffic control device brightness and other characteristics on drivers’ ability to

quickly and accurately identify the presence of a pedestrian and then discern the pedestrian’s

direction of travel.

For flashing traffic control devices, there are two important and competing considerations in

designing the brightness of traffic control devices:

• Is the brightness high enough to command the driver’s attention and elicit the desired

response (e.g., yielding to pedestrians)?

• Is the brightness low enough that it does not impair a driver’s ability to see pedestrians

because of disability or discomfort glare?

For a well-designed traffic control device, the answers to both questions need to be yes, yet the

measure of brightness associated with these two questions may not be the same.

At the conclusion of the closed-course study, crossing sign assemblies were identified for

evaluation in the field (open-road phase).

Study Objective

The objective of this study was to investigate how LED brightness and the flash pattern used

with LEDs affect the ability to detect pedestrians. The measures of effectiveness for the closed-

course study were as follows:

• Time to correctly identify pedestrian walking direction.

• Percentage of the tests where the participant correctly identified the cutout pedestrian

walking direction.

• Participants’ rating of discomfort glare.

Overview of Study Approach

The intent of the static closed-course study was to quantify drivers’ ability to detect pedestrians

within and around a crosswalk (a measure of disability glare) and quantify discomfort glare

ratings associated with LEDs in traffic control devices. Participants drove the study vehicle to

the starting location where they parked the vehicle at a set distance of 200 ft away from the sign

assemblies that consisted of a pedestrian crossing sign with LEDs within the sign face and LEDs

in rectangular beacons above and below the sign. After the participants placed the vehicle into

park, they were asked to wear occlusion glasses, which obscure the participants’ vision by

becoming opaque when there is no power supplied to them or clear when power is supplied.

Wearing these glasses was similar to wearing sunglasses and involved no more risk than that

typically encountered while sitting in a parked vehicle.

Once the participants’ vision was occluded, technicians placed a static cutout photo of a

pedestrian (either 54 inches tall to represent a child or 70 inches tall to represent an adult) within

the crosswalk located near the sign assemblies. An experimenter then restored the participants’

vision, and they were asked to identify the direction the pedestrian was traveling (i.e., to the left,

to the right, or not present) as quickly as possible using a button box. This type of research

approach—identifying the walking direction of a pedestrian in a photo cutout—has been used

previously to examine crosswalk lighting.

(37)

When the participants pressed a button on the

button box, the glasses turned opaque again. Following the identification of the pedestrian’s

direction, the researcher asked the participants to rate the intensity of the LED (comfortable,

irritating, or unbearable) before asking the field crew to set up the next condition. This process

was repeated for various combinations of LED brightness, LED locations, pedestrian positions,

and flash patterns. This portion of the study was stationary, and, after completion, the

participants drove to the check-in location and completed a laptop survey that asked a series of

queries to obtain the participants’ opinions regarding flash patterns for LEDs used with signs. At

the end of the study, the participants were compensated for their participation.

To increase the number of flash patterns tested in the study but to keep within a reasonable

testing period, data were collected within two sets. Within each set, two flash patterns were

tested for the LEDs in rectangular beacons, and two flash patterns were tested for the LEDs

within the sign. For pattern set I (descriptions provided in the following Course Development

section), the study was conducted during both the daytime and nighttime. For pattern set II, the

study was only conducted during the nighttime. During the testing of set I, it was determined that

nighttime was the more critical condition, which is why only nighttime data were collected

during set II.

COURSE DEVELOPMENT

Riverside Campus

The runway system on the Texas A&M University (TAMU) Riverside campus served as the

test roadway for data collection. The runways offered a mixture of long straightaways, short

intersecting segments, and curves. Researchers selected one of the taxiways so that the study site

would look more similar to a two-lane road rather than a wider paved surface area, which is a

characteristic of the runways. The location selected was approximately 40 ft wide. Edgeline and

centerline markings were added to give the site a more urban feel. Each lane was approximately

12 ft wide.

Pedestrian Crossing Assemblies Selected for Study

Initially, researchers planned to have the different study assemblies located in different parts

of the TAMU Riverside campus. During development, the researchers realized that a single

assembly could include LEDs in the beacons above and below the sign and that the sign could

have the LEDs embedded within the sign (see figure 1). Having all device combinations on

one post decreased the amount of participant time that had to be spent driving between the

different study locations, which meant more tests could be conducted per participant. Having all

device combinations at one site also decreased the course preparation efforts in that only one site

rather than several sites had to be prepared to have the desired urban feel, such as adding

edgeline and centerline markings.

Figure 1. Photo. Study assembly containing LEDs above, below, and within the sign.

The location of the LEDs used in this study included the following:

• LEDs in rectangular beacons located above the sign. The bottom edge of the beacon

housing was approximately 11.6 ft from the pavement.

• LEDs in rectangular beacons located below the sign. The bottom edge of the beacon

housing was approximately 7.0 ft from the pavement.

• LEDs embedded within the sign. The height to the middle of the sign was approximately

9.5 ft.

Study Site

At the beginning of each participant run, the participants drove a Texas A&M Transportation

Institute (TTI) vehicle to the study site (see figure 2) and parked the vehicle near the orange

barrel (see figure 3 and figure 4). Figure 4 shows a photograph of the view for the participants.

At the site, the participants saw two study assemblies: one on each side of the two-lane street.

Vehicles were parked on the cross street upstream and downstream of the study site to aid in

giving the urban feel and to provide a hiding space for the technicians that were changing the

LED settings and moving the pedestrian cutout. Transverse white pavement crosswalk markings

were installed at the site (see figure 5).

Figure 2. Illustration. Route for closed-course study.

Figure 3. Illustration. Layout for the study site.

Figure 4. Photo. View of the study assemblies.

Figure 5. Photo. Back view of study site.

To be more efficient, the study was designed so that data were collected from two participants

simultaneously. Each participant was in a unique car so that a participant’s response would not

be heard by the other participant. The vehicles were parked next to each other at the study site,

thus simulating vehicles approaching a pedestrian crossing on a multilane roadway (see figure 3

and figure 4, which illustrate how the vehicles were parked). The participants were located 200 ft

from the LED assemblies. The 200-ft distance was selected because it represents stopping sight

distance (SSD) when traveling 30 mi/h.

(38)

Street lighting was present at the site for the nighttime testing. Two work zone light towers were

rented for the study and placed on either side of the approach on the cross street. During course

preparation, researchers positioned these light towers in a manner that simulated street lighting.

Prior to collecting data for each set of nighttime participants, the luminance reading at the

three pedestrian positions were taken to ensure a consistent street lighting level was present.

The average of these readings was about 26 lux.

Cutout Pedestrian

To ensure consistency with the pedestrian characteristics, the research team decided to use a

photograph of a pedestrian. The photograph was cut out to mimic the shape of a walking

pedestrian (see figure 6). Two cutouts were created to reflect two heights: adult and child. The

70-inch version reflected the average height of adults between 1999 and 2002, while the 54-inch

version reflected the average height of a child in the same time period.

(39)

Figure 7 shows a

researcher removing the short cutout photograph (center of road) after installing the tall cutout

photograph (right side of road).

Figure 6. Photo. View of 54-inch cutout pedestrian used in study.

Figure 7. Photo. Researcher removing short cutout pedestrian after placing tall cutout

pedestrian.

The cutout photographs were glued on both sides of a pole that extended a few inches below

the shoe in the photograph. This extension was placed into one of three holes drilled into the

pavement. The holes were located just to the right of the edgeline in the center of the road (i.e.,

on the lane line) and just to the left of the edgeline, as shown in figure 8. The positions near the

edgeline pavement markings reflected the condition of a pedestrian waiting to cross the street.

The center of the street represented a pedestrian in the crosswalk. The holes were drilled between

the two crosswalk lines, as shown in figure 6 and figure 8. Because the photographs were glued

to both sides of the pole, the cutout pedestrian could be rotated to appear to be walking to the left

or to the right.

Figure 8. Illustration. Plan view showing pedestrian cutout positions.

Flash Pattern for Assemblies

Several flash patterns were used within the study. For the LEDs in rectangular beacons, the

patterns shown in table 3 were used. The light bar containing the rectangular beacons had

two unique beacons (with each beacon containing eight LEDs). When the beacon was turned on

varied depending on the beacon location (i.e., left side or right side), as illustrated in table 3.

Table 3. Flash patterns used with LEDs located in rectangular beacons

above or below the sign.

Cumulative

Time (ms)

Sets I and II:

No Flashes Dark

Set I: Wig-Wag

Alternating

Sets I and II:

2-5 Flash Pattern

Set II: Two 125-ms

Simultaneous

Pulses

Left

Time

On (ms)

Right

Time

On (ms)

Left

Time

On (ms)

Right

Time

On (ms)

Left

Time

On (ms)

Right

Time

On (ms)

Left

Time

On (ms)

Right

Time

On (ms)

100

125

150

175

200

225

250

275

300

325

350

375

400

425

450

475

500

525

550

575

600

625

650

675

700

725

750

775

800

BEC

825

BEC

850

BEC

875

BEC

900

BEC

925

BEC

950

BEC

975

BEC

Cycle length

(ms)

N/A

1,000

800

Number of

cycles/min

N/A

BEC = Beyond end of cycle.

N/A = Not applicable.

Note: Yellow shading represents when the beacons were on.

Table 4 shows the flash pattern used for the LEDs within the sign. While there are eight unique

points of lights within an embedded diamond sign, the researchers decided that all eight LEDs

would be illuminated at the same time within the sign as is currently used in practice. Therefore,

there was not a left and right designation for the LEDs within the sign. The 2-5 flash pattern used

with the assemblies was selected based on FHWA official interpretation 4(09)-21 (I) released

on June 13, 2012, regarding the RRFB.

(9)

It has two slower flashes on one side followed by

five rapid flashes on other side.

Table 4. Flash patterns used with LEDs within sign.

Cumulative

Time (ms)

Sets I and II:

No Flashing

Set I: Five Pulses

Similar to Right

Side of RRFB

Sets I and II:

One 100-ms Pulse

Set II: Two 125-ms

Pulses Similar to

Left Side of RRFB

Time On (ms)

100

125

150

175

200

225

250

275

300

325

350

375

400

425

450

475

500

525

550

575

600

625

650

675

700

725

750

775

800

BEC

825

BEC

850

BEC

875

BEC

900

BEC

925

BEC

950

BEC

975

BEC

Cycle length

(ms)

N/A

800

1,000

800

Number of

cycles/min

N/A

N/A = Not applicable.

Note: Yellow shading represents when the beacons were on.

Brightness of LEDs

The characteristics of the LEDs may affect the detection of pedestrians. Table 5 lists the

characteristics of the LEDs used with pattern set I, while table 6 provides similar values for

pattern set II. To quantify the brightness of the pulsing lights, researchers used the photometric

range within the TTI Visibility Laboratory. For each RRFB beacon and LED sign, a technician

measured the 95th percentile peak intensity (called “measured intensity” in table 5 and table 6)

and the optical power of the device. The researcher took the measurements at a vertical angle of

0 degrees and a horizontal angle of 0 degrees.

Peak luminous intensity is defined as the maximum luminous intensity for a given flash. The

peak intensity can be much higher than the typical intensity within a pulse. Therefore, the

95th percentile intensity is used to provide a more representative value. The 95th percentile

luminous intensity is the luminous intensity that 95 percent of the instantaneous intensity

measurements are less than or equal to during the duration of the flash; instantaneous intensities

measured during the dark period are not included in this measurement.

According to SAE Standard J595, optical power is defined as the integrated total of all flashes in

a minute, in candela-s/min.

(15)

Stated in a general way, optical power represents the area under

the curve. It provides an appreciation of both the intensity of the pulses and the amount of time

the LEDs are illuminated.

Table 5. LED characteristics for set I.

LED

Location

Flash Pattern

Target

Intensity

(Candela)

Measured

Intensity

(Candela)

Optical Power

(Candela-s/min)

Pulse Rate

(Number of

Pulses/Cycle

Length)

On Ratio

(Percent)

Above

2-5

600

622

25,600

8.75

Above

2-5

1,400

1,426

58,800

8.75

Above

2-5

2,200

2,207

91,000

8.75

Above

Wig-wag

600

605

36,300

2.00

100

Above

Wig-wag

1,400

1,442

86,500

2.00

100

Above

Wig-wag

2,200

2,237

134,200

2.00

100

Below

2-5

600

675

27,900

8.75

Below

2-5

1,400

1,450

59,800

8.75

Below

2-5

2,200

2,249

92,700

8.75

Below

Wig-wag

600

633

38,000

2.00

100

Below

Wig-wag

1,400

1,458

87,400

2.00

100

Below

Wig-wag

2,200

2,256

135,300

2.00

100

Within

100

600

649

3,900

1.00

Within

100

1,400

1,471

8,800

1.00

Within

100

2,200

2,225

13,300

1.00

Within

Five pulses

600

652

14,700

6.25

Within

Five pulses

1,400

1,454

32,700

6.25

Within

Five pulses

2,200

2,216

49,900

6.25

Note: Flash patterns are defined as follows: 2-5 = 2-5 flash pattern; wig-wag = wig-wag flash pattern; and 100 = one 100-ms

flash pattern.

Table 6. LED characteristics for set II.

LED

Location

Flash

Pattern

Target

Intensity

(Candela)

Measured

Intensity

(Candela)

Optical Power

(Candela-s/min)

Pulse Rate

(Number of

Pulses/Cycle

Length)

On Ratio

(Percent)

Above

125(2)

600

622

11,700

2.50

Above

125(2)

1,400

1,441

27,000

2.50

Above

125(2)

2,200

2,308

43,300

2.50

Above

2-5

600

622

25,600

8.75

Above

2-5

1,400

1,426

58,800

8.75

Above

2-5

2,200

2,207

91,000

8.75

Below

125(2)

600

619

11,600

2.50

Below

125(2)

1,400

1,436

26,900

2.50

Below

125(2)

2,200

2,269

42,500

2.50

Below

2-5

600

675

27,900

8.75

Below

2-5

1,400

1,450

59,800

8.75

Below

2-5

2,200

2,249

92,700

8.75

Within

100

600

652

3,900

1.00

Within

100

1,400

1,469

8,800

1.00

Within

100

2,200

2,227

13,400

1.00

Within

125(2)

600

646

12,100

2.50

Within

125(2)

1,400

1,464

27,400

2.50

Within

125(2)

2,200

2,227

41,800

2.50

Note: Flash patterns are defined as follows: 2-5 = 2-5 flash pattern; wig-wag = wig-wag flash pattern; 100 =

one 100-ms flash pattern; and 125(2) = two 125-ms flashes.

Previous research has demonstrated that LED characteristics can influence whether an object is

detected.

(40)

Because the amount of time the LEDs are on may influence a driver’s ability to

detect a pedestrian, a measure of the on time was developed. The on ratio variable (see table 5

and table 6) is defined to be the percentage of the 25-ms increments within a cycle where the

LEDs within the beacon or sign are illuminated. The percentage of the cycle where the LEDs are

dark would be determined as 1 minus the on ratio. For example, the 2-5 pattern would have an

off ratio of 31 percent (100 percent – 69 percent). In the wig-wag pattern, there was no dark

period, as demonstrated by having an on ratio of 100 percent. To provide an appreciation of how

often the LEDs are pulsing, the pulse rate was determined as the number of pulses divided by the

cycle length. For example, the 2-5 pattern had 7 pulses within the 0.8-s cycle for a pulse rate of

8.75, while the rapid-flashing LEDs within a sign had 5 pulses within the 0.8-s cycle for a pulse

rate of 6.25.

Combinations Studied

The variables for participant characteristics and site characteristics presented within this closed-

course study are as follows:

• Lighting: Day (natural lighting) or night (street lighting).

• Gender: Male or female.

• Age: Young (less than 55 years old) or old (55 years old or greater).

• Lane: Left lane or right lane.

• Viewing position: 200 ft upstream from assemblies.

Study assemblies characteristics included the following:

• LED location: LEDs in a rectangular beacon below the sign, LEDs in a rectangular

beacon above the sign, or LEDs within the sign.

• Flash pattern (three per set; see table 3 and table 4):

o Set I: No rectangular beacon above or below the sign, 2-5 pattern, or wig-wag

(alternating) pattern.

o Set I: No LEDs within the sign, five pulses (five pulses similar to the right side of the

RRFB), or one 100-ms pulse (single pulse).

o Set II: No rectangular beacon above or below the sign, 2-5 pattern, or two 125-ms

pulses (simultaneous).

o Set II: No LEDs within sign, five pulses (rapid right side of RRFB), or two 125-ms

pulses (two pulses similar to left side of RRFB).

• Target intensity (i.e., brightness): 0, 600, 1,400, and 2,200 candelas.

The cutout pedestrian characteristics include the following:

• Pedestrian position: None, right side, center, or left side.

• Pedestrian height (when present): Tall (70 inches) or short (54 inches).

• Pedestrian direction (when present): Left or right.

Over 260 tests would be needed for a participant to see all possible combinations of study

assembly and pedestrian characteristics. Preliminary data collection efforts demonstrated that

about 100 tests could be conducted within the available 60-min data collection period.

A presentation order of the possible combinations between the study assembly and cutout

pedestrian characteristics was developed using a random number generator in a spreadsheet.

The order was then modified so that a participant would only see a particular combination once

and so that a similar number of viewings per combination would occur. Table 7 shows the

combinations tested. A total of 15 tests were conducted for each combination of pedestrian

height and position. For example, the short cutout pedestrian when located in the center of the

roadway was viewed in 15 tests. For the 7 combinations possible when considering pedestrian

position and height, the 15 tests per combination resulted in a total of 105 tests per participant.

For those tests when a cutout pedestrian was present (105 − 15 = 90 tests), half of the tests had

the cutout pedestrian moving toward the left, while the other half of the tests had the cutout

pedestrian moving toward the right.

Initially, the goal was to randomize the presentation order for all characteristics tested (i.e., LED

location, flash pattern, brightness level, and cutout pedestrian position, height, and direction).

Preliminary efforts demonstrated that the changes required of the technicians to switch from

one LED location to another would consume too much time. Therefore, the study was subdivided

into three blocks. Within the first block, all the tests associated with one of the LED locations

would be conducted (e.g., rectangular below). A short break would be provided to the participant

while the field crew switched the wires to operate the next LED location (e.g., LED within sign).

Another break would divide the second block from the third block. Each block included 35 tests.

The presentation of the device order was different for different sets of participants; some

participants saw the above block first, some saw the below block first, and others saw the

LED sign block first.

Table 7. Number of variable combinations tested during the closed-course study.

Location

of LED

Flash

Pattern

Target

Intensity

(Candela)

Number of Tests

Pedestrian

Cutout

Present

Short Pedestrian

Cutout Position

Tall Pedestrian

Cutout Position

Total

Left

Side

Center

Right

Side

Left

Side

Center

Right

Side

Within

None

Other

600

1,400

2,200

Rapid

600

1,400

2,200

Below

None

Other

600

1,400

2,200

Rapid

600

1,400

2,200

Above

None

Other

600

1,400

2,200

Rapid

600

1,400

2,200

Grand Total

105

Note: Blank cells indicate that the combination was not tested.

Note that within the table, flash patterns are defined as follows:

• None: LEDs were not illuminated.

• Rapid: The 2-5 pattern was used when LEDs were above or below the sign, while

five pulses (five pulses similar to the right side of the RRFB) were used when the LEDs

were located within the sign.

• Other:

o Set I: Wig-wag (alternating) was used when LEDs were above or below the sign, and

a 100-ms pulse (single pulse) was used when LEDs were within the sign.

o Set II: Two 125-ms pulses (simultaneous) were used when LEDs were above or

below the sign, while two 125-ms pulses (two pulses similar to left side of RRFB)

were used when LEDs were within the sign.

Concluding Survey

After participants completed the closed-course portion of the study, they were asked to complete

a laptop survey that asked a series of queries to obtain the participants’ opinions regarding flash

patterns for beacons used with pedestrian crossing signs. The two initial queries included a video

filmed from a driver’s position as the vehicle moved toward a crosswalk with a waiting

pedestrian. The participants always saw the same sign assembly; however, the LEDs and flash

pattern used (if any) varied between the two queries. The same question was used with each

query. Figure 9 shows the starting view for the first two queries (a close-up example of the sign

assembly is shown in figure 1). The wording of the question and answers used with queries 1 and

2 are as follows:

As a driver of an automobile approaching the crosswalk shown in the video, how would you

react in this situation?

1. I would slow and allow the pedestrian to cross the roadway.

2. I would stop and allow the pedestrian to cross the roadway.

3. I would confirm the pedestrian is not crossing before proceeding.

4. I would continue driving at the same speed.

Figure 9. Photo. View at start of the driving video for the concluding survey for

queries 1 and 2.

Researchers wanted to determine how drivers viewed the requirement to yield to the pedestrian

when a pedestrian crossing sign did not have active supplemental LEDs. Therefore, the video for

one of the two initial queries for all participants had no LEDs active (condition termed “sign”

within the survey and is similar to the flash pattern “none” when wearing the occlusion glasses).

About half of the participants had the sign-only video with their first query, while the other half

of the participants had the sign-only video with their second query. Table 8 lists the videos

shown for each query by participant group.

Table 9 identifies the flash pattern used with queries 1 and 2, which were the moving videos

shown from a driver’s perspective. Table 10 shows illustrations of the flash patterns used

with queries 3 and 4, which were stationary videos showing a close-up of the pedestrian

crossing assembly.

Table 8. Video assignments and flash patterns for each query by participant group.

Participant

Group

Driving Video Flash Pattern

Stationary Video Flash Pattern

Query 1

Query 2

Query 3

Query 4

Video at Top of

Screen

Video at Top of

Screen

Video A at Left

Side of Screen

Video B at Right

Side of Screen

Video A at Left

Side of Screen

Video B at Right

Side of Screen

Sign

Below; 2-5

Within; 100

Below; 25(4)+200

Within; 125(2)

Below; 125(2)

Sign

Below; wig-wag

Below; 25(4)+200

Within; 25(4)+200

Sign

Within; 125(2)

Sign

Within; 100

Below; 25(4)+200

Within; 100

Below; 25(4)+200

Within; 125(2)

Below; 2-5

Sign

Below; 125(2)

Below; 2-5

Within; 25(4)+200

Within; 125(2)

Below; wig-wag

Sign

Within; 100

Within; 25(4)+200

Within; 100

Within; 125(2)

Within; 100

Sign

Within; 100

Below; 2-5

Sign

Within; 25(4)+200

Sign

Below; 2-5

Within; 125(2)

Within; 25(4)+200

Sign

Within; 100

Sign

Below; wig-wag

Sign

Below; 125(2)

Within; 25(4)+200

Sign

Within; 100

Below; 125(2)

Within; 25(4)+200

Below; 2-5

Sign

Below; 2-5

Sign

Within; 25(4)+200

Below; 125(2)

Below; 2-5

Within; 125(2)

Below; wig-wag

Sign

Below; 125(2)

Within; 100

Within; 25(4)+200

Below; 2-5

Within; 100

Sign

Within; 125(2)

Sign

Within; 125(2)

Within; 100

Sign

Below; 2-5

Below; 125(2)

Below; 25(4)+200

Below 25(4)+200

Below; 125(2)

Sign

Below; wig-wag

Below; 2-5

Within; 100

Within; 25(4)+200

Within; 100

Sign

Within; 100

Below; 2-5

Within; 25(4)+200

Below; 25(4)+200

Below; 2-5

Sign

Below; 25(4)+200

Sign

Within; 100

Sign

Below; wig-wag

Sign

Within; 125(2)

Below; 25(4)+200

Below; 2-5

Below; 25(4)+200

Within; 100

Sign

Below; 2-5

Within; 100

Below; 125(2)

Sign

Below; 2-5

Below; 125(2)

Sign

Within; 125(2)

Below; 2-5

Sign

Below; wig-wag

Sign

Below; 25(4)+200

Below; 2-5

Sign

Within; 100

Below; 2-5

Below; 125(2)

Within; 125(2)

Note: Flash patterns are defined as follows: sign = no active LEDs; 2-5 = 2-5 flash pattern; wig-wag = wig-wag flash pattern; 100 = one 100-ms flash pattern;

25(4)+200 = four 25-ms flashes and one 200-ms flash; and 125(2) = two 125-ms flashes.

Table 9. Flash patterns for queries 1 and 2 showing moving videos from driver perspective.

Cumulative

Time

(ms)

Below; Wig-Wag

Flash Pattern

Below; 2-5 Flash Pattern

Within; 100-ms

Flash Pattern

No LEDs or

Flash Pattern

Left Time

On (ms)

Right Time

On (ms)

Left Time

On (ms)

Right Time

On (ms)

Time On (ms)

100

125

150

175

200

225

250

275

300

325

350

375

400

425

450

475

500

525

550

575

600

625

650

675

700

725

750

775

800

BEC

825

BEC

850

BEC

875

BEC

900

BEC

925

BEC

950

BEC

975

BEC

Note: Yellow shading represents when the beacons were on.

Table 10. Flash patterns used for queries 3 and 4 with the video showing a close-up view.

Cumulative

Time

(ms)

Below; 2-5

Flash Pattern

Below;

Two 125-ms

Flashes

Below;

Four 25-ms

Flashes and

One 200-ms

Flash

Within;

100-ms

Flash

Pattern

Within;

Two

125-ms

Flashes

Within;

Four 25-ms

Flashes and

One 200-ms

Flash

Pattern

Left

Time

(ms)

Right

Time

(ms)

Left

Time

(ms)

Right

Time

(ms)

Left

Time

(ms)

Right

Time

(ms)

Time On

(ms)

Time On

(ms)

Time On

(ms)

Time

(ms)

100

125

150

175

200

225

250

275

300

325

350

375

400

425

450

475

500

525

550

575

600

625

650

675

700

725

750

775

Note: Yellow shading represents when the beacons were on.

Queries 3 and 4 asked the participants to judge the urgency of the message conveyed by the

crosswalk treatment. The participants saw a close-up of two side-by-side assemblies labeled

video A and video B. The flash patterns and which LEDs were active varied, as listed in table 8.

The design of the study resulted in four to five participants seeing each pair with the specific

placement on the screen (i.e., left side or right side). If placement on the screen was not

considered, then each device pair was viewed, on average, by nine participants. The wording

of the question and answers used with queries 3 and 4 were as follows:

In your opinion, which video conveys a more urgent need for a driver to yield to a pedestrian?

1. Video A conveys a more urgent need.

2. Video B conveys a more urgent need.

3. The level of urgency is similar in both videos.

4. Neither video conveys an urgent need for a driver to yield to a pedestrian.

The final query asked the participants to count how many flashes they observed in the left and

right beacons for a light bar that was located in the room with them.

DATA COLLECTION

Study Periods

The study was conducted under both daytime and nighttime conditions between Wednesday,

November 13, 2013, and Thursday, December 12, 2013, with several days lost due to rain.

Sunset occurred at approximately 5:30 p.m. during the study. The study took about 1.5 h from

meeting the participant to the participant receiving their payment. About one-third of the

participants drove during daylight hours, and two-thirds drove during nighttime conditions

with an approximately even split between flash pattern sets I and II. The following start times

were used:

• 12 p.m.

• 1:30 p.m.

• 6:30 p.m.

• 8 p.m.

Participants

Participants were recruited from the area using TTI’s pool of previous research subjects list.

Over the phone, the potential participants were told that the study was confidential and the

records of the study would be kept private. They were also told that their participation was

voluntary and that they were free to withdraw from the study at any time.

The initial intent was to recruit a group of participants composed of one-quarter males over the

age of 55, one-quarter females over the age of 55, one-quarter males under the age of 55, and

one-quarter females under the age of 55. Within each of those demographic groups, the goal was

to have an even distribution between those who participated during the daytime and nighttime

within pattern set I. Therefore, the following divisions were used in structuring participant

recruitment:

• Light level: Day or night.

• Age group: Young (younger than 55 years old) and old (55 years old or older).

• Gender: Male or female.

When pattern set II was added to the study, data were only collected during the nighttime.

The male/female, young/old divisions resulted in four participant categories. The research goal

was to have 8 participants in each of these categories, resulting in 32 participants per day or per

night. Table 11 summarizes the number of participants by pattern set (I or II) and light level (day

or night) that participated in the study.

Participants were at least 18 years old and possessed a valid driver’s license with no restrictions.

Upon completion of the survey, participants received monetary compensation of $50.

Table 11. Distribution of participants.

Day or

Night

Pattern

Set

Old

Female

Old

Male

Old

Total

Young

Female

Young

Male

Young

Total

Young

Total

Day

Night

Grand Total

Participant’s Tasks

The tasks for the participants for this closed-course study were as follows:

1. After vision was restored by the occlusion glasses, participants were asked to indicate via a

button push whether the pedestrian was walking to the left or to the right.

2. Following the driver’s identification of the pedestrian direction, participants were asked to

state whether the intensity of the LEDs was comfortable, irritating, or unbearable.

3. Participants responded to survey queries presented in a conference room at the conclusion of

the study.

Instrumented Vehicle

Two similar vehicles—2009 sports utility vehicles—served as the participant cars for this

experiment. The headlamps for these vehicles were 35 inches from the ground and 27 inches

from center of the vehicle. Prior to the start of the study, the headlamps on both vehicles were

properly aligned by TTI staff members.

Participant Intake

Participant intake was headquartered at TTI’s Environmental Emissions Research Facility on the

Riverside campus. This location was selected because it was near the driving route, had public

parking available, included restroom facilities, and was available for both daytime and nighttime

use during the data collection period. After meeting with a member of the research team to review

the informed consent documentation and complete the demographic questionnaire, participants

were given an overview of the study, including how the data were to be collected. They were also

given a Dvorine color vision test.

To ensure consistency, the research team used scripts and slide shows to aid in providing

instructions to each participant. The script used during intake was as follows:

“Now, let me tell you a little about your tasks. There are two parts. For the first part, you will be

driving a State-owned passenger vehicle on a closed course we have set up on airport runways,

taxiway, and roadways here at the Riverside campus. The vehicle is specially equipped to record

and measure various driving characteristics, but drives just like a normal car. A researcher will

be in the car with you at all times and will direct you when, where, and how fast you will need to

go. The fastest you will be asked to drive is 40 mi/h.

For one part, you will be driving a course marked with white and yellow striping just as you

would see on an actual road. Part of the route is not striped, and when we reach these segments,

I will point you to the reflective pavement markings/line in the pavement that will act as our

road’s “center line.” Once we arrive to the study location, we will ask you to park your vehicle

next to the orange barrels. There will be another participant in a vehicle next to you. We are

running two participants simultaneously to more efficiently collect data for this study.

Once the vehicle is in position we will ask you to place it into park. We will then ask you to place

the occlusion glasses over your eyes and glasses if you have glasses. The occlusion glasses will

block your vision until the start of the test. When you are ready, we will clear the occlusion

glasses and restore your vision. You are to tell us via a button push whether the pedestrian in the

downstream crosswalk is walking to the left, to the right, or is not present. We will practice the

button pushes prior to driving to the study sites. After you indicate which way the pedestrian is

walking, I will ask you to indicate if the beacon glare is comfortable, irritating, or unbearable.

• Comfortable (where the glare is not annoying and the signal is easy to look at).

• Irritating (where the glare is uncomfortable, however you are still able to look at it

without the urge to look away).

• Unbearable (where the glare is so intense that you want to avoid looking at it).

After completing the tests you will return here for a brief laptop survey. After the laptop survey

we will provide your payment.”

Initial Button Push Training

As part of the intake process, the participants practiced with a button box while responding to

photographs of the crossing. The objectives for this part of the study were as follows:

• Train participants to recognize the pedestrians as well as absence of the pedestrians.

• Provide the opportunity for the participants to become familiar with using three buttons

to record their responses.

During the training tests, the participant pressed a button when they determined the direction the

pedestrian was walking. Because of the software used for this test and available response pads

for this software, the button box used for this training had seven buttons. Figure 10 through

figure 12 show three of the photos along with the accompanying instructions used in the initial

training. A random mix of tall and short cutout pedestrians moving to the right and to the left and

in positions 1, 3, or 5 (illustrated in figure 8) were used within the training.

When the pedestrian in the

crosswalk is moving to the left

(as shown) please press (3)

(do so now).

Figure 10. Photo. Training example with pedestrian facing left.

When the pedestrian in the

crosswalk is moving to the right

(as shown) please press (5)

(do so now).

Figure 11. Photo. Training example with pedestrian facing right.

When no pedestrian is present in

the crosswalk (as shown) please

press (4) (do so now).

Figure 12. Photo. Training example with no pedestrian.

Vehicle Review

Participants were escorted to the TTI vehicle and given a walk-through of the vehicle’s features.

They were provided with the opportunity to adjust the seat and mirrors and to become

accustomed to the controls of the vehicle.

Participants were informed that they would drive the vehicle on a closed course and were told to

drive at a speed not exceeding 40 mi/h on the runways. They were asked to drive the runway

system as though it was a regular roadway and were reminded that they had complete control of

the vehicle at all times. A researcher accompanied the participant in the back seat, controlling the

data collection equipment and providing direction. Participants were told to keep the vehicle’s

headlamps on the low setting if testing at night. They were told to drive to the study site and to

position the vehicle by the barrel. Once in position, they were told to place the vehicle into park.

Data Collection at Study Site

At the study site, the participants were reminded that they would be wearing occlusion glasses

that would block their vision until the start of the test and that they would provide responses via

a three-button box within the vehicle. The researcher handed the participant the button box and

asked them to become acquainted with the button box and to determine how best to hold the box

comfortably in their hands. When the participant indicated they were comfortable with the box,

they were provided the occlusion glasses, which they placed on their face over their eyeglasses if

they were wearing any.

After the participants indicated that the glasses and button box were comfortable, the practice

testing began for at least three scenarios. After the practice, the testing began. When the

participant had indicated readiness and the field crew indicated readiness for the cutout

pedestrian and study assembly, the researcher cleared the glasses and restored vision. The

participants were then asked to indicate via a pushbutton whether the pedestrian in the

downstream crosswalk was walking to the left or to the right or whether the pedestrian was

not present.

The participants were provided the following instruction in case they felt the brightness was too

bright for them:

If you find the brightness level of the beacons to be agonizing and you are not comfortable

completing the task for a particular test, please look away from the crosswalk and tell me. I will

block your vision for that test and will radio the field crew to setup for the next test.

After the participants pushed a button on the button box, which would darken the glasses, they

were to provide their rating of the brightness of the lights on the traffic control device. The

three rating levels used were as follows:

• Comfortable: The glare was not annoying, and the signal was easy to look at.

• Irritating: The glare was uncomfortable; however, participants could still look at it

without the urge to look away.

• Unbearable: The glare was so intense that participants wanted to avoid looking at it.

After the participants indicated the rating level, the researcher radioed the field crew and told

them to set up for the next test. This process was repeated until the participants had completed all

the tests at the site.

The participants were also provided these additional instructions:

If at any point in time you wish to stop, or would like a break, let me know and we will stop

or allow you an opportunity to rest.

Please leave the vehicle in park during these tests and while you are wearing the

occlusion glasses.

DATA REDUCTION

Participant Demographics

Table 12 lists the demographic information for the 98 participants. The large number that

selected retired for employment (34 percent) is a reflection of the emphasis on having half of the

drivers over 55 years old.

Table 12. Demographic information for participants.

Characteristic

Set I, Day

Set I, Night

Set II, Night

Total

Number

Percent

Number

Percent

Number

Percent

Number

Percent

Gender

Female

Male

Age group

< 55 years old

≥ 55 years old

Employment

Full time

Part time

Retired

Student/part

time

Other

Miles driven

per year

< 10,000 mi

10,000–

15,000 mi

> 15,000 mi

Normal

driving

conditions

Rural roads

City streets

Freeways

Mixed

Data Cleaning

Before proceeding with the statistical analyses, the data were reviewed to identify and remove

tests that needed to be eliminated due to miscoded information regarding the response type, the

wrong LEDs being activated in the assembly, or incorrect pedestrian size, position, or direction.

In a few cases, participants would self-correct a button push. To have all response times only

reflect initial reactions, the detection time results for a given test with duplicate responses were

eliminated. These data were included in the detection accuracy evaluations. Instances where

animals crossed in front of the vehicles were eliminated as well.

Responses

The computer software program along with the response pad unit were used to record the time

between the occlusion glasses being cleared and the participants pressing a button in the response

pad. These data were recorded within a spreadsheet that contained an experiment label, a time

stamp, and the corresponding detection time. For each experiment, the researcher asked about the

glare immediately after each participant pressed a button in the response pad. The experimenters

manually recorded discomfort glare ratings using preprinted data sheets.

Each of the experiment labels corresponded to predetermined combinations of pedestrian height,

position, brightness, and flash pattern. The sequence of experiments was random within the

blocking structure described previously in this report. The spreadsheet with detection time data

was later combined with the corresponding experiment conditions and discomfort data per

experiment label.

Box Plots

For some analyses, results were presented visually in the form of box plots or quantitatively in

the form of statistical analysis. Box plots presented in this report were generated using the

convention that the central line in the box represents the median data point (see figure 13). The

top of the box represents the 75th percentile, and the bottom represents the 25th percentile. Thus,

the relative position of the median score within the 75th and 25th percentiles can give some

indication about the skewness of the data. The height of the box is known as the “interquartile

range” (IQR). The “whiskers” represent the data that lie 1.5 times beyond the IQR. If all data

below the 25th percentile and above the 75th percentile are within 1.5 times the IQR, then the

end of the whisker represents the greatest or smallest value. Otherwise, all outliers beyond

1.5 times the IQR, added or subtracted from the 25th and 75th percentiles, respectively, are

plotted using small black open circles.

Figure 13. Illustration. Box plot details.

Additionally, it should be noted that a box plot representing a large sample provides more

confidence on its quartiles than another box plot representing a smaller sample. For this reason,

when two or more box plots are drawn together, the following two metrics of sample sizes

are represented:

• The box plot width is drawn proportional to the square root of the sample size, n.

• A triangular notch is symmetrically cut around the median and has a total width of

. This feature allows a preliminary graphic assessment of differences of

medians, since such notch width has been proposed to roughly represent a 95 percent

confidence interval around the median.

(41)

For example, two medians are significantly

different if the notches of two box plots do not overlap, but nothing can be said (that is,

preliminarily) if there is overlap between notches.

Mosaic Plots

For some analyses, results were presented visually in the form of mosaic plots. Mosaic plots

divide each dimension of a rectangular space in sizes relative of the levels of a variable assigned

to that dimension. Thus, this type of plot can represent two variables at the time, where each

variable may have two or more levels. Figure 14 shows the details of a mosaic plot when the

variable assigned to the height is the number of correct/incorrect pedestrian detections.

Figure 14. Illustration. Mosaic plot details.

Potential Outliers

Preliminary statistical analyses were examined for outlying data points in the fit. Data points

identified in this way were tested for their impact on the analysis. Most of the cases identified in

this stage came from a young participant with distinctive fast detection times and high accuracy

3.16 × IQR



Variable X

Outcome

IncorrectCorrect

Levels of

variable X

Width drawn in

proportion to total

number of detections

in this level, relative to

all levels of X

Height drawn

proportional to

number of

incorrect

detections,

relative to all

detections in this

level of X

Height drawn

proportional to

number of correct

detections,

relative to all

detections in this

level of X

Proportion of correct

detections at this level

of X

in the daytime dataset. The data from this participant were identified in the analysis stage. The

analysis was tested for sensitivity to this subset, but it was verified that the conclusions remained

virtually unchanged, with or without these data. For robustness, results in this report include the

data from this participant.

FINDINGS

Detection Time to Correctly Identify Pedestrian Walking Direction

During the daytime, the average detection time to pedestrian direction was 1,137 ms from a

sample of 2,998 correct detections. At night, the average detection time was notably longer—

1,376 ms from a sample of 6,091 correct detections. This roughly represents a 25 percent

increase in detection time at night. Table 13 shows the average detection time for the daytime

data, while table 14 provides the nighttime average detection time for pattern set I. Nighttime

average detection time for pattern set II is in table 15 along with nighttime average for both

pattern sets I and II.

Table 13. Daytime average detection time for set I.

Target

Intensity

(Candela)

Flash

Pattern

Location

of LED

Older Participants

Younger Participants

Combined Participants

Number of

Participants

Average

Detection

Time

(ms)

Number of

Participants

Average

Detection

Time

(ms)

Number of

Participants

Average

Detection

Time

(ms)

600

100

Within

1,356

967

136

1,167

600

Five pulses

Within

1,170

1,015

138

1,097

600

2-5

Above

1,219

929

141

1,075

600

2-5

Below

1,336

1,068

145

1,220

600

Wig-wag

Above

1,197

907

144

1,056

600

Wig-wag

Below

1,200

963

144

1,096

1,400

100

Within

1,311

972

138

1,149

1,400

Five pulses

Within

1,211

968

140

1,095

1,400

2-5

Above

1,339

979

146

1,166

1,400

2-5

Below

1,276

969

144

1,144

1,400

Wig-wag

Above

1,318

960

145

1,145

1,400

Wig-wag

Below

1,247

938

144

1,107

2,200

100

Within

1,311

1,013

142

1,170

2,200

Five pulses

Within

1,286

972

142

1,136

2,200

2-5

Above

1,291

966

148

1,140

2,200

2-5

Below

1,566

1,065

143

1,349

2,200

Wig-wag

Above

1,333

912

147

1,132

2,200

Wig-wag

Below

1,332

1,025

143

1,199

None

Sign

Above

1,190

910

140

1,052

None

Sign

Below

1,240

985

148

1,128

None

Sign

Within

1,145

940

140

1,046

Total

1,601

1,281

1,397

971

2,998

1,137

Note: Flash patterns are defined as follows: 100 = one 100-ms flash pattern; 2-5 = 2-5 flash pattern; wig-wag = wig-wag flash pattern; and sign = no

active LEDs.

Table 14. Nighttime average detection time for set I.

Target

Intensity

(Candela)

Flash

Pattern

Location

of LED

Older Participants

Younger Participants

Combined Participants

Number of

Participants

Average

Detection

Time

(ms)

Number of

Participants

Average

Detection

Time

(ms)

Number of

Participants

Average

Detection

Time

(ms)

600

100

Within

1,781

1,106

151

1,419

600

Five pulses

Within

1,609

1,120

150

1,338

600

2-5

Above

1,525

1,208

152

1,352

600

2-5

Below

1,700

1,270

144

1,464

600

Wig-wag

Above

1,654

1,215

151

1,424

600

Wig-wag

Below

1,900

1,459

151

1,666

1,400

100

Within

1,609

1,184

149

1,380

1,400

Five pulses

Within

1,596

1,147

154

1,351

1,400

2-5

Above

1,511

1,237

149

1,362

1,400

2-5

Below

1,822

1,527

147

1,663

1,400

Wig-wag

Above

1,495

1,240

145

1,358

1,400

Wig-wag

Below

1,870

1,520

137

1,676

2,200

100

Within

1,526

1,098

155

1,291

2,200

Five pulses

Within

1,603

1,231

148

1,399

2,200

2-5

Above

1,623

1,298

149

1,444

2,200

2-5

Below

1,706

1,363

136

1,517

2,200

Wig-wag

Above

1,745

1,277

149

1,500

2,200

Wig-wag

Below

2,567

1,979

128

2,241

None

Sign

Above

1,345

1,173

151

1,250

None

Sign

Below

1,747

1,184

153

1,442

None

Sign

Within

1,500

1,059

154

1,260

Grand Total

1,418

1,680

1685

1,274

3,103

1,459

Note: Flash patterns are defined as follows: 100 = one 100-ms flash pattern; 2-5 = 2-5 flash pattern; wig-wag = wig-wag flash pattern; and sign = no

active LEDs.

Table 15. Nighttime average detection time for set II and combined total for sets I and II.

Target

Intensity

(Candela)

Flash

Pattern

Location

of LED

Older Participants

Younger Participants

Combined Participants

Number of

Participants

Average

Detection

Time

(ms)

Number of

Participants

Average

Detection

Time

(ms)

Number of

Participants

Average

Detection

Time

(ms)

600

100

Within

1,227

1,257

142

1,242

600

125(2)

Above

1,167

1,192

141

1,179

600

125(2)

Below

1,195

1,246

138

1,221

600

125(2)

Within

1,132

1,311

145

1,226

600

2-5

Above

1,254

1,262

148

1,258

600

2-5

Below

1,558

1,383

137

1,461

1,400

100

Within

1,218

1,350

142

1,287

1,400

125(2)

Above

1,170

1,322

145

1,250

1,400

125(2)

Below

1,236

1,302

146

1,269

1,400

125(2)

Within

1,191

1,327

141

1,260

1,400

2-5

Above

1,249

1,303

146

1,278

1,400

2-5

Below

1,479

1,451

137

1,464

2,200

100

Within

1,202

1,242

145

1,222

2,200

125(2)

Above

1,191

1,312

151

1,255

2,200

125(2)

Below

1,394

1,343

139

1,368

2,200

125(2)

Within

1,320

1,470

141

1,399

2,200

2-5

Above

1,304

1,412

144

1,362

2,200

2-5

Below

1,390

1,481

125

1,445

None

Sign

Above

1,224

1,158

147

1,188

None

Sign

Below

1,385

1,214

143

1,294

None

Sign

Within

1,189

1,217

145

1,203

Total Set II

1,415

1,265

1573

1,312

2,988

1,290

Combined Total Sets I and II

2,833

1,473

3258

1,292

6,091

1,376

Note: Flash patterns are defined as follows: 100 = one 100-ms flash pattern; 125(2) = two 125-ms flashes; 2-5 = 2-5 flash pattern; and sign = no

active LEDs.

Box plots were generated to demonstrate trends in the data before conducting the formal

statistical analysis. The plots in figure 15 for daytime and figure 16 for nighttime demonstrate

that detection time tended to be shorter for lower intensity and longer for higher intensity

regardless of the location of the LEDs. This trend was more obvious at night (see figure 16).

The trends held even when the data were sorted by pedestrian position rather than LED locations

(see figure 17 for daytime and figure 18 for nighttime). The groups of boxes clearly tend to be

higher to the right of the plot, which corresponds to higher intensities.

Figure 17 and figure 18 demonstrate a clear trend regarding the pedestrian position in the

crosswalk. The time to correctly identify that there was no pedestrian in the crosswalk appears

similar at different levels of LED intensity and at day and nighttime (i.e., the green boxes). The

median detection time for that case was about 1,200 ms (i.e., the added horizontal line in the

plots). In all other correct responses, it is clear that nighttime had longer times, but the relative

trends appear constant; a pedestrian at the center of the crosswalk triggered faster detections than

either pedestrian at the right or the left side of the crosswalk.

Figure 15. Graph. Daytime detection time by LED location and target intensity.

Figure 16. Graph. Nighttime detection time by LED location and target intensity.

Note: The horizontal line represents the median detection time.

Figure 17. Graph. Daytime detection time by pedestrian position and target intensity.

Note: The horizontal line represents the median detection time with no pedestrian present.

Figure 18. Graph. Nighttime detection time by pedestrian position and target intensity.

It should be noted that the plots make evident the fact that the data are heavily skewed toward

longer detection times, especially at night. This means that the data are more disperse at values

above the median than below the median. To control for this characteristic, the statistical analysis

was performed using the natural logarithm of the detection time. This data transformation

reduced the skewness while preserving the percentile ranks in the data. More details are provided

in the Statistical Analysis section in this chapter.

Accuracy of Detecting Pedestrian Direction

Accuracy of detecting pedestrian direction was determined by the number of participants who

correctly detected the direction of the cutout pedestrian to the number of participants for the

given characteristics (e.g., flash pattern, etc.). Table 16 shows the accuracy rate for daytime,

while table 17 shows similar data for nighttime. During the daytime, the average rate of correct

detections of pedestrian direction was 98 percent from a sample of 3,053 detections. At night, the

average detection rate was notably lower, 93 percent, from a sample of 6,515 detections.

Table 16. Daytime accuracy of correct detection for set I.

Target

Intensity

(Candela)

Flash

Pattern

Location

of LED

Older

Participant

Accuracy

(Percent)

Younger

Participant

Accuracy

(Percent)

All

Participant

Accuracy

(Percent)

Sample

Size

600

Five pulses

Within

140

600

Wig-wag

Above

100

150

600

Wig-wag

Below

149

600

100

Within

141

600

2-5

Above

100

143

600

2-5

Below

100

148

1,400

Five pulses

Within

100

141

1,400

Wig-wag

Above

100

148

1,400

Wig-wag

Below

100

146

1,400

100

Within

140

1,400

2-5

Above

100

148

1,400

2-5

Below

146

2,200

Five pulses

Within

100

142

2,200

Wig-wag

Above

100

147

2,200

Wig-wag

Below

149

2,200

100

Within

100

143

2,200

2-5

Above

150

2,200

2-5

Below

148

None

Sign

Above

100

143

None

Sign

Below

100

150

None

Sign

Within

100

141

Grand Total

3,053

Note: Flash patterns are defined as follows: wig-wag = wig-wag flash pattern; 100 = one 100-ms flash pattern;

2-5 = 2-5 flash pattern; and sign = no active LEDs.

Table 17. Nighttime accuracy of correct detection.

Target

Intensity

(Candela)

Flash

Pattern

Location

of LED

Set I

Set II

All

Participant

Accuracy

(Percent)

Sample

Size

Older

Participant

Accuracy

(Percent)

Younger

Participant

Accuracy

(Percent)

Older

Participant

Accuracy

(Percent)

Younger

Participant

Accuracy

(Percent)

600

Five pulses

Within

160

600

Wig-wag

Above

154

600

Wig-wag

Below

159

600

100

Within

312

600

125(2)

Above

153

600

125(2)

Below

151

600

125(2)

Within

154

600

2-5

Above

100

309

600

2-5

Below

311

1,400

Five pulses

Within

100

159

1,400

Wig-wag

Above

150

1,400

Wig-wag

Below

158

1,400

100

Within

308

1,400

125(2)

Above

154

1,400

125(2)

Below

152

1,400

125(2)

Within

150

1,400

2-5

Above

310

1,400

2-5

Below

313

2,200

Five pulses

Within

160

2,200

Wig-wag

Above

154

2,200

Wig-wag

Below

157

2,200

100

Within

100

313

2,200

125(2)

Above

155

2,200

125(2)

Below

151

2,200

125(2)

Within

153

2,200

2-5

Above

311

2,200

2-5

Below

308

None

Sign

Above

100

312

None

Sign

Below

100

310

None

Sign

Within

314

Grand Total

6,515

Note: Flash patterns are defined as follows: wig-wag = wig-wag flash pattern; 100 = one 100-ms flash pattern; 125(2) = two 125-ms flashes; 2-5 = 2-5 flash

pattern; and sign = no active LEDs.

NS = Flash pattern was not studied within the set.

Mosaic plots were generated to demonstrate trends in the data before conducting a formal

statistical analysis. The plots in figure 19 and figure 20 demonstrate that the percent of

correct detections tends to be lower for higher target intensity at night. This trend is not seen

in the daytime data.

Figure 19. Graph. Daytime correct detection rate by target intensity.

Figure 20. Graph. Nighttime correct detection rate by target intensity.

Figure 21 and figure 22 demonstrate that when subdividing the data by flash pattern, no trend

appeared clear for daytime. It appears that the 2-5 (rapid) and wig-wag patterns tended to have

slightly lower correct detection rates than the rest of patterns for nighttime condition.

Figure 21. Graph. Daytime correct detection rate by flash pattern.

Figure 22. Graph. Nighttime correct detection rate by flash pattern.

Finally, the trends by age are demonstrated by figure 23 for daytime and figure 24 for nighttime.

It seems clear the older participants tended to be less accurate than young participants.

Figure 23. Graph. Daytime detection rate by age.

Figure 24. Graph. Nighttime detection rate by age.

Discomfort

After the participants indicated the direction the cutout pedestrian was traveling, they stated

whether the intensity of the LEDs was comfortable, irritating, or unbearable. As expected,

during the daytime, almost all of the participants were comfortable with the LEDs, as shown

in figure 25 (older drivers) and figure 26 (younger drivers). Only the target intensity of

2,200 candelas was associated with more than a 10 percent level of irritating responses.

Figure 25. Graph. Older driver daytime discomfort rating for set I.

Figure 26. Graph. Younger driver daytime discomfort rating for set I.

During nighttime, more participants considered the LEDs to be unbearable, as illustrated

in figure 27 and figure 28 for set I and figure 29 and figure 30 for set II. Trends in the data

show that a larger proportion of the participants felt the flash patterns with the higher intensities

were irritating or unbearable. Within set I, the wig-wag pattern with a target intensity of

2,200 candelas had the lowest number of participants, indicating it was comfortable for both

older and younger drivers.

Reasons the participants gave an unbearable rating include the following:

• It was almost impossible to see the pedestrian.

• There was too much glare.

• Lights were too distracting.

Figure 27. Graph. Older driver nighttime discomfort rating for set I.

Figure 28. Graph. Younger driver nighttime discomfort rating for set I.

Figure 29. Graph. Older driver nighttime discomfort rating for set II.

Figure 30. Graph. Younger driver nighttime discomfort rating for set II.

Concluding Survey

Queries 1 and 2—Driver Responses

The initial two queries asked the participants to indicate how the driver in the video would react

to the pedestrian attempting to cross at the crosswalk. Table 18 highlights the number of

participants who selected each of the potential responses for both queries 1 and 2 with the

percent of participants shown in parentheses. Figure 31 shows a plot of the findings. For all

of the devices studied, the answer selected by the majority of the participants was “stop.” For

two devices—the wig-wag pattern on the LEDs located below the sign and the sign without any

active LEDs—had about one-third of the participants selecting the “confirm pedestrian is not

crossing” answer while less than 17 percent selected that answer for the other two devices tested.

Stated in another manner, the 2-5 below and the 100 ms within had more correct responses

(“slow” or “stop” and “allow the pedestrian to cross”) than the sign without LEDs or the sign

with the LEDs below in a wig-wag pattern. The multiple flashes within a short time period,

as is present with the 2-5 pattern, may be better at communicating the need to stop for a

yellow device.

Table 18. Results for survey queries 1 and 2.

Flash Pattern

Number of Participants (Percent of Participants)

Slow

Stop

Confirm

Continue

Total

Within; 100

7 (22)

20 (63)

5 (16)

0 (0)

32 (100)

Below; wig-wag

3 (9)

18 (56)

11 (34)

0 (0)

32 (100)

Below; 2-5

5 (15)

23 (67)

6 (17)

0 (0)

34 (100)

Sign

23 (23)

44 (45)

29 (30)

2 (2)

98 (100)

I would slow and allow the pedestrian to cross the roadway.

I would stop and allow the pedestrian to cross the roadway.

I would confirm the pedestrian is not crossing before proceeding.

I would continue driving at the same speed.

Note: Flash patterns are defined as follows: 100 = one 100-ms flash pattern; wig-wag = wig-wag flash pattern;

2-5 = 2-5 flash pattern; and sign = no active LEDs.

Figure 31. Graph. Results for survey queries 1 and 2.

Queries 3 and 4—Flash Pattern and LED Location

Queries 3 and 4 explored whether certain flash patterns and LED locations affected the

participants’ sense of urgency in needing to yield to a pedestrian. Each participant saw two pairs

of videos. The results for queries 3 and 4 were combined for this review along with whether the

sign was shown on the left side or the right side of the screen. To facilitate review of the results,

findings were repeated for each pair combination (e.g., the results were shown for both the

comparison of the within LEDs being on for 100 ms to sign (i.e., no active beacons) as well as

sign (i.e., no active beacons) to within LEDs being on for 100 ms). Table 19 and table 20 show

the results.

Table 19 contains the comparisons between having a sign with no active LEDs and the other

combinations. For all of these comparisons, the majority of the participants selected the device

with an active LED as communicating more urgency for yielding. For the comparison between

sign and either below LEDs with a 2-5 flash pattern or below LEDs with four 25-ms flashes and

one 100-ms flash, all of the participants felt the flashing device communicated a greater urgency

to yield than the sign without an active LED. A majority of the participants (60 percent) felt the

within LEDs with two 125-ms flashes communicated a greater urgency; however, 30 percent felt

that neither device communicated urgency.

Table 19 also shows the results for the comparisons with the devices when the LEDs below

the sign were active. When the 2-5 flash pattern was used with the LEDs below the sign, the

participants felt it communicated a greater urgency as compared to the three patterns tested

with the LEDs within the sign. The 2-5 pattern below the sign was favored by 90 percent of

participants compared to the single 100-ms flash within the sign, by 78 percent compared to

two 125-ms flashes within the sign, and 78 percent compared to four 25-ms flashes and

one 200-ms flash within the sign. The comparison of the 2-5 flash pattern to the flash pattern

with four 25-ms flashes and one 200-ms flash used with the below LEDs showed that the

majority of the participants felt both devices communicated similar urgency (66 percent). The

flash pattern for the below LEDs with four 25-ms flashes and one 200-ms flash was a subset of

the below 2-5 flash pattern in that it was the “5” portion of the 2-5 pattern. Perhaps it was the

multiple pulses that helped to communicate the urgency. The comparison of the 2-5 flash pattern

with the pattern that only had the two pulses (below LEDs with two 125-ms flashes) had fewer

participants feeling that both of these devices communicated a similar urgency (only 33 percent).

This result provides some support that the multiple pulses helped to communicate urgency.

A total of 22 percent of the participants felt the below LEDs with two 125-ms flashes

communicated greater urgency as compared to the below 2-5 flash pattern, which added caution

to the observation that more flashes were associated with greater urgency. The location of the

LEDs may be another factor.

The comparison of the same number of flashes being used at different LEDs locations shows that

participants believed the LEDs below the sign demonstrated more urgency than LEDs within the

sign. For example, when two 125-ms pulses were used, the participants felt the LEDs below

communicated a greater urgency (70 percent). The results for the four 25-ms flashes and

one 200-ms flash also revealed that more participants felt the LEDs below (78 percent) showed

greater urgency.

Within the comparisons of different flash patterns used with the within LEDs (see table 20),

almost all of the participants (80 percent) felt the two-pulse pattern (i.e., two 125-ms pulses) and

the five-pulse pattern (i.e., four 25-ms pulses and one 200-ms pulse) communicated the same

urgency. The participants indicated that the two-pulse pattern (56 percent) communicated greater

urgency over the one-pulse pattern (i.e., within LEDs with one 100-ms flash), or they felt those

two patterns communicated a similar urgency (33 percent).

Table 19. Percent of participants who felt a sense of urgency to yield for signs with no

active LEDs and LEDs below the sign.

Device 1

Device 2

Device 1

Urgent

(Percent)

Device 2

Urgent

(Percent)

Similar

Urgency

Both

Devices

(Percent)

Neither

Device

Conveys

Urgency

(Percent)

Sign

Within; 100

Sign

Within; 125(2)

Sign

Within; 25(4)+200

Sign

Below; 2-5

100

Sign

Below; 125(2)

Sign

Below; 25(4)+200

100

Below; 2-5

Within; one 100

Below; 2-5

Within; 125(2)

Below; 2-5

Within; 25(4)+200

Below; 2-5

Below; 125(2)

Below; 2-5

Below; 25(4)+200

Below; 2-5

Sign

100

Below; 125(2)

Within; one 100

Below; 125(2)

Within; 125(2)

Below; 125(2)

Within; 25(4)+200

Below; 125(2)

Below; 2-5

Below; 125(2)

Below; 25(4)+200

Below; 125(2)

Sign

Below; 25(4)+200

Within; one 100

Below; 25(4)+200

Within; 125(2)

Below; 25(4)+200

Within; 25(4)+200

Below; 25(4)+200

Below; 2-5

Below; 25(4)+200

Below; 125(2)

Below; 25(4)+200

Sign

100

Note: Flash patterns are defined as follows: 100 = one 100-ms flash pattern; 125(2) = two 125-ms flashes;

25(4)+200 = four 25-ms flashes and one 200-ms flash; 2-5 = 2-5 flash pattern; and sign = no active LEDs.

Table 20. Percent of participant results for sense of urgency to yield for LEDs

above the sign.

Device 1

Device 2

Device 1

Urgent

(Percent)

Device 2

Urgent

(Percent)

Similar

Urgency

Both

Devices

(Percent)

Neither

Device

Conveys

Urgency

(Percent)

Within; 100

Within; 125(2)

Within; 100

Within; 25(4)+200

Within; 100

Below; 2-5

Within; 100

Below; 125(2)

Within; 100

Below; 25(4)+200

Within; 100

Sign

Within; 125(2)

Within; 100

Within; 125(2)

Within; 25(4)+200

Within; 125(2)

Below; 2-5

Within; 125(2)

Below; 125(2)

Within; 125(2)

Below; 25(4)+200

Within; 125(2)

Sign

Within; 25(4)+200

Within; 100

Within; 25(4)+200

Within; 125(2)

Within; 25(4)+200

Below; 2-5

Within; 25(4)+200

Below; 125(2)

Within; 25(4)+200

Below; 25(4)+200

Within; 25(4)+200

Sign

Note: Flash patterns are defined as follows: 100 = one 100-ms flash pattern; 125(2) = two 125-ms flashes;

25(4)+200 = four 25-ms flashes and one 200-ms flash; 2-5 = 2-5 flash pattern; and sign = no active LEDs.

Queries 5 and 6—Number of Pulses

For queries 5 and 6, the participants were asked to indicate how many flashes they could see on

the left side and the right side of an active light bar. The research team used the term “flashes”

rather than “pulses” because it has more common usage for the participants. The correct term

would be pulses because “A flash is a light pulse or a train of light pulses, where a dark interval

of at least 160 ms separates the light pulse or the last pulse of the train of light pulses from the

next pulse or the first pulse of the next train of light pulses.”

(15)

(pg. 4)

The 2-5 flash pattern was used and had five pulses on the left side of the light bar and two pulses

on the right side of the light bar. The frequency and percent of the responses by number of pulses

is listed in table 21. One participant said there were eight flashes on each side. The researcher

who worked with that participant believed the participant was counting the number of unique

LEDs within the beacon rather than counting the number of pulses.

The majority of the participants (77 percent) correctly counted two pulses. A few of the

participants (four) correctly counted five pulses. The majority of the participants (55 percent)

saw three pulses when five pulses were present.

Table 21. Number of pulses on light bar.

Number

of Pulses

Response for Side with Five Pulses

Response for Side with Two Pulses

Frequency

Percent

Frequency

Percent

Total

100

Figure 32. Graph. Number of pulses by percent of participants.

STATISTICAL ANALYSIS

The following subsections describe the statistical analyses performed on the data collected. It

should be noted that when the term “significant” appears alone in these subsections, it indicates

statistically significant (i.e., the differences found were unlikely to be a random variation

but a systematic, measurable trend). Whenever appropriate, an explicit distinction is made to

differentiate this term from “practical significance,” which refers to the scale of a difference.

For example, a difference may be found statistically significant in one of the analyses, but its

magnitude could be such that is too small to be considered practically significant.

Pedestrian Detection Time

The data were initially split by daytime and nighttime conditions. Each set was analyzed using

linear mixed effects models (LMMs). These kinds of models combine characteristics from

both linear regression and analysis of variance (ANOVA). The model was specified such that

appropriate accounts were given to the data structure, known associations between variables, and

systematic variation in the response variable. The analysis treated the codependency of data

points from the same drivers including a random effect for each participant in the experiment.

The analysis incorporated fixed effects for other variables of interest. In the case of detection

time, the fixed effect variables were age, intensity, flash pattern, pedestrian height, and

pedestrian position. Estimates, confidence intervals, and conclusions were later extracted for

these effects. Due to the heavy skewness of the data, the analysis was performed over the natural

logarithm of the detection time (see figure 33).

Figure 33. Equation. Natural logarithm of detection time.

Where:

ln(Detection_Time

) = Natural logarithm of detection time for experiment i.

= Vector of variable levels for experiment i.

= Vector of coefficients for all variables in experiment (fixed effects, estimated).

= Random effect for participant j (estimated).

= Residual error for experiment i and subject j.

This statistical specification with a logged response allowed researchers to make inferences

about the median detection time instead of the mean detection time. Because a logarithm

transformation affects the distribution of the residual errors, the transformed mean is not equal to

the original mean. However, an analysis on the transformed mean is equivalent to analyzing the

median of the original scale (i.e., detection time) as long as normality or near-normality is

achieved after the transformation. This is so because the normal distribution is symmetrical

and also because the quartile distribution of the data is not affected by the log transformation.

Therefore, each coefficient in the vector can be interpreted (after exponentiation) as a

multiplicative change on the median detection time per unit of explanatory variable.

All statistical analyses were performed using open-source statistical software. (See

references 42–45.)

Daytime Time to Detect Pedestrian Direction

Table 22 shows the ANOVA on the variables in this analysis. These results indicate that target

intensity had an impact on the detection time after accounting for the rest of variables in the

table. In fact, except for two variables (LED location and lane) all of the experimental factors

had a significant impact on detection time. The variables used in the following tables are defined

as follows:

• Target intensity: Intensity of LEDs (0, 600, 1,400, or 2,200 candelas).

• Flash pattern: LED flash pattern (none, 2-5 flash pattern, wig-wag, one 100-ms flash, or

five pulses).

• LED location: Location of LEDs in the assembly (above sign, below sign, or

within sign).

• Pedestrian height: Height of the pedestrian when present (tall or short).

lnDetection_Time

 = X

× β + α

+ ϵ

• Pedestrian position: Pedestrian position on crosswalk, when present (center, left,

or right).

• Lane: Lane of vehicle with participant (left or right).

• Age: Age of participant (ranging from 19 to 85 years old).

Table 22. Daytime ANOVA for detection time fixed effects.

Variable

Numerator Degrees

of Freedom (DF)

Denominator

F-value

p-value

Reference

2956

10730.10

< 0.0001

Target intensity

2956

22.44

< 0.0001

Flash pattern

2956

4.74

0.0008

LED location

2956

0.85

0.4270

Pedestrian height

2956

19.59

< 0.0001

Pedestrian position

2956

95.61

< 0.0001

Lane

2.11

0.1571

Age

7.37

0.0112

The coefficient estimates for daytime are shown in table 23. The comparison of flash patterns

shows that 2-5 flash pattern produced longer detection times, though the p-values should be

adjusted for multiple comparisons. The reference levels used for the base model in this study

were no flash pattern, LED location is above, pedestrian height is tall, pedestrian position is

none, and lane is left. The coefficient for target intensity in table 23 is small and statistically

insignificant. The trend, however, is positive as is the trend at night (discussed in following

subsection). Therefore, the no significance could be explained by the effect being smaller than

the statistical power in the study. Indeed, examining the magnitude of the effect this coefficient

implies, all other factors held equal (i.e. flash pattern, LED location in assembly, pedestrian

height, pedestrian position, lane, and age of participant), the median response time increased by

0.00114 percent per additional candela of intensity (i.e., exp(0.0000114) = 1.0000114), which is

about one-fourth of the magnitude of the same trend at night.

Table 23. Daytime detection time fixed effects coefficients.

Coefficient

Value

Standard Error

t-value

p-value

Reference

6.39000

0.21700

2956

29.5039

< 0.0001

Target intensity

0.00001

2956

1.6846

0.0922

Flash

pattern

2-5

0.05040

0.01800

2956

2.8054

0.0051

100

0.04340

0.02380

2956

1.8231

0.0684

Five flashes

0.04320

0.02380

2956

1.8135

0.0699

Wig-wag

0.02260

0.01800

2956

1.2548

0.2097

LED

location

Below

0.00686

0.01000

2956

0.6850

0.4934

Within

-0.00422

0.02370

2956

-0.1783

0.8585

Pedestrian height—short

0.03850

0.00825

2956

4.6637

< 0.0001

Pedestrian

position

Center

-0.22200

0.01320

2956

-16.8046

< 0.0001

Left

-0.16700

0.01320

2956

-12.6137

< 0.0001

Right

-0.16400

0.01330

2956

-12.3883

< 0.0001

Lane—right

0.17300

0.13400

1.2902

0.2075

Age

0.00947

0.00349

2.7139

0.0112

Reference level used in model for each categorical variable base value: flash pattern = sign, LED location = above,

pedestrian height = tall, pedestrian position = no pedestrian, and lane = left.

p-values for discrete factors with three or more levels need a multiple comparison adjustment.

Note: Flash patterns are defined as follows: 2-5 = 2-5 flash pattern; 100 = one 100-ms flash pattern; and wig-wag =

wig-wag flash pattern.

The adjusted comparisons, shown in table 24, constitute evidence of the 2-5 flash pattern being

the only flash pattern with statistically significantly longer detection times than no LEDs flashing

during daytime conditions.

Table 24. Daytime simultaneous tests for general linear hypothesis of detection time flash

pattern effects.

Condition 1

Condition 2

Estimate

Standard

Error

z-value

p-value

Significance

2-5 flash pattern

None

0.05037

0.01796

2.805

0.0175

Wig-wag

None

0.02264

0.01805

1.254

0.5029

One 100-ms flash

None

0.04337

0.02379

1.823

0.1952

Five flashes

None

0.04323

0.02384

1.813

0.1986

Estimate is the difference between fixed effects coefficients corresponding to conditions 1 and 2.

Adjusted p-values were reported using a single-step method.

Significance values are as follows: blank cell = p > 0.10; ~ = p < 0.10; * = p < 0.05; ** = p < 0.01; and

*** = p < .0.001.

Each row in in table 24 represents a scientific hypothesis being tested statistically from the

model results. For example, the first row of this table corresponds to the hypothesis that the

natural logarithm of the average detection time under the 2-5 flash pattern is no different from

the natural logarithm of the detection time when no flashing is present. The estimate for the

difference of natural logarithm of detection time under each of these conditions is shown under

the column titled “Estimate.” The last four columns provide the basis for the assessment of the

statistical significance of said difference in natural logarithm of detection time.

For the four intensity levels used in the study, table 25 shows that the magnitude of the intensity

effect on median detection time is negligible during the daytime.

Table 25. Daytime magnitude of detection time intensity effect.

Target Intensity

(Candela)

Estimated Increase in Median

Detection Time (Percent)

0 (reference level)

600

0.7

1,400

1.6

2,200

2.4

There was a moderate impact of pedestrian height in detection time. Using the corresponding

coefficient in table 23, results indicate that there was a 3.9 percent increase in detection time

when using a short pedestrian cutout instead of a tall pedestrian cutout.

Nighttime Time to Detect Pedestrian Direction

Table 26 shows the ANOVA on the variables of interest. The results indicate that at night, all of

the experimental factors had a significant impact on detection time except for lane.

Regarding flash patterns, the 2-5 flash pattern is again the pattern that triggered longer detection

times. For nighttime, wig-wag was also associated with longer detection times.

Table 26. Nighttime ANOVA for detection time fixed effects.

Variable

Numerator DF

Denominator DF

F-value

p-value

Reference

6,016

39772.22

< 0.0001

Target intensity

6,016

85.61

< 0.0001

Flash pattern

6,016

30.74

< 0.0001

LED location

6,016

73.12

< 0.0001

Pedestrian height

6,016

22.06

< 0.0001

Pedestrian position

6,016

149.86

< 0.0001

Lane

1.46

0.2322

Age

8.74

0.0045

The coefficient estimates for nighttime are shown in table 27.

Table 27. Nighttime fixed effects coefficients for detection time.

Coefficient

Value

Standard Error

t-value

p-value

Reference

6.6900

0.1060

6,016

63.2133

0.0000

Target intensity

0.0000

6,016

5.9727

0.0000

Flash

pattern

2-5

0.0580

0.0154

6,016

3.7787

< 0.00

100

-0.0328

0.0184

6,016

-1.7841

0.0745

125(2)

-0.0077

0.0162

6,016

-0.4770

0.6330

Five pulses

-0.0172

0.0213

6,016

-0.8106

0.4180

Wig-wag

0.1280

0.0175

6,016

7.3196

< 0.001

LED

location

Below

0.1160

0.0091

6,016

12.7706

< 0.001

Within

0.0582

0.0139

6,016

4.1920

< 0.001

Pedestrian height—short

0.0357

0.0075

6,016

4.7693

0.0000

Pedestrian

position

Center

-0.1310

0.0118

6,016

-11.1563

< 0.001

Left

0.0490

0.0119

6,016

4.1094

< 0.001

Right

0.0465

0.0119

6,016

3.9206

< 0.001

Lane—right

0.0950

0.0717

1.3252

0.1900

Age

0.0056

0.0019

2.9558

0.0045

Reference level used in model for each categorical variable base value: flash pattern = sign, LED location =

above, pedestrian height = tall, pedestrian position = no pedestrian, and lane = left.

p-values for discrete factors with three or more levels need a multiple comparison adjustment.

Note: Flash patterns are defined as follows: 2-5 = 2-5 flash pattern; 100 = one 100-ms flash pattern; 125(2) =

two 125-ms flashes; and wig-wag = wig-wag flash pattern.

Table 28 shows the differences among flash patterns with statistical significance adjusted for

multiple comparisons.

Table 28. Nighttime simultaneous tests for general linear hypothesis of flash patterns on

detection time.

Condition 1

Condition 2

Estimate

Standard

Error

z-value

p-value

Significance

2-5 flash pattern

None

0.058007

0.015354

3.778

< 0.001

***

Wig-wag

None

0.12806

0.017492

7.321

< 0.001

***

One 100-ms flash

None

-0.03284

0.018403

-1.784

0.248

Two 125-ms flashes

None

-0.00776

0.016156

-0.48

0.983

Five pulses

None

-0.0172

0.021267

-0.809

0.871

Estimate is the difference between fixed effects coefficients corresponding to conditions 1 and 2.

Adjusted p-values were reported using a single-step method.

Significance values are as follows: blank cell = p > 0.10; ~ = p < 0.10; * = p < 0.05; ** = p < 0.01; and *** = p < 0.001.

There was statistical evidence of the 2-5 flash pattern delaying detection. The magnitude of this

delay was very similar to the daytime delay (6 percent increase in median detection time at night

versus 5.2 percent at daytime). However, at night, there was also evidence that the wig-wag flash

pattern also delayed pedestrian detection. This delay is a substantial increase in detection time.

Other variables being equal, median detection time was 13.7 percent longer for the wig-wag

pattern than the median detection time with no LEDs active (i.e., 13.7 percent=[exp(0.12806) –

1.0] × 100 percent).

The coefficient for target intensity at night was highly significant as opposed to being

insignificant during the daytime. This coefficient was also larger at night by a factor of about 3.7

compared to the daytime. It is estimated that the median response time increased by

0.00369 percent per additional candela of intensity (i.e., exp(0.0000369) = 1.0000369) after

controlling for other experimental factors. Table 29 shows the magnitude of the estimated impact

of LED intensity. These magnitudes were substantial increases in median detection time.

Table 29. Nighttime magnitude of intensity effect on detection time.

Target Intensity

(Candela)

Estimated Increase in Median

Detection Time (Percent)

0.0 (reference level)

600

2.2

1,400

5.3

2,200

8.5

There was a moderate impact of pedestrian height on detection time. Using the coefficient in

table 27, results indicate that there was a 3.6 percent increase in detection time when using a

short pedestrian cutout instead of a tall pedestrian cutout.

Table 30 shows the relative effects of LED location with statistical significance adjusted for

simultaneous comparisons. The shortest nighttime detection times occurred when the LEDs were

located above the sign. Using this position as a reference level, the median detection time was

6 percent longer when the LEDs were located within the sign and 12.3 percent longer when the

LEDs were located below the sign as compared to above. Finally, a comparison between LEDs

within the sign against LEDs below the sign indicated that the median detection time was

6 percent longer at LEDs below the sign. All three contrasts were statistically significant.

Table 31 summarizes these findings.

Table 30. Nighttime simultaneous tests for effect of LED location on detection time.

Condition 1

Condition 2

Estimate

Standard

Error

z-value

p-value

Significance

Below

Above

0.116373

0.009112

12.771

< 0.001

***

Within

Above

0.058172

0.013877

4.192

< 0.001

***

Below

Within

0.058202

0.013906

4.185

< 0.001

***

Estimate is the difference between fixed effects coefficients corresponding to conditions 1 and 2.

Adjusted p-values were reported using a single-step method.

Significance values are as follows: blank cell = p > 0.10; ~ = p < 0.10; * = p < 0.05; ** = p < 0.01; and *** = p < 0.001.

Table 31. Nighttime magnitude of LED location effect on detection time.

LED Location

Estimated Increase in Median

Detection Time (Percent)

Above

0.0 (reference level)

Within

6.0

Below

12.3

Key Findings Regarding Detection Time

For the analysis focusing on detection time, (i.e., the time it took participants to indicate the

direction of the cutout pedestrian from the moment the occlusion glasses were cleared), results

indicate the following:

• As expected, detection time was longer at night than during the day.

• The age of the participants had an impact on detection time, both during the day and at

night. Detection time for younger participants was shorter than detection time for older

participants. It is estimated that there was an increase of 1 percent in median detection

time per year of age during the daytime. At night, age made less of a difference. The

corresponding estimate for this effect was an increase of 0.5 percent in median detection

time per year of age.

• Pedestrian height had a very similar impact on detection time during the day and at night.

Detection time was longer when the pedestrian cutout was short rather than tall. Results

indicate an increase in median detection time of 3.9 percent during the daytime and an

increase of 3.6 percent at night for the short pedestrian cutout compared to a tall cutout.

• In the case of pedestrian position, trends were the same for daytime and nighttime:

detection times were longer when the pedestrian cutout was located on either side of the

crosswalk compared to when it was located at the center of the road.

• Flash pattern also had a significant impact on detection time, though most of the

differences between the different flash patterns were not statistically significant. Only

two flash patterns produced significantly longer detection times than the base level of no

flash pattern: the 2-5 flash pattern (used above and below sign) used during the day and at

night and the wig-wag pattern (used above and below sign) at night only. Compared to no

active flash pattern, there was an increase in median detection time for the 2-5 flash

pattern by 5.2 percent during the day and of 6 percent at night. The wig-wag pattern

caused an increase in median detection time of 13.7 percent.

LED location had a significant impact at night but not during the day. At night, detection was

fastest when the LEDs were above the signs after controlling for other factors. Compared to the

above sign LED location, the median detection time increased by 6 percent when the LEDs were

within the sign and increased by an additional 6 percent when the LEDs were below the sign for

a total increase of 12.3 percent when the below location was compared to the above location.

Accuracy of Detecting Pedestrian Direction

Similar to the analysis of detection time, the data were split by daytime and nighttime conditions.

In the detection time analysis, only data representing the correct responses by the participant

were utilized. For the accuracy analysis; however, the dataset also included the instances when

participants indicated the incorrect walking direction. This analysis used both subsets (i.e.,

correct and incorrect answers) to evaluate changes in the rate of correct to total answers due to

the different variables considered in the experimental design.

The analysis of the resulting dataset was performed on the framework of generalized linear

mixed effects models (GLMMs). Similarly to LMMs, these kinds of models combine

characteristics from both generalized linear regression and analysis of deviance. The analysis

treated the co-dependency of data points from the same drivers including a random effect for

each participant in the experiment. In doing so, the model gave an appropriate account to the

correlation structure in the data. The model also included a simultaneous parametric estimation

for the variables of interest (i.e., as fixed effects).

Similar to the analysis of detection time, the fixed effect variables were age, intensity, flash

pattern, pedestrian height, and pedestrian position. Estimates, confidence intervals, and

conclusions were later extracted for these effects. The formal specification of the statistical

model for accuracy analysis is shown in figure 34.

Figure 34. Equation. Accuracy analysis.

Where:

Logit = Logit transformation, such that Logit(x) = Ln[x/(1 − x)].

Accuracy_Rate

= Accuracy rate at experiment i for participant j.

= Vector of k variables whose levels are set for experiment i.

Since each experiment was only recorded once per participant, the best estimator for the

accuracy rate was a binary variable, Z, representing the outcome of 1 if the response was correct,

0 otherwise. This variable was utilized as the response in the analysis. To make this a statistical

model, an assumption that Z was binomially distributed was made, as shown in figure 35.

Figure 35. Equation. Accuracy rate.

The accuracy rate in the equation shown in figure 30 was estimated in the model. The known

parameter n is the number of valid data points for each experiment/participant combination,

which equals 1 in this study. The experiment design was such that only one response was

obtained per experiment/participant combination. The model is then as shown in figure 36.

Figure 36. Equation. Logit model.

Where:

X' = Vector of explanatory variables.

= Random effect, estimated for each participant.

The variables and in the equation shown in figure 36 were estimated by maximum

likelihood.

LogitAccuracy_Rate

 = X'

× β + α

Z~Binomial

(

Accuracy_Rate, n = 1

)

Logit

(

)

= X' × β + α

Model Interpretation

The statistical specification linked the logit of the accuracy rate to a linear combination of the

factors in the experiment design. Because the logit transformation is the natural logarithm of the

odds of correct responses, inferences about the impact of changing experimental factors X to the

accuracy rate should be made as follows: a marginal change of one variable in the linear

predictor represents a multiplicative change in the odds of participant j correctly identifying

the pedestrian direction. For experimental factor X

, with two levels, A and B, the equation in

figure 37 defines the odds ratio that corresponds to coefficient, .

Figure 37. Equation. Odds ratio corresponding to levels A and B of factor X

Where:

= Odds of level A of factor x

= Odds of level B of factor x

The equation in figure 37 indicates that the odds ratio (i.e., the ratio of odds of correct answers at

level A to odds of correct answers at level B) is the exponential of the difference between levels

multiplied by the corresponding regression coefficient. All statistical analyses were performed

using R, an open-source statistical software. (See references 42–45.)

Daytime Accuracy of Detecting Pedestrian Direction

Table 32 shows the proportion of deviance corresponding by each variable in the analysis (i.e.,

analogous to an ANOVA table). This table indicates that target intensity had little or no impact

on accuracy after accounting for the rest of the variables in the experiment design. Similarly,

flash pattern did not have any influence on the odds of correctly detecting the pedestrian cutout.

In contrast, this table shows that pedestrian position and participant age were the only

two variables that were influential to accuracy of pedestrian detection. Specific coefficient

estimates for daytime are shown in table 33.

Table 32. Daytime analysis of deviance for accuracy fixed effects.

Variables

Numerator DF

Sum of Squares

Mean Squares

F-value

p-value

Target intensity

0.812

0.367

Flash pattern

5.266

1.316

0.261

LED location

1.370

0.685

0.504

Pedestrian height

1.934

0.164

Pedestrian position

10.004

3.335

0.019

Lane

1.230

0.267

Age

12.130

< 0.001

The statistics in this table are based on maximum likelihood estimates convergence to normality by virtue of the law of

large numbers. Therefore, p-values were obtained from the limit case when DF in the denominator tends to infinity.

In table 33, only two coefficients were statistically significant: age of the participants and the

position of the cutout when it faced to the right. The coefficient for age indicates that there was

exp β

(

A − B

)

=

i1 = A

= B

= 

= 

an inverse relationship between age and accuracy, as was expected. Except for the variables

explicitly depicted, the rates in figure 38 correspond to the reference levels listed in table 33.

From figure 38, it is evident that accuracy at daytime was high in general, with about 3 percent

reduction in accuracy rates for the oldest participants when compared to the youngest

participants in the study.

For the second statistically significant variable (i.e., pedestrian position to the right), the

three p-values in table 33 should be adjusted for multiple comparisons. Table 34 shows multiple

comparisons of interest and corresponding adjusted p-values. Although the trends are similar

when comparing left and right sides with the center position, only the comparison between right

side and center positions offers suggestive evidence of a real difference.

Table 33. Daytime accuracy fixed effects coefficients.

Daytime Coefficients

Value

Standard

Error

z-value

p-value

Reference

9.5480

1.3160

7.258

< 0.001

Target intensity

0.0004

0.0002

1.640

0.101

Flash pattern

2-5

-0.5800

0.6180

-0.938

0.34

100

-1.2440

1.1540

-1.079

0.281

Five flashes

-0.2500

1.2490

-0.200

0.841

Wig-wag

-0.9990

0.6120

-1.631

0.103

LED location

Below

-0.3600

0.3320

-1.085

0.278

Within

0.2380

1.1790

0.202

0.840

Pedestrian height—short

-0.4350

0.2990

-1.453

0.146

Pedestrian

position

Center

-0.8780

0.8170

-1.075

0.282

Left

-1.5140

0.7840

-1.931

0.054

Right

-1.8500

0.7730

-2.391

0.017

Lane—right

-0.3470

0.4520

-0.768

0.442

Age

-0.0490

0.0140

-3.483

< 0.001

Reference level used in model for each categorical variable base value: flash pattern = sign, LED

location = above, pedestrian height = tall, pedestrian position = no pedestrian, and lane = left.

p-values for discrete factors with three or more levels need a multiple comparison adjustment.

Note: Flash patterns are defined as follows: 2-5 = 2-5 flash pattern; 100 = one 100-ms flash pattern; and

wig-wag = wig-wag flash pattern.

Table 34. Daytime simultaneous tests for general linear hypothesis of flash pattern

accuracy effects.

Condition 1

Condition 2

Estimate

Standard

Error

z-value

Pr (> |z|)

Significance

Odds

Ratio

Center

No pedestrian

-0.878

0.817

-1.075

0.6516

0.416

Left Side

Center

-0.636

0.419

-1.520

0.3660

0.529

Right Side

Center

-0.972

0.399

-2.438

0.0534

0.378

Both Sides

Center

-0.804

0.3735

-2.153

0.1073

0.448

Left Side

Right Side

0.3355

0.3324

1.009

0.6948

1.399

Estimate is the difference between fixed effects coefficients corresponding to conditions 1 and 2.

Adjusted p-values were reported using a single-step method.

Significance values are as follows: blank cell = p > 0.10; ~ = p < 0.10; * = p < 0.05; ** = p < 0.01; and *** = p < 0.001.

Figure 38 and figure 39 show accuracy rates by pedestrian cutout position and age after

accounting for other experimental factors. The extreme difference in accuracy curves when the

pedestrian cutout is present is between the center and right-side positions as shown in figure 39.

This difference was negligible for younger drivers and was a modest 3 percent for older drivers.

Figure 38. Graph. Daytime estimated accuracy rate by age and pedestrian position.

20 30

50 60 70 80 90

0 20 40 60 80 100

Age (years of age)

Accuracy Rate (%)

Ped Position

None

Center

Left-Side

Right-Side

Figure 39. Graph. Close-up view of daytime estimated accuracy rate by age and pedestrian

position.

Nighttime Accuracy of Detecting Pedestrian Direction

Table 35 shows the analysis of deviance of the model in this analysis. These results indicate that

at night, all the experimental factors have a significant impact on the accuracy rate, except for

lane. The coefficient estimates for nighttime are shown in table 36.

Table 35. Nighttime analysis of deviance for accuracy fixed effects.

Variables

Numerator

Sum of

Squares

Mean

Squares

F-value

p-value

Target intensity

14.174

< 0.001

Flash pattern

13.470

2.694

0.019

LED location

50.289

25.145

< 0.001

Pedestrian height

15.686

< 0.001

Pedestrian position

95.724

31.908

< 0.001

Lane

1.363

0.243

Age

11.166

< 0.001

The statistical quantifiers in this table are based on maximum likelihood estimates convergence to normality

by virtue of the law of large numbers. Therefore, p-values were calculated in the limit when DF in the

denominator tended to infinity.

Table 36. Nighttime accuracy fixed effects coefficients.

Nighttime Coefficients

Value

Standard

Error

t-value

p-value

Reference

8.2800

0.6580

12.575

< 0.0001

Target intensity

-0.0003

0.0001

-2.789

0.0053

Flash pattern

2-5

-0.2810

0.2440

-1.149

0.2506

100

0.3250

0.3020

1.073

0.2831

125(2)

0.2070

0.2590

0.802

0.4223

Five flashes

0.1160

0.3430

0.338

0.7357

Wig-wag

-0.4010

0.2750

-1.458

0.1447

LED

location

Below

-1.0900

0.1410

-7.762

< 0.0001

Within

-0.7780

0.2320

-3.349

0.0008

Pedestrian height—short

-0.4540

0.1160

-3.918

0.0001

Pedestrian

position

Center

-1.8900

0.4090

-4.625

< 0.0001

Left

-2.9200

0.4000

-7.305

0.0000

Right

-2.8400

0.4010

-7.101

0.0000

Lane—right

0.3300

0.3200

1.031

0.3025

Age

-0.0283

0.0085

-3.342

0.0008

Reference level used in model for each categorical variable base value: flash pattern = sign, LED

location = above, pedestrian height = tall, pedestrian position = no pedestrian, and lane = left.

p-values for discrete factors with three or more levels need a multiple comparison adjustment.

Note: Flash patterns are defined as follows: 2-5 = 2-5 flash pattern; 100 = one 100-ms flash pattern;

125(2) = two 125-ms flashes; and wig-wag = wig-wag flash pattern.

There was no evidence of a difference in accuracy of answers due to different flash patterns after

accounting for other relevant factors. Compared to the no flash pattern, only the 2-5 and wig-wag

flash patterns seemed to have hindered accuracy (i.e., negative coefficients), but the data did

not offer statistical evidence that these differences in fact diverged from zero. However, it is

interesting that these two flash patterns were the same for which the analysis of detection time

found evidence of being counterproductive.

Also, similar to the results of the detection time analysis, the accuracy analysis found that

intensity of the LEDs had an adverse effect. Using the base conditions from table 36, figure 40

and figure 41 demonstrate in absolute terms the impact of target intensity across the range of

ages of participants. Also shown in figure 41 is the significant impact of participant age in

accuracy of detection. Table 37 shows the odds ratios for the four intensity levels in this study.

This table demonstrates that the odds of correct detections fell with increasing intensity.

Figure 40. Graph. Nighttime estimated accuracy rate by age and LED intensity.

Figure 41. Graph. Close-up view of nighttime estimated accuracy rate by age and LED

intensity.

20 30 40 50 60 70

0 20 40 60 80 100

Age (years of age)

Accuracy Rate (%)

Target Intensity

0 cd

600 cd

1400 cd

2200 cd

Table 37. Nighttime odds ratio of correct detection intensity levels.

Target Intensity

(Candela)

Odds Ratio

(Correct Detections)

1.00 (reference level)

600

0.86

1,400

0.71

2,200

0.58

Table 38 shows the relative effects of pedestrian location with statistical significance adjusted for

simultaneous comparisons. Compared to pedestrian in the center of the crosswalk, placing the

pedestrian on either side caused a significant drop in the odds of accurate answers. The

difference between left and right sides was found to be not significant.

Table 38. Nighttime simultaneous linear hypotheses for pedestrian position effect on

accuracy.

Condition 1

Condition 2

Estimate

Standard

Error

z-value

Pr( >|z|)

Significance

Odds

Ratio

Center

No pedestrian

-1.88952

0.40859

-4.625

< 0.001

***

0.151

Left side

Center

-1.03111

0.15249

-6.762

< 0.001

***

0.357

Right side

Center

-0.95434

0.15456

-6.175

< 0.001

***

0.385

Both sides

Center

-0.99272

0.13994

-7.094

< 0.001

***

0.371

Left side

Right side

-0.07677

0.12629

-0.608

0.914

0.926

Estimate is the difference between fixed effects coefficients corresponding to conditions 1 and 2.

Adjusted p-values were reported using a single-step method.

Significance values are as follows: blank cell = p > 0.10; ~ = p < 0.10; * = p < 0.05; ** = p < 0.01; and *** = p < 0.001.

Table 39 shows the relative effects of LED location with statistical significance adjusted for

simultaneous comparisons. The most accurate detections at night occurred, as well as the shortest

detection times, when the LEDs were located above the sign. Other variables kept equal, the

odds of accurate detection with LEDs below the sign were about one-third of the odds with

LEDs above sign. The data did not provide evidence supporting any significant difference in

odds of accurate detection when comparing LEDs located below and within the sign.

Table 39. Nighttime simultaneous linear hypotheses on LED location effect on accuracy.

Condition 1

Condition 2

Estimate

Standard

Error

z-value

Pr( >|z|)

Significance

Odds

Ratio

Below

Above

-1.0943

0.141

-7.762

< 0.001

***

0.335

Within

Above

-0.7783

0.2324

-3.349

0.00206

0.459

Below

Within

-0.316

0.2161

-1.462

0.30099

0.729

Estimate is the difference between fixed effects coefficients corresponding to conditions 1 and 2.

Adjusted p-values were reported using a single-step method.

Significance values are as follows: blank cell = p > 0.10; ~ = p < 0.10; * = p < 0.05; ** = p < 0.01; and *** = p < 0.001.

Key Findings Regarding Accuracy of Detection

For the analysis focusing on accuracy of detection, results indicate the following:

• As expected, detection accuracy was higher during the daytime than at night.

• The age of participants had an impact on accuracy, both during the daytime and at night.

Accuracy at daytime was high in general. The analysis indicated, however, a measurable

but small reduction in accuracy by age. For example, it was estimated that there was a

difference of about 3 percent in accuracy rate between the oldest participants (85 years

old) and the youngest participants (19 years old) when they were presented with no

flashing LEDs and the tall pedestrian was placed at either the right or the left side.

Similarly, the age of the participant had a significantly larger effect under nighttime

conditions compared to daytime conditions. For the same scenario described (i.e., no

LED and the tall pedestrian cutout positioned at either the right or the left side of the

crosswalk) the difference between accuracy rates of a participant 85 years old and

another participant 19 years old was estimated at about 8 percent.

• Regarding pedestrian height, accuracy was higher when the pedestrian cutout was tall

compared to when it was short.

• Pedestrian position had an impact on accuracy. The evidence for this effect was strong at

night but only suggestive during the daytime. In general, locating the pedestrian at the

center of the crosswalk had more accurate responses compared to when the cutout was

located at either side of the crosswalk. In particular for daytime, only the difference

between right-side and center position had a statistical significance, and it was minimal.

For nighttime, however, the trends were the same, but the data provided convincing

evidence of an impact of pedestrian position: lower accuracy could be attributed to

placing the pedestrian cutout on either side of the crosswalk (i.e., closer to the LED

assembly) compared to the center position. This finding suggests that being further away

from the active LEDs makes accurately detecting pedestrian walking direction easier. As

with daytime, no statistical difference was found between the left and right sides at night.

• No evidence was found of the flash pattern having a significant impact on accuracy

during the day and at night.

• LED location had a significant impact on accuracy at night but not during the day. At

night, detection was most accurate when the LEDs were above the signs after controlling

for other factors. Placing the LEDs within the sign led to slightly better accuracy rates

than the below location, which was similar to the trends observed for detection time

across LED locations. However, this small difference in accuracy rates was not found

statistically significant.

Discomfort Glare

The discomfort data obtained from participants’ responses were categorized by daytime and

nighttime conditions for the analysis. Similar to the analysis of detection times, the only

discomfort data used in the analysis were those provided after a correct response was given

on pedestrian direction. The discomfort analysis statistically evaluated the changes in the

expressed discomfort that could be attributed to the different variables considered in the

experimental design.

The analysis used GLMMs to account for repeated measures taken from the same participants.

Similar to the previous two analyses, variables age, intensity, flash pattern, pedestrian height,

and pedestrian position were coded as fixed effects, with their corresponding standard errors and

confidence intervals. Random intercepts per participant were included as a random effect to

induce the correlation expected between all responses from each participant.

The discomfort level expressed by the participants could be described as a discrete partition of

a continuous non-observable variable that indicated true discomfort. In other words, the true

discomfort experienced was ideally a continuous, monotonic function. The goal of the analysis

was, in essence, to characterize the relationship between the unobserved real discomfort and

the three-level, discrete variable corresponding to the question asked to participants after each

experiment (where the only possible answers were comfortable, irritating, and unbearable).

The relationship between the true and discrete discomfort variables can be idealized by the plot

shown in figure 42 for a given level of an experimental factor:

Figure 42. Graph. Idealized relationship between discrete and real discomfort scales.

It can be logically concluded from this graph that the cumulative frequency of answers from all

participants defines incremental thresholds in the real discomfort level. Therefore, the statistical

analysis quantified how the thresholds changed in response to the variability of the factors in the

experiment design. These changes should directly correspond to changes in the idealized

continuous discomfort scale.

Given that the odds corresponding to the cumulative frequency at any of the two thresholds are

proportional to the odds corresponding to the cumulative frequency at the other threshold, then

Cumulative Frequency

Real Discomfort Level

P(‘comfortable’)+P(‘Irritating’)

P(‘comfortable’)

P(‘comfortable’)+P(‘irritating’) +P(‘unbearable’) = 1.0

Irritating threshold

Comfortable range

Irritating range

Unbearable range

Unbearable threshold

any of these cumulative frequencies can be related to the explanatory factors, as shown in

figure 43.

Figure 43. Equation. Cumulative frequency.

Where:

Cumulative_frequency

dij

= Cumulative frequency of answers below threshold d in the discretized

scale of discomfort at experiment i for participant j.

= Log-odds of the threshold for discomfort level d at base conditions (estimated).

Model Interpretation

The statistical specification linked the logit of the cumulative rate in the discomfort scale to a

linear combination of the factors in the experiment design. Because the logit transformation is

the natural logarithm of the odds, inferences about the impact of changing experimental factors X

to the discomfort rate cannot be made in a linear fashion. Instead, inferences should be made

similar to interpreting a logistic model as follows: for a given threshold d, a positive marginal

change of one variable in the linear predictor represents a multiplicative increase in the odds of

the participant indicating any higher level of discomfort compared to the odds of any lower level

of discomfort. In other words, for a given threshold d, a positive coefficient indicates an increase

in the odds of indicating any higher level of discomfort at that threshold. Similarly, a negative

coefficient indicates a decrease in the odds of any higher level of discomfort at that threshold.

For experimental factor X

with two levels, A and B, the equation is shown in figure 44.

Figure 44. Equation. Odds ratio for levels A and B of variable X

at a maximum level of

discomfort.

The quantities in parenthesis shown in figure 44 are odds ratios (i.e., the ratio of odds of level of

discomfort d or below at X

= A to odds of level of discomfort d or below at X

= B). This

relationship implies that the odds ratio at both thresholds should be proportionally related to

changes in all factors in the experimental design. Such condition is a critical assumption of the

model. Re-expressing the last equality yields an equivalent form that can be used to verify the

proportional odds assumption of the model, as shown in figure 45.

Figure 45. Equation. Revised odds ratio.

The researchers verified that this equality reasonably held for various marginal odds obtained

from partitioning the data by levels of the variables of interest (i.e., intensity and flash pattern) as

well as by the age of participants. Therefore, the researchers found this model specification

appropriate for analyzing the discomfort response. All statistical analyses were performed using

R, an open-source statistical software. (See references 42–46.)

Logit Cumulative_frequency

dij

 = θ

− X'

× β + α



exp-β

[

A − B

]

= 

≤ 1,X

= A

d ≤ 1,X

= B



= 

d ≤ 2,X

= B







d ≤ 1,X

= A

d ≤

2,X

= A

 = 

≤ 1,X

= B

d ≤ 2,X

= B

 



X in the study

Daytime Discomfort

As an alternative to an analysis of deviance, Table 40 shows the likelihood ratio tests for models

that incrementally add each experimental variable in the analysis.

Table 40. Daytime likelihood ratio tests for incremental discomfort fixed effects.

Variable

Log

Likelihood

Chi-Squared

Statistic

p-value

Reference

N/A

-248.5

N/A

Target intensity

-230.96

35.0783

< 0.001

Flash pattern

-193.54

74.8384

< 0.001

LED location

-193.5

0.0775

0.96198

Pedestrian height

-192.82

1.3694

0.24192

Pedestrian position

-189.57

6.5003

0.08965

Lane

-189.54

0.0463

0.82961

Age

-189.53

0.0165

0.89784

The statistical quantifiers in this table were based on the expected convergence to normality of

the log-likelihood function by virtue of the law of large numbers.

N/A = Not applicable.

Table 40 indicates that, after accounting for target intensity and flash pattern, little or no gains in

explanatory power resulted from including additional variables. A notable exception in this table

is the inclusion of pedestrian position; it showed a minor improvement that is barely statistically

insignificant. However, the deviances should be taken as a preliminary assessment of the

importance of variables in the analysis. To draw conclusions, specific coefficient estimates for a

daytime model accounting for all variables simultaneously were obtained, as shown in table 41.

The only coefficient statistically significant in table 41 corresponds to LED target intensity. This

is not surprising, given that the vast majority of discomfort answers during daytime were

comfortable. This resulted in a problematic statistical estimation of the first two coefficients in

the table (i.e., the discomfort thresholds). Even though the information about the thresholds of

the discomfort scale is limited, there is strong evidence of the discomfort increasing with

increasing intensity (per the target intensity coefficient in the table). All other factors held equal

(i.e., flash pattern, LED location in assembly, pedestrian height, pedestrian position, lane, and

age of participant), the odds of a higher level of discomfort increased by 0.089 percent per

additional candela of intensity (i.e., (1 − exp(8.88E-04 ) × 100 percent = 0.089 percent).

Table 42 shows the odds ratios for increase discomfort level at the four intensity values

included in this research.

Table 41. Daytime discomfort fixed effect coefficients.

Coefficient

Value

Standard

Error

z-value

p-value

Intercept

First threshold

(comfortable | irritating)

39.87

451.2

0.088

0.930

Second threshold

(irritating | unbearable)

44.83

451.21

0.099

0.921

Target intensity

0.0009

0.0002

3.9340

0.0004

Flash

pattern

2-5

27.6000

452.0000

0.0610

0.951

100

25.7000

696.0000

0.0370

0.971

Five flashes

29.7000

696.0000

0.0430

0.966

Wig-wag

25.7000

452.0000

0.0570

0.955

LED

location

Below

-0.0721

0.3330

-0.2170

0.829

Within

-1.8200

298.0000

-0.0060

0.995

Pedestrian height—short

0.3120

0.2860

1.0900

0.2760

Pedestrian

position

Center

-0.4310

0.4740

-0.9090

0.364

Left

-0.1780

0.4570

-0.3890

0.697

Right

0.4820

0.4580

1.0510

0.293

Lane—right

-0.5840

2.7500

-0.2120

0.8320

Age

-0.0093

0.0718

-0.1290

0.8980

Reference level used in model for each categorical variable base value: flash pattern = sign, LED location =

above, pedestrian height = tall, pedestrian position = no pedestrian, and lane = left.

p-values for discrete factors with three or more levels need a multiple comparison adjustment. Therefore,

p-values in this table should not be used unless they correspond to a continuous variable or to a discrete factor

of two levels.

Note: Flash patterns are defined as follows: 2-5 = 2-5 flash pattern; 100 = one 100-ms flash pattern; and

wig-wag = wig-wag flash pattern.

Table 42. Daytime odds ratios for higher level of discomfort by target intensity level.

Target Intensity

(Candela)

Odds Ratio

1.00 (reference level)

600

1.70

1,400

3.47

2,200

7.05

Nighttime Discomfort

Table 43 shows a preliminary assessment of the importance of experimental variables in the

results based the deviance breakdown of the nighttime data. These results indicate that at night,

all the experimental factors influenced the discomfort level.

Table 43. Nighttime likelihood ratio tests for incremental discomfort fixed effects.

Variable

Log

Likelihood

Chi-squared

Statistic

p-value

Reference

N/A

-3699.1

N/A

Target intensity

-3272.5

853.2187

< 0.001

Flash pattern

-3145.2

254.7609

< 0.001

LED location

-3104.0

82.2763

< 0.001

Pedestrian height

-3101.6

4.7844

0.02872

Pedestrian position

-3045.6

112.1452

< 0.001

Lane

-3043.2

4.6482

0.03109

Age

-3035.3

15.8501

< 0.001

The statistical quantifiers in this table were based on the expected convergence to normality of the

log-likelihood function by virtue of the law of large numbers.

N/A = Not applicable.

The coefficient estimates for nighttime are shown in table 44, which can be used to draw formal

conclusions about the variables influencing discomfort at night. Results indicate that all factors

had a bearing in the discomfort level expressed by participants, except for the lane (right or left)

where the participant parked.

Table 44. Nighttime discomfort fixed effect coefficients.

Coefficient

Value

Standard

Error

z-value

p-value

Intercept

First threshold

(comfortable | irritating)

4.2135

0.4709

8.9470

< 0.001

Second threshold

(irritating | unbearable)

6.8120

0.4763

14.3030

< 0.001

Target intensity (candela)

0.0010

0.0001

18.8520

< 0.001

Flash

pattern

2-5

2.5600

0.2640

9.7190

< 0.001

100

1.2800

0.2820

4.5300

< 0.001

125(2)

2.1700

0.2660

8.1470

< 0.001

Five flashes

2.0600

0.2930

7.0460

< 0.001

Wig-wag

2.2400

0.2750

8.1720

< 0.001

LED

location

Below

0.6200

0.0802

7.7340

< 0.001

Within

0.6440

0.1390

4.6260

< 0.001

Pedestrian height—short

-0.1490

0.0666

-2.2370

0.0253

Pedestrian

position

Center

-0.1230

0.1080

-1.1350

0.2564

Left

0.5290

0.1060

4.9870

< 0.001

Right

0.4420

0.1080

4.0920

< 0.001

Lane—right

0.2500

0.2660

0.9400

0.3471

Age

-0.0153

0.0070

-2.1790

0.0293

Reference level used in model for each categorical variable base value: flash pattern = sign, LED location =

above, pedestrian height = tall, pedestrian position = no pedestrian, and lane = left.

p-values for discrete factors with three or more levels need a multiple comparison adjustment.

Note: Flash patterns are defined as follows: 2-5 = 2-5 flash pattern; 100 = one 100-ms flash pattern; 125(2) =

two 125-ms flashes; and wig-wag = wig-wag flash pattern.

Target Intensity

Not surprisingly, target intensity of the LEDs had a positive relationship with nighttime

discomfort level. After accounting for all other factors, this analysis indicates that the odds of

higher discomfort increased by 0.102 percent per additional candela of intensity. Table 45 shows

the odds ratios corresponding to the target intensities used in the study. Similarly, there was

convincing evidence of a reduction in discomfort levels associated with placing the short

pedestrian in the crosswalk compared to placing the tall pedestrian.

Table 45. Nighttime odds ratios of higher discomfort by target intensity level.

Target Intensity

(Candela)

Odds Ratio

1.00 (reference level)

600

1.84

1,400

4.17

2,200

9.43

Flash Pattern

There was also strong evidence of an increase in discomfort under all different flash patterns

compared to no LEDs flashing after accounting for other relevant factors. Positive coefficients

indicate that the odds of higher discomfort were statistically higher than the base condition of no

flash pattern. It was of interest; however, to evaluate simultaneous comparisons to determine if

there was any particularly flash pattern associated with a high risk of discomfort scores. Due to

the particular statistical specification of this analysis, the researchers carried the multiple

comparisons by computing the multivariate Hotelling’s T

statistic in contrast with the previous

two analyses. This is a single measure of significance for a set of independent simultaneous

hypotheses that involve the coefficient estimates and their corresponding covariance. Since there

were six different flash patterns, this methodology allowed up to five simultaneous comparisons

for this factor. The researchers defined three comparisons that address the question of interest,

shown in table 46. The unique and small p-value for this table (1.676E-05) indicates that the test

rejected the prospect that all hypotheses were true simultaneously. The last two columns in this

table show the expected range of variation in odds ratio for each hypothesis in an overall

95 percent confidence region associated with the simultaneous comparisons. From these

columns, the results indicate the following:

• The odds ratio between the group of all the flash patterns to no flash pattern at all was

statistically different from 1. The odds of a higher discomfort level were significantly

larger for the group of all flash patterns.

• The odds ratio for higher discomfort was not different from 1 when comparing the 2-5 or

wig-wag flash patterns to the rest of flash patterns (not including none).

• The odds ratio for higher discomfort was not different from 1 when comparing the

2-5 flash pattern to the wig-wag flash pattern.

Table 46. Nighttime simultaneous hypotheses for flash pattern discomfort effect.

Hypothesis on Odds Ratios

Minimum Estimate

of Odds Ratio

Maximum Estimate

of Odds Ratio

(All flashing) ÷ (None) = 1

2.10

29.42

(2-5 or wig-wag) ÷ (All others flashing) = 1

0.91

3.41

(2-5) ÷ (wig-wag) = 1

0.79

2.40

For this table, a multivariate T

statistic was computed to test the three hypotheses

simultaneously. The corresponding T

statistic was 108.07; this statistic follows the

F-distribution with 16 DF in the numerator and 6,075 DF in the denominator. The corresponding

F-statistic is then F(16; 6,075) = 6.738. Because the corresponding critical F-statistic for a

95 percent confidence of simultaneous comparisons is 1.645, the result of this statistical test

indicates that there is convincing evidence that at least one hypothesis in table 46 is such that it

the confidence interval does not contain 1.0. From this table, it is clear that such hypothesis is the

one comparing all flashing patters to none.

LED Location

Table 47 shows the relative effects of LED location with statistical significance adjusted for

simultaneous comparisons. The T

statistic corresponding to this table indicates convincing

evidence of higher discomfort when the LEDs were located below the sign compared to when

they were located above the sign. In contrast, there was no sufficient evidence that locating

LEDs within the sign resulted in higher discomfort as compared to above the sign. For this table,

the resulting T

statistic was 64.9134; the corresponding F-statistic from a F(16, 6,074)

distribution was 4.047. The critical F-statistic for a 95 percent confidence of all simultaneous

comparisons was 1.645, with a corresponding p-value of 9.033E-08 for a test on the

two hypotheses simultaneously.

Table 47. Nighttime simultaneous hypotheses on LED location discomfort effect.

Hypothesis

Minimum Estimate

of Odds Ratio

Maximum Estimate

of Odds Ratio

(Below) ÷ (Above) = 1

1.23

2.81

(Within) ÷ (Above) = 1

0.93

3.89

Pedestrian Position

Table 48 shows the expected ranges for the relative effects of pedestrian position, given that they

were compared simultaneously. There was convincing evidence of higher discomfort when the

pedestrian was located at either side compared to when the pedestrian was located at the center

of the crosswalk. In contrast, there was no sufficient evidence that having the pedestrian in the

crosswalk resulted in higher discomfort compared to when no pedestrian was present. The

statistic for this table was 73.225; the test statistic was F(16, 6,074) = 4.565. The critical

F-statistic for a 95 percent confidence of all simultaneous comparisons was 1.645, with

corresponding p-value of 3.353E-09.

Table 48. Nighttime tests for simultaneous hypotheses on discomfort effect of pedestrian

location.

Hypothesis

Minimum Estimate

for Odds Ratio

Maximum Estimate

for Odds Ratio

(Pedestrian) ÷ (No pedestrian) = 1

0.81

2.16

(Either side) ÷ (Center) = 1

1.24

2.73

(Left side) ÷ (Right side) = 1

0.59

1.42

Age

Finally, this analysis found that driver age influenced the odds of higher discomfort after

controlling for other factors in the experimental design. This decreasing discomfort trend is

shown in figure 46 when no pedestrian was in the crosswalk and the LEDs were set at

2,200 candelas using the 2-5 flash pattern.

Figure 46. Graph. Estimated cumulative probabilities by age of participant for the discrete

scale of discomfort when using 2,200 candelas of intensity and the 2-5 flash pattern.

Key Findings Regarding Discomfort of Detection

For the analysis focusing on discomfort, results indicate the following:

• There were clear differences in discomfort between daytime and nighttime. It was

estimated that the odds of increasing discomfort were only influenced by LED intensity

during the day, whereas almost all experimental factors had an impact at night.

• LED intensity had a significant impact on discomfort levels during the nighttime only.

• The age of participants had an impact on discomfort levels during the nighttime only.

Discomfort tended to decrease with increasing participant age.

• Pedestrian position had an impact on discomfort level during the nighttime only. There

was no evidence for increasing discomfort levels when the pedestrian was present

compared to when the pedestrian was absent. However, locating the pedestrian at any

side of the crosswalk yielded higher discomfort compared to when the pedestrian was

located at the center of the crosswalk. This effect is probably associated with the

proximity to the active LEDs. No evidence of higher discomfort was found when the

pedestrian cutout was placed on the right side compared to when it was placed on

the left side.

• For nighttime only, there was convincing evidence of the flash pattern having an impact

on discomfort levels. Not surprisingly, this analysis found a significant increase of

discomfort associated with any flash pattern compared to no flash pattern at all.

• For nighttime only, LED location had a significant impact on discomfort levels. This

analysis found evidence of higher discomfort levels when the LEDs were located below

the sign compared to LEDs above the signs after controlling for other factors. Although

the trend is similar when comparing LEDs within the sign to LEDs above the sign, this

analysis did not find this difference statistically significant.

CHAPTER 4. DRIVER-YIELDING RESULTS FOR BEACONS PLACED ABOVE OR

BELOW CROSSING SIGN IN AN OPEN-ROAD SETTING

INTRODUCTION

For the open-road study, the test conditions were set to determine driver yielding when the

beacons were located above or below the warning sign. Both placements were studied at all

sites so that a similar driver population would see both treatments. This chapter describes the

methodology and results from the open-road study that investigated the effects of the placement

of yellow rapid-flashing beacons above or below the pedestrian crossing sign.

Due to the findings documented in this report, FHWA issued another interpretation: Official

Interpretation #4(09)-58 (I)—Placement of RRFBs Units Above Sign.

(3)

This permits the

placement of the beacons either above or below the crossing warning sign.

Study Overview

When IA-11 was issued in July 2008 for the RRFB, the only position of the beacons described in

the document was below the crossing warning sign and above the supplemental downward

diagonal arrow plaque.

(4)

As described in chapter 3 of this report, the position of the beacons had

an effect on drivers’ time to detect the presence and direction of crossing pedestrians as well as

discomfort glare during nighttime conditions on a closed course. Prior to developing the

proposed provisions for incorporating a rapid-flashing beacon traffic control device into the

MUTCD, it is important to determine which beacon position is most beneficial from a driver

yielding perspective.

(1)

This study sought to determine if mounting the beacons above the

pedestrian crossing sign was more effective in terms of driver yielding than the traditional

position below the sign.

Study Objective

The objective of this study discussed in this chapter was to determine benefits of different

positions for the RRFBs used with pedestrian crossing signs in an open-road setting. Because the

closed-course study presented in chapter 3 indicated that benefits may exist for placing the

beacons above the sign, the open-road study investigated if drivers yielded differently to RRFBs

placed above versus below the pedestrian crossing sign.

STUDY DEVELOPMENT

Study Sites

Near the conclusion of the closed-course study described in chapter 3, the researchers talked to

agency representatives and made requests during professional society meetings, seeking agencies

that would be willing to participate in the open-road research. Four agencies volunteered:

Aurora, IL; Douglas County, CO; Marshall, TX; and Phoenix, AZ. As a minimum, the agencies

were asked to identify at least two locations that either had existing RRFBs below the pedestrian

crossing sign that could be moved to the position above the sign or that would allow the beacons

to be installed in one position and then moved to the other position after the initial data

collection. Table 49 lists the 13 sites included in the study. The average daily traffic (ADT)

values were provided by the agencies in Arizona, Colorado, and Texas. Researchers estimated

the ADT for the Illinois sites based on 1-h counts made from the video recordings.

Table 49. Study site characteristics for above-below study.

Site

Posted

Speed

Limit

(mi/h)

Total

Crossing

Distance

(ft)

Crossing

Distance

Refuge

(ft)

ADT

Crosswalk

Marking

Pattern

Presence

Advanced

Stop or

Yield

Lines

Number of

Through

or Left-

Turn

Lanes

Crossed by

Pedestrians

Median

Type

Intersection

Geometry

Pedestrians

Crossing

per Hour

AZ-PH-04

23,700

Ladder

Yes

Raised

Midblock

with median

jog (50)

AZ-PH-05

8,700

Ladder

Yes

TWLTL

Three legs

288

CO-DC-02

45 and

c,d

7,900

Ladder

Raised

Three legs

CO-DC-03

2,600

Ladder

None

Four legs

CO-DC-04

4,900

Ladder

None

Four legs

CO-DC-05

16,100

Ladder

Yes

Raised

Three legs

CO-DC-06

35 and

19,800

Ladder

Yes

Raised

Midblock (50)

CO-DC-07

18,800

Ladder

Yes

Raised

Midblock (50)

IL-AU-02

30,800

Diagonal

TWLTL

Midblock (30)

IL-AU-03

8,900

Diagonal

None

Midblock

(360)

IL-AU-04

9,400

Transverse

Yes

Raised

Four legs

TX-MA-01

1,400

Diagonal

None

Midblock

(300)

137

TX-MA-02

4,900

Diagonal

None

Three legs

Note: Sites are labeled as XX-YY-##, where XX represents the two-letter State code; YY represents the two-letter city code, and ## represents the site number within the city.

NR = No refuge; TWLTL = Two-way left-turn lane.

The distance (ft) to nearest intersection or major driveway is shown in parentheses (measured from the center of the crossing to the center of the nearest driveway/intersection).

This indicates the number of pedestrian crossings per hour during the daytime data collection period when the beacons were located below the crossing sign.

Speed limit varied by approach.

Site also includes the following two advance traffic control assemblies: pedestrian crossing (W11-2) warning sign with AHEAD (W16-9P) plaque, and SPEED LIMIT 25 (R2-1)

regulatory sign with WHEN FLASHING (S4-4P) plaque and a 12-inch circular beacon that is activated when the pedestrian pushes the pushbutton at the crossing.

Study Assemblies

Examples of the study assemblies are shown in figure 47 and figure 48. The beacons were

mounted on a roadside pole to supplement either a W11-2 (pedestrian) or W11-15 (trail) crossing

warning sign with a diagonal downward arrow (W16-7p) plaque and located at or immediately

adjacent to a marked crosswalk. The flash pattern used at the study sites was the 2-5 flash

pattern. Table 50 provides information on installation order along with the dates of the data

collection.

Figure 47. Photo. Example of RRFB placed above the sign.

Figure 48. Photo. Example of RRFB placed below the sign.

Table 50. Installation and data collection dates.

Site

Existing

Beacons on

Assembly

Initial

Position

Date

Above

Installed

Date

Above

Data

Collection

Date

Below

Installed

Date

Below

Data

Collection

AZ-PH-04

RRFB

Below

2/20/2015

2/26/2015

Existing

2/17/2015

AZ-PH-05

RRFB

Below

2/20/2015

2/27/2015

Existing

2/16/2015

CO-DC-02

Activated

Above

3/18/2015

4/14/2015

4/27/2015

5/13/2015

CO-DC-03

Activated

Above

3/18/2015

4/14/2015

4/27/2015

5/14/2015

CO-DC-04

Activated

Above

3/18/2015

4/15/2015

4/27/2015

5/14/2015

CO-DC-05

Activated

Below

4/27/2015

5/14/2015

3/27/2015

4/13/2015

CO-DC-06

Activated

Below

4/27/2015

5/13/2015

3/27/2015

4/13/2015

CO-DC-07

Activated

Below

4/27/2015

5/13/2015

3/18/2015

4/14/2015

IL-AU-02

RRFB

Below

10/16/2014

10/28/2014

Existing

10/10/2014

IL-AU-03

RRFB

Below

10/16/2014

10/28/2014

Existing

10/10/2014

IL-AU-04

RRFB

Below

10/16/2014

10/29/2014

Existing

10/11/2014

TX-MA-01

RRFB

Below

3/25/2015

4/9/2015

Existing

2/12/2015

TX-MA-02

RRFB

Below

3/25/2015

4/10/2015

Existing

2/13/2015

Existing = RRFB was installed below the sign at site prior to the study.

Activated = Pedestrian-activated yellow circular 12-inch flashing beacons were activated.

Rotation

To account for the possibility that device installation order could affect the results, the RRFB

was installed above the sign first in some locations and second in other locations. For the

13 study sites, the RRFB was installed initially above the sign in 3 of the sites and was

previously installed or initially installed below the sign at the remaining sites.

DATA COLLECTION AND REDUCTION

Study Periods

The study was conducted between October 2014 and May 2015. Following installation of the

device in its initial position, the research team collected after data. Once the after data were

obtained, the research team requested that the device be installed in the second position

(i.e., RRFBs above the sign were moved below the sign and vice versa). After receiving

confirmation that the devices had been moved, the research team collected after data for the

second position.

Data were collected primarily during the daytime when vehicles were free-flowing. Because few

studies have collected data at night, the research team wanted to obtain some data for nighttime

conditions. The characteristics of the beacons and the site may have different impacts on driver

yielding during night conditions as compared to daytime conditions. Therefore, nighttime data

were collected for one site within each city.

Staged Pedestrian Protocol

The research team used a staged pedestrian protocol to collect driver yielding data to ensure that

oncoming drivers received a consistent presentation of approaching pedestrians. Under this

protocol, a member of the research team acted as a pedestrian using the crosswalk to stage the

conditions under which driver yielding would be observed. Each staged pedestrian wore similar

clothing (gray t-shirt, blue jeans, and gray tennis shoes) and followed specific instructions in

crossing the roadway. The staged pedestrian was accompanied by a second researcher, who

observed and recorded the yielding data on pre-printed datasheets.

Prior to the staged crossing maneuvers, researchers placed markers (either small contractor flags

or cones) at the edge of the traveled way at a distance corresponding to the AASHTO SSD value

for the posted speed limit at that site; one marker was placed in each direction approaching the

crosswalk.

(38)

SSD is 200 ft for 30 mi/h, 305 ft for 40 mi/h, and 360 ft for 45 mi/h. After the

study site had been prepared, the researchers followed the predetermined staged pedestrian

protocol, which was defined as follows:

1. The staged pedestrian approached the crosswalk as oncoming vehicles approached the SSD

marker activating the RRFB.

2. The staged pedestrian reached the edge of the crosswalk in time to place one foot in the

crosswalk (e.g., off the edge of the curb or curb ramp) within approximately 1 s of the

approaching driver(s) reaching the SSD marker.

3. The staged pedestrian waited to cross until approaching drivers yielded or until all

approaching drivers had traveled through the crosswalk.

4. The observer recorded how many motorists did/did not yield as well as how many were in a

position to yield for each crossing maneuver. Drivers were considered to be in position to

yield if they were upstream of the SSD marker when the staged pedestrian was positioned at

the edge of the crosswalk. Each such vehicle that did not yield was counted as was each

yielding vehicle. Of the vehicles in a position to yield, a vehicle was considered to be

yielding if the driver slowed or stopped for the purpose of allowing the waiting pedestrian to

cross. Any vehicles traveling in a platoon behind yielding vehicles were not counted because

those drivers did not have the opportunity to make a decision on whether to yield to the

pedestrian; therefore, the maximum number of yielding vehicles possible for each crossing

maneuver was equal to the number of travel lanes through which the crosswalk passed.

5. Yielding was observed separately for each direction of vehicular travel because Arizona,

Colorado, Illinois, and Texas law is written such that drivers must yield to pedestrians in or

approaching their half of the roadway.

6. The observer noted any unusual events or noteworthy comments for each crossing.

7. Once the crosswalk was clear (i.e., the approaching vehicle had either stopped or passed

through the crossing), the staged pedestrian crossed the street and waited on the sidewalk or

roadside until all vehicles visible during that crossing traveled through the crosswalk. After

all such vehicles had left the study site, the staged pedestrian prepared for the next crossing

maneuver.

The protocol called for the completion of a minimum of 20 staged crossing maneuvers in each

direction of travel for a total of 40 crossings. Observation periods were chosen such that vehicle

traffic was heavy enough to create frequent yielding situations but not heavy enough for

congestion to affect speeds. Data were always collected during daylight and in good weather,

avoiding rain, wet pavement, dusk or dawn, or other conditions that could affect a driver’s ability

to see and react to a waiting staged pedestrian.

A minimum of 40 (and a desired 60) staged pedestrian crossings were collected at each site

within each time period during daytime. Because of the length of time needed to collect the

crossing, a minimum of 40 staged pedestrians were collected at night.

Driver Yielding

After completing the data collection, researchers entered the crossing data and the site

characteristics data from the field worksheets into an electronic database. The average yielding

rate for a site was calculated, as shown in figure 49; however, data for individual crossings were

used in the statistical evaluation.

Figure 49. Equation. Driver yielding rate.

Table 51 lists the driver yielding rates for each site and beacon position along with the number

of staged pedestrian crossings for the nighttime data collection periods. Driver yielding to

staged pedestrians at night averaged 68 percent for the above position and 65 percent for the

below position.

Table 51. Nighttime driver yielding rate by site and beacons position.

Site

Number of

Staged

Crossings for

Above Position

Driver Yielding

for Above

Position

(Percent)

Number of

Staged

Crossings for

Below Position

Driver Yielding

for Below

Position

(Percent)

AZ-PH-05

CO-DC-06

IL-AU-03

TX-MA-01

Total

205

201

Table 52 shows similar results for the daytime data collection periods. During the daytime, driver

yielding to staged pedestrians averaged 64 percent for the above position and 61 percent for the

below position. For several sites, neither beacon position showed a large increase in driver

yielding as compared to the other. The range of driver yielding to staged pedestrians at these

sites ranged from 19 to 98 percent.

Yielding rate =

Number of yielding vehicles

Number of yielding vehicles + Number of non ̵ yielding vehicles

Table 52. Daytime driver yielding rate by site and beacon position.

Site

Number of

Staged

Crossings for

Above Position

Driver Yielding

for Above

Position

(Percent)

Number of

Staged

Crossings for

Below Position

Driver Yielding

for Below

Position

(Percent)

AZ-PH-04

AZ-PH-05

CO-DC-02

CO-DC-03

CO-DC-04

CO-DC-05

CO-DC-06

CO-DC-07

IL-AU-02

IL-AU-03

IL-AU-04

TX-MA-01

TX-MA-02

Total

762

745

RESULTS

When a driver approaches a pedestrian crossing, the driver either yields and stops (or slows) the

vehicle or does not yield to the waiting pedestrian. This binary behavior (yield or no yield) can

be modeled using logistic regression. A significant advantage of using logistic regression is it

permits consideration of individual crossing data rather than reducing all the data at a site to only

one value. For the dataset available within this study, that means that over 1,900 data points

could be available (i.e., all the unique staged crossings recorded) rather than only 34 data points

(i.e., the number of study sites by number of assemblies and by day or night). For the analyses

that focused on comparing the below position to the above position, that means 1,507 data points

rather than 26 data points were available. These larger sample sizes could result in finding

significant relationships that would not be apparent with a smaller dataset. Additionally, it is

possible to utilize random effects to account for site-specific differences since such differences

induce a correlation structure in the dataset.

Using logistic regression to model the relationships assumes that the logit transformation of

the outcome variable (i.e., yielding rate) has a linear relationship with the predictor variables,

which results in challenges in interpreting the regression coefficients. The interpretation of such

coefficients is not on the yield rate changes directly but a change in the odds of motorists

yielding (odds are defined as the ratio of the number of yielding motorists to the number of non-

yielding motorists). The regression coefficients can be transformed and interpreted as odds ratios

of different levels of the corresponding independent variable. In other words, a unit change of the

independent variable corresponds to a change in the odds of motorists yielding, which is an

alternative way to express a change in yielding rate. More details on these types of models can be

found in the literature.

(47)

All the statistical analyses were performed using R, an open-source

statistical language and environment, and two open-source packages for fitting GLMMs.

(48,44,45)

COMPARISON OF BELOW TO ABOVE

Because a previous study that included RRFBs found posted speed limit, crossing distance, and

city influenced driver yielding, the initial analyses were also conducted with those variables.

(35)

In addition, a variable to reflect the intersection configuration was included, as preliminary

reviews indicated that the number of approach legs may be related to yielding results.

Preliminary modeling revealed a correlation between road type (e.g., number of lanes and

median treatment) and speed limit present in the dataset; therefore, only posted speed limit was

included in the final model. Models were examined that included other variables, such as total

crossing distance; however, the best results were found when the variables shown in table 53

were included. The reference level for a driver yielding in the model was estimated for the

following conditions: an above sign during the daytime in Arizona with a three-leg intersection.

From the preliminary review of the results in table 52, it appears that there were only minor, if

any, differences between the above and below position for the RRFBs. The results from the

GLMM are shown in table 53, and these results support that observation. The results indicate

that there were no significant differences between the two beacon locations (p-value = 0.1611).

The day/night variable was significant (p-value = 0.0005), which indicates that there were day/

night differences for this dataset regarding driver yielding. It appears that Illinois had notably

lower driver yielding as compared to the base State, Arizona. An adjusted p-value for multiple

comparisons is required to make a formal assessment. Texas and Colorado were not different

from Arizona. The model also indicates that the driver yielding at the midblock offset

configuration was statistically different from the driver yielding at the three-legged intersections.

A caution with this finding is offered since there was only one site with a midblock offset

configuration in the dataset.

Table 53. GLMM results comparing below to above.

Variable

Estimate

Standard

Error

t-value

p-value

Significance

Reference

0.10770

1.04333

0.103

0.917783

Below

-0.09931

0.07087

-1.401

0.161107

Night

-0.41899

0.12048

-3.478

0.000506

***

Posted speed limit

0.05858

0.02718

2.155

0.031185

State

Colorado

-0.26452

0.56242

-0.470

0.638119

Illinois

-2.18731

0.64555

-3.388

0.000703

***

Texas

0.02124

0.60734

0.035

0.972104

Intersection

configuration

Four legs

-0.07459

0.47508

-0.157

0.875249

Midblock

-0.49582

0.44650

-1.110

0.266803

Offset midblock

-2.11671

0.57363

-3.690

0.000224

***

Estimate = Natural logarithm of the ratio = Odds (coefficient level)/Odds (reference level). In the case of reference level,

estimate is the log-odds of the average yielding rate at the reference level.

t-value = Conservative estimate of the z-value, which is the standard normal score for the estimate, given the hypothesis

that the actual odds ratio equals 1.

p-value: Probability that the observed log-odds ratio is at least as extreme as the estimate, given the hypothesis that the

actual odds ratio equals 1.

Reference level driver yielding in the model is estimated for the following conditions: above, day, Arizona, and

three-legged intersection.

These p-values require an adjustment for multiple comparisons if inferences about different yielding rates among States or

among configuration are intended.

Significance values are as follows: blank cell = p > 0.10; ~ = p < 0.10; * = p < 0.05; ** = p < 0.01; and *** = p < 0.001.

CHAPTER 5. DRIVER-YIELDING RESULTS FOR THREE RRFB PATTERNS IN AN

OPEN-ROAD SETTING

INTRODUCTION

This chapter describes the methodology and results from an open-road study that examined

different flash patterns for use with yellow RRFBs.

Study Overview

When IA-11 was issued in July 2008 for the RRFBs, the only flash pattern that had been tested

was the 2-5 flash pattern.

(4)

Because the 2-5 flash pattern appears be a 2-3 flash pattern according

to the human eye, several devices were installed with the 2-3 flash pattern rather than the

2-5 flash pattern. Only after looking at the flash pattern using an oscilloscope were transportation

professionals able to determine that the original devices had a 2-5 flash pattern, which is why

FHWA changed the flash pattern from a 2-3 flash pattern to a 2-5 flash pattern in Official

Interpretation 4(09)-21.

(9)

An inability to accurately determine the number of pulses within the 2-5 RRFB flash pattern was

later confirmed in the closed-course study (see chapter 3). The same study found that certain

flash patterns (i.e., those that could be characterized as having limited or no dark periods within

the flash pattern) negatively influenced the amount of time participants needed to identify a

pedestrian’s direction of travel. Prior to developing the proposed provisions for incorporating the

RRFB a rapid-flashing beacon traffic control device into the MUTCD, it is important to

determine which flash patterns are acceptable from the perspectives of effectiveness and

simplicity.

(1)

This study sought to determine if less complicated flash patterns and flash pattern

with different proportions of dark and light periods could be as or more effective than the

2-5 flash pattern.

Study Objective

The objective of this study was to determine if the use of simpler flash patterns or flash patterns

with a greater proportion of dark periods resulted in different driver yielding rates at uncontrolled

crosswalks in an open-road setting. This study’s measure of effectiveness (MOE) was the

number of drivers who did and did not yield at crosswalks during staged pedestrian crossings.

STUDY DEVELOPMENT

Study Sites

The cities of College Station, TX, and Garland, TX, along with TAMU agreed to participate in

the study by providing locations where the research team could install temporary equipment.

Table 54 lists the sites included in the study. A goal was to try to match the distribution of site

characteristics used in the original FHWA study on RRFBs.

(16)

For example, the research team

preferred locations on multilane roads so that yielding behavior associated with the multiple

threats issue could be observed. Because of limited ability to mount temporary beacons on

overhead mast arms, the research team did not consider locations where the RRFB had been

installed on mast arms over the roadway.

Table 54. List of sites for rapid flash pattern study.

Site ID

Posted Speed

Limit (mi/h)

Number of

Lanes

Median

Crossing

Distance (ft)

CS-02

Flush

CS-03

Flush

GA-02

Flush

GA-06

Raised

GA-07

Raised

GA-10

Raised

GA-11

Raised

GA-13

Raised

Temporary Light Bar

To conduct an in-field evaluation of multiple flash patterns, the research team needed to be able

to set the flash pattern and brightness of the beacons at the study sites in a quick, reliable, and

consistent manner. Because of the difficulties with working with different equipment in different

cities and unknown characteristics for the beacons at these locations (such as brightness), the

research team designed temporary controllers to be used with temporary light bars. In the field,

the temporary light bars were mounted in front of existing RRFB light bars.

The temporary light bar setup was designed such that it was not obvious that the beacons being

observed during the staged pedestrian crossings were any different from the permanent RRFB

equipment. Figure 50 shows an example of TTI personnel installing the temporary light bar at a

site, and figure 51 shows an example of the installed light bar being used by a staged pedestrian.

The staged pedestrian had a remote control to activate the light bars and activated the device if a

non-staged pedestrian approached the crossing while the temporary light bars were installed.

Figure 50. Photo. Installation of the light bar in field.

Figure 51. Photo. CS-02 study site with installed temporary light bars and staged

pedestrian crossing.

Flash Patterns

The study budget and parameters made it possible to test four different conditions at each study

site. One of the four conditions was reserved for collecting driver yielding data with the existing

equipment. Data were collected with the existing equipment in order to control for differences

between the existing equipment and the temporary equipment. The other three conditions used

the temporary equipment. Of the three remaining conditions, one condition was reserved for the

2-5 flash pattern.

To determine flash patterns for the other two conditions, a flash pattern workshop was held at

TTI. The workshop included a selection of licensed transportation engineering professionals,

representatives of FHWA, and TTI research staff. The patterns were initially reviewed using a

mockup of a rectangular beacon light bar and a controller in a conference room. Several pre-

developed patterns were shown to the participants. Based on participant comments, new patterns

were developed. For example, some flash patterns were changed to have more dark periods or to

have periods where both beacons were on. A reason for wanting increased dark periods for some

of the flash patterns for this study was a preliminary finding from a closed-course research study

(see chapter 3) that indicated drivers could determine the direction a pedestrian was walking in a

crosswalk more quickly when the flashing traffic control devices had larger dark periods.

After identifying a short list of potential patterns during the meeting in the conference room,

the meeting moved to a TTI closed-course location to look at the potential patterns in the field

during the nighttime setting. The participants parked the vehicle 200 ft from a crosswalk on a

two-lane approach with RRFB assemblies located on both sides of the roadway. The patterns

developed during the conference room meeting were demonstrated to the meeting participants in

the field. Based on the meeting participants’ comments, two potential patterns were selected.

These two patterns were demonstrated to FHWA representatives, and final approval was given to

use these two flash patterns as the two remaining conditions for the open-road study.

100

Figure 52 illustrates the three patterns selected for testing in the field using the temporary light

bars. The patterns considered in this study included the following:

• Temporary light bar and pattern using a combination of long and short flashes

(i.e., blocks).

• Temporary light bar and a pattern using a combination of wig-wag and

simultaneous flashes.

• Temporary light bar and the 2-5 flash pattern.

• Existing equipment and the 2-5 flash pattern or 2-3 pattern (whichever was present at the

site). Because of when the cities installed the existing RRFBs, some of the sites may have

had the 2-3 flash pattern rather than the 2-5 flash pattern with the existing equipment.

101

Figure 52. Illustration. Flash patterns studied.

(49)

102

Brightness of LEDs

Preliminary findings from the closed-course study (see chapter 3) indicate that brightness of the

beacons can influence how quickly a participant can detect a pedestrian within a crosswalk.

Therefore, the same brightness level was used for the three flash patterns tested with the

temporary light bars. Table 55 shows the target and measured intensity for the beacons when

measured at horizontal and vertical angles of 0 degrees. The table also shows the measured

optical power along with the on and off ratios (i.e., percent of the cycle where at least one of the

beacons was on or where both beacons were dark, respectively).

Table 55. Brightness measurements.

Flash Pattern with

Temporary

Equipment

Target

Intensity

(Candela)

Measured

Target

Intensity

(Candela)

Optical Power

(Candela-s/min)

On Ratio

(Percent)

Off Ratio

(Percent)

2-5

1,400

1,414

58,300

Blocks

1,400

1,415

63,700

Wig-wag and

simultaneous (WW+S)

1,400

1,418

42,500

Sample Size

Based on a statistical analysis of past driver yielding data at RRFB locations in Texas, the

research team estimated it would take between 7 and 13 sites to obtain a sufficient sample of

data to permit detection of at least a 5 percent difference in driver yielding.

(36)

With available

resources for the study, a total of eight sites were selected for testing. Based on previous

experience, the minimum number of staged pedestrian crossings for each condition was set at 40.

Flash Pattern Order

The order that treatments were presented could have had an effect on results; therefore, flash

pattern order for the sites was randomized. Table 56 lists the order that the flash patterns were

installed at each site.

103

Table 56. Flash pattern order by test site location.

Site ID

Initial Flash Pattern

Second Flash Pattern

Third Flash Pattern

Fourth Flash Pattern

GA-02

Temporary; 2-5

Existing; 2-5 or 2-3

Temporary; blocks

Temporary; WW+S

CS-02

Existing; 2-5 or 2-3

Temporary; blocks

Temporary; WW+S

Temporary; 2-5

CS-03

Temporary; blocks

Temporary; WW+S

Temporary; 2-5

Existing; 2-5 or 2-3

GA-06

Temporary; WW+S

Temporary; 2-5

Existing; 2-5 or 2-3

Temporary; blocks

GA-07

Temporary; 2-5 flash

Existing; 2-5 or 2-3

Temporary; blocks

Temporary; WW+S

GA-10

Existing; 2-5 or 2-3

Temporary; blocks

Temporary; WW+S

Temporary; 2-5

GA-11

Temporary; blocks

Temporary; WW+S

Temporary; 2-5

Existing; 2-5 or 2-3

GA-13

Temporary; WW+S

Temporary; 2-5

Existing; 2-5 or 2-3

Temporary; blocks

Note: Flash patterns are defined as follows: 2-5 = 2-5 flash pattern and 2-3 = 2-3 flash pattern.

DATA COLLECTION AND REDUCTION

Study Periods

The data were collected during daytime conditions in February and March 2014. The research

team avoided Monday mornings and Friday afternoons along with weekends because travel

patterns for those time periods can be different from travel patterns associated with a

typical weekday.

Staged Pedestrian Protocol

The research team used a staged pedestrian protocol to collect driver yielding data to ensure that

oncoming drivers received a consistent presentation of approaching pedestrians. Under this

protocol, a member of the research team acted as a pedestrian using the crosswalk to stage the

conditions under which driver yielding would be observed. Each staged pedestrian wore similar

clothing (gray t-shirt, blue jeans, and gray tennis shoes) and followed specific instructions in

crossing the roadway. The staged pedestrian was accompanied by a second researcher, who

observed and recorded the yielding data on pre-printed datasheets. Additional information on the

staged pedestrian protocol followed is available in chapter 4 of this report or in “Driver Yielding

to Traffic Control Signals, Pedestrian Hybrid Beacons, and Rectangular Rapid-Flashing Beacons

in Texas.”

(36)

DATA REDUCTION

After completing the data collection, researchers entered the crossing data and the site

characteristics data from the field worksheets into an electronic database. The average yielding

rate for a site was calculated; however, data for individual crossings were used in the statistical

evaluation. Table 57 lists the driver yielding rates for each site, type of light bar, and flash

pattern. As shown in the final row of the table, the three flash patterns used with the temporary

light bar had similar average driver yielding rates—between 78 and 80 percent. When comparing

the results for the individual sites, some sites did have larger differences between the different

flash patterns.

104

Table 57. Driver yielding rate by site and pattern.

Site

Temporary Light

Bars with WW+S

(Percent)

Temporary

Light Bars with

Blocks (Percent)

Temporary Light

Bars with

2-5 Flash Pattern

(Percent)

Existing Light

Bars with 2-5 or

2-3 Flash Patterns

(Percent)

CS-02

CS-03

GA-02

GA-06

GA-07

GA-10

GA-11

GA-13

Total

RESULTS

When a driver approaches a crossing, the driver either yields and stops the vehicle or does not

yield to the waiting staged pedestrian. This binary behavior (yield or no yield) can be modeled

using logistic regression. A significant advantage of using logistic regression is it permits

consideration of individual crossing data rather than reducing all the data at a site to only

one value. For the dataset available within this study, that means over 1,100 data points could

be available (i.e., all the unique staged crossings recorded) rather than only 32 data points (i.e.,

the number of study sites by number of flash patterns). The larger sample size provides more

detailed data and could result in finding significant relationships that would not be apparent with

a smaller dataset.

Using logistic regression to model the relationships assumes that the logit transformation of the

outcome variable (i.e., yielding rate) has a linear relationship with the predictor variables, which

results in challenges in interpreting the regression coefficients. Odds ratios can be used to

illustrate how to interpret the logistic regression results. The interpretation of such coefficients is

not on the yield rate changes directly but a change in the odds of motorists yielding (odds are

defined as the ratio of the number of yielding motorists to the number of non-yielding motorists).

The regression coefficients can be transformed and interpreted as odds ratios of different levels

of the corresponding independent variable. In other words, the odds ratio is the expected change

in the odds of motorists yielding per unit change of the independent variable. More details on

these types of models can be found in the literature.

(47)

All the statistical analyses were

performed using R, an open-source statistical language, and environment and two open-source

packages for fitting GLMMs.

(48,45)

Patterns Used with Temporary Light Bars

From the preliminary review of the results in table 57, it appears that there were only minor, if

any, differences between the tested flash patterns. The results from the GLMM are shown in

table 58. Statistical significance of coefficients was obtained from comparing the coefficient

(i.e., parameter estimate) to a value of zero. If an estimate is found to be statistically different

from zero, then the variable has a statistically significant effect on the odds of driver yielding.

105

Additionally, if the coefficient is different from zero, then the odds ratio is different from 1.

Conversely, coefficients without statistical significance indicate an odds ratio indistinguishable

from one, thus indicating that the variable has no bearing on driver yielding rate. In this study,

the reference level for a driver yielding in the model was estimated as follows: temporary light

bar with a 2-5 flash pattern in College Station, TX.

Table 58. Linear mixed-effects model results for flash patterns used with temporary light

bars.

Variable

Estimate

Standard Error

t-value

p-value

Reference

1.3864637

0.9582977

941

1.4467986

0.1483

Temporary; blocks

0.1662325

0.1503383

941

1.1057233

0.2691

Temporary; WW+S

0.1164097

0.1452238

941

0.8015884

0.4230

Garland, TX

0.8213472

0.5663119

1.4503443

0.2067

Crossing distance (ft)

-0.0090980

0.0184713

-0.4925464

0.6432

Estimate = Natural logarithm of the ratio = odds (coefficient level)/odds (reference level). In the case of reference

level, estimate is the log-odds of the average yielding rate at the reference level.

t-value = Conservative estimate of the z-value, which is the standard normal score for the estimate, given the

hypothesis that the actual odds ratio equals 1.

p-value = Probability that the observed log-odds ratio is at least as extreme as the estimate, given the hypothesis

that the actual odds ratio equals 1.

Reference level driver yielding in the model is estimated for the following conditions: 2-5 flash pattern used with

temporary light bars in College Station, TX.

Because a previous study on RRFBs found that posted speed limit, crossing distance, and city

influenced driver yielding, the analysis considered those variables initially. However, for this set

of sites, posted speed limit and crossing distance were correlated; therefore, posted speed limit

was removed. Site selection was heavily influenced by whether four lanes were present and

whether the beacons were located on the roadside rather than overhead. In other words, site

selection was not a function of the posted speed limit and crossing distance, and a high number

of sites had one posted speed limit (40 mi/h for six of the eight sites), which did not provide a

sufficient range for parameter estimation on that variable. The city (Garland, TX, or College

Station, TX) was included as a fixed effect, with the results shown in table 59. Both city and

crossing distance were found to be not significant for this dataset.

The p-values from table 58 were adjusted to allow multiple comparisons, as shown in table 59.

The table indicates that there were no significant differences between the 2-5 flash pattern and

the WW+S flash pattern (p-value = 0.707), between the 2-5 flash pattern and the blocks flash

pattern (p-value = 0.517), between the blocks flash pattern and WW+S flash pattern (p-value =

0.941), or between blocks, WW+S, or the 2-5 flash pattern (p-value = 0.516).

106

Table 59. Simultaneous comparisons on flash pattern differences.

Hypothesis

Estimate

Standard

Error

z-value

Pr( >|z|)

Temporary; blocks − Temporary 2-5 flash pattern = 0

0.16623

0.14994

1.109

0.517

Temporary; WW+S − Temporary; 2-5 flash

pattern = 0

0.11641

0.14484

0.804

0.707

Temporary; blocks − Temporary; WW+S = 0

0.04982

0.14872

0.335

0.941

(Temporary; blocks and Temporary; WW+S) −

(Temporary; 2-5 flash pattern) = 0

0.14132

0.12728

1.11

0.516

Adjusted p-values were reported using a single-step method.

2-5 Flash Pattern

The previous evaluation kept the temporary light bar constant, while this evaluation kept the

2-5 flash pattern constant. Comparing the results between the 2-5 flash pattern used with the

temporary light bars and the results when the 2-5 flash pattern was used with the existing

equipment indicates that a difference may exist. As shown in table 57, the average yielding for

the 2-5 flash pattern with temporary light bars was 78 percent, while the average yielding for the

existing equipment was slightly higher at 81 percent. Overall, the driver yielding rates were

higher for the existing light bars for the Garland, TX, sites, and the driver yielding rates were

lower for the existing light bars for the College Station, TX, sites.

Table 60 shows the results for the LMM, which found that the equipment (p-value = 0.0010) and

the city (p-value = 0.0205) were both significant. Because these statistical significant differences

existed, they indicate that characteristics of the city, the roadway, and the beacons other than

flash pattern had an effect on driver yielding. Even with accounting for crossing distance and

city, a statistical significant difference was found between the existing and temporary light bars.

Therefore, other characteristics that were not measured (i.e., brightness) are possibly influencing

a driver’s decision to yield or not yield. The reference level driver yielding in the model was

estimated as having existing light bars in College Station, TX.

Table 60. LMM results comparing the 2-5 flash pattern with temporary and existing

equipment.

Variable

Estimate

Standard Error

t-value

p-value

Reference

1.4748929

0.8625094

644

1.71002

0.0877

Temporary beacons

-0.5002792

0.1516542

644

-3.298814

0.0010

Garland, TX

1.6766262

0.5014311

3.343682

0.0205

Crossing distance (ft)

-0.0131371

0.0166508

-0.788978

0.4659

Estimate = Natural logarithm of the ratio = odds (coefficient level)/odds (reference level). In the case of reference

level, estimate is the log-odds of the average yielding rate at the reference level.

t-value = Conservative estimate of the z-value, which is the standard normal score for the estimate, given the

hypothesis that the actual odds ratio equals 1.

p-value = Probability that the observed log-odds ratio is at least as extreme as the estimate, given the hypothesis

that the actual odds ratio equals 1.

Reference level driver yielding in the model is estimated for the following conditions: existing light bars in

College Station, TX.

107

CHAPTER 6. PHB STUDY

INTRODUCTION

This chapter describes the methodology and results from a study that examined driver and

pedestrian behavior at PHBs. The PHB, or HAWK as it is known in Tucson, AZ, is a traffic

control device used at pedestrian crossings. The crossing typically has the crosswalk across only

one of the major road approaches. The PHB’s vehicular display faces are typically located on

mast arms over the major approaches to an intersection and in some locations on the roadside.

An example is shown in figure 53 for an installation in Tucson, AZ, and in figure 54 for an

installation in Austin, TX. The face of the PHB consists of two red indications above a single

yellow indication. It rests in a dark mode, but when activated by a pedestrian, it first displays a

few seconds of flashing yellow followed by a steady yellow change interval and then displays a

steady red indication to drivers, which creates a gap for pedestrians to cross the major roadway.

During the flashing pedestrian clearance interval, the PHB displays an alternating flashing red

indication to allow drivers to proceed after stopping if the pedestrians have cleared their half of

the roadway, thereby reducing vehicle delays.

Figure 53. Photo. Example of PHB installation in Tucson, AZ.

108

Figure 54. Photo. Example of PHBs being used in Austin, TX.

The PHB has shown great potential for improving pedestrian safety; however, questions remain

regarding under what roadway conditions—such as crossing distance (i.e., number of lanes) and

posted speed limit—should it be considered for use.

(26,27)

In addition, there are questions about

the device’s operations. For example, a current topic of discussion within the profession is the

way drivers treat a PHB when it is dark. PHBs dwell in a dark mode for drivers until activated

by a pedestrian. A concern among some is that drivers will see a dark PHB and treat it as a

Stop sign, similar to the required behavior for a dark traffic signal that has experienced a

power outage.

The STC of the NCUTCD assists in developing language for chapter 4 of the MUTCD.

(1)

It is

interested in research and/or assistance in refining material on the PHB. The PHB was first

included in the 2009 MUTCD, which discusses the design and operations of the device along

with guidance for installation categorized by low speed (roadways where speeds are 35 mi/h or

less) and high speed (roadways where speeds are more than 35 mi/h).

(1)

The 2009 MUTCD also

indicates that the PHB “…should be installed at least 100 ft from side streets or driveways that

are controlled by Stop or Yield signs”

(1)

(pg. 449) In 2011, the STC recommended to remove

that statement because it was a significant change from what was reviewed and approved by

the National Committee in 2007 and what was proposed in the Notice of Proposed Amendment

(NPA) for the 2009 MUTCD.

(50)

The statement was added to the PHB 2009 MUTCD discussion

just prior to publication. The STC provided the following concerns with the 100-ft distance (with

additional details added by this study’s research team based on reviewer comments):

• The result of the added 100-ft guidance, if followed, is that these beacons could not be

used at unsignalized intersections or driveways. The NPA language did not include any

limitations (either standard or guidance) on the locations for use of the PHB; therefore,

the 100-ft change was not subject to public review and comment.

• The 100-ft offset listed in the guidance is not supported by research or experimentation

with this device. Most sites used for experimentation when the PHB was being tested

were intersection or driveway locations which were the natural crossing locations.

109

Therefore, the typical use of the device as tested, which ultimately proved to be

successful, is recommended against in the 2009 MUTCD.

• All of the sites included in the FHWA study that evaluated the safety effectiveness of

these devices were at stop-controlled intersections or major driveways.

(27)

The study was

performed just prior to the publication of the 2009 MUTCD.

• The 100-ft guidance, if followed, causes increased mobility difficulties and discomfort

for pedestrians with disabilities and forces all pedestrians to experience increased

inconvenience if they must divert away from their desired crossing location at an

intersection or driveway to a different crossing point located 100 ft or more away which

would likely lead to 200 ft or more out-of-way travel. If the PHB is not placed at the

natural crossing locations, it is likely it will not be used by most pedestrians, and their

value as a safety device could be compromised.

Because of the questions being asked regarding driver and pedestrian behaviors with PHBs,

FHWA sponsored a study to record behaviors at existing sites.

Study Objective

The objective of this study was to determine actual driver and pedestrian behaviors at locations

with a PHB.

STUDY SITES

Through existing contacts and research team knowledge along with responses to requests, the

research team compiled a preliminary list of PHB locations. Data for key variables (posted speed

limit, number of through lanes, and the type of median treatment) were gathered and added to the

list for the PHBs in communities with multiple installations. Pedestrian crossings on higher

speed roadways and with wider crossings have historically experienced lower driver yielding, so

posted speed limit and crossing distance (as reflected by number of lanes) were selected as key

variables. The goal was to have at least eight sites with higher posted speed limits (defined for

this study as being 40 mi/h or higher) and four sites with lower posted speed limits (defined for

this study as being 35 mi/h or lower). The presence of a median can provide refuge for a

crossing, which may affect the measures of effectiveness considered for this study, so it was also

included in the original study matrix. Because of efficiencies in data collection, data were

collected for a total of 20 sites. Roadway and traffic characteristics for the sites are listed in

table 61.

110

Table 61. Site characteristics.

Site

Name

Roadway

Configuration

Number

Approach

Legs

Posted

Speed

Limit

(mi/h)

ADT

Pedsetrians/

Hour

Number

Through

Lanes

Park

Lane/

Bike Lane

Width (ft)

Median

Type

Median

Width

(ft)

Total

Crossing

Distance

(ft)

TU-003

Intersection

7,400

4.8

NA/6

TWLTL

TU-004

Intersection

7,600

9.3

NA/6

TWLTL

TU-007

Intersection

8,700

8.9

NA/6

TWLTL

TU-021

Intersection

31,000

8.2

NA/5

TWLTL

TU-037

Intersection

27,500

11.1

NA/5

TWLTL

TU-042

Intersection

5,100

14.2

NA/NA

Raised

TU-059

Intersection

28,400

3.1

NA/4

Raised

TU-070

Intersection

29,900

3.6

NA/4

Raised

TU-072

Intersection

41,300

7.6

NA/6

Raised

119

TU-073

Intersection

13,800

13.3

NA/6

Raised

TU-090

Intersection

10,100

1.1

NA/7

Raised

TU-091

Intersection

5,200

2.5

13/5

Raised

112

AU-04

Intersection

26,600

11.5

NA/NA

Raised

AU-07

Midblock (50)

24,600

23.3

NA/NA

Raised

AU-11

Intersection

26,900

6.4

8/NA

TWLTL

AU-16

Intersection

28,500

18.5

NA/NA

TWLTL

AU-21

Midblock (60)

27,100

20.0

NA/NA

None

AU-22

Midblock (70)

19,600

38.3

NA/6

TWLTL

AU-24

Intersection

14,100

20.7

NA/NA

Raised

AU-27

Midblock (80)

21,200

10.7

NA/6

Raised

NA = Not applicable.

Site name is denoted as AA-XXX, where AA represents the two-letter city code and XXX represents the number assigned to the site.

Number of pedestrians per hour did not include any research team member crossings. They were observed during data collection (typically over a 4-h daytime period).

PHB is located within coordinated corridor where the timing of when the PHB is active is influenced by the nearby coordinated corridor.

For midblock roadway configuration, the number in parentheses shows the distance (ft) measured from center of crossing to center of nearest driveway/intersection to

the nearest intersection or major driveway.

111

The cities of Tucson, AZ and, Austin, TX, had the greatest variety in site characteristics of

interest to this project and were selected for the study. Differences in practices between the

two cities include the following:

• The Tucson, AZ, PHB faces had back plates with yellow reflective borders (see

figure 53), while the Austin, TX, PHB faces did not have back plates (see figure 54).

• The signs used at most of the Tucson, AZ, sites included the CROSSWALK STOP ON

RED (symbolic circular red) (R10-23) sign (see figure 55) and an internally illuminated

PEDESTRIAN CROSSING or CROSSWALK sign (see figure 56). The sign used at the

crossing for the Austin, TX, sites is shown in figure 57. This sign was selected to help

educate drivers regarding appropriate behavior during the flashing red. Recently, FHWA

has received numerous inquiries regarding how to address comprehension issues with the

flashing red phase and is now recommending that if an alternative legend to the R10-23

sign is used, that it be the sign shown in figure 58.

• In advance of the crossing, Tucson, AZ, frequently installed a pedestrian crossing

warning sign (W11-2) (see figure 59). School crossing signs were used at school sites.

• Austin, TX, included the STOP HERE ON RED (R10-6, R10-6a) sign at the stop line.

• The red clearance time (i.e., the elapsed time between start of the vehicular steady red

indication and start of the pedestrian walk indication) was 1 s at the Tucson, AZ, sites and

2 s at the Austin, TX, sites.

• The steady red interval was 8 s for Tucson, AZ, sites and ranged between 9 and 12 s for

the Austin, TX, sites.

• When the Tucson, AZ, sites had a median, a PHB face and a CROSSWALK STOP

ON RED (symbolic circular red) (R10-23) sign was frequently included on a post in

the median.

Figure 55. Photo. Example of sign used in Tucson, AZ.

112

Figure 56. Photo. Example of internally illuminated sign used in Tucson, AZ.

Figure 57. Photo. Sign used in Austin, TX.

The currently preferred format for the type of sign shown in figure 57 is shown in figure 58.

113

Figure 58. Photo. Sign recommended by FHWA to address comprehension issues with the

flashing red phase.

Figure 59. Photo. Example of advance warning sign used in Tucson, AZ.

The crosswalk markings were always located on only one side of the intersection. The PHBs had

between 3 and 4 s of flashing yellow and between 3 and 4 s of steady yellow, consistent with city

policies regarding clearance intervals at signalized intersections. For both cities, the flashing red

duration varied based on the site’s crossing width and ranged from 15 to 29 s.

DATA COLLECTION AND REDUCTION

Data using a multiple video camera setup were collected in November 2014 for the Austin, TX,

sites and in February 2015 for the Tucson, AZ, sites. All observations were collected during

daytime dry weather conditions between 6:30 a.m. and 6:30 p.m. The observers and the video

114

recording device were placed to be inconspicuous from the pedestrians, bicyclists, and motorists.

The goal was to record a minimum of 50 pedestrian crossing events or 4 h of data (the smaller of

the two) at each location, where each crossing event consisted of one or more pedestrian(s)

crossing the entire width of the street. If it appeared that fewer than 50 pedestrian crossing events

would occur within the 4-h block, research team members would cross the street to increase the

sample size of pedestrian crossings. Additional information on the staged pedestrian protocol is

available in Characteristics of Texas Pedestrian Crashes and Evaluation of Driver Yielding at

Pedestrian Treatments.

(35)

The research team members sought to complete data collection efforts

at two sites per day, accounting for travel time between sites and the need to notify local

stakeholders (e.g., school personnel) of their activity. Hence, the periods of peak vehicle and

pedestrian volumes were not necessarily observed.

The video footage was reviewed in several rounds to extract the required observations for

analysis. After the first two rounds, a list of vehicle arrivals, pedestrian arrivals, pedestrian

departures, and PHB actuations was assembled and sorted by site and time. The beacons and

pedestrian signal indications were determined for each event in this list through a series of

computations using the timestamps and the known timing parameters for each PHB.

In the next rounds of video footage review, the computed beacon indications were verified, and

additional detailed observations were extracted including the following:

• Vehicle position relative to the pedestrian for vehicles arriving on a steady or flashing red

beacon indication.

• Driver yielding behavior during steady or flashing red beacon indication.

• Button presses by arriving pedestrians.

• Categorization of pedestrians as staged or non-staged.

• Conflict occurrences.

• Recording whether each driver arriving on steady or flashing red stopped before

proceeding through the crosswalk.

• Recording whether drivers stayed stopped throughout the flashing red indication.

Additional efforts were also undertaken to record any instances of major street vehicles stopping

while the beacon indication was dark as well as to identify minor road driver behaviors during a

beacon actuation. The final dataset reflected over 78 h of video data and included 1,149 PHB

actuations and 1,979 pedestrians who crossed the street.

DRIVER BEHAVIOR FINDINGS

Driver Behavior During Dark Indication

Selected videos were reviewed to identify each occurrence when a vehicle stopped at the

crossing when the PHB was displaying a dark indication. There were several events; however, in

115

almost all cases, it was because of congestion. There were a few cases where the driver stopped

because of a bus or truck loading/unloading or because a pedestrian was in the crosswalk.

Therefore, none of the drivers who stopped at the crossing when the PHB was dark appeared to

be confused regarding the device.

Driver Position Relative to Pedestrian Position During Steady or Flashing Red Indications

When the Driver Drove Across the Crosswalk

The position of the driver and the pedestrian for each driver that drove across the crosswalk

during a steady or flashing red indication was identified. Figure 60 provides an illustration of

driver and pedestrian positions for a crossing. For each cycle and for each approach, the number

of cycles by approach where a given pedestrian-vehicle position combination occurred was

counted. Table 62 summarizes the findings for the 1,252 cycle approaches (1,149 cycles plus

103 cycles where pedestrians were crossing in both directions) for the 20 sites included in the

study. The pedestrian positions were (1) edge of the street clearly indicating the desire to cross,

(2) within the crosswalk on the initial approach (or first half of the crossing), or (3) within the

crosswalk on the second approach (or second half of the crossing). The vehicle was either on the

same approach as the pedestrian or the other approach relative to the pedestrian.

As an example, if a pedestrian was crossing a street that was oriented north and south and was on

the initial approach (say the southbound approach), and if the vehicle was on the other approach,

then the vehicle would be on the northbound approach. In this example, the northbound vehicle

can legally enter the crosswalk during the flashing red portion of the cycle while the pedestrian is

walking eastbound and crossing the southbound approach. Both Arizona and Texas State laws

indicate that vehicles must yield the right-of-way to pedestrians within a crosswalk that are in the

same half of the roadway as the vehicle. So drivers in cases A to F in table 62 would be

considered as not yielding to the pedestrian. Both States also indicate that vehicles are to yield

right-of-way if a pedestrian is approaching closely enough from the opposite side of the roadway

to constitute a danger. Cases G and H in table 62 could possibly fit this situation; however, in the

opinion of those reducing the data, these observations did not have the pedestrian that close to

the vehicle approaching from the other approach. For all those observations, the vehicle on the

other approach entered the crosswalk shortly after the steady red indication had started and the

pedestrian had recently left the curb. A driver entering the intersection during the steady red

indication would be in violation of the beacon indication. Cases A, C, E, G, and I reflect

combinations when the driver would be in violation of the beacon.

For those cases when the driver should yield, the combination with the most yielding violations

was when the pedestrian was at the edge of the street and the drivers entered the crosswalk on the

steady red indication. Case A occurred for 7.7 percent of the observed cycle approaches (96 of

1,252). Most of these non-yielding (and violation) events occurred soon after the beacon changed

to steady red. While it could be argued that a vehicle was not required to yield to the pedestrian

in this situation since the pedestrian was not on the pavement, in the research team’s opinion the

pedestrians in these situation were clearly communicating the intent to cross and were not on the

pavement due to safety concerns. Of course, these drivers were clearly in violation of the steady

red indication. While the research team included cases A and B in the non-yielding counts, the

counts are shown in table 62 so readers can draw their own conclusions. Few, but not zero as

preferred, cycles had a driver entering the crosswalk when the pedestrian was on the same

116

approach (see cases B to F). Reviewing the situations when a vehicle enters the crosswalk when

the pedestrian is in the second half of their crossing (see cases E and F) revealed several

occurrences when the vehicles entered the crosswalk soon after the pedestrian departed the lane.

Figure 60. Illustration. Pedestrian and driver positions when the pedestrian is on the initial

approach and vehicles are present on the same approach and on the other approach.

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Table 62. Pedestrian position and vehicle approach during steady or flashing red

indications.

Case

Pedestrian Position

Vehicle

Approach

Beacon

Indication

Percent of

Cycles

Approaches

Non-Yielding

and/or

Violation

Initial approach, edge of

street

Same

Steady red

7.7

Non-yielding

;

violation

Initial approach, edge of

street

Same

Flashing red

0.0

Non-yielding

Initial approach, moving

within crosswalk

Same

Steady red

0.4

Non-yielding;

violation

Initial approach, moving

within crosswalk

Same

Flashing red

0.2

Non-yielding

Second approach, moving

within crosswalk

Same

Steady red

0.2

Non-yielding;

violation

Second approach, moving

within crosswalk

Same

Flashing red

1.7

Non-yielding

Initial approach, moving

within crosswalk

Other

Steady red

8.8

Violation

Initial approach, moving

within crosswalk

Other

Flashing red

0.2

Neither

Second approach, moving

within crosswalk

Other

Steady red

1.0

Violation

Second approach, moving

within crosswalk

Other

Flashing red

23.6

Neither

Pedestrian and vehicle positions are relative to where the pedestrian started the crossing (see figure 60 for an example).

Results reflect the percent of the 1,252 cycle approaches when the combination of pedestrian and vehicle position occurred.

The total number of cycle approaches (1,252) reflect 1,149 cycles plus 103 cycles when pedestrians were crossing in both

directions for the 20 sites included in the study.

Demonstrates whether this case reflects a non-yielding driver and/or a violation of the red indication.

While it could be argued that a vehicle is not required to yield to the pedestrian in this situation since the pedestrian was not

“on the pavement,” the pedestrians in these situation were clearly communicating the intent to cross and remained on the curb

rather than on the pavement due to safety concerns. These drivers were in violation of the steady red indication.

Driver Yielding Behavior During Steady or Flashing Red Indications

For each pedestrian crossing when the PHB was showing steady or flashing red, the number of

drivers that yielded and did not yield was determined. The driver yielding rates reflected all

available pedestrian crossings regardless of whether the pedestrian was a member of the research

team. A driver was considered to have not yielded to the pedestrian if the driver crossed the

crosswalk markings when the PHB was in either the steady red or flashing red indications and

the pedestrian was at the edge of the street clearly communicating to drivers the intent to cross or

was walking on the same approach as the driver. When the crossing pedestrian was a member of

the research team, the team member would place a foot on the pavement to clearly communicate

the intent to cross. Using only staged pedestrian crossings would have required a much longer

data collection period per site because of the large number of pedestrians crossing at the

intersections. Therefore, the study included non-staged pedestrians. If it appeared that the

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non-staged pedestrian was not clearly communicating the intent to cross, perhaps by not standing

near the edge of the sidewalk, the pedestrian crossing was not included in the study.

Counting the number of vehicles that did or did not yield to a crossing pedestrian was easier with

video data when compared to gathering the data in the field. The video could be replayed to

determine the exact position of a vehicle when the signal indication changed. In addition, the

video allowed for greater consistency between data collectors, as an event could be reviewed by

more than one person. Table 63 provides the driver yielding values for the 20 sites. Overall,

driver yielding for these 20 sites averaged 96 percent. In almost all of the crossings, drivers

appropriately yielded to the crossing pedestrians.

Table 63. Driver yielding values for all 20 sites.

Site

Number

of PHB

Actuations

Number

of Drivers

Yielding

Number of

Drivers Not

Yielding

Driver

Yielding

(Percent)

TU-003

TU-004

162

TU-007

183

TU-021

131

TU-037

248

TU-042

187

TU-059

151

100

TU-070

159

TU-072

230

TU-073

368

TU-090

100

TU-091

AU-04

147

AU-07

256

AU-11

169

AU-16

195

AU-21

139

AU-22

171

AU-24

182

AU-27

Total

1,149

3,359

145

Driver yielding = Percent of approaching drivers who should have yielded and did so.

When reviewing the results by city, Tucson, AZ, had an average yielding rate of 97 percent

while Austin, TX, had an average yielding rate of 94 percent. Affecting the average result for

Austin, TX, were two sites: AU-11 and AU-27. Almost all of the non-yielding vehicles at

AU-11 were northbound vehicles that crossed the crosswalk very soon after the PHB turned

steady red and frequently moved at lower speeds due to high vehicle volumes present or due to

an active reduced speed limit for the school zone. At AU-27, about half of the non-yielding

vehicles entered the crosswalk very soon after the PHB turned to steady red (six vehicles), with

the remaining non-yielding vehicles (four vehicles) entering the crosswalk before the pedestrian

119

completely cleared that half of the roadway. Other potential differences between Austin, TX, and

Tucson, AZ, that could affect yielding rates could be the length of time the PHB treatment has

been used in the city (they have been in Tucson, AZ, for many more years than Austin, TX), the

use of back plates (common in Tucson, AZ, not in Austin, TX), beacon face mounting locations

(Tucson, AZ, typically mounted one face over the approach, one to the right of the approach, and

one in the median if a raised median was present, while Austin, TX, typically mounted two faces

over the approach), and the use of supplemental signs on the mast arm (Tucson, AZ, sites

typically included a CROSSWALK or PEDESTRIAN CROSSWALK sign in addition to the

regulatory CROSSWALK STOP ON RED (symbolic circular red) (R10-23) sign while

Austin, TX, used a combination sign that provided the additional information of STOP ON

FLASHING RED (symbolic flashing circular red) THEN PROCEED IF CLEAR).

There were different driver behaviors within those situations when drivers did not yield to the

pedestrians. Reviewing the conditions when drivers were non-compliant revealed that several of

the non-compliant drivers entered the crossing just after the PHB changed from steady yellow to

steady red and the pedestrian was at the edge of the street. Given that these drivers were provided

with at least 7 to 8 s of warning by way of the flashing and steady yellow indications, and based

on the posted speed limits for these sites (30 to 45 mi/h), the drivers did have sufficient warning

to stop upstream of the crossing but decided not to stop. In a few of the cases, the driver

proceeded through the crosswalk just after the pedestrian had cleared the lane.

Driver Behavior During Flashing Red Indication

For about 20 percent of the observed PHB actuations, vehicles were not present during the

flashing red indication. When a queue of vehicles was present during the flashing red indication,

about half of the crossing actuations included at least one driver who did not completely stop

prior to entering the crosswalk. About 5 percent of the actuations included at least one driver

who stopped on the flashing red indication and remained stopped until the dark indication. This

behavior was observed at about the same frequency in both cities. In some cases, these drivers

might not have realized that they could proceed after stopping if their half of the crosswalk was

clear of pedestrians. However, there were many cases where the stopped drivers could not

proceed for one or more of the following reasons:

• The driver arrived in the last few seconds of the flashing red indication.

• The driver’s half of the crosswalk was continually occupied by pedestrians, some of

whom may have been slow or may have started their crossing after the start of the

flashing red indication (i.e., during their flashing do not walk indication).

• Minor movement drivers were proceeding without complying with their requirements

to stop.

Impact of PHB Actuation on Minor Movement Drivers

Interested practitioners have raised questions about how PHBs affect minor movements that do

not pass through the beacon-controlled crosswalk. These movements may include one left-turn

movement originating from the major street (LT), up to two through movements on the

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minor-street approaches (TH1 and TH2), a left-turn movement originating from the minor street

(LT1), and a right-turn movement originating from the minor street (RT2). These movements are

illustrated in figure 61. Specifically, the following questions have been asked:

• When the PHB is active, do minor movement drivers take advantage of the gaps that have

been created in the major street traffic and complete their movements more easily?

• When the PHB is showing the flashing red indication, does the intersection function like

a four-way stop-controlled intersection?

Figure 61. Illustration. Minor movements at a PHB-controlled crosswalk.

The research team conducted a review of the video footage to answer these questions. The

review included 17 of the 20 PHB sites. Sites TU-091, AU-07, and AU-27 were excluded

because they lacked the relevant movements due to site layout (midblock crossing, stop line

located well downstream of the movement, or the movement is prohibited) or the movements

existed but were negligible in volume. Not all minor movements were present at every site

because some of the sites were three-legged intersections or the fourth leg was a driveway

with minimal traffic (e.g., TU-070). The minor movements permitted at the sites are listed in

table 64.

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Table 64. Minor movements permitted at the sites.

Movements Present

Sites

LT, LT1, TH1, TH2, and RT2

(four-legged intersection)

TU-003, TU-021, TU-037, TU-042,

TU-059, TU-072, TU-073, TU-090,

AU-04, AU-16, and AU-24

LT and LT1 (three-legged intersection)

TU-004, TU-070, and AU-11

LT and RT2 (midblock site with

nearby driveway)

AU-21 and AU-22

A total of 850 minor movement vehicles were observed. This set of vehicles includes only those

that arrived and/or departed while the PHB was active (i.e., not displaying the dark indication).

The vehicle distribution by site and movement code is provided in table 65. Note that the pairing

of the LT and RT2 movements represents a significant portion of the sample size, particularly at

sites TU-042, TU-072, TU-073, AU-21, and AU-22. These movements at these sites represent

about 62 percent of the sample size (525 of 850 vehicles). Additional turning movements were

possible but not included in the minor movement analysis. Turning vehicles that originated from

the minor approaches and passed through the crosswalk are included in the other analyses

documented in this chapter.

Table 65. Minor movement vehicle distribution by site.

Site

Movement Code

LT1

RT2

TH1

TH2

Total

TU-003

TU-004

TU-007

TU-021

TU-037

TU-042

154

116

283

TU-059

TU-070

TU-072

TU-073

TU-090

AU-04

AU-11

AU-16

AU-21

AU-22

AU-24

Grand Total

303

178

316

850

The vehicle distribution by movement code and PHB indication on vehicle arrival is provided in

table 66. Arrival is defined as the time that the vehicle stopped at the stop line on its approach or

the time that the vehicle crossed the stop line if it did not stop. The vehicle distribution by

movement code and PHB indication on its departure is provided in table 67. Departure is defined

as the time that the vehicle exited the intersection.

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Table 66. Minor movement vehicle distribution upon vehicle arrival.

Movement

Code

PHB Indication

Dark

Flashing

Yellow

Steady

Yellow

Steady

Red

Flashing

Red

Total

172

303

LT1

178

RT2

186

316

TH1

TH2

Grand Total

125

229

443

850

Table 67. Minor movement vehicle distribution upon vehicle departure.

Movement

Code

PHB Indication

Dark

Flashing

Yellow

Steady

Yellow

Steady

Red

Flashing

Red

Total

107

160

303

LT1

178

RT2

111

165

316

TH1

TH2

Grand Total

340

407

850

As shown by the total count numbers in the rightmost columns of table 66 and table 67, the

turning movements were the most commonly observed movements, especially movements LT

and RT2, which were complementary. The through movements were less common, partly

because these movements did not exist at the three-legged intersection sites.

The total count numbers in the bottom rows of table 66 and table 67 generally reflect the

proportion of time that the PHB was active. The number of vehicles arriving or departing on the

dark indication is small because vehicles that both arrived and departed on the dark indication

were excluded from this analysis.

The distribution of minor movement vehicles by PHB indication upon arrival and PHB

indication upon departure is shown in table 68. As shown, most vehicles departed on either

steady red or flashing red regardless of when they arrived. However, a small number of vehicles

experienced notable delay because they arrived while the PHB was active but could not depart

until the next dark indication. Specifically, nine vehicles arrived during flashing red but did not

depart until the steady yellow or steady red indication during the next PHB actuation. These long

delays occurred during periods when the major street volume was sufficiently high enough that

no acceptable turning or crossing gaps were available during the dark indication, and the major

street drivers were aggressive during the flashing red indication such that they either did not

stop consistently or stopped briefly and proceeded without waiting for other drivers’ movements

to proceed.

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Table 68. Minor movement vehicle distribution by PHB indication.

PHB

Indication

Upon Arrival

PHB Indication Upon Departure

Dark

Flashing

Yellow

Steady

Yellow

Steady

Red

Flashing

Red

Total

Dark

108

125

Flashing yellow

Steady yellow

Steady red

180

229

Flashing red

340

443

Grand Total

340

407

850

To examine stop compliance, drivers were classified as violators under the following conditions:

• The driver was making the LT movement, arrived during a steady or flashing red

indication, and did not stop. At all sites, the stop line was marked across all through and

left-turn lanes, such that these drivers were required to stop even though they do not pass

through the crosswalk.

• The driver was making the LT1, TH1, TH2, or RT2 movement and did not stop. In other

words, the driver failed to stop at the STOP sign.

Each driver in the database was thus classified as a violator or non-violator, and violation rates

were computed for each site based on the amount of video footage that was collected at the site

(i.e., violations per hour). These findings are shown in table 69. The percentage of drivers not

stopping varied widely, from 30.8 to 89.9 percent. Many of these percentages were computed

based on the small number of minor movement vehicles that were observed while the PHBs were

active. In terms of rate, violations at most sites were less common than five violations/h (or

one violation every 12 min).

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Table 69. Minor-movement violation rates by site.

Site

Number of

Vehicles

Percent Not

Stopping

Violation Rate

(Violations/h)

TU-003

0.4

TU-004

2.6

TU-007

0.5

TU-021

2.2

TU-037

1.9

TU-042

283

46.7

TU-059

7.2

TU-070

1.5

TU-072

3.9

TU-073

7.5

TU-090

1.6

AU-04

6.3

AU-11

3.0

AU-16

0.8

AU-21

10.2

AU-22

40.6

AU-24

5.4

The following seven sites were found to have violation rates in excess of five violations/h:

TU-042, TU-059, TU-073, AU-04, AU-21, AU-22, and AU-24. A more focused examination of

these sites was conducted by computing violation rates for each movement. The results of this

examination are shown in table 70. Similar computations were performed for all movements at

all sites, but none of the movements omitted from table 70 had violation rates higher than

four violations/h.

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Table 70. Selected minor-movement violation rates by site and movement code.

Site

Movement

Code

Number of

Vehicles

Percent

Not Stopping

Violation Rate

(Violations/h)

Notes

TU-042

154

25.4

Entering a collector near a

high school campus

RT2

116

20.4

Exiting a collector near a

high school campus

TU-059

LT1

4.2

Exiting a residential

neighborhood with a nearby

elementary school campus

TU-073

RT2

4.9

Exiting a residential

neighborhood with a nearby

high school campus

AU-04

LT1

4.1

Exiting a collector near a

supermarket

AU-21

RT2

7.8

Exiting a supermarket

AU-22

15.4

Entering a supermarket

RT2

25.2

Exiting a supermarket

AU-24

LT1

4.1

Exiting a supermarket

All of the movements listed in table 70 were located close to major traffic generators such as

schools or supermarkets. In fact, the majority of the violations observed at site TU-042 occurred

in the morning period before classes began for the day. Violations were also frequent at site

AU-22; however, it should be noted that the LT movement at this site occurred from a TWLTL,

and the stop line for the PHB was marked in the adjacent through lanes but not in the TWLTL,

suggesting that the stopping requirement might not have been intended for this movement. This

marking practice differed from the practice at other Austin, TX, sites, where stop lines were

extended through all approach lanes. Sites AU-21 and AU-22 were similar in that their LT and

RT2 movements went into or out of supermarkets, but the LT movement at site AU-22 did not

have a high violation rate. The major street at this site was a four-lane undivided city arterial, so

left-turning drivers did not have a turn bay and were often blocked from completing the LT

movement while the PHB was active.

Based on the preceding information, the following observations could be made:

• Minor movement drivers took advantage of gaps that were created in the major street

traffic while the PHBs were active.

• PHB sites did not necessarily operate like four-way stops while the flashing red

indication was shown. At sites near major traffic generators, movements entering or

exiting the traffic generator tended to dominate the operation of the intersection, and stop

compliance for these movements tended to be low.

These trends may occur at any type of unsignalized pedestrian crossing treatment while

pedestrians are present. They are likely not unique to PHB-controlled crossings.

126

PEDESTRIAN BEHAVIORS FINDINGS

Pedestrian Departures by Indication

Of the 1,979 pedestrians crossing the street, 290 were research team members who always

crossed when the beacons showed steady or flashing red to the motorists. Most of the remaining

1,689 general public pedestrians departed when the beacon showed a steady red indication to

the drivers. As shown in table 71, 80 percent departed during the steady red or flashing red

indications. Approximately 13 percent of the pedestrians departed while the PHB was still in the

vehicle clearance intervals.

Table 71. Pedestrian departures by indication.

Site

Dark

Flashing

Yellow

Steady

Yellow

Steady

Red

Flashing

Red

Total

TU-003

TU-004

TU-007

TU-021

TU-037

TU-042

101

155

TU-059

TU-070

TU-072

TU-073

TU-090

TU-091

AU-04

AU-07

132

198

AU-11

AU-16

AU-21

104

AU-22

123

190

AU-24

130

209

AU-27

Grand Total

(Percent)

124 (7)

43 (3)

163 (10)

1,203 (71)

156 (9)

1,689 (100)

The PHB was located within the coordinated corridor where the timing of when the PHB was active was

influenced by the nearby coordinated signals.

For the pedestrian crossings observed that did not include the research team member crossings,

only 124 pedestrians (7 percent) left during the dark indication. For the majority of these

pedestrians, the roadway volume was such that the pedestrian was able to find sufficient gaps to

cross. The volume per minute per lane was less than four vehicles/min/lane for the majority of

these crossings. Figure 62 shows the cumulative distributions of the 1 min/lane volume for those

pedestrians that departed during the dark indication (i.e., blue dashed line) and those that

departed during an active indication (i.e., red solid line). Pedestrians were more likely to wait for

127

the PHB to be active before starting to cross at the higher roadway volumes, as shown by the

location of the red solid line to the right of the blue dashed line. For example, the cumulative

distributions reached the value of 80 percent at volumes of about six vehicles/min/lane for the

blue dashed line and about eight vehicles/min/lane for the red solid line. This trend shows that

only 20 percent of non-compliant pedestrians were still willing to cross if the roadway volume

exceeded six vehicles/min/lane (i.e., 1,440 vehicles/h at a four-lane site). Less than 5 percent of

non-compliant pedestrians crossed when the roadway volume exceeded 10 vehicles/min/lane.

Conversely, roughly half of the compliant pedestrians crossed at roadway volumes exceeding

six vehicles/min/lane.

Figure 62. Graph. Volume cumulative distribution when pedestrian started the crossing.

Two of the PHB sites were located within coordinated corridors where the timing of PHB

activation was influenced by the nearby coordinated signals. The other 18 sites were at “hot

button” sites, where the PHB sequence starts following the push of the pushbutton. About

one-third of the pedestrians (36 of 124) who departed during the dark indication were at the sites

where the PHB is coordinated with nearby signals. While a large value, there were other sites

with more pedestrians departing during the dark indication, both in terms of number of

pedestrians and proportion of pedestrians observed at the site. The percentage of pedestrians

departing during the dark indication was 13 percent at the coordinated sites and 6 percent at the

hot button sites. Departures on dark were much less frequent at the coordinated site that had

pedestrian pushbuttons with red lights that illuminate when the button is pressed. The

coordinated site with the red-lighted buttons had 10 percent of pedestrians departing on dark,

while the coordinated site with the non-lighted buttons had 20 percent of pedestrians departing

on dark.

Pedestrian Actuation of the PHB

Of the 1,979 pedestrians crossing the street, 290 were research team members who crossed using

a staged pedestrian protocol and always activated the PHB. The remaining 1,689 general public

pedestrians were coded by whether they pushed the pedestrian pushbutton or did not push the

pushbutton subdivided by whether the PHB was already active or not active when they arrived to

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the crossing. Table 72 shows the number of pedestrians by action. Overall, most pedestrians

(average of 91 percent) who could have activated the PHB did. As can be seen in table 72, there

were some sites with lower percent actuations (e.g., AU-04) and other sites where every

pedestrian crossed with an activated PHB (e.g., TU-03, TU-04, and AU-11).

Table 72. Number of pedestrians by site who pushed, did not push, or did not push because

PHB was active.

Site

Pushed

Did Not

Push

Did Not Push;

Already

Active

Total

Number of

Pedestrians

Percent

TU-003

100

TU-004

100

TU-007

TU-021

TU-037

TU-042

116

155

TU-059

TU-070

TU-072

TU-073

TU-090

TU-091

AU-04

AU-07

165

198

AU-11

100

AU-16

AU-21

104

AU-22

147

190

AU-24

162

209

AU-27

Grand Total

1,397

135

157

1,689

Percent = Percentage reflecting the ratio of the number of pedestrians that pushed the button to the

number of pedestrians that if they would have pushed the button would have triggered a change in

the device (i.e., those that pushed and those that did not when the device was dark). In other words,

this column does not include those pedestrians who arrived when the PHB was active.

The PHB was located within coordinated corridor where the timing of when the PHB was active was

influenced by the nearby coordinated signals.

A plot of the percentage of pedestrians who activated the PHB when arriving to the crossing

when the PHB was not already active is shown by posted speed limit in figure 63, by crossing

distance in figure 64, and by hourly volume in figure 65 . A review of these plots shows trends

for the highest values. A high number of pedestrians (93 percent) activated the device on the

45-mi/h posted speed limit road. For the 40-mi/h or less roads, a large range of actuation was

observed—between 75 and 100 percent. The percentage of pedestrians pushing the button was

always greater than 83 percent for the longer crossing distances (i.e., longer than 110 ft).

129

The 1-min volume count nearest to the arrival time of the pedestrian was determined. The

number of pedestrians by their action was summed for each 1-min count value for all 20 sites.

The 1-min counts with less than 20 pedestrians crossing were omitted for the plot shown in

figure 65. The 1-min count was adjusted to an hourly equivalent value by multiplying the 1-min

count by 60. When the hourly volume for both approaches was 1,500 vehicles/h or more, the

percent of pedestrians activating the PHB was always 92 percent or more.

Figure 63. Graph. Percentage of pedestrians pushing the button, by posted speed limit.

Figure 64. Graph. Percentage of pedestrians pushing the button, by crossing distance.

Figure 65. Graph. Percentage of pedestrians pushing the button, by 1-min volume counts

adjusted to hourly counts.

130

Some of the pedestrians who activated the PHB departed prior to the walk indication. The

beacon indication when the pedestrian departed is shown in table 73. Most of the pedestrians

who departed after pushing the pushbutton either left during the steady red (82 percent) or the

flashing red (2 percent). About 1 percent of those who activated the device left while the device

was still dark, with most occurring at the two locations where the PHB was within a coordinated

corridor (sites TU-072 and AU-07).

Table 73. Indication when pedestrian departed for those that activated the PHB.

Site

Dark

Flashing

Yellow

Steady

Yellow

Steady Red

Flashing

Red

Total

TU-003

TU-004

TU-007

TU-021

TU-037

TU-042

116

TU-059

TU-070

TU-072

TU-073

TU-090

TU-091

AU-04

AU-07

129

165

AU-11

AU-16

AU-21

AU-22

109

147

AU-24

129

162

AU-27

Grand Total

(Percent)

(1.2)

(3.0)

162

(11.6)

1,148

(82.2)

(2.0)

1,397

(100)

The PHB was located within coordinated corridor where the timing of when the PHB was active was

influenced by the nearby coordinated signals.

CONFLICTS FINDINGS

All occurrences of pedestrian/vehicle conflicts and erratic maneuvers were noted when observed

in the video footage. A conflict is defined based on the following criteria:

• The vehicle suddenly changes path, speed, or both.

• The vehicle’s brake lights illuminate unexpectedly during a turning maneuver.

• The pedestrian exhibits one or more of the following behaviors:

o Slows down due to vehicle presence.

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o Stops and waits for the vehicle to pass through the crosswalk.

o Steps sideways to avoid a vehicle.

o Runs or speeds up to avoid a vehicle.

o Turns back to origin curb to avoid a vehicle.

o Makes another sudden change in path or speed in response to an

approaching vehicle.

In the 78 h of video footage, 54 conflicts were observed. The distribution of the conflicts

categorized by vehicle maneuver was 38 for through vehicles, 10 for left-turning vehicles, and

6 for right-turning vehicles. Distribution by vehicle maneuver was analyzed because of concerns

about turning traffic operations while the PHB was serving pedestrians. While major street

through vehicles were stopped by the PHB, turning drivers originating from the minor

approaches could see significant gaps form on the major street and may have taken this

opportunity to turn, possibly leading to conflicts with pedestrians in the crosswalk.

The conflicts were tabulated by vehicular beacon indication and vehicle maneuver, as shown in

table 74. Beacon indication can be used to classify pedestrians as compliant (i.e., they departed

the corner on a steady red indication while the walk indication was displayed) or non-compliant

(i.e., they departed on one of the other beacon indications while the flashing or steady do not

walk indication was displayed). Slightly less than half of the observed conflicts occurred during

the dark beacon indication and involved a through vehicle. These conflicts usually involved

pedestrians who either crossed without pushing the button or pushed the button but did not wait

for the walk indication and then paused in the raised curb median while crossing. The latter case

occurred most frequently at site TU-072 (14 conflicts), which had 6 lanes and operated in

coordinated mode.

Table 74. Vehicle and pedestrian conflicts by beacon indication and vehicle maneuver.

Beacon

Indication

Vehicle Maneuver

Through

Left Turn

Right Turn

Total

Dark

Flashing yellow

Steady yellow

Steady red

Flashing red

Total

Indicates

pedestrians who were not in compliance with signal indication.

A notable number of conflicts involving left-turning vehicles was observed at site AU-21. This

site was located near a well-patronized supermarket and a bus stop, with a driveway located

about 45 ft away from the crosswalk. At that distance, drivers making left turns out of the

supermarket parking lot would be able to turn onto the major street but would still be oriented

diagonally when encountering the crosswalk. Many of these drivers encroached on the crosswalk

while attempting to complete their turning maneuvers, leading to conflicts if pedestrians were

132

present. A similar geometric layout was present at site AU-22, but this site did not exhibit

conflicts because the driveway was located farther away from the crosswalk (about 60 ft) and

afforded left-turning drivers more room to complete their turning maneuvers and wait for

pedestrians to clear.

To account for exposure differences across the sites, conflict rates were computed using

three conflict rate measures, as provided in table 75. The measure of conflicts per pedestrian-

vehicle accounted for the influence of both pedestrian and vehicle exposure. For all three rate

measures, the conflict rate was found to be higher for non-compliant pedestrians than for

compliant pedestrians. These rates were interpreted in terms of total exposure (pedestrians or

vehicles or pedestrian and vehicles). That is, at a site that experiences 100 pedestrians crossing

per hour, it is expected that 2.73 conflicts would be observed per hour, of which 1.06 would

involve compliant pedestrians and 1.67 would involve non-compliant pedestrians.

Table 75. Pedestrian-vehicle conflict rates.

Pedestrian

Group

Conflicts per

100 Observed

Pedestrians

Conflicts per

10,000 Vehicles

All

2.73

4.15

Compliant

1.06

1.61

Non-compliant

1.67

2.54

Notable conflict rates were observed at site TU-072 because of the high number of non-

compliant pedestrians. This site operated in coordinated mode with buttons that did not provide

audible or visual cues indicating that the button press had been registered. As a result, many

pedestrians pushed the button but may have believed that the PHB was malfunctioning or may

have grown impatient while waiting for service and crossed the major street before the walk

indication was displayed. Similar behavior was not observed at site AU-07, which operated in

coordinated mode but with buttons that had small red lights that illuminated when the button

was pressed.

Notable conflict rates for both compliant and non-compliant pedestrians were also observed at

several sites where the PHBs were located near supermarkets and multiple bus stops. At these

sites, many bus riders walked through the supermarket parking lots or ran across the major street

while transferring between bus lines. The presence of bus stops near an access point with

significant turning vehicle volumes tended to result in higher conflict rates.

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CHAPTER 7. SUMMARY/CONCLUSIONS, DISCUSSION,

AND FUTURE RESEARCH NEEDS

OVERVIEW

This chapter provides summaries and conclusions along with a discussion of the implications of

the findings or each of the studies. The chapter concludes with a list of future research needs.

CLOSED-COURSE STUDY

Summary/Conclusions

The closed-course study was designed to quantify drivers’ ability to detect pedestrians within

and around a crosswalk (a measure of disability glare) and quantify discomfort glare ratings

associated with LEDs in traffic control devices. Participants drove the study vehicle to the

starting location where they parked the vehicle 200 ft from sign assemblies that consisted of a

pedestrian crossing sign with LEDs within the sign face and LEDs in rectangular beacons above

and below the sign. After the driver placed the vehicle into park, they were asked to set occlusion

glasses on their faces. The occlusion glasses obscured the participants’ vision by going opaque

when there was no power supplied to them and going clear when power was supplied.

Once the participants’ vision was occluded, technicians placed a static cutout photo of a

pedestrian (either 54 inches tall to represent a child or 70 inches tall to represent an adult) within

the crosswalk located near the sign assemblies. An experimenter then restored the participants’

vision, and the participants were asked to identify the direction the pedestrian was traveling (to

the left, to the right, or not present) as quickly as possible using a button box. When the

participants pressed the button on the button box, the glasses turned opaque again. Following the

participants’ identification of the pedestrian’s direction, the researcher asked the participants to

rate the intensity of the LED (comfortable, irritating, or unbearable) before asking the field crew

to set up the next condition. This process was repeated for various combinations of LED

brightness, LED locations, pedestrian positions, and flash patterns. This portion of the study was

stationary, and after completion, the participants drove to the check-in location and completed a

laptop survey that asked a series of questions to obtain the participants’ opinions regarding flash

patterns for LEDs used with signs.

To increase the number of flash patterns tested in the study but to keep within a reasonable

testing period, data were collected within two sets. Within each set, two flash patterns were

tested for the LEDs in rectangular beacons and two flash patterns for the LEDs within sign. For

set I, the study was conducted during both daytime and nighttime. For set II, the study was only

conducted during the nighttime. During the testing of set I, it was determined that night was the

more critical condition, which is why only nighttime data were collected during set II.

A summary of the findings for LED intensity and location along with flash pattern is provided in

table 76. Table 77 summarizes the findings for pedestrian height, pedestrian position, and

participant age. Following is an overview of the key findings from this research study.

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Table 76. Summary of results for LED intensity and location along with flash pattern.

Variable

LED Intensity

Flash Pattern

LED Location

Detection

time

• Night: Detection time was

longer when intensity was

higher—8.5 percent greater

for 2,200 candelas compared

to when the LEDs were off.

• Day: 2.4 percent greater for

2,200 candelas compared to

when LEDs were off.

• Night: 2-5 flash pattern

when used above or below

sign yielded detection

time 6 percent longer

than no flash pattern at

all. Using the wig-wag

pattern above or below

sign resulted in a

13.7 percent increase in

detection time compared

to no flash pattern at all.

• Day: 2-5 flash pattern

when used above or below

sign caused a 5.2 percent

increase in detection time

compared to no flash

pattern at all.

• Night: Detection time was

fastest when LEDs were

above the signs. In

comparison, there was a

6 percent increase in

detection time when

LEDs were within the

sign and 12 percent

increase when LEDs were

below the sign.

• Day: Not statistically

significant.

Detection

accuracy

• Night: Accuracy was lower

when intensity was higher.

Odds of accurate detection

for 2,200 candelas reduced

to 0.58 times the odds for

0 candelas.

• Day: Not statistically

significant.

• Night: Not statistically

significant.

• Day: Not statistically

significant.

• Night: Higher accuracy

when LEDs were above

the sign. In comparison,

odds of accurate

responses for LEDs below

were 0.54 times the odds

for LEDs above.

• Day: Not statistically

significant.

Discomfort

• Night: Discomfort was

higher when intensity was

higher. Odds of increased

discomfort for

2,200 candelas was

9.43 times the odds when

LEDs were off.

• Day: Discomfort was higher

when intensity was higher.

Odds of increased

discomfort for

2,200 candelas was

7.05 times the odds when

LEDs were off.

• Night: When LEDs were

active, the odds of

increased discomfort

were about 8 times the

odds for no LEDs flashing

(statistically significant).

No difference between

either 2-5 or wig-wag

patterns compared to the

rest of patterns. No

difference between 2-5 and

wig-wag patterns.

• Day: Not statistically

significant.

• Night: Odds of higher

discomfort when the

LEDs were below

1.86 times the odds when

LEDs were above. No

difference between within

and above locations.

• Day: Not statistically

significant.

Note: Bold text indicates the results were statistically significant.

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Table 77. Summary of results for pedestrian height and position and participant age.

Variable

Ped Height

Pedestrian Position

Participant Age

Detection

time

• Night: Detection time was

longer when the

pedestrian was short

rather than tall. There

was a 3.6 percent

detection time increase for

the short pedestrian.

• Day: Detection time was

longer when the

pedestrian was short

rather than tall. There

was a 3.9 percent increase

for the short pedestrian.

• Night: Detection time

shorter for center position

as compared to either side.

• Day: Detection time

shorter for center position

as compared to either side.

• Night: 0.5 percent increase in

detection time per additional

year of driver age.

• Day: 1 percent increase in

detection time per year of

driver age.

Detection

accuracy

• Night: Detection accuracy

was higher when the

pedestrian was tall

compared to short. Odds

of accurate detection for

the short pedestrian were

0.65 times the odds for the

tall pedestrian.

• Day: Detection accuracy

was higher when the

pedestrian was tall

compared to short. Odds

of accurate detection for

the short pedestrian were

0.64 times the odds for the

tall pedestrian.

• Night: Center had more

accurate responses

compared to either side.

Higher accuracy at center

compared to either side.

• Day: Same trend as night,

but evidence is only

suggestive.

• Night: Odds of accurate

detections for oldest

participants (85 years old)

about 0.16 times the odds for

the youngest (21 years old).

• Day: Odds of accurate

detections for oldest

participants (83 years old)

are about 0.04 times the odds

for the youngest

(19 years old). However, this

difference is not practically

significant, since both

accuracy rates for these

participants are above 95%.

Discomfort

• Night: Odds of higher

discomfort for the short

pedestrian were 0.86 times

the odds for the tall

pedestrian.

• Day: Not statistically

significant.

• Night: Odds of higher

discomfort nearly doubled

(i.e., multiplicative factor

of 1.84) when placing

pedestrian at either side,

compared to center of the

crosswalk. No difference in

discomfort level between

pedestrian and no-pedestrian

conditions.

• Day: Not statistically

significant.

• Night: Odds of higher

discomfort changed by a

multiplicative factor of 0.98

with each additional year of

age.

• Day: Not statistically

significant.

Note: Bold text indicates the results were statistically significant.

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Average nighttime detection time for the participants to search and determine which direction a

cutout pedestrian was walking was 1.473 and 1.292 s for older and younger participants,

respectively. Average daytime detection time for the participants was, as expected, faster

(1.281 and 0.971 s for older and younger participants, respectively, during the day).

LED intensity had a measurable adverse impact on detection time at night but not during the day.

Under nighttime conditions, detection time increased 8.5 percent for 2,000 candelas compared to

0 candelas (no LEDs). Similar to detection time, LED intensity adversely affected accuracy at

night but not during the day. Regarding discomfort glare, LED intensity had an adverse impact

under both daytime and nighttime conditions.

LED location affected nighttime detection times but had no detectable daytime effect. At night,

detection time was 6 percent longer for LEDs below compared to LEDs within (or 12 percent

longer for LEDs below compared to LEDs above). Likewise, detection times with LEDs within

were 6 percent longer than for LEDs above. Discomfort glare was different by LED position at

night with higher discomfort level with LEDs below compared to LEDs above.

Flash pattern affected detection times during both nighttime and daytime conditions. During the

day, only the 2-5 flash pattern had a significantly larger detection time (5.2 percent longer) than

no flash pattern. At night, both the 2-5 and wig-wag flash patterns were found to delay detection

compared to no pattern (increases of 6.0 and 13.7 percent, respectively). For accuracy and

discomfort glare, no significant differences among flash patterns were found under both daytime

and nighttime conditions.

Pedestrian height impacted detection time both day and night. During the day, detection time for

the short pedestrian increased by 3.9 percent during the day and by 3.6 percent at night compared

to detection time for the tall pedestrian. Similarly, accuracy was higher when the experiment

involved the tall pedestrian instead of the short pedestrian.

Pedestrian position had an impact on detection time, both during the day and at night. Under both

conditions, detection was faster when the pedestrian was located at the center of the crosswalk.

Also, for both light conditions, detection times for pedestrian at left or at right were not

statistically different from each other. Accuracy trends by pedestrian position were similar to

detection time trends, though only at night were these trends statistically significant. Pedestrian

position was found to influence discomfort glare at night with higher discomfort when searching

for the pedestrian at either side of the crosswalk as compared to when the pedestrian was at

the center.

Age of participants drew a clear gradient of increasing detection times, both during the daytime

and nighttime. Accuracy of detection decreased by age, both during the day and at night.

The survey found that multiple flashes within a short time period were better at communicating

the need to stop for a pedestrian at a crosswalk as compared to few or no flashes such as the

wig-wag or no LED illuminated conditions.

The survey also found that when observing close-up views of a sign assembly consisting of a

pedestrian crossing sign and LEDs either embedded or below the sign, the patterns that used

137

multiple pulses communicated greater urgency in needing to yield to a pedestrian. The

participants indicated that LEDs below communicated more urgency than the LEDs within.

When asked to count the number of pulses in a light bar with the 2-5 flash pattern, the majority

of the participants (77 percent) correctly counted two pulses; however, almost none of the

participants correctly counted the five faster pulses. Only four participants provided the correct

answer. The majority of the participants (55 percent) saw three pulses when five pulses

were presented.

Discussion

The flash pattern along with the brightness of LEDs, whether used within beacons or embedded

in a sign, can help draw drivers’ attention to a device and the area around the device. However,

characteristics of the LEDs, such as brightness or flash pattern, can also make it more difficult

for drivers to see objects around a device (disability glare) or result in drivers looking away from

a device (discomfort glare). This study used several measures to gain an understanding of how

brightness and flash pattern affect driver’s ability to detect a pedestrian within a crosswalk.

These measures included time to correctly identify pedestrian walking direction and participant’s

rating of discomfort glare.

The brightness intensity of the LEDs used in this study ranged from 0 candelas (i.e., the LEDs

were not on) to 2,200 candelas. In another FHWA study, devices installed in the field were

measured with higher brightness intensity; the range used in this closed-course study did not

reflect the wider range currently being used in on-road installations.

(5)

The brightness of LEDs in

the field appears to be highly variable. Part of the reason could be that current requirements only

specify a minimum intensity. The minimum intensity is defined within SAE Standard J595; the

minimum measured at a horizontal angle of 0 degrees and vertical angle of 0 degrees for class I

yellow peak luminous intensity is 600 candelas.

(15)

For this study, brightness intensity did not have a significant impact on detection time for

daytime conditions while being significant for nighttime conditions. Nighttime detection time

increased by 8.5 percent at 2,200 candelas (the maximum used in the study) as compared to

when the LEDs were off. The brighter the LEDs, the longer it took for the participants to

determine which direction the pedestrian was facing. In other words, lower brightness was

associated with reduced disability glare.

Some of the flash patterns used with the devices were associated with longer detection times.

Of the six flash patterns tested, only two flash patterns—the 2-5 and the wig-wag flash

patterns—were associated with statistically significant longer detection times as compared to the

no flash pattern condition. Both of these patterns had longer on times (the 2-5 flash patter was on

69 percent of the cycle, and the wig-wag pattern was on 100 percent of the cycle) as compared to

the other patterns (range of 10 to 38 percent on time). The LEDs being constantly on may have

caused the participants to look away from the LEDs. In addition, the lack of sufficient dark

period(s) between the flashes may have limited the participants’ ability to adequately search for

the pedestrian. A better flash pattern than the current 2-5 flash pattern should retain multiple

pulses (since the survey results found that participants felt patterns with multiple pulses were

associated with greater urgency), more dark periods (since the study found longer detection time

138

for patterns with less dark periods), and a maximum intensity that limits discomfort when

attempting to detect objects while still commanding driver attention (i.e., resulting in high

driver yielding).

The findings for pedestrian position and LED location indicate that the distance between the

pedestrian and the light source affected the ability to quickly detect the pedestrian. When the

pedestrians were located at the edge of the crosswalk (i.e., next to the assembly) and when the

LEDs were located below the sign (i.e., closer to the pedestrian), detection time was longer and

detection accuracy was lower. These findings support the idea of placing the LEDs above rather

than below the sign and investigating the benefits of locating the LEDs over the roadway rather

than on the roadside.

The shorter height pedestrian required more time to detect and had lower detection accuracy,

which were expected findings. The smaller target provided by a child-sized pedestrian was a

known concern for pedestrian crosswalks.

This study found strong evidence that there was potential value in mounting the LEDs above the

sign instead of below. Nighttime detection time was fastest when LEDs were above the signs,

with a 6 percent increase when LEDs were within the sign and a 12 percent increase when LEDs

were below the sign. Both of these findings were statistically significant. This finding supports

the idea that separating the pedestrian from the light source may benefit the driver’s ability to

search and identify the location of the pedestrian.

ABOVE-BELOW (OPEN-ROAD) STUDY

Summary/Conclusion

Based on the findings from the closed-course study, the following combination was examined in

open-road settings: beacons located above the sign as compared to when the beacons were

located below the sign.

The RRFB in positions above and below the pedestrian crossing sign were installed at 13 sites

located in 4 States (Aurora, IL; Douglas County, CO; Marshall, TX; and Phoenix, AZ). At all

13 sites, after collecting data for the initial beacon position, the beacons were moved to the

opposite position. The same flash pattern was used regardless of beacon position. The research

team used a staged pedestrian protocol to collect driver yielding data to ensure that oncoming

drivers received a consistent presentation of approaching pedestrians.

Because a previous study that included RRFBs found that posted speed limit, crossing distance,

and city influenced driver yielding, the initial analyses were conducted with those variables

along with the beacon shape variable.

(35)

An indicator variable for nighttime conditions was

included in the final model to determine if the nighttime results were significantly different from

daytime results. From the preliminary review of the findings (average daytime yielding was

64 percent when the beacons were above the sign and 60 percent when the beacons were below

the sign), it appears that there were only minor, if any, differences between the above and below

positions. The results from the GLMM indicate that there were no significant differences

between the two positions (p-value = 0.1611).

139

In conclusion, the position of the yellow RRFB did not have an impact on whether a driver

decided to yield to the waiting pedestrians. Variables that did have an impact on driver yielding

include posted speed limit, intersection configuration, and city (yielding was lower in Illinois

compared to the other States included in study).

Discussion

With respect to the location of the LEDs, the findings from the closed-course study for

pedestrian position and LED location indicate that the distance between the pedestrian and the

light source affected the ability to quickly detect the pedestrian. When the pedestrians were

located at the edge of the crosswalk (i.e., next to the assembly) and when the LEDs were located

below the sign (i.e., closer to the pedestrian), detection time was longer. These findings support

the idea of placing the LEDs above rather than below the sign.

The open-road study found that the position of the RRFB (either above or below the sign) did not

affect a driver’s decision to yield. With the apparent benefits identified from the closed-course

study (i.e., lower discomfort and improved ability to detect the pedestrian, as measured by

identifying the direction the cutout pedestrian is traveling) and the finding that there was no

difference in driver yielding due to position, locating the beacons above the sign could improve

the overall effectiveness of this treatment. Based on these findings, FHWA is considering issuing

an official interpretation to permit the placing of the beacons above the sign.

FLASH PATTERN (OPEN-ROAD) STUDY

Summary/Conclusions

When IA-11 was issued in July 2008 for the RRFB, the only flash pattern that had been tested

was the 2-5 flash pattern in which the beacon pulses two times on one side followed by

five faster pulses on the other side.

(4)

However, because the 2-5 flash pattern appears to the

human eye to be a 2-3 flash pattern, IA-11 specified a 2-3 flash pattern and, up until official

interpretation 4(09)-21 (I), many devices were installed with the 2-3 flash pattern rather than the

2-5 flash pattern.

(9)

The inability to accurately determine the number of pulses within a pattern

was later confirmed in the closed-course study (see chapter 3). The same closed-course study

found that certain flash patterns—those that could be characterized as having limited or no dark

periods within the flash pattern—negatively influenced the amount of time participants needed to

identify the direction a pedestrian is walking. Prior to developing the proposed provisions for

incorporating a rapid-flashing beacon traffic control device into the MUTCD, it is important to

determine which flash patterns are acceptable from the perspectives of effectiveness and

simplicity.

(1)

There is a desire to know if a less complicated flash pattern or a flash pattern

with different dark/light proportions would be equally or more effective than the 2-5 or

2-3 flash patterns.

An open-road study was conducted to examine different flash patterns with yellow RRFBs.

The objective of the study was to determine if the use of simpler flash patterns used with

RRFBs resulted in different driver yielding rates at uncontrolled crosswalks. The MOE was the

number of drivers who did and did not yield for a staged pedestrian who activated the RRFBs

and was attempting to cross the roadway. The study included eight sites located in either

140

College Station, TX, or Garland, TX. Most of the sites (seven out of eight) had four lanes with a

40- or 45-mi/h posted speed limit. The remaining site had two lanes and a 30-mi/h posted

speed limit.

A temporary light bar and controller were developed to permit the research team to have control

over several of the beacons characteristics, such as flash pattern and brightness. The light bar

was designed such that it was not obvious that the beacons being observed were any different

from the permanent RRFB light bar they were mounted to. A remote control was used to activate

the temporary light bar.

A flash pattern workshop along with meetings with FHWA resulted in the selection of the

following patterns for testing:

• Pattern using a combination of long and short flashes (blocks).

• Pattern using a combination of WW+S flashes.

• The 2-5 flash pattern.

The research team used a staged pedestrian approach to evaluate driver yielding for the different

patterns. Each staged pedestrian wore similar clothing (gray t-shirt, blue jeans, and gray tennis

shoes) and followed specific instructions in crossing the roadway. A second researcher, who

observed and recorded the yielding data on pre-printed datasheets, accompanied the staged

pedestrian. Data were collected in February and March 2014.

The average driver yielding was 80 percent for the WW+S flash pattern, 80 percent for the

blocks pattern, and 78 percent for the 2-5 flash pattern. While there was a small numeric

difference of 2 percent, the statistical analysis found that this difference was not statistically

significant. Logistic regression was used to model the yielding and not yielding relationships for

the individual crossings. The results from the GLMM indicate that there were no significant

differences between the tested flash patterns. The WW+S flash and block patterns developed as

part of this research study were as effective as the 2-5 flash pattern.

Discussion

This study, combined with the closed-course study that found drivers were better at judging

pedestrian direction when there were more dark periods (see chapter 3), suggest an advantage in

using a flash pattern with a longer dark period during night time conditions and that this

advantage was not offset by a reduction in driver yielding during the daytime conditions. This

suggests the profession should consider using a flash pattern with increased dark periods when

specifiying the pattern for RRFBs.

The findings from the research effort were presented to the NCUTCD STC during its June 2014

meeting. The STC recommended that the WW+S flash pattern should be used with future rapid-

flashing pedestrian treatments. Based on the findings from this research, FHWA issued an

official interpretation on July 25, 2014, to permit agencies to use either the previously approved

2-5 flash pattern or the optional WW+S flash pattern.

(2)

Although both flash patterns are

available for use, the official interpretation mentions that FHWA favors the WW+S flash pattern

141

because it has a greater percentage of dark time when both beacons of the RRFB are off and

because the beacons are on for less total time. The greater percentage of dark time is important

because this will make it easier for drivers to read the sign and to see the waiting pedestrian,

especially under nighttime conditions. The less total on time will make the RRFB more energy

efficient, which is important since they are usually powered by solar energy.

PHB STUDY

Summary/Conclusions

The PHB has shown great potential in improving safety and driver yielding; however, questions

have been asked regarding actual driver and pedestrian behaviors. A total of 20 locations in

Tucson, AZ, and Austin, TX, were selected for inclusion in this study representing a range of

posted speed limit, median type, and number of major roadway lanes. Data were collected using

a multiple video camera setup. The final dataset reflected over 78 h of video data and included

1,979 pedestrians.

The videos were reviewed to identify each occurrence when a vehicle stopped at the crossing

when the PHB was displaying a dark indication. None of the drivers who stopped at the crossing

when the PHB was dark appeared to be confused regarding the device. In the cases when a queue

was present during the flashing red indication, about half of the crossings included at least

one driver who did not completely stop prior to entering the crosswalk. Overall, driver yielding

for these 20 sites averaged 96 percent. In almost all of the crossings, drivers appropriately

yielded to the crossing pedestrians.

For the pedestrian crossings observed, only 124 of the 1,689 pedestrians (7 percent) departed

during a dark indication. For the majority of these pedestrians, the roadway volume was such

that the pedestrian was able to find sufficient gaps to cross. The 1-min/lane volume count was

less than 4 vehicles/min for the majority of these crossings. An examination of departures on the

dark indication revealed that pedestrians were more likely to depart on dark at coordinated sites

compared to hot-button sites (13 versus 7 percent), but departures on dark were much less

frequent at the coordinated site that had pushbuttons with red lights that illuminated when the

button was pressed. The coordinated site with the red-lighted buttons had 10 percent of

pedestrians departing on dark, while the coordinated site with the non-lighted buttons had

20 percent of pedestrians departing on dark.

Of the 1,979 arriving pedestrians, 290 were research team members who always activated the

PHB. For the remaining 1,689 general public pedestrians, 157 did not push the button because

the PHB was already active. For those who arrived when the PHB was not active, 91 percent

pushed the button and activated the PHB. A review of the data by site characteristics shows

trends for the highest values. A greater number of pedestrians activated the device when on

45-mi/h posted speed limit roads as compared to 40 mi/h or less roads. The percentage of

pedestrians pushing the button was always greater than 80 percent for the longer crossing

distances (longer than 110 ft). When the hourly volume for both approaches was 1,500 vehicles/h

or more, the percent of pedestrians activating the PHB was always 90 percent or more.

142

All occurrences of pedestrian/vehicle conflicts and erratic maneuvers were noted when observed

in the video footage. The conflict rate was found to be higher for non-compliant pedestrians than

for compliant pedestrians. Slightly less than half of the observed conflicts occurred during the

dark beacon indication and involved a through vehicle. These conflicts usually involved

pedestrians who either crossed without pushing the button or pushed the button but did not wait

for their walk indication and then paused in the raised-curb median while crossing.

Notable conflict rates for both compliant and non-compliant pedestrians were observed at several

sites where the PHBs were located near supermarkets and multiple bus stops. At these sites,

many bus riders would walk through the supermarket parking lots or run across the major street

while transferring between bus lines. The presence of bus stops near access points with

significant turning vehicle volumes tended to result in higher conflict rates.

Discussion

The PHB has shown great potential in improving safety. It is also associated with less delay for

the major roadway as compared to a full TCS because of the PHB’s flashing red indication that

permits stop-and-go operations if the pedestrians have finished crossing their half of the

roadway.

Experience of city traffic engineers has indicated that drivers did not understand that they can

start the stop-and-go operations once the crosswalk is clear. In response to this need, a sign was

created and has been installed in several cities. FHWA now recommends that a slightly different

wording be used on such a sign (see figure 58 for an example).

The results from this research have shown, however, that drivers are not always stopping on the

flashing red before proceeding through the cleared crossing. In about half of the actuations where

a queue existed on the approach, at least one of the drivers in the queue did not come to a

complete stop before driving through the crossing. A small number of drivers (about 5 percent)

was observed staying stopped on the flashing red indication, sometimes when it would have been

clear to proceed, but often because pedestrians were still crossing or conflicting minor movement

vehicles were occupying the intersection.

Within the 78 h of video data reviewed, conflicts were observed with most of the conflicts

associated with non-compliant pedestrians. Several conflicts were observed at a site with a

nearby access point (e.g., driveway), which could indicate that access points should be limited

within a certain distance to the PHB, especially if they serve major traffic generators. Additional

research is needed to determine the distance(s) access points should be restricted. The research

should also consider the type of access point or the anticipated volume from the access point as

well as proximity to bus stops where pedestrians may be making transfers between bus lines.

Most of the PHB sites included in this study were at intersections or major driveways. Including

midblock sites was a priority for the study, and four locations were identified. The midblock

PHBs had driveways/intersections that were within 80 ft of the PHB. All of these sites were in

Austin, TX. Conflicts were not counted at two of these sites because of minimal cross street

volume or restricted movements (e.g., right in/right out turns only). For the other two sites,

1 had minimal conflicts, but the remaining site had 11 conflicts. Examination of the video

143

footage revealed that the conflicts at this site occurred when left-turning drivers departed a major

traffic generator and did not have adequate space on the major street to complete the turning

maneuver before encountering the midblock crosswalk. This site did not have a median, so the

back-left corner of the turning vehicles were still in the opposing through lanes when the

vehicles were stopped in the diagonal position. The drivers wanted to move out of their awkward

position (diagonal, partly encroaching on opposing through lanes) and sometimes encroached on

pedestrians in the crosswalk while doing so. Hence, guidance for the placement of PHBs and/or

access points near PHBs needed to account for turning vehicle paths.

While drivers stopping at a dark PHB were observed, it did not appear that the stopping was

caused by a driver being confused with the dark device. Rather, drivers stopped because of

congestion from a nearby driveway or intersection or because of crossing pedestrians or

stopped buses.

This study identified high driver yielding (greater than 94 percent) for the site with the widest

crossing and the site with the 45-mi/h posted speed limit. This finding, along with findings from

previous studies and the overall high yielding for PHBs identified in this research (overall

96 percent), supports the use of this device at a variety of locations, including on high-speed and

wide roads, at residential intersections, and elsewhere.

(29,33)

FUTURE RESEARCH NEEDS

While several research studies have examined the effectiveness of the RRFB and the PHB, many

research needs remain, as presented in this section. Several of these ideas were presented in a

previous FHWA report but are included here for completeness.

(5)

Based on the research conducted as part of this study, along with discussions held at professional

society meetings and with other practitioners, additional research questions regarding RRFBs

used at pedestrian crossings are as follows:

• Appropriate brightness level of RRFBs: The brightness of the RRFBs can help draw a

driver’s attention to a device and the area around the device. It can also result in drivers

looking away from the device because the brightness is irritating or unbearable. When the

discomfort glare is unbearable, drivers are more likely to divert their eyes away from the

discomfort, which might result in drivers missing people or objects located near the glare

source. Recommendations are needed for a maximum brightness for beacons used with

pedestrian crossing signs and for other traffic control devices with embedded LEDs or

supplemental beacons. The maximum brightness should vary between daytime and

nighttime conditions.

• When RRFBs should be dimmed and by how much: Guidance is needed on whether to

dim these devices during low light conditions and, if so, by how much. A study of

disability glare and discomfort glare in both bright and dark conditions can be used to

determine appropriate maximum nighttime and daytime brightness for RRFB. The

investigation into brightness levels should consider an open-road portion to be able to

associate different motorist yielding behavior with the different brightness levels.

144

• Appropriate use of RRFB assemblies on only one side of the roadway approach: The

original IA for the RRFB requires the assembly to be located on the right-hand and left-

hand sides of the roadway. There may be street widths where having two assemblies

provides limited benefits. If so, the additional cost savings in purchasing and maintaining

fewer devices at a site could provide additional resources to treat other locations.

• Appropriate installation of RRFB assemblies overhead rather than on the roadside:

FHWA issued an interpretation in 2009 that indicated overhead mounting is appropriate

and that if overhead mounting is used, a minimum of only one sign per approach is

required, and it should be located over the approximate center of the lanes of the

approach.

(6)

Presence of buses and street width are two examples of site conditions where

RRFBs could be installed overhead rather than roadside, but there might be other criteria

that should be considered when making this decision. In addition to identifying the

applicable criteria, developing numeric guidance for these criteria is also needed (e.g., at

what roadway width should overhead rather than roadside installation be considered).

The guidance might also need to consider additional variables beyond primary

characteristics such as roadway width. For example, if the sidewalks at the site are

adjacent to the face of curb, then the roadside assembly might need to be located more

than 5 ft from the curb, which would place the assembly beyond the driver’s cone of

vision. The research would need to consider if placing the beacons above the sign would

satisfy some of the concerns expressed in this research idea discussion. This research

effort may also need to consider if larger beacons are needed for overhead application

along with some adjustments to direct them to the approaching driver since many LEDs

are directional.

• Identification of roadway and traffic variables that influence driver yielding: This

research would examine the relationship between driver yielding rate and geometric

and/or traffic variables. Additional research is needed to identify what characteristics are

influential so that the characteristics (e.g., better enforcement or overall street design) of

those communities with higher driver yielding could be reproduced.

• Identification of optimal use of signing and pavement markings at pedestrian

crossings: This research would examine how signing can be used to improve a pedestrian

crossing. Types of signs at some pedestrian crosswalks include warning signs used in

advance of the crossing (sometimes with flashing beacon), signs at the yield or stop bar

of the crossing to inform drivers of the appropriate place to stop, and signs at the

crosswalk on the mast arm structure or roadside. Examples of signs being used at the

crossing include regulatory signs such as the crosswalk stop on red [ball] and internally

illuminated signs that say PEDESTRIAN CROSSING. Research questions could include

(1) do the signs influence drivers’ alertness at the crossing, (2) do the signs help to

communicate the likelihood that a pedestrian will be at the crossing, or (3) how long are

signs needed that provide information on stop-and-go behavior.

• How driver yielding for LED-embedded pedestrian crossing signs compares to

RRFBs: Another flashing pedestrian treatment is signs with LEDs embedded into the

sign face. The performance of this treatment as measured by driver yielding is needed.

145

Research needs associated with the PHB include the following:

• Optimal location of the PHB. At some sites, a logical place for a PHB is near a bus

stop or other major traffic generator that may not be at a street intersection or a major

driveway. At one of the sites in this research project, several conflicts were observed

between vehicles and pedestrians with the vehicles turning in and out of a nearby

driveway contributing to several of the conflicts. This research would examine tradeoffs

for locating the PHB near a major access point. These tradeoffs may include changes in

crosswalk utilization, pedestrian compliance, or conflict rate. A range of pedestrian

volumes, access point volumes, and distances between crosswalk and access point should

be considered along with identifying alternative treatments such as restricting some

turning movements at the access point.

• Pedestrian compliance at coordinated PHBs: PHBs can be programmed to begin

service to pedestrians instantly upon actuation (i.e., hot-button operation) or to begin

service in coordination with adjacent traffic signals. Compared to instant service PHB

operation, coordinated PHB operation reduces vehicular delay by preserving progression

bandwidth and avoiding stopping the platoon of major street vehicles. However, a

pedestrian actuating a coordinated PHB will often see delayed service and may conclude

that the device is malfunctioning and then cross the street without the assistance and

protection of the device. Research is needed to quantify pedestrian compliance trends as

they are influenced by PHB operational mode (instant service versus coordinated),

vehicular volumes, provision of visual or auditory feedback/guidance devices for waiting

pedestrians (e.g., small indicator lights above the pushbutton that illuminate upon

pressing), and other site characteristics. This insight would be used to formulate guidance

on choosing between coordinated and instant service mode for a PHB.

• PHBs within signal system: Research is needed to determine the optimal background

cycle length for a PHB (e.g., what should be the minimal major street green time between

subsequent PHB activations?). The study could also investigate the minimum separation

between a PHB and a signal that will allow a roadway to operate adequately. Investigate

how that separation distance should change for various roadway features such as width

of roadways.

• BikeHAWK: Research is needed to evaluate modifications to the PHB that can better

accommodate bicyclist crossings along with pedestrians. Tucson, AZ, has developed a

modified PHB called BikeHAWK, but there is a concern with the potential for late entry

by bicyclists. Even though there is an R9-5 sign that instructs bicyclists to use the

pedestrian signal, it is not known whether bicyclists know that there is a flashing red

during the countdown. Even though the bicyclist can cross in the remaining time, a motor

vehicle may be proceeding through the crossing. Other issues also exist in attempting to

modify a PHB to better accommodate bicyclists along with pedestrians.

Other research needs for pedestrian treatments include the following:

• Guidance on selection of appropriate pedestrian crossing treatment for a particular

location: In general, the PHB has higher yielding rates but costs more than RRFB

146

assembly. The RRFB is more effective than many other pedestrian treatments; however, a

Texas study found lower compliance for the RRFB for longer crossing distances.

(35)

This

finding indicates that there is a crossing distance width for which a device other than the

RRFB should be considered. The dataset included sites with total crossing distances that

ranged between 38 and 120 ft. A research study with an objective of developing

guidelines for selecting appropriate pedestrian crossing treatments would help to improve

uniformity across the country. The study would also need to identify the site conditions

that should be considered (e.g., roadway volume, pedestrian volume, crossing distance,

posted speed limit, typical pedestrian walking speed at the site, etc.).

• Minimum number of pedestrians to justify a pedestrian treatment: There is a

growing use of the PHB and the RRFB for pedestrian crossings. Establishing

guidance that can be consistently applied would help to facilitate use of these devices

in appropriate settings. A particular question is whether there is a minimum number

of pedestrians before a device should be considered. The MUTCD contains graphs

that illustrate when to consider a PHB, and these graphs include a minimum of

20 pedestrians/h.

(1)

When deciding to recommend this minimum pedestrian number, the

NCUTCD based its decision on a value developed through engineering judgment during

an FHWA study on whether to mark crosswalks.

(51)

Research is needed to more fully

consider what should be the minimum pedestrian value used for selecting various traffic

control devices. For example, should this minimum number be a function of crossing

distance or posted speed limit? In addition, should it consider the distance to the nearest

crossing? A location that is only a few hundred feet from an established crossing should

have a higher minimum number compared with a crossing that is more than one-fourth or

one-half of a mile from a signal on a wide high-speed arterial.

• Number of pedestrians induced as a result of installation of selected pedestrian

treatments: The primary objective of this study would be to determine reasonable values

for estimates of latent pedestrian crossing demand (i.e., estimated number of pedestrians

that would now use the site because of the installation of a specific pedestrian treatment).

The results of the research could improve the process for selecting pedestrian treatments.

The research should make appropriate suggestions for changes to key reference

documents, such as design manuals or the MUTCD.

(1)

Improved guidance should help to

improve conditions for pedestrians by identifying appropriate devices for crossings,

which should improve pedestrian mobility and reduce the number of pedestrian crashes.

• Drivers’ search patterns near flashing beacons: There was evidence in this study that

the closed-course drivers were more accurate in seeing objects beyond the signs with

flashing beacons compared with seeing objects beyond the distractor signs. This could be

an artifact of this study or it could be because the flashing beacons attracted the eye to the

area. Additional research could focus on drivers’ search patterns when a flashing beacon

is present to test the theory that the presence of the beacons or LEDs encourages drivers

to search a particular area. By varying the brightness of the beacons along with the light

source (e.g., beacons or LED-embedded signs), the study could also investigate whether

drivers need additional time to search an area because of the brightness of the device.

147

• Pedestrians’ attitudes toward using treatments: Observations of pedestrians in the

open-road portion of this study (and in other studies) have documented crossing

pedestrians that did not activate the beacon treatments when they were provided. Some

of those pedestrians were not within the treated crosswalk to be able to use the

beacon, while others crossed at the crosswalk but chose not to activate the beacon. This

study would explore pedestrian decisionmaking and examine why pedestrians who have

the opportunity to use a treatment (such as an RRFB) to support their crossing choose not

to do so. For example, at crosswalks marked as school crossings, do adult pedestrians

think that the treatment is for use only by schoolchildren? Results from this study could

feed into the suggested educational campaign mentioned previously, and results could

also be used to support guidance on where treatments should be installed and what

information (e.g., instructional plaques next to the pushbutton) should be provided to

crossing pedestrians.

• Estimating pedestrian exposure: With ADT being the key predictor of vehicle crashes,

there is a desire to have similar types of data for pedestrians. With limited resources for

collecting counts—vehicle, bicycles, or pedestrians—researchers could study what are

the most effective means for obtaining pedestrian exposure.

• Distance between crossings: How far will a pedestrian be willing to walk to reach a

crossing with a pedestrian treatment? How does that distance change based on the

treatment type (e.g., PHB versus crosswalk markings only), on the presence of a median,

on the posted speed limit of the major street, and other factors that influence pedestrians

walking behavior? These are all questions that could be investigated with further studies.

• National education campaign on the RRFBs and/or PHBs: Research is needed to

determine what education campaigns have been used by cities and jurisdictions that have

implemented RRFBs and whether they were successful. For example, are there common

themes that could be used on a national level? The campaigns could also include other

considerations of pedestrian behaviors such as the need to activate the pushbutton as well

as cautions against distracted walking and walking during nighttime conditions, blind

spots around commercial vehicles, and others. Education campaigns could be directed

toward drivers, pedestrians, or both. The portion of the campaign could be directed to

police who have to enforce the device to provide them with information on what is and is

not a violation within their State laws.

149

ACKNOWLEDGMENTS

This research is sponsored by the FHWA as part of the “Evaluation of Pedestrian Hybrid

Beacons and Rapid Flashing Beacons” project.

The project was under the direction of Ann Do of FHWA. The following panel members

provided comments and advice and generously donating their time and effort during the project:

George Branyan, Michael Cynecki, Dwight Kingsbury, Peter Koonce, Ransford McCourt,

Richard Nassi, and Gary Schatz. Their advice and guidance was greatly appreciated during the

course of the study.

Several TTI employees assisted the authors with the closed-course study or the open-road

studies by collecting data, including Susan T. Chrysler, Daniel P. Walker, Nicholas S. Wood,

Ivan G. Lorenz, Nada D. Trout, Sandra K. Stone, Laura L. Higgins, Richard A. Zimmer, and

Diana G. Wallace.

Several TTI student employees also assisted the authors with either the closed-course or

open-road studies by collecting or reducing the data, including Benjamin C. Starnes, Shannon E.

Duffie, George F. Gillette, II, Farinoush Sharifi, Eathan R. Langdale, Brittany C. Bednarz,

Aaron C. White, Carla Beltran, Maria C. Rodriguez, Darshan Padmanabha, Sarah E. Motes,

Kent Krueger, Amanda B. Martin, Julie L. Smith, Rachel D. Musgrove, Kathryn E. Bender,

Kelsey R. Zorn, Kendra A. Zorn, Carla B. Kolber, Apoorba Bibeka, and Crystal Salazar.

The research team is also grateful to the representatives and staff of the cities that participated in

and facilitated the open-road study, including Aurora, IL (Eric Gaullt); Austin, TX (Jim Dale,

Ronnie Bell, and Jonathan Lammert); Douglas County, CO (Amy E. Branstetter, Duane Cleere,

Leonard Cheslock, and Carlos Zambrano); Garland, TX (Dave Timbrell and Chris Moore);

Marshall, TX (William ‘Bill’ Marshall, J.C. Hughes, and Buzz Snyder); Phoenix, AZ (Kerry

Wilcoxon); and Tucson, AZ (Richardo Montano and Diahn Swartz).

The authors of this report also value the comments provided by reviewers of draft versions of

this material and members of the NCUTCD.

151

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